{"meta":{"query_hash":"7645002cb9d8","filters":{"topic":"Transportation Planning and Optimization"},"cohort_total":2034,"direct_labels_cover":0,"predictions_cover":2034,"exported":2034,"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/7645002cb9d8","api":"https://metacan.xera.ac/api/v1/cohort?topic=Transportation+Planning+and+Optimization"},"results":[{"id":"W103308084","doi":"","title":"STRADA - THE WORLD'S NEWEST BUS OPERATOR SELECTION TOOLKIT","year":2011,"lang":"en","type":"article","venue":"CONGRESS - DUBAI 2011","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Suite; Process (computing); Attendance; Test (biology); Product (mathematics); Presentation (obstetrics); Strengths and weaknesses; Selection (genetic algorithm); Engineering; Operations research; Computer science; Process management; Knowledge management; Marketing; Engineering management; Business; Psychology; Geography; Political science; Artificial intelligence; Medicine; Social psychology","score_opus":0.04291984990278771,"score_gpt":0.28459354024688854,"score_spread":0.24167369034410083,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W103308084","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.64481676,0.0006813601,0.0032727963,0.0030858896,0.006883565,0.0012383845,0.000072011484,0.00093492033,0.33901432],"genre_scores_gemma":[0.9728029,0.00005086951,0.0006795059,0.00022977003,0.00023702333,0.000022914637,0.000022055463,0.000012901311,0.025942098],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.99910337,0.00011488883,0.00016494753,0.00017573271,0.00021540502,0.0002256302],"domain_scores_gemma":[0.9994876,0.000056340145,0.00008248732,0.00012734326,0.00015442942,0.00009179467],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00029108065,0.00009427857,0.000088951725,0.000059648464,0.0006543457,0.00009707011,0.0002310544,0.00006887276,0.0035699091],"category_scores_gemma":[0.00003846877,0.00007381223,0.000037946662,0.00025585218,0.000178227,0.0003350425,0.000006348262,0.00012620492,0.00021654305],"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":[0.00023582054,0.00036280186,0.5032177,0.000028913995,0.00017178831,0.0000427207,0.084367245,0.0013922079,0.00018120189,0.22678556,0.16157557,0.021638468],"study_design_scores_gemma":[0.000889262,0.0001126342,0.357311,0.00006874519,0.0001349547,0.000004016392,0.0052322936,0.0006237428,0.0014825354,0.000736447,0.6327378,0.0006665512],"about_ca_topic_score_codex":0.0040568914,"about_ca_topic_score_gemma":0.017891524,"teacher_disagreement_score":0.47116223,"about_ca_system_score_codex":0.000028947554,"about_ca_system_score_gemma":0.0001491078,"threshold_uncertainty_score":0.9983891},"labels":[],"label_agreement":null},{"id":"W105801430","doi":"","title":"Understanding TDM and its Role in the Delivery of Sustainable Urban Transport","year":2009,"lang":"en","type":"article","venue":"European Transport Conference, 2009Association for European Transport (AET)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Benchmarking; Public transport; Sustainable transport; Sustainability; Work (physics); Business; Process management; Transport engineering; Management science; Engineering; Marketing","score_opus":0.041992997048041374,"score_gpt":0.2481021052516377,"score_spread":0.20610910820359632,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W105801430","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6922649,0.0005452222,0.021868587,0.0017706774,0.00021676201,0.0024731215,0.00041205384,0.00042094055,0.28002775],"genre_scores_gemma":[0.99498045,0.00048015124,0.00026163846,0.00035579826,0.0001553355,0.000011900415,0.0008624865,0.000064736596,0.0028275263],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9958609,0.00065665867,0.0011853293,0.0006382779,0.0008244468,0.000834383],"domain_scores_gemma":[0.9983946,0.0001564252,0.0005224048,0.00026510068,0.0004534489,0.00020799857],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.004304917,0.0004048957,0.00051133806,0.0003891515,0.0008028154,0.00007132289,0.00062260503,0.00014257313,0.00008554755],"category_scores_gemma":[0.00004840934,0.0003948067,0.00026093054,0.0008945087,0.00020174743,0.00076396245,0.0000027859596,0.000365221,0.000009669444],"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":[0.0006454447,0.00086745835,0.34991014,0.00026319534,0.00016385945,0.0005063633,0.20215926,0.0037925057,0.00052925217,0.43886322,0.0008278126,0.0014714887],"study_design_scores_gemma":[0.0024862818,0.0002892725,0.94147366,0.00017115448,0.00026528022,0.0000021272178,0.021159686,0.00016545702,0.00005244615,0.001035452,0.032239515,0.0006596634],"about_ca_topic_score_codex":0.00025080692,"about_ca_topic_score_gemma":0.0010769683,"teacher_disagreement_score":0.5915635,"about_ca_system_score_codex":0.0001630179,"about_ca_system_score_gemma":0.00029616145,"threshold_uncertainty_score":0.9998504},"labels":[],"label_agreement":null},{"id":"W107101374","doi":"10.1007/978-1-4419-0158-3_16","title":"Double-Layer and Hybrid Dynamics of Equilibrium Problems: Applications to Markets of Environmental Products","year":2009,"lang":"en","type":"book-chapter","venue":"Springer optimization and its applications","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Disequilibrium; Variational inequality; Oligopoly; Product (mathematics); Economics; Mathematical economics; Mathematical optimization; Computer science; Mathematics; Cournot competition","score_opus":0.017660611231170242,"score_gpt":0.24783325334648992,"score_spread":0.23017264211531968,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W107101374","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0021432529,0.0065721124,0.44574878,0.00703749,0.00019475086,0.01871313,0.002856013,0.00052952656,0.51620495],"genre_scores_gemma":[0.61166364,0.02021469,0.09384892,0.0003423555,0.0004900106,0.001408126,0.0044209803,0.00026022023,0.26735106],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9985299,0.000017149472,0.0005209055,0.00045905187,0.0003053212,0.00016764096],"domain_scores_gemma":[0.99891585,0.00004513279,0.0004193101,0.00026404508,0.0002041019,0.00015155962],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00020490216,0.00021463846,0.00028835196,0.00024120236,0.00021223283,0.000038037302,0.00016561805,0.00017049474,0.00013736958],"category_scores_gemma":[0.00001039519,0.00025142657,0.000042181066,0.00014733426,0.00018418307,0.00015980349,0.00003385939,0.00012219846,0.0000063837433],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010609949,0.00035242495,0.00039916526,0.00066664914,0.0001470936,6.261944e-7,0.0036668088,0.13647959,0.0008277853,0.811011,0.00032791175,0.046014838],"study_design_scores_gemma":[0.0028512531,0.00029972475,0.0037087072,0.00081174116,0.0009886688,0.000010936266,0.0008730173,0.03722451,0.0016781932,0.0076073664,0.94130886,0.0026370299],"about_ca_topic_score_codex":0.0000146354805,"about_ca_topic_score_gemma":0.000027143398,"teacher_disagreement_score":0.9409809,"about_ca_system_score_codex":0.00006105871,"about_ca_system_score_gemma":0.00008835077,"threshold_uncertainty_score":0.9999938},"labels":[],"label_agreement":null},{"id":"W1120147903","doi":"10.3727/108354202108749998","title":"Recall Salience: Concept, Use, and Estimation","year":2002,"lang":"en","type":"article","venue":"Tourism Analysis","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Salience (neuroscience); Recall; Estimation; Psychology; Social psychology; Econometrics; Economics; Sociology; Cognitive psychology; Management","score_opus":0.027100856789542244,"score_gpt":0.27776074348203916,"score_spread":0.25065988669249695,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1120147903","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7211792,0.00060930505,0.25551322,0.0039759898,0.0001550483,0.00022071184,0.000034630044,0.0003704933,0.0179414],"genre_scores_gemma":[0.98830426,0.0001955035,0.006787715,0.00010577905,0.00003835611,0.0000025829027,0.000042689826,0.0000028522418,0.0045202402],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99936825,0.00006903067,0.00012410217,0.00013529077,0.0001943755,0.00010892674],"domain_scores_gemma":[0.9996502,0.00008436244,0.000061703184,0.00007530501,0.000058553465,0.00006991712],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016513607,0.00004768755,0.00009883507,0.00013457595,0.00028284476,0.00010765902,0.00005164191,0.000053787207,0.0006543451],"category_scores_gemma":[0.00010103443,0.00004804511,0.000042631287,0.0007815426,0.00010077386,0.00034013038,0.0000023828577,0.00004130119,0.000014886421],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011332957,0.00012436767,0.38925573,0.000010050253,0.0005963505,0.00003192778,0.11990411,0.4009541,0.00001696839,0.016482754,0.027013257,0.04559906],"study_design_scores_gemma":[0.0004455994,0.000033610595,0.36125675,0.000017226525,0.0016538331,5.9199823e-7,0.0030800768,0.60925287,0.000026678825,0.00077870936,0.023009166,0.00044488572],"about_ca_topic_score_codex":0.0018704883,"about_ca_topic_score_gemma":0.0006634708,"teacher_disagreement_score":0.26712507,"about_ca_system_score_codex":0.000013619773,"about_ca_system_score_gemma":0.0000073555266,"threshold_uncertainty_score":0.71646196},"labels":[],"label_agreement":null},{"id":"W1126157700","doi":"10.1016/j.jtte.2015.08.002","title":"Evaluation of connected vehicle impact on mobility and mode choice","year":2015,"lang":"en","type":"article","venue":"Journal of Traffic and Transportation Engineering (English Edition)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"CIMA+ (Canada); McMaster University","funders":"University of Toronto","keywords":"Mode (computer interface); Vehicle-to-vehicle; Transport engineering; Computer science; Mode choice; Vehicle Information and Communication System; Mode of transport; Simulation; Public transport; Engineering; Road traffic; Computer network","score_opus":0.022000094748640422,"score_gpt":0.29308643188592615,"score_spread":0.27108633713728575,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1126157700","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99700767,0.00017580306,0.0020008706,0.00008031446,0.00045645438,0.00011868667,0.000040839466,0.000034820707,0.000084543106],"genre_scores_gemma":[0.9991272,0.00005235018,0.0004896018,0.000007835937,0.00026054741,0.0000032435596,0.000048712252,0.000007514273,0.0000029837993],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9986826,0.00007452139,0.00035709873,0.00009123368,0.00069712056,0.00009738864],"domain_scores_gemma":[0.9979228,0.00017583677,0.00021149033,0.000042730673,0.0014838793,0.0001632604],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014368937,0.00008673365,0.00016507838,0.00014421198,0.000061732506,0.000023117365,0.0000426937,0.000076496995,0.000018992967],"category_scores_gemma":[0.00052776746,0.00008052003,0.000049501003,0.00019434768,0.000053230116,0.00047241306,3.3966788e-7,0.00012403508,1.6497987e-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":[0.00005466863,0.000075118754,0.0031775255,0.00001828426,0.000041748433,0.0000011747798,0.014381397,0.9803437,0.000021770553,0.0004160433,0.00013622013,0.0013323497],"study_design_scores_gemma":[0.0036243373,0.00046394212,0.8484472,0.00019324855,0.00045121426,0.0000020466332,0.004353343,0.14118916,0.00011142194,0.00013730752,0.00080040214,0.00022634356],"about_ca_topic_score_codex":0.000054128945,"about_ca_topic_score_gemma":0.00009823764,"teacher_disagreement_score":0.8452697,"about_ca_system_score_codex":0.00006517326,"about_ca_system_score_gemma":0.00021755902,"threshold_uncertainty_score":0.32835117},"labels":[],"label_agreement":null},{"id":"W1232129636","doi":"10.1007/s11116-015-9643-9","title":"Social interactions in transportation: analyzing groups and spatial networks","year":2015,"lang":"en","type":"article","venue":"Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Universität für Bodenkultur Wien; Deutsche Forschungsgemeinschaft","keywords":"TRIPS architecture; Travel behavior; Work (physics); Demand management; Context (archaeology); Mode choice; Transport engineering; Economics; Regional science; Operations research; Marketing; Sociology; Business; Microeconomics; Public transport; Geography; Engineering","score_opus":0.037759074596110435,"score_gpt":0.3195234263295202,"score_spread":0.28176435173340975,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1232129636","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8496017,0.00011526866,0.14632806,0.0013665402,0.0003609023,0.0002869098,0.000027416048,0.00017751678,0.0017357222],"genre_scores_gemma":[0.99827534,0.00005794899,0.0005649461,0.000058995913,0.0002001108,0.000022654127,0.0006873668,0.000012961138,0.00011968178],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99889016,0.00008435318,0.00034691335,0.00021544771,0.0002527057,0.00021043229],"domain_scores_gemma":[0.99955386,0.000050170947,0.0001080637,0.000046301157,0.00011939711,0.00012219368],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038324695,0.000107511885,0.00014763357,0.00015702614,0.00026182528,0.0000492407,0.00006129573,0.000085483116,0.000045029432],"category_scores_gemma":[0.000015120341,0.00012856415,0.000040300754,0.00045887608,0.000094545256,0.0005119469,2.952532e-7,0.00016030921,0.0000031617024],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020084475,0.000104016064,0.7077819,0.00001971451,0.00002411544,0.00002744843,0.17726006,0.07752151,0.000015511356,0.025429372,0.0003459677,0.011269514],"study_design_scores_gemma":[0.0010083643,0.000022503362,0.98118997,0.00002923451,0.000050924435,2.3283759e-7,0.008499576,0.004683718,0.000006587928,0.0008311128,0.0034652043,0.00021258027],"about_ca_topic_score_codex":0.008471164,"about_ca_topic_score_gemma":0.21142147,"teacher_disagreement_score":0.27340803,"about_ca_system_score_codex":0.00006180567,"about_ca_system_score_gemma":0.00009345762,"threshold_uncertainty_score":0.9981315},"labels":[],"label_agreement":null},{"id":"W125206768","doi":"","title":"Enhancing Household Travel Surveys Using Smart Card Data","year":2009,"lang":"en","type":"article","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Smart card; Transit (satellite); Travel behavior; Data collection; Identification (biology); Survey data collection; Travel survey; Computer science; Transport engineering; Geography; Public transport; Statistics; Engineering; Computer security; Mathematics","score_opus":0.04356665342859213,"score_gpt":0.2854181330602873,"score_spread":0.24185147963169518,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W125206768","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12859203,0.00045384237,0.8665224,0.0013395916,0.00022244862,0.00039243404,0.00015591376,0.00068787305,0.0016334839],"genre_scores_gemma":[0.9174049,0.00017212817,0.08042645,0.0009837915,0.00024734327,0.000012869797,0.00023779688,0.000031392374,0.0004833599],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99752903,0.00046549516,0.00040675295,0.00039700864,0.0004917119,0.0007100226],"domain_scores_gemma":[0.9985934,0.00011864108,0.00018365505,0.0006974826,0.00009163807,0.00031517493],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003362708,0.0002079428,0.0002642688,0.00025472918,0.0007051981,0.00020703746,0.0006260585,0.00026049782,0.000018031678],"category_scores_gemma":[0.00031716572,0.00023577339,0.00007495874,0.00065883045,0.00008413919,0.0009076886,0.00003249352,0.00027200658,0.00000430266],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014621299,0.0010941573,0.71338826,0.00007057623,0.00017697846,0.00015413834,0.028530223,0.099692404,0.028672025,0.06702221,0.010129034,0.050923783],"study_design_scores_gemma":[0.00078675034,0.00011547512,0.90229917,0.0001777404,0.0001438578,0.000024798954,0.0035215928,0.07767873,0.0064355554,0.0010138451,0.0066043413,0.0011981346],"about_ca_topic_score_codex":0.04813033,"about_ca_topic_score_gemma":0.02582027,"teacher_disagreement_score":0.7888128,"about_ca_system_score_codex":0.00027930818,"about_ca_system_score_gemma":0.00042674522,"threshold_uncertainty_score":0.991956},"labels":[],"label_agreement":null},{"id":"W133325607","doi":"","title":"Modeling transport networks with design pattern: application to hybrid traffic simulations","year":2007,"lang":"en","type":"article","venue":"international conference on Modelling and simulation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Université Laval","funders":"","keywords":"Cellular automaton; Computer science; Representation (politics); Grid; Transport network; Node (physics); Distributed computing; Macro; Scale (ratio); Theoretical computer science; Artificial intelligence; Engineering; Computer network","score_opus":0.06665067689109186,"score_gpt":0.3228076725955885,"score_spread":0.2561569957044967,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W133325607","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2175407,0.000005204338,0.78098345,0.0003287423,0.00008572423,0.0003039502,0.000009029933,0.00010473262,0.00063846924],"genre_scores_gemma":[0.98942846,0.00002591067,0.009931889,0.00015973601,0.00014938661,0.00001174553,0.0001865188,0.000016704096,0.000089620014],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986892,0.000043856064,0.0002966026,0.00032031533,0.00043789684,0.00021212637],"domain_scores_gemma":[0.9991214,0.0001658431,0.000074625124,0.00009578099,0.00040440273,0.00013790013],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005430822,0.00013451687,0.0001109603,0.00020539424,0.000369305,0.00008710845,0.000118309705,0.000079720776,0.000029619658],"category_scores_gemma":[0.000013959377,0.0001367825,0.000024912008,0.0001715526,0.000036507052,0.0002473895,0.000002617094,0.00012847807,0.000004837902],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017651923,0.000031980355,0.0005770263,0.0000017713899,0.000012083511,0.0000018512723,0.0023644182,0.98418057,0.0000048227644,0.002762769,9.371214e-7,0.009885234],"study_design_scores_gemma":[0.00028876134,0.000051041385,0.00018040264,0.00005060537,0.00001535544,3.639491e-7,0.0003213665,0.998524,0.0000087626995,0.00028236926,0.00010359925,0.00017337927],"about_ca_topic_score_codex":0.00027469732,"about_ca_topic_score_gemma":0.0005097658,"teacher_disagreement_score":0.7718878,"about_ca_system_score_codex":0.000063750274,"about_ca_system_score_gemma":0.00005765767,"threshold_uncertainty_score":0.5577828},"labels":[],"label_agreement":null},{"id":"W141620605","doi":"","title":"A review of methodologies employed in roadside surveys of drinking and driving","year":2008,"lang":"en","type":"review","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Commission; Best practice; Survey data collection; European commission; Transport engineering; Geography; Engineering; Political science; Business; European union; Law","score_opus":0.19569637283075317,"score_gpt":0.44652349059717633,"score_spread":0.2508271177664232,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W141620605","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000008934112,0.99511284,0.002426358,0.000014579773,0.000043750148,0.00030974366,0.000001498386,0.000025922633,0.0020563626],"genre_scores_gemma":[0.0000261669,0.9853199,0.014477946,0.000011875815,0.000009921043,0.000010411067,0.000031394746,0.00000812254,0.00010426017],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9967779,0.002068296,0.0006999522,0.0001630645,0.00017533649,0.000115447445],"domain_scores_gemma":[0.997928,0.001380577,0.00048390625,0.00009873669,0.000083119005,0.000025609577],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003413395,0.00011593661,0.0011512951,0.00016684768,0.00004803443,0.000005270855,0.0001334771,0.0001425814,0.00003143665],"category_scores_gemma":[0.0013502216,0.00009520857,0.0001265709,0.0005412554,0.00013534538,0.00007461654,0.000011373312,0.00010613387,3.8468846e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[3.3236904e-7,0.000010178356,0.0070837103,0.02305058,0.00002790148,0.0000021352641,0.0016614565,0.000023905292,3.4687996e-8,0.000650958,0.00013986477,0.96734893],"study_design_scores_gemma":[0.00011570839,0.000024247438,0.0050221295,0.35493878,0.00022568535,0.000003277627,0.0004097093,0.0000033539623,7.6418456e-7,0.00012720059,0.6387862,0.00034294094],"about_ca_topic_score_codex":0.0016408744,"about_ca_topic_score_gemma":0.00424298,"teacher_disagreement_score":0.967006,"about_ca_system_score_codex":0.000019977459,"about_ca_system_score_gemma":0.00024851842,"threshold_uncertainty_score":0.38824928},"labels":[],"label_agreement":null},{"id":"W145675971","doi":"","title":"Commercial Vehicle Dispatch System","year":2007,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Software deployment; Taxis; Economic dispatch; Key (lock); Fleet management; Software; Transport engineering; Wireless; Computer science; Engineering; Computer security; Telecommunications; Operating system; Electric power system","score_opus":0.017073059742657475,"score_gpt":0.2987167666713253,"score_spread":0.2816437069286678,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W145675971","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.38764134,0.000013404227,0.110585295,0.0008846526,0.00044722485,0.00010318122,0.0000023382706,0.00040312376,0.49991944],"genre_scores_gemma":[0.9962337,0.000001891923,0.0013877255,0.00011781072,0.00011817637,7.939216e-7,0.0000075244952,0.0000027373928,0.0021296244],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9995608,0.000021130916,0.00008771889,0.000057712314,0.000146989,0.00012570508],"domain_scores_gemma":[0.9997976,0.000048016256,0.000020476517,0.00003740442,0.00003829456,0.000058226153],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046134653,0.000026120102,0.00003711964,0.000022962886,0.00027243875,0.000021115997,0.00005235785,0.000039277216,0.00008725019],"category_scores_gemma":[0.000012208279,0.00002471815,0.00001552936,0.000137361,0.000032988926,0.00007434614,0.0000012898187,0.000029516103,0.000052293817],"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":[0.000028869068,0.0000400868,0.47379607,0.000013048978,0.0000071554327,0.000011639149,0.02100711,0.0005642907,0.000036333615,0.48298156,0.003688363,0.017825486],"study_design_scores_gemma":[0.00038360475,0.000018804893,0.8752761,0.000024586489,0.000013053877,3.6138607e-7,0.029903688,0.0006196154,0.00020774144,0.00006937492,0.093299106,0.00018395027],"about_ca_topic_score_codex":0.0031751543,"about_ca_topic_score_gemma":0.0080119055,"teacher_disagreement_score":0.6085924,"about_ca_system_score_codex":0.000030346673,"about_ca_system_score_gemma":0.000022951053,"threshold_uncertainty_score":0.47999048},"labels":[],"label_agreement":null},{"id":"W1483427682","doi":"10.4000/bifea.5857","title":"Innovaciones tecnológicas aplicadas al transporte colectivo en Quito. Optimización en la evaluación de la demanda con GPS y SIG","year":2004,"lang":"es","type":"article","venue":"Bulletin de l’Institut français d’études andines","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ministère des Transports","funders":"","keywords":"Global Positioning System; Geography; Transport engineering; Cartography; Welfare economics; Humanities; Forestry; Computer science; Engineering; Telecommunications; Economics; Art","score_opus":0.009208003723731808,"score_gpt":0.29534815822950555,"score_spread":0.28614015450577374,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1483427682","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8611897,0.0025813086,0.04302127,0.03756534,0.0010008045,0.0020383475,0.0003708052,0.001134214,0.05109819],"genre_scores_gemma":[0.98227507,0.005411731,0.009780201,0.00091834983,0.0006024222,0.00016768802,0.00014982071,0.000089790665,0.0006049197],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9954171,0.000799019,0.0009299036,0.00091878814,0.0009455732,0.000989583],"domain_scores_gemma":[0.9975213,0.0010311411,0.0003547699,0.00037559454,0.00031644126,0.0004007942],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0026193815,0.0007031661,0.00078656425,0.0003586566,0.001373674,0.0005482905,0.000695299,0.0010621647,0.00080616865],"category_scores_gemma":[0.0005060955,0.0007046677,0.0003309058,0.0010192702,0.0011694622,0.00028311298,0.000039113547,0.0008063592,0.00012336262],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014616617,0.0023327428,0.10229942,0.00069480704,0.0007643984,0.0009842662,0.079406545,0.52926105,0.002641843,0.2673609,0.007966513,0.004825857],"study_design_scores_gemma":[0.005160269,0.00031437457,0.06275933,0.0008322629,0.0004710954,0.00011259406,0.003187027,0.0006897714,0.0006954059,0.00047266795,0.9241522,0.0011530304],"about_ca_topic_score_codex":0.005103421,"about_ca_topic_score_gemma":0.0010972484,"teacher_disagreement_score":0.9161857,"about_ca_system_score_codex":0.00031009453,"about_ca_system_score_gemma":0.0010458741,"threshold_uncertainty_score":0.9999264},"labels":[],"label_agreement":null},{"id":"W1484784010","doi":"10.3390/publications3030174","title":"Themed Volumes: A Blessing or a Curse?","year":2015,"lang":"en","type":"article","venue":"Publications","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Blessing; Curse; Volume (thermodynamics); Launched; Space (punctuation); Perspective (graphical); Production (economics); Management; Operations research; Computer science; Business; Engineering; Sociology; Economics; History","score_opus":0.12177692423630254,"score_gpt":0.37188038445886906,"score_spread":0.2501034602225665,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1484784010","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030619908,0.00019733477,0.14285667,0.09170886,0.0006962866,0.00068233587,0.000042133073,0.0016073786,0.7315891],"genre_scores_gemma":[0.95015246,0.000017458957,0.012097785,0.00030254477,0.00016105617,0.00008468087,0.00012224719,0.000009089886,0.037052706],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99940157,0.00005019684,0.000107472624,0.00010228491,0.0002040656,0.00013441106],"domain_scores_gemma":[0.99935216,0.00005252562,0.000055266428,0.000112462476,0.00028019847,0.00014741444],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035575006,0.00003769585,0.00004616925,0.000078986704,0.0003479934,0.00014809825,0.00012468713,0.000040753635,0.00020406932],"category_scores_gemma":[0.000537118,0.000033362227,0.00001638435,0.00060035684,0.00008555442,0.00037730753,0.0000034365673,0.000036405505,0.000055041324],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003122368,0.00030180113,0.0892356,0.000009453845,0.0000339469,0.0000014227996,0.16168976,0.002233179,0.000014892557,0.4908412,0.23505113,0.020556385],"study_design_scores_gemma":[0.00023733209,0.000008871742,0.008981032,0.000008221397,0.000013164704,5.387372e-7,0.011360657,0.0016782355,0.0000041738776,0.001492668,0.9761181,0.000097036245],"about_ca_topic_score_codex":0.00041879845,"about_ca_topic_score_gemma":0.0015485231,"teacher_disagreement_score":0.91953254,"about_ca_system_score_codex":0.00004345054,"about_ca_system_score_gemma":0.0005696629,"threshold_uncertainty_score":0.26765192},"labels":[],"label_agreement":null},{"id":"W1486407966","doi":"","title":"Congestive Traffic Failure: The Case for High-Occupancy and Express Toll Lanes in Canadian Cities","year":2011,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Carpool; Toll; Revenue; Traffic congestion; Business; Occupancy; Transport engineering; Congestion pricing; Economic impact analysis; Economic cost; Finance; Economics; Engineering; Civil engineering","score_opus":0.04139674209177319,"score_gpt":0.3154491915377643,"score_spread":0.2740524494459911,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1486407966","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98653585,0.00015434076,0.0000029375735,0.0013974793,0.0002536228,0.0010520496,0.00029399825,0.000023500954,0.0102862455],"genre_scores_gemma":[0.9961109,0.0019481393,0.00051653833,0.00003430754,0.000097834854,0.00031554187,0.00009657367,0.000017776383,0.00086235354],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.99843895,0.00026551163,0.000273622,0.0003914433,0.00009075326,0.0005397181],"domain_scores_gemma":[0.9986657,0.0007619571,0.0000818891,0.00021110296,0.0000940915,0.00018528174],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015929334,0.00013485491,0.00022793858,0.00032312915,0.00043150937,0.00015426395,0.00027020878,0.00030414347,0.00003862415],"category_scores_gemma":[0.0003393225,0.0001287447,0.000036238434,0.000081511396,0.00046082446,0.000096036805,0.000034789053,0.0005297875,7.815937e-7],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00051950756,0.00017794785,0.18562774,0.00066533335,0.00023261918,0.0014977707,0.38388425,0.17081267,0.000004133768,0.08468789,0.0010998187,0.17079031],"study_design_scores_gemma":[0.0057761013,0.00043617203,0.23960608,0.0017037356,0.00012728242,0.00010066591,0.4868645,0.026473045,0.000057303892,0.022926144,0.21240398,0.0035249717],"about_ca_topic_score_codex":0.39875168,"about_ca_topic_score_gemma":0.9658375,"teacher_disagreement_score":0.5670858,"about_ca_system_score_codex":0.000375529,"about_ca_system_score_gemma":0.0012805879,"threshold_uncertainty_score":0.6052521},"labels":[],"label_agreement":null},{"id":"W1488176543","doi":"10.5038/2375-0901.5.3.2","title":"Transit Itinerary Calculation on the Web: Based on a Transit User Information System","year":2002,"lang":"en","type":"article","venue":"Journal of Public Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Heuristics; Transit (satellite); Computer science; The Internet; Pedestrian; Geographic information system; Transport engineering; World Wide Web; Public transport; Engineering; Geography","score_opus":0.02846728815267116,"score_gpt":0.23639861266769524,"score_spread":0.20793132451502408,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1488176543","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.61030823,0.00007399619,0.30355415,0.05963073,0.0014726153,0.0011050485,0.00018481948,0.00037774703,0.023292674],"genre_scores_gemma":[0.9982975,0.000030782878,0.00047809441,0.00077448465,0.0001762458,0.000010970658,0.00010565776,0.000013142815,0.000113084505],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9975345,0.00029614018,0.0007604727,0.000109873,0.0010723721,0.00022661473],"domain_scores_gemma":[0.9984872,0.00022025108,0.0005313886,0.00012295054,0.000492396,0.00014585863],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013242497,0.00014899649,0.00018871129,0.000419623,0.00045740715,0.00017316881,0.00019504275,0.00015127889,0.00026659362],"category_scores_gemma":[0.00007948358,0.000113089896,0.00018755892,0.00066167815,0.00006699768,0.0014732839,1.8759464e-7,0.00028824262,0.000026015141],"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":[0.00045709443,0.00043669128,0.015939437,0.00016408117,0.00012752092,0.000043141343,0.060491547,0.75261116,0.0001259544,0.13839383,0.010729161,0.020480372],"study_design_scores_gemma":[0.009819198,0.0012708103,0.47281575,0.001156464,0.0005063928,0.00001268899,0.019042341,0.2604885,0.00029555216,0.00013151114,0.23334369,0.0011170821],"about_ca_topic_score_codex":0.00004741255,"about_ca_topic_score_gemma":0.00020817407,"teacher_disagreement_score":0.49212265,"about_ca_system_score_codex":0.00013040894,"about_ca_system_score_gemma":0.00016143314,"threshold_uncertainty_score":0.46116722},"labels":[],"label_agreement":null},{"id":"W1489472458","doi":"","title":"Using Revealed and Stated Preference Data to Estimate the Demand and Consumption Benefits of Sporting Events: An Application to National Hockey League Game Trips 1","year":2009,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Metropolitan area; Revealed preference; TRIPS architecture; Ticket; Consumption (sociology); Economic surplus; Preference; Economics; Advertising; Business; Marketing; Microeconomics; Welfare; Geography; Transport engineering; Engineering; Computer science","score_opus":0.18753753587819466,"score_gpt":0.44261378082230235,"score_spread":0.2550762449441077,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1489472458","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9969947,0.000063824176,0.0008922904,0.00040005744,0.000034564357,0.0010257169,0.00021876855,0.000020248328,0.00034983788],"genre_scores_gemma":[0.988853,0.0014994578,0.008969372,0.000038365877,0.000055610584,0.000042013522,0.0004857497,0.000011894456,0.000044546206],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.998133,0.00025884324,0.0004909281,0.00056322815,0.00030671558,0.00024730063],"domain_scores_gemma":[0.99873286,0.00025739148,0.0002635477,0.0003431243,0.00025556976,0.0001475061],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003654577,0.00011607726,0.00021282713,0.00023502548,0.00024180456,0.000095536954,0.00037065032,0.00015196772,0.0000043325463],"category_scores_gemma":[0.00068625476,0.00011816867,0.0000127057165,0.0001549576,0.00012728231,0.00022632825,0.0001468887,0.00028323705,4.6531576e-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":[0.00015405589,0.00007314911,0.07304167,0.00009553565,0.000022750459,5.329196e-7,0.0071219164,0.8206017,0.00027673744,0.0009516819,0.000008208168,0.09765205],"study_design_scores_gemma":[0.00046546312,0.00008060625,0.83926713,0.00053637143,0.00002355273,0.0000017381346,0.0013465487,0.15581223,0.000044414955,0.0014325844,0.0006799183,0.0003094108],"about_ca_topic_score_codex":0.00061449426,"about_ca_topic_score_gemma":0.0048069647,"teacher_disagreement_score":0.76622546,"about_ca_system_score_codex":0.0001957475,"about_ca_system_score_gemma":0.000323334,"threshold_uncertainty_score":0.48187783},"labels":[],"label_agreement":null},{"id":"W1491102286","doi":"","title":"PUBLIC TRANSPORT CROWDING: THE CURRENT STATE OF FORECASTING TECHNIQUES IN THE UK AND AUSTRALIA","year":2011,"lang":"en","type":"article","venue":"European Transport Conference 2011Association for European Transport (AET)Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Public transport; Crowding; Metropolitan area; Transport engineering; Regional science; Operations research; Strategic planning; Transportation planning; Geography; Computer science; Business; Engineering; Marketing","score_opus":0.2909681127512197,"score_gpt":0.3686195441073205,"score_spread":0.07765143135610081,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1491102286","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93269026,0.00025676706,0.029169794,0.0014545592,0.00042620182,0.0042212983,0.0010706928,0.0003798092,0.03033062],"genre_scores_gemma":[0.9952703,0.0009246432,0.0014626305,0.00008946699,0.00015638112,0.00013879625,0.0011147333,0.00011058648,0.00073243433],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9919105,0.0019264595,0.0020019494,0.0009468245,0.0019201135,0.0012941106],"domain_scores_gemma":[0.9962084,0.0004838458,0.0008057611,0.0005505271,0.0016168845,0.00033456646],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.014190305,0.00052213855,0.0005785645,0.0006409231,0.0013465858,0.00012459511,0.0013804502,0.00012407124,0.00018109287],"category_scores_gemma":[0.00015090172,0.00042771178,0.0003575213,0.0014621387,0.0012652542,0.00088560046,0.0000058339765,0.001210284,0.000017894283],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038049286,0.0004469359,0.80540276,0.00029790614,0.00011697796,0.00011980578,0.14614013,0.00014027844,0.00013607593,0.020926293,0.00037628858,0.025516052],"study_design_scores_gemma":[0.0009613553,0.00021432807,0.97260165,0.00026337517,0.0001366694,7.5280076e-7,0.0027616275,0.000037174832,0.00015051635,0.0010644402,0.021312062,0.00049604004],"about_ca_topic_score_codex":0.0032059879,"about_ca_topic_score_gemma":0.036925357,"teacher_disagreement_score":0.1671989,"about_ca_system_score_codex":0.0001076615,"about_ca_system_score_gemma":0.00045690447,"threshold_uncertainty_score":0.9999535},"labels":[],"label_agreement":null},{"id":"W1492683818","doi":"10.17226/13950","title":"Bus Use of Shoulders","year":2006,"lang":"en","type":"book","venue":"Transportation Research Board eBooks","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Shoulders; Computer science; Medicine","score_opus":0.12341950643405879,"score_gpt":0.37930275288635423,"score_spread":0.25588324645229543,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1492683818","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003352618,0.000093176175,0.002192547,0.0002447369,0.00027132654,0.0013344606,0.00061044167,0.00028421963,0.9916165],"genre_scores_gemma":[0.019141512,0.000086405686,0.0016337566,0.000040050174,0.00019403081,0.00005418946,0.0016542517,0.000072785115,0.977123],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99582,0.0002829354,0.00071697024,0.00044788644,0.002175636,0.00055657956],"domain_scores_gemma":[0.99747854,0.0005637584,0.0002879424,0.0003045341,0.0011637514,0.00020148726],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009907598,0.00024010679,0.00039637295,0.000673536,0.00049639033,0.00010961246,0.0003246175,0.0006294603,0.0002642001],"category_scores_gemma":[0.00006102213,0.00027393835,0.00021047145,0.00020943239,0.0010474089,0.0002028679,0.0000024951744,0.0007410741,0.000029877161],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041837292,0.00014369823,0.0068566515,0.0006958027,0.00023829822,0.0001483668,0.03310687,0.011242075,0.00007352923,0.38803053,0.55134106,0.0077047586],"study_design_scores_gemma":[0.00037970944,0.00006862799,0.0099125225,0.00029207102,0.00008100921,3.9768615e-8,0.00043198856,0.000025835767,0.00004973332,0.0041162404,0.98431206,0.00033016544],"about_ca_topic_score_codex":0.017275909,"about_ca_topic_score_gemma":0.051684923,"teacher_disagreement_score":0.432971,"about_ca_system_score_codex":0.00019797262,"about_ca_system_score_gemma":0.0018242424,"threshold_uncertainty_score":0.9999713},"labels":[],"label_agreement":null},{"id":"W1492781394","doi":"10.1109/icsmc.2005.1571298","title":"Windsor-Detroit Border Crossing Problem: Conflict Analysis of the Schwartz Report","year":2005,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Windsor; Truck; Transport engineering; Terrorism; Operations research; Computer science; Engineering; Geography; Archaeology; Environmental science","score_opus":0.021394585468107342,"score_gpt":0.33550568076070475,"score_spread":0.3141110952925974,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1492781394","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7982597,0.000075618045,0.019657899,0.005649307,0.0001164046,0.0002454063,0.000010124191,0.00015759958,0.17582798],"genre_scores_gemma":[0.9764821,0.000005630262,0.005079381,0.00015187505,0.000038865368,0.0000025329125,0.000016883705,0.000003638144,0.018219117],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9991283,0.000044372064,0.00026427303,0.0001259583,0.00031086625,0.00012627734],"domain_scores_gemma":[0.9994602,0.000036907604,0.00016770026,0.00014483769,0.0001540071,0.00003635089],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047159326,0.000048260452,0.000112858404,0.00009068765,0.00053484656,0.00008171167,0.000113163616,0.00005320119,0.00071775727],"category_scores_gemma":[0.00006826073,0.000035255773,0.00009993853,0.0011149435,0.00019646704,0.00016197802,0.0000042537845,0.000052730742,0.0000035300834],"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":[0.000008266471,0.000059442416,0.7976702,0.0000046136665,0.00025328112,0.000002323255,0.03383733,0.1552985,0.00014564075,0.00924621,0.00080442283,0.0026698103],"study_design_scores_gemma":[0.00025038648,0.000009153369,0.7173773,0.000019160343,0.00056919374,0.0000010814537,0.0024230464,0.0054682656,0.0005473841,0.000078533856,0.2730787,0.00017777257],"about_ca_topic_score_codex":0.0021411993,"about_ca_topic_score_gemma":0.00691749,"teacher_disagreement_score":0.2722743,"about_ca_system_score_codex":0.000022978767,"about_ca_system_score_gemma":0.00016469066,"threshold_uncertainty_score":0.7858939},"labels":[],"label_agreement":null},{"id":"W149436234","doi":"10.5822/978-1-61091-222-8_6","title":"The Reshape America Index","year":2013,"lang":"en","type":"book-chapter","venue":"Island Press/Center for Resource Economics eBooks","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Amenity; Quarter (Canadian coin); Index (typography); Business; Agricultural economics; Geography; Economics; Finance; Computer science","score_opus":0.020900563496138152,"score_gpt":0.2380958487267014,"score_spread":0.21719528523056325,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W149436234","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00008411784,0.00027462677,0.00058539544,0.00040265342,0.00038271723,0.0008855408,0.00061385724,0.00008603109,0.9966851],"genre_scores_gemma":[0.0022325586,0.00057806034,0.00015507486,0.00045833082,0.00068269647,0.00011161196,0.00043371136,0.00007840814,0.99526954],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99865675,0.000036982736,0.00043545195,0.00036421724,0.00014159233,0.00036501556],"domain_scores_gemma":[0.99869317,0.0003261982,0.00043196534,0.00032529407,0.00009567343,0.00012768203],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002772804,0.00025532595,0.0002745237,0.0000623981,0.0009201567,0.0003867832,0.00046392437,0.00035425375,0.00015553478],"category_scores_gemma":[0.000019553429,0.0002193729,0.0002048384,0.0000050953017,0.00033561513,0.00006304778,0.000028590932,0.00028518555,0.00005755896],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00049427385,0.000022474915,0.00022173359,0.00006740669,0.0004928718,0.0000035492474,0.01873199,0.003143977,4.062636e-7,0.49754506,0.3805931,0.09868316],"study_design_scores_gemma":[0.0003919901,0.00002970984,0.000012760931,0.000047392015,0.000035328016,5.6490944e-7,0.00016311047,0.0005993577,0.0000010994294,0.007972399,0.99048376,0.00026252764],"about_ca_topic_score_codex":0.00028521265,"about_ca_topic_score_gemma":0.0021372137,"teacher_disagreement_score":0.6098907,"about_ca_system_score_codex":0.000068694615,"about_ca_system_score_gemma":0.00010269055,"threshold_uncertainty_score":0.8945767},"labels":[],"label_agreement":null},{"id":"W1495948796","doi":"10.1016/j.tra.2011.11.013","title":"Understanding the impacts of a combination of service improvement strategies on bus running time and passenger’s perception","year":2012,"lang":"en","type":"article","venue":"Transportation Research Part A Policy and Practice","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Service (business); Transport engineering; Public transport; Bus priority; Order (exchange); Transit (satellite); Level of service; Travel time; Operations research; Computer science; Engineering; Business; Marketing; Finance","score_opus":0.2055664551319852,"score_gpt":0.451265909072814,"score_spread":0.2456994539408288,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1495948796","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9753166,0.00010212015,0.0013631184,0.015076198,0.000045366458,0.00053394603,0.000041376297,0.00002928328,0.0074919895],"genre_scores_gemma":[0.99888664,0.00052766735,0.00020408015,0.00017222976,0.000071977025,0.000012839834,0.000052646865,0.000006660195,0.000065281645],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9984388,0.0004048655,0.0002422933,0.000107598666,0.00056445046,0.00024203777],"domain_scores_gemma":[0.9976815,0.0016334456,0.00018692738,0.000076351695,0.0003240012,0.000097763674],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034315195,0.000070166025,0.00010300506,0.00017329137,0.0004780986,0.000059110876,0.000056284516,0.00007045049,0.000037536774],"category_scores_gemma":[0.00039513182,0.0000589664,0.000017140264,0.0005331547,0.00022681846,0.001007751,0.000002084579,0.00016497727,0.0000022706054],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007603503,0.00033348758,0.01087561,0.00027420346,0.000081704544,0.000001041472,0.38121176,0.0015792882,0.0069861286,0.59612,0.00026207571,0.0015143384],"study_design_scores_gemma":[0.0016969554,0.00082472025,0.5983855,0.0003428896,0.00016377866,0.0000018133934,0.38316768,0.00128825,0.00057400786,0.005498499,0.0077492963,0.00030659174],"about_ca_topic_score_codex":0.008687002,"about_ca_topic_score_gemma":0.0015477756,"teacher_disagreement_score":0.59062153,"about_ca_system_score_codex":0.00005757872,"about_ca_system_score_gemma":0.00020228795,"threshold_uncertainty_score":0.99791425},"labels":[],"label_agreement":null},{"id":"W1501806917","doi":"10.4102/jtscm.v3i1.52","title":"A decision-support system for improving road transportation performance in Gauteng, South Africa: Developing a data acquisitioning system","year":2009,"lang":"en","type":"article","venue":"Journal of Transport and Supply Chain Management","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Transport Canada","funders":"","keywords":"Deliverable; Decision support system; Process (computing); Order (exchange); Process management; Computer science; Transport engineering; Sustainability; Systems engineering; Risk analysis (engineering); Operations research; Business; Engineering; Data mining","score_opus":0.021575947786969963,"score_gpt":0.2598359457919334,"score_spread":0.23825999800496345,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1501806917","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4085,0.00035634046,0.5875488,0.000529509,0.0005884491,0.0010535459,0.000094344396,0.000093204224,0.0012358262],"genre_scores_gemma":[0.959166,0.00014928445,0.04025748,0.000039486935,0.00010988383,0.000013831255,0.00016360752,0.000011696357,0.000088735054],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99793315,0.00004007883,0.00091375964,0.0002789666,0.0005219243,0.00031211445],"domain_scores_gemma":[0.9991202,0.000043378863,0.00042173362,0.0001573531,0.0001494422,0.00010790834],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024718952,0.00015576169,0.00032740264,0.00037940257,0.00036033907,0.00007092487,0.0003227863,0.00008946308,0.0000054876546],"category_scores_gemma":[0.0000089683945,0.00014972223,0.00007355612,0.00032557384,0.000031210766,0.000713938,0.0000030470635,0.00012137682,5.724869e-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":[0.0023279323,0.00031320515,0.46974018,0.0051281485,0.00042228622,0.0010223793,0.18562362,0.06598019,0.00004378117,0.045748625,0.00037342613,0.22327624],"study_design_scores_gemma":[0.003270805,0.00023849793,0.92556936,0.002083335,0.00037597347,0.0000107372625,0.050348286,0.013969907,0.000010055852,0.00008030424,0.0036174343,0.00042532643],"about_ca_topic_score_codex":0.00003391662,"about_ca_topic_score_gemma":0.00012480804,"teacher_disagreement_score":0.55066603,"about_ca_system_score_codex":0.00016248612,"about_ca_system_score_gemma":0.00013982951,"threshold_uncertainty_score":0.6105495},"labels":[],"label_agreement":null},{"id":"W1502832553","doi":"","title":"Environment Energy Assessment of Trips (EEAT): An updated approach to assess the environmental impacts of urban mobility, The case of Lille Region","year":2010,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Center for Northern Studies","funders":"","keywords":"TRIPS architecture; Energy (signal processing); Transport engineering; Environmental science; Environmental planning; Geography; Computer science; Engineering; Statistics","score_opus":0.023927841426546633,"score_gpt":0.26749427049020097,"score_spread":0.24356642906365433,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1502832553","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8537553,0.00017835695,0.13241766,0.0017729207,0.00013274838,0.0008760497,0.00021904758,0.000045481578,0.010602427],"genre_scores_gemma":[0.98410994,0.0002856487,0.014030409,0.000027448605,0.00001578654,0.000059970844,0.00082123355,0.000020544017,0.0006290296],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9928808,0.005249118,0.00065995427,0.0004497057,0.00054023624,0.00022015104],"domain_scores_gemma":[0.9962681,0.0006693733,0.00094156753,0.0016106087,0.00035850884,0.0001518424],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0058788112,0.0002085445,0.0003307829,0.00011613566,0.00043743837,0.00008076976,0.0010609609,0.0002683424,0.000075304146],"category_scores_gemma":[0.00018554606,0.00016067157,0.00015932652,0.00025545093,0.0007390944,0.000118152784,0.00027389036,0.00041274633,5.170439e-7],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014788144,0.011099893,0.07589762,0.00036632802,0.0005470484,0.000021004582,0.40433142,0.05582797,0.015211522,0.40493968,0.0017700274,0.02983958],"study_design_scores_gemma":[0.0047443667,0.000043900585,0.4230663,0.002261104,0.0016142592,0.0001157424,0.07243903,0.2535704,0.17993453,0.007773017,0.051009446,0.0034278987],"about_ca_topic_score_codex":0.013434683,"about_ca_topic_score_gemma":0.0048215883,"teacher_disagreement_score":0.39716667,"about_ca_system_score_codex":0.00010874819,"about_ca_system_score_gemma":0.00030028616,"threshold_uncertainty_score":0.9931349},"labels":[],"label_agreement":null},{"id":"W1503783645","doi":"10.1002/atr.1230","title":"Special issue on public transport systems","year":2013,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","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":"Public transport; Estimation; Transport engineering; Investment (military); Operations research; Computer science; Process (computing); Transportation planning; Strategic planning; Selection (genetic algorithm); Transit (satellite); Demand forecasting; Business; Economics; Engineering; Marketing","score_opus":0.01491284502907886,"score_gpt":0.2723069745351367,"score_spread":0.25739412950605783,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1503783645","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9357176,0.00019281537,0.008747536,0.0052177906,0.009703329,0.0009855133,0.00004069352,0.00015222443,0.039242536],"genre_scores_gemma":[0.9903058,0.00026788306,0.0021668153,0.000118285934,0.00596146,0.000012282553,0.00006689207,0.000018773875,0.0010818401],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9981779,0.0000722951,0.00067631394,0.0001352673,0.0006989199,0.00023934059],"domain_scores_gemma":[0.99848294,0.000080332116,0.0005050806,0.00008255944,0.00064896676,0.00020012264],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038005403,0.00012402437,0.00023244915,0.00023371515,0.00023398186,0.00006500686,0.00016545068,0.00010854623,0.0007230485],"category_scores_gemma":[0.000041956555,0.00011624612,0.00012057056,0.0003609711,0.00007366972,0.0013322311,1.7814105e-7,0.00020246532,0.000054446376],"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":[0.0004301743,0.0005641002,0.040409576,0.00010890737,0.0001541971,0.00011220378,0.07661091,0.7500386,0.00046220157,0.043457262,0.018156508,0.069495305],"study_design_scores_gemma":[0.0014766852,0.00031847955,0.39952826,0.00015845172,0.00006477959,0.0000029091761,0.016392898,0.000036510388,0.000064057815,0.00047658497,0.58119667,0.00028371465],"about_ca_topic_score_codex":0.0001533789,"about_ca_topic_score_gemma":0.00033249197,"teacher_disagreement_score":0.75000215,"about_ca_system_score_codex":0.00007471494,"about_ca_system_score_gemma":0.00017445644,"threshold_uncertainty_score":0.7916874},"labels":[],"label_agreement":null},{"id":"W1504312776","doi":"10.1016/s0927-0507(06)14010-4","title":"Chapter 10 Traffic Equilibrium","year":2006,"lang":"en","type":"book-chapter","venue":"Handbooks in operations research and management science","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":26,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Traffic network; Nash equilibrium; Computer science; Incentive; Network packet; Telecommunications network; Operations research; Mathematical economics; Transport engineering; Engineering; Computer network; Mathematics; Economics; Microeconomics","score_opus":0.07130149340000251,"score_gpt":0.36357979704696164,"score_spread":0.2922783036469591,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1504312776","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0008650025,0.00031263582,0.00046578242,0.00062094955,0.000117281765,0.0007856446,0.000013472514,0.000059437538,0.9967598],"genre_scores_gemma":[0.053716145,0.0017481119,0.0013537762,0.000043871583,0.000086812885,0.00005336938,0.00005225416,0.000016390653,0.94292927],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9973937,0.00003497393,0.00027475363,0.000556637,0.0012422632,0.0004976526],"domain_scores_gemma":[0.9992358,0.000051029263,0.000026814956,0.00023755942,0.000292625,0.00015617153],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0021284553,0.00014513472,0.00014212831,0.0011254227,0.0013488174,0.0005943287,0.0004091591,0.00011166462,0.0005712413],"category_scores_gemma":[0.00003744304,0.0001491007,0.000026263331,0.0002671478,0.002145022,0.00043878224,0.00008199377,0.00026515307,0.000091651294],"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":[0.000012732924,0.000025653266,0.000017802326,0.000029291587,0.000006357559,0.000027619251,0.0014268052,0.014803393,0.000014174421,0.9755783,0.00262079,0.005437046],"study_design_scores_gemma":[0.00084731175,0.00021096694,0.00055335916,0.0007657973,0.000024717376,0.0000014766559,0.0011792568,0.011185106,0.000034062457,0.008619199,0.97582936,0.0007493631],"about_ca_topic_score_codex":0.0003246501,"about_ca_topic_score_gemma":0.0054345154,"teacher_disagreement_score":0.9732086,"about_ca_system_score_codex":0.00013609751,"about_ca_system_score_gemma":0.00020776903,"threshold_uncertainty_score":0.9999513},"labels":[],"label_agreement":null},{"id":"W1506532353","doi":"10.1002/atr.208","title":"Semi‐dynamic traffic assignment model with mode and route choices under stochastic travel times","year":2012,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":33,"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; Mode choice; Mode (computer interface); Reliability (semiconductor); Computer science; Transit (satellite); Travel time; Operations research; Flow network; Simulation; Engineering; Mathematical optimization; Mathematics","score_opus":0.011020484885157163,"score_gpt":0.28154275167517756,"score_spread":0.2705222667900204,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1506532353","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.58997834,0.0002460779,0.40925905,0.0001988087,0.00009803743,0.0001061668,0.000014805052,0.000018458293,0.00008027248],"genre_scores_gemma":[0.98494923,0.00017997526,0.014473564,0.0000451587,0.000058479094,0.000004630598,0.00004113856,0.000015719661,0.00023210447],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99885833,0.00003388406,0.00034516864,0.00010572742,0.0004367956,0.0002201172],"domain_scores_gemma":[0.9992188,0.00006298573,0.00035132258,0.00004169654,0.00015490454,0.00017027804],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002520304,0.000119487144,0.00018374062,0.00011079223,0.00018499869,0.000028181212,0.00006203083,0.0000689978,0.000012233266],"category_scores_gemma":[0.0000085646725,0.000101549784,0.000045166224,0.00015183249,0.000086734995,0.0012273476,3.1955764e-7,0.00013962298,5.4795674e-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":[0.00013605267,0.00006522988,0.0010905586,0.000011756782,0.000025309677,0.0000011661355,0.024454381,0.9714053,0.00021481409,0.0011179029,0.0000042352754,0.0014732843],"study_design_scores_gemma":[0.005259535,0.00049321476,0.81954175,0.00048375857,0.0007905719,0.00001696284,0.037878126,0.13275358,0.00015846603,0.0015782014,0.00021995216,0.0008259099],"about_ca_topic_score_codex":0.000022372422,"about_ca_topic_score_gemma":0.0005740328,"teacher_disagreement_score":0.8386517,"about_ca_system_score_codex":0.00007099574,"about_ca_system_score_gemma":0.00013114256,"threshold_uncertainty_score":0.414108},"labels":[],"label_agreement":null},{"id":"W1509732782","doi":"","title":"Approche multimodale de la mobilité urbaine : développement d'un outil d'aide à la prise de décision","year":2007,"lang":"fr","type":"preprint","venue":"Archipelago (Université du Québec à Montréal)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Humanities; Political science; Art","score_opus":0.012122310705296934,"score_gpt":0.2267947329509476,"score_spread":0.21467242224565067,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1509732782","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7323674,0.0023845595,0.25338066,0.0046184175,0.00020925299,0.000584796,0.000052671327,0.00025098794,0.0061512734],"genre_scores_gemma":[0.8662131,0.00307058,0.12592663,0.00026014983,0.00019779596,0.000025296895,0.00016595771,0.00007177318,0.0040687183],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.99548256,0.0012881877,0.000598975,0.0009268733,0.0007629576,0.0009404642],"domain_scores_gemma":[0.9963377,0.00178078,0.0004488311,0.00055473926,0.00022160035,0.00065634307],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0029363958,0.0005808174,0.0005574517,0.0003537153,0.0011848927,0.000114461676,0.0009047356,0.00084943866,0.00032155722],"category_scores_gemma":[0.00031324956,0.0006860789,0.0004120488,0.00061079627,0.00093467376,0.00026452102,0.000270739,0.0011592715,0.00007206109],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00044664156,0.00068244064,0.04066832,0.00013964661,0.00023952432,0.00043055738,0.70433044,0.12590268,0.00017144861,0.008327,0.000760169,0.117901154],"study_design_scores_gemma":[0.004395747,0.00023385411,0.5794787,0.00074204145,0.0009121492,0.000068718924,0.15288001,0.08595869,0.00021335036,0.010959088,0.1622193,0.0019383646],"about_ca_topic_score_codex":0.42653096,"about_ca_topic_score_gemma":0.18654263,"teacher_disagreement_score":0.55145043,"about_ca_system_score_codex":0.0015806365,"about_ca_system_score_gemma":0.002015996,"threshold_uncertainty_score":0.99955904},"labels":[],"label_agreement":null},{"id":"W1510649035","doi":"10.1109/wsc.2003.1261620","title":"A discrete event simulation for the crew assignment process in North American freight railroads","year":2004,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Crew; Schedule; Discrete event simulation; Crew scheduling; Process (computing); Operations research; Software; Computer science; Transport engineering; Event (particle physics); Government (linguistics); Simulation software; Engineering; Aeronautics; Simulation","score_opus":0.019803358422349482,"score_gpt":0.33724244257606495,"score_spread":0.31743908415371547,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1510649035","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.33856302,0.000023851344,0.65517306,0.0036259496,0.000081819424,0.00084973814,0.000008513507,0.00007244664,0.0016016196],"genre_scores_gemma":[0.99833477,0.00001566377,0.001111,0.00016270981,0.000046679463,0.000085737964,0.000029630322,0.00000489137,0.00020892655],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9993632,0.000025737161,0.00014668019,0.00011514391,0.00020813654,0.00014109064],"domain_scores_gemma":[0.99965376,0.00013479656,0.00007219636,0.00005463882,0.00004888683,0.000035741232],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021253241,0.000052170126,0.00006335275,0.000031748747,0.00025219194,0.000034471068,0.00008587849,0.000017345052,0.000025599813],"category_scores_gemma":[0.000054887892,0.00003581524,0.000029077113,0.00036034634,0.00007132525,0.00012379934,0.0000015244372,0.000038125134,0.0000024026433],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023459292,0.00002072209,0.033136714,0.00000273474,0.000004014951,2.2201868e-7,0.01283185,0.9502073,3.6587582e-7,0.0019481201,0.000009033022,0.0018154687],"study_design_scores_gemma":[0.002327465,0.00014845993,0.8720638,0.00003141318,0.000042903197,7.786513e-8,0.018813888,0.09748467,0.000055966942,0.0016240308,0.0070904046,0.00031688678],"about_ca_topic_score_codex":0.0014744481,"about_ca_topic_score_gemma":0.031190421,"teacher_disagreement_score":0.85272264,"about_ca_system_score_codex":0.00006892706,"about_ca_system_score_gemma":0.00010088345,"threshold_uncertainty_score":0.9864878},"labels":[],"label_agreement":null},{"id":"W1514408998","doi":"10.1016/b978-0-08-097086-8.71018-x","title":"Transportation, Supply, and Congestion","year":2015,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta; University of British Columbia","funders":"","keywords":"Singapore Area Licensing Scheme; Traffic congestion; Congestion pricing; Business; Transport engineering; Runway; Geography; Engineering","score_opus":0.02675884391370985,"score_gpt":0.2747466242248678,"score_spread":0.24798778031115792,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1514408998","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00007627988,0.0009693165,0.000039191687,0.0002709724,0.00026552557,0.00031263748,0.000101825986,0.0001267185,0.99783754],"genre_scores_gemma":[0.0065573268,0.00040169986,0.0005170408,0.0000914365,0.00020857209,0.000012227169,0.0004181684,0.000032813543,0.99176073],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99893737,0.000028096232,0.00025957634,0.0002493011,0.00037493085,0.00015073144],"domain_scores_gemma":[0.9992788,0.000042351894,0.00015705917,0.00010731263,0.00024647964,0.00016801302],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034688838,0.00017131015,0.0002130599,0.0000936295,0.00022967647,0.000051697,0.00008419102,0.00031695925,0.00024715063],"category_scores_gemma":[0.000017875866,0.00018637182,0.000049496583,0.00001290532,0.00023978254,0.00007983289,0.0000017877655,0.00018012681,0.000035883775],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017411981,0.0000036738822,0.00033379596,0.000031628475,0.000032766573,0.000014009021,0.012161189,0.000060638384,8.458659e-7,0.1779268,0.0011237543,0.8082935],"study_design_scores_gemma":[0.00019013,0.000019594721,0.0005916688,0.00013310029,0.00009060946,5.777531e-7,0.00014709741,0.0000022916938,5.8048533e-7,0.012401366,0.9862069,0.00021612221],"about_ca_topic_score_codex":0.000016631919,"about_ca_topic_score_gemma":0.0032349436,"teacher_disagreement_score":0.9850831,"about_ca_system_score_codex":0.00004868036,"about_ca_system_score_gemma":0.00024794505,"threshold_uncertainty_score":0.7600022},"labels":[],"label_agreement":null},{"id":"W1514796059","doi":"10.17226/14666","title":"Uses of Social Media in Public Transportation","year":2012,"lang":"en","type":"book","venue":"National Academies Press eBooks","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":90,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Social media; Public transport; Business; Sociology; Political science; Transport engineering; Computer science; Engineering; World Wide Web","score_opus":0.09746001431096343,"score_gpt":0.32624915098691437,"score_spread":0.22878913667595094,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1514796059","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0004690793,0.00038785135,0.000077039054,0.00024551057,0.0003217389,0.00029840498,0.0006153247,0.00009056766,0.99749446],"genre_scores_gemma":[0.4567956,0.00026191268,0.00044971192,0.00015956498,0.0017391264,0.00011690904,0.0032238944,0.0000624706,0.5371908],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99744856,0.00013081961,0.0005891177,0.00021229874,0.001351089,0.00026813956],"domain_scores_gemma":[0.99838907,0.0005425383,0.00046574388,0.000044710814,0.00047805265,0.00007990379],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008294795,0.0001748644,0.00029367048,0.000327333,0.00018049263,0.000026784519,0.00027129715,0.0009387136,0.00015088065],"category_scores_gemma":[0.00023798906,0.00020048206,0.00010024413,0.00005587544,0.0003331254,0.00028449745,0.0000046880855,0.0005361275,0.0000056705667],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001963712,0.00002380282,0.00039382742,0.00006839054,0.00003138196,6.1621813e-7,0.050864685,0.00010952999,0.000003977838,0.93581176,0.011340043,0.0013323355],"study_design_scores_gemma":[0.00037356903,0.0000049400646,0.010811567,0.0000928565,0.00005142315,1.2105518e-7,0.0004667407,0.0000083868235,0.000035451583,0.0150180785,0.97284776,0.00028909463],"about_ca_topic_score_codex":0.00009009038,"about_ca_topic_score_gemma":0.0014071134,"teacher_disagreement_score":0.96150774,"about_ca_system_score_codex":0.00021821866,"about_ca_system_score_gemma":0.00091903284,"threshold_uncertainty_score":0.8175421},"labels":[],"label_agreement":null},{"id":"W1515773341","doi":"10.1016/s0927-0507(06)14002-5","title":"Chapter 2 Public Transit","year":2006,"lang":"en","type":"book-chapter","venue":"Handbooks in operations research and management science","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":191,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis","funders":"","keywords":"Public transport; Crew scheduling; Transport engineering; Traffic congestion; Transit (satellite); Population; Computer science; Scheduling (production processes); Operations research; Crew; Engineering; Operations management; Aeronautics","score_opus":0.09868667080001484,"score_gpt":0.3674446001439776,"score_spread":0.2687579293439628,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1515773341","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00016809149,0.00029432337,0.003799297,0.0016252935,0.00010149439,0.00075007684,0.000013182985,0.000049516664,0.99319875],"genre_scores_gemma":[0.104855105,0.0049077584,0.0021191507,0.0000896379,0.00008450392,0.00007114556,0.00005856407,0.00001807831,0.88779604],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99742895,0.00003583508,0.0002564605,0.00051981583,0.0012618054,0.0004971589],"domain_scores_gemma":[0.9992211,0.00004108709,0.000021926362,0.00021978594,0.0003233031,0.00017280634],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.002323769,0.00013352229,0.00013269867,0.001308874,0.0020062132,0.0007346796,0.00039976087,0.00010763545,0.00030259253],"category_scores_gemma":[0.00003158693,0.00013700376,0.000024767256,0.00029383568,0.0021372177,0.00052976934,0.000051977247,0.00029218383,0.000044465716],"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":[0.0000040494856,0.000016611226,0.000052554416,0.000016725402,0.000004740837,0.000017332668,0.0010825936,0.0010508277,0.00000639493,0.99261016,0.0006382317,0.0044997707],"study_design_scores_gemma":[0.00062423805,0.00010917424,0.0009830903,0.00043755007,0.00001579806,0.0000011658452,0.0010801579,0.0022742678,0.000019832003,0.020968605,0.9729597,0.0005264292],"about_ca_topic_score_codex":0.0006660424,"about_ca_topic_score_gemma":0.014495168,"teacher_disagreement_score":0.97232145,"about_ca_system_score_codex":0.00013602461,"about_ca_system_score_gemma":0.00022355567,"threshold_uncertainty_score":0.999293},"labels":[],"label_agreement":null},{"id":"W1516250982","doi":"","title":"Testing Daganzo’s Behavioral Theory for Multi-lane Freeway Traffic","year":2002,"lang":"en","type":"preprint","venue":"eScholarship (California Digital Library)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Federal Highway Administration; U.S. Department of Transportation","keywords":"Merge (version control); Bottleneck; Transport engineering; Three-phase traffic theory; Traffic bottleneck; Computer science; Geography; Traffic congestion reconstruction with Kerner's three-phase theory; Engineering; Traffic optimization; Traffic congestion; Floating car data; Operations management; Information retrieval","score_opus":0.07705680947229802,"score_gpt":0.29755048780607585,"score_spread":0.2204936783337778,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1516250982","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8054124,0.0017398737,0.044019096,0.0023275393,0.0036597014,0.0066747284,0.07677468,0.008576534,0.05081541],"genre_scores_gemma":[0.9160349,0.00003659017,0.061148476,0.0002663959,0.0007234201,0.00021261696,0.010448131,0.00021869014,0.010910787],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9970046,0.00022966124,0.0006977054,0.00081142195,0.00055258075,0.00070400425],"domain_scores_gemma":[0.9980727,0.0005521023,0.0004093243,0.00040065483,0.00015460438,0.00041060883],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0005802549,0.00048131283,0.000454057,0.0002497771,0.0006839529,0.0024076672,0.0008369164,0.0007332317,0.0004592078],"category_scores_gemma":[0.00080842286,0.00045787066,0.0003165984,0.0004206835,0.00026274656,0.0019256426,0.00011963395,0.0009147701,0.00032681224],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008412702,0.005123105,0.525961,0.0017384244,0.0005136713,0.0002987738,0.014949611,0.04665572,0.00004348373,0.021813046,0.021116588,0.36094534],"study_design_scores_gemma":[0.006863913,0.0007001249,0.045883838,0.0029588512,0.0009092657,0.000014502781,0.0042429892,0.026322696,0.00027958563,0.045414526,0.85854083,0.007868892],"about_ca_topic_score_codex":0.000026814829,"about_ca_topic_score_gemma":0.000039119717,"teacher_disagreement_score":0.8374242,"about_ca_system_score_codex":0.00007615314,"about_ca_system_score_gemma":0.00039094329,"threshold_uncertainty_score":0.99978733},"labels":[],"label_agreement":null},{"id":"W1516453488","doi":"10.2139/ssrn.1805206","title":"Thou Shalt Not Covet Thy (Suburban) Neighbor’s Car","year":2011,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Quest University Canada","funders":"","keywords":"Thou; Theology; Philosophy","score_opus":0.023577559239612696,"score_gpt":0.2661475724325862,"score_spread":0.2425700131929735,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1516453488","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6102492,0.0045971447,0.13401972,0.006328292,0.002738574,0.00063919846,0.00003334843,0.00062814675,0.24076635],"genre_scores_gemma":[0.99190915,0.0027052495,0.0004304709,0.0002829572,0.00033831532,0.0000029809107,0.000009110032,0.000016019132,0.004305729],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.997756,0.00013187474,0.00022816821,0.00015157329,0.00035631497,0.0013760802],"domain_scores_gemma":[0.9994649,0.000044651522,0.00014979797,0.00009217547,0.00012436975,0.00012407445],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015390664,0.00010377408,0.00012078891,0.00008535784,0.00061412447,0.000058525256,0.00026000402,0.000104025916,0.00029859671],"category_scores_gemma":[0.00008123506,0.000097281954,0.000085305895,0.00022279506,0.00008318857,0.00033894766,0.00000429718,0.0008248305,0.000054588058],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000116957905,0.00007718943,0.021290323,0.0000030539413,0.00009543682,0.000011435701,0.028970411,0.00025977907,0.00006030028,0.9410021,0.00070906704,0.0074039525],"study_design_scores_gemma":[0.004581836,0.0015262971,0.069489,0.00014017319,0.00046681083,0.00024273281,0.11263461,0.0006229841,0.00089276594,0.6258127,0.1815429,0.0020471716],"about_ca_topic_score_codex":0.0015207191,"about_ca_topic_score_gemma":0.008270276,"teacher_disagreement_score":0.38165992,"about_ca_system_score_codex":0.0003433939,"about_ca_system_score_gemma":0.001983152,"threshold_uncertainty_score":0.4723411},"labels":[],"label_agreement":null},{"id":"W1516853131","doi":"","title":"Guidelines for Planning and Implementation of Transit Priority Measures","year":2013,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Transit (satellite); Transport engineering; Reliability (semiconductor); Public transport; Traffic congestion; Business; Computer science; Engineering","score_opus":0.09602618412722587,"score_gpt":0.41524123243702926,"score_spread":0.3192150483098034,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1516853131","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5457944,0.00021596967,0.44411677,0.005614896,0.00011561383,0.0008327587,0.000017275937,0.00009285326,0.0031994323],"genre_scores_gemma":[0.97176576,0.000021535834,0.02784353,0.00009848054,0.000032720833,0.00001739767,0.000014922061,0.000002357331,0.00020328678],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9996145,0.000014788918,0.00014414934,0.000059593145,0.00010006036,0.00006691414],"domain_scores_gemma":[0.9995508,0.000041502484,0.000040566592,0.00002070238,0.00032004673,0.000026366075],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024171558,0.000028167071,0.000050780385,0.000025406553,0.0001168095,0.000019455036,0.000024568199,0.000024364143,0.00008400528],"category_scores_gemma":[0.000042191332,0.000025423114,0.000013794804,0.000047789566,0.00002683861,0.00015674288,6.1935316e-7,0.0000106029465,4.3342615e-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":[0.00003386092,0.00003552916,0.60350335,0.000106901396,0.00005907505,2.1183904e-7,0.12109248,0.0069734426,0.0030781045,0.06033001,0.01959473,0.18519232],"study_design_scores_gemma":[0.0018428746,0.00013680186,0.8207837,0.000056343913,0.000077133525,2.920352e-7,0.12597866,0.0031959075,0.0048835855,0.0054796105,0.03723276,0.00033236912],"about_ca_topic_score_codex":0.0039610215,"about_ca_topic_score_gemma":0.0013690233,"teacher_disagreement_score":0.42597133,"about_ca_system_score_codex":0.0000040350096,"about_ca_system_score_gemma":0.000035202906,"threshold_uncertainty_score":0.5987906},"labels":[],"label_agreement":null},{"id":"W1517741681","doi":"","title":"Institutional Aspects of Bus Rapid Transit Operation","year":2001,"lang":"en","type":"article","venue":"eScholarship (California Digital Library)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Federal Highway Administration; California Department of Transportation; Federal Transit Administration; U.S. Department of Transportation","keywords":"Bus rapid transit; Brainstorming; Public transport; Transport engineering; Business; Transit (satellite); Marketing; Public relations; Engineering; Political science","score_opus":0.017743019266447734,"score_gpt":0.23545691135706165,"score_spread":0.21771389209061393,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1517741681","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.496348,0.00027907794,0.008848818,0.0023736935,0.0002628847,0.00035140812,0.0013689164,0.0004648124,0.48970237],"genre_scores_gemma":[0.9968367,0.000094655275,0.00083202985,0.00021598165,0.00012964391,0.0000061618357,0.0012964846,0.000015832511,0.0005724704],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99881184,0.00006808997,0.00030535026,0.0002000244,0.00040109255,0.00021360963],"domain_scores_gemma":[0.99953735,0.000062187144,0.00008234464,0.00010552968,0.000059765298,0.0001528516],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00017230198,0.0001133881,0.00013421237,0.00012275692,0.00032954922,0.0003749175,0.00019294987,0.00011610965,0.001023068],"category_scores_gemma":[0.00011126479,0.000115280585,0.00008027742,0.00047980645,0.00017515624,0.0035379368,0.000007612851,0.00014153127,0.00028110063],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00042169046,0.000641708,0.41322002,0.00006646251,0.000086666834,0.00010129971,0.0025553105,0.013589029,0.00033969228,0.5298594,0.0016586443,0.037460014],"study_design_scores_gemma":[0.0013100284,0.00012221036,0.0688798,0.00013553166,0.00003565623,0.000009505386,0.00054323766,0.0004678975,0.0019045329,0.015566681,0.9104533,0.00057158264],"about_ca_topic_score_codex":0.000023405582,"about_ca_topic_score_gemma":0.000033178083,"teacher_disagreement_score":0.9087947,"about_ca_system_score_codex":0.000023035884,"about_ca_system_score_gemma":0.0002990261,"threshold_uncertainty_score":0.99989015},"labels":[],"label_agreement":null},{"id":"W1519413051","doi":"10.1002/atr.185","title":"Integrated smart feeder/shuttle transit service: simulation of new routing strategies","year":2011,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"European Commission","keywords":"Routing (electronic design automation); Operations research; Service (business); Transport engineering; Construct (python library); Computer science; Simulation; Engineering; Computer network","score_opus":0.0355373849364589,"score_gpt":0.3022844932395727,"score_spread":0.26674710830311377,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1519413051","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.74870676,0.000085144806,0.24924763,0.00016807423,0.0003784964,0.00014919628,0.000011583438,0.00004452242,0.0012085907],"genre_scores_gemma":[0.9772563,0.000072249044,0.022440353,0.000034305853,0.00006826374,8.0602945e-7,0.000053474934,0.000013296135,0.000060976992],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9985534,0.000057804158,0.00073223305,0.00010825035,0.00039376604,0.0001545756],"domain_scores_gemma":[0.99830437,0.0000698991,0.00069751544,0.0000640243,0.0007561148,0.00010806979],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003511122,0.000111859714,0.00022717341,0.0001603052,0.00013030934,0.000022922766,0.00012832144,0.0001034757,0.00014351426],"category_scores_gemma":[0.000036337722,0.00010944965,0.00010027396,0.0005349288,0.00004571222,0.0013029011,3.1622113e-7,0.00015451382,0.0000015681346],"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":[0.00034051613,0.0000631755,0.0055646203,0.000028683222,0.000035482088,0.000006053289,0.13735837,0.84769404,0.001089404,0.0027542156,0.000010255708,0.005055172],"study_design_scores_gemma":[0.003986533,0.00053678453,0.79243326,0.00067805836,0.00041769684,0.0000026438724,0.1829735,0.003872329,0.0041050888,0.007047161,0.0034205324,0.0005264231],"about_ca_topic_score_codex":0.0010097489,"about_ca_topic_score_gemma":0.0040465742,"teacher_disagreement_score":0.8438217,"about_ca_system_score_codex":0.00003473563,"about_ca_system_score_gemma":0.0004569143,"threshold_uncertainty_score":0.4463227},"labels":[],"label_agreement":null},{"id":"W1520187214","doi":"10.1002/atr.1271","title":"Estimation of bus dwell time using univariate time series models","year":2014,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Univariate; Dwell time; Series (stratigraphy); Estimation; Time series; Statistics; Computer science; Econometrics; Mathematics; Engineering; Multivariate statistics; Medicine; Geology","score_opus":0.0116107642212453,"score_gpt":0.2659796423232687,"score_spread":0.2543688781020234,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1520187214","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.67272824,0.000043308046,0.32599315,0.00016197325,0.00019822022,0.000110436136,0.000014382973,0.000026256552,0.0007240394],"genre_scores_gemma":[0.9088823,0.00005973016,0.09072323,0.000015428128,0.00006999502,5.6220233e-7,0.000047591184,0.000012380575,0.00018875195],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99870193,0.000090850604,0.000586296,0.00009220904,0.00039974187,0.00012896777],"domain_scores_gemma":[0.9986163,0.00007876728,0.00076252664,0.000066371686,0.00040312263,0.000072933035],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005491585,0.000091515845,0.00022716203,0.00015852702,0.00014260132,0.000017723974,0.000099531484,0.00007901463,0.000053517484],"category_scores_gemma":[0.000051949683,0.0000938124,0.00008198874,0.00029130786,0.00007406392,0.0011939072,4.426023e-7,0.00008962335,0.0000035517764],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013639563,0.000029846651,0.00014244874,0.00001867297,0.000016013157,0.0000020442712,0.008549363,0.97938895,0.0042298553,0.0045522186,0.0000075399503,0.0029266626],"study_design_scores_gemma":[0.006368637,0.0011866346,0.061667655,0.0013709241,0.0009819231,0.000018700302,0.0062229275,0.835913,0.011424519,0.07010913,0.0035756784,0.0011602892],"about_ca_topic_score_codex":0.00005664999,"about_ca_topic_score_gemma":0.0000375744,"teacher_disagreement_score":0.2361541,"about_ca_system_score_codex":0.000042352473,"about_ca_system_score_gemma":0.00013555956,"threshold_uncertainty_score":0.38255587},"labels":[],"label_agreement":null},{"id":"W1524058871","doi":"10.1007/s11116-009-9255-3","title":"Random covariance heterogeneity in discrete choice models","year":2010,"lang":"en","type":"article","venue":"Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Flexibility (engineering); Schedule; Computer science; Phone; Operations research; Task (project management); Transport engineering; Engineering; Systems engineering; Economics","score_opus":0.022981017856216406,"score_gpt":0.30613061278455506,"score_spread":0.28314959492833863,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1524058871","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9206594,0.000019732153,0.07545779,0.00043799815,0.0003379835,0.00026794025,0.000032776465,0.000120894234,0.0026654464],"genre_scores_gemma":[0.99455315,0.000038948692,0.0047871307,0.000073778414,0.00007213334,0.000028179145,0.00023661304,0.000009791036,0.00020025241],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.999068,0.000052965173,0.00024817805,0.00019447874,0.00024514168,0.00019122132],"domain_scores_gemma":[0.9996058,0.00008270718,0.00007486573,0.00009649038,0.00006583975,0.00007435093],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039939332,0.00008271501,0.0001142903,0.00007301976,0.00017866606,0.00003272778,0.0001059952,0.00011628169,0.00008821343],"category_scores_gemma":[0.000031926895,0.00009014689,0.00004334621,0.00028727046,0.000078465986,0.0005363195,3.136704e-7,0.00017241164,0.000008023364],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028523387,0.00013718677,0.40592685,0.000035788,0.000019615829,0.000016631851,0.0398866,0.42389247,0.0022572377,0.12430614,0.00007889491,0.003157343],"study_design_scores_gemma":[0.0030504577,0.00001900321,0.9698863,0.000037723705,0.000034461646,2.1225188e-7,0.0006520904,0.015444042,0.00048645478,0.003931175,0.00610621,0.00035185667],"about_ca_topic_score_codex":0.00495717,"about_ca_topic_score_gemma":0.16903912,"teacher_disagreement_score":0.5639594,"about_ca_system_score_codex":0.000014897274,"about_ca_system_score_gemma":0.000077636345,"threshold_uncertainty_score":0.84612375},"labels":[],"label_agreement":null},{"id":"W1524822577","doi":"10.1002/atr.207","title":"An alternative definition of dynamic user optimum on signalised road networks","year":2012,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Science Foundation","keywords":"Path (computing); Transport engineering; Flow network; Computer science; State (computer science); Mathematical optimization; Traffic flow (computer networking); Travel time; Operations research; Engineering; Mathematics; Algorithm; Computer network","score_opus":0.016812852278403668,"score_gpt":0.3021296813174324,"score_spread":0.2853168290390287,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1524822577","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.89339036,0.0001363588,0.10511864,0.000083110834,0.00065178255,0.00013473387,0.000020653795,0.000022297227,0.00044209498],"genre_scores_gemma":[0.9811523,0.00029497058,0.018160129,0.00005220677,0.00017088982,0.0000030257988,0.00013815935,0.000013786646,0.000014561137],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.998617,0.00011843023,0.0005272209,0.000090221205,0.00045456292,0.0001925525],"domain_scores_gemma":[0.9985814,0.00008821208,0.00073394936,0.00007052536,0.00038352137,0.00014240912],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005321788,0.000101412144,0.0001900115,0.00016410627,0.00012539524,0.000014168586,0.000113507616,0.000088183464,0.00005344595],"category_scores_gemma":[0.000028549026,0.00009866096,0.0000954667,0.00025178606,0.00006584111,0.0013312694,2.3630535e-7,0.00015972156,0.0000012190507],"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":[0.00045887276,0.00025370994,0.008003894,0.000010259357,0.00003351849,0.0000043393215,0.020736486,0.9559448,0.001055974,0.008566689,0.000009571111,0.0049218987],"study_design_scores_gemma":[0.0025239957,0.00086447917,0.978306,0.00030273295,0.00019982079,0.0000018869873,0.012029578,0.0022038652,0.00093121256,0.001857932,0.0004411271,0.00033734576],"about_ca_topic_score_codex":0.000056426943,"about_ca_topic_score_gemma":0.00016133267,"teacher_disagreement_score":0.9703021,"about_ca_system_score_codex":0.00006216158,"about_ca_system_score_gemma":0.000062319275,"threshold_uncertainty_score":0.40232772},"labels":[],"label_agreement":null},{"id":"W1525014717","doi":"10.1002/atr.1267","title":"Taxis in road pricing zone: should they pay the congestion charge?","year":2014,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","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":"Specialized Research Fund for the Doctoral Program of Higher Education of China; National Natural Science Foundation of China","keywords":"Taxis; Singapore Area Licensing Scheme; Road pricing; Congestion pricing; Transport engineering; Traffic congestion; Road traffic; Business; Computer science; Environmental science; Engineering","score_opus":0.016490483410089338,"score_gpt":0.2899338026159346,"score_spread":0.27344331920584525,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1525014717","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96159995,0.00009602692,0.031909432,0.0047108484,0.0007837852,0.00024198653,0.0000050921335,0.00003588724,0.0006170194],"genre_scores_gemma":[0.99696314,0.00031789232,0.0022182865,0.00012519532,0.00017567322,0.0000048773,0.000021920454,0.000009693944,0.0001633057],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9986393,0.0001503635,0.00050761056,0.000103402665,0.00043495948,0.00016440274],"domain_scores_gemma":[0.99901575,0.00015284406,0.00046103855,0.00007047026,0.00023949916,0.000060409326],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001208013,0.00008748678,0.00016238984,0.0001292565,0.00020606554,0.000029797353,0.00013482192,0.000078371915,0.00002892638],"category_scores_gemma":[0.00013711101,0.000052657568,0.00006938118,0.0002800738,0.000053297434,0.00058709056,3.6808746e-7,0.00025799533,0.0000028125364],"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":[0.00025666744,0.000098787285,0.07432355,0.000026144035,0.000023165365,0.000012049692,0.0950425,0.6888981,0.001077562,0.007219709,0.00006033301,0.13296144],"study_design_scores_gemma":[0.0010663872,0.00010802585,0.9774664,0.00015372045,0.000043352382,0.000001461877,0.005531092,0.00034134596,0.00022093982,0.001045986,0.013882325,0.0001389714],"about_ca_topic_score_codex":0.00035600096,"about_ca_topic_score_gemma":0.0051680137,"teacher_disagreement_score":0.90314287,"about_ca_system_score_codex":0.000054366665,"about_ca_system_score_gemma":0.00008591785,"threshold_uncertainty_score":0.2883873},"labels":[],"label_agreement":null},{"id":"W1525625978","doi":"10.1002/atr.1246","title":"Traffic signal timing problems with environmental and equity considerations","year":2013,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"University of Hong Kong; National Natural Science Foundation of China","keywords":"Mathematical optimization; Signal timing; Computer science; Traffic network; Simulated annealing; Flow network; Equity (law); Network planning and design; Heuristic; Transport engineering; Simulation; Operations research; Real-time computing; Engineering; Traffic signal; Mathematics; Computer network","score_opus":0.01839346826902756,"score_gpt":0.26661015336810573,"score_spread":0.24821668509907818,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1525625978","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99223995,0.00012858125,0.006516358,0.0005808876,0.00006296974,0.00021535844,0.0000059129334,0.000020325357,0.00022967583],"genre_scores_gemma":[0.98311317,0.00014825653,0.016564582,0.00005152349,0.00004459459,0.0000064632527,0.000023602352,0.00000747945,0.000040309176],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9991457,0.00003351307,0.00030796992,0.00008779244,0.0003028808,0.00012214188],"domain_scores_gemma":[0.9994321,0.00007483437,0.0002665571,0.000029446624,0.000095981966,0.000101085345],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017894714,0.0000725619,0.00011392842,0.00007325318,0.00025696945,0.000053400185,0.000035052584,0.000042611133,0.00030754536],"category_scores_gemma":[0.000008031554,0.00006324585,0.000028286577,0.00006766709,0.000112274756,0.00096630084,4.115241e-7,0.00010635098,0.0000024257008],"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":[0.000058816404,0.00010554226,0.0121742105,0.000021376496,0.00004141807,0.000017039425,0.05338174,0.9080444,0.004271972,0.0012953378,0.000064403685,0.020523718],"study_design_scores_gemma":[0.0032873312,0.00070543465,0.95689785,0.00024484962,0.0002150475,0.000029569399,0.032173272,0.00077855843,0.00047925714,0.002880081,0.001858406,0.00045035375],"about_ca_topic_score_codex":0.000027072694,"about_ca_topic_score_gemma":0.00039141675,"teacher_disagreement_score":0.9447236,"about_ca_system_score_codex":0.000031897605,"about_ca_system_score_gemma":0.000085853215,"threshold_uncertainty_score":0.3367406},"labels":[],"label_agreement":null},{"id":"W1525972495","doi":"10.1108/bepam-03-2013-0004","title":"A game-theoretic model for roadway performance management","year":2014,"lang":"en","type":"article","venue":"Built Environment Project and Asset Management","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Asset (computer security); Asset management; Multitude; Computer science; Game theory; Set (abstract data type); IT asset management; Risk analysis (engineering); Management science; Operations research; Knowledge management; Business; Microeconomics; Economics; Computer security; Engineering; Finance","score_opus":0.018311758078922994,"score_gpt":0.2655456986617421,"score_spread":0.2472339405828191,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1525972495","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12354591,0.00007833892,0.778339,0.000720132,0.00014611651,0.002709155,0.000017904775,0.00018904358,0.09425435],"genre_scores_gemma":[0.9563211,0.0017623842,0.025637055,0.00018583414,0.000046124296,0.00037570522,0.00008261607,0.000017432476,0.015571729],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99887747,0.00004677992,0.00018390939,0.000312956,0.00029286297,0.00028605197],"domain_scores_gemma":[0.99966884,0.00002341043,0.00007468337,0.00017070846,0.0000069947428,0.000055341014],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006219704,0.00013359437,0.000113202244,0.00010598495,0.00032235173,0.00007460633,0.0001479267,0.000045259156,0.00002392839],"category_scores_gemma":[0.0000030934202,0.00013054206,0.000040062998,0.000077096614,0.000101147336,0.00014784948,0.000033900382,0.00004179641,0.000012824258],"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":[0.0001658257,0.00037726137,0.013478314,0.0010401313,0.00022406268,0.0000045925326,0.01189022,0.18914281,0.000009368325,0.51159674,0.004013963,0.26805672],"study_design_scores_gemma":[0.0017501909,0.00018106114,0.01899694,0.00010569253,0.00025129216,3.333555e-7,0.0021825777,0.5858943,0.000018339608,0.0045969402,0.38547477,0.00054757687],"about_ca_topic_score_codex":0.00001859964,"about_ca_topic_score_gemma":0.000012438885,"teacher_disagreement_score":0.83277524,"about_ca_system_score_codex":0.000047384303,"about_ca_system_score_gemma":0.0000068078234,"threshold_uncertainty_score":0.5323351},"labels":[],"label_agreement":null},{"id":"W1527156413","doi":"10.1002/atr.1252","title":"Analysis of factors that influence the sensor location problem for freeway corridors","year":2013,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","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":"Fundamental Research Funds for the Central Universities; National High-tech Research and Development Program; Central University Basic Research Fund of China; National Natural Science Foundation of China","keywords":"Computer science; Transport engineering; Scheme (mathematics); Value (mathematics); Operations research; Intelligent transportation system; Engineering; Machine learning; Mathematics","score_opus":0.01408023464002443,"score_gpt":0.27818426152557996,"score_spread":0.2641040268855555,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1527156413","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94313234,0.000057764206,0.055827096,0.00039261338,0.00014118796,0.00037343038,0.000025697986,0.000013849773,0.00003599321],"genre_scores_gemma":[0.99158156,0.000090715774,0.008091991,0.00003505191,0.000021414662,0.000013652671,0.00009409003,0.000007237187,0.00006427781],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9988032,0.000051056977,0.00052859867,0.000092965354,0.0003965088,0.0001276991],"domain_scores_gemma":[0.9974972,0.0002912617,0.000901781,0.00007794931,0.0011717739,0.000060014554],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003567854,0.00008304198,0.00021454289,0.00022917922,0.00018980073,0.000027407788,0.00014268425,0.00006171903,0.000034094868],"category_scores_gemma":[0.00009073891,0.000060217255,0.00016277278,0.0008688429,0.000095319396,0.0008217912,3.017761e-7,0.00008349014,4.948551e-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":[0.000040559084,0.000026444182,0.16883293,0.000018763656,0.00014194728,2.2138354e-7,0.02669856,0.80217546,0.0005230727,0.00049879536,0.000019273093,0.0010239622],"study_design_scores_gemma":[0.0003321163,0.00006629396,0.9801565,0.000040105086,0.0005583092,9.070713e-8,0.0170095,0.0004540194,0.00050353934,0.0003776798,0.000425434,0.0000764021],"about_ca_topic_score_codex":0.0006005819,"about_ca_topic_score_gemma":0.0017549428,"teacher_disagreement_score":0.8113236,"about_ca_system_score_codex":0.00003388776,"about_ca_system_score_gemma":0.00010807828,"threshold_uncertainty_score":0.24555883},"labels":[],"label_agreement":null},{"id":"W1528572877","doi":"10.1002/atr.1309","title":"A multi‐class transit assignment model for estimating transit passenger flows—a case study of Beijing subway network","year":2015,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":40,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"National Key Research and Development Program of China; State Key Laboratory of Rail Traffic Control and Safety; National Natural Science Foundation of China","keywords":"Beijing; Urban rail transit; Transport engineering; Transit (satellite); Class (philosophy); Computer science; Shanghai china; Estimation; Flow network; Traffic flow (computer networking); Preference; Rail transit; Public transport; Scheme (mathematics); Operations research; Engineering; China; Mathematical optimization; Statistics; Geography; Mathematics; Computer network; Artificial intelligence","score_opus":0.05265371492748807,"score_gpt":0.33563273151693906,"score_spread":0.282979016589451,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1528572877","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.49054235,0.000057383204,0.5085786,0.00008866715,0.00029865507,0.00039197132,0.000013986684,0.000019396834,0.000008984263],"genre_scores_gemma":[0.7392075,0.000012960475,0.26054683,0.000016443622,0.00013513627,0.00001747549,0.000022368416,0.000019487725,0.000021787711],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977674,0.00011035821,0.0010367562,0.00018832072,0.00062944205,0.0002677045],"domain_scores_gemma":[0.9979824,0.0001656681,0.00074506167,0.00009132031,0.0008095242,0.00020603137],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012197188,0.00016448757,0.0003876893,0.00013318736,0.0002821461,0.000027987488,0.00012038041,0.000102878636,0.0000040096556],"category_scores_gemma":[0.000061134844,0.00016382747,0.00015169894,0.0003205601,0.00003969082,0.0007351615,4.546736e-7,0.00017844507,1.5883143e-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":[0.00031256565,0.00030576612,0.001953997,0.000037054226,0.000060898805,0.000118994976,0.14590907,0.84891,0.00025501158,0.000060119626,0.00001612859,0.0020604234],"study_design_scores_gemma":[0.009155029,0.0009327074,0.0036788369,0.00027099493,0.00062916824,0.000027689408,0.1322684,0.8521648,0.000072906034,0.00030262367,0.00013670523,0.0003601086],"about_ca_topic_score_codex":0.00012646157,"about_ca_topic_score_gemma":0.008382499,"teacher_disagreement_score":0.24866517,"about_ca_system_score_codex":0.00010033021,"about_ca_system_score_gemma":0.00028430083,"threshold_uncertainty_score":0.668069},"labels":[],"label_agreement":null},{"id":"W1533913647","doi":"","title":"Traffic model for Copenhagen; updating of the trip matrices","year":2006,"lang":"en","type":"article","venue":"Technical University of Denmark, DTU Orbit (Technical University of Denmark, DTU)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Transport Canada","funders":"","keywords":"TRIPS architecture; Trip distribution; Transport engineering; Revealed preference; Probit; Travel survey; Public transport; Operations research; Travel behavior; Work (physics); Traffic congestion; Geography; Computer science; Econometrics; Engineering; Mathematics","score_opus":0.01628830009194739,"score_gpt":0.23306039559168207,"score_spread":0.21677209549973467,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1533913647","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.68281066,0.00047799808,0.22801523,0.0027422542,0.00022568178,0.0026999405,0.0006470679,0.00049815234,0.081883],"genre_scores_gemma":[0.9612467,0.00018561869,0.031569265,0.000022104281,0.000025300282,3.1200514e-7,0.000036812446,0.000018037017,0.0068958327],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9975801,0.0002182988,0.0005039722,0.00048425148,0.00077417854,0.00043918227],"domain_scores_gemma":[0.9974452,0.00062543235,0.00079552195,0.00046458447,0.00051613763,0.00015308913],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00096362334,0.00024009249,0.0005990809,0.00025845392,0.00080876466,0.000016988077,0.001526837,0.0005528841,0.00059730053],"category_scores_gemma":[0.00020914793,0.00026856802,0.00055085414,0.0011961298,0.0013723078,0.00039263867,0.00016777847,0.00036992834,0.000017532146],"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":[0.008128284,0.006348547,0.020384897,0.0017596355,0.00076925673,0.00016693617,0.012045602,0.4043238,0.03491848,0.25496525,0.24305053,0.013138826],"study_design_scores_gemma":[0.044068385,0.0033934577,0.30337253,0.004806567,0.008542646,0.00007142432,0.07906775,0.26178923,0.008641859,0.013368708,0.2639147,0.008962752],"about_ca_topic_score_codex":0.0014199957,"about_ca_topic_score_gemma":0.005445946,"teacher_disagreement_score":0.28298762,"about_ca_system_score_codex":0.00022074647,"about_ca_system_score_gemma":0.00044445373,"threshold_uncertainty_score":0.99997663},"labels":[],"label_agreement":null},{"id":"W1534951127","doi":"10.1002/atr.1255","title":"Toll pricing framework under logit‐based stochastic user equilibrium constraints","year":2013,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Toll; Marginal cost; Logit; Variational inequality; Road pricing; Mathematical optimization; Computer science; Congestion pricing; Path (computing); Flow network; Economics; Operations research; Econometrics; Microeconomics; Mathematics; Transport engineering; Engineering; Computer network; Traffic congestion","score_opus":0.01694462741406569,"score_gpt":0.29227817651322013,"score_spread":0.2753335490991544,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1534951127","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.48099425,0.000054961183,0.51685435,0.001149637,0.0005040936,0.00020480285,0.000006376999,0.000042927317,0.00018858211],"genre_scores_gemma":[0.9522118,0.000014987734,0.04724371,0.00027573973,0.0001339169,0.0000060436214,0.000028159235,0.000015143086,0.00007051915],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9984899,0.00006398283,0.00057169783,0.00013346085,0.0005029671,0.00023796559],"domain_scores_gemma":[0.9984393,0.00028447807,0.0005205723,0.00007820251,0.00052131363,0.00015614904],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029093967,0.00011981488,0.00020640096,0.00015951715,0.00015679594,0.00006264239,0.00013490902,0.00012957717,0.00040171048],"category_scores_gemma":[0.00011984884,0.00011497341,0.00010532047,0.00030888093,0.00016103903,0.0009656732,4.5620303e-7,0.00024489942,0.00001207125],"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":[0.00006148111,0.000056031047,0.0036081166,0.0000150774995,0.000021755637,0.000007511378,0.0057702432,0.98145,0.000795612,0.006154465,0.00005523903,0.0020044243],"study_design_scores_gemma":[0.005316795,0.0006112096,0.89712554,0.001450311,0.0003913805,0.000007654108,0.046357773,0.0019170505,0.0010793908,0.042883478,0.0017784832,0.0010809448],"about_ca_topic_score_codex":0.000055284174,"about_ca_topic_score_gemma":0.000086667314,"teacher_disagreement_score":0.979533,"about_ca_system_score_codex":0.00006904214,"about_ca_system_score_gemma":0.00026072515,"threshold_uncertainty_score":0.46884796},"labels":[],"label_agreement":null},{"id":"W1535246175","doi":"10.2139/ssrn.2432178","title":"Distributional Consequences of Public Policies: An Example from the Management of Urban Vehicular Travel","year":2014,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Public transport; Business; Public economics; Transport engineering; Environmental planning; Regional science; Economics; Geography; Engineering","score_opus":0.026111634260919846,"score_gpt":0.2685204119283564,"score_spread":0.24240877766743657,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1535246175","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8698347,0.0005473636,0.12618378,0.0018334185,0.00009124491,0.00009681253,0.00002816942,0.000014623366,0.0013698984],"genre_scores_gemma":[0.9987328,0.00070569274,0.00024181374,0.000032751836,0.000104992025,0.0000018852122,0.00006270014,0.0000034890716,0.000113888025],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99857616,0.00022053222,0.00023016614,0.00008750286,0.000362245,0.00052341935],"domain_scores_gemma":[0.9994477,0.000088022425,0.00020355932,0.000086767664,0.00012298432,0.00005093576],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021227454,0.00005544734,0.000097825374,0.000029950532,0.00033129146,0.00003323884,0.00026850647,0.000039357787,0.000041903302],"category_scores_gemma":[0.000021004691,0.00004163303,0.00005114029,0.00017797857,0.00027865925,0.00015043854,0.000003772489,0.00022847288,7.883895e-7],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008933296,0.000034470577,0.015698433,0.0000019402432,0.000091187096,1.7151662e-7,0.003985865,0.0003655184,0.00009216591,0.97801214,0.000031205796,0.0016779698],"study_design_scores_gemma":[0.0018697906,0.00043029804,0.26432338,0.00011411146,0.00026136078,0.000010383762,0.19777569,0.0006440868,0.0005523197,0.5203015,0.013352385,0.00036465892],"about_ca_topic_score_codex":0.008053528,"about_ca_topic_score_gemma":0.00402287,"teacher_disagreement_score":0.45771062,"about_ca_system_score_codex":0.00011116976,"about_ca_system_score_gemma":0.0007006043,"threshold_uncertainty_score":0.9985519},"labels":[],"label_agreement":null},{"id":"W1535836935","doi":"10.1111/j.1468-0270.2011.02126.x","title":"INTER‐CITY BUS SERVICES IN CANADA – TIME FOR DEREGULATION","year":2012,"lang":"en","type":"article","venue":"Economic Affairs","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Deregulation; Government (linguistics); Productivity; Business; Service (business); Quality (philosophy); Service quality; Economics; Public economics; Marketing; Market economy; Economic growth","score_opus":0.009836336382015051,"score_gpt":0.23259543008886227,"score_spread":0.2227590937068472,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1535836935","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9771405,0.000037881837,0.0006849399,0.0007254561,0.00055635354,0.0001992317,0.000032952034,0.000032592485,0.020590095],"genre_scores_gemma":[0.9986769,0.0000032730961,0.0005079022,0.000038936385,0.0001204476,0.000010469187,0.00006475963,0.0000044321887,0.00057288713],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9995924,0.000019230934,0.00012225866,0.00007419619,0.00003237698,0.00015949037],"domain_scores_gemma":[0.9997822,0.000056569894,0.000057177316,0.000041143794,0.000010272488,0.00005264997],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020156197,0.000040693132,0.00006493743,0.000028198056,0.00008360227,0.000014513992,0.00006121119,0.00003060587,0.0002865144],"category_scores_gemma":[0.0000058731707,0.000047781916,0.000014500542,0.000029454995,0.000012998551,0.00028762623,0.0000022003403,0.000020868469,0.000024242261],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000041337484,0.000021318701,0.9236463,0.000021364189,0.000016071454,2.705245e-7,0.022893155,0.03785213,0.0000051617726,0.0066843354,0.0033113498,0.0055072405],"study_design_scores_gemma":[0.0009383646,0.000019979117,0.8181614,0.000047079775,0.00002183259,3.8704e-7,0.042525012,0.041102275,0.000111003836,0.00070173776,0.09593023,0.00044065734],"about_ca_topic_score_codex":0.7699402,"about_ca_topic_score_gemma":0.98648727,"teacher_disagreement_score":0.21654704,"about_ca_system_score_codex":0.00043284392,"about_ca_system_score_gemma":0.0002986005,"threshold_uncertainty_score":0.31371316},"labels":[],"label_agreement":null},{"id":"W1537192819","doi":"10.1002/atr.1200","title":"A method to estimate the historical US air travel demand","year":2012,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Air travel; Transport engineering; Environmental science; Computer science; Econometrics; Economics; Engineering; Aviation; Aerospace engineering","score_opus":0.018409690077971583,"score_gpt":0.35449445132457574,"score_spread":0.33608476124660414,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1537192819","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28647724,0.00035407714,0.708366,0.0033050457,0.0008369933,0.00018612138,0.0000051499487,0.000026048961,0.00044337226],"genre_scores_gemma":[0.83758074,0.00004131169,0.16165707,0.00021692665,0.00026518552,0.0000049367686,0.0000073023034,0.000009515811,0.00021703132],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9987763,0.00011136303,0.00041228224,0.00007283735,0.00041720193,0.00020997027],"domain_scores_gemma":[0.99909586,0.00014481077,0.0002910716,0.000054747503,0.00022348083,0.00019001293],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010992286,0.00008062919,0.00015736723,0.00009416888,0.00026079672,0.0000130891995,0.000120848665,0.000056029585,0.000022489343],"category_scores_gemma":[0.00005423394,0.000060569197,0.00009033783,0.0003177007,0.000023014829,0.0005805872,3.8206e-7,0.000143032,0.0000032945102],"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":[0.00028203698,0.00014467472,0.047517236,0.000017783339,0.000040986786,0.000010469701,0.1211713,0.80436575,0.0014192262,0.008333275,0.000664748,0.016032523],"study_design_scores_gemma":[0.0005006051,0.000095604075,0.9168799,0.000041209703,0.000112845904,0.0000046665223,0.0032917701,0.00003598008,0.00044161902,0.00038945096,0.07807668,0.00012964552],"about_ca_topic_score_codex":0.00007707385,"about_ca_topic_score_gemma":0.00027343788,"teacher_disagreement_score":0.8693627,"about_ca_system_score_codex":0.00014756001,"about_ca_system_score_gemma":0.000081622435,"threshold_uncertainty_score":0.24699402},"labels":[],"label_agreement":null},{"id":"W1538179740","doi":"10.1109/vnis.1989.98780","title":"Evaluating the benefits and interactions of route guidance and traffic control strategies using simulation","year":2003,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ministry of Transportation of Ontario; Queen's University","funders":"","keywords":"Computer science; Control (management); Transport engineering; Travel time; Operations research; Simulation; Engineering; Artificial intelligence","score_opus":0.09310942155457488,"score_gpt":0.4035535369871179,"score_spread":0.310444115432543,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1538179740","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97602516,0.00021473946,0.021567764,0.000106949075,0.000047156638,0.00010563134,0.000002796015,0.000017548537,0.0019122398],"genre_scores_gemma":[0.9964183,0.000017674463,0.003466492,0.000021570888,0.000010840575,0.000001421649,0.0000011852379,0.0000020154341,0.000060507846],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995678,0.000101167505,0.000113240596,0.00006479808,0.00009721531,0.00005575287],"domain_scores_gemma":[0.9995376,0.00026674088,0.00006675068,0.000029263547,0.00008350675,0.000016122849],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003981662,0.000032093692,0.000047492405,0.000019955265,0.00029659257,0.000052844396,0.000017598437,0.000018482699,0.0000184228],"category_scores_gemma":[0.000115570925,0.000024724523,0.000008527587,0.00008528191,0.00006516241,0.00023721704,6.82957e-7,0.000027259868,1.0833386e-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":[0.0000045734723,0.0000037645646,0.004542034,0.0000021951548,0.000004168306,2.4001759e-8,0.005685384,0.9655842,0.0000813289,0.021467732,6.1462964e-7,0.0026239504],"study_design_scores_gemma":[0.00034236856,0.00002719498,0.025222732,0.000032253945,0.000036949645,4.2301863e-7,0.013862559,0.9599451,0.000020617259,0.00030882124,0.00013998816,0.000061013212],"about_ca_topic_score_codex":0.00021900315,"about_ca_topic_score_gemma":0.00080137566,"teacher_disagreement_score":0.021158911,"about_ca_system_score_codex":0.000007397877,"about_ca_system_score_gemma":0.000053963187,"threshold_uncertainty_score":0.22811802},"labels":[],"label_agreement":null},{"id":"W1539292066","doi":"10.1002/atr.1307","title":"Behavior of taxi customers in hailing vacant taxis: a nested logit model for policy analysis","year":2015,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"University Research Committee, Emory University; University of Hong Kong; National Natural Science Foundation of China","keywords":"Taxis; Multinomial logistic regression; Nested logit; Mixed logit; Discrete choice; Computer science; Market segmentation; Logit; Logistic regression; Operations research; Econometrics; Transport engineering; Marketing; Business; Economics; Mathematics; Engineering","score_opus":0.04113446549666637,"score_gpt":0.35278317563643,"score_spread":0.31164871013976364,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1539292066","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.83113205,0.00009114882,0.16794634,0.00034107742,0.00011530279,0.00024386712,0.000051769606,0.000014592233,0.00006384972],"genre_scores_gemma":[0.97012496,0.00008128137,0.029559016,0.000021806558,0.000045361136,0.000013246625,0.00007715405,0.000008913964,0.000068234585],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9985722,0.000040090315,0.0007184836,0.00010873746,0.00039614338,0.00016438452],"domain_scores_gemma":[0.9983619,0.000060569677,0.00070966943,0.000058980073,0.00068590365,0.00012300097],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006024919,0.00008578398,0.00029601195,0.00070773455,0.000057350087,0.000011340727,0.00011077438,0.00008379132,0.000002982333],"category_scores_gemma":[0.00013205879,0.000086464766,0.00016674933,0.0012523547,0.00005607171,0.00054626516,3.5466786e-7,0.00009692788,1.6310449e-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":[0.00023143459,0.00008011188,0.0278204,0.000012731208,0.00004301643,0.00000515304,0.026503837,0.9424493,0.000265059,0.0010455253,0.0000070884166,0.0015363496],"study_design_scores_gemma":[0.012881917,0.00082999095,0.7564522,0.00038585466,0.0036833177,0.0000027739484,0.07838867,0.13858332,0.0012243247,0.005231522,0.0014103,0.00092581694],"about_ca_topic_score_codex":0.00067165075,"about_ca_topic_score_gemma":0.006903822,"teacher_disagreement_score":0.80386597,"about_ca_system_score_codex":0.00012302313,"about_ca_system_score_gemma":0.0005709716,"threshold_uncertainty_score":0.3852495},"labels":[],"label_agreement":null},{"id":"W1540192699","doi":"10.1002/atr.1238","title":"Optimal transit fare in a bimodal network under demand uncertainty and bounded rationality","year":2013,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":14,"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":"Bounded rationality; Public transport; Bounded function; Heuristic; Sensitivity (control systems); Mode choice; Mathematical optimization; Computer science; Mode (computer interface); Rationality; Operations research; Transit (satellite); Logit; Economics; Econometrics; Mathematics; Microeconomics; Transport engineering; Engineering","score_opus":0.011719574254876257,"score_gpt":0.27464956675815255,"score_spread":0.2629299925032763,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1540192699","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9598887,0.00024943298,0.03771156,0.0015748913,0.00018795826,0.00023367845,0.0000073647325,0.000017961951,0.00012843385],"genre_scores_gemma":[0.9812384,0.00025484414,0.018163312,0.000121329096,0.00011346024,0.000008282513,0.00005375585,0.0000082306215,0.00003835381],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9986801,0.0001004378,0.0005417657,0.00013102242,0.0003478982,0.00019875621],"domain_scores_gemma":[0.999162,0.00011133121,0.000280342,0.00004435365,0.0002899163,0.000112028785],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048325284,0.00010083151,0.00019834643,0.00010316644,0.00020299401,0.000057599857,0.00007136499,0.00009409138,0.00008950807],"category_scores_gemma":[0.00002543766,0.00009758112,0.00005928234,0.00031193413,0.00009947901,0.00090980146,4.0063478e-7,0.00017049225,0.0000010888787],"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":[0.0001219302,0.000036572244,0.024622412,0.000012453907,0.000015856254,0.000005929318,0.010998996,0.9558208,0.000099088385,0.006991343,0.000029095232,0.0012455174],"study_design_scores_gemma":[0.0015388618,0.000080258156,0.9795838,0.00008861131,0.000036340407,0.0000019409842,0.007437159,0.0009290886,0.000017210486,0.009209568,0.0009261887,0.00015096202],"about_ca_topic_score_codex":0.00037715535,"about_ca_topic_score_gemma":0.004497196,"teacher_disagreement_score":0.9549614,"about_ca_system_score_codex":0.00006223028,"about_ca_system_score_gemma":0.00016635799,"threshold_uncertainty_score":0.39792424},"labels":[],"label_agreement":null},{"id":"W1540990492","doi":"","title":"Assessing the Value of TMCs and Methods to Evaluate the Long Term Effects of ITS: Measuring Congestion, Productivity and Benefi t Flow from Implementation","year":2004,"lang":"en","type":"article","venue":"eScholarship (California Digital Library)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Truck; Productivity; Investment (military); Transport engineering; Traffic congestion; Traffic flow (computer networking); Work (physics); Operations research; Dimension (graph theory); Engineering; Operations management; Environmental economics; Business; Computer science; Economics; Computer security; Mathematics; Economic growth","score_opus":0.027551168473843354,"score_gpt":0.3306824938207443,"score_spread":0.30313132534690096,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1540990492","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98777145,0.0004727794,0.009612653,0.0012222986,0.00007842134,0.00049497356,0.00015700133,0.00004407847,0.00014632047],"genre_scores_gemma":[0.9914891,0.000033889828,0.008241046,0.00006282218,0.00005133847,0.000015907579,0.00008502233,0.000013551706,0.0000073013384],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9986832,0.00037831513,0.00026147274,0.0002133012,0.0003189565,0.00014475817],"domain_scores_gemma":[0.99903876,0.0005455987,0.00013235869,0.00012349898,0.000081552455,0.000078219404],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009905638,0.00010576661,0.0001435599,0.000052967745,0.0003301591,0.00053288863,0.00013856842,0.00005014558,0.000008344877],"category_scores_gemma":[0.0006872056,0.00007318558,0.000031699405,0.00030422327,0.00013698952,0.0023583095,0.000033585424,0.00011660315,0.0000016012369],"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":[0.000059978855,0.000076757686,0.8699994,0.00015386165,0.000083040024,0.0000018728057,0.0070035895,0.004774532,0.0031126207,0.0033204588,0.000009792284,0.11140406],"study_design_scores_gemma":[0.0003958826,0.00003553006,0.961939,0.00019642462,0.00006644507,5.1745803e-7,0.00053510314,0.00007067397,0.032766193,0.0037087852,0.00017350534,0.00011194181],"about_ca_topic_score_codex":0.000067829984,"about_ca_topic_score_gemma":0.000055512206,"teacher_disagreement_score":0.11129212,"about_ca_system_score_codex":0.000018055962,"about_ca_system_score_gemma":0.00011439713,"threshold_uncertainty_score":0.5138657},"labels":[],"label_agreement":null},{"id":"W1543070735","doi":"10.1787/5k8zvv94rmd1-en","title":"Maintained Hypotheses and Questions in Search of Answers","year":2011,"lang":"en","type":"paratext","venue":"Discussion papers","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Urbanization; Computer science; Regional science; Operations research; Data science; Positive economics; Mathematical economics; Sociology; Economics; Mathematics; Economic growth","score_opus":0.026850908613092847,"score_gpt":0.30748206663392635,"score_spread":0.2806311580208335,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1543070735","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021290237,0.0007084771,0.00028444245,0.0020097906,0.0009034491,0.00063820794,0.00022383637,0.000060915998,0.97388065],"genre_scores_gemma":[0.79359925,0.003399947,0.0006718842,0.000068616035,0.00006862026,0.000024122095,0.00032712382,0.000026720778,0.20181371],"study_design_codex":"qualitative","study_design_gemma":"not_applicable","domain_scores_codex":[0.9989408,0.00019867747,0.0002261482,0.0002069797,0.00024104166,0.00018631574],"domain_scores_gemma":[0.99960166,0.00007318412,0.00008985203,0.000095571806,0.000049295,0.000090456284],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00032376617,0.00011076488,0.00019553598,0.00021634277,0.0001584579,0.000019602106,0.00011369858,0.0002205218,0.002163173],"category_scores_gemma":[0.00007166069,0.00007369273,0.00004336993,0.00025982843,0.00027486213,0.00008102261,0.000008164419,0.0001657386,0.000054391887],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00072306365,0.0008905828,0.09674914,0.001064613,0.00023057948,0.000048700927,0.6459346,0.017694775,0.0012847523,0.06615275,0.08362916,0.08559727],"study_design_scores_gemma":[0.0016867495,0.00022845202,0.17177561,0.0027513646,0.00012251925,0.0000011329271,0.15125564,0.0001234478,0.00018187994,0.00082269806,0.66974765,0.0013028769],"about_ca_topic_score_codex":0.0029877664,"about_ca_topic_score_gemma":0.0023065484,"teacher_disagreement_score":0.772309,"about_ca_system_score_codex":0.000036688387,"about_ca_system_score_gemma":0.0002361095,"threshold_uncertainty_score":0.99874896},"labels":[],"label_agreement":null},{"id":"W1543690589","doi":"10.1002/atr.205","title":"A multi‐class mean‐excess traffic equilibrium model with elastic demand","year":2012,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":34,"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":"China Scholarship Council; Tongji University; National Science Foundation","keywords":"Price elasticity of demand; Travel time; Variational inequality; Elasticity (physics); Computer science; Function (biology); Risk aversion (psychology); Class (philosophy); Mathematical optimization; Econometrics; Operations research; Economics; Mathematical economics; Microeconomics; Mathematics; Expected utility hypothesis; Transport engineering; Engineering; Artificial intelligence","score_opus":0.01832041953930727,"score_gpt":0.2857894207781338,"score_spread":0.26746900123882655,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1543690589","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8287776,0.00025003645,0.17015202,0.0002055924,0.00030199278,0.00013468985,0.000010711514,0.000042382886,0.00012493195],"genre_scores_gemma":[0.9498256,0.00010553718,0.04966934,0.000042436277,0.00014858156,0.00000438646,0.000031504143,0.000018904424,0.00015372621],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9985389,0.000055896446,0.0004674557,0.000109167064,0.0005340939,0.00029444706],"domain_scores_gemma":[0.99880844,0.00007981978,0.00047528642,0.000065126515,0.00035180085,0.00021954408],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004639256,0.00012731597,0.00021083512,0.0001432639,0.00016470507,0.000028442999,0.000117496806,0.00008583674,0.000019724408],"category_scores_gemma":[0.000028121662,0.00010881867,0.000079409445,0.00030166996,0.000088528446,0.0016527592,3.8087853e-7,0.000176788,0.0000022125232],"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":[0.0002412628,0.00011874557,0.0048038294,0.000021366714,0.00002674015,0.000006351139,0.041203283,0.9517755,0.0006710454,0.00040709603,0.000017857257,0.00070694415],"study_design_scores_gemma":[0.020532575,0.0013732847,0.7526028,0.0014301214,0.001963186,0.00004869513,0.06823058,0.1400616,0.002152321,0.0006156745,0.008683732,0.0023054136],"about_ca_topic_score_codex":0.000009799919,"about_ca_topic_score_gemma":0.00054123945,"teacher_disagreement_score":0.8117139,"about_ca_system_score_codex":0.000057513364,"about_ca_system_score_gemma":0.00018586882,"threshold_uncertainty_score":0.44374967},"labels":[],"label_agreement":null},{"id":"W1543980706","doi":"10.1002/atr.1194","title":"Comparative study on travel time reliability indexes for highway users and operators","year":2012,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Meijo University","keywords":"Reliability (semiconductor); Computer science; Index (typography); Travel time; Operator (biology); Measure (data warehouse); Level of service; Variation (astronomy); Transport engineering; Service (business); Function (biology); Operations research; Data mining; Engineering","score_opus":0.02443281402411519,"score_gpt":0.3280695409857674,"score_spread":0.3036367269616522,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1543980706","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9954552,0.000051177052,0.003453072,0.00017332117,0.00029417843,0.0004325465,0.000024318888,0.000016196038,0.0000999841],"genre_scores_gemma":[0.9954272,0.000025107202,0.0043067713,0.00003161316,0.00010690106,0.000009965058,0.000022923186,0.000007492312,0.0000620198],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.9989966,0.00008380605,0.00038204854,0.00009960286,0.00028649013,0.00015145261],"domain_scores_gemma":[0.99908745,0.00019827868,0.0002746085,0.00004669285,0.00026974812,0.00012321227],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007037314,0.00009089896,0.00021965831,0.000093216535,0.0002263462,0.000020372652,0.00005830066,0.00005223348,0.000012539166],"category_scores_gemma":[0.0000476059,0.00008017506,0.00005206577,0.00015182885,0.00006232064,0.0007783451,2.5022712e-7,0.000104029205,0.0000011637968],"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":[0.0014293564,0.0011047232,0.3628368,0.000030827356,0.00010499848,0.000002981615,0.49921548,0.12942255,0.001520952,0.0027740838,0.0001825488,0.00137467],"study_design_scores_gemma":[0.0013883461,0.0005072456,0.9619793,0.00003063941,0.00007570639,2.45538e-7,0.03443313,0.000018477895,0.00047505484,0.000120663666,0.000861434,0.00010970133],"about_ca_topic_score_codex":0.00001856673,"about_ca_topic_score_gemma":0.000095291674,"teacher_disagreement_score":0.59914255,"about_ca_system_score_codex":0.000042823518,"about_ca_system_score_gemma":0.00006174545,"threshold_uncertainty_score":0.32694438},"labels":[],"label_agreement":null},{"id":"W1545564034","doi":"10.3386/w8278","title":"Ownership and Use Taxes as Congestion Correcting Instruments","year":2001,"lang":"en","type":"report","venue":"National Bureau of Economic Research","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"TRIPS architecture; Externality; Revenue; Economics; Congestion pricing; Excise; Tax revenue; Tax deferral; Car ownership; Microeconomics; Traffic congestion; Marginal cost; Public economics; Business; Public transport; Tax reform; Transport engineering; Finance; State income tax","score_opus":0.447699686332323,"score_gpt":0.5359663174201584,"score_spread":0.08826663108783545,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1545564034","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1657873,0.00023168277,0.000015354046,0.0010043763,0.0011487451,0.0006341374,0.0001420421,0.000050518305,0.83098584],"genre_scores_gemma":[0.9539138,0.0029627306,0.00022951589,0.000015368862,0.0005080748,0.000028275159,0.0006940903,0.000019363542,0.04162877],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99728924,0.00027106376,0.00041667226,0.00033025217,0.0014226177,0.000270134],"domain_scores_gemma":[0.99672765,0.0011345001,0.00031110775,0.000088820576,0.0016211965,0.000116724455],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00491439,0.000114032424,0.00022444714,0.00057312497,0.00042799808,0.00017320931,0.00016537745,0.00036298065,0.00034790777],"category_scores_gemma":[0.0038514666,0.00012523608,0.000050497896,0.00020388357,0.00036562668,0.00040333174,0.000019713756,0.00041937255,0.000034497687],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002878915,0.00019201261,0.42114064,0.00029209084,0.00040518332,0.000018363298,0.008376074,0.008540499,0.000022061226,0.42169356,0.08225148,0.056780156],"study_design_scores_gemma":[0.0028643876,0.0005139271,0.12450071,0.0019633358,0.0001572973,0.00009815214,0.0121474415,0.0019135462,0.0001883943,0.26330692,0.5906084,0.0017374455],"about_ca_topic_score_codex":0.03447062,"about_ca_topic_score_gemma":0.005915108,"teacher_disagreement_score":0.78935707,"about_ca_system_score_codex":0.0009059153,"about_ca_system_score_gemma":0.0036167747,"threshold_uncertainty_score":0.97195894},"labels":[],"label_agreement":null},{"id":"W1546938461","doi":"","title":"The Potential for Premium-intermodal Services to Reduce Freight CO2 Emissions in the Quebec City - Windsor Corridor: A Stated Preference Application","year":2006,"lang":"en","type":"article","venue":"Infoscience (Ecole Polytechnique Fédérale de Lausanne)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Transport engineering; Truck; Windsor; Destinations; Revealed preference; Greenhouse gas; Business; Agricultural economics; Geography; Environmental science; Engineering; Tourism; Economics; Econometrics","score_opus":0.013753919243084054,"score_gpt":0.28287223498970754,"score_spread":0.2691183157466235,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1546938461","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.690346,0.00007256107,0.2934896,0.00718091,0.00028767134,0.0037129715,0.00017882859,0.00040470305,0.004326731],"genre_scores_gemma":[0.9914175,0.000024956575,0.0048807403,0.00046182467,0.00012822749,0.0010133644,0.00007988152,0.00001104924,0.0019824095],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99798954,0.00018560937,0.00042023163,0.0003742163,0.00046234514,0.00056808046],"domain_scores_gemma":[0.9988331,0.00028192438,0.00021843256,0.00033948873,0.00020468631,0.00012235339],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0015197002,0.00015812385,0.0001372742,0.00012582136,0.001350554,0.00035763707,0.0010862596,0.00016134063,0.000015873606],"category_scores_gemma":[0.00012988846,0.00011910884,0.000061955696,0.0009575362,0.00029152306,0.00043526178,0.000030394893,0.00020744087,0.000005178787],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017848362,0.0018651336,0.17734881,0.0002800553,0.00004760531,0.00003231547,0.12638225,0.2200602,0.10429362,0.26437277,0.04740242,0.056129985],"study_design_scores_gemma":[0.0016939687,0.00048580268,0.6285752,0.00064866064,0.00009373786,0.000015027759,0.01848066,0.109232865,0.028746096,0.023250515,0.18712299,0.0016544954],"about_ca_topic_score_codex":0.040990334,"about_ca_topic_score_gemma":0.114603326,"teacher_disagreement_score":0.45122638,"about_ca_system_score_codex":0.0001716626,"about_ca_system_score_gemma":0.00046571775,"threshold_uncertainty_score":0.9999496},"labels":[],"label_agreement":null},{"id":"W1546963243","doi":"10.1007/978-3-642-19789-5_6","title":"GIS-based Map-matching: Development and Demonstration of a Postprocessing Map-matching Algorithm for Transportation Research","year":2011,"lang":"en","type":"book-chapter","venue":"Lecture notes in geoinformation and cartography","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":37,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Map matching; Toolbox; Python (programming language); Computer science; Scripting language; Global Positioning System; Matching (statistics); Data mining; Algorithm; Geographic information system; Blossom algorithm; Flexibility (engineering); Geography; Cartography; Mathematics","score_opus":0.02916391254899594,"score_gpt":0.29205952103844557,"score_spread":0.26289560848944965,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1546963243","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004401753,0.0009958615,0.985003,0.0002875972,0.00017719713,0.0010534512,0.00013030626,0.000086353364,0.007864493],"genre_scores_gemma":[0.7783822,0.000488854,0.2156568,0.00028792297,0.00014814937,0.00013385327,0.004128783,0.000070352355,0.0007030936],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.998419,0.00003893287,0.00060534687,0.00022805188,0.0004669302,0.00024175536],"domain_scores_gemma":[0.99885017,0.0002743326,0.00033868398,0.000081771665,0.00037255973,0.00008250003],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009170073,0.00020528458,0.0002576326,0.00077961426,0.0005187656,0.00009744449,0.00008350616,0.00039445527,0.000019093839],"category_scores_gemma":[0.000018914538,0.00021415002,0.000072788585,0.00017046588,0.00022181161,0.0003859645,0.0000030640717,0.00029874442,8.1462105e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016996598,0.0000454114,0.0020800834,0.0015373025,0.00007782082,0.0000025208037,0.2442877,0.0051635103,0.000017720575,0.050031856,0.00003654622,0.69654953],"study_design_scores_gemma":[0.011069115,0.0015156858,0.028269611,0.014147154,0.0008582947,0.000006588037,0.021020371,0.02888523,0.0040725875,0.59401435,0.29013604,0.0060050096],"about_ca_topic_score_codex":0.00037822966,"about_ca_topic_score_gemma":0.0044253296,"teacher_disagreement_score":0.77398044,"about_ca_system_score_codex":0.000037584112,"about_ca_system_score_gemma":0.00029206244,"threshold_uncertainty_score":0.8732784},"labels":[],"label_agreement":null},{"id":"W1551189305","doi":"10.1108/9781848558458-034","title":"Survey Mode Integration and Data Fusion: Methods and Challenges","year":2009,"lang":"en","type":"book-chapter","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Merge (version control); Sensor fusion; Survey data collection; Computer science; Survey methodology; Data integration; Data science; Context (archaeology); Process (computing); Data collection; Mode (computer interface); Data mining; Geography; Information retrieval; Artificial intelligence; Statistics; Mathematics; Human–computer interaction","score_opus":0.20911227523185671,"score_gpt":0.41794715600359483,"score_spread":0.20883488077173812,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1551189305","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000014867382,0.010653202,0.030308552,0.0023261795,0.00015785352,0.00021462202,0.0001806474,0.00012425175,0.9560198],"genre_scores_gemma":[0.0032728785,0.24167077,0.11334443,0.0002678033,0.00027706748,0.0000026053624,0.0046584657,0.00003524147,0.63647074],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99912745,0.00013125886,0.00015809906,0.00033616394,0.00016445661,0.000082558676],"domain_scores_gemma":[0.9993294,0.00022862265,0.00008605878,0.00019989214,0.00008603345,0.00006999393],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013804468,0.000118267926,0.0001658738,0.000067264744,0.00019539797,0.00006550065,0.00012699954,0.00025354343,0.0001187759],"category_scores_gemma":[0.00009602099,0.00010622127,0.000010491928,0.000017976792,0.00010068184,0.00020472478,0.000018462277,0.00011676555,0.0000021114056],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008710444,0.0000040519044,0.000038209517,0.000010566501,0.000011685272,0.0000010176971,0.0033745435,0.000011242851,6.957121e-7,0.23611894,0.00079078967,0.75962955],"study_design_scores_gemma":[0.0002967563,0.000078110876,0.016502157,0.00021476945,0.000114389506,0.0000015471937,0.001370102,0.0053049093,0.0000029157452,0.03179243,0.943661,0.00066094496],"about_ca_topic_score_codex":0.0009942686,"about_ca_topic_score_gemma":0.028015396,"teacher_disagreement_score":0.9428702,"about_ca_system_score_codex":0.00001010966,"about_ca_system_score_gemma":0.000056326196,"threshold_uncertainty_score":0.98972076},"labels":[],"label_agreement":null},{"id":"W1551577277","doi":"10.1016/j.trpro.2015.06.001","title":"Activity-based Market Equilibrium for Capacitated Multimodal Transport Systems","year":2015,"lang":"en","type":"article","venue":"Transportation research procedia","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Toronto Metropolitan University","funders":"University of Toronto; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Multimodal transport; Computer science; Transport engineering; Engineering","score_opus":0.14789953301789585,"score_gpt":0.4090297267424301,"score_spread":0.2611301937245343,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1551577277","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.87108546,0.00020460134,0.10698442,0.0029665076,0.0010281424,0.0053896243,0.0010631005,0.001458549,0.009819574],"genre_scores_gemma":[0.9921213,0.000015564181,0.0035923303,0.00003835107,0.00020895308,0.0006883643,0.00072110596,0.000057263514,0.0025567662],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9966533,0.0002716871,0.0004013521,0.00045003765,0.0014843157,0.00073926756],"domain_scores_gemma":[0.99677616,0.00041045187,0.00012017476,0.00017218136,0.0019923288,0.000528693],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003506713,0.00017945826,0.00024655552,0.00036274813,0.00045542393,0.000098403885,0.00027245478,0.00024596765,0.00005988419],"category_scores_gemma":[0.0003331462,0.00019213211,0.000097409815,0.0009771348,0.0003509582,0.00059422234,7.5348873e-7,0.00030464612,0.000020756575],"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":[0.0089119235,0.00203319,0.2642362,0.0029550754,0.0003071227,0.00014475822,0.18596992,0.38307253,0.0030059796,0.063196115,0.08220394,0.0039632237],"study_design_scores_gemma":[0.018945897,0.001658398,0.45776984,0.0006723807,0.00028050612,0.0000011516864,0.045824703,0.19439232,0.0027993873,0.0021510366,0.27301532,0.002489041],"about_ca_topic_score_codex":0.0041022114,"about_ca_topic_score_gemma":0.004277254,"teacher_disagreement_score":0.19353363,"about_ca_system_score_codex":0.00015976487,"about_ca_system_score_gemma":0.001737765,"threshold_uncertainty_score":0.78349197},"labels":[],"label_agreement":null},{"id":"W1552936668","doi":"10.18757/ejtir.2002.2.1.3680","title":"Conditional Choice Modelling of Time Allocation Among Spouses in Transport Settings","year":2001,"lang":"en","type":"article","venue":"European journal of transport and infrastructure research","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ministry of Transportation of Ontario","funders":"","keywords":"Spouse; Outcome (game theory); Sample (material); Set (abstract data type); Econometrics; Function (biology); Conditional expectation; Suite; Conditional probability; Statistics; Computer science; Psychology; Mathematics; Mathematical economics; Sociology; Geography","score_opus":0.03831156940318819,"score_gpt":0.31097564116658355,"score_spread":0.27266407176339535,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1552936668","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9778027,0.00014365399,0.01592235,0.00039185016,0.000049036295,0.00011668168,0.000013706559,0.00001164544,0.0055483608],"genre_scores_gemma":[0.9970177,0.0007737805,0.0017342435,0.000021019689,0.00016425722,5.3085637e-7,0.000053396787,0.000015904097,0.0002192141],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9978895,0.0003396821,0.0006158677,0.00014244736,0.000762108,0.00025036393],"domain_scores_gemma":[0.9989211,0.00013346571,0.00022162109,0.00006674149,0.00051229645,0.00014479544],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003207263,0.00009493732,0.0002069969,0.00042032838,0.00019843712,0.00001961253,0.00020812714,0.00005826854,0.00022094198],"category_scores_gemma":[0.00004931492,0.00008919051,0.00006345458,0.00051756034,0.00038395057,0.00048204165,0.0000023378238,0.0004577565,0.0000016641142],"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":[0.00018219552,0.000051030685,0.7522719,0.000044797078,0.00002877316,0.000159074,0.01577616,0.22899179,0.00033598172,0.00049692224,0.00018767918,0.0014737226],"study_design_scores_gemma":[0.0007265824,0.00010592945,0.9873366,0.00021701532,0.000020142032,0.000012047012,0.0013716762,0.0011772314,0.000052183783,0.00039705227,0.008472827,0.000110739915],"about_ca_topic_score_codex":0.00020800157,"about_ca_topic_score_gemma":0.00015515943,"teacher_disagreement_score":0.2350647,"about_ca_system_score_codex":0.000032523018,"about_ca_system_score_gemma":0.00019095473,"threshold_uncertainty_score":0.36370832},"labels":[],"label_agreement":null},{"id":"W1557660147","doi":"","title":"Past Presidents' Award for Merit in Transportation Engineering: Assessing Transportation Policy Using an Activity-Based Microsimulation Model of Travel Demand","year":2006,"lang":"en","type":"article","venue":"ITE journal","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Microsimulation; Telecommuting; Demand management; Transport engineering; Scheduling (production processes); Transportation planning; Operations research; Computer science; Engineering; Economics; Operations management","score_opus":0.03452593936604329,"score_gpt":0.3305878045873825,"score_spread":0.2960618652213392,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1557660147","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5260886,0.000009252852,0.47359014,0.00009032325,0.000044339376,0.00012099995,0.000036466197,0.000013249005,0.00000665715],"genre_scores_gemma":[0.9638067,0.0000061460064,0.03577935,0.000013466567,0.00019686946,0.0000050202093,0.00016033449,0.000019210309,0.000012913503],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988047,0.00006111231,0.00046052886,0.00014384136,0.00031157734,0.00021822788],"domain_scores_gemma":[0.9992959,0.000068466,0.00032401728,0.000050298673,0.00019097076,0.00007035913],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005098193,0.00011186777,0.00017517753,0.00037449345,0.00024229554,0.00009961134,0.00007502079,0.00012209576,0.00000332488],"category_scores_gemma":[0.00001985052,0.00013045347,0.000083588355,0.000323798,0.000034045708,0.0010764751,1.7346755e-7,0.00011790588,5.349938e-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":[0.00006449735,0.00009945728,0.031277917,0.000035030316,0.000004909513,0.0000011987224,0.0037148295,0.91895497,0.04514866,0.00037012534,0.0000024523113,0.00032593426],"study_design_scores_gemma":[0.0009782645,0.000022868913,0.38249627,0.00009343676,0.00004431447,3.1372053e-7,0.00029019572,0.61193615,0.0035575405,0.0004308031,0.000018557803,0.00013132591],"about_ca_topic_score_codex":0.0015090582,"about_ca_topic_score_gemma":0.001977779,"teacher_disagreement_score":0.43781078,"about_ca_system_score_codex":0.0001210233,"about_ca_system_score_gemma":0.0003685917,"threshold_uncertainty_score":0.53197384},"labels":[],"label_agreement":null},{"id":"W1557708027","doi":"","title":"Urban Transportation Indicators - Third Survey","year":2005,"lang":"en","type":"article","venue":"","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":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Metropolitan area; Census; Geography; Transport engineering; Survey data collection; Urban area; Survey methodology; Urban planning; Regional science; Engineering; Population; Civil engineering; Environmental health","score_opus":0.019054396772374988,"score_gpt":0.28627962787816746,"score_spread":0.2672252311057925,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1557708027","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.80392194,0.00007699573,0.016924327,0.0023315814,0.0003010393,0.00026054028,0.000061752326,0.00061571476,0.17550611],"genre_scores_gemma":[0.9915774,0.00003059727,0.0018157958,0.00024068245,0.00009265257,0.000003596358,0.00025116198,0.0000049694254,0.0059831426],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9993242,0.000076025295,0.00014190075,0.0001080406,0.00021908697,0.00013074238],"domain_scores_gemma":[0.99972135,0.00006021594,0.000046559377,0.000051414107,0.000044554065,0.00007592604],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048555323,0.000047277408,0.000055686538,0.0000838854,0.00023134067,0.000029025956,0.00007741158,0.00006707563,0.0005007649],"category_scores_gemma":[0.000033106462,0.000046852463,0.000023026354,0.0003693396,0.00005381097,0.000238305,2.935299e-7,0.000050213686,0.00006906251],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011853082,0.00003838671,0.9134386,0.0000016890298,0.000007261046,5.7546924e-7,0.025771651,0.001575329,0.0000039994165,0.029368598,0.025294023,0.004488037],"study_design_scores_gemma":[0.0001199799,0.00000620505,0.86009467,0.000002411634,0.000005242282,1.5368256e-8,0.0006701194,0.00006398763,0.000044885433,0.000022360407,0.13889116,0.000078973295],"about_ca_topic_score_codex":0.002583914,"about_ca_topic_score_gemma":0.03952833,"teacher_disagreement_score":0.18765546,"about_ca_system_score_codex":0.000022702654,"about_ca_system_score_gemma":0.00007630674,"threshold_uncertainty_score":0.9779978},"labels":[],"label_agreement":null},{"id":"W1560650779","doi":"","title":"Trucking onder Warschau en Montreal","year":2004,"lang":"nl","type":"article","venue":"Data Archiving and Networked Services (DANS)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Political science; Humanities; Theology; Art; Philosophy","score_opus":0.016340368610792144,"score_gpt":0.26757861373017505,"score_spread":0.2512382451193829,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1560650779","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9529299,0.0022733435,0.025510106,0.0021130512,0.0012954755,0.00051148277,0.0011579008,0.0004270447,0.0137816835],"genre_scores_gemma":[0.9890131,0.0025554495,0.003214151,0.00034167484,0.0007999047,0.000002628295,0.0033121118,0.00004113827,0.0007198488],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.99725366,0.0003221961,0.00046792664,0.00082142174,0.00046959036,0.0006652071],"domain_scores_gemma":[0.9984302,0.00034341202,0.00023884335,0.0006571507,0.00003997705,0.0002904279],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.00092200824,0.00033410647,0.00034331006,0.00010919564,0.0013155661,0.00035287128,0.0011677084,0.0001886707,0.000051149025],"category_scores_gemma":[0.0000175202,0.0003275251,0.000057628822,0.0004886391,0.0002089022,0.000911892,0.00020658252,0.00038015362,0.000048411966],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025178297,0.0002762215,0.08413018,0.0005753404,0.00035968574,0.00010473982,0.53225195,0.33668542,0.00008134482,0.0049488232,0.00066103064,0.039673463],"study_design_scores_gemma":[0.0029975786,0.0001730064,0.6177511,0.0023159094,0.00063315424,0.000022717015,0.07084491,0.27623507,0.000014211441,0.0055994727,0.02185265,0.001560237],"about_ca_topic_score_codex":0.022173874,"about_ca_topic_score_gemma":0.05937355,"teacher_disagreement_score":0.5336209,"about_ca_system_score_codex":0.000051628816,"about_ca_system_score_gemma":0.00013700526,"threshold_uncertainty_score":0.99998456},"labels":[],"label_agreement":null},{"id":"W1563478981","doi":"10.1002/atr.1245","title":"Trial and error method for optimal tradable credit schemes: The network case","year":2013,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":29,"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":"Convergence (economics); Function (biology); Computer science; Mathematical optimization; Scheme (mathematics); Economics; Econometrics; Mathematics; Macroeconomics","score_opus":0.025459254856231125,"score_gpt":0.3368817686599219,"score_spread":0.3114225138036908,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1563478981","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5929681,0.0003719552,0.4031135,0.0017919135,0.000809254,0.00083630654,0.000013662601,0.000024039173,0.00007123408],"genre_scores_gemma":[0.61593,0.00017683288,0.3827485,0.00010743063,0.00077582384,0.00004931081,0.000024974473,0.000014025167,0.00017309444],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9989329,0.00008235316,0.00046247648,0.00010330686,0.00023673968,0.00018222665],"domain_scores_gemma":[0.9987213,0.0003787849,0.0004095191,0.00005280435,0.00034336888,0.00009423597],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008894312,0.000085727916,0.00017076444,0.00003700668,0.0004585171,0.000059643382,0.00008046458,0.00007243282,0.00004615383],"category_scores_gemma":[0.00008165622,0.000063438565,0.000092521164,0.00018247853,0.00006388379,0.0007417339,3.6696895e-7,0.00012997774,4.913703e-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":[0.0058892365,0.00009696265,0.0005703997,0.00004112342,0.00010913678,0.00006339811,0.03690202,0.9154269,0.00016979197,0.007766347,0.0024018222,0.030562872],"study_design_scores_gemma":[0.16500759,0.006122071,0.06163812,0.0006658849,0.00244801,0.00044302695,0.25805745,0.02878482,0.00078724045,0.023982974,0.45003054,0.002032277],"about_ca_topic_score_codex":0.00013493221,"about_ca_topic_score_gemma":0.0004397775,"teacher_disagreement_score":0.88664204,"about_ca_system_score_codex":0.000019759067,"about_ca_system_score_gemma":0.00010975533,"threshold_uncertainty_score":0.35265893},"labels":[],"label_agreement":null},{"id":"W1565759525","doi":"10.1002/atr.216","title":"Alternate capacity reliability measures for transportation networks","year":2012,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":67,"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":"Center for Advanced Study, University of Illinois at Urbana-Champaign; Program for New Century Excellent Talents in University; Tongji University; Ministry of Education of the People's Republic of China; National Science Foundation","keywords":"Reliability (semiconductor); Measure (data warehouse); Reliability engineering; Computer science; Level of service; Transport engineering; Engineering; Data mining","score_opus":0.024075616782214128,"score_gpt":0.2952670172052056,"score_spread":0.27119140042299145,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1565759525","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6448866,0.00027118062,0.35265502,0.0002930226,0.0013849697,0.00030934808,0.000051863088,0.000044790686,0.000103164355],"genre_scores_gemma":[0.96999574,0.00036248728,0.028820178,0.00006507771,0.00053225394,0.000015201875,0.00014602266,0.000017969818,0.000045096192],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.998254,0.000086619846,0.0007239642,0.00012722802,0.0004846304,0.000323571],"domain_scores_gemma":[0.9981421,0.00017728769,0.00065399776,0.00008293149,0.00073648564,0.00020717221],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013677345,0.00013011195,0.0002388023,0.00010974434,0.00028116835,0.000023531284,0.00012793006,0.00012777933,0.000022352075],"category_scores_gemma":[0.000114787574,0.00012806241,0.00019725067,0.00024424598,0.000085883934,0.0015101202,1.4388189e-7,0.00017946822,7.879209e-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":[0.00060432137,0.00018601512,0.094372064,0.000048868693,0.000049417005,0.000001928834,0.03912032,0.8473219,0.0003842982,0.009188811,0.00016218661,0.008559864],"study_design_scores_gemma":[0.002546655,0.00022935632,0.9421841,0.00013760402,0.000327056,0.0000017620963,0.005304055,0.00028864635,0.00084926124,0.0025941,0.04514026,0.00039714435],"about_ca_topic_score_codex":0.0000913942,"about_ca_topic_score_gemma":0.0007789106,"teacher_disagreement_score":0.84781206,"about_ca_system_score_codex":0.00008776334,"about_ca_system_score_gemma":0.00008984831,"threshold_uncertainty_score":0.52222335},"labels":[],"label_agreement":null},{"id":"W1567863984","doi":"10.1002/atr.1314","title":"Modeling distributions of travel time variability for bus operations","year":2015,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":64,"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":"Department of Transport and Main Roads, Queensland Government; China Scholarship Council","keywords":"Computer science; Reliability (semiconductor); Robustness (evolution); Normality; Travel time; Flexibility (engineering); Statistics; Econometrics; Transport engineering; Power (physics); Engineering; Mathematics","score_opus":0.027118272625564235,"score_gpt":0.31226079345184454,"score_spread":0.28514252082628033,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1567863984","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3074481,0.000032151973,0.6915714,0.0003149267,0.00019711372,0.00017129438,0.00013242965,0.000011684351,0.00012089124],"genre_scores_gemma":[0.9305456,0.000030389887,0.06903479,0.000009349285,0.00008129486,0.00000652831,0.00023346017,0.0000063315915,0.00005223071],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99886894,0.000056804998,0.00058455626,0.00008458782,0.00029357112,0.00011154506],"domain_scores_gemma":[0.99814606,0.00008764329,0.00019353462,0.000058001184,0.0014003356,0.00011445713],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009261146,0.00006465507,0.0001782238,0.00007707709,0.00015106313,0.000013626254,0.000087601366,0.00006476412,0.00001387944],"category_scores_gemma":[0.00030344934,0.000064738684,0.00009771573,0.0002249632,0.00005219629,0.00057297526,2.6966836e-7,0.000074251344,5.9690046e-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":[0.00012527633,0.00007883357,0.00042252033,0.000010836302,0.000015414065,5.447509e-7,0.011300147,0.9774446,0.00076713646,0.009207583,0.000022896302,0.00060419895],"study_design_scores_gemma":[0.028326018,0.0032149244,0.1866723,0.0010714941,0.0024552576,0.000013755353,0.10502283,0.553185,0.009018566,0.099054046,0.009756321,0.0022094976],"about_ca_topic_score_codex":0.00006921255,"about_ca_topic_score_gemma":0.00028458485,"teacher_disagreement_score":0.62309754,"about_ca_system_score_codex":0.000064441745,"about_ca_system_score_gemma":0.00041805938,"threshold_uncertainty_score":0.2639967},"labels":[],"label_agreement":null},{"id":"W1572821023","doi":"","title":"Un esquema de \"Road Pricing\": La autopista 407 ETR de Toronto","year":2000,"lang":"es","type":"article","venue":"Carreteras: Revista técnica de la Asociación Española de la Carretera","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Humanities; Political science; Art","score_opus":0.004774429946839197,"score_gpt":0.2877649077581163,"score_spread":0.2829904778112771,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1572821023","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8870489,0.0059712855,0.0022162548,0.0016364127,0.00038151306,0.0009499391,0.00040462153,0.0008953917,0.10049569],"genre_scores_gemma":[0.98381686,0.0051601366,0.0055339066,0.0013745525,0.0010809023,0.00014033665,0.00021577599,0.00020429239,0.002473249],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9872158,0.007065125,0.0012936685,0.0012634831,0.001065857,0.0020960686],"domain_scores_gemma":[0.9945361,0.00252049,0.00064496254,0.00097286876,0.0002717166,0.0010538565],"candidate_categories":["metaepi_narrow","scholarly_communication","research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0057995296,0.0009915321,0.0012067471,0.0001295361,0.0012819642,0.002667852,0.0013069869,0.0019111581,0.0016896031],"category_scores_gemma":[0.000823789,0.0011406173,0.00087765616,0.0006713981,0.0012464871,0.00083433616,0.0000689622,0.0015303105,0.000058025864],"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":[0.00088880816,0.000876971,0.52317494,0.001670547,0.001314431,0.0016614653,0.19615221,0.0053415834,0.0024655433,0.11300159,0.0091328975,0.14431903],"study_design_scores_gemma":[0.0029019439,0.00026064407,0.3351452,0.0021266732,0.0009922233,0.0002221504,0.003441607,0.004213911,0.00027673878,0.0008429888,0.6472341,0.0023418337],"about_ca_topic_score_codex":0.0052071665,"about_ca_topic_score_gemma":0.00024710788,"teacher_disagreement_score":0.63810116,"about_ca_system_score_codex":0.0033279145,"about_ca_system_score_gemma":0.0017900444,"threshold_uncertainty_score":0.9993846},"labels":[],"label_agreement":null},{"id":"W1573851328","doi":"","title":"Restorable mesh network design under demand uncertainty: toward \"future proofed transport investments","year":2004,"lang":"en","type":"article","venue":"Optical Fiber Communication Conference","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Robustness (evolution); Computer science; Mathematical optimization; Network planning and design; On demand; Robust optimization; Set (abstract data type); Integer programming; Integer (computer science); Operations research; Computer network; Algorithm; Engineering; Mathematics","score_opus":0.0656009990651954,"score_gpt":0.3059807548342898,"score_spread":0.24037975576909443,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1573851328","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02915525,0.0013603752,0.81743014,0.052590396,0.0005492795,0.0022334266,0.000024911687,0.0008750706,0.095781155],"genre_scores_gemma":[0.8743353,0.0007761125,0.12248786,0.0006186029,0.00010324097,0.00007212543,0.00020089929,0.00001744544,0.0013884173],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.998234,0.000334445,0.00035403596,0.00027671608,0.0004277283,0.00037307525],"domain_scores_gemma":[0.99865884,0.00023567691,0.00012258382,0.00046208038,0.00029862594,0.00022222027],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008060785,0.00016662195,0.00020656973,0.00004459984,0.0007709486,0.000120463934,0.0005383263,0.00022661258,0.00036757247],"category_scores_gemma":[0.00007681482,0.00016745074,0.00005618939,0.00040609893,0.00035754862,0.0003655631,0.000018098803,0.00030055188,0.00007240045],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007872689,0.00011157111,0.0005760587,0.000011799764,0.00003679081,0.0000027000654,0.008829858,0.33021918,0.000016032714,0.6579329,0.0005352189,0.0016491724],"study_design_scores_gemma":[0.008984717,0.0006033778,0.075218834,0.0016394082,0.0006833984,0.0000107847345,0.019044539,0.0129602235,0.000760284,0.5549126,0.3216686,0.0035132265],"about_ca_topic_score_codex":0.0004796063,"about_ca_topic_score_gemma":0.0008164322,"teacher_disagreement_score":0.84518003,"about_ca_system_score_codex":0.00014543819,"about_ca_system_score_gemma":0.0006894954,"threshold_uncertainty_score":0.6828443},"labels":[],"label_agreement":null},{"id":"W1575708395","doi":"10.17226/13919","title":"Uses of Higher Capacity Buses in Transit Service","year":2008,"lang":"en","type":"book","venue":"Transportation Research Board eBooks","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Transit (satellite); Service (business); Computer science; Transport engineering; Business; Public transport; Engineering; Marketing","score_opus":0.13310861204084373,"score_gpt":0.3663868653250945,"score_spread":0.23327825328425078,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1575708395","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.067592174,0.00033622174,0.00028710227,0.00082908786,0.0003406221,0.0017527804,0.0006231596,0.00027985658,0.927959],"genre_scores_gemma":[0.28073385,0.00064753747,0.0011513246,0.00019557354,0.00025219872,0.00012920369,0.0008075561,0.00011184884,0.71597093],"study_design_codex":"qualitative","study_design_gemma":"not_applicable","domain_scores_codex":[0.9957668,0.00041803197,0.00081850245,0.0004977346,0.0019122019,0.00058668654],"domain_scores_gemma":[0.9977332,0.00044497926,0.00023644447,0.00028478113,0.0010928488,0.00020775052],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00095682964,0.0002759477,0.0005292024,0.0007890728,0.00037858824,0.00003843201,0.00044021642,0.00066101644,0.00033596222],"category_scores_gemma":[0.000028275439,0.00031498662,0.00015736393,0.0004369116,0.0007755077,0.00019040206,0.0000016690228,0.0009109616,0.000035103712],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001561685,0.0005883199,0.040030073,0.003254316,0.00043128667,0.0005090641,0.50100803,0.013681548,0.00041529714,0.3911477,0.043512985,0.0038596652],"study_design_scores_gemma":[0.0021074738,0.0001702424,0.18507873,0.0014916027,0.000116042705,3.136408e-7,0.002748654,0.000028247805,0.00034788795,0.0071396297,0.7997715,0.0009997006],"about_ca_topic_score_codex":0.025230538,"about_ca_topic_score_gemma":0.18239002,"teacher_disagreement_score":0.7562585,"about_ca_system_score_codex":0.00022885104,"about_ca_system_score_gemma":0.0017910809,"threshold_uncertainty_score":0.9999302},"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,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_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","score_opus":0.016049075026061895,"score_gpt":0.26725716271552213,"score_spread":0.25120808768946024,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1577646917","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9216682,0.000029391038,0.071841106,0.00016355142,0.00039565904,0.000181013,0.00011639648,0.000028184488,0.005576538],"genre_scores_gemma":[0.98959106,0.000043585063,0.010119505,0.000013737565,0.00005668988,0.0000017812788,0.000067071356,0.0000063597263,0.00010022827],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.9988157,0.000085273954,0.0005488573,0.00009283595,0.00033785813,0.00011947296],"domain_scores_gemma":[0.9988211,0.000095291565,0.00050957507,0.00007679243,0.00040882936,0.00008843128],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00059213105,0.00007586322,0.00018282949,0.000096574404,0.000118604636,0.000005279202,0.00010937293,0.0000731679,0.00020110016],"category_scores_gemma":[0.00009933976,0.00007260308,0.000111915346,0.00027204526,0.00010029978,0.0003765573,2.3505146e-7,0.00012180353,0.0000031371922],"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":[0.0033660654,0.002889754,0.16204591,0.0001403633,0.00029516127,0.000056963105,0.4403376,0.11525337,0.012510255,0.24421665,0.00084702147,0.01804088],"study_design_scores_gemma":[0.00075200753,0.00030281226,0.9889689,0.00008165986,0.00009188189,5.867355e-7,0.002464054,0.000022676853,0.00298885,0.0033789363,0.0008313222,0.000116323725],"about_ca_topic_score_codex":0.00005329282,"about_ca_topic_score_gemma":0.0000630468,"teacher_disagreement_score":0.826923,"about_ca_system_score_codex":0.000043860327,"about_ca_system_score_gemma":0.00013840903,"threshold_uncertainty_score":0.29606673},"labels":[],"label_agreement":null},{"id":"W1579493581","doi":"10.1111/j.1538-4632.2010.00795.x","title":"Aggregating Data for the Flow‐Intercepting Location Model: A Geographic Information System, Optimization, and Heuristic Framework. 截流选址模型的数据集计: 一个地理信息系统、优化和探索性框架","year":2010,"lang":"en","type":"article","venue":"Geographical Analysis","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Alberta; Wilfrid Laurier University","funders":"","keywords":"Heuristic; Computer science; Flow (mathematics); Flow network; Point (geometry); Traffic flow (computer networking); Operations research; Point of interest; Data mining; Mathematical optimization; Artificial intelligence; Engineering; Mathematics; Computer security","score_opus":0.015707767927874934,"score_gpt":0.28043514926905044,"score_spread":0.2647273813411755,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1579493581","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011699039,0.000094806914,0.9856934,0.0014451985,0.00016660785,0.00040981,0.00013923142,0.00021099222,0.00014091206],"genre_scores_gemma":[0.89221954,0.00015323555,0.1060536,0.00018573957,0.00012430154,0.00006779861,0.0011723365,0.000010700405,0.000012767682],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981918,0.00010322358,0.00055206125,0.00037385602,0.00046229383,0.00031678146],"domain_scores_gemma":[0.9977818,0.0005477701,0.00034528875,0.0005875492,0.0006113327,0.00012623453],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0018153868,0.00016407842,0.00024316476,0.000547427,0.0017106946,0.0004761666,0.0005119318,0.00024601378,0.0000364506],"category_scores_gemma":[0.0013755722,0.00013467664,0.00015038274,0.0029705074,0.00032028227,0.00087119057,0.000046973688,0.00033348272,0.0000017426863],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015289499,0.00001459455,0.033602845,0.000052292366,0.0002394143,1.7753693e-7,0.002346631,0.9529327,6.8242605e-7,0.0073142457,0.000048645838,0.0034324855],"study_design_scores_gemma":[0.00016036951,0.000009979011,0.0080332495,0.00004255703,0.0012768494,6.078235e-7,0.0022089824,0.9873142,3.311813e-7,0.0003400959,0.00044429777,0.00016848225],"about_ca_topic_score_codex":0.0012184235,"about_ca_topic_score_gemma":0.0025412317,"teacher_disagreement_score":0.88052046,"about_ca_system_score_codex":0.000012848727,"about_ca_system_score_gemma":0.00007041606,"threshold_uncertainty_score":0.99958897},"labels":[],"label_agreement":null},{"id":"W1580727515","doi":"10.1002/atr.1275","title":"Investigating the irregularity of bus routes: highlighting how underlying assumptions of bus models impact the regularity results","year":2014,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Stochastic modelling; Work (physics); Mathematical model; Simulation; Engineering; Mathematics","score_opus":0.04865504485181907,"score_gpt":0.32095253017979314,"score_spread":0.27229748532797404,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1580727515","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.87279147,0.00019659552,0.121961445,0.0041682264,0.0002652704,0.0002365611,0.00004962715,0.000025601565,0.00030517945],"genre_scores_gemma":[0.98423827,0.00013935198,0.015406432,0.000030509147,0.00008981705,0.0000025859727,0.000043461154,0.000011291926,0.00003826962],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99778056,0.00031134693,0.00086048926,0.0001345386,0.0007209495,0.00019210915],"domain_scores_gemma":[0.9964253,0.0006492995,0.0018531127,0.00018088448,0.0008088539,0.000082564555],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023566647,0.00012676191,0.0002806174,0.000108672575,0.00052339013,0.0000464178,0.00028311674,0.000102033715,0.0000038361045],"category_scores_gemma":[0.0005025588,0.00008268048,0.00020430435,0.00051973225,0.0002875512,0.00093905645,0.0000016034833,0.00029103082,1.122176e-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":[0.000079731966,0.000040861083,0.0040374324,0.000035075376,0.000054316963,7.150758e-7,0.03847346,0.93429995,0.0022089826,0.017853396,0.000022912734,0.0028931685],"study_design_scores_gemma":[0.0024484498,0.00032459013,0.92129755,0.0007841283,0.00042369778,0.0000040853765,0.025744176,0.014995902,0.0019858726,0.031018801,0.00066206313,0.00031066325],"about_ca_topic_score_codex":0.000437946,"about_ca_topic_score_gemma":0.0014311377,"teacher_disagreement_score":0.919304,"about_ca_system_score_codex":0.000058211263,"about_ca_system_score_gemma":0.00021597759,"threshold_uncertainty_score":0.40255466},"labels":[],"label_agreement":null},{"id":"W1581368314","doi":"10.1177/0361198105192700124","title":"Making Automatic Passenger Counts Mainstream","year":2005,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Rounding; Sampling (signal processing); Transit (satellite); Parsing; Transport engineering; Real-time computing; Data mining; Public transport; Engineering; Detector; Telecommunications","score_opus":0.12305495956327672,"score_gpt":0.43333488512680657,"score_spread":0.31027992556352985,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1581368314","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96911466,0.0003272854,0.002888702,0.012324833,0.0010872115,0.000950533,0.000058331454,0.000118047996,0.013130415],"genre_scores_gemma":[0.9904781,0.0006685723,0.0048975046,0.00009313714,0.0004645628,0.000025629755,0.000015294405,0.000035204175,0.003322014],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9938181,0.0010142961,0.0009050623,0.00024566622,0.0033326228,0.00068423396],"domain_scores_gemma":[0.99621624,0.00067333935,0.00033666045,0.00026182152,0.0022594132,0.00025252605],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0044197924,0.00015144813,0.00025528425,0.0006829233,0.0010316988,0.00015967654,0.0007258243,0.00018784266,0.0017772774],"category_scores_gemma":[0.00020659325,0.00012373253,0.00023256915,0.0016579487,0.0005846033,0.0006527107,0.0000021475294,0.0012327585,0.00014626286],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00082080404,0.0008793071,0.4911907,0.0004060146,0.00039067236,0.00021404181,0.06172512,0.0150213055,0.00060958945,0.0893441,0.14414917,0.19524917],"study_design_scores_gemma":[0.00081343215,0.00012754569,0.58144647,0.00037323855,0.000048696427,3.0264079e-7,0.0047690026,0.0004880482,0.00010955521,0.0016388752,0.40997854,0.00020628436],"about_ca_topic_score_codex":0.0025429723,"about_ca_topic_score_gemma":0.041747354,"teacher_disagreement_score":0.26582938,"about_ca_system_score_codex":0.00028413598,"about_ca_system_score_gemma":0.00087392936,"threshold_uncertainty_score":0.99913526},"labels":[],"label_agreement":null},{"id":"W1585994046","doi":"","title":"A Copula-Based Joint Model of Commute Mode Choice and Number of Non-Work Stops during the Commute","year":2011,"lang":"en","type":"article","venue":"Texas ScholarWorks (Texas Digital Library)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Multinomial logistic regression; Copula (linguistics); Metropolitan area; Econometrics; Discrete choice; Mode choice; Nested logit; Mode (computer interface); Context (archaeology); Logit; Geography; Transport engineering; Statistics; Computer science; Economics; Mathematics; Engineering","score_opus":0.029532973828988028,"score_gpt":0.25781772230774314,"score_spread":0.2282847484787551,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1585994046","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94976425,0.00010524679,0.0047088,0.00026712497,0.00006279428,0.00031734552,0.00017057979,0.00014044916,0.04446339],"genre_scores_gemma":[0.9920818,0.00003795968,0.0058892816,0.00014505802,0.000030549072,0.0000066239772,0.00008887165,0.000036003636,0.0016838327],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9984771,0.00011063538,0.00049061363,0.00023544804,0.00038666837,0.00029951098],"domain_scores_gemma":[0.99887484,0.00017930818,0.00031416232,0.00038790648,0.00008847492,0.00015527665],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023595641,0.00019428725,0.00030223493,0.00009046515,0.0003250909,0.00022966973,0.0004750469,0.00016933164,0.00015202872],"category_scores_gemma":[0.00007067947,0.00016363211,0.000114337556,0.0005380669,0.00047973968,0.0023111252,0.00007134141,0.00037842692,0.00001492257],"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":[0.00010579375,0.00017430191,0.975061,0.000058669037,0.00004169997,0.0000020486034,0.0024447716,0.017797269,0.000007507957,0.0033957837,0.00053189084,0.00037923572],"study_design_scores_gemma":[0.0014475487,0.000060927952,0.9726513,0.00049825426,0.00007426026,0.000001227626,0.00038179703,0.019440044,0.001238301,0.0025540371,0.0011390531,0.0005132374],"about_ca_topic_score_codex":0.00038670708,"about_ca_topic_score_gemma":0.00005307359,"teacher_disagreement_score":0.042779554,"about_ca_system_score_codex":0.000016719425,"about_ca_system_score_gemma":0.00014036271,"threshold_uncertainty_score":0.6672724},"labels":[],"label_agreement":null},{"id":"W1587659076","doi":"10.4337/1840649690.00013","title":"Capital Stock and Productivity in French Transport: An International Comparison","year":2002,"lang":"en","type":"book-chapter","venue":"Edward Elgar Publishing eBooks","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Ministère des Transports","keywords":"Stock (firearms); Productivity; Business; Economics; Geography; Macroeconomics; Archaeology","score_opus":0.035862589459085485,"score_gpt":0.2749695794903594,"score_spread":0.2391069900312739,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1587659076","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03235845,0.000116695584,0.00022458857,0.00075649313,0.001282054,0.00044058912,0.000107028,0.00023735562,0.96447676],"genre_scores_gemma":[0.6644906,0.00004150057,0.0008302032,0.000065426,0.0008252594,0.000016333426,0.000547071,0.00005004752,0.33313355],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99794084,0.0000643303,0.00043349326,0.000573263,0.0007078919,0.0002801849],"domain_scores_gemma":[0.9991101,0.00005925743,0.0002579507,0.00021146917,0.00018574558,0.00017549992],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007866779,0.00024789377,0.00032683794,0.00034360084,0.0002548764,0.00064337056,0.00039120094,0.00047577894,0.00028578323],"category_scores_gemma":[0.000100629724,0.00028694337,0.00006402518,0.00003667121,0.00022049343,0.0016192407,0.000014563528,0.0007427152,0.0000066595],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009011068,0.00029728148,0.1824714,0.00013465756,0.0002247407,0.0001321959,0.3068921,0.0018643741,0.000008878058,0.38383338,0.010458563,0.11359233],"study_design_scores_gemma":[0.0010405717,0.00012177166,0.11568806,0.00040358497,0.000090823974,0.0000051254938,0.0011112809,0.00073296786,0.0000054344337,0.009155621,0.87038535,0.00125942],"about_ca_topic_score_codex":0.0027831777,"about_ca_topic_score_gemma":0.021060776,"teacher_disagreement_score":0.85992676,"about_ca_system_score_codex":0.0001411248,"about_ca_system_score_gemma":0.00017283481,"threshold_uncertainty_score":0.9999583},"labels":[],"label_agreement":null},{"id":"W1587664141","doi":"10.1177/0361198105192700129","title":"Assessing Transit Level of Service along Travel Corridors","year":2005,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo; Stantec (Canada)","funders":"","keywords":"Transit (satellite); Transport engineering; Service (business); Service quality; Level of service; Public transport; Bus rapid transit; Quality (philosophy); Travel time; Service level; Computer science; Business; Engineering; Marketing","score_opus":0.26785203885585285,"score_gpt":0.4543490128367907,"score_spread":0.18649697398093784,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1587664141","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.966706,0.00019775782,0.015870947,0.014618036,0.0005652704,0.0008763643,0.00013418829,0.000039293718,0.0009921842],"genre_scores_gemma":[0.9898817,0.0006202381,0.007932138,0.00013952571,0.0003555507,0.00002768884,0.00004119051,0.000055506756,0.00094649295],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.98934793,0.0019096679,0.0020086097,0.00041535482,0.0053218226,0.0009966386],"domain_scores_gemma":[0.9899236,0.0014016062,0.00081012707,0.00039529044,0.0069804005,0.000488971],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.008514325,0.00026466558,0.000568207,0.0011843217,0.0013670513,0.00022300993,0.0013097422,0.000323659,0.00050026143],"category_scores_gemma":[0.0002823101,0.00022607288,0.00043080066,0.0038350376,0.0009688846,0.0020429257,0.0000031500804,0.0019112547,0.000019478564],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0029658843,0.0015605153,0.5795034,0.0009507098,0.0005621309,0.00015197671,0.18026662,0.13001004,0.012247436,0.025388248,0.010882229,0.055510804],"study_design_scores_gemma":[0.001743169,0.0002053183,0.95297694,0.00056016754,0.00009929708,4.2930432e-7,0.02645535,0.0004550093,0.002573583,0.0009103208,0.013756564,0.00026387678],"about_ca_topic_score_codex":0.049531624,"about_ca_topic_score_gemma":0.35083583,"teacher_disagreement_score":0.3734735,"about_ca_system_score_codex":0.0002959233,"about_ca_system_score_gemma":0.0019993482,"threshold_uncertainty_score":0.999933},"labels":[],"label_agreement":null},{"id":"W1590919438","doi":"10.1177/0361198105192700109","title":"Performance-Driven Transit Funding Model","year":2005,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Transit (satellite); Transit system; Equity (law); Transport engineering; Per capita; Business; Public transport; Computer science; Operations research; Engineering; Political science","score_opus":0.1423993452953462,"score_gpt":0.4129456223901276,"score_spread":0.2705462770947814,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1590919438","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9789946,0.00012555171,0.0077522327,0.009081481,0.00047203674,0.00085700536,0.000049856375,0.000078551224,0.0025886574],"genre_scores_gemma":[0.98563945,0.0020646078,0.008512562,0.00009199302,0.0004870567,0.000046815636,0.00002611866,0.0000564698,0.0030749298],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9907253,0.0012120529,0.0014403065,0.000418336,0.004985228,0.0012188079],"domain_scores_gemma":[0.9940327,0.0007052938,0.00045801964,0.00037844648,0.003845772,0.0005798002],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0062997406,0.00025210707,0.00041753397,0.0011863662,0.002248421,0.0002111911,0.0012943518,0.00029690732,0.0004811518],"category_scores_gemma":[0.00016387903,0.00021082563,0.00040498556,0.0025837086,0.0009896616,0.0016717197,0.0000033078836,0.0021347257,0.000048390593],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001195517,0.00037231963,0.33256063,0.00017681085,0.00014919082,0.000035289882,0.05406747,0.56980187,0.0012893215,0.012755126,0.008505622,0.019090826],"study_design_scores_gemma":[0.0029911783,0.0005275565,0.8462168,0.0007214702,0.00015076142,5.3296037e-7,0.011575817,0.03161361,0.00095447066,0.0014150805,0.10324923,0.0005834746],"about_ca_topic_score_codex":0.00504643,"about_ca_topic_score_gemma":0.09352398,"teacher_disagreement_score":0.5381883,"about_ca_system_score_codex":0.00047250406,"about_ca_system_score_gemma":0.001538261,"threshold_uncertainty_score":0.9990505},"labels":[],"label_agreement":null},{"id":"W1592479498","doi":"10.17226/13614","title":"Bus and Rail Transit Preferential Treatments in Mixed Traffic","year":2010,"lang":"en","type":"book","venue":"Transportation Research Board eBooks","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Transit (satellite); Transport engineering; Computer science; Engineering; Public transport","score_opus":0.05817404033138266,"score_gpt":0.35029326251107346,"score_spread":0.2921192221796908,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1592479498","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4823874,0.00040026125,0.00048390712,0.00067135034,0.00084196235,0.004007954,0.00068178604,0.0005148672,0.51001054],"genre_scores_gemma":[0.40779933,0.00043207448,0.0005816034,0.000026749209,0.00022868194,0.0002017158,0.0015648863,0.0000873332,0.58907765],"study_design_codex":"qualitative","study_design_gemma":"not_applicable","domain_scores_codex":[0.9962853,0.00033882714,0.0006178171,0.0006776595,0.0014000229,0.0006803421],"domain_scores_gemma":[0.99868476,0.00028390446,0.00014130394,0.00023423426,0.00032565184,0.0003301382],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0010621633,0.0003293379,0.00045002616,0.0007549328,0.0005760959,0.00014965764,0.00028262121,0.0009346694,0.00032509983],"category_scores_gemma":[0.000026950584,0.00036122138,0.00012862783,0.0001776075,0.00073200074,0.00016883401,0.0000023509826,0.0013766062,0.000030314208],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0033203254,0.0011942398,0.013622524,0.0019615383,0.0010889515,0.0010299765,0.48768482,0.007539832,0.0009684154,0.21980119,0.022732016,0.23905618],"study_design_scores_gemma":[0.010248632,0.00058813643,0.2679018,0.0013532952,0.00058321486,0.0000010574635,0.005540336,0.00028704733,0.00020651588,0.013700982,0.6972672,0.0023217807],"about_ca_topic_score_codex":0.0034725624,"about_ca_topic_score_gemma":0.20402639,"teacher_disagreement_score":0.67453516,"about_ca_system_score_codex":0.00018629679,"about_ca_system_score_gemma":0.0013398782,"threshold_uncertainty_score":0.99988395},"labels":[],"label_agreement":null},{"id":"W1592723517","doi":"10.21949/1527279","title":"An Assessment of Automatic Passenger Counters","year":2015,"lang":"en","type":"article","venue":"ROSA P","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"U.S. Department of Transportation","keywords":"Scheduling (production processes); Transport engineering; Reliability (semiconductor); Computer science; Passenger transport; Transit (satellite); Operations research; Engineering; Operations management; Public transport","score_opus":0.032584490916150816,"score_gpt":0.38243985394462315,"score_spread":0.3498553630284723,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1592723517","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9599122,0.000018316405,0.01810057,0.0005392057,0.0003491525,0.000112774454,0.000020022831,0.0001502244,0.020797556],"genre_scores_gemma":[0.99345845,0.0000033060878,0.006287111,0.000038490994,0.000026821086,0.0000022892978,0.00003215481,0.0000025542665,0.00014884601],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9995424,0.000051786457,0.00008289892,0.000050377093,0.000209626,0.000062885396],"domain_scores_gemma":[0.99974763,0.000018833425,0.00004547377,0.0000535484,0.00007212316,0.000062392486],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003342188,0.000024495192,0.00004854554,0.000021443962,0.00004914697,0.00001748096,0.00005349887,0.000025985159,0.000048089234],"category_scores_gemma":[0.000019034345,0.000023688479,0.000010664208,0.000084447536,0.000034702192,0.00013267944,8.0743416e-7,0.000021254868,0.0000036994352],"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":[0.000013822402,0.00041426148,0.68303764,0.000054800897,0.0000438676,0.000009785243,0.13790898,0.05984895,0.00041432702,0.09282251,0.01675417,0.008676864],"study_design_scores_gemma":[0.0012597404,0.00026414593,0.756194,0.000090204136,0.00006186976,5.5045905e-7,0.039687674,0.15484965,0.00017380221,0.0012522712,0.045798093,0.000368036],"about_ca_topic_score_codex":0.00025710775,"about_ca_topic_score_gemma":0.00022275196,"teacher_disagreement_score":0.09822132,"about_ca_system_score_codex":0.000027671704,"about_ca_system_score_gemma":0.0001492413,"threshold_uncertainty_score":0.09659881},"labels":[],"label_agreement":null},{"id":"W1592859723","doi":"10.1111/j.1538-4632.2009.00741.x","title":"The Pickup Problem: Consumers' Locational Preferences in Flow Interception","year":2009,"lang":"en","type":"article","venue":"Geographical Analysis","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Wilfrid Laurier University; University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Interception; Pickup; TRIPS architecture; Computer science; Service (business); Flow network; Operations research; Flow (mathematics); Interrupt; Mathematical optimization; Business; Marketing; Artificial intelligence; Telecommunications; Engineering; Mathematics; Transmission (telecommunications); Image (mathematics); Ecology","score_opus":0.011069220042452057,"score_gpt":0.2736998318842551,"score_spread":0.2626306118418031,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1592859723","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8914006,0.00061504776,0.059478357,0.027344666,0.00017251862,0.0004935878,0.000020665917,0.00025559403,0.020218983],"genre_scores_gemma":[0.9975881,0.0005706791,0.0014029315,0.00012407512,0.000022306387,0.000011157989,0.000095511474,0.000001296449,0.00018391559],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9989684,0.00014961422,0.00022633525,0.00017142222,0.00030654029,0.00017768922],"domain_scores_gemma":[0.999566,0.00012025997,0.00006240152,0.00008598252,0.00010682922,0.00005853663],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005906405,0.0000610614,0.00010323225,0.00027690976,0.00043219724,0.00009622496,0.00015047162,0.00007328663,0.00016568307],"category_scores_gemma":[0.000053833402,0.000045044122,0.0001137716,0.0024275212,0.00019528662,0.00011353194,0.000002563007,0.000110165165,0.000008968659],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003584756,0.00009598307,0.8890968,0.000002240649,0.00014895857,0.000001175611,0.0049840226,0.0309162,0.0000031840234,0.019095609,0.00016441148,0.055455565],"study_design_scores_gemma":[0.00011581561,0.00001969391,0.98050505,0.00000777569,0.00016866907,6.4011786e-8,0.001127468,0.0061559714,7.24324e-7,0.009290243,0.0025214765,0.00008704833],"about_ca_topic_score_codex":0.0015888883,"about_ca_topic_score_gemma":0.03682926,"teacher_disagreement_score":0.106187545,"about_ca_system_score_codex":0.000016232823,"about_ca_system_score_gemma":0.000042280135,"threshold_uncertainty_score":0.9807461},"labels":[],"label_agreement":null},{"id":"W159313701","doi":"","title":"Let Your Plan do the Driving: Effective Performance Measurement","year":2012,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Plan (archaeology); Process (computing); Process management; Corporate governance; Strategic planning; Master plan; Business; Performance measurement; Operations management; Computer science; Engineering; Engineering management; Marketing; Finance","score_opus":0.04485022318289594,"score_gpt":0.2846425375883323,"score_spread":0.23979231440543636,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W159313701","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.77808267,0.00017489189,0.007235371,0.00143534,0.0006800543,0.00042434165,0.000001392068,0.00017103,0.21179491],"genre_scores_gemma":[0.9987101,0.00002373686,0.00020834156,0.000103849845,0.00016882303,0.000016951717,0.0000025541117,0.0000031980485,0.0007624516],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9992905,0.00007730278,0.00006488951,0.000057102417,0.00034024674,0.0001699377],"domain_scores_gemma":[0.9997537,0.00004397087,0.000030037529,0.00005825077,0.00006140611,0.000052626285],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001044161,0.00004240631,0.000039034487,0.000016770085,0.00045575274,0.000035357705,0.00008054318,0.000028780909,0.00013865693],"category_scores_gemma":[0.00005027181,0.000027306342,0.000017999824,0.00010256047,0.00004603054,0.00020153646,0.0000025277723,0.00005307379,0.00005251896],"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":[0.000007912639,0.000039149596,0.9082246,0.000004369898,0.000016230726,1.18134345e-7,0.063847356,0.0013044032,0.0000270194,0.01392422,0.0025077856,0.010096873],"study_design_scores_gemma":[0.00012776327,0.000018045646,0.9069581,0.000015465872,0.000014619359,2.1606502e-7,0.005986263,0.00016848573,0.00020257114,0.000026111487,0.08639232,0.00009005976],"about_ca_topic_score_codex":0.00018187912,"about_ca_topic_score_gemma":0.0002501302,"teacher_disagreement_score":0.22062743,"about_ca_system_score_codex":0.00004125917,"about_ca_system_score_gemma":0.000029097848,"threshold_uncertainty_score":0.35053277},"labels":[],"label_agreement":null},{"id":"W1597531677","doi":"","title":"Évaluation de l'intermodalité à Montréal. Développement d'outils de planification","year":2011,"lang":"en","type":"article","venue":"INRIA a CCSD electronic archive server","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Humanities; Political science; Art","score_opus":0.04021175936095073,"score_gpt":0.28287767901464594,"score_spread":0.24266591965369522,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1597531677","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8012239,0.000158427,0.18541326,0.0010397034,0.00010223535,0.00039756086,0.000012503205,0.00019228522,0.011460153],"genre_scores_gemma":[0.99133575,0.0003177011,0.007337335,0.00022423104,0.00007744841,0.00005570289,0.00013241761,0.000015138987,0.00050425413],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9984409,0.00030832642,0.00023407865,0.00021818439,0.00026007075,0.0005383896],"domain_scores_gemma":[0.99949986,0.00006648327,0.00011358383,0.00014682046,0.00006747561,0.000105807914],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009777584,0.000109971465,0.00010125093,0.000087473476,0.0003145285,0.00003161063,0.00019851483,0.00007721685,0.00025570623],"category_scores_gemma":[0.00007133535,0.000118707525,0.000058257076,0.00015689536,0.00006542889,0.00016388416,0.000008388672,0.00016066342,0.000042483523],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029002904,0.00020856748,0.059259355,0.000019398167,0.000116701085,0.0000076236934,0.29808843,0.0036750878,0.0014212463,0.6242668,0.0015498174,0.011096931],"study_design_scores_gemma":[0.0011272269,0.00021098695,0.83579004,0.00006363336,0.000119085424,0.0000049531263,0.004111772,0.009279906,0.0020701669,0.12275615,0.024000425,0.00046562028],"about_ca_topic_score_codex":0.0058601513,"about_ca_topic_score_gemma":0.027288899,"teacher_disagreement_score":0.7765307,"about_ca_system_score_codex":0.00032183042,"about_ca_system_score_gemma":0.0006188589,"threshold_uncertainty_score":0.9904605},"labels":[],"label_agreement":null},{"id":"W1599430102","doi":"","title":"On the Existence of Optimal Taxes for Network Congestion Games with Heterogeneous Users","year":2014,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Inefficiency; Computer science; Latency (audio); Homogeneous; Price of anarchy; Network congestion; Mathematical optimization; Heterogeneous network; Computer network; Microeconomics; Economics; Mathematics; Monetary economics; Telecommunications","score_opus":0.02103939639910675,"score_gpt":0.26344109183968123,"score_spread":0.2424016954405745,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1599430102","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.76346976,0.000019109175,0.22392502,0.001768346,0.00014028432,0.00041475278,0.000010146807,0.000082030914,0.010170519],"genre_scores_gemma":[0.98953956,0.0000075998773,0.00948772,0.00018544847,0.000045842935,0.000016224549,0.0000072139574,0.0000037922816,0.0007065752],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995717,0.000050614122,0.000076254815,0.000078398305,0.0001244566,0.000098615536],"domain_scores_gemma":[0.9994077,0.00037570344,0.000063135085,0.00005896389,0.000072126335,0.000022380289],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029276733,0.000039493163,0.000055254826,0.000010602501,0.00019249976,0.000019111225,0.00006837011,0.000028086399,0.00003163801],"category_scores_gemma":[0.00007336689,0.000024150138,0.00001957759,0.00007570337,0.000113463146,0.000040723466,9.345608e-7,0.000021480064,0.0000012415248],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007950007,0.000010608303,0.0030155734,0.0000040703976,0.000008843797,1.0243634e-7,0.0020801611,0.7512269,0.000003868801,0.24204999,0.0008114624,0.0007089017],"study_design_scores_gemma":[0.008584767,0.011075619,0.14328407,0.0017186038,0.0007188598,0.000008910257,0.044969216,0.50211155,0.008459278,0.054679457,0.22107324,0.0033164665],"about_ca_topic_score_codex":0.00016193601,"about_ca_topic_score_gemma":0.0007895101,"teacher_disagreement_score":0.2491154,"about_ca_system_score_codex":0.000005554969,"about_ca_system_score_gemma":0.000021592032,"threshold_uncertainty_score":0.1480572},"labels":[],"label_agreement":null},{"id":"W160073687","doi":"","title":"IMPACTS OF VARIOUS TRAFFIC PARAMETERS ON TRANSIT SIGNAL PRIORITY EFFECTIVENESS","year":2004,"lang":"en","type":"article","venue":"The Journal of Public Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"VisSim; Headway; Transit (satellite); Bus priority; SIGNAL (programming language); Traffic volume; Transport engineering; Traffic simulation; Traffic signal; Volume (thermodynamics); Simulation software; Simulation; Real-time computing; Engineering; Software; Automotive engineering; Computer science; Microsimulation; Public transport; Operating system","score_opus":0.022136595752315115,"score_gpt":0.2812630454707848,"score_spread":0.25912644971846965,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W160073687","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9462089,0.00007534023,0.05218143,0.0010227022,0.0001864223,0.0001971748,0.00001582793,0.000024800825,0.00008742163],"genre_scores_gemma":[0.9990268,0.000114032024,0.000737728,0.00004091402,0.00004750611,0.0000014821729,0.000018253337,0.000009816347,0.000003479388],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9981987,0.00039364578,0.0004868659,0.00008214791,0.0006417521,0.0001968716],"domain_scores_gemma":[0.9986222,0.00035034155,0.00047250403,0.00007959072,0.00035035855,0.00012501945],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026258074,0.000104917206,0.00021486275,0.00018077108,0.0002217062,0.00003116292,0.00021213008,0.00009359333,0.000023219],"category_scores_gemma":[0.00006695665,0.00007817613,0.00013537673,0.00047916113,0.00017008088,0.00046057865,2.7236905e-7,0.0002232177,0.0000013155878],"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":[0.00068387575,0.00037372374,0.008947273,0.000063141015,0.00012644815,0.000014477511,0.07275616,0.8978413,0.0011656677,0.0093551045,0.000010696003,0.008662182],"study_design_scores_gemma":[0.0034167157,0.0009987531,0.98495615,0.0002850116,0.00031783068,0.000006961036,0.00461974,0.000076950615,0.0030287278,0.0018424505,0.00022760822,0.00022313066],"about_ca_topic_score_codex":0.0006316329,"about_ca_topic_score_gemma":0.0023460903,"teacher_disagreement_score":0.97600883,"about_ca_system_score_codex":0.000096616364,"about_ca_system_score_gemma":0.00051224924,"threshold_uncertainty_score":0.318793},"labels":[],"label_agreement":null},{"id":"W1603637980","doi":"","title":"URBAN PLANNING AND URBAN IMPACT OF LIGHT RAIL SYSTEMS: INTRODUCING 'TRANSIT CITY' LRT IN TORONTO","year":2010,"lang":"en","type":"article","venue":"Public transport international","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Transport engineering; Light rail transit; Plan (archaeology); Public transport; Transit (satellite); Urban planning; Land use; Transit-oriented development; Land-use planning; Business; Rail transit; Environmental planning; Bus rapid transit; Light rail; Transportation planning; Geography; Engineering; Civil engineering","score_opus":0.01456045077868818,"score_gpt":0.29817422791775394,"score_spread":0.28361377713906577,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1603637980","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9673378,0.00030516082,0.0034623565,0.0013332628,0.0012020818,0.00023097591,0.00009807471,0.00009856983,0.025931703],"genre_scores_gemma":[0.9985238,0.000030497044,0.00039286373,0.000022229784,0.00037289385,0.000015008194,0.00020913355,0.000013278125,0.0004203053],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9985092,0.00004496333,0.00044542493,0.00028003808,0.00046116303,0.00025919706],"domain_scores_gemma":[0.99930346,0.000065103,0.00014910965,0.00011850612,0.00021929631,0.00014453665],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00081388466,0.00013345809,0.00020551794,0.00020023614,0.00012244923,0.000086410815,0.00025629255,0.00014557147,0.000561545],"category_scores_gemma":[0.00007403118,0.00013387021,0.00008106577,0.00019808886,0.00010297672,0.0009073125,0.0000035603964,0.000215387,9.565944e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000374149,0.00006858496,0.96033776,0.000012596551,0.000051032435,0.00000570549,0.021190012,0.00086131523,0.00037501613,0.016483702,0.00038746293,0.00018938804],"study_design_scores_gemma":[0.00068021315,0.000032510794,0.97583365,0.000067489884,0.000016025124,0.0000033992271,0.0020892525,0.0009567692,0.000032294894,0.00003320755,0.020055953,0.0001992444],"about_ca_topic_score_codex":0.013834569,"about_ca_topic_score_gemma":0.014604857,"teacher_disagreement_score":0.031185975,"about_ca_system_score_codex":0.00016437595,"about_ca_system_score_gemma":0.00023635318,"threshold_uncertainty_score":0.9927324},"labels":[],"label_agreement":null},{"id":"W1606447628","doi":"10.1007/s11116-018-9954-8","title":"Use of repeated cross-sectional travel surveys for developing meta models of activity-travel scheduling processes","year":2018,"lang":"en","type":"article","venue":"Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Ontario Ministry of Economic Development and Innovation","keywords":"Transferability; Computer science; Scheduling (production processes); Meta-analysis; Econometrics; Machine learning; Engineering; Mathematics; Operations management; Logit","score_opus":0.18975532919604263,"score_gpt":0.3677765170722017,"score_spread":0.1780211878761591,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1606447628","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.49374488,0.000013353109,0.50565296,0.000025117914,0.000071269285,0.00020954398,0.00016925271,0.000028567289,0.00008508235],"genre_scores_gemma":[0.96139723,0.000039539154,0.037915356,0.000010039994,0.000045012668,0.000030791263,0.0004038888,0.000015042536,0.00014312458],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99867105,0.000092492,0.0004798558,0.00023601635,0.00034866735,0.00017189373],"domain_scores_gemma":[0.9977254,0.0002771203,0.0003592082,0.000080313286,0.0015176409,0.000040296294],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010131049,0.000110797584,0.00024403195,0.00012924016,0.00027673913,0.000032465512,0.00009193555,0.0001342922,0.000026631575],"category_scores_gemma":[0.00015760718,0.00011504807,0.00009993251,0.00051030645,0.00022275082,0.00087177,5.244153e-7,0.000053139567,4.094615e-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":[0.0008535375,0.0003316027,0.5320663,0.0012025587,0.00093591394,0.0000012646227,0.062175963,0.3101391,0.017212536,0.07396289,0.000011033119,0.0011073106],"study_design_scores_gemma":[0.0004655523,0.00007209419,0.9543176,0.00005313866,0.00021704882,1.1899567e-7,0.00061313435,0.0043608556,0.038433436,0.0012490352,0.000030451047,0.00018752618],"about_ca_topic_score_codex":0.001871482,"about_ca_topic_score_gemma":0.0050999313,"teacher_disagreement_score":0.4677376,"about_ca_system_score_codex":0.000023922637,"about_ca_system_score_gemma":0.00045044147,"threshold_uncertainty_score":0.46915242},"labels":[],"label_agreement":null},{"id":"W162229576","doi":"","title":"Transportation in Toronto","year":2014,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Business; Geography; Transport engineering; Engineering","score_opus":0.010125906573018371,"score_gpt":0.28939741478909703,"score_spread":0.27927150821607866,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W162229576","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16941606,0.000024476323,0.04575594,0.00081970496,0.00017268477,0.00009900319,0.0000013882336,0.00015057644,0.78356016],"genre_scores_gemma":[0.99654204,0.000018438359,0.0018787446,0.00011079899,0.000027961802,0.0000029632695,0.000018997367,0.000001829109,0.001398224],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9996887,0.000029872519,0.0000722895,0.000055008266,0.000085729196,0.000068435955],"domain_scores_gemma":[0.99989825,0.000023510785,0.000012544764,0.000025331166,0.00001608798,0.000024289493],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020737684,0.000020422054,0.000030177132,0.00001223813,0.000052934916,0.0000094389625,0.000030197338,0.00002819908,0.0005880186],"category_scores_gemma":[0.000015233117,0.000020834847,0.000008999628,0.00005937523,0.000014094227,0.00016425272,9.15122e-8,0.000014385329,0.000009221705],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008179272,0.000027953549,0.15228567,0.0000032295009,0.000001286221,5.1314356e-7,0.031352516,0.003274158,0.00001927214,0.8001979,0.00044405193,0.01238525],"study_design_scores_gemma":[0.0003580696,0.000016989134,0.89635056,0.00001013624,0.000003486107,1.525193e-8,0.010040503,0.0011603432,0.000032889693,0.0013968836,0.09051433,0.00011579117],"about_ca_topic_score_codex":0.028734844,"about_ca_topic_score_gemma":0.45588386,"teacher_disagreement_score":0.82712597,"about_ca_system_score_codex":0.000037131682,"about_ca_system_score_gemma":0.00001528704,"threshold_uncertainty_score":0.9777329},"labels":[],"label_agreement":null},{"id":"W1631273695","doi":"10.1111/peps.12060","title":"On the Distribution of Job Performance: The Role of Measurement Characteristics in Observed Departures from Normality","year":2013,"lang":"en","type":"article","venue":"Personnel Psychology","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":55,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Normality; Range (aeronautics); Distribution (mathematics); Econometrics; Artifact (error); Psychology; Statistics; Computer science; Social psychology; Mathematics; Artificial intelligence; Engineering","score_opus":0.05305126977959082,"score_gpt":0.2851986998797762,"score_spread":0.23214743010018538,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1631273695","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99665344,0.00005310365,0.00019126722,0.0014936606,0.00013778472,0.0001699789,0.00004002947,0.0000083518535,0.0012523986],"genre_scores_gemma":[0.999635,0.000059012094,0.000025743653,0.00016326501,0.000035716745,0.000023249699,0.000047488724,0.0000024315088,0.000008090755],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99918985,0.00019049652,0.00019839779,0.00009216913,0.00021728936,0.00011181492],"domain_scores_gemma":[0.9994419,0.0001233913,0.0001647201,0.00012612688,0.00012599933,0.00001786833],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00059862493,0.00004978102,0.00009419124,0.000015157409,0.00013145103,0.000008310144,0.00015701742,0.000063784115,0.00016855219],"category_scores_gemma":[0.00017923582,0.000031136504,0.00002845931,0.00011847758,0.00015720022,0.000052503234,0.0000029273717,0.000095035386,0.000007772919],"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":[0.00007516238,0.00010370798,0.95899785,0.0000061176374,0.00001779445,1.3647238e-7,0.032832447,0.00028006095,0.00066748203,0.004292541,0.0004597529,0.002266924],"study_design_scores_gemma":[0.00013106024,0.000027521106,0.9938236,0.0000149857915,0.0000061155733,4.3541966e-8,0.004324415,0.00063770235,0.00010863759,0.00055538665,0.00033640504,0.000034133525],"about_ca_topic_score_codex":0.0032586532,"about_ca_topic_score_gemma":0.00080025074,"teacher_disagreement_score":0.03482572,"about_ca_system_score_codex":0.000021393684,"about_ca_system_score_gemma":0.000024349089,"threshold_uncertainty_score":0.49261308},"labels":[],"label_agreement":null},{"id":"W1677231726","doi":"10.5555/1929237.1929260","title":"Braess's paradox for flows over time","year":2010,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Transpose; Conjecture; Network topology; Context (archaeology); Flow (mathematics); Computer science; Mathematics; Flow network; Topology (electrical circuits); Mathematical economics; Mathematical optimization; Discrete mathematics; Combinatorics; Physics; Geography; Geometry; Computer network; Quantum mechanics","score_opus":0.03596065590270301,"score_gpt":0.20279388915693283,"score_spread":0.16683323325422983,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1677231726","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9562627,0.0000028577047,0.030761883,0.00014496497,0.00031039098,0.00017536899,0.000019669658,0.00014621703,0.012175967],"genre_scores_gemma":[0.9885325,0.000008786038,0.0007526405,0.00006546013,0.00009220097,4.2582894e-7,0.000026145834,0.0000064386013,0.010515397],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995263,0.000024502726,0.00005897783,0.00018626757,0.000043321827,0.00016061973],"domain_scores_gemma":[0.9996029,0.000091937065,0.00004381181,0.00010625413,0.000068443296,0.00008661589],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016863433,0.000057316352,0.000067040964,0.00005281554,0.0003289139,0.000026908152,0.00014835664,0.00009349017,0.00047809666],"category_scores_gemma":[0.00004357556,0.00006771094,0.000054420114,0.0002354493,0.00007690704,0.00024548997,0.0000043098066,0.00007822622,0.00008568467],"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":[0.00015968157,0.00013159167,0.085782975,0.000014215207,0.00004525581,0.000030282452,0.004525037,0.08515535,0.0008827237,0.8162494,0.0059800562,0.0010434485],"study_design_scores_gemma":[0.0039997622,0.0001426647,0.09689302,0.00003585001,0.00024156147,0.00000109289,0.0017458507,0.4116431,0.00026366353,0.035925068,0.4479141,0.0011942754],"about_ca_topic_score_codex":0.00021722016,"about_ca_topic_score_gemma":0.0012259168,"teacher_disagreement_score":0.78032434,"about_ca_system_score_codex":0.000016785278,"about_ca_system_score_gemma":0.000066958455,"threshold_uncertainty_score":0.5234823},"labels":[],"label_agreement":null},{"id":"W1697015183","doi":"10.1002/atr.197","title":"Modeling and optimization of transportation systems","year":2011,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Operations research; Public transport; Service (business); Transport engineering; Computer science; Sizing; Engineering; Business; Marketing","score_opus":0.02404201703697013,"score_gpt":0.268557331539643,"score_spread":0.2445153145026729,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1697015183","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.53747576,0.00036438435,0.4613941,0.000028428949,0.00033281624,0.00014328933,0.000018654193,0.000021672839,0.00022091171],"genre_scores_gemma":[0.9434836,0.0009247864,0.055455323,0.00000726904,0.00004562366,0.0000026793082,0.00005261828,0.000011669032,0.000016420163],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9986539,0.000052381925,0.0007254549,0.000102095786,0.0003499549,0.00011619808],"domain_scores_gemma":[0.99869883,0.00003217433,0.0005299104,0.000051636267,0.00060037005,0.00008707896],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040524505,0.000088952,0.00021051631,0.0001874488,0.00010972493,0.000011849877,0.00007572682,0.000087777385,0.000022250226],"category_scores_gemma":[0.000023216993,0.000090461675,0.000062562605,0.00024788044,0.00006496838,0.00087808794,1.6408988e-7,0.00009325213,1.7427234e-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":[0.00016318687,0.000041243955,0.0035898348,0.000044294462,0.000020336685,0.000004677808,0.044713397,0.9452964,0.00012658416,0.0053264624,0.000002053829,0.0006715286],"study_design_scores_gemma":[0.012726839,0.0021769365,0.44042778,0.0026555513,0.0019315871,0.000019277275,0.2484308,0.28117532,0.0023846866,0.0045311055,0.0016572737,0.0018828356],"about_ca_topic_score_codex":0.00029819226,"about_ca_topic_score_gemma":0.0003216384,"teacher_disagreement_score":0.6641211,"about_ca_system_score_codex":0.000021308873,"about_ca_system_score_gemma":0.0000958973,"threshold_uncertainty_score":0.368892},"labels":[],"label_agreement":null},{"id":"W1701620708","doi":"","title":"Modélisation des coûts aux usagers du réseau routier québécois","year":2009,"lang":"fr","type":"article","venue":"Espace École de technologie supérieure (École de technologie supérieure)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Humanities; Political science; Art","score_opus":0.02340058339418911,"score_gpt":0.2895788547928999,"score_spread":0.2661782713987108,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1701620708","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.63140196,0.0127302585,0.19442493,0.1306841,0.0020814359,0.003284138,0.00039618116,0.01690274,0.008094268],"genre_scores_gemma":[0.96727544,0.0046240333,0.015159795,0.0022181931,0.00039441214,0.0002960158,0.00019889147,0.0001747992,0.009658447],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99130255,0.0004762495,0.0014168714,0.0020109168,0.0013528648,0.0034405729],"domain_scores_gemma":[0.99511254,0.00054371404,0.0009005493,0.0019158458,0.0008845048,0.00064281904],"candidate_categories":["metaepi_narrow","sts","research_integrity"],"consensus_categories":["metaepi_narrow","sts","research_integrity"],"category_scores_codex":[0.0021349848,0.0014526261,0.0013054903,0.0013937883,0.0022904826,0.00056991784,0.002939766,0.0046725282,0.0003287093],"category_scores_gemma":[0.004623521,0.001656882,0.0006134469,0.0061838566,0.004376661,0.001689398,0.00023865345,0.0028661266,0.00027715613],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005707109,0.002136796,0.11266953,0.0003227095,0.00042210458,0.0016872877,0.027697597,0.5545704,0.0036382773,0.15296853,0.04176232,0.10155377],"study_design_scores_gemma":[0.0070515997,0.0050313976,0.11275177,0.0019305677,0.0016170666,0.00051468116,0.17770718,0.19745699,0.018793115,0.09645828,0.37242782,0.008259555],"about_ca_topic_score_codex":0.008579575,"about_ca_topic_score_gemma":0.021280631,"teacher_disagreement_score":0.3571134,"about_ca_system_score_codex":0.0022652196,"about_ca_system_score_gemma":0.0029022433,"threshold_uncertainty_score":0.9998223},"labels":[],"label_agreement":null},{"id":"W170967342","doi":"","title":"Sistemas de Autobuses Rápidos: un nuevo modo de transporte público metropolitano","year":2005,"lang":"es","type":"article","venue":"Carreteras: Revista técnica de la Asociación Española de la Carretera","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Political science; Humanities; Geography; Art","score_opus":0.007164813907627245,"score_gpt":0.3001212586796462,"score_spread":0.29295644477201893,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W170967342","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9449931,0.010487924,0.012380757,0.0055733016,0.0005236003,0.0012331478,0.0010198959,0.001205712,0.022582602],"genre_scores_gemma":[0.97778815,0.0039141234,0.013069535,0.0018662121,0.001786348,0.00017598801,0.00029144203,0.00026491762,0.00084328227],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.98702884,0.005987318,0.0017244046,0.0013970428,0.0013154513,0.00254696],"domain_scores_gemma":[0.99374616,0.002450628,0.0009903474,0.0010567177,0.00041402905,0.0013421316],"candidate_categories":["metaepi_narrow","sts","scholarly_communication","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0058862437,0.0011722286,0.0015620424,0.00037320275,0.0014586393,0.0022408552,0.0014686108,0.0019083326,0.00052421994],"category_scores_gemma":[0.0009290036,0.0013420787,0.0013460927,0.0014780388,0.0022241154,0.0009736119,0.000057416404,0.0018435309,0.000057177796],"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":[0.00060405827,0.0009292245,0.673356,0.001878701,0.0017785013,0.0008082792,0.13039355,0.014456024,0.0041572494,0.14774607,0.006498513,0.017393807],"study_design_scores_gemma":[0.0054365518,0.00039536756,0.24985352,0.003407617,0.0032073963,0.00027655013,0.010041601,0.010493894,0.0017467134,0.0018288807,0.7090581,0.0042537707],"about_ca_topic_score_codex":0.0009109903,"about_ca_topic_score_gemma":0.00013489022,"teacher_disagreement_score":0.7025596,"about_ca_system_score_codex":0.0035552033,"about_ca_system_score_gemma":0.0020636604,"threshold_uncertainty_score":0.99984133},"labels":[],"label_agreement":null},{"id":"W1715191156","doi":"10.3141/2311-03","title":"Impacts of Transit Priority on Signal Coordination: Case Study of Toronto, Ontario, Canada","year":2012,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Centre for Social Innovation","funders":"","keywords":"Transit (satellite); SIGNAL (programming language); Transport engineering; Bus priority; Signal timing; Downtown; Computer science; Analytic hierarchy process; Traffic signal; Operations research; Engineering; Simulation; Real-time computing; Public transport; Geography","score_opus":0.0809491796722078,"score_gpt":0.393234923650928,"score_spread":0.3122857439787202,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1715191156","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9964332,0.00019141838,0.00030341893,0.000617972,0.00057879265,0.0012296657,0.000070771384,0.000013829947,0.0005609077],"genre_scores_gemma":[0.9988317,0.00014766281,0.00041354634,0.00001743838,0.0001603452,0.000026819713,0.000015657786,0.000029597482,0.00035721852],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9900184,0.0019623914,0.0016847524,0.00027283636,0.0052260146,0.0008355638],"domain_scores_gemma":[0.99241555,0.0013059763,0.00082932645,0.00035110879,0.004540893,0.0005571488],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0064101387,0.00022633692,0.00054258783,0.00040892407,0.0008952366,0.000045355686,0.00068518776,0.00018603382,0.0010233343],"category_scores_gemma":[0.00016566912,0.00018825146,0.0002465075,0.0013080257,0.00048329547,0.0010086062,0.0000026644927,0.001275219,0.0000010131874],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011619686,0.001267922,0.8829366,0.00013057125,0.00018284521,0.00021930743,0.10465297,0.003823636,0.0002696241,0.0018866232,0.0016839497,0.0017839632],"study_design_scores_gemma":[0.0018566799,0.0011182231,0.9212461,0.0002057553,0.00010435965,0.0000015570109,0.07083876,0.000020881225,0.0005077681,0.00007844237,0.003849862,0.00017161167],"about_ca_topic_score_codex":0.99536216,"about_ca_topic_score_gemma":0.99962497,"teacher_disagreement_score":0.038309492,"about_ca_system_score_codex":0.001047736,"about_ca_system_score_gemma":0.0040965714,"threshold_uncertainty_score":0.99988985},"labels":[],"label_agreement":null},{"id":"W1754485582","doi":"10.1002/atr.1198","title":"Effectiveness of en route traffic information in developing countries using conventional discrete choice and neural‐network models","year":2012,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Artificial neural network; Computer science; Discrete choice; Transport engineering; Operations research; Artificial intelligence; Machine learning; Engineering","score_opus":0.01455997137322764,"score_gpt":0.29313500773512025,"score_spread":0.2785750363618926,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1754485582","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9319584,0.00028874332,0.06725048,0.00006646015,0.00026195886,0.00014095989,0.000011030162,0.0000067575324,0.000015235755],"genre_scores_gemma":[0.9863407,0.000370296,0.013159239,0.000016457883,0.000057998033,0.000002190567,0.00004713983,0.0000047562276,0.0000012228118],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9988756,0.00013733425,0.00050713157,0.000048244336,0.0002842022,0.00014746921],"domain_scores_gemma":[0.9989847,0.00025962142,0.00045941607,0.000024221328,0.00022118837,0.000050840063],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009885355,0.000072799,0.00017582533,0.00013158735,0.000101407415,0.000016777032,0.00004673577,0.00006257118,0.0000029292262],"category_scores_gemma":[0.000031411262,0.00007321047,0.00004013519,0.00023856171,0.000053242253,0.0028695478,5.744852e-7,0.00008928072,8.3384506e-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":[0.000120154065,0.000009593978,0.07940488,0.000102012564,0.0000114537615,4.1379027e-7,0.012742782,0.9001594,0.000030815794,0.006940298,4.31511e-7,0.00047774715],"study_design_scores_gemma":[0.0011614333,0.000034888388,0.988859,0.0005208123,0.00005114804,0.0000017791237,0.0019618291,0.0062403143,0.000055368386,0.0004174563,0.0005775677,0.000118411124],"about_ca_topic_score_codex":0.0000617057,"about_ca_topic_score_gemma":0.0001622341,"teacher_disagreement_score":0.9094541,"about_ca_system_score_codex":0.0000786142,"about_ca_system_score_gemma":0.00012159123,"threshold_uncertainty_score":0.2985436},"labels":[],"label_agreement":null},{"id":"W1765284097","doi":"10.71781/10066","title":"Network pricing problems : complexity, polyhedral study and solution approaches","year":2008,"lang":"en","type":"book","venue":"Library and Archives Canada (Government of Canada)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Toll; Mathematical optimization; Linear programming; Flow network; Integer programming; Integer (computer science); Shortest path problem; Cutting-plane method; Path (computing); Mathematics; Convex hull; Polyhedron; Regular polygon; Computer science; Combinatorics; Graph","score_opus":0.02524263984497622,"score_gpt":0.17454775497717023,"score_spread":0.14930511513219402,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1765284097","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004383582,0.0007228101,0.0003060264,0.0019236902,0.0002235606,0.000673219,0.00011986029,0.00003102495,0.99161625],"genre_scores_gemma":[0.6293882,0.00046763665,0.001568064,0.00033326232,0.00027729388,0.000012322054,0.00009855078,0.00003172507,0.36782295],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99766135,0.000104027924,0.0002805183,0.00026122245,0.0014193323,0.00027355822],"domain_scores_gemma":[0.9993317,0.00014863965,0.00024130444,0.0000952466,7.692029e-7,0.00018234234],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000027162208,0.00018146634,0.00027301605,0.000023100276,0.0007306459,0.000033436543,0.00013826715,0.000055654396,0.000008727357],"category_scores_gemma":[0.0000018289531,0.0001917439,0.000017919452,0.000059961836,0.00023510643,0.00024446181,0.00004351383,0.00017310645,8.9923813e-10],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040782295,0.00014570817,0.1664007,0.00058985513,0.00035388148,0.00012906756,0.017566822,0.0086004585,0.000019465315,0.78000104,0.015873006,0.009912181],"study_design_scores_gemma":[0.002722844,0.00074888056,0.5182381,0.0017634085,0.0004724387,0.000017894115,0.04114367,0.016200049,0.000076666685,0.032432716,0.38328904,0.002894279],"about_ca_topic_score_codex":0.0122494465,"about_ca_topic_score_gemma":0.25781927,"teacher_disagreement_score":0.7475683,"about_ca_system_score_codex":0.000015361538,"about_ca_system_score_gemma":0.0028938823,"threshold_uncertainty_score":0.9943281},"labels":[],"label_agreement":null},{"id":"W1778268826","doi":"10.1002/atr.1213","title":"A rule‐based model for integrated operation of bus priority signal timings and traveling speed","year":2012,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"VisSim; Bus priority; Schedule; Local bus; SIGNAL (programming language); Real-time computing; Computer science; Bus rapid transit; Control bus; System bus; Automotive engineering; Simulation; Public transport; Engineering; Transport engineering; Microsimulation; Computer hardware","score_opus":0.025581671201803977,"score_gpt":0.3075099216050705,"score_spread":0.2819282504032665,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1778268826","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5558923,0.00009555746,0.44361445,0.00010350026,0.00009750495,0.00014517245,0.000024038343,0.000009722935,0.000017777236],"genre_scores_gemma":[0.8986882,0.00006223708,0.101037264,0.00002982088,0.00006901514,0.00000227421,0.00007821034,0.000008612389,0.000024376597],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989787,0.00003353242,0.00049911387,0.00007680532,0.00026611434,0.00014577096],"domain_scores_gemma":[0.99882823,0.000093396324,0.00044280232,0.000031163694,0.0005066638,0.00009772831],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006473726,0.00008269392,0.00018293908,0.00010048232,0.00014140563,0.000016020125,0.00005219881,0.00007800418,0.000010930468],"category_scores_gemma":[0.000053648244,0.00007931495,0.00007002775,0.00014474247,0.000054913864,0.0008176779,2.028188e-7,0.00009486812,1.13033785e-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":[0.00041328854,0.00008605985,0.008384951,0.0000502738,0.000016791546,5.020109e-7,0.0351335,0.9407731,0.009736851,0.0012086693,0.0000066248303,0.004189375],"study_design_scores_gemma":[0.010523271,0.00072882505,0.36835262,0.00075179763,0.0008027371,0.0000036822928,0.026511753,0.5634276,0.024511749,0.002390749,0.001188548,0.0008066497],"about_ca_topic_score_codex":0.000037412614,"about_ca_topic_score_gemma":0.0001746757,"teacher_disagreement_score":0.3773455,"about_ca_system_score_codex":0.000036222114,"about_ca_system_score_gemma":0.00019744546,"threshold_uncertainty_score":0.323437},"labels":[],"label_agreement":null},{"id":"W1778626794","doi":"","title":"The Naturalistic Driver Model: A Review of Distraction, Impairment and Emergency Factors","year":2005,"lang":"en","type":"review","venue":"eScholarship (California Digital Library)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Distraction; Agency (philosophy); Transit (satellite); Engineering; Transport engineering; Psychology; Operations research; Library science; Gerontology; Political science; Sociology; Medicine; Computer science; Public transport; Social science","score_opus":0.030227216843375208,"score_gpt":0.3059339251010639,"score_spread":0.27570670825768867,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1778626794","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00002999102,0.99221337,0.00007865047,0.00025570503,0.00016091578,0.0006247939,0.0028510417,0.000107801076,0.0036777188],"genre_scores_gemma":[0.00070829893,0.99474484,0.00012310044,0.000038254253,0.000094030554,0.000029355404,0.002331574,0.000041215288,0.0018893449],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99784577,0.0001838115,0.00087855064,0.00031650072,0.00048387388,0.00029147917],"domain_scores_gemma":[0.9986257,0.00026156008,0.00060500135,0.00022813509,0.00007595652,0.0002036451],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003177957,0.00031510578,0.00063036487,0.000079985395,0.00047153805,0.00034273544,0.00038409646,0.00022212797,0.0002196146],"category_scores_gemma":[0.00035696538,0.00020864511,0.0003567323,0.00046470398,0.00017753548,0.0015648945,0.000034312612,0.00042051804,0.000059541275],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019990443,0.00021407224,0.004811618,0.042525545,0.0002766427,0.000007032503,0.00041925703,0.000045889014,1.8714386e-8,0.011967748,0.018281214,0.92143095],"study_design_scores_gemma":[0.00003848141,0.000010757598,0.000115119954,0.010418838,0.00020602415,9.135703e-7,0.000032040924,0.000012954368,4.878146e-8,0.00036000312,0.98856914,0.00023568925],"about_ca_topic_score_codex":0.000007579928,"about_ca_topic_score_gemma":0.000011388009,"teacher_disagreement_score":0.9702879,"about_ca_system_score_codex":0.000052477917,"about_ca_system_score_gemma":0.00035867834,"threshold_uncertainty_score":0.8508301},"labels":[],"label_agreement":null},{"id":"W1790684974","doi":"10.1002/atr.1219","title":"Traffic rationing and pricing in a linear monocentric city","year":2012,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":35,"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":"Hong Kong University of Science and Technology; National Natural Science Foundation of China","keywords":"Rationing; Toll; Economics; Microeconomics; Business","score_opus":0.01566250136748498,"score_gpt":0.2924762114655758,"score_spread":0.2768137100980908,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1790684974","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98855066,0.00049064157,0.010272205,0.00018454193,0.0003107536,0.00009032438,0.0000014146561,0.000014368238,0.00008509887],"genre_scores_gemma":[0.9847337,0.0004367655,0.014634218,0.000026575533,0.00012606797,0.0000013740718,0.000010583062,0.000005468759,0.000025222122],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9991498,0.00004628857,0.0003716464,0.00005876722,0.00021748435,0.00015598579],"domain_scores_gemma":[0.9994292,0.00007396727,0.00026629865,0.000024150659,0.00011566951,0.00009068582],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00055439456,0.00005725159,0.00011823912,0.00017990566,0.00012053037,0.000013511322,0.00003713047,0.000049740767,0.000012954014],"category_scores_gemma":[0.00006439317,0.0000582119,0.000030513922,0.00035068343,0.00002909812,0.0010632323,2.354071e-7,0.00012475217,3.937595e-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":[0.0000935691,0.00007859507,0.3093401,0.000016400227,0.000008458943,0.000005953693,0.09167302,0.5852862,0.0001682347,0.0008577969,0.000006955952,0.012464756],"study_design_scores_gemma":[0.0008705483,0.000038326503,0.9889254,0.00007974365,0.000028166876,0.0000017746889,0.008166351,0.0003918764,0.000076801116,0.00006929935,0.0012581793,0.000093531555],"about_ca_topic_score_codex":0.000028654054,"about_ca_topic_score_gemma":0.00032738477,"teacher_disagreement_score":0.67958534,"about_ca_system_score_codex":0.00004611644,"about_ca_system_score_gemma":0.00006358658,"threshold_uncertainty_score":0.23738123},"labels":[],"label_agreement":null},{"id":"W1802316727","doi":"10.1002/atr.1278","title":"Optimization of headways with stop‐skipping control: a case study of bus rapid transit system","year":2014,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":62,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"National Key Research and Development Program of China; Program for New Century Excellent Talents in University; National Natural Science Foundation of China; National Science Foundation","keywords":"Headway; Operating cost; Scheduling (production processes); Bus rapid transit; Transport engineering; Beijing; Computer science; Operations research; Engineering; Public transport; Operations management; Waste management","score_opus":0.010185271355157082,"score_gpt":0.2563698964778908,"score_spread":0.24618462512273373,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1802316727","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.63692826,0.000051416697,0.36246362,0.000037598205,0.00012102293,0.0002756008,0.000011409403,0.000020462654,0.00009058182],"genre_scores_gemma":[0.98469293,0.00003452554,0.015166149,0.00000925564,0.000054815777,0.0000049733644,0.000016498889,0.000015577747,0.000005281361],"study_design_codex":"simulation_or_modeling","study_design_gemma":"qualitative","domain_scores_codex":[0.998025,0.00019806354,0.00093171303,0.00013219127,0.0005723389,0.00014069572],"domain_scores_gemma":[0.99750626,0.0001522508,0.001232415,0.000096862925,0.00092272594,0.00008945599],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007737056,0.000121276695,0.00042088912,0.0002347008,0.00016670748,0.000012402691,0.0000972047,0.000069960624,0.000010630609],"category_scores_gemma":[0.000029663575,0.000108066124,0.00008420408,0.00044478822,0.000072415634,0.00055288064,2.3388093e-7,0.00011856361,9.455128e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006677403,0.00020986733,0.0049240333,0.000103954,0.00007100833,0.00009223258,0.056794733,0.9348759,0.00019625627,0.00047362823,0.000001101403,0.0015895454],"study_design_scores_gemma":[0.03824354,0.011537834,0.10369545,0.0022757058,0.002445591,0.00030854074,0.8080425,0.03114836,0.0009683051,0.000059933718,0.00037339688,0.00090080535],"about_ca_topic_score_codex":0.0003971546,"about_ca_topic_score_gemma":0.0014348276,"teacher_disagreement_score":0.90372753,"about_ca_system_score_codex":0.000042042513,"about_ca_system_score_gemma":0.00012281851,"threshold_uncertainty_score":0.44068086},"labels":[],"label_agreement":null},{"id":"W181418386","doi":"","title":"Study of the Commercial Vehicle Value of Time for Operation at Border Crossings","year":2009,"lang":"en","type":"article","venue":"2009 ANNUAL CONFERENCE AND EXHIBITION OF THE TRANSPORTATION ASSOCIATION OF CANADA - TRANSPORTATION IN A CLIMATE OF CHANGE","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Context (archaeology); Work (physics); Truck; Business; Phone; Value (mathematics); Order (exchange); Marketing; Transport engineering; Engineering; Finance; Computer science; Geography","score_opus":0.023563795765254858,"score_gpt":0.2874118476432047,"score_spread":0.26384805187794985,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W181418386","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9953696,0.000027045393,0.000099310644,0.0015718273,0.0001437915,0.000909016,0.0016929992,0.000008310635,0.00017810862],"genre_scores_gemma":[0.99931747,0.000076286386,0.000103751896,0.000078909936,0.000017145756,0.00002486861,0.0002513296,0.000007958558,0.00012227848],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99770105,0.00015659542,0.0010224102,0.00016767571,0.00076728966,0.00018500589],"domain_scores_gemma":[0.9972038,0.00019254038,0.0014421571,0.0001221454,0.0009991871,0.000040149465],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00066747575,0.00012872068,0.00037080373,0.00010185296,0.00022885484,0.000008435921,0.00016265828,0.00013201819,0.000050042825],"category_scores_gemma":[0.000085119085,0.00011381166,0.00009990232,0.00043024844,0.00012144958,0.00033053372,0.0000014106743,0.0000802129,5.4099473e-8],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015943231,0.0009698144,0.6678775,0.00047175086,0.000101792866,5.8696247e-7,0.28513047,0.02298502,0.0049982173,0.013974044,0.0005285536,0.0013679268],"study_design_scores_gemma":[0.002251367,0.00023170645,0.9841225,0.00018955015,0.000116317045,1.8396458e-8,0.008545438,0.0003058932,0.0038347528,0.00015966303,0.00013132281,0.00011143506],"about_ca_topic_score_codex":0.051035326,"about_ca_topic_score_gemma":0.6268086,"teacher_disagreement_score":0.57577324,"about_ca_system_score_codex":0.00009489742,"about_ca_system_score_gemma":0.00038075692,"threshold_uncertainty_score":0.95528394},"labels":[],"label_agreement":null},{"id":"W1820292428","doi":"10.1002/atr.1199","title":"Dynamic process models of combined traffic assignment and control with different signal updating strategies","year":2012,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":25,"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":"SIGNAL (programming language); Representation (politics); Computer science; Convergence (economics); Process (computing); Traffic flow (computer networking); Control theory (sociology); Signal timing; Mathematical optimization; Flow network; Control (management); Operations research; Simulation; Engineering; Mathematics; Economics; Artificial intelligence","score_opus":0.008684775610101223,"score_gpt":0.2658669027596685,"score_spread":0.2571821271495673,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1820292428","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.86296815,0.00015492634,0.13649532,0.000086114225,0.00006513639,0.0001448375,0.000010156544,0.00001321778,0.00006214979],"genre_scores_gemma":[0.99681616,0.00009353536,0.0030142698,0.000010088483,0.000022215663,0.0000045477173,0.000024320094,0.000008966061,0.0000058697315],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9988898,0.00004894929,0.00042388745,0.00007158314,0.00041006735,0.00015571639],"domain_scores_gemma":[0.9989876,0.000073570794,0.0005713211,0.000027134573,0.00024168738,0.000098650824],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026430018,0.0000949089,0.00021326107,0.00008403173,0.000113540875,0.000019897865,0.00005147466,0.000045768982,0.000011608343],"category_scores_gemma":[0.000005153171,0.00007495684,0.000036928737,0.000120786906,0.00008073733,0.0012614796,1.7522842e-7,0.000101910424,4.5256623e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004578338,0.00012835035,0.009915794,0.000055236455,0.00004149301,0.0000018111266,0.032364547,0.952559,0.0006791022,0.002005086,4.889909e-7,0.0017912514],"study_design_scores_gemma":[0.01077737,0.0016622886,0.7640265,0.00078165275,0.00066477736,0.00000613878,0.19898742,0.018575992,0.0011359651,0.002794193,0.000029756724,0.00055790454],"about_ca_topic_score_codex":0.0000070934966,"about_ca_topic_score_gemma":0.0001615057,"teacher_disagreement_score":0.933983,"about_ca_system_score_codex":0.000028997829,"about_ca_system_score_gemma":0.000101101,"threshold_uncertainty_score":0.30566514},"labels":[],"label_agreement":null},{"id":"W1824331421","doi":"10.1139/cjce-2014-0555","title":"Better estimation of origin–destination matrix using automated intersection movement count data","year":2015,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Intersection (aeronautics); Traffic count; Count data; Matrix (chemical analysis); Computer science; Algorithm; Statistics; Link (geometry); A priori and a posteriori; Mathematics; Mathematical optimization; Traffic congestion; Engineering","score_opus":0.039848234780922955,"score_gpt":0.29900850332649326,"score_spread":0.2591602685455703,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1824331421","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.46981862,0.00014484418,0.5277322,0.00028968957,0.0013458367,0.00010098681,0.000030127463,0.000059603954,0.00047808953],"genre_scores_gemma":[0.9904386,0.000002621782,0.009392712,0.000015465856,0.00009780337,2.894305e-7,0.000032408454,0.0000073855163,0.000012723776],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993159,0.000023876091,0.00026752782,0.000056948746,0.00021912022,0.000116623545],"domain_scores_gemma":[0.9992428,0.000022573506,0.00018622274,0.000073566946,0.00027162491,0.00020321649],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006978135,0.000052059244,0.00008999765,0.0002815994,0.000060441726,0.000043239008,0.00013376461,0.000043373653,0.000025084153],"category_scores_gemma":[0.00025804638,0.0000582182,0.000016582875,0.0002335315,0.000021205238,0.0005286194,0.0000022838956,0.000072347575,7.136901e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000002548045,0.0000028810612,0.0030468472,0.000011775321,0.000012125883,0.000006193944,0.0027896727,0.9927915,0.00007072471,0.00036310256,0.00066839845,0.0002342641],"study_design_scores_gemma":[0.00021557767,0.000022709306,0.0035070647,0.00015054343,0.000030388319,0.0000055366963,0.00063136546,0.9932654,0.000036418616,0.00006350693,0.001999993,0.000071519615],"about_ca_topic_score_codex":0.008698834,"about_ca_topic_score_gemma":0.06628871,"teacher_disagreement_score":0.52062,"about_ca_system_score_codex":0.0003409797,"about_ca_system_score_gemma":0.0008682218,"threshold_uncertainty_score":0.99790233},"labels":[],"label_agreement":null},{"id":"W1828260571","doi":"10.1002/atr.1315","title":"A G/G(<i>n</i>)/<i>C</i>/<i>C</i>state‐dependent simulation model for metro station corridor width design","year":2015,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":28,"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":"Southwest Jiaotong University; National Natural Science Foundation of China; Southwest University; Louisiana State University","keywords":"Metro station; Queueing theory; Offset (computer science); Simulation; Computer simulation; Interval (graph theory); Computer science; Mathematics; Engineering; Transport engineering; Statistics; Combinatorics","score_opus":0.054785844560952625,"score_gpt":0.33222717390209205,"score_spread":0.2774413293411394,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1828260571","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.106090195,0.0001285621,0.89224416,0.00023379193,0.0005877625,0.00053920655,0.000065916,0.000055351687,0.000055075794],"genre_scores_gemma":[0.81531346,0.000063185515,0.18401262,0.000102431506,0.00012080085,0.00001885844,0.00010962436,0.000024215897,0.00023480895],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99780256,0.00012179719,0.00082607404,0.00018356097,0.000808254,0.00025773066],"domain_scores_gemma":[0.9967385,0.00031222383,0.00090093777,0.00008692258,0.0017122313,0.00024916534],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014843517,0.00015564928,0.0002673699,0.0002426309,0.00021949876,0.000055661283,0.00014775746,0.00009630423,0.0000067497326],"category_scores_gemma":[0.00029484465,0.00015887395,0.000125705,0.0003381565,0.000055026947,0.0010758503,5.866127e-7,0.00013673259,0.0000019578383],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011305303,0.00007617987,0.0007828439,0.000015870331,0.000026190268,0.0000047119693,0.032219186,0.9602036,0.00022209456,0.00045689617,0.0001870505,0.0046748403],"study_design_scores_gemma":[0.011131603,0.0011804202,0.009038989,0.00018801901,0.00047721155,0.0000022363365,0.019568238,0.9157795,0.0015357248,0.03677438,0.0035584597,0.00076522847],"about_ca_topic_score_codex":0.000039538332,"about_ca_topic_score_gemma":0.00053176965,"teacher_disagreement_score":0.7092233,"about_ca_system_score_codex":0.00019125461,"about_ca_system_score_gemma":0.0006612328,"threshold_uncertainty_score":0.64786917},"labels":[],"label_agreement":null},{"id":"W1834591371","doi":"10.1002/atr.1202","title":"Multicriteria evaluation on accessibility‐based transportation equity in road network design problem","year":2012,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":53,"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":"Maximization; Equity (law); Gini coefficient; Pareto principle; Multi-objective optimization; Minification; Computer science; Network planning and design; Mathematical optimization; Standard deviation; Index (typography); Econometrics; Operations research; Economics; Mathematics; Statistics; Inequality","score_opus":0.07337150714083268,"score_gpt":0.4006051002236567,"score_spread":0.327233593082824,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1834591371","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8735649,0.00033795502,0.12297241,0.0003996667,0.0011538327,0.0010901032,0.000019977497,0.00005791434,0.00040323657],"genre_scores_gemma":[0.9344905,0.00007526945,0.06482174,0.0001002746,0.00026800623,0.00003460185,0.00017937318,0.000018924038,0.000011332612],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99684197,0.00045387057,0.0010258579,0.00019283101,0.0010639204,0.00042157262],"domain_scores_gemma":[0.99813277,0.00021644724,0.0007862798,0.00010904586,0.00057426974,0.00018119844],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0041587297,0.00017423174,0.0002824241,0.00025570148,0.00021155649,0.000043198925,0.00016836083,0.00015656638,0.00011010307],"category_scores_gemma":[0.00012509628,0.00017475488,0.000116484116,0.00068145024,0.00006328165,0.0018088509,3.8180218e-7,0.00025623856,0.0000030785945],"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":[0.00073404337,0.00027917686,0.037382476,0.000032851585,0.000012637696,0.0000042672204,0.017467104,0.92002475,0.000419058,0.0007048061,0.00003535447,0.022903483],"study_design_scores_gemma":[0.0025593357,0.00019922847,0.9906529,0.00031529838,0.00012398836,2.5954225e-7,0.0017976739,0.0019540365,0.0003878886,0.0013190247,0.0004653637,0.00022499879],"about_ca_topic_score_codex":0.00009622225,"about_ca_topic_score_gemma":0.0012100181,"teacher_disagreement_score":0.95327044,"about_ca_system_score_codex":0.00029992984,"about_ca_system_score_gemma":0.0003975698,"threshold_uncertainty_score":0.71262974},"labels":[],"label_agreement":null},{"id":"W1836139326","doi":"10.1007/3-540-44849-7_30","title":"Improving Customer Proximity to Railway Stations","year":2003,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":23,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Dynamic programming; Algorithm","score_opus":0.01739495702249567,"score_gpt":0.2720882694312704,"score_spread":0.25469331240877474,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1836139326","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00010451462,0.00004317888,0.9779929,0.0011322761,0.000982473,0.00044936631,0.000012981245,0.000100250865,0.01918208],"genre_scores_gemma":[0.2363572,0.00005328993,0.7464185,0.0062928423,0.00079217507,0.000038700036,0.00005501562,0.0000729539,0.009919349],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9979366,0.000038809707,0.00028161658,0.0006206376,0.00071846537,0.0004038675],"domain_scores_gemma":[0.99891746,0.00019920547,0.00014628115,0.00026417014,0.0002874512,0.00018543744],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008464384,0.00020373566,0.00020469935,0.00049172784,0.000621378,0.00024297222,0.000491268,0.00020578071,0.00010024067],"category_scores_gemma":[0.0002187463,0.00020761626,0.000051054085,0.00056982465,0.00044151593,0.00035108905,0.00003280639,0.0003387814,0.00005441871],"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":[0.000012300345,0.000039671973,0.0007353771,0.00003946736,0.00001065294,0.000025710686,0.030126287,0.46167144,0.000067685585,0.05067652,0.0002970796,0.4562978],"study_design_scores_gemma":[0.0026825017,0.0007716707,0.004950448,0.0023899665,0.00023076711,0.000025539064,0.00010666814,0.15321526,0.0020693396,0.21842803,0.60658234,0.008547484],"about_ca_topic_score_codex":0.00029561773,"about_ca_topic_score_gemma":0.0021505686,"teacher_disagreement_score":0.6062852,"about_ca_system_score_codex":0.00024714548,"about_ca_system_score_gemma":0.0006792074,"threshold_uncertainty_score":0.84663457},"labels":[],"label_agreement":null},{"id":"W1844890431","doi":"10.5931/djim.v9i1.3361","title":"Collaborative public transportation feasibility study: Development of a database prototype","year":2013,"lang":"en","type":"article","venue":"Dalhousie Journal of Interdisciplinary Management","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Database; Computer science; Public transport; Transport engineering; World Wide Web; Engineering","score_opus":0.029606741159716166,"score_gpt":0.33545980145376714,"score_spread":0.30585306029405096,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1844890431","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.977176,0.00003514115,0.015922727,0.0007781126,0.00024369851,0.0019805685,0.000016024434,0.000026337359,0.0038213762],"genre_scores_gemma":[0.9823095,0.00001123839,0.017310882,0.000014997834,0.000036748403,0.000066406945,0.000038269,0.000009433116,0.0002025313],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.99799985,0.00017893102,0.0008579365,0.00016190778,0.00063106767,0.00017028779],"domain_scores_gemma":[0.9982754,0.000036866204,0.00062490726,0.00014705973,0.0007992348,0.000116512376],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012706453,0.00011538255,0.00022858915,0.00027464537,0.000323476,0.0000659123,0.00029296035,0.000036261423,0.00022736455],"category_scores_gemma":[0.000024827354,0.00010018336,0.000060799168,0.0005523758,0.000091125694,0.000801179,0.000032843345,0.00011343742,0.000008966844],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001232778,0.0070238174,0.1591039,0.0004987235,0.0011061551,0.00016070952,0.7822556,0.0027188244,0.0002106134,0.005277302,0.004008218,0.03640334],"study_design_scores_gemma":[0.0018334347,0.00092511973,0.6628768,0.0002640675,0.00014085458,0.0000010103192,0.33059698,0.000084189785,0.00012651697,0.00055627077,0.0023295376,0.00026523098],"about_ca_topic_score_codex":0.000028973416,"about_ca_topic_score_gemma":0.0010090005,"teacher_disagreement_score":0.50377285,"about_ca_system_score_codex":0.00011083409,"about_ca_system_score_gemma":0.00023812764,"threshold_uncertainty_score":0.4085359},"labels":[],"label_agreement":null},{"id":"W1847971474","doi":"","title":"Maximum Spin-Off","year":2008,"lang":"en","type":"article","venue":"ITS International","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Smart card; Ultra high frequency; Telecommunications; Transit (satellite); Radio-frequency identification; Service (business); Contactless smart card; Engineering; Computer security; Transport engineering; Smart city; Computer science; Public transport; Internet of Things; Business","score_opus":0.03276241756727885,"score_gpt":0.3204814775906784,"score_spread":0.28771906002339953,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1847971474","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.37321374,0.00012184622,0.00767346,0.0061029145,0.002758256,0.00011550929,0.00003091765,0.00025193338,0.60973144],"genre_scores_gemma":[0.9803399,0.00016501156,0.0013216158,0.00024295901,0.00031301705,0.0000037684392,0.00004765324,0.0000040859686,0.017562026],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994278,0.000017268312,0.00009095456,0.00008517189,0.00029427442,0.00008454401],"domain_scores_gemma":[0.9997469,0.0000226199,0.00003472889,0.000033007622,0.00012300482,0.000039691757],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00008961538,0.000034436678,0.000033226264,0.00005210216,0.00022055297,0.000019137662,0.00013231192,0.0000352112,0.001360485],"category_scores_gemma":[0.00007394972,0.000037027614,0.000024210405,0.000076610784,0.00005290274,0.00017321335,0.000003445044,0.0000442236,0.00021673486],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009890295,0.00041934216,0.21727954,0.000007553509,0.00013704358,0.00018587595,0.07338029,0.018088575,0.00028168154,0.5580745,0.09999514,0.032051552],"study_design_scores_gemma":[0.00028792454,0.0000122724105,0.07566881,0.000010696802,0.000003473071,0.0000054020816,0.0004325574,0.0008745501,0.00013789501,0.00076823885,0.92168105,0.00011714151],"about_ca_topic_score_codex":0.00008715778,"about_ca_topic_score_gemma":0.00009252455,"teacher_disagreement_score":0.8216859,"about_ca_system_score_codex":0.000031876414,"about_ca_system_score_gemma":0.00007092835,"threshold_uncertainty_score":0.9995524},"labels":[],"label_agreement":null},{"id":"W1848460606","doi":"10.1002/atr.1287","title":"An augmented Lagrangian origin‐based algorithm for link‐capacitated traffic assignment problem","year":2014,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Hunan Provincial Innovation Foundation for Postgraduate; National Natural Science Foundation of China","keywords":"Augmented Lagrangian method; Lagrangian; Algorithm; Mathematical optimization; Graph; Link (geometry); Computer science; Function (biology); Sketch; Assignment problem; Mathematics; Traffic network; Theoretical computer science; Applied mathematics; Combinatorics","score_opus":0.013895945567513968,"score_gpt":0.293012155882136,"score_spread":0.2791162103146221,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1848460606","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09801563,0.000041551088,0.90010357,0.000733411,0.00045299984,0.00042664938,0.00004682114,0.000085359454,0.00009398497],"genre_scores_gemma":[0.7069636,0.000038102018,0.2922169,0.00010047132,0.00023736637,0.000018421857,0.00035345988,0.0000201508,0.000051498755],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99838674,0.00011679401,0.00060801447,0.00016737042,0.00048457572,0.00023650366],"domain_scores_gemma":[0.998463,0.0001309819,0.0005666056,0.00007000056,0.0005813277,0.00018809554],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008325345,0.00013359082,0.00022362612,0.00018795021,0.00028594164,0.00004776168,0.00013353013,0.000108025095,0.000028969203],"category_scores_gemma":[0.00002337538,0.00012938856,0.00013242941,0.00030771067,0.00006249386,0.0007048153,1.208723e-7,0.00013505881,0.0000011827999],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018426421,0.00016502904,0.0003366171,0.000028108656,0.00003113353,0.0000033315744,0.008569178,0.69439614,0.0008434202,0.0008089463,0.000048186503,0.29458562],"study_design_scores_gemma":[0.04484513,0.010665349,0.14506906,0.0017062082,0.0018188138,0.0000068845598,0.036194123,0.4105611,0.0069848537,0.006106932,0.33301988,0.0030216735],"about_ca_topic_score_codex":0.000019743195,"about_ca_topic_score_gemma":0.0003368939,"teacher_disagreement_score":0.608948,"about_ca_system_score_codex":0.000081579645,"about_ca_system_score_gemma":0.00018207703,"threshold_uncertainty_score":0.5276312},"labels":[],"label_agreement":null},{"id":"W1850471256","doi":"","title":"Integrated Trip-Fare Calculator - A Sought-After Feature in a Transit Agency Website (Poster)","year":2014,"lang":"fr","type":"article","venue":"Transportation 2014: Past, Present, Future - 2014 Conference and Exhibition of the Transportation Association of Canada // Transport 2014 : Du passé vers l'avenir - 2014 Congrès et Exposition de 'Association des transports du Canada","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Transit (satellite); Calculator; Agency (philosophy); Computer science; Quality (philosophy); World Wide Web; Advertising; Business; Transport engineering; Public transport; Engineering; Sociology","score_opus":0.005745425148613223,"score_gpt":0.2039185707897054,"score_spread":0.19817314564109217,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1850471256","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.91237414,0.0017929547,0.012920363,0.05157927,0.0042684847,0.0026067058,0.013088149,0.00018194834,0.0011879718],"genre_scores_gemma":[0.98259395,0.0050833086,0.00053283555,0.0011758651,0.00067449745,0.00022514307,0.0066164746,0.0001416839,0.002956264],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.989015,0.0016024144,0.0031902783,0.0013292738,0.0032929268,0.0015701242],"domain_scores_gemma":[0.9914433,0.00067815115,0.0036158524,0.0006242894,0.0028276322,0.00081081793],"candidate_categories":["metaepi_narrow","research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002818722,0.0012316061,0.0017649303,0.00046002984,0.0008231601,0.00014554121,0.00068072847,0.001466487,0.0015832391],"category_scores_gemma":[0.00008649827,0.0013260832,0.0007780864,0.0012157687,0.00047498624,0.0017282896,0.0000049737173,0.0012903857,0.000006802853],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013902871,0.00057823333,0.85215515,0.0014936175,0.0008149445,0.00010142036,0.0289383,0.026624463,0.0005029388,0.00643818,0.07981504,0.0011474033],"study_design_scores_gemma":[0.005105317,0.00018626837,0.8341583,0.0009207847,0.0013991672,0.0000055300093,0.0024103608,0.0008182476,0.0007291343,0.00042333693,0.15257274,0.0012708422],"about_ca_topic_score_codex":0.7041882,"about_ca_topic_score_gemma":0.99033827,"teacher_disagreement_score":0.28615004,"about_ca_system_score_codex":0.0032848364,"about_ca_system_score_gemma":0.006643888,"threshold_uncertainty_score":0.9998298},"labels":[],"label_agreement":null},{"id":"W1851273911","doi":"10.1002/atr.1295","title":"Integration of signal timing estimation model and dynamic traffic assignment in feedback loops: system design and case study","year":2014,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Federal Highway Administration; Mountain-Plains Consortium","keywords":"SIGNAL (programming language); Computer science; Queue; Intersection (aeronautics); Traffic simulation; Signal timing; Traffic generation model; Coupling (piping); Simulation; Real-time computing; Traffic signal; Engineering","score_opus":0.020519356577656096,"score_gpt":0.2958488919119749,"score_spread":0.2753295353343188,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1851273911","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5511291,0.000031483287,0.44858366,0.000020408927,0.000032369204,0.00018867386,0.000001466951,0.000008036261,0.0000047813983],"genre_scores_gemma":[0.94050795,0.000038693077,0.059412885,0.0000036112408,0.000009698307,0.000005600329,0.000008849265,0.000007745081,0.000004951162],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99871725,0.00016665897,0.0006111291,0.000120545614,0.00028887132,0.00009552451],"domain_scores_gemma":[0.99909085,0.00014716526,0.0004925636,0.000035022127,0.00017302055,0.00006139441],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010573765,0.00009208,0.00020983005,0.00020832014,0.000114711525,0.000024250337,0.0000357323,0.000058241738,0.000001336082],"category_scores_gemma":[0.000029416151,0.00008901301,0.000025032856,0.00018484352,0.000047220175,0.0005986661,4.3915378e-7,0.000106069754,6.836611e-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":[0.000113983544,0.000074314026,0.00089744665,0.000040845647,0.000008975109,0.000032160042,0.052088287,0.9191814,0.0006509985,0.0001639922,3.3221966e-7,0.026747253],"study_design_scores_gemma":[0.0016409532,0.00037932082,0.029570434,0.00028714072,0.00010099407,0.000025744012,0.05827175,0.90942174,0.00006541149,0.00012738189,9.2927394e-7,0.000108189975],"about_ca_topic_score_codex":0.000081624385,"about_ca_topic_score_gemma":0.0012428359,"teacher_disagreement_score":0.38937885,"about_ca_system_score_codex":0.000075989745,"about_ca_system_score_gemma":0.000069770955,"threshold_uncertainty_score":0.3629845},"labels":[],"label_agreement":null},{"id":"W1859196492","doi":"","title":"METRO's BRT 25: Analysis of Top Bus Rapid Transit Projects in North America","year":2007,"lang":"en","type":"article","venue":"Metrologia","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Bus rapid transit; Transit (satellite); Transport engineering; Public transport; Business; Agricultural economics; Capital cost; Engineering; Economics; Electrical engineering","score_opus":0.021145868126867376,"score_gpt":0.29094100516653654,"score_spread":0.2697951370396692,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1859196492","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96729285,0.00011906565,0.026833685,0.00015880282,0.000067784575,0.00012140347,0.000018705654,0.000054114244,0.0053335945],"genre_scores_gemma":[0.99568176,0.000049856764,0.003953278,0.00012307093,0.000019963301,0.000004096523,0.00008651875,0.0000038456947,0.00007761454],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99889576,0.000114681956,0.00028753854,0.00017921066,0.00027688895,0.000245932],"domain_scores_gemma":[0.999463,0.00017614492,0.00012326628,0.00010287887,0.00008364462,0.00005103963],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00088333344,0.0000726093,0.0002625927,0.00080694706,0.000079761,0.000011083755,0.00013724047,0.00008310936,0.00015986359],"category_scores_gemma":[0.00015844229,0.00006846817,0.00009831806,0.005080461,0.00011530283,0.0000878636,0.0000024183664,0.000092523595,0.0000029568125],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000052456035,0.000062867985,0.96358466,0.000003828687,0.00012876005,0.00000824434,0.009758781,0.018035762,0.00004104536,0.0002229377,0.000027746015,0.008072909],"study_design_scores_gemma":[0.00020335891,0.00006909956,0.99338186,0.0000016329878,0.00020891885,2.4387983e-8,0.003197686,0.0006254602,0.000081059916,0.0000060168086,0.0021438664,0.00008099407],"about_ca_topic_score_codex":0.003740248,"about_ca_topic_score_gemma":0.048076384,"teacher_disagreement_score":0.044336136,"about_ca_system_score_codex":0.00004408018,"about_ca_system_score_gemma":0.000053290467,"threshold_uncertainty_score":0.9692937},"labels":[],"label_agreement":null},{"id":"W186420439","doi":"","title":"Réflexion sur la tarification du réseau routier québécois","year":2010,"lang":"fr","type":"article","venue":"CIRANO Project Reports","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Political science; Humanities; Psychology; Art","score_opus":0.015056294555612746,"score_gpt":0.2794919746554491,"score_spread":0.26443568009983637,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W186420439","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.74882114,0.0017512552,0.007731344,0.01645781,0.018086674,0.0016889733,0.00004457841,0.0007350972,0.20468314],"genre_scores_gemma":[0.9730639,0.00036578492,0.0019569157,0.00018460686,0.0008163641,0.000056099372,0.00023604103,0.000037551512,0.023282731],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.99753165,0.00025029306,0.00068158633,0.0005649937,0.0005572738,0.00041421453],"domain_scores_gemma":[0.99833393,0.00015644927,0.0005580923,0.00041522886,0.00038742865,0.00014888418],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0016416563,0.00023329012,0.00024809767,0.00015410568,0.00050998677,0.00018025706,0.00014803116,0.0005343386,0.00044761162],"category_scores_gemma":[0.00048093416,0.0002546937,0.00014799924,0.0007324043,0.00042184326,0.0007472997,0.000009708392,0.0004728554,0.000050615145],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001651504,0.0015981685,0.39167854,0.00043141522,0.00024165047,0.0016932138,0.2085417,0.003154893,0.0044866037,0.15368807,0.065669805,0.16865079],"study_design_scores_gemma":[0.00023475988,0.00004470654,0.04254733,0.0000778165,0.000116197334,0.00016699718,0.0009401636,0.0006197953,0.00036372052,0.001237834,0.9532442,0.0004064956],"about_ca_topic_score_codex":0.09730949,"about_ca_topic_score_gemma":0.031323906,"teacher_disagreement_score":0.8875744,"about_ca_system_score_codex":0.00014203403,"about_ca_system_score_gemma":0.0024966397,"threshold_uncertainty_score":0.9999905},"labels":[],"label_agreement":null},{"id":"W1865916800","doi":"10.1139/cjce-2014-0231","title":"A cell-based multiple vehicle type dynamic user equilibrium model with physical queues","year":2015,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","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":"National Natural Science Foundation of China","keywords":"Queue; Cell Transmission Model; Mathematical optimization; Discretization; Computer science; Variational inequality; Traffic flow (computer networking); Type (biology); Queueing theory; Flow (mathematics); Genetic algorithm; Dissipation; Simulation; Control theory (sociology); Mathematics; Intersection (aeronautics); Engineering; Computer network; Artificial intelligence","score_opus":0.013435513819909251,"score_gpt":0.22433466787462494,"score_spread":0.21089915405471568,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1865916800","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.89346874,0.0002213607,0.10341486,0.0006375006,0.00029709304,0.00007483059,0.000009690332,0.000042474363,0.0018334396],"genre_scores_gemma":[0.99830323,0.000001170184,0.0014264873,0.00003343924,0.000058183858,5.933371e-7,0.0000037343216,0.000013597649,0.00015954567],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994788,0.000013605422,0.00010119118,0.000053903816,0.00016950087,0.00018295746],"domain_scores_gemma":[0.99919057,0.000030394132,0.00005309655,0.000046657224,0.00021056159,0.00046875034],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000150152,0.00006113161,0.000092746704,0.00011701206,0.00005070814,0.000044137174,0.000096642856,0.000035003715,0.000008121106],"category_scores_gemma":[0.00007607209,0.000058236274,0.000026004142,0.00018984012,0.000029854846,0.00019665873,7.083044e-7,0.00010819709,0.0000019685021],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007817222,0.0000052496434,0.0029328533,0.000004741676,0.0000048289894,0.000018449988,0.0036649264,0.9927422,0.00010540323,0.00006429081,0.00043615518,0.000013116739],"study_design_scores_gemma":[0.00040350802,0.000055865115,0.00073372433,0.000046484187,0.000014513396,0.0000012053341,0.00039768312,0.99558485,0.0000644311,0.000018981988,0.0025811016,0.00009766001],"about_ca_topic_score_codex":0.0018750604,"about_ca_topic_score_gemma":0.14469649,"teacher_disagreement_score":0.14282143,"about_ca_system_score_codex":0.00012518647,"about_ca_system_score_gemma":0.0016459157,"threshold_uncertainty_score":0.8709106},"labels":[],"label_agreement":null},{"id":"W1867972197","doi":"10.1002/atr.1192","title":"Discretised route travel time models based on cumulative flows","year":2012,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Travel time; Transport engineering; Computer science; Environmental science; Engineering","score_opus":0.019577418842628546,"score_gpt":0.2919508973056617,"score_spread":0.27237347846303317,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1867972197","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7445935,0.00011994894,0.24794039,0.0007582394,0.0007953939,0.00033803735,0.00008096486,0.0000628254,0.0053107208],"genre_scores_gemma":[0.98320276,0.000040579613,0.016108954,0.000118684264,0.00020345859,0.0000038318617,0.00012125196,0.000016605876,0.00018390248],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9984862,0.00009430556,0.00047872926,0.00009937869,0.00060177234,0.00023960105],"domain_scores_gemma":[0.99891686,0.0001386627,0.00043191988,0.000069914946,0.00025515244,0.00018750908],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00052372477,0.00012000443,0.00020503253,0.00016674303,0.00019672881,0.000020192072,0.00010248892,0.00008634324,0.000108719636],"category_scores_gemma":[0.000037352595,0.00011132097,0.00013032174,0.00027288633,0.000045619225,0.001419182,2.1542273e-7,0.00016322281,0.000007970552],"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":[0.00027113163,0.00011079686,0.0017419804,0.0000064900487,0.0000147567625,0.0000042048837,0.02583396,0.9675766,0.00020787831,0.002762443,0.000035298686,0.0014344195],"study_design_scores_gemma":[0.0063796025,0.0006415305,0.8716452,0.000511505,0.00036322043,0.0000018746508,0.012734603,0.097061455,0.0008911806,0.004080982,0.00487503,0.00081380515],"about_ca_topic_score_codex":0.000029095281,"about_ca_topic_score_gemma":0.00007647037,"teacher_disagreement_score":0.87051517,"about_ca_system_score_codex":0.00008283528,"about_ca_system_score_gemma":0.00013105889,"threshold_uncertainty_score":0.45395374},"labels":[],"label_agreement":null},{"id":"W1874879095","doi":"10.3968/j.ans.1715787020110402.800","title":"Spatial Structure of Road Infrastructure In Ekiti State, Nigeria: Options for Transformation","year":2011,"lang":"en","type":"article","venue":"Advances in natural science/Advances in natural sciences","topic":"Transportation Planning and Optimization","field":"Social Sciences","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":"Index (typography); Transport engineering; Spatial network; Node (physics); Graph; Business; Computer science; Geography; Engineering; Mathematics","score_opus":0.008147128168512964,"score_gpt":0.30503690270907885,"score_spread":0.29688977454056587,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1874879095","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97605443,0.011760355,0.0015123215,0.00023239164,0.0038619933,0.0014130155,0.000095634845,0.000083192695,0.0049866494],"genre_scores_gemma":[0.9795128,0.0031441727,0.017132062,0.000054298223,0.00006423147,0.000030583906,0.000026888212,0.000007982201,0.000026941047],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99624276,0.00013672821,0.00088372995,0.0007069067,0.0011043014,0.0009255428],"domain_scores_gemma":[0.99879956,0.00024645947,0.0004085566,0.00015765046,0.0002741285,0.000113647715],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.001520104,0.00027501603,0.0004023643,0.001055646,0.00067057874,0.000094034505,0.0011458409,0.00014895656,0.000051047733],"category_scores_gemma":[0.000563976,0.00022869537,0.00007889122,0.005211433,0.0030024324,0.009139599,0.0000306325,0.00047432072,6.809686e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005704507,0.00018225789,0.42473152,0.0001695778,0.000006108151,0.000009405017,0.07221975,0.18310092,0.0041113696,0.05156029,0.000008317379,0.26333],"study_design_scores_gemma":[0.0032640141,0.00048285868,0.8169269,0.00064638554,0.000012859372,0.0000074536833,0.026728446,0.012950089,0.009649395,0.12326965,0.00460294,0.001459006],"about_ca_topic_score_codex":0.0019608652,"about_ca_topic_score_gemma":0.08626942,"teacher_disagreement_score":0.39219537,"about_ca_system_score_codex":0.00032954637,"about_ca_system_score_gemma":0.00063325797,"threshold_uncertainty_score":0.9997108},"labels":[],"label_agreement":null},{"id":"W1879825720","doi":"10.1139/cjce-2014-0202","title":"Merging transit schedule information with a planning network to perform dynamic multimodal assignment: lessons from a case study of the Greater Toronto and Hamilton Area","year":2014,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Complement (music); Aggregate (composite); Schedule; Multimodal transport; Operations research; Transport engineering; Engineering","score_opus":0.008731541233222126,"score_gpt":0.2203177396960153,"score_spread":0.2115861984627932,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1879825720","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93699396,0.0000512182,0.06244099,0.00010088105,0.00011634475,0.00010910624,0.0000057048082,0.000006181605,0.00017560502],"genre_scores_gemma":[0.9990736,0.0000016272128,0.00084929174,0.00002077802,0.000040497485,0.0000022705287,0.0000015829064,0.0000061730616,0.0000041268795],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99942017,0.000026675858,0.00019304016,0.000050726783,0.00015242604,0.00015696464],"domain_scores_gemma":[0.9995396,0.000035892623,0.00009129203,0.000056890833,0.00006975586,0.00020657846],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026512917,0.000070715876,0.00011488917,0.000069510956,0.00023774673,0.000055676963,0.00007699274,0.000033727476,0.000017340097],"category_scores_gemma":[0.000024796984,0.000055995424,0.000018839739,0.00011339821,0.00001602122,0.00039181212,0.0000014799431,0.000093551374,7.586536e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000075166295,0.0000025675076,0.026707025,0.0000047532276,0.0000237438,0.000020076806,0.10431021,0.8685651,0.0000064577725,0.00002432437,0.0000067742208,0.00032145722],"study_design_scores_gemma":[0.0022198411,0.0004402191,0.27582294,0.0010940691,0.00022409213,0.00013223117,0.10777356,0.6098937,0.000019879202,0.000011499649,0.0019517333,0.00041628446],"about_ca_topic_score_codex":0.036700845,"about_ca_topic_score_gemma":0.710494,"teacher_disagreement_score":0.67379314,"about_ca_system_score_codex":0.00011792982,"about_ca_system_score_gemma":0.00012827956,"threshold_uncertainty_score":0.96971387},"labels":[],"label_agreement":null},{"id":"W1882745332","doi":"10.1139/cjce-2015-0004","title":"Maximizing toll revenue and level of service on managed lanes with a dynamic feedback-control toll pricing strategy","year":2015,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Toll; VisSim; Revenue; Congestion pricing; Value of time; Pricing strategies; Road pricing; Dynamic pricing; Traffic congestion; Level of service; Toll road; Transport engineering; Computer science; Operations research; Business; Engineering; Economics; Microsimulation; Travel time; Microeconomics; Finance","score_opus":0.028683723870739725,"score_gpt":0.22759315624124568,"score_spread":0.19890943237050596,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1882745332","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9450075,0.00083442556,0.04783014,0.0018613887,0.00038450153,0.00021996154,0.00009202299,0.0000239061,0.003746136],"genre_scores_gemma":[0.99895376,0.000009449367,0.000820089,0.000050972616,0.00004020746,5.7458794e-7,0.0000019645918,0.0000097254715,0.000113258844],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99944824,0.000023851757,0.00016614266,0.000057893623,0.00014911103,0.00015478756],"domain_scores_gemma":[0.9993704,0.000042806674,0.00011627504,0.000043164306,0.00014483159,0.00028249656],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003436597,0.000066983026,0.00014091331,0.00019129834,0.000068962036,0.000037253038,0.00008153473,0.000040710634,0.0000067675155],"category_scores_gemma":[0.000062055515,0.00006431462,0.000016112097,0.00017715708,0.000018940056,0.00015251465,7.326837e-7,0.00011530564,3.9974333e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002556899,0.0000031286565,0.004026728,0.00004485551,0.000031168813,0.000069559035,0.007637749,0.98709226,0.000043143056,0.0004841828,0.00009389498,0.000447781],"study_design_scores_gemma":[0.015620762,0.001704774,0.6700146,0.00817165,0.00071484735,0.00038700548,0.05585651,0.22412087,0.0001846497,0.0011277066,0.019897139,0.002199465],"about_ca_topic_score_codex":0.009953625,"about_ca_topic_score_gemma":0.6055431,"teacher_disagreement_score":0.76297134,"about_ca_system_score_codex":0.000080327896,"about_ca_system_score_gemma":0.00044198846,"threshold_uncertainty_score":0.9966392},"labels":[],"label_agreement":null},{"id":"W1893183190","doi":"","title":"Why take the risk","year":2015,"lang":"en","type":"article","venue":"International railway journal","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Transport engineering; Rail traffic; Business; Engineering","score_opus":0.033766755394872165,"score_gpt":0.31416641521264743,"score_spread":0.28039965981777526,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1893183190","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16030131,0.0005880619,0.15635224,0.16817719,0.015907697,0.00027271535,0.00006414811,0.00022372059,0.49811292],"genre_scores_gemma":[0.98883617,0.00023714229,0.0017592107,0.0021724014,0.001719314,0.000003126496,0.000012592392,0.000007994998,0.005252039],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99887025,0.00014477881,0.0001633532,0.00006539349,0.0006397995,0.000116445066],"domain_scores_gemma":[0.9991399,0.00010029205,0.00015610471,0.000047872923,0.00042893252,0.00012687623],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001227768,0.000046834983,0.0000428234,0.000059821352,0.00046267867,0.000235124,0.00031864145,0.000036552952,0.0005047237],"category_scores_gemma":[0.00062432484,0.00003260088,0.000051214214,0.000104563806,0.00008690453,0.00028490886,0.000005383473,0.00021614354,0.0000652049],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010712401,0.00008222314,0.13547292,6.6487644e-7,0.00015462752,0.000040787603,0.055598706,0.054087993,0.000009630826,0.052866645,0.6851364,0.01644232],"study_design_scores_gemma":[0.00030531807,0.00001251782,0.009092183,0.000007632949,0.000010538528,0.00001743811,0.0038279789,0.0006250807,0.00000558983,0.005189427,0.9808525,0.000053807653],"about_ca_topic_score_codex":0.0004508162,"about_ca_topic_score_gemma":0.0006303235,"teacher_disagreement_score":0.82853484,"about_ca_system_score_codex":0.00007876169,"about_ca_system_score_gemma":0.00018607984,"threshold_uncertainty_score":0.55263704},"labels":[],"label_agreement":null},{"id":"W1899085136","doi":"10.1002/atr.1226","title":"Designing Emission Charging Schemes for Transportation Conformity","year":2013,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China; China Scholarship Council; University of Florida","keywords":"Conformity; Complementarity (molecular biology); Toll; Scheme (mathematics); Mathematical optimization; Computer science; Transport engineering; Operations research; Simulation; Engineering; Mathematics","score_opus":0.01753643866791943,"score_gpt":0.2951808611829633,"score_spread":0.27764442251504384,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1899085136","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.64051,0.00014267716,0.35746893,0.0006406943,0.00046735533,0.0005358456,0.00002343866,0.00006054353,0.00015051769],"genre_scores_gemma":[0.9212974,0.00017499665,0.07787598,0.00007599745,0.00016461161,0.000027879469,0.00019346572,0.0000175397,0.00017215162],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9984926,0.000035935722,0.0006951695,0.00013364664,0.00040995562,0.00023269647],"domain_scores_gemma":[0.99809265,0.00015503076,0.0006698713,0.000057773672,0.00087340333,0.00015129041],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004922746,0.00012322806,0.00021915093,0.00016276113,0.0003597897,0.00005240377,0.00011983986,0.00010877638,0.00009242475],"category_scores_gemma":[0.00008885497,0.00012004682,0.0001482414,0.00024118414,0.00005554001,0.0017712729,1.5497986e-7,0.00013942465,0.0000025244237],"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":[0.0015796168,0.0004914873,0.08497682,0.00059566443,0.00029017113,0.000029069246,0.26099718,0.30681363,0.12269739,0.030532219,0.0026177224,0.18837903],"study_design_scores_gemma":[0.012000282,0.0010428812,0.652041,0.0014630918,0.0006533715,0.0000051027987,0.0930336,0.0011821873,0.06878809,0.014002594,0.15423135,0.0015564123],"about_ca_topic_score_codex":0.000056674147,"about_ca_topic_score_gemma":0.00013182436,"teacher_disagreement_score":0.5670642,"about_ca_system_score_codex":0.000056727622,"about_ca_system_score_gemma":0.0001782213,"threshold_uncertainty_score":0.4895367},"labels":[],"label_agreement":null},{"id":"W1901137330","doi":"10.1139/cjce-2014-0464","title":"Proof-of-concept for methodological framework to evaluate operational strategies with travel demand models","year":2015,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Transportation Planning and Optimization","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":"Virginia Department of Transportation","keywords":"Software deployment; Operational planning; Computer science; Operations research; Process (computing); Toll; Key (lock); Transport engineering; Flow network; Traffic flow (computer networking); Occupancy; Engineering","score_opus":0.11299837282102812,"score_gpt":0.3322879200149424,"score_spread":0.21928954719391427,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1901137330","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010287553,0.00019708494,0.9879848,0.00051249703,0.00017229526,0.00013006752,0.000011518778,0.000004879021,0.00069934555],"genre_scores_gemma":[0.8669516,0.0000010420697,0.13285528,0.000034842178,0.00011695051,0.000004723541,0.0000029082432,0.0000062692434,0.00002641071],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993489,0.00004305016,0.00019478705,0.000060646325,0.00019720747,0.00015545983],"domain_scores_gemma":[0.9989077,0.00014636472,0.0000671426,0.00003326111,0.00043390846,0.00041162447],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009800652,0.000059362512,0.00014289698,0.00012330773,0.000073743424,0.00005421702,0.00010272266,0.0000623404,0.00002417883],"category_scores_gemma":[0.00044257328,0.000050459235,0.000030014431,0.0001569116,0.0000356311,0.0002657889,6.640064e-7,0.00008946899,9.446756e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015395102,0.000002279768,0.00012456156,0.0000052279975,0.0000147795445,0.0000034379343,0.013825955,0.9571573,0.0000057733646,0.028558178,0.00015507698,0.00013200122],"study_design_scores_gemma":[0.0051626735,0.0035224808,0.009143986,0.0022487163,0.00047540333,0.00008758478,0.07014538,0.7821843,0.0027907316,0.102051444,0.02036273,0.0018245418],"about_ca_topic_score_codex":0.0004590039,"about_ca_topic_score_gemma":0.030227704,"teacher_disagreement_score":0.856664,"about_ca_system_score_codex":0.00006053466,"about_ca_system_score_gemma":0.0018838635,"threshold_uncertainty_score":0.9874681},"labels":[],"label_agreement":null},{"id":"W1903756953","doi":"10.3141/2350-06","title":"Integrated Intervening Opportunities Model for Public Transit Trip Generation–Distribution","year":2013,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"TRIPS architecture; Public transport; Transport engineering; Socioeconomic status; Trip distribution; Trip generation; Work (physics); Distribution (mathematics); Geography; Transit (satellite); Census; Regional science; Engineering; Mathematics; Population; Sociology","score_opus":0.3592215201654481,"score_gpt":0.4191521983017274,"score_spread":0.05993067813627928,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1903756953","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6844081,0.00019565783,0.2913716,0.020157842,0.0007392126,0.0023527225,0.00039877446,0.0000909176,0.00028513715],"genre_scores_gemma":[0.9874153,0.0009072561,0.0067881835,0.00010528554,0.0003496165,0.0003786426,0.0005721967,0.000057875553,0.0034256538],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.99188524,0.0016453414,0.0017206853,0.0004312863,0.0032146357,0.0011027965],"domain_scores_gemma":[0.98665875,0.0009441065,0.00058062497,0.00033168262,0.010904293,0.00058053207],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.007105704,0.00027443204,0.00045545804,0.00092516,0.0022362394,0.00056825933,0.0010593468,0.00033466317,0.0005345692],"category_scores_gemma":[0.00058093225,0.00022763567,0.00052243227,0.0019485869,0.0009563641,0.0020512065,0.000003054221,0.0014765393,0.00001446823],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0027269225,0.0017307317,0.11035641,0.00090401125,0.0009930548,0.00008094499,0.120203346,0.14938402,0.00741159,0.22309536,0.27040282,0.11271079],"study_design_scores_gemma":[0.009852734,0.001979321,0.3336047,0.0014436577,0.00042676157,0.0000013091192,0.09480288,0.22830951,0.0018586911,0.026425721,0.29955164,0.0017430738],"about_ca_topic_score_codex":0.013923707,"about_ca_topic_score_gemma":0.079285316,"teacher_disagreement_score":0.3030072,"about_ca_system_score_codex":0.0004500431,"about_ca_system_score_gemma":0.0021618556,"threshold_uncertainty_score":0.9990627},"labels":[],"label_agreement":null},{"id":"W1910878437","doi":"10.1002/atr.1301","title":"Statistical inference on transit route‐level origin–destination flows using automatic passenger counter data","year":2015,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Markov chain Monte Carlo; Enumeration; Computer science; Statistical inference; Markov chain; Inference; Monte Carlo method; Transit (satellite); Flow (mathematics); Algorithm; Data mining; Mathematical optimization; Public transport; Mathematics; Statistics; Transport engineering; Engineering; Artificial intelligence","score_opus":0.13398898161334716,"score_gpt":0.39273481923540904,"score_spread":0.2587458376220619,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1910878437","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.54962337,0.000035620335,0.44861227,0.00037684143,0.00070503715,0.00017437055,0.0002334558,0.00004900246,0.0001900126],"genre_scores_gemma":[0.8952919,0.000041655323,0.10390435,0.00007498379,0.00016942693,0.000001877251,0.00046680585,0.000016386666,0.000032609307],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9978012,0.0001338817,0.00069959805,0.00020510342,0.00094868283,0.00021153183],"domain_scores_gemma":[0.9981498,0.0002576417,0.0004900385,0.00017026991,0.0007183113,0.00021391532],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00095678295,0.00014519051,0.00024742272,0.00018192205,0.00017256301,0.00006867651,0.0002637042,0.00009959733,0.000057909856],"category_scores_gemma":[0.0003386223,0.00013872131,0.00004322864,0.00032638278,0.00007066716,0.0016036924,0.0000011037158,0.0002278948,0.0000060250973],"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":[0.000309518,0.00019832102,0.011267293,0.0000444372,0.000048745263,0.00007792441,0.022911752,0.93963903,0.00027682396,0.0060781282,0.00024925105,0.018898802],"study_design_scores_gemma":[0.008689977,0.0010646768,0.7979042,0.0012310727,0.0008401867,0.00002205004,0.016249532,0.14514229,0.00019585936,0.005083501,0.022373723,0.0012029589],"about_ca_topic_score_codex":0.0001608638,"about_ca_topic_score_gemma":0.0012308094,"teacher_disagreement_score":0.7944967,"about_ca_system_score_codex":0.00016988411,"about_ca_system_score_gemma":0.0006678202,"threshold_uncertainty_score":0.5656891},"labels":[],"label_agreement":null},{"id":"W1911860930","doi":"10.1139/cjce-2012-0433","title":"A comparative study on sampling strategies for truck destination choice model: case of Seoul Metropolitan Area","year":2013,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Sampling (signal processing); Truck; Stratified sampling; Sample (material); Simple random sample; Statistics; Sampling design; Sample size determination; Computer science; Lot quality assurance sampling; Transport engineering; Mathematics; Engineering; Population","score_opus":0.060395535811214984,"score_gpt":0.31647538135683845,"score_spread":0.25607984554562346,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1911860930","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7655363,0.000042445496,0.23288245,0.000055442673,0.000114047325,0.00022339467,0.0000141449045,0.000010068199,0.0011217165],"genre_scores_gemma":[0.99667734,8.704602e-7,0.003227409,0.000006835251,0.000052605566,0.0000071363797,0.0000025185466,0.0000075916278,0.000017665545],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994034,0.000021673872,0.00024466903,0.00005976867,0.000112398455,0.000158073],"domain_scores_gemma":[0.99904424,0.00018780756,0.00013466277,0.000041840252,0.0003806542,0.00021076726],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025938894,0.0000693615,0.00015438195,0.0002759963,0.00014674028,0.00006978956,0.00007650426,0.000033878063,0.00002164036],"category_scores_gemma":[0.00017086242,0.000071122326,0.00003965332,0.00017459955,0.000025737303,0.00032895937,5.6742596e-7,0.000088740795,2.514174e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000025793631,0.000009207699,0.0023896976,0.000009505949,0.000023801878,0.000010612162,0.020031093,0.97282666,0.000028695818,0.0045641884,0.00006679427,0.000037166476],"study_design_scores_gemma":[0.0015841174,0.0007533828,0.039547194,0.00034300066,0.0001674383,0.0000518609,0.2728725,0.68103945,0.00008279189,0.0027289612,0.00036265506,0.00046664584],"about_ca_topic_score_codex":0.015747694,"about_ca_topic_score_gemma":0.39236486,"teacher_disagreement_score":0.37661716,"about_ca_system_score_codex":0.00018672552,"about_ca_system_score_gemma":0.0005339707,"threshold_uncertainty_score":0.9908065},"labels":[],"label_agreement":null},{"id":"W1918916900","doi":"10.1002/atr.1332","title":"Automated Box–Jenkins forecasting tool with an application for passenger demand in urban rail systems","year":2015,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":54,"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":"Box–Jenkins; Public transport; Demand forecasting; Scheduling (production processes); Computer science; MATLAB; Operations research; Transportation planning; Selection (genetic algorithm); Transport engineering; Time series; Industrial engineering; Engineering; Artificial intelligence; Machine learning; Autoregressive integrated moving average; Operations management","score_opus":0.02495964502793252,"score_gpt":0.29318214279994076,"score_spread":0.26822249777200824,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1918916900","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.83049005,0.000092322865,0.1684226,0.00012220812,0.00021477448,0.0004926487,0.0000148754325,0.00008423611,0.00006630045],"genre_scores_gemma":[0.97498083,0.000020169462,0.024593135,0.000017102457,0.00014904655,0.00003740506,0.00015620829,0.000016429607,0.000029696801],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9987056,0.00006719358,0.0005449766,0.0001354887,0.00037337048,0.00017333752],"domain_scores_gemma":[0.9985396,0.00008309588,0.00055494433,0.00006345873,0.00064172136,0.00011714256],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008582027,0.000097076474,0.00019520053,0.00016361443,0.00012278676,0.00004173003,0.00008938863,0.00008203592,0.0000012309839],"category_scores_gemma":[0.000058282796,0.00008805416,0.000036513262,0.0003432017,0.000035034565,0.0011367431,2.1257789e-7,0.00009610101,3.293425e-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":[0.00061101787,0.00007785345,0.039122324,0.000032284766,0.000014769193,0.000010326512,0.026010761,0.9306095,0.0001899916,0.001516408,0.00004874306,0.0017560043],"study_design_scores_gemma":[0.018320672,0.0028768836,0.71097296,0.0010439957,0.00038941138,0.000025234618,0.073715,0.16317917,0.0005474223,0.0016743304,0.026114106,0.0011407722],"about_ca_topic_score_codex":0.00012387535,"about_ca_topic_score_gemma":0.002110601,"teacher_disagreement_score":0.76743037,"about_ca_system_score_codex":0.0000992302,"about_ca_system_score_gemma":0.00026217944,"threshold_uncertainty_score":0.35907447},"labels":[],"label_agreement":null},{"id":"W1919208341","doi":"10.1002/atr.1269","title":"Braess' paradox in a generalised traffic network","year":2014,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Context (archaeology); Node (physics); Traffic flow (computer networking); Flow network; Computer science; Link (geometry); Extension (predicate logic); Symmetry (geometry); Traffic network; Volume (thermodynamics); Mathematics; Mathematical economics; Statistical physics; Topology (electrical circuits); Mathematical optimization; Physics; Combinatorics; Geography; Computer network; Geometry; Quantum mechanics","score_opus":0.010161213974274845,"score_gpt":0.2710729594042398,"score_spread":0.260911745429965,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1919208341","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97904116,0.00014949263,0.01908707,0.0006004836,0.00057508424,0.00010862108,0.0000023899142,0.0000280139,0.00040768948],"genre_scores_gemma":[0.9834506,0.00022605677,0.015799485,0.00012082543,0.00030525614,0.0000027870049,0.00002967353,0.000009767722,0.00005554668],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.998734,0.000116187286,0.00052882324,0.00009814333,0.00033031346,0.00019254557],"domain_scores_gemma":[0.99924016,0.00009763457,0.00036575404,0.000052321753,0.00015612233,0.00008802107],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00070744334,0.00007926229,0.00018679904,0.00012526392,0.000116508374,0.000021224605,0.00010481054,0.00007379668,0.000029799969],"category_scores_gemma":[0.000048138343,0.000079959405,0.000075844095,0.00041697148,0.00004306848,0.00049700984,1.8785153e-7,0.00014581784,0.0000016081574],"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":[0.000107827735,0.00003443339,0.010440339,0.000006907023,0.0000058946603,0.000008395148,0.011147071,0.9656183,0.000058850343,0.0034801634,0.00008012863,0.009011688],"study_design_scores_gemma":[0.0036220101,0.00022396349,0.9520204,0.0002525888,0.00006614198,0.000002098518,0.0035708426,0.0019002848,0.000055635166,0.0029559596,0.035015408,0.0003146753],"about_ca_topic_score_codex":0.000035445864,"about_ca_topic_score_gemma":0.0025059541,"teacher_disagreement_score":0.963718,"about_ca_system_score_codex":0.00003865,"about_ca_system_score_gemma":0.00009286997,"threshold_uncertainty_score":0.326065},"labels":[],"label_agreement":null},{"id":"W1919665013","doi":"10.3141/2502-08","title":"Greenhouse Gas Impact of Ridership on Sheppard Subway Line, Toronto, Canada","year":2015,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Greenhouse gas; Transport engineering; Kilometer; Transit (satellite); Bus rapid transit; Public transport; Travel time; Mode choice; Environmental science; Mode (computer interface); Agricultural economics; Engineering; Computer science; Economics","score_opus":0.15305483476820736,"score_gpt":0.42449779962448636,"score_spread":0.271442964856279,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1919665013","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9928293,0.00028008706,0.00049024815,0.0027711955,0.0007229436,0.0007953768,0.00017353996,0.00003386573,0.0019034051],"genre_scores_gemma":[0.9965117,0.0010971247,0.00064269383,0.000035734945,0.00029510367,0.000025388723,0.000045053755,0.00005092626,0.001296249],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9886265,0.0020308145,0.0014921249,0.00037073158,0.0064980746,0.000981771],"domain_scores_gemma":[0.9896781,0.0013476303,0.0007024482,0.00046773537,0.0068916683,0.0009124246],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006772383,0.00026828414,0.0005320636,0.00055055437,0.0007085963,0.000110545,0.001222069,0.0002379653,0.00054593146],"category_scores_gemma":[0.0007701895,0.00020603249,0.00045241413,0.001909303,0.0007461097,0.00083083636,0.0000031307416,0.0014465493,0.000008074231],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0057377033,0.000684275,0.795389,0.00016904896,0.00040179698,0.0001480118,0.03522338,0.07778776,0.00030587698,0.0073445537,0.07062472,0.006183899],"study_design_scores_gemma":[0.0024570723,0.00222998,0.9566858,0.00041731083,0.00007456841,2.2526213e-7,0.016302906,0.00020849472,0.00049868104,0.0014724826,0.019335207,0.00031729793],"about_ca_topic_score_codex":0.93885756,"about_ca_topic_score_gemma":0.992668,"teacher_disagreement_score":0.16129678,"about_ca_system_score_codex":0.0015176719,"about_ca_system_score_gemma":0.008404148,"threshold_uncertainty_score":0.9972173},"labels":[],"label_agreement":null},{"id":"W1921360096","doi":"10.3141/2351-14","title":"Effects of Fare Payment Types and Crowding on Dwell Time","year":2013,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":53,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"McGill University","funders":"Mitacs","keywords":"Dwell time; Crowding; Payment; Crowding out; Computer science; Transport engineering; Service (business); Business; Economics; Marketing; Engineering; Psychology","score_opus":0.042521210551906066,"score_gpt":0.36226506138797077,"score_spread":0.3197438508360647,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1921360096","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99357575,0.00024608435,0.00043681325,0.0035198492,0.000279021,0.0011558436,0.000021154652,0.00002711403,0.0007383856],"genre_scores_gemma":[0.995718,0.0011984941,0.001137721,0.000050408962,0.000118900796,0.00005182268,0.00001214309,0.0000306981,0.0016818255],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99408567,0.0012177969,0.00091391045,0.0002855502,0.0028967522,0.00060034165],"domain_scores_gemma":[0.9944395,0.0018877808,0.00040841594,0.00020719366,0.0027193534,0.0003377763],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029694226,0.0001737172,0.0003530378,0.00074180606,0.00079754065,0.00013139493,0.0005514809,0.00018095082,0.00047938304],"category_scores_gemma":[0.00031668975,0.00013343542,0.00019155383,0.0013225859,0.0007645353,0.000605713,0.000003005434,0.0010017361,0.000040318973],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0032059182,0.0017363268,0.6991012,0.002259979,0.0007779018,0.0001938344,0.11216851,0.013733302,0.032680884,0.049503293,0.032096077,0.05254275],"study_design_scores_gemma":[0.0012227367,0.0006016908,0.97910184,0.00066121476,0.000057632766,1.0563247e-7,0.0036765172,0.00012968211,0.0036594202,0.0025958202,0.008115765,0.00017759038],"about_ca_topic_score_codex":0.01374324,"about_ca_topic_score_gemma":0.007124978,"teacher_disagreement_score":0.2800006,"about_ca_system_score_codex":0.00012708006,"about_ca_system_score_gemma":0.0004064395,"threshold_uncertainty_score":0.9928243},"labels":[],"label_agreement":null},{"id":"W1922744897","doi":"10.1109/rrcon.1995.395173","title":"An integrated systems engineering approach to managing the implementation of new transit technologies","year":2002,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Hatch (Canada)","funders":"","keywords":"Doors; Transit (satellite); Computer science; Systems engineering; Telecommunications; Transit system; Public transport; Transport engineering; Engineering; Operating system","score_opus":0.025730834966378156,"score_gpt":0.2757167048566114,"score_spread":0.24998586989023328,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1922744897","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03441838,0.00009747973,0.9568352,0.0015411833,0.00009778068,0.00038257067,0.0000043417754,0.00054043136,0.0060826046],"genre_scores_gemma":[0.9907741,0.000021090022,0.008846044,0.000013688164,0.000013102091,0.000009177239,0.000010333295,0.0000037290704,0.00030876583],"study_design_codex":"simulation_or_modeling","study_design_gemma":"qualitative","domain_scores_codex":[0.9995716,0.000018353043,0.00011619951,0.000077968994,0.00012322729,0.00009262758],"domain_scores_gemma":[0.9998353,0.000013027874,0.00002603112,0.00006924793,0.00003243874,0.000023961033],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015374584,0.000040662093,0.000052652547,0.00007090925,0.000092985945,0.000041978485,0.00012183645,0.000030253688,0.000028687997],"category_scores_gemma":[0.000006996906,0.000029824636,0.000012188705,0.00038209773,0.000015387188,0.00012774636,0.0000011252066,0.000035271787,0.0000017719287],"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":[0.0000029916052,0.000027607595,0.0025701523,0.000021134992,0.000020244326,2.9912323e-7,0.10488803,0.7776381,0.00024831764,0.07095764,0.0014281261,0.04219738],"study_design_scores_gemma":[0.0003493059,0.00007194556,0.005327843,0.000040117236,0.000037652764,6.798699e-7,0.7524501,0.22037493,0.0008704008,0.00006201908,0.020167835,0.00024714836],"about_ca_topic_score_codex":0.004389386,"about_ca_topic_score_gemma":0.00038951833,"teacher_disagreement_score":0.9563557,"about_ca_system_score_codex":0.000015468018,"about_ca_system_score_gemma":0.000011277863,"threshold_uncertainty_score":0.6635468},"labels":[],"label_agreement":null},{"id":"W1931749568","doi":"10.1111/itor.12205","title":"A review on cost allocation methods in collaborative transportation","year":2015,"lang":"en","type":"review","venue":"International Transactions in Operational Research","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":284,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Computer science; Cost allocation; Operations research; Flexibility (engineering); Profit (economics); Game theory; Cooperative game theory; Management science; Economics; Microeconomics; Mathematics","score_opus":0.30703357849286755,"score_gpt":0.6077634543396133,"score_spread":0.30072987584674576,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1931749568","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[6.4698145e-7,0.9661041,0.011736284,0.002730976,0.0007722072,0.00334225,0.00047574082,0.000053605432,0.0147842085],"genre_scores_gemma":[0.000035059496,0.98601377,0.0060125487,0.00019860189,0.00016830879,0.0024706759,0.0026487936,0.000042454787,0.0024098137],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99276006,0.0030809306,0.0012382009,0.0006572311,0.001834482,0.00042911785],"domain_scores_gemma":[0.9959271,0.001608537,0.0001980065,0.00022612905,0.0018719777,0.00016827908],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.008525101,0.0003036043,0.0007831273,0.0018826514,0.00034291885,0.00017079193,0.0006063293,0.0004277925,0.0011969773],"category_scores_gemma":[0.00082176796,0.00030998662,0.00017673982,0.0035226468,0.00024348855,0.0006047412,0.000003183084,0.0013442076,0.00014113242],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005067073,0.00040944613,0.000011589548,0.0016625009,0.00007043144,0.000019932548,0.0034276966,0.011870877,2.1687512e-7,0.0138295,0.002047277,0.9665999],"study_design_scores_gemma":[0.00036208433,0.00003745925,0.000026205758,0.018056188,0.000042815216,0.0000014459674,0.0008449295,0.0003067885,6.261457e-7,0.00023198882,0.97982734,0.00026213462],"about_ca_topic_score_codex":0.0014891417,"about_ca_topic_score_gemma":0.012449161,"teacher_disagreement_score":0.97778004,"about_ca_system_score_codex":0.0021892898,"about_ca_system_score_gemma":0.0047405018,"threshold_uncertainty_score":0.9999352},"labels":[],"label_agreement":null},{"id":"W1933129527","doi":"10.1002/atr.1224","title":"Optimization of bus routing strategies for evacuation","year":2013,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":38,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Lagrangian relaxation; Routing (electronic design automation); Public transport; Computer science; Relaxation (psychology); School bus; Mathematical optimization; Operations research; Integer programming; Integer (computer science); Transport engineering; Transit (satellite); Emergency evacuation; Engineering; Algorithm; Mathematics; Computer network","score_opus":0.016299230392536588,"score_gpt":0.2982572621184227,"score_spread":0.28195803172588607,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1933129527","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.33022267,0.00006770585,0.66854155,0.0002765128,0.00028795796,0.0003270123,0.000009293047,0.00002057892,0.00024674102],"genre_scores_gemma":[0.8861338,0.000114861745,0.11350653,0.000019401898,0.00009099217,0.000011433128,0.00007585407,0.00000945456,0.00003770873],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99880797,0.00003960439,0.0006290986,0.000081329876,0.00031745658,0.00012456859],"domain_scores_gemma":[0.99772084,0.00012849104,0.0008255416,0.000045147583,0.001225189,0.0000548117],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038869286,0.00007387744,0.00016541264,0.0001363624,0.00014179673,0.000037530586,0.000084621955,0.000071193244,0.00006235359],"category_scores_gemma":[0.00009075633,0.00007342388,0.00009000892,0.0002715075,0.000050628598,0.0016102367,2.3393777e-7,0.000064547676,5.9627195e-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":[0.0000649147,0.000031595537,0.0017576077,0.000026469002,0.000015630583,3.218581e-7,0.016107218,0.9704808,0.0010438692,0.006662537,0.000031866333,0.0037771452],"study_design_scores_gemma":[0.011124542,0.0017937215,0.59718645,0.0011314757,0.00076762226,0.0000040813484,0.28538474,0.05611183,0.0051076333,0.03664219,0.0035813989,0.0011643211],"about_ca_topic_score_codex":0.00010367652,"about_ca_topic_score_gemma":0.00014111,"teacher_disagreement_score":0.914369,"about_ca_system_score_codex":0.000035078727,"about_ca_system_score_gemma":0.00021787337,"threshold_uncertainty_score":0.2994139},"labels":[],"label_agreement":null},{"id":"W1933295065","doi":"10.1002/atr.1282","title":"A stochastic computational model for yellow time determination and its application","year":2014,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","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":"Tongji University; Science and Technology Commission of Shanghai Municipality; National Natural Science Foundation of China","keywords":"Randomness; Intersection (aeronautics); Monte Carlo method; Sensitivity (control systems); Computer science; Traffic flow (computer networking); Line (geometry); Stochastic modelling; Field (mathematics); Mathematical optimization; Simulation; Algorithm; Applied mathematics; Mathematics; Statistics; Engineering; Transport engineering","score_opus":0.010658069857455146,"score_gpt":0.28310370478774993,"score_spread":0.2724456349302948,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1933295065","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16728295,0.000030995852,0.83201367,0.000293108,0.0000677018,0.00023624854,0.000015250506,0.000020960175,0.00003908926],"genre_scores_gemma":[0.93496823,0.000017730696,0.064644,0.00005011313,0.00006817027,0.000014622978,0.00011304102,0.000010094059,0.00011397454],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991349,0.000029949233,0.00035779225,0.00010783869,0.0002661726,0.0001033249],"domain_scores_gemma":[0.99878585,0.00018433039,0.0003918739,0.000030047147,0.0005323116,0.00007560564],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044297613,0.000072612536,0.00013172594,0.00011726797,0.00019439522,0.000018931105,0.000059575403,0.00006112148,0.000003832387],"category_scores_gemma":[0.00009206916,0.00007749468,0.000049487015,0.00011522209,0.000033703443,0.0005239067,3.0708753e-7,0.000059843962,0.0000013081292],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008678215,0.000023084674,0.00006372509,0.000017434286,0.000005748188,1.5409442e-7,0.007038758,0.97735953,0.00055535673,0.005141859,0.000010196941,0.009697381],"study_design_scores_gemma":[0.00094586314,0.00008482374,0.0066520777,0.000037138743,0.00005812493,8.646977e-7,0.00031524146,0.9829093,0.000033993554,0.008537183,0.00032714717,0.00009821852],"about_ca_topic_score_codex":0.0000022541908,"about_ca_topic_score_gemma":0.00005107203,"teacher_disagreement_score":0.7676853,"about_ca_system_score_codex":0.00003087639,"about_ca_system_score_gemma":0.00007890055,"threshold_uncertainty_score":0.31601414},"labels":[],"label_agreement":null},{"id":"W1933586805","doi":"10.1002/atr.1270","title":"Investigating the impact of stochastic vehicle arrivals to optimal stop spacing and headway for a feeder bus route","year":2014,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Headway; Transit (satellite); Transport engineering; Service (business); Travel time; Key (lock); Transit system; Variance (accounting); Engineering; Operations research; Computer science; Simulation; Public transport; Computer security; Economics","score_opus":0.017690579195460663,"score_gpt":0.3213689490401666,"score_spread":0.3036783698447059,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1933586805","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7553505,0.000037507158,0.2438219,0.00048057386,0.00008332885,0.00018761356,0.000013406427,0.00000854802,0.000016668755],"genre_scores_gemma":[0.97165745,0.0000083667055,0.028151175,0.00004302173,0.000099574696,0.0000052539363,0.000010312027,0.000009862562,0.000014995942],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9990446,0.00005342262,0.00041399812,0.00009194018,0.0002482038,0.00014785987],"domain_scores_gemma":[0.9987505,0.0002944408,0.00044065458,0.000051561085,0.00034402602,0.000118793636],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006988032,0.000082597624,0.00018762088,0.000088617875,0.00023167144,0.00002924439,0.00008045315,0.000041993655,0.0000037990003],"category_scores_gemma":[0.00028321016,0.00006209122,0.00009179498,0.0002092426,0.000071251496,0.00035712263,5.7577347e-7,0.000090372174,1.5889084e-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":[0.000094392126,0.00001320897,0.0040246355,0.000014104546,0.000021620142,2.6704262e-7,0.048168436,0.9403679,0.0038035803,0.00081305386,0.000013388085,0.0026654187],"study_design_scores_gemma":[0.0029516446,0.0012872225,0.9535593,0.0005329319,0.00020362691,0.000003634793,0.025893712,0.010934644,0.0011665203,0.002891106,0.00026016677,0.0003155199],"about_ca_topic_score_codex":0.00016835924,"about_ca_topic_score_gemma":0.00022967577,"teacher_disagreement_score":0.94953465,"about_ca_system_score_codex":0.000035703262,"about_ca_system_score_gemma":0.000117032505,"threshold_uncertainty_score":0.25320062},"labels":[],"label_agreement":null},{"id":"W1939024356","doi":"10.1002/atr.1262","title":"Modeling transit technology selection in a linear transportation corridor","year":2014,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Headway; Transit (satellite); Transport engineering; Population; Maximization; Public transport; Investment (military); Profit maximization; Price elasticity of demand; Computer science; Profit (economics); Operations research; Economics; Engineering; Microeconomics","score_opus":0.00997923228335535,"score_gpt":0.27610135309199757,"score_spread":0.2661221208086422,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1939024356","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.66880095,0.00005721862,0.3299445,0.0005779958,0.00031875455,0.00014169278,0.000004704381,0.0000569398,0.00009721693],"genre_scores_gemma":[0.9742939,0.0002083846,0.025195759,0.000050786137,0.0001355271,0.0000074001036,0.000052076633,0.000018553488,0.000037625032],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.998266,0.00008074107,0.00082315446,0.00017490528,0.00042050055,0.00023469035],"domain_scores_gemma":[0.9990152,0.00005304098,0.00033409504,0.000050182505,0.00045873589,0.000088706234],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006526666,0.00013032138,0.00027025255,0.0005990687,0.00016437969,0.000015684624,0.00012648613,0.00018892862,0.000022678205],"category_scores_gemma":[0.000069005546,0.00014133047,0.00009968767,0.0009878204,0.000055496646,0.0007947079,1.3437072e-7,0.00030780042,0.0000018975542],"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":[0.00020819393,0.00006588039,0.011523279,0.000019741949,0.000010330356,0.0000067283636,0.012499353,0.96403855,0.0013205986,0.0030634482,0.000004074317,0.00723983],"study_design_scores_gemma":[0.017541803,0.0020129867,0.6155756,0.0016279655,0.0006465894,0.000019319057,0.05076807,0.26880398,0.0034520356,0.020209678,0.017459229,0.0018827448],"about_ca_topic_score_codex":0.00014827805,"about_ca_topic_score_gemma":0.010198301,"teacher_disagreement_score":0.69523454,"about_ca_system_score_codex":0.00008358579,"about_ca_system_score_gemma":0.00015609473,"threshold_uncertainty_score":0.57632893},"labels":[],"label_agreement":null},{"id":"W1939152881","doi":"10.1002/atr.1253","title":"Incentive subsidy scheme design with elastic transport demand","year":2013,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Subsidy; Incentive; Price elasticity of demand; Public transport; Maximization; Profit maximization; Scheme (mathematics); Service (business); Profit (economics); Computer science; Operations research; Transport engineering; Economics; Microeconomics; Business; Engineering; Mathematics; Marketing","score_opus":0.011908662695772648,"score_gpt":0.2557881968559841,"score_spread":0.24387953416021144,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1939152881","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.62567437,0.00010807537,0.37309685,0.00029038754,0.00018986373,0.00031958462,0.0000047661306,0.00003783934,0.00027823684],"genre_scores_gemma":[0.9225864,0.00020131095,0.07689487,0.000051988398,0.00008150525,0.000012347162,0.000031761803,0.00001647124,0.00012338783],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99847853,0.00007572932,0.0005209091,0.0001411678,0.0005564323,0.0002272589],"domain_scores_gemma":[0.9984313,0.00012132992,0.00049009634,0.00006458769,0.0007279938,0.00016465646],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034024953,0.00013277926,0.00022226309,0.00015359954,0.00022367084,0.00003112024,0.00012760448,0.0000821018,0.00017336755],"category_scores_gemma":[0.000026808691,0.00011223697,0.00007461621,0.0003934952,0.00012059876,0.0014734893,1.9689087e-7,0.00018322658,0.000008536868],"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":[0.0007496236,0.00019631247,0.09829766,0.00004358992,0.00012452704,0.000077539706,0.047015347,0.8458457,0.0021980638,0.0024671766,0.000108379216,0.002876098],"study_design_scores_gemma":[0.0025352866,0.0004985182,0.98115647,0.00026566326,0.0001783742,0.000004700635,0.011342672,0.00019843079,0.0009090248,0.001218848,0.001368653,0.00032336792],"about_ca_topic_score_codex":0.0000927116,"about_ca_topic_score_gemma":0.0004179773,"teacher_disagreement_score":0.8828588,"about_ca_system_score_codex":0.0000549424,"about_ca_system_score_gemma":0.00024353396,"threshold_uncertainty_score":0.45768908},"labels":[],"label_agreement":null},{"id":"W1944531729","doi":"10.1111/j.1538-4632.2010.00803.x","title":"Entropy‐Based Spatial Interaction Models for Trip Distribution. 基于熵的空间相互作用模型在出行分布中的应用","year":2010,"lang":"en","type":"article","venue":"Geographical Analysis","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"McMaster University","keywords":"Entropy maximization; Computer science; Trip distribution; Entropy (arrow of time); Maximization; Constraint (computer-aided design); Principle of maximum entropy; Context (archaeology); Utility maximization; Operations research; Mathematical economics; Econometrics; Mathematical optimization; Mathematics; Artificial intelligence; Geography","score_opus":0.01339961373032302,"score_gpt":0.28567433769839506,"score_spread":0.27227472396807206,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1944531729","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11437191,0.0000093432745,0.88233393,0.0020852142,0.00028722547,0.00018161882,0.00019552409,0.00013624263,0.00039896832],"genre_scores_gemma":[0.9942358,0.000021899607,0.0032349022,0.00010844072,0.00017658352,0.00005110397,0.0020676549,0.0000056895637,0.00009791961],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988363,0.00007228906,0.00025404117,0.00026888153,0.00032247693,0.00024606028],"domain_scores_gemma":[0.9991217,0.00019570462,0.00011647909,0.0001457244,0.00027150978,0.00014888056],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000459085,0.00009666487,0.00018218851,0.0002890291,0.00056376425,0.00010280812,0.00013393186,0.00016236232,0.00036026665],"category_scores_gemma":[0.00014980312,0.000093124116,0.0004374592,0.0015650007,0.00014805913,0.0002017971,0.000003198037,0.00017918929,0.0000041768585],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00052515266,0.0006322802,0.22560431,0.000028267446,0.0011502719,0.0000043030595,0.0018420247,0.5312222,0.0002539341,0.22197206,0.0013345105,0.015430644],"study_design_scores_gemma":[0.0009684847,0.000059845886,0.061176963,0.0000068289964,0.002114334,1.6315965e-7,0.000365463,0.8877237,0.000105468,0.0090871025,0.038028616,0.00036305576],"about_ca_topic_score_codex":0.003654964,"about_ca_topic_score_gemma":0.014292664,"teacher_disagreement_score":0.8798639,"about_ca_system_score_codex":0.000016633992,"about_ca_system_score_gemma":0.000057397006,"threshold_uncertainty_score":0.7975642},"labels":[],"label_agreement":null},{"id":"W1947070923","doi":"10.1002/atr.202","title":"A reliability‐based traffic assignment model for multi‐modal transport network under demand uncertainty","year":2012,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":48,"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":"Modal; Reliability (semiconductor); Computer science; Transport engineering; Traffic model; Reliability engineering; Engineering; Computer network; Chemistry","score_opus":0.03235958933205837,"score_gpt":0.3166703119471247,"score_spread":0.28431072261506635,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1947070923","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3286908,0.00015642865,0.6700432,0.00022465743,0.00044147373,0.00035229465,0.00003348162,0.00003775646,0.000019945612],"genre_scores_gemma":[0.87243044,0.00007542803,0.12680724,0.00014364782,0.0002624017,0.00002644235,0.00014391827,0.000022099466,0.00008836325],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99798894,0.00008080665,0.0007847813,0.00016855862,0.00053771434,0.00043919752],"domain_scores_gemma":[0.99850744,0.00016297848,0.000548182,0.000084451305,0.00043923967,0.0002577018],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013178445,0.00017034094,0.00029839677,0.00010181376,0.00034832896,0.000016625216,0.00012286089,0.00015310533,0.000022010152],"category_scores_gemma":[0.000032827716,0.00016186653,0.0002503809,0.00026327255,0.000092756985,0.00088400824,2.5044523e-7,0.00017978712,6.5424285e-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":[0.00052150147,0.00023851694,0.0056350376,0.000035315777,0.000027450944,0.0000012510352,0.012362245,0.97887963,0.000103917526,0.001120448,0.00004696486,0.0010277444],"study_design_scores_gemma":[0.009862301,0.0004352489,0.3314564,0.00034110024,0.0007731961,0.0000017299454,0.00948429,0.64049786,0.0001469974,0.001525076,0.004629418,0.00084635353],"about_ca_topic_score_codex":0.000017078724,"about_ca_topic_score_gemma":0.0009794623,"teacher_disagreement_score":0.5437397,"about_ca_system_score_codex":0.00015971577,"about_ca_system_score_gemma":0.00034974192,"threshold_uncertainty_score":0.66007257},"labels":[],"label_agreement":null},{"id":"W1949101686","doi":"10.1002/atr.1195","title":"Design of a tabu search algorithm for assigning optimal bus sizes and frequencies in urban transport services","year":2012,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Division of Mathematical Sciences","keywords":"Tabu search; Mathematical optimization; Guided Local Search; Constraint (computer-aided design); Convergence (economics); Computer science; Public transport; Algorithm; Function (biology); Pattern search; Search algorithm; Mathematics; Engineering; Transport engineering; Economics","score_opus":0.022727154416468387,"score_gpt":0.2987616662608607,"score_spread":0.2760345118443923,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1949101686","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6017241,0.00074241846,0.3970324,0.000059214795,0.00014505943,0.00024382793,0.000023602404,0.000011633411,0.000017748025],"genre_scores_gemma":[0.759854,0.0003602239,0.2396283,0.000011985233,0.00007992541,0.000007586781,0.00003138268,0.000010258546,0.00001631327],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9986306,0.000070289156,0.0005845994,0.00010096043,0.00036504964,0.00024853903],"domain_scores_gemma":[0.9989385,0.0002203088,0.0003672899,0.000040357976,0.0003259151,0.000107602085],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010860185,0.00010339646,0.00025497517,0.00021226302,0.00012010657,0.000012376875,0.00009223469,0.000090618276,0.00000953047],"category_scores_gemma":[0.000015568496,0.000102151214,0.00006107591,0.0002847916,0.00008629758,0.0012404912,3.296453e-7,0.00012321181,1.09651275e-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":[0.00040546237,0.00017067972,0.1305054,0.00017696751,0.000047677702,0.000008189264,0.23930383,0.6030242,0.006715024,0.00071566517,0.0000044114986,0.018922508],"study_design_scores_gemma":[0.004288933,0.00060362706,0.9128135,0.0006334658,0.00023160351,0.0000042227452,0.06915812,0.0039865556,0.0066700308,0.00036131064,0.0008246027,0.00042401787],"about_ca_topic_score_codex":0.00013457228,"about_ca_topic_score_gemma":0.000189971,"teacher_disagreement_score":0.7823081,"about_ca_system_score_codex":0.000036941197,"about_ca_system_score_gemma":0.00013503004,"threshold_uncertainty_score":0.41656056},"labels":[],"label_agreement":null},{"id":"W1950380357","doi":"10.1111/mice.12023","title":"Activity‐Based Travel Scenario Analysis with Routing Problem Reoptimization","year":2013,"lang":"en","type":"article","venue":"Computer-Aided Civil and Infrastructure Engineering","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":55,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Heuristics; Mathematical optimization; Computer science; Genetic algorithm; Heuristic; Metaheuristic; Routing (electronic design automation); Schedule; Vehicle routing problem; Mathematics; Computer network","score_opus":0.004225081752610376,"score_gpt":0.19227353467338604,"score_spread":0.18804845292077565,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1950380357","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14016102,0.000013614558,0.85865796,0.00015427993,0.00006691415,0.00017807982,0.0000025073375,0.00016640784,0.0005992419],"genre_scores_gemma":[0.8956475,0.000008027104,0.104082145,0.00005437712,0.000106269865,0.000012028641,0.000037155205,0.000013546823,0.00003891827],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990916,0.000033155688,0.00016704138,0.0002503523,0.00020735626,0.0002504558],"domain_scores_gemma":[0.99950427,0.00006435525,0.000093001465,0.000102499216,0.000112265865,0.00012358149],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012434783,0.00015754462,0.00020354161,0.00022676814,0.0002651021,0.00021010333,0.000092105875,0.00009923003,0.00007419804],"category_scores_gemma":[0.000013440791,0.00014209279,0.000044000866,0.0007841145,0.000043395776,0.00042563328,0.000007815668,0.00015185858,0.0000011125994],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000042273964,0.0000071205673,0.012473429,0.000019401094,0.00006926241,0.0000011086844,0.0022114008,0.9809057,0.00008958573,0.000646416,0.000048338272,0.0035240205],"study_design_scores_gemma":[0.00023678906,0.000030751748,0.23396651,0.00004227472,0.000078831166,6.9080335e-7,0.00009419869,0.7651695,0.00004335641,0.000026863585,0.00013665126,0.00017355882],"about_ca_topic_score_codex":0.00040234753,"about_ca_topic_score_gemma":0.000196587,"teacher_disagreement_score":0.7554865,"about_ca_system_score_codex":0.000043342745,"about_ca_system_score_gemma":0.000060330956,"threshold_uncertainty_score":0.57943755},"labels":[],"label_agreement":null},{"id":"W1951272079","doi":"","title":"Réingéniere des processus décisionnels en situation d'urgence d'une société de transport collectif","year":2010,"lang":"fr","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Humanities; Political science; Philosophy","score_opus":0.018334578111107554,"score_gpt":0.30303937800115177,"score_spread":0.2847047998900442,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1951272079","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7898545,0.0004102114,0.15228786,0.006292169,0.0022503412,0.0004505646,0.00003918395,0.00029617266,0.048118945],"genre_scores_gemma":[0.92090327,0.00047074736,0.021674767,0.00023555107,0.00045972038,0.000029451552,0.00006179082,0.00002412323,0.056140557],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.9980987,0.00012954944,0.00041588285,0.00037463233,0.0004772753,0.00050400925],"domain_scores_gemma":[0.9987645,0.00032547652,0.00015865992,0.0001387102,0.00035496216,0.000257696],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00101905,0.00020627724,0.00021258861,0.00009819429,0.0011047832,0.00008602409,0.00026374037,0.00044078144,0.0039509023],"category_scores_gemma":[0.00022581959,0.00022095494,0.000112095106,0.0013598577,0.000506948,0.00078047515,0.000004575462,0.00039103854,0.000090774105],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016682214,0.00079777854,0.14125079,0.00040401443,0.0001365951,0.000054390828,0.5327703,0.049648218,0.0048971307,0.07547129,0.0063330666,0.18806966],"study_design_scores_gemma":[0.0031450999,0.00043367725,0.5120636,0.00065982813,0.00084746483,0.000058983915,0.10136057,0.04423472,0.0076325373,0.0874536,0.23945403,0.0026559057],"about_ca_topic_score_codex":0.0053326422,"about_ca_topic_score_gemma":0.024070868,"teacher_disagreement_score":0.4314097,"about_ca_system_score_codex":0.000117322845,"about_ca_system_score_gemma":0.000827629,"threshold_uncertainty_score":0.9969596},"labels":[],"label_agreement":null},{"id":"W1951930509","doi":"10.1002/9781118445112.stat00193","title":"Estimation of Travel Distance","year":2014,"lang":"en","type":"other","venue":"Wiley StatsRef: Statistics Reference Online","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University; Royal Military College of Canada","funders":"","keywords":"Euclidean distance; Distance measures; Norm (philosophy); Empirical research; Econometrics; Relevance (law); Mathematics; Computer science; Euclidean geometry; Statistics; Artificial intelligence; Geometry","score_opus":0.02948896734146376,"score_gpt":0.32797103965300456,"score_spread":0.2984820723115408,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1951930509","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000014488007,0.00026814896,0.85765135,0.00006332169,0.00030507668,0.00027230862,0.01843536,0.00015915607,0.122830756],"genre_scores_gemma":[0.01866832,0.0045219427,0.51238114,0.000090600945,0.00034602656,0.000023737919,0.028184237,0.00037454916,0.43540946],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99808764,0.00014617477,0.0004982894,0.00033561542,0.0006409715,0.00029132608],"domain_scores_gemma":[0.9985306,0.00018787364,0.00069418456,0.0002674029,0.00018133604,0.00013855746],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002375333,0.00024867506,0.0004389545,0.00021567686,0.00012614028,0.00003471316,0.00027308628,0.00030740557,0.0016605748],"category_scores_gemma":[0.00027962448,0.0002578837,0.00003538218,0.00025829257,0.00035865218,0.000055722947,0.000008174508,0.00022587953,0.000051677758],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000046067187,0.0002780341,0.00037943787,0.0005049678,0.000084214706,0.000009201933,0.0029359553,0.0043834806,0.0000055279424,0.45742136,0.4562644,0.07768735],"study_design_scores_gemma":[0.0008443209,0.00016732336,0.0017364086,0.0016658826,0.00021668558,5.316903e-7,0.0010735863,0.01780226,0.000008144173,0.008629133,0.9669467,0.00090899155],"about_ca_topic_score_codex":0.002200775,"about_ca_topic_score_gemma":0.011861338,"teacher_disagreement_score":0.51068234,"about_ca_system_score_codex":0.00005590049,"about_ca_system_score_gemma":0.0003550582,"threshold_uncertainty_score":0.99998736},"labels":[],"label_agreement":null},{"id":"W1956327980","doi":"10.1002/atr.1233","title":"A Pareto‐improving hybrid policy for transportation networks","year":2013,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China; National Science Foundation","keywords":"Pareto principle; Pareto optimal; Transport engineering; Operations research; Computer science; Flow network; Multi-objective optimization; Engineering; Mathematical optimization; Operations management; Mathematics","score_opus":0.00993881376117147,"score_gpt":0.2784583590414652,"score_spread":0.26851954528029376,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1956327980","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.47528884,0.00019151888,0.52142113,0.0014444786,0.0006920851,0.0006998021,0.000053516993,0.00007384343,0.00013479551],"genre_scores_gemma":[0.9739412,0.00024525868,0.024640804,0.00016947552,0.0005299614,0.000043598768,0.00028570564,0.000024652378,0.00011934979],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99835676,0.000044883764,0.0007602322,0.00015751162,0.00038422976,0.0002963888],"domain_scores_gemma":[0.99800676,0.00015198861,0.0007291169,0.000075920594,0.00086297526,0.00017325739],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000383685,0.00013825542,0.0002383203,0.00022868309,0.00030247495,0.0000651573,0.00014776966,0.00009162784,0.000046910238],"category_scores_gemma":[0.0001011026,0.00013970459,0.00018379591,0.00035188947,0.00006853815,0.0014575205,1.8458731e-7,0.00015618662,0.00000206541],"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":[0.0002667811,0.00009948836,0.0069660186,0.0000700728,0.000054633998,0.000009825016,0.018321712,0.89739555,0.0008296499,0.009192137,0.0005584293,0.06623568],"study_design_scores_gemma":[0.007843383,0.001099043,0.9047861,0.00044438048,0.0005022401,0.000006195709,0.024175426,0.005905262,0.0013271704,0.014224777,0.038572244,0.0011137888],"about_ca_topic_score_codex":0.00042108193,"about_ca_topic_score_gemma":0.0009661015,"teacher_disagreement_score":0.89782006,"about_ca_system_score_codex":0.00008810779,"about_ca_system_score_gemma":0.00028965922,"threshold_uncertainty_score":0.56969875},"labels":[],"label_agreement":null},{"id":"W1956400035","doi":"10.1002/atr.1313","title":"Solving the stochastic multi‐class traffic assignment problem with asymmetric interactions, route overlapping, and vehicle restrictions","year":2015,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"","funders":"National Research Foundation of Korea","keywords":"Computer science; Path (computing); Mathematical optimization; Logit; Sensitivity (control systems); Class (philosophy); Scheme (mathematics); Algorithm; Mathematics; Engineering; Artificial intelligence","score_opus":0.0221491365394115,"score_gpt":0.28639307815998477,"score_spread":0.2642439416205733,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1956400035","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7328275,0.00040403166,0.26380652,0.0017818953,0.00047130228,0.00037885085,0.00001107943,0.000055841454,0.00026299668],"genre_scores_gemma":[0.9868921,0.000109462104,0.012672203,0.000038507016,0.000083942796,0.000009779802,0.000014520682,0.000010812809,0.00016867906],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.998807,0.000078554156,0.0003860948,0.00011595141,0.0004622694,0.0001500882],"domain_scores_gemma":[0.99880123,0.00015432759,0.00046558867,0.000052371328,0.00038814728,0.0001383426],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045837718,0.00009438786,0.00013386614,0.00018606469,0.0003589371,0.00006769669,0.00007909439,0.000040847408,0.0000037182215],"category_scores_gemma":[0.00007206724,0.00006940669,0.00004099881,0.0005548821,0.00007831338,0.0008224065,7.1786235e-7,0.00022229459,8.021257e-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":[0.00013226127,0.00007324179,0.0043505165,0.000006574909,0.000034614925,0.000006873752,0.021784892,0.9659279,0.000057110108,0.00065130263,0.00006598905,0.006908748],"study_design_scores_gemma":[0.008721701,0.001162437,0.80625117,0.0007126705,0.0007032526,0.000040975254,0.13506834,0.027923187,0.00006165164,0.00043109216,0.018259816,0.00066369533],"about_ca_topic_score_codex":0.000110274595,"about_ca_topic_score_gemma":0.0018870125,"teacher_disagreement_score":0.9380047,"about_ca_system_score_codex":0.00013047356,"about_ca_system_score_gemma":0.00024269588,"threshold_uncertainty_score":0.28303227},"labels":[],"label_agreement":null},{"id":"W1956724351","doi":"10.1002/atr.1225","title":"Berth assignment planning for multi‐line bus stops","year":2013,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":27,"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; Heuristic; Plan (archaeology); Bus priority; Line (geometry); Computer science; Engineering; Transport engineering; Operations research; Public transport; Real-time computing; Computer network","score_opus":0.03321880831804417,"score_gpt":0.33130350384236945,"score_spread":0.29808469552432526,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1956724351","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.44701377,0.00021602063,0.5507633,0.0007532145,0.00062403124,0.00044408417,0.000017934639,0.000038226386,0.00012941245],"genre_scores_gemma":[0.8819097,0.00008802786,0.1173151,0.00008858578,0.00016514903,0.000021607757,0.00007336277,0.000012878425,0.0003255753],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9988534,0.000031946296,0.00050075975,0.00010398818,0.00032869633,0.00018119837],"domain_scores_gemma":[0.9987492,0.00010370344,0.0004695662,0.000047228026,0.0005146752,0.00011561692],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030994797,0.000090653346,0.00016642288,0.00011047067,0.00022204206,0.000035493333,0.00009715589,0.00006975115,0.00006324631],"category_scores_gemma":[0.00006340004,0.00008522327,0.00009645947,0.00015392163,0.0000392773,0.0007939805,2.6605846e-7,0.00010736411,0.0000027188846],"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":[0.00016988114,0.00013376662,0.012704038,0.00003255171,0.000045583976,0.000008032223,0.038727555,0.9245986,0.0018229878,0.0015949927,0.00021706686,0.019944947],"study_design_scores_gemma":[0.00812582,0.0008815241,0.90100074,0.00047222993,0.00026428854,0.0000033660428,0.043303132,0.0033481854,0.001646638,0.0022576083,0.038055405,0.00064103876],"about_ca_topic_score_codex":0.00006855761,"about_ca_topic_score_gemma":0.00017670212,"teacher_disagreement_score":0.9212504,"about_ca_system_score_codex":0.00005449429,"about_ca_system_score_gemma":0.00010412706,"threshold_uncertainty_score":0.3475304},"labels":[],"label_agreement":null},{"id":"W1960068173","doi":"10.7202/705474ar","title":"La prévision en temps réel des charges de polluants dans un réseau d'assainissement urbain","year":2005,"lang":"fr","type":"article","venue":"Revue des sciences de l eau","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"École de Technologie Supérieure; Institut National de la Recherche Scientifique","funders":"","keywords":"Humanities; Physics; Philosophy","score_opus":0.06328974662904216,"score_gpt":0.35153661935732844,"score_spread":0.2882468727282863,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1960068173","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8797667,0.006701466,0.06878396,0.009475731,0.00060645666,0.00036819367,0.000066910274,0.00019734976,0.034033217],"genre_scores_gemma":[0.90434307,0.0030657414,0.08561794,0.00020842296,0.0004084435,0.000016345142,0.000012437885,0.000017655451,0.006309928],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.996832,0.00088876014,0.00040646692,0.0004773808,0.0005182779,0.00087708596],"domain_scores_gemma":[0.9987051,0.00049752573,0.00020154988,0.00015957498,0.000098099525,0.00033817583],"candidate_categories":["sts"],"consensus_categories":["sts"],"category_scores_codex":[0.0038384325,0.00022762862,0.00022680305,0.00015650364,0.0019837865,0.00027812223,0.0005895188,0.0002586859,0.0003177946],"category_scores_gemma":[0.00034420026,0.00023292126,0.00011363483,0.0012114564,0.002901367,0.0008430629,0.000022086986,0.00021409575,0.00008832628],"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":[0.000024171539,0.0004160933,0.20991348,0.0002557901,0.00004140289,0.000057738824,0.40948248,0.19186759,0.0014128237,0.11017598,0.00056645705,0.075785995],"study_design_scores_gemma":[0.001410207,0.0009130411,0.5311825,0.0033132306,0.00031439608,0.00022776802,0.07892812,0.2568925,0.0033774942,0.027827967,0.0938438,0.0017689139],"about_ca_topic_score_codex":0.005317682,"about_ca_topic_score_gemma":0.015672075,"teacher_disagreement_score":0.33055437,"about_ca_system_score_codex":0.00077556254,"about_ca_system_score_gemma":0.0006544881,"threshold_uncertainty_score":0.9998122},"labels":[],"label_agreement":null},{"id":"W1964778031","doi":"10.1002/atr.5670390303","title":"A reliability-based network design problem","year":2005,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":81,"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":"Reliability (semiconductor); Network planning and design; Computer science; Maximization; Flow network; Mathematical optimization; Monte Carlo method; Measure (data warehouse); Traffic flow (computer networking); Reliability engineering; Mathematics; Data mining; Statistics; Engineering","score_opus":0.014486467100245613,"score_gpt":0.2782703891356487,"score_spread":0.26378392203540313,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1964778031","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09835903,0.00022806258,0.898116,0.0019588456,0.0003800271,0.00033614846,0.000005257197,0.000080675825,0.00053597376],"genre_scores_gemma":[0.5840223,0.00007149515,0.4154462,0.00012422854,0.0002467691,0.0000037568145,0.000014793762,0.000007840919,0.00006267181],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99863833,0.000116033465,0.0005374876,0.000104505234,0.0004139733,0.0001896474],"domain_scores_gemma":[0.9987883,0.00014983141,0.00045146188,0.00006311619,0.00044113657,0.000106182706],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009478811,0.000084132625,0.00015451213,0.0000848918,0.00021301932,0.000023728182,0.00010858127,0.00007454528,0.00006086294],"category_scores_gemma":[0.000039525257,0.00008087973,0.00009317039,0.00034747215,0.000058352016,0.0007026146,1.6204332e-7,0.00014872712,0.0000037205987],"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":[0.00021224289,0.000054622007,0.0016725785,0.00000913309,0.0000074393633,0.0000046205914,0.006317142,0.9828134,0.00011435662,0.0012571048,0.00027119817,0.007266151],"study_design_scores_gemma":[0.012676271,0.0017554958,0.42567936,0.0012387667,0.0006712382,0.000006771058,0.010479879,0.010685696,0.00263116,0.02021933,0.51240486,0.0015511847],"about_ca_topic_score_codex":0.000015094183,"about_ca_topic_score_gemma":0.0002899708,"teacher_disagreement_score":0.97212774,"about_ca_system_score_codex":0.000076043325,"about_ca_system_score_gemma":0.00029811793,"threshold_uncertainty_score":0.32981795},"labels":[],"label_agreement":null},{"id":"W1965567461","doi":"10.1016/j.retrec.2008.05.024","title":"Public transport policy in Canada and the United States: Developing political commitment from the federal government","year":2008,"lang":"en","type":"article","venue":"Research in Transportation Economics","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Government (linguistics); Politics; Context (archaeology); Public transport; Public administration; Investment (military); Public policy; Political science; Business; Economic policy; Economics; Economic growth; Law; Geography","score_opus":0.11407607957280465,"score_gpt":0.32722906626345677,"score_spread":0.21315298669065214,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1965567461","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9389708,0.000039749124,0.0002742992,0.05949185,0.000054689393,0.00039846668,0.00017892002,0.0000100452935,0.0005811576],"genre_scores_gemma":[0.9959162,0.0021967904,0.00017372236,0.0010988665,0.00004966893,0.000059909085,0.00045747028,0.000009556542,0.000037828686],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99804133,0.00037823868,0.0004332169,0.00020378552,0.0004332628,0.0005101419],"domain_scores_gemma":[0.99868214,0.0009454743,0.000056795216,0.00011336117,0.00006708164,0.00013515729],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013739187,0.00009221023,0.0001514419,0.0001037769,0.00061269215,0.000051147097,0.00022189812,0.00005408293,0.000020104839],"category_scores_gemma":[0.000062664934,0.00007193569,0.000023337523,0.00054026284,0.0004890312,0.00018288322,0.0000024666087,0.00029814237,9.621942e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007995208,0.000018757653,0.73538554,0.0000050190965,0.000012796472,0.000013828062,0.019811403,0.011912176,7.312683e-8,0.23244803,0.00004807504,0.000264347],"study_design_scores_gemma":[0.0013785715,0.0000065967347,0.96013635,0.000022264576,0.0000024551289,3.2639355e-7,0.022908162,0.0024217428,0.0000034070117,0.003906379,0.009111137,0.00010262619],"about_ca_topic_score_codex":0.98744935,"about_ca_topic_score_gemma":0.9975316,"teacher_disagreement_score":0.22854164,"about_ca_system_score_codex":0.0017233035,"about_ca_system_score_gemma":0.003450117,"threshold_uncertainty_score":0.612036},"labels":[],"label_agreement":null},{"id":"W1965675711","doi":"10.5399/osu/jtrf.49.3.2576","title":"Empirical Examination of Passing Lane Operational Benefits on Rural Two-Lane Highways","year":2012,"lang":"en","type":"article","venue":"Journal of the Transportation Research Forum","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Montana State University","keywords":"Transport engineering; Upstream (networking); Empirical research; Rural area; Operational effectiveness; Computer science; Geography; Business; Engineering; Operations management; Telecommunications; Statistics; Mathematics","score_opus":0.10030245119111066,"score_gpt":0.4072610325812567,"score_spread":0.30695858139014603,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1965675711","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9886926,0.00013953818,0.0015739626,0.007237275,0.0005292719,0.0002083276,0.000041043066,0.000019352094,0.001558663],"genre_scores_gemma":[0.99826264,0.00006836522,0.0009925008,0.00010292881,0.0002691197,0.0000038980106,0.00004131495,0.000010035824,0.0002491696],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99703974,0.0004886446,0.0005064997,0.00007413502,0.0015566333,0.0003343674],"domain_scores_gemma":[0.99826,0.0003903111,0.00030967747,0.00009660639,0.0007958842,0.00014748363],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029259298,0.00008081264,0.00015124811,0.00025147756,0.0005910801,0.00004531066,0.00025260277,0.000085171436,0.00010281402],"category_scores_gemma":[0.00020098186,0.000058461683,0.000109351255,0.00053267315,0.00018352745,0.0005748812,0.000002460256,0.00035903996,0.0000057123],"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":[0.000420491,0.0006820828,0.73710287,0.000048758855,0.00009259825,0.000005776822,0.04505755,0.05222211,0.00112494,0.1459135,0.006847778,0.010481565],"study_design_scores_gemma":[0.00068539265,0.00014165569,0.98875517,0.00014535464,0.000024911886,0.0000012452424,0.005596845,0.00017939587,0.00096894614,0.00029374787,0.003124591,0.00008276723],"about_ca_topic_score_codex":0.0002671633,"about_ca_topic_score_gemma":0.0009072775,"teacher_disagreement_score":0.2516523,"about_ca_system_score_codex":0.00010124315,"about_ca_system_score_gemma":0.00021018863,"threshold_uncertainty_score":0.45461702},"labels":[],"label_agreement":null},{"id":"W1965973529","doi":"10.1002/atr.5670420106","title":"ITS applications for work zones","year":2008,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Software deployment; Dissemination; Work (physics); Intelligent transportation system; Investment (military); Transport engineering; Computer science; Traffic congestion; Variable (mathematics); Operations research; Risk analysis (engineering); Engineering; Business; Telecommunications; Software engineering","score_opus":0.02355816108996672,"score_gpt":0.30415302042929704,"score_spread":0.2805948593393303,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1965973529","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3633529,0.00066989625,0.63296545,0.0010544511,0.0005838706,0.00069508207,0.000034674555,0.00007233104,0.0005713646],"genre_scores_gemma":[0.93924546,0.000770004,0.059106942,0.000055329856,0.00026828,0.000036572703,0.000062160645,0.000010982151,0.00044424346],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99914724,0.000019317946,0.00037806472,0.000080247,0.0002542188,0.00012090735],"domain_scores_gemma":[0.9988452,0.00013176519,0.0003456489,0.000042856555,0.0005552367,0.000079261605],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020300093,0.000061696184,0.00012406606,0.00009672631,0.00035789845,0.000009118133,0.0000910955,0.000055496086,0.000021392234],"category_scores_gemma":[0.000046561418,0.000062432664,0.00009511026,0.00032250388,0.00005092181,0.00046803337,1.4339375e-7,0.0000716779,0.0000026238372],"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":[0.00096212374,0.00036713053,0.032335453,0.000100676465,0.000102372556,0.000027104024,0.09778192,0.79591745,0.00083113275,0.042382244,0.0014115749,0.02778079],"study_design_scores_gemma":[0.0033320705,0.000286867,0.5831189,0.00015446737,0.00018792474,0.00000782979,0.008638521,0.000031052954,0.0008370684,0.0036020053,0.3993629,0.0004404023],"about_ca_topic_score_codex":0.0000044128346,"about_ca_topic_score_gemma":0.0000642662,"teacher_disagreement_score":0.7958864,"about_ca_system_score_codex":0.00002748676,"about_ca_system_score_gemma":0.00013886773,"threshold_uncertainty_score":0.27527016},"labels":[],"label_agreement":null},{"id":"W1967000285","doi":"10.1016/s0965-8564(01)00032-5","title":"Developing passenger car equivalents for heavy vehicles on freeways during queue discharge flow","year":2002,"lang":"en","type":"article","venue":"Transportation Research Part A Policy and Practice","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":113,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Merge (version control); Queue; Highway Capacity Manual; Transport engineering; Queueing theory; Traffic flow (computer networking); Computer science; Engineering; Mathematics; Statistics; Level of service","score_opus":0.22078160936672295,"score_gpt":0.4559848071914637,"score_spread":0.23520319782474078,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1967000285","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7471148,0.0008113562,0.014521155,0.20529346,0.00057563395,0.0036014472,0.00095527334,0.0005886369,0.026538236],"genre_scores_gemma":[0.9884298,0.0023179366,0.004808959,0.0006412383,0.00055073615,0.00019326038,0.00015874623,0.000027391909,0.002871972],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9976797,0.00048231002,0.00029342607,0.00031760705,0.0006881675,0.0005388083],"domain_scores_gemma":[0.9964819,0.0027097296,0.000099196775,0.00012116206,0.00038233673,0.00020569994],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0017448436,0.0001232975,0.00014230759,0.000250251,0.0015553916,0.00014600661,0.00012144819,0.00012638417,0.00010240419],"category_scores_gemma":[0.002011156,0.00012818648,0.0000464284,0.0005529962,0.00016972669,0.0008124679,0.000002213091,0.0002642975,0.000031774747],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0022046508,0.00062190084,0.0090959985,0.0006730092,0.00022352244,0.000059953545,0.22418249,0.008513257,0.00065017905,0.73091465,0.012551476,0.010308912],"study_design_scores_gemma":[0.0024686905,0.00027750543,0.04648136,0.00032476897,0.00007474895,0.0000022501342,0.013918056,0.0023129152,0.0010112775,0.00212075,0.93038696,0.0006207339],"about_ca_topic_score_codex":0.0023342052,"about_ca_topic_score_gemma":0.0021436685,"teacher_disagreement_score":0.9178355,"about_ca_system_score_codex":0.000090872745,"about_ca_system_score_gemma":0.00017611851,"threshold_uncertainty_score":0.9997445},"labels":[],"label_agreement":null},{"id":"W1968515861","doi":"10.1080/19439962.2010.487636","title":"Managing Large-Scale Multimodal Emergency Evacuations","year":2010,"lang":"en","type":"article","venue":"Journal of Transportation Safety & Security","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":53,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto","funders":"University of Toronto","keywords":"Transport engineering; Scheduling (production processes); Transit (satellite); Computer science; Routing (electronic design automation); Vehicle routing problem; Poison control; Population; Public transport; Operations research; Engineering; Computer network; Operations management","score_opus":0.009640518955554025,"score_gpt":0.29559329409769697,"score_spread":0.28595277514214296,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1968515861","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8626974,0.00012788775,0.121594205,0.0058383616,0.0030761396,0.00028986327,0.0002484384,0.000110166235,0.006017546],"genre_scores_gemma":[0.9919382,0.0004991498,0.0068307132,0.00006460734,0.00034139672,0.0000025473926,0.00013438071,0.000013191013,0.0001758121],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99804986,0.00009317003,0.00082223356,0.00014479582,0.0006437845,0.0002461849],"domain_scores_gemma":[0.9983929,0.000069464164,0.00053704524,0.00011202051,0.00068723544,0.00020135407],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0013642879,0.00011625882,0.00019936942,0.00017604846,0.000581298,0.000031326897,0.00021598,0.00014021248,0.0012751616],"category_scores_gemma":[0.000071367256,0.000119410164,0.00019936438,0.00041897333,0.00006862519,0.00076266134,0.0000011285792,0.0004709513,0.000010932494],"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":[0.00060633704,0.0010642316,0.48190877,0.00010468653,0.00020876552,0.00006927925,0.3990888,0.029162481,0.0016891006,0.07560948,0.0033425614,0.0071454956],"study_design_scores_gemma":[0.0020834524,0.00007822817,0.8270489,0.00006171064,0.00025495992,0.0000048714383,0.016520241,0.0027676832,0.00018568292,0.009579536,0.14098004,0.0004346988],"about_ca_topic_score_codex":0.00020175267,"about_ca_topic_score_gemma":0.015481187,"teacher_disagreement_score":0.38256857,"about_ca_system_score_codex":0.00003169533,"about_ca_system_score_gemma":0.0002102612,"threshold_uncertainty_score":0.9996378},"labels":[],"label_agreement":null},{"id":"W1968766303","doi":"10.2495/sdp-v3-n1-1-15","title":"Urban road pricing and sustainable transportation systems: the Thessaloniki central area case","year":2008,"lang":"en","type":"article","venue":"International Journal of Sustainable Development and Planning","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Road pricing; Sustainable transport; Revenue; Transport engineering; Urban area; Order (exchange); Equity (law); Sustainable development; Environmental economics; Business; Transport network; Environmental planning; Regional science; Environmental resource management; Sustainability; Environmental science; Geography; Economics; Finance; Traffic congestion; Engineering; Economy","score_opus":0.017943897529391975,"score_gpt":0.254932702469307,"score_spread":0.23698880493991503,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1968766303","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9921581,0.0013115456,0.0046458244,0.0004468439,0.00026479387,0.00016112899,0.0000019266151,0.000019271976,0.0009905708],"genre_scores_gemma":[0.9966336,0.00020780515,0.0005593276,0.00005352953,0.0001826384,0.0000047587714,0.000015869377,0.0000074890672,0.0023350106],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.99866325,0.000072977615,0.00039011682,0.000107208296,0.00044117635,0.00032527416],"domain_scores_gemma":[0.9986051,0.00013290439,0.0002978529,0.00003221607,0.000826952,0.00010501953],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009345998,0.000101628975,0.0001323823,0.00021818014,0.0010787662,0.00019499782,0.00013465223,0.000059137543,0.000007675692],"category_scores_gemma":[0.00012368982,0.00007956839,0.000026001577,0.00016320944,0.00010327526,0.0006672516,0.00000816583,0.00015546869,1.8720579e-7],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021840111,0.000050261737,0.26338533,0.00008173764,0.00022938669,0.040756967,0.64017135,0.017618116,0.000004264216,0.03395069,0.002053682,0.0014798084],"study_design_scores_gemma":[0.0008186755,0.000036492733,0.17060028,0.00017348939,0.000041282252,0.0012409635,0.75691956,0.00053115224,0.000027993601,0.0001263471,0.06926682,0.0002169497],"about_ca_topic_score_codex":0.0009846473,"about_ca_topic_score_gemma":0.000036211575,"teacher_disagreement_score":0.11674819,"about_ca_system_score_codex":0.00014721826,"about_ca_system_score_gemma":0.00045944983,"threshold_uncertainty_score":0.82971066},"labels":[],"label_agreement":null},{"id":"W1969065232","doi":"10.3846/16484142.2014.1003324","title":"Modelling bus delay at bus stop","year":2015,"lang":"en","type":"article","venue":"Transport","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Queueing theory; Transfer (computing); Block (permutation group theory); Queuing delay; Computer science; Simulation; Real-time computing; Mathematics; Computer network","score_opus":0.06535190371015827,"score_gpt":0.2931136581688241,"score_spread":0.22776175445866587,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1969065232","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.77852565,0.0001795398,0.1602217,0.0006005248,0.00046307294,0.00019300969,0.000021715865,0.00036046052,0.05943435],"genre_scores_gemma":[0.98626906,0.00006838403,0.0039140093,0.00009022726,0.000101311605,0.000006648119,0.00011898847,0.000012839783,0.009418524],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9990069,0.000029615332,0.0001809657,0.00017208481,0.0003823979,0.00022802924],"domain_scores_gemma":[0.9995068,0.00001971151,0.000045835102,0.000097638454,0.000120854485,0.000209161],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038374448,0.000082794744,0.000106472544,0.000046932353,0.00025001593,0.000013068485,0.00011450076,0.00009457773,0.00013937576],"category_scores_gemma":[0.000006555628,0.00008648638,0.00004541755,0.00018780942,0.00006272863,0.00018008359,0.0000010735364,0.00007410476,0.000080777754],"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":[0.00005427887,0.000032376018,0.047057424,0.0000054298985,0.000010229369,0.000033204917,0.04298388,0.90082383,0.000004544824,0.008267552,0.0004881539,0.00023907603],"study_design_scores_gemma":[0.0033536288,0.00015536805,0.019891098,0.00010393967,0.00025221365,0.000008886029,0.012196468,0.061390456,0.00022660961,0.0033628293,0.8977168,0.0013416648],"about_ca_topic_score_codex":0.0032344973,"about_ca_topic_score_gemma":0.0020174265,"teacher_disagreement_score":0.89722866,"about_ca_system_score_codex":0.00007340276,"about_ca_system_score_gemma":0.00016680207,"threshold_uncertainty_score":0.4889614},"labels":[],"label_agreement":null},{"id":"W1969465931","doi":"10.1016/j.trb.2003.10.002","title":"Locating and pricing park-and-ride facilities in a linear monocentric city with deterministic mode choice","year":2004,"lang":"en","type":"article","venue":"Transportation Research Part B Methodological","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":166,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Park and ride; TRIPS architecture; Transport engineering; Mode (computer interface); Profit (economics); Service (business); Business; Microeconomics; Boundary (topology); Operations research; Computer science; Traffic congestion; Center (category theory); Economics; Public transport; Marketing; Mathematics; Engineering","score_opus":0.36820037193464555,"score_gpt":0.4901318088787902,"score_spread":0.12193143694414466,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1969465931","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9332665,0.00006937889,0.06516245,0.0005968355,0.000025934427,0.00037224992,0.000018303976,0.000072663606,0.00041567895],"genre_scores_gemma":[0.946742,0.0002518217,0.052679762,0.00003775921,0.000039091632,0.00006111384,0.00003556086,0.0000073383794,0.00014552825],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9974739,0.00087130425,0.00032983322,0.00036937091,0.00052766385,0.00042795268],"domain_scores_gemma":[0.99733394,0.0021723234,0.00006561495,0.000080306156,0.00017178128,0.00017605232],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034006883,0.00011442247,0.00022310004,0.00015393506,0.00048742027,0.000056405566,0.00009272337,0.00012967743,0.000042487354],"category_scores_gemma":[0.0012812557,0.000096755066,0.000020169788,0.000614715,0.00052539137,0.00021660457,0.0000031805334,0.00037498787,0.000002246921],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002917242,0.00009924048,0.8633982,0.00013619376,0.000013672218,0.00007205195,0.04621462,0.07030397,0.00006114085,0.015227943,0.0000058176574,0.0041754413],"study_design_scores_gemma":[0.0013703172,0.00020109923,0.9787467,0.00014824375,0.000021448232,0.000001908822,0.011139532,0.0022385581,0.00013180512,0.0046135024,0.0011217896,0.00026507306],"about_ca_topic_score_codex":0.0074228453,"about_ca_topic_score_gemma":0.012413878,"teacher_disagreement_score":0.11534853,"about_ca_system_score_codex":0.000057010362,"about_ca_system_score_gemma":0.00018378621,"threshold_uncertainty_score":0.9991868},"labels":[],"label_agreement":null},{"id":"W1969948224","doi":"10.1007/s10109-014-0203-1","title":"Revisiting the destination ranking procedure in development of an Intervening Opportunities Model for public transit trip distribution","year":2014,"lang":"en","type":"article","venue":"Journal of Geographical Systems","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Ranking (information retrieval); Public transport; Transit (satellite); Transport engineering; Distribution (mathematics); Regional science; Geography; Trip distribution; Computer science; Engineering; Mathematics; Information retrieval","score_opus":0.07818250393637544,"score_gpt":0.3044256836619658,"score_spread":0.22624317972559038,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1969948224","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.47657597,0.00008337488,0.5222905,0.00075572997,0.00007538286,0.00015698967,0.00000345848,0.000009323059,0.000049218077],"genre_scores_gemma":[0.9979668,0.0000136550825,0.0018236842,0.00001022232,0.00012990953,0.000010839464,0.00003118105,0.0000045897787,0.000009108914],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984867,0.00025004317,0.0007047546,0.000070396156,0.00035168178,0.00013642597],"domain_scores_gemma":[0.9986961,0.00018082737,0.00056070247,0.000037933085,0.0004621306,0.00006229497],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0049659987,0.00005897828,0.00017528531,0.00014933218,0.0002851958,0.00008561307,0.00014152248,0.00007302861,9.3141836e-7],"category_scores_gemma":[0.0004094843,0.000044109653,0.00007651324,0.0002844805,0.000056720142,0.00034297814,0.0000019507825,0.00011827918,2.1543922e-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":[0.00042897876,0.00026846357,0.1264721,0.0012055495,0.00014292276,0.000004829306,0.0910435,0.51504695,0.00027661351,0.11341046,0.000043004635,0.1516566],"study_design_scores_gemma":[0.0015727148,0.00013683896,0.052187126,0.001893185,0.00007459063,0.000012671459,0.020252604,0.9202297,0.000033475728,0.0011264547,0.0022466816,0.00023395813],"about_ca_topic_score_codex":0.000031807525,"about_ca_topic_score_gemma":0.0001877273,"teacher_disagreement_score":0.52139086,"about_ca_system_score_codex":0.000037531066,"about_ca_system_score_gemma":0.00016524408,"threshold_uncertainty_score":0.21935244},"labels":[],"label_agreement":null},{"id":"W1970112009","doi":"10.3141/2230-01","title":"Investigating the Role of Social Networks in Start Time and Duration of Activities","year":2011,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Endogeneity; Duration (music); Relevance (law); Travel behavior; Context (archaeology); Econometrics; Scheduling (production processes); Econometric model; Time allocation; Computer science; Psychology; Economics; Microeconomics; Operations management; Geography; Political science","score_opus":0.0801667180968357,"score_gpt":0.3565217242938685,"score_spread":0.2763550061970328,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1970112009","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99698657,0.00017428707,0.0003744245,0.0009603265,0.0001013666,0.0005474457,0.000030617794,0.0000106252055,0.0008143158],"genre_scores_gemma":[0.9981749,0.00044625238,0.0010810038,0.000013489403,0.0000686189,0.00001976583,0.000013071091,0.000018951192,0.00016393673],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99455506,0.0016246554,0.0012044043,0.00018927462,0.0019854032,0.00044121535],"domain_scores_gemma":[0.9965313,0.0009363142,0.0006997638,0.00015438731,0.0015507765,0.00012742926],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0062114336,0.00012675676,0.00031987834,0.00056964316,0.00066954375,0.00004261071,0.00051220035,0.00016884365,0.00016257066],"category_scores_gemma":[0.00021108336,0.00009429044,0.0001461058,0.0016551426,0.0015654552,0.000633148,0.000003469699,0.0010008496,8.8087626e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007591409,0.00020708842,0.7797532,0.000103355575,0.00008976115,0.0000072871358,0.18199062,0.0036932183,0.0034017083,0.02381909,0.00029987987,0.005875656],"study_design_scores_gemma":[0.0005872481,0.00019489629,0.96145767,0.00020229667,0.00003085472,8.28874e-8,0.030089533,0.0006708748,0.0015580351,0.0042329314,0.0008796912,0.00009588674],"about_ca_topic_score_codex":0.023495365,"about_ca_topic_score_gemma":0.07711475,"teacher_disagreement_score":0.18170448,"about_ca_system_score_codex":0.00007484678,"about_ca_system_score_gemma":0.0005464126,"threshold_uncertainty_score":0.98300725},"labels":[],"label_agreement":null},{"id":"W1970361744","doi":"10.3141/1887-11","title":"Using Geographic Information Systems to Inform Bus Maintenance Base Location Decisions: Sketch-Level Methodology for Estimating Bus Deadhead Costs","year":2004,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Relocation; Geographic information system; Transport engineering; Operations research; Computer science; Process (computing); Shortest path problem; Sketch; Cost estimate; Total cost; Engineering; Business; Geography","score_opus":0.322530903050626,"score_gpt":0.4741630851913395,"score_spread":0.1516321821407135,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1970361744","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.47027954,0.000084222665,0.52222735,0.003781136,0.0010409723,0.0022864619,0.00016188789,0.000050330327,0.00008808134],"genre_scores_gemma":[0.76388115,0.00023221137,0.2350172,0.00014197177,0.0002282638,0.00023354268,0.00010172224,0.000042997985,0.00012093919],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9908457,0.0014360889,0.0022046473,0.00040342024,0.0039081858,0.0012019704],"domain_scores_gemma":[0.98236656,0.0034327116,0.00091917097,0.00041612907,0.012225373,0.0006400421],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.015144951,0.0002997691,0.00054566376,0.0022240581,0.0024780761,0.0004067146,0.0010602162,0.00035881635,0.000048841153],"category_scores_gemma":[0.004618199,0.00025782085,0.00032580292,0.005172444,0.00059143687,0.0022662731,0.0000062389045,0.001257662,0.000022579887],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002237907,0.00023966344,0.035226643,0.00042768297,0.00017981672,0.000029970452,0.030823877,0.8025936,0.0006496292,0.11032846,0.0017312055,0.015531538],"study_design_scores_gemma":[0.010854206,0.0022529531,0.8004405,0.008367647,0.0003848842,0.000004585249,0.082600184,0.020254469,0.0013734802,0.026786948,0.045069788,0.0016103705],"about_ca_topic_score_codex":0.05045765,"about_ca_topic_score_gemma":0.0770806,"teacher_disagreement_score":0.78233916,"about_ca_system_score_codex":0.0010397141,"about_ca_system_score_gemma":0.0024191628,"threshold_uncertainty_score":0.9999874},"labels":[],"label_agreement":null},{"id":"W1970740637","doi":"10.1016/s0094-1190(02)00002-5","title":"Private roads, competition, and incentives to adopt time-based congestion tolling","year":2002,"lang":"en","type":"article","venue":"Journal of Urban Economics","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":55,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Toll; Congestion pricing; Toll road; Microeconomics; Duopoly; Incentive; Price elasticity of demand; Competition (biology); Economics; Business; Road pricing; Revenue; Traffic congestion; Dilemma; Industrial organization; Finance; Cournot competition; Transport engineering","score_opus":0.01546114126786572,"score_gpt":0.22438736282700267,"score_spread":0.20892622155913695,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1970740637","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9907585,0.00011720967,0.0047262865,0.0032859882,0.00017210649,0.00006703019,0.000006483316,0.00001350961,0.00085292314],"genre_scores_gemma":[0.9918954,0.00024914942,0.0071766092,0.00024027619,0.0001498875,5.0648777e-7,0.0000039929478,0.0000051998127,0.00027899924],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99951434,0.000040692732,0.00023795608,0.000063344414,0.000057964284,0.000085727115],"domain_scores_gemma":[0.9994747,0.00005596223,0.00024643945,0.000032148928,0.000091644695,0.00009909326],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030318537,0.000046700083,0.00011144815,0.000095592215,0.00013858033,0.00007130048,0.00006104203,0.00003068291,0.000077040175],"category_scores_gemma":[0.000055535736,0.000050615305,0.000027130343,0.00007104738,0.000058362937,0.00023983537,0.0000019795787,0.000059730468,0.000012556958],"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":[0.0002491424,0.00025020045,0.37454274,0.000028650817,0.00010056126,0.000014222187,0.029045196,0.52745765,0.00015937042,0.044487126,0.009191034,0.01447408],"study_design_scores_gemma":[0.0041410774,0.00085056527,0.38030836,0.0005369369,0.00018476807,0.000016133325,0.0039780336,0.09663059,0.00056878035,0.0014676091,0.51036054,0.0009565905],"about_ca_topic_score_codex":0.0000126842615,"about_ca_topic_score_gemma":0.000044812627,"teacher_disagreement_score":0.5011695,"about_ca_system_score_codex":0.000057188718,"about_ca_system_score_gemma":0.00003018605,"threshold_uncertainty_score":0.20640321},"labels":[],"label_agreement":null},{"id":"W1971870243","doi":"10.1016/j.ejor.2012.09.016","title":"A pro-active real-time control approach for dynamic vehicle routing problems dealing with the delivery of urgent goods","year":2012,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":98,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Vehicle routing problem; Tabu search; Control (management); Routing (electronic design automation); Process (computing); Quality (philosophy); Operations research; Order (exchange); Diversity (politics); Point (geometry); Mathematical optimization; Artificial intelligence; Engineering; Computer network; Mathematics","score_opus":0.06960539709992418,"score_gpt":0.3517991659851844,"score_spread":0.2821937688852602,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1971870243","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8413178,0.0003144932,0.12688984,0.0034402716,0.00008356587,0.001434198,0.000048083613,0.000020770383,0.026451036],"genre_scores_gemma":[0.9909436,0.00004519608,0.008479281,0.000022939696,0.00017654937,0.000007593125,0.000014527545,0.000013797627,0.00029648544],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977129,0.00092097744,0.00028696138,0.00007850642,0.00076137163,0.00023928485],"domain_scores_gemma":[0.9980619,0.0004205253,0.00019549351,0.00005442458,0.0011440892,0.00012355405],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008025107,0.00005618311,0.00010616432,0.00008596058,0.0006320467,0.0000688716,0.00018359625,0.00001683126,0.00003495753],"category_scores_gemma":[0.00029844642,0.00003647003,0.00004667905,0.00019359993,0.00016253424,0.00033541975,0.000007013484,0.00019557824,0.0000023356745],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002665511,0.0008021322,0.023587104,0.000099567995,0.00052703265,0.0000146390375,0.12608129,0.767014,0.028875519,0.03849566,0.0011441009,0.010693407],"study_design_scores_gemma":[0.015592292,0.0056549804,0.4062615,0.001163607,0.0004298984,0.00008512033,0.089597136,0.45496276,0.002470003,0.00020027188,0.02230966,0.0012727837],"about_ca_topic_score_codex":0.000044517816,"about_ca_topic_score_gemma":0.000008938808,"teacher_disagreement_score":0.3826744,"about_ca_system_score_codex":0.000064548374,"about_ca_system_score_gemma":0.0003754845,"threshold_uncertainty_score":0.48612565},"labels":[],"label_agreement":null},{"id":"W1972361483","doi":"10.1016/j.trb.2014.11.005","title":"Link-based day-to-day network traffic dynamics and equilibria","year":2014,"lang":"en","type":"article","venue":"Transportation Research Part B Methodological","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":91,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China; Natural Science Foundation of Inner Mongolia; University of Windsor; National Key Research and Development Program of China; University of Minnesota","keywords":"Link (geometry); Equivalence (formal languages); Uniqueness; Traffic flow (computer networking); Stationary state; Extension (predicate logic); State (computer science); Stability (learning theory); Computer science; Applied mathematics; Mathematical optimization; Mathematics; Physics; Mathematical analysis; Algorithm","score_opus":0.2742218187429174,"score_gpt":0.4680565967569427,"score_spread":0.19383477801402532,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1972361483","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.36661628,0.00005920913,0.6201775,0.009105398,0.0003408112,0.00072201656,0.000043040392,0.00034497594,0.0025907645],"genre_scores_gemma":[0.90951747,0.00009194821,0.08820592,0.0004399986,0.0005230443,0.000093250266,0.00041403645,0.000022713279,0.0006916337],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9919752,0.0054724314,0.00043033363,0.00051252113,0.00089312677,0.000716376],"domain_scores_gemma":[0.9915886,0.007365093,0.00007695795,0.00018816827,0.00031623035,0.00046498174],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01932411,0.00015329466,0.000296316,0.00017804682,0.00082309986,0.00011502205,0.0002499574,0.00028294866,0.00033440863],"category_scores_gemma":[0.0025451551,0.00014260944,0.00007362068,0.0009931143,0.00048118585,0.00014702891,0.0000039792594,0.00043069772,0.000034363493],"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":[0.0004254965,0.00014146326,0.06563961,0.00008408852,0.0000381038,0.00002586258,0.012008798,0.5902517,0.00011706659,0.24138747,0.00424162,0.085638724],"study_design_scores_gemma":[0.0014023894,0.0007247406,0.7252824,0.0001777967,0.00006647093,2.9686788e-7,0.0018301079,0.14028458,0.000060390532,0.007300853,0.12214297,0.0007270263],"about_ca_topic_score_codex":0.0001864086,"about_ca_topic_score_gemma":0.005756482,"teacher_disagreement_score":0.65964276,"about_ca_system_score_codex":0.00005218391,"about_ca_system_score_gemma":0.00019048691,"threshold_uncertainty_score":0.669739},"labels":[],"label_agreement":null},{"id":"W1972711377","doi":"10.1016/j.trc.2013.01.010","title":"Bounded-rationality based day-to-day evolution model for travel behavior analysis of urban railway network","year":2013,"lang":"en","type":"article","venue":"Transportation Research Part C Emerging Technologies","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":74,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"National Key Research and Development Program of China; Program for New Century Excellent Talents in University; State Key Laboratory of Rail Traffic Control and Safety; National Natural Science Foundation of China","keywords":"Bounded rationality; Path (computing); Computer science; Bounded function; Econometrics; Operations research; Mathematics; Artificial intelligence","score_opus":0.0871051156264171,"score_gpt":0.3868092807859839,"score_spread":0.2997041651595668,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1972711377","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21969709,0.00006603878,0.7739799,0.003285804,0.00010158732,0.0017571326,0.000304832,0.00059086975,0.000216722],"genre_scores_gemma":[0.9739976,0.000038544516,0.023001336,0.000028111128,0.000034921257,0.0014483273,0.0009476847,0.000017803928,0.00048567902],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9971102,0.00020798735,0.0006330211,0.00044444308,0.0009936168,0.0006107466],"domain_scores_gemma":[0.99749225,0.00060541835,0.00018730396,0.00032494075,0.0012846385,0.00010544047],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027703573,0.00015704408,0.00032096836,0.0009717888,0.00093454233,0.00007746123,0.0004080047,0.00025598938,0.00013779526],"category_scores_gemma":[0.0005748837,0.00016719951,0.00018885915,0.0035913228,0.00045820614,0.00039300136,0.0000046221894,0.00022230447,0.0000068319946],"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":[0.00003776508,0.00015406303,0.1422827,0.000043346066,0.00015971714,6.74578e-7,0.008004735,0.7303711,0.00039816217,0.11147294,0.0056896466,0.0013851363],"study_design_scores_gemma":[0.00054019736,0.000108256565,0.5311409,0.000081923055,0.00048487744,1.0077368e-8,0.010446938,0.4439636,0.0005007241,0.010217325,0.002106829,0.0004083872],"about_ca_topic_score_codex":0.0019050617,"about_ca_topic_score_gemma":0.012283087,"teacher_disagreement_score":0.7543005,"about_ca_system_score_codex":0.0001345006,"about_ca_system_score_gemma":0.0004212178,"threshold_uncertainty_score":0.71878386},"labels":[],"label_agreement":null},{"id":"W1973383868","doi":"10.1007/s10479-014-1725-0","title":"A general rapid network design, line planning and fleet investment integrated model","year":2014,"lang":"en","type":"article","venue":"Annals of Operations Research","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":50,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"Ministerio de Economía y Competitividad","keywords":"Network planning and design; Operations research; Computer science; Transportation planning; Theory of computation; Flow network; Profit (economics); Public transport; Integrated business planning; Strategic planning; Process (computing); Operational planning; Transport engineering; Mathematical optimization; Engineering; Economics","score_opus":0.3250954444193009,"score_gpt":0.46765132002324783,"score_spread":0.14255587560394695,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1973383868","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24134654,0.0010428728,0.73757577,0.007839502,0.000082697115,0.0006975891,0.000022807519,0.0000880463,0.011304182],"genre_scores_gemma":[0.92552125,0.0004450356,0.07105301,0.00053635944,0.00015433948,0.000043560292,0.00009733432,0.00001000994,0.0021390961],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998407,0.00057646824,0.00020504236,0.00015688936,0.0003811517,0.0002734669],"domain_scores_gemma":[0.9989209,0.00016467983,0.000020365624,0.00010479836,0.0006694928,0.00011975708],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003065678,0.00006132454,0.00010645445,0.00014729501,0.00076284265,0.00011139766,0.00011612216,0.000070848466,0.000040785322],"category_scores_gemma":[0.0004075908,0.000056611858,0.000018772073,0.00047204734,0.00022160994,0.00020312732,0.000012107765,0.00015001462,0.0000037939467],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021010512,0.000025307521,0.00045875067,0.0000035598239,0.00000982708,4.2720959e-7,0.0054376754,0.94943523,0.000117726144,0.038246784,0.0051410883,0.0011026365],"study_design_scores_gemma":[0.00015194243,0.00013089912,0.0006798216,0.00004253663,0.0000034204218,1.3038675e-7,0.00061817036,0.99132127,0.00037702944,0.0017322194,0.004865628,0.00007693858],"about_ca_topic_score_codex":0.00073702703,"about_ca_topic_score_gemma":0.00031456642,"teacher_disagreement_score":0.6841747,"about_ca_system_score_codex":0.000010356454,"about_ca_system_score_gemma":0.0002646543,"threshold_uncertainty_score":0.5867246},"labels":[],"label_agreement":null},{"id":"W1974140618","doi":"10.3141/2042-05","title":"Active Transit Signal Priority for Streetcars","year":2008,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":45,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Public transport; Transport engineering; Traffic congestion; Operations research; Transit (satellite); Computer science; Bus priority; Transportation planning; Engineering","score_opus":0.1345007327215228,"score_gpt":0.41807424562596085,"score_spread":0.28357351290443805,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1974140618","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9725452,0.00015898554,0.018207332,0.005443789,0.0007095056,0.0019336421,0.00024856825,0.00006705807,0.00068592094],"genre_scores_gemma":[0.9916986,0.0013975659,0.0046350807,0.000058095586,0.0004379355,0.00011226706,0.00005936697,0.000053893375,0.0015471755],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99104446,0.0016035194,0.0013179595,0.00045367295,0.0044788616,0.0011015265],"domain_scores_gemma":[0.990187,0.001933593,0.0005130597,0.0003217325,0.006504245,0.0005403969],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0052846223,0.00025069722,0.00048085747,0.00091342465,0.0030307667,0.00010775053,0.0011108636,0.0003205144,0.0003642385],"category_scores_gemma":[0.00030521763,0.00021208657,0.00058027916,0.0023800128,0.0015732975,0.0010593433,0.0000022838315,0.0017753177,0.000011632074],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.011671784,0.0015383331,0.6497578,0.00055361807,0.0007544414,0.00038342157,0.1971344,0.029558806,0.0031174056,0.036876947,0.03577643,0.03287663],"study_design_scores_gemma":[0.0027644446,0.00067762507,0.8965383,0.00024189733,0.000092393515,6.1711614e-7,0.012969816,0.00017368492,0.0013676474,0.003590461,0.081261806,0.0003213006],"about_ca_topic_score_codex":0.016689735,"about_ca_topic_score_gemma":0.104458265,"teacher_disagreement_score":0.24678051,"about_ca_system_score_codex":0.00034878243,"about_ca_system_score_gemma":0.0023533895,"threshold_uncertainty_score":0.9982672},"labels":[],"label_agreement":null},{"id":"W1974241804","doi":"10.4028/www.scientific.net/amm.488-489.1439","title":"Research on Hyperpaths in the Urban Rail Transit Network","year":2014,"lang":"en","type":"article","venue":"Applied Mechanics and Materials","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ministry of Transportation of Ontario","funders":"","keywords":"Urban rail transit; Convergence (economics); Path (computing); Shortest path problem; Computer science; Algorithm; Beijing; Line (geometry); Mathematical optimization; Scale (ratio); Mathematics; Transport engineering; Engineering; Theoretical computer science; Computer network; Geography; Graph","score_opus":0.03919311803647274,"score_gpt":0.31291123945438337,"score_spread":0.27371812141791063,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1974241804","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.81988984,0.0000970104,0.08005197,0.003995799,0.0012703919,0.0018408903,0.000032436415,0.00017681048,0.09264488],"genre_scores_gemma":[0.9984596,0.00009168496,0.0006364497,0.00047348064,0.00022804517,0.00003898666,0.000016198712,0.00000609144,0.00004946593],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99908215,0.00023463658,0.00011612375,0.00013095306,0.0002309654,0.00020515603],"domain_scores_gemma":[0.9996615,0.0001771296,0.000024780344,0.000084848245,0.000021258222,0.000030465708],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037444113,0.00004944032,0.00008890178,0.000036803107,0.00033714014,0.00009385325,0.00010655937,0.000072767114,0.000036096128],"category_scores_gemma":[0.000017310907,0.00003730958,0.000007663853,0.00017068499,0.000021446675,0.0000240666,0.000003553425,0.000069198475,0.000011993446],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028366641,0.000011101706,0.0000030004871,0.000004241496,0.0000012010322,4.5051056e-7,0.0064247,0.00021072692,0.0006104929,0.9915823,0.00054918305,0.00057425525],"study_design_scores_gemma":[0.0017846285,0.00027626933,0.0014994047,0.00011476047,0.000031820382,8.397709e-7,0.030092552,0.00033844472,0.0030246747,0.7190228,0.2433182,0.0004955663],"about_ca_topic_score_codex":0.00020484262,"about_ca_topic_score_gemma":0.00029346126,"teacher_disagreement_score":0.27255943,"about_ca_system_score_codex":0.0000076137585,"about_ca_system_score_gemma":0.000019640434,"threshold_uncertainty_score":0.25930434},"labels":[],"label_agreement":null},{"id":"W1975005734","doi":"10.3141/1722-02","title":"Parameter Estimation Strategies for Large-Scale Urban Models","year":2000,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Scale (ratio); Estimation; Process (computing); Bayesian probability; Estimation theory; Piecewise; Calibration; Function (biology); Mathematical optimization; Data mining; Econometrics; Algorithm; Mathematics; Statistics; Artificial intelligence; Engineering","score_opus":0.10126834373809768,"score_gpt":0.40794419725665504,"score_spread":0.30667585351855736,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1975005734","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8771307,0.00021134473,0.113516845,0.0044803456,0.0005458503,0.0019803885,0.00023318155,0.00009226402,0.0018090633],"genre_scores_gemma":[0.97536755,0.000930866,0.0196774,0.000088054294,0.00029929815,0.00016717816,0.00010031618,0.00005715043,0.0033121963],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9923855,0.0012980885,0.0013265241,0.00042644842,0.0034655987,0.0010978307],"domain_scores_gemma":[0.9937484,0.0015016657,0.00037644056,0.0003643394,0.0036001499,0.00040898728],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0065051783,0.00023596286,0.00040688895,0.0007013597,0.001996995,0.00038194895,0.00095496577,0.00029536602,0.0008865156],"category_scores_gemma":[0.00015884858,0.00019687673,0.0004353882,0.0018980193,0.00062196975,0.002070107,0.0000016965977,0.0012518931,0.000022746537],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.005548172,0.0011772912,0.07735826,0.0005340262,0.00035267486,0.00005814747,0.19444631,0.46280923,0.00030467426,0.17047599,0.04846657,0.038468637],"study_design_scores_gemma":[0.007104504,0.0015181495,0.45757988,0.0010018917,0.00029841036,5.896929e-7,0.07068481,0.043192618,0.00035734026,0.18002082,0.23723699,0.0010039969],"about_ca_topic_score_codex":0.008298684,"about_ca_topic_score_gemma":0.07606959,"teacher_disagreement_score":0.4196166,"about_ca_system_score_codex":0.00021805173,"about_ca_system_score_gemma":0.0013656545,"threshold_uncertainty_score":0.99930227},"labels":[],"label_agreement":null},{"id":"W1975106191","doi":"10.1007/s11769-000-0040-7","title":"Some measures of increasing rail transit riderships: Case studies","year":2000,"lang":"en","type":"article","venue":"Chinese Geographical Science","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Public transport; Transport engineering; Revenue; Business; Transit (satellite); Service quality; Government (linguistics); Transit system; Service (business); Transportation planning; Rail transit; Bus rapid transit; Profit (economics); Quality (philosophy); Finance; Engineering; Marketing; Economics","score_opus":0.04096690692038538,"score_gpt":0.33011310934829613,"score_spread":0.2891462024279107,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1975106191","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99603635,0.00088947767,0.000115776005,0.0011882494,0.0001302299,0.000116739255,0.000006138182,0.00009362655,0.0014234119],"genre_scores_gemma":[0.9983559,0.00061374187,0.0007578348,0.000114562084,0.00009841444,0.0000046623554,0.0000019276156,0.0000043501555,0.000048560185],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99823666,0.00015986137,0.00025962573,0.00028765254,0.0007305056,0.00032572533],"domain_scores_gemma":[0.99911034,0.0002626423,0.00006132195,0.00013649148,0.00023932966,0.00018986588],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0023980346,0.00010751673,0.00018823324,0.00021721802,0.0011278802,0.00006300606,0.0002687141,0.0000621414,0.000087164764],"category_scores_gemma":[0.00070066197,0.00008365071,0.00008382801,0.0024861582,0.002731452,0.0006448594,0.0000061382766,0.000115791656,0.0000041803755],"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":[0.00038224304,0.00048852427,0.6652122,0.00012668007,0.00013380175,0.00060773536,0.18740779,0.024880093,0.002163796,0.0610929,0.00010244228,0.05740178],"study_design_scores_gemma":[0.0027484423,0.00034039665,0.8220491,0.00045528548,0.00025581606,0.0004236439,0.033843692,0.0030747517,0.00053269655,0.13126431,0.0032285922,0.0017832706],"about_ca_topic_score_codex":0.0020727892,"about_ca_topic_score_gemma":0.0015888192,"teacher_disagreement_score":0.15683688,"about_ca_system_score_codex":0.000019264617,"about_ca_system_score_gemma":0.00013601892,"threshold_uncertainty_score":0.99998254},"labels":[],"label_agreement":null},{"id":"W1975840612","doi":"10.1002/atr.5670430207","title":"Multiple criteria optimization method for the vehicle assignment problem in a bus transportation company","year":2009,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Sample (material); Pareto principle; Metaheuristic; Memetic algorithm; Mathematical optimization; Computer science; Operations research; Process (computing); Decision maker; Transportation theory; Multi-objective optimization; Local search (optimization); Engineering; Mathematics","score_opus":0.020021426178307246,"score_gpt":0.3312920350591256,"score_spread":0.3112706088808183,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1975840612","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.048606493,0.00020032366,0.94703376,0.0029293697,0.00032535582,0.0007705168,0.00003735757,0.000035933186,0.00006090531],"genre_scores_gemma":[0.7278723,0.0002173691,0.27149263,0.00014346515,0.0000929775,0.000022578071,0.00012100232,0.000010714697,0.000026925485],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9982345,0.00011048905,0.00082374463,0.00016434265,0.00043773223,0.00022915385],"domain_scores_gemma":[0.99844575,0.00038029268,0.0005854071,0.000078307945,0.00042993532,0.00008031525],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010077435,0.0001317896,0.00023804487,0.00016762252,0.00028112726,0.000049007776,0.00016008246,0.00009656454,0.000026714375],"category_scores_gemma":[0.00006750361,0.0001121873,0.00013150375,0.0004442058,0.00003898856,0.00080199697,1.6162576e-7,0.0001606731,3.3081255e-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":[0.00048113763,0.000121354205,0.0024072966,0.000015807871,0.000015638112,0.0000045947477,0.022704532,0.9586091,0.0011341013,0.0013860163,0.000053518645,0.013066889],"study_design_scores_gemma":[0.008448328,0.0007844723,0.8654157,0.0003627181,0.00031695602,0.000001998654,0.01893395,0.09207794,0.001084133,0.003950435,0.008119191,0.0005042063],"about_ca_topic_score_codex":0.000115441486,"about_ca_topic_score_gemma":0.0014862914,"teacher_disagreement_score":0.8665312,"about_ca_system_score_codex":0.00010063702,"about_ca_system_score_gemma":0.00013700561,"threshold_uncertainty_score":0.45748654},"labels":[],"label_agreement":null},{"id":"W1975864868","doi":"10.1080/15472450.2012.710158","title":"Perspectives on Future Transportation Research: Impact of Intelligent Transportation System Technologies on Next-Generation Transportation Modeling","year":2012,"lang":"en","type":"article","venue":"Journal of Intelligent Transportation Systems","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":88,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Intelligent transportation system; Advanced Traffic Management System; Transport engineering; Emerging technologies; Transportation planning; Computer science; Engineering; Risk analysis (engineering); Systems engineering; Engineering management; Business","score_opus":0.16328600893126,"score_gpt":0.38094789020249126,"score_spread":0.21766188127123126,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1975864868","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.91568524,0.0027905004,0.076869406,0.00030383715,0.0021561668,0.0012708892,0.00045366937,0.00025174257,0.00021858183],"genre_scores_gemma":[0.9940561,0.0028630972,0.00086188316,0.000010734264,0.0011050517,0.000068491805,0.00089723634,0.00007942631,0.000057948153],"study_design_codex":"simulation_or_modeling","study_design_gemma":"qualitative","domain_scores_codex":[0.99251294,0.00060665695,0.0028744703,0.0005224255,0.0027045605,0.00077892176],"domain_scores_gemma":[0.99423724,0.00033129333,0.0017434058,0.00035941158,0.0029734063,0.00035522663],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0035218152,0.00053226994,0.0008893543,0.0016974595,0.00061971566,0.00015406369,0.0004877335,0.00063472724,0.000060431856],"category_scores_gemma":[0.00010061115,0.00047110734,0.00075443706,0.0016625844,0.0002446622,0.0017446782,5.280002e-7,0.0009180024,0.00001788137],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011665544,0.00084238243,0.016745767,0.00037350156,0.00039402052,0.000026747754,0.18982244,0.7032349,0.0023334015,0.08221026,0.0003787963,0.0024712544],"study_design_scores_gemma":[0.0019163284,0.0028247356,0.048592146,0.0028670542,0.0007051973,0.000006155049,0.916694,0.014579138,0.007582551,0.00017240802,0.0028056789,0.001254635],"about_ca_topic_score_codex":0.0020038509,"about_ca_topic_score_gemma":0.0015979637,"teacher_disagreement_score":0.72687155,"about_ca_system_score_codex":0.0009693287,"about_ca_system_score_gemma":0.000487783,"threshold_uncertainty_score":0.99977404},"labels":[],"label_agreement":null},{"id":"W1976465188","doi":"10.1002/atr.5670370102","title":"A system dynamics approach to land use/transportation system performance modeling Part I: Methodology","year":2003,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":62,"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":"Causal loop diagram; System dynamics; Land use; Population; Robustness (evolution); Computer science; Population growth; Econometrics; Operations research; Transport engineering; Engineering; Civil engineering; Economics","score_opus":0.05245851534272749,"score_gpt":0.29682518383815937,"score_spread":0.24436666849543187,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1976465188","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.48511153,0.000045748155,0.513276,0.0000324596,0.00062459474,0.00022725835,0.0000273088,0.0000699951,0.0005851363],"genre_scores_gemma":[0.8663548,0.000086082844,0.13319445,0.000024224522,0.00009426633,0.000015446669,0.00014701308,0.000023916446,0.00005980068],"study_design_codex":"simulation_or_modeling","study_design_gemma":"qualitative","domain_scores_codex":[0.9976337,0.0002802513,0.0009705064,0.00023456385,0.0005823954,0.00029858487],"domain_scores_gemma":[0.99828774,0.00012564458,0.0005668168,0.00011686718,0.00069592404,0.0002070035],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014107781,0.0001756729,0.00038171854,0.00027569136,0.00034061272,0.000053278294,0.00014439545,0.00016134763,0.0000038517555],"category_scores_gemma":[0.00007708757,0.00017558443,0.00012578875,0.00056267146,0.000035982703,0.0010610188,2.65022e-7,0.00022139176,0.0000025301601],"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":[0.00017157073,0.0000386296,0.018051228,0.00017708328,0.000031793665,0.000009178136,0.012093826,0.9230823,0.000032815286,0.04578801,0.000007040759,0.00051656313],"study_design_scores_gemma":[0.009930758,0.0011567336,0.29549778,0.004020231,0.0018312293,0.000114273396,0.3458525,0.32532364,0.00067622465,0.00041374506,0.012500777,0.0026820933],"about_ca_topic_score_codex":0.00014992195,"about_ca_topic_score_gemma":0.00069719716,"teacher_disagreement_score":0.5977586,"about_ca_system_score_codex":0.00028958634,"about_ca_system_score_gemma":0.0002058831,"threshold_uncertainty_score":0.71601254},"labels":[],"label_agreement":null},{"id":"W1976859829","doi":"10.3141/1883-20","title":"<i>Highway Capacity Manual</i> and Highway Capacity Software 2000 and Advanced Transportation Modeling Tools: Focus Group Findings","year":2004,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Ministry of Education, India; Ministry of Earth Sciences","keywords":"Highway Capacity Manual; Transport engineering; Focus group; Strengths and weaknesses; Agency (philosophy); Engineering; Software; Computer science; Operations research; Engineering management; Business; Level of service; Psychology; Marketing","score_opus":0.0868895014678727,"score_gpt":0.35207906843080855,"score_spread":0.26518956696293583,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1976859829","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9685033,0.0004582538,0.024930317,0.0037917604,0.00046307928,0.0012828055,0.0003069127,0.00012152151,0.00014205057],"genre_scores_gemma":[0.97823966,0.0032884965,0.017774964,0.00007616654,0.00017950009,0.00008516747,0.00010126105,0.0000724958,0.0001823026],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9913403,0.0011021565,0.0016670349,0.0007505492,0.003927928,0.0012120443],"domain_scores_gemma":[0.99402094,0.0010011256,0.00047906148,0.00036742963,0.0033771722,0.0007543002],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0057095657,0.00041421264,0.00063867273,0.0009974771,0.0024220145,0.00040544427,0.00075425976,0.00044394392,0.0001079363],"category_scores_gemma":[0.00043224808,0.0003659591,0.00027894316,0.0023682478,0.0014223427,0.0025409784,0.0000042671995,0.0022561373,0.000007246443],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0051396736,0.0012281048,0.36598772,0.001382965,0.0006022736,0.000336086,0.30085897,0.19250095,0.007387904,0.09068026,0.0010617883,0.032833315],"study_design_scores_gemma":[0.00645421,0.0009421005,0.94252735,0.0013396677,0.00022811323,0.000002108436,0.014126773,0.0004487782,0.0012823626,0.019724188,0.012039808,0.00088455825],"about_ca_topic_score_codex":0.039794248,"about_ca_topic_score_gemma":0.22630979,"teacher_disagreement_score":0.57653964,"about_ca_system_score_codex":0.0004814026,"about_ca_system_score_gemma":0.0009798396,"threshold_uncertainty_score":0.99987924},"labels":[],"label_agreement":null},{"id":"W1976909768","doi":"10.1139/l03-010","title":"Assessment of streetcar transit priority options using microsimulation modelling","year":2003,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Microsimulation; Headway; Transit (satellite); Transport engineering; Bus priority; Status quo; Service (business); Public transport; Computer science; Operations research; Engineering; Business; Economics","score_opus":0.0238855108364393,"score_gpt":0.26759496061451443,"score_spread":0.24370944977807513,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1976909768","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21154457,0.00020150053,0.78686196,0.000023332024,0.00024077776,0.000043544565,0.000006761792,0.0000056702215,0.0010719016],"genre_scores_gemma":[0.9741401,0.000022460788,0.025774978,0.000003542211,0.000038692015,1.7627453e-7,0.0000019136126,0.0000067760284,0.000011343711],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993913,0.00003278591,0.000247498,0.000046415702,0.00014134707,0.0001406421],"domain_scores_gemma":[0.99943733,0.000031797852,0.00010785075,0.000039125498,0.0001883355,0.00019553717],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042561916,0.00005101826,0.00010582614,0.000208143,0.00013619248,0.000027309574,0.000057182606,0.00005105231,0.00005693261],"category_scores_gemma":[0.00003889389,0.00006118477,0.000052252635,0.00021023533,0.00002273326,0.00020344039,2.4680884e-7,0.00010555881,7.436484e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[3.7555048e-7,0.0000030691292,0.006896309,0.000008723987,0.000009756268,0.0000032104304,0.0017837241,0.9839913,0.00030252687,0.0069817067,0.0000032613439,0.000016016746],"study_design_scores_gemma":[0.00044276332,0.000028979472,0.01575669,0.00025647294,0.00008648603,0.000007470836,0.0012558751,0.9772927,0.00018742903,0.00022452754,0.004256629,0.00020393883],"about_ca_topic_score_codex":0.0025437663,"about_ca_topic_score_gemma":0.062297624,"teacher_disagreement_score":0.76259553,"about_ca_system_score_codex":0.00014520778,"about_ca_system_score_gemma":0.0010716696,"threshold_uncertainty_score":0.954813},"labels":[],"label_agreement":null},{"id":"W1977247155","doi":"10.1109/cgames.2012.6314559","title":"A visualization framework for simulating fuel consumption through serious games","year":2012,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Nipissing University","funders":"Nipissing University","keywords":"Visualization; Computer science; Fuel efficiency; Traffic flow (computer networking); Plan (archaeology); Data visualization; Consumption (sociology); Virtual reality; Traffic simulation; Simulation; Transport engineering; Human–computer interaction; Computer security; Engineering; Artificial intelligence; Automotive engineering","score_opus":0.056100290244538824,"score_gpt":0.3928634406405911,"score_spread":0.33676315039605226,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1977247155","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.046976995,0.00016581631,0.94729835,0.00022970751,0.00049844885,0.00029965068,0.0000054190673,0.00024710322,0.004278504],"genre_scores_gemma":[0.9172785,0.000072924835,0.08144442,0.00030330004,0.00027461615,0.00002156732,0.00006707629,0.000009559451,0.000528031],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99934036,0.000049587285,0.00014669003,0.00009418513,0.00015242484,0.00021675097],"domain_scores_gemma":[0.9994544,0.00027519962,0.000080459824,0.000051671534,0.00008502777,0.000053289557],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029686652,0.00005866567,0.00007042066,0.000026330963,0.00033717038,0.000050564067,0.00004225114,0.00010816718,0.00023176653],"category_scores_gemma":[0.00027390665,0.000059141545,0.000031999156,0.0001375273,0.000044199245,0.000526385,0.0000020345817,0.000035960948,0.000020963627],"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":[0.000015739128,0.000042731277,0.11028152,0.000028072162,0.000009376413,6.15762e-8,0.052811924,0.0064965417,0.000011356532,0.8282125,0.00033401357,0.001756157],"study_design_scores_gemma":[0.0030209746,0.00022525313,0.23650946,0.0004569896,0.00031082486,0.0000018168893,0.053383324,0.058448166,0.000822321,0.14648093,0.4983939,0.0019460374],"about_ca_topic_score_codex":0.00013953817,"about_ca_topic_score_gemma":0.00011232976,"teacher_disagreement_score":0.8703015,"about_ca_system_score_codex":0.000027414837,"about_ca_system_score_gemma":0.000026227795,"threshold_uncertainty_score":0.2593276},"labels":[],"label_agreement":null},{"id":"W1977613281","doi":"10.5539/emr.v3n1p56","title":"The Finnish Road Weather Business Ecosystem - Turning Societal Benefits into Business and the Other Way Round","year":2014,"lang":"en","type":"article","venue":"Engineering Management Research","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Teknologian Tutkimuskeskus VTT; Tekes","keywords":"Business ecosystem; Value network; Value (mathematics); Product (mathematics); Business; Ecosystem; Service (business); Process (computing); Business model; Scenario analysis; Value chain; Customer value; Process management; Business value; Environmental resource management; Marketing; Knowledge management; Computer science; Environmental science; Ecology; Economics; Mathematics","score_opus":0.019425339053979387,"score_gpt":0.2851465661120942,"score_spread":0.26572122705811485,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1977613281","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6306849,0.005008999,0.19373566,0.062966265,0.002795131,0.004269898,0.000013102267,0.0011263909,0.09939964],"genre_scores_gemma":[0.9936623,0.00043898227,0.0006067727,0.000024816758,0.00024140264,0.000075873824,0.0000053458575,0.000021660335,0.004922841],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99849254,0.00020573722,0.00015240017,0.00019150757,0.00060357,0.00035425503],"domain_scores_gemma":[0.9990144,0.0005050782,0.0000354873,0.00018518946,0.00021157903,0.00004829766],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.004357201,0.00009504379,0.0000982417,0.00008568228,0.0013512027,0.00052195817,0.00031824215,0.000051764335,0.000015695208],"category_scores_gemma":[0.00026069887,0.000061042396,0.000026030608,0.0007535018,0.0001605702,0.00016058407,0.00005172824,0.00017796058,0.000015852553],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000039956725,0.0000203808,0.0011555228,0.0001946019,0.00010758859,0.0000025462236,0.012504524,0.53581446,0.000009882618,0.40796873,0.0009773759,0.04120443],"study_design_scores_gemma":[0.0017216074,0.000019413903,0.15295675,0.0003619244,0.000046992714,7.5760215e-7,0.007098797,0.122862466,0.0000092792,0.0009769233,0.7135378,0.00040726244],"about_ca_topic_score_codex":0.001283259,"about_ca_topic_score_gemma":0.00096347753,"teacher_disagreement_score":0.7125605,"about_ca_system_score_codex":0.00005378935,"about_ca_system_score_gemma":0.00001781964,"threshold_uncertainty_score":0.9999489},"labels":[],"label_agreement":null},{"id":"W1977868407","doi":"10.3141/2111-18","title":"Beyond Generating Transit Performance Measures","year":2009,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":44,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Oregon Department of Transportation","keywords":"Transit (satellite); Metropolitan area; Transport engineering; Computer science; Performance indicator; Public transport; Performance measurement; Automatic vehicle location; Telecommunications; Operations research; Engineering; Business; Geography","score_opus":0.09511339537847162,"score_gpt":0.39169780475759336,"score_spread":0.2965844093791217,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1977868407","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97879523,0.00034017913,0.007710332,0.009095903,0.0007306318,0.00088098884,0.000032068787,0.00007626082,0.0023383892],"genre_scores_gemma":[0.9914923,0.0019829376,0.0043900977,0.00018429644,0.00049095915,0.000024401266,0.000025512125,0.00003598677,0.0013735183],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9897948,0.0018016801,0.0014376836,0.00041418854,0.005455874,0.0010957572],"domain_scores_gemma":[0.9931781,0.00066347135,0.00046282212,0.000372523,0.00482191,0.00050117116],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0082976995,0.0002534406,0.00042656175,0.0009788855,0.0025067544,0.00027196933,0.0011798441,0.00028056273,0.00028480316],"category_scores_gemma":[0.00026036787,0.00020727619,0.0003724646,0.0029067013,0.0007880646,0.0012443669,0.0000016325199,0.0020842503,0.000020401572],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0035016863,0.0011025228,0.45852846,0.00030973993,0.0003393579,0.00025251237,0.1192507,0.113108106,0.011618502,0.05029424,0.02236132,0.21933286],"study_design_scores_gemma":[0.0016689706,0.00078926154,0.9311221,0.00039523953,0.000079914025,4.8092846e-7,0.0073091686,0.00047215444,0.001395363,0.003574986,0.052830294,0.00036201987],"about_ca_topic_score_codex":0.005979429,"about_ca_topic_score_gemma":0.046409186,"teacher_disagreement_score":0.4725937,"about_ca_system_score_codex":0.00027043576,"about_ca_system_score_gemma":0.0012544332,"threshold_uncertainty_score":0.9987919},"labels":[],"label_agreement":null},{"id":"W1978200301","doi":"10.1016/j.trf.2013.08.005","title":"Travel behavior changes and responses to advanced traveler information in prolonged and large-scale network disruptions: A case study of west LRT line construction in the city of Calgary","year":2013,"lang":"en","type":"article","venue":"Transportation Research Part F Traffic Psychology and Behaviour","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":53,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Transport engineering; Downtown; Travel behavior; Geography; Scale (ratio); Public transport; Transit (satellite); Mode choice; Business; Engineering; Cartography","score_opus":0.0669889294303758,"score_gpt":0.4062854428031677,"score_spread":0.3392965133727919,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1978200301","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9971831,0.000068482994,0.00017952721,0.0005923066,0.000046082492,0.0018547694,0.000044868433,0.000012504626,0.000018357669],"genre_scores_gemma":[0.99852574,0.00024058155,0.0005960471,0.00003720077,0.000011669871,0.00051186857,0.000059625214,0.0000043254718,0.000012957489],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9983477,0.0004999674,0.00043325534,0.00020346843,0.00026734485,0.00024828725],"domain_scores_gemma":[0.99935466,0.00019355316,0.000098996854,0.000096122385,0.00017865087,0.0000780019],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016608896,0.00009123932,0.00018706679,0.00032003812,0.00024493536,0.000022648006,0.00006717039,0.00013155406,0.000026607422],"category_scores_gemma":[0.000034950768,0.00008014253,0.000012766037,0.0006083776,0.00031868357,0.00029548482,0.0000021325807,0.00025598824,3.6545265e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003045938,0.00056903355,0.8441118,0.000022088674,0.00000488924,0.000033330605,0.13676211,0.00031878313,0.000031462932,0.000116484385,0.000012315721,0.017713085],"study_design_scores_gemma":[0.0013521707,0.0004894392,0.9165612,0.000035924,0.000022017499,0.0000125636425,0.0813309,0.00008026877,0.0000032984794,0.000019447814,0.000023768704,0.00006899687],"about_ca_topic_score_codex":0.0030466253,"about_ca_topic_score_gemma":0.114699095,"teacher_disagreement_score":0.11165247,"about_ca_system_score_codex":0.000008085289,"about_ca_system_score_gemma":0.000039672883,"threshold_uncertainty_score":0.90145534},"labels":[],"label_agreement":null},{"id":"W1978223900","doi":"10.3138/cpp.2013-073","title":"The Effectiveness and Distributional Effects of the Tax Credit for Public Transit","year":2014,"lang":"en","type":"article","venue":"Canadian Public Policy","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"Saint Mary's University","funders":"","keywords":"TRIPS architecture; Tax credit; Transit (satellite); Public economics; Public transport; Economics; Business; Transport engineering; Engineering","score_opus":0.009665502982295859,"score_gpt":0.24992054123190155,"score_spread":0.2402550382496057,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1978223900","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5909318,0.0005507286,0.056392398,0.3224125,0.002484568,0.0037198444,0.0016973692,0.00015861336,0.021652205],"genre_scores_gemma":[0.99921453,0.000008120659,0.000018316126,0.00025476763,0.00021945921,0.000048044032,0.000033609525,0.000005266201,0.00019791408],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9990974,0.00026069584,0.00009193526,0.00009927529,0.00014491173,0.0003057899],"domain_scores_gemma":[0.9986096,0.0008174106,0.000048473652,0.000108156506,0.0001491279,0.00026722543],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008956591,0.00005679316,0.0000733339,0.000077075514,0.0012008114,0.00012387603,0.00020581114,0.000068192945,0.00000458731],"category_scores_gemma":[0.0024292439,0.000039630202,0.00004399235,0.00036824585,0.0003332159,0.00012825037,0.000002964275,0.00005395727,5.7879924e-7],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004645679,0.0000046688333,0.0112392735,0.000022674169,0.000013827601,5.6234274e-8,0.00042095833,0.00001382374,0.0000051245074,0.97858596,0.001651034,0.008037936],"study_design_scores_gemma":[0.00041355388,0.000024655841,0.60090023,0.000015075444,0.000011516831,3.319648e-7,0.00008508726,0.00015050417,0.0000329499,0.005847594,0.39244086,0.00007764993],"about_ca_topic_score_codex":0.062080238,"about_ca_topic_score_gemma":0.24985829,"teacher_disagreement_score":0.9727384,"about_ca_system_score_codex":0.00012958745,"about_ca_system_score_gemma":0.0019581688,"threshold_uncertainty_score":0.94416547},"labels":[],"label_agreement":null},{"id":"W1978799619","doi":"10.3141/1771-18","title":"Demand Matrix Adjustment for Multimodal Freight Networks","year":2001,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Inro Consultants (Canada); Université de Montréal","funders":"","keywords":"Transportation planning; Computer science; Flow network; Operations research; Bilevel optimization; Strategic planning; Transport engineering; Mathematical optimization; Optimization problem; Engineering; Business; Algorithm; Mathematics","score_opus":0.08280561172563286,"score_gpt":0.41664662487643855,"score_spread":0.3338410131508057,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1978799619","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8946241,0.0010768704,0.09023323,0.008241431,0.0019531543,0.0029082634,0.00011247081,0.00009359518,0.00075684406],"genre_scores_gemma":[0.9832624,0.004729932,0.0071087587,0.000077460325,0.000915393,0.0001705825,0.000077979275,0.00006330935,0.0035941591],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99190116,0.0014821201,0.0014148823,0.0004363112,0.00356923,0.0011963117],"domain_scores_gemma":[0.99187845,0.0017795161,0.0005380418,0.00035487383,0.0049021407,0.0005469821],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.007426769,0.00024993392,0.0004393592,0.00086586666,0.0019869795,0.00019612045,0.0010669772,0.00033093445,0.00046187465],"category_scores_gemma":[0.00031475068,0.00020226983,0.00048844283,0.002322007,0.0007990619,0.000828875,0.0000031951063,0.0014678242,0.00001342837],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.006304232,0.00094705087,0.6577918,0.00029458845,0.0004585258,0.00017101598,0.029798018,0.19978465,0.00064380676,0.04416069,0.03865154,0.020994108],"study_design_scores_gemma":[0.0032289487,0.0005857416,0.81740695,0.00034248142,0.00012504644,4.9350325e-7,0.0072643734,0.0029092445,0.000138286,0.00364185,0.16401787,0.00033868512],"about_ca_topic_score_codex":0.012971066,"about_ca_topic_score_gemma":0.096037135,"teacher_disagreement_score":0.19687541,"about_ca_system_score_codex":0.00032573286,"about_ca_system_score_gemma":0.0009062038,"threshold_uncertainty_score":0.9993123},"labels":[],"label_agreement":null},{"id":"W1979297853","doi":"10.1016/j.tranpol.2005.11.013","title":"Modelling and evaluation of road pricing in Paris","year":2006,"lang":"en","type":"article","venue":"Transport Policy","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":70,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Toll; Value of time; Road pricing; Congestion pricing; Toll road; Revenue; Travel time; Transport engineering; Economics; Welfare; Value (mathematics); Computer science; Traffic congestion; Engineering; Finance","score_opus":0.038663460267798405,"score_gpt":0.33077399947150843,"score_spread":0.29211053920371,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1979297853","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9711759,0.00013550631,0.007988989,0.00027685944,0.00001729647,0.00014578502,0.0000047477765,0.000025075697,0.02022986],"genre_scores_gemma":[0.9987396,0.000088761335,0.00094655406,0.000011838914,0.000057046782,0.0000054194606,0.00003118688,0.000004496545,0.00011510826],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99924207,0.000046435085,0.00020948598,0.00009523955,0.00028807845,0.0001186849],"domain_scores_gemma":[0.99978673,0.000015548416,0.000057737077,0.00004198426,0.000073962656,0.000024015046],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00071427453,0.000044908604,0.00008444018,0.00014883777,0.00006940911,0.000005361151,0.00003229299,0.000052196305,0.000018840683],"category_scores_gemma":[0.000006231374,0.000049717215,0.000016933445,0.00032083958,0.000052833966,0.00012236019,4.054919e-7,0.000039266077,5.391499e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005716601,0.000020291403,0.11387392,0.000007999061,0.0000016037017,3.7102777e-7,0.010014425,0.86233544,0.000014554508,0.011926087,0.0000037756918,0.0017958106],"study_design_scores_gemma":[0.0009784296,0.0000151238755,0.82353115,0.00007422792,0.000062143015,2.1495336e-7,0.0014709651,0.167109,0.0001363553,0.0057308627,0.0007189094,0.00017263005],"about_ca_topic_score_codex":0.107073404,"about_ca_topic_score_gemma":0.0069546644,"teacher_disagreement_score":0.7096572,"about_ca_system_score_codex":0.000036520312,"about_ca_system_score_gemma":0.00018430054,"threshold_uncertainty_score":0.8988727},"labels":[],"label_agreement":null},{"id":"W1979398619","doi":"10.3141/2111-11","title":"Emergent Curbside Intercity Bus Industry","year":2009,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"TRIPS architecture; Public transport; Competition (biology); Transport engineering; Atlanta; Engineering; Business; Finance; Metropolitan area","score_opus":0.12796545826427086,"score_gpt":0.43877474696659946,"score_spread":0.3108092887023286,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1979398619","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9751221,0.00021395297,0.0024492566,0.017889183,0.0010388438,0.00089894317,0.000049288883,0.00007523277,0.0022631825],"genre_scores_gemma":[0.99401736,0.0012454938,0.0017987427,0.00018764423,0.00042908787,0.000024398138,0.000027523658,0.00003287664,0.002236893],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9899159,0.0020709566,0.0015306742,0.00042631465,0.004954208,0.0011019601],"domain_scores_gemma":[0.99291736,0.0007010454,0.00055775314,0.00042104468,0.004744634,0.0006581748],"candidate_categories":["sts","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0075194943,0.0002502907,0.00042781865,0.0010784958,0.0016871556,0.00022031576,0.001349397,0.0005043543,0.00090152887],"category_scores_gemma":[0.00048398512,0.00020608127,0.0004201546,0.0032653934,0.000847412,0.0010468293,0.0000030981623,0.0038807117,0.00003285252],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0026182213,0.0011929623,0.7686682,0.00016185708,0.0002608695,0.00032709748,0.05835184,0.014396121,0.0018033972,0.05724496,0.061227325,0.033747196],"study_design_scores_gemma":[0.0009967667,0.00042897943,0.9226875,0.0003159414,0.000048240272,2.601707e-7,0.0077040787,0.000054872267,0.0004377123,0.00571555,0.061383974,0.0002261233],"about_ca_topic_score_codex":0.017918253,"about_ca_topic_score_gemma":0.07588695,"teacher_disagreement_score":0.15401936,"about_ca_system_score_codex":0.00036699165,"about_ca_system_score_gemma":0.0013127249,"threshold_uncertainty_score":0.9996125},"labels":[],"label_agreement":null},{"id":"W1979918729","doi":"10.1177/0037549709359354","title":"Using Multi-agent Geo-simulation Techniques for the Detection of Risky Areas for Trains","year":2010,"lang":"en","type":"article","venue":"SIMULATION","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Université de Tunis; Fonds Québécois de la Recherche sur la Nature et les Technologies; Université Laval","keywords":"Train; Computer science; Process (computing); Scale (ratio); Domain (mathematical analysis); Variety (cybernetics); Geographic information system; Agent-based model; Distributed computing; Industrial engineering; Simulation; Artificial intelligence; Engineering; Geography","score_opus":0.0842587107926425,"score_gpt":0.395686592429663,"score_spread":0.3114278816370205,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1979918729","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.110404044,0.000011413793,0.88804567,0.00005823498,0.00029803353,0.0010281716,0.000027967055,0.00008477724,0.000041698095],"genre_scores_gemma":[0.97104216,0.0000034772231,0.028574757,0.000016889458,0.00019633221,0.0000472882,0.000046911446,0.000011599455,0.000060592436],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993558,0.000034715726,0.00021807647,0.00012319333,0.00015022917,0.000118007],"domain_scores_gemma":[0.9987072,0.0006374122,0.00019418627,0.00008881854,0.00034658363,0.000025847776],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006047957,0.000065227054,0.000077188444,0.000072621435,0.00057449285,0.000030295767,0.00006370092,0.00012483959,0.00000867527],"category_scores_gemma":[0.00041387245,0.000057144225,0.000072601986,0.00015193204,0.000061884995,0.00019858034,0.0000017666848,0.00006009486,2.9576486e-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":[0.000049467497,0.000024586903,0.0015973918,0.000011994739,0.000008217271,1.3422545e-8,0.003418774,0.9573356,0.0055131232,0.00071975496,0.0000010652551,0.031320047],"study_design_scores_gemma":[0.0003235692,0.00002360001,0.014787279,0.000009156532,0.000049264618,3.1650654e-8,0.0004589249,0.97586614,0.002674818,0.0004544142,0.0052799582,0.00007285125],"about_ca_topic_score_codex":0.00032150213,"about_ca_topic_score_gemma":0.001367475,"teacher_disagreement_score":0.8606381,"about_ca_system_score_codex":0.00003121691,"about_ca_system_score_gemma":0.000048581813,"threshold_uncertainty_score":0.44185928},"labels":[],"label_agreement":null},{"id":"W1980082063","doi":"10.3141/2175-06","title":"Comparison of Agent-Based Transit Assignment Procedure with Conventional Approaches","year":2010,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of British Columbia; University of Toronto","funders":"","keywords":"Public transport; Transit (satellite); Transport engineering; Microsimulation; Computer science; Process (computing); Trip distribution; Traffic congestion; Transportation planning; Aggregate (composite); Mode choice; Service (business); Travel behavior; Operations research; Engineering; Business","score_opus":0.1736866407550783,"score_gpt":0.42196416522866925,"score_spread":0.24827752447359094,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1980082063","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97884613,0.00009636823,0.013806312,0.0048493436,0.00045847124,0.0011644815,0.00008188847,0.0000366839,0.00066030317],"genre_scores_gemma":[0.99306583,0.00006564756,0.0060719163,0.000024511071,0.00012522165,0.00006991165,0.000066010456,0.000040452007,0.00047048298],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99107337,0.0010975779,0.0013897719,0.00037653442,0.005342292,0.00072046695],"domain_scores_gemma":[0.993995,0.000897621,0.0007562991,0.00033172092,0.0036678314,0.00035150902],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005298046,0.00022699697,0.00048968376,0.0008236554,0.0010753952,0.00011881221,0.000956635,0.00028044666,0.0005595368],"category_scores_gemma":[0.00016368712,0.00017058979,0.0003304426,0.0021161896,0.0017061078,0.0005540486,0.0000017379284,0.0022690895,0.0000063000844],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0023182777,0.0010976676,0.9031269,0.0003748244,0.00018829672,0.00003060168,0.019221582,0.04517762,0.0032468918,0.020219732,0.0018011264,0.0031965128],"study_design_scores_gemma":[0.0026178104,0.0007668687,0.95810026,0.00041981848,0.00012426326,2.4922403e-7,0.013672697,0.0007711639,0.0047043175,0.0008266585,0.017725479,0.00027041874],"about_ca_topic_score_codex":0.0045563933,"about_ca_topic_score_gemma":0.104104236,"teacher_disagreement_score":0.09954784,"about_ca_system_score_codex":0.00012345024,"about_ca_system_score_gemma":0.0021233843,"threshold_uncertainty_score":0.98581904},"labels":[],"label_agreement":null},{"id":"W1980711996","doi":"10.1504/writr.2009.023307","title":"Intelligent transport system for developing countries","year":2009,"lang":"en","type":"article","venue":"World Review of Intermodal Transportation Research","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Scarcity; Developing country; Intelligent transportation system; Commodity; Business; Traffic congestion; Consumption (sociology); Advanced Traffic Management System; Transport engineering; Environmental economics; Finance; Economic growth; Engineering; Economics","score_opus":0.08056286358308247,"score_gpt":0.42280736101759725,"score_spread":0.3422444974345148,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1980711996","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0460824,0.06533843,0.71037376,0.07176124,0.002268863,0.01501349,0.0009847624,0.0012149211,0.08696214],"genre_scores_gemma":[0.9649689,0.026865246,0.005935818,0.0005122612,0.00010081799,0.00012819195,0.00040391853,0.00001643607,0.0010683977],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99742776,0.00018172256,0.00084815413,0.00028029535,0.00088356494,0.0003785062],"domain_scores_gemma":[0.99791354,0.0002690823,0.0001924976,0.00015962178,0.0013612496,0.00010399582],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00280558,0.00013225318,0.0003603128,0.0003704539,0.000392101,0.000023784101,0.00031411066,0.00006955188,0.00011334862],"category_scores_gemma":[0.00006297613,0.00012650553,0.0001783813,0.0010223947,0.00020842178,0.00020855556,7.6795317e-7,0.00017721772,0.000009949457],"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":[0.00022373418,0.00008903707,0.0032697225,0.014300666,0.000053262855,0.00001221749,0.010066865,0.00024968124,0.00004322241,0.9547082,0.002750445,0.014232915],"study_design_scores_gemma":[0.0011410635,0.00037954302,0.088086694,0.06697679,0.00019959401,9.5262146e-7,0.008446548,0.00016215236,0.0036883007,0.0017002228,0.8284267,0.00079143315],"about_ca_topic_score_codex":0.00029506447,"about_ca_topic_score_gemma":0.0022292277,"teacher_disagreement_score":0.953008,"about_ca_system_score_codex":0.000157586,"about_ca_system_score_gemma":0.00045207026,"threshold_uncertainty_score":0.5158746},"labels":[],"label_agreement":null},{"id":"W1981740305","doi":"10.3141/1854-05","title":"Application of Artificial Neural Network Models to Activity Scheduling Time Horizon","year":2003,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Wilfrid Laurier University","funders":"Luonnontieteiden ja Tekniikan Tutkimuksen Toimikunta; Natural Resources Canada; Natural Sciences and Engineering Research Council of Canada; Canadian Natural Resources Limited","keywords":"Time horizon; Scheduling (production processes); Computer science; Artificial neural network; Schedule; Operations research; Process (computing); Artificial intelligence; Machine learning; Engineering; Mathematical optimization; Operations management; Mathematics","score_opus":0.10615792340341995,"score_gpt":0.39342556614123925,"score_spread":0.2872676427378193,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1981740305","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9251122,0.00008637661,0.070499234,0.0018912595,0.00044675037,0.0012411904,0.00003995055,0.000040441704,0.00064259203],"genre_scores_gemma":[0.9887671,0.00018554562,0.010275779,0.000028554154,0.00025767577,0.0000624955,0.000016831304,0.000043175012,0.00036285713],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9912601,0.00231037,0.0012832133,0.00041316482,0.003802314,0.0009307987],"domain_scores_gemma":[0.9933723,0.00103321,0.0006054937,0.00038695915,0.0041144392,0.0004875591],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008486806,0.00021387902,0.00045826536,0.0007808853,0.0012266395,0.00011787246,0.00081768894,0.0002506018,0.0001657559],"category_scores_gemma":[0.0003106082,0.00018969257,0.00032359897,0.0037961788,0.00058010186,0.00089153985,0.0000031785055,0.0013408088,0.000021867792],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014781058,0.00034087038,0.05073309,0.00010197209,0.0001001585,0.000015970067,0.012256423,0.8594238,0.0060480423,0.05246286,0.001268357,0.01577035],"study_design_scores_gemma":[0.003581682,0.0028347317,0.8342117,0.0010817009,0.00032481688,8.3879917e-7,0.016673822,0.037429634,0.008670787,0.057653427,0.03622544,0.0013113997],"about_ca_topic_score_codex":0.011151157,"about_ca_topic_score_gemma":0.037482496,"teacher_disagreement_score":0.8219942,"about_ca_system_score_codex":0.00022222316,"about_ca_system_score_gemma":0.0009938731,"threshold_uncertainty_score":0.9954337},"labels":[],"label_agreement":null},{"id":"W1981764214","doi":"10.1002/atr.149","title":"A computational model for the probit‐based dynamic stochastic user optimal traffic assignment problem","year":2010,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computational complexity theory; Computer science; Mathematical optimization; Probit; Set (abstract data type); Cell Transmission Model; Covariance; Point (geometry); Algorithm; Computational model; Iterative method; Mathematics; Machine learning; Statistics; Engineering; Traffic congestion","score_opus":0.011661121704888896,"score_gpt":0.2870791087532425,"score_spread":0.2754179870483536,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1981764214","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20912881,0.000023795463,0.7887414,0.0012463405,0.00028708339,0.00050084095,0.00003585482,0.000027413293,0.000008424133],"genre_scores_gemma":[0.7764328,0.000006131157,0.2232405,0.00006105798,0.000046561254,0.00003833701,0.00008001316,0.00001377319,0.000080825004],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986586,0.000026094876,0.0005013976,0.0001303114,0.0004973473,0.00018622694],"domain_scores_gemma":[0.99855006,0.00030173062,0.00047670002,0.00005597571,0.0005269451,0.00008858856],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056070596,0.0001120382,0.00014899974,0.000102615944,0.00040729987,0.000047077192,0.0001512381,0.000081869955,0.000019025249],"category_scores_gemma":[0.000041285643,0.00008884241,0.0001331258,0.00017938318,0.000105893676,0.00042646143,3.4704695e-7,0.00024404368,6.3655204e-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":[0.00022208541,0.00008660811,0.00005166268,0.000015298356,0.000023128192,0.0000011538104,0.008442076,0.98601645,0.000247447,0.0025635913,0.000023915654,0.0023065608],"study_design_scores_gemma":[0.0016695586,0.000115696836,0.005874905,0.000048527123,0.0001287111,0.0000011665254,0.0015229053,0.98885494,0.0000116925585,0.0012249261,0.0004084081,0.00013855135],"about_ca_topic_score_codex":0.00000462819,"about_ca_topic_score_gemma":0.00064657,"teacher_disagreement_score":0.567304,"about_ca_system_score_codex":0.00004968857,"about_ca_system_score_gemma":0.00048676223,"threshold_uncertainty_score":0.36228883},"labels":[],"label_agreement":null},{"id":"W1982886411","doi":"10.3141/2054-08","title":"Weekly Rhythm in Joint Time Expenditure for All At-Home and Out-of-Home Activities","year":2008,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Canadian Natural Resources","funders":"Transport Canada; McLean Foundation","keywords":"Rhythm; Travel behavior; Frame (networking); Set (abstract data type); Preference; Revealed preference; Behavioral pattern; Behavioral modeling; Maximization; Computer science; Simulation; Econometrics; Operations research; Statistics; Engineering; Psychology; Transport engineering; Economics; Mathematics; Artificial intelligence; Social psychology; Telecommunications; Medicine","score_opus":0.11057407960385754,"score_gpt":0.38625597466112305,"score_spread":0.2756818950572655,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1982886411","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99363506,0.00028334095,0.0007716471,0.0032876148,0.0004158386,0.001163955,0.0001595637,0.000027698512,0.0002552845],"genre_scores_gemma":[0.99188715,0.0024891016,0.002389087,0.000034262328,0.000180739,0.00008822323,0.000060004284,0.00004441404,0.0028270413],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99380136,0.001010392,0.0013032091,0.0003710199,0.0027135161,0.000800485],"domain_scores_gemma":[0.99588263,0.001381773,0.00053350534,0.00024959835,0.0016202453,0.0003322569],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0045672203,0.00021437109,0.000543222,0.0010929779,0.00095612294,0.000058233985,0.00050427637,0.000267624,0.00026569355],"category_scores_gemma":[0.00017442313,0.00018199046,0.0002913881,0.0010295867,0.0012524017,0.0008037886,0.0000045387724,0.0010544438,0.000006931993],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0061609116,0.0007425291,0.7611165,0.0007309376,0.00032452767,0.00030079175,0.18565106,0.0059718015,0.010014371,0.0075838594,0.01778533,0.0036174026],"study_design_scores_gemma":[0.0025061646,0.0005637175,0.96034247,0.00036816107,0.000041388696,9.635525e-7,0.008380819,0.00014154823,0.0011584914,0.002153901,0.024061905,0.000280468],"about_ca_topic_score_codex":0.0061565614,"about_ca_topic_score_gemma":0.060294624,"teacher_disagreement_score":0.19922599,"about_ca_system_score_codex":0.00027594113,"about_ca_system_score_gemma":0.0007113019,"threshold_uncertainty_score":0.95685256},"labels":[],"label_agreement":null},{"id":"W1983062415","doi":"10.1080/15568318.2011.626970","title":"The Paradox of Public Transport Peak Spreading: Universities and Travel Demand Management","year":2012,"lang":"en","type":"article","venue":"International Journal of Sustainable Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"University of British Columbia","keywords":"Public transport; Demand management; Travel behavior; Transport engineering; Passenger transport; Business; Economics; Economic geography; Engineering; Macroeconomics","score_opus":0.01323562835027279,"score_gpt":0.271960706721115,"score_spread":0.2587250783708422,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1983062415","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9601217,0.0008188075,0.03150968,0.0018655374,0.00047612633,0.00018665908,0.0000143189945,0.000014994273,0.004992212],"genre_scores_gemma":[0.9963548,0.0013341651,0.00061592134,0.000021085049,0.00010177877,0.0000022228285,0.000030998173,0.000006826788,0.0015321686],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99861276,0.000068410554,0.00041350944,0.000066022534,0.00060688617,0.00023242348],"domain_scores_gemma":[0.998596,0.0001136409,0.00036424515,0.000044452132,0.00077161944,0.00011001476],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012611767,0.00007798799,0.000117085525,0.00022181128,0.00027736597,0.000046490117,0.00022345917,0.000050756313,0.000024591222],"category_scores_gemma":[0.000016987295,0.000066321954,0.000072766576,0.00017679816,0.00018890966,0.0010938336,0.0000015717447,0.000089171146,4.1621942e-7],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021298238,0.00011630414,0.1315666,0.000054756114,0.0002823741,0.00007631999,0.059539393,0.0016386263,0.000032211396,0.8041454,0.00010434548,0.002230699],"study_design_scores_gemma":[0.0008714539,0.000045761215,0.66809857,0.000051548042,0.00013762184,0.0000060566404,0.3027937,0.0000238078,0.00013433212,0.0016705743,0.026051402,0.00011515111],"about_ca_topic_score_codex":0.00018288808,"about_ca_topic_score_gemma":0.00011825906,"teacher_disagreement_score":0.8024748,"about_ca_system_score_codex":0.00011172278,"about_ca_system_score_gemma":0.00015968908,"threshold_uncertainty_score":0.2704531},"labels":[],"label_agreement":null},{"id":"W1983123393","doi":"10.3141/2146-06","title":"Evaluating, Comparing, and Improving Metro Networks: Application to Plans for Toronto, Canada","year":2010,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Transport engineering; Plan (archaeology); Public transport; Transit (satellite); Work (physics); Metro station; Light rail transit; Computer science; Street network; Operations research; Business; Geography; Engineering","score_opus":0.08918938802012787,"score_gpt":0.43437187175102976,"score_spread":0.3451824837309019,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1983123393","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9583271,0.00011674212,0.03530129,0.0031642485,0.000928986,0.0018555302,0.00008967917,0.00003326824,0.00018316096],"genre_scores_gemma":[0.988011,0.00016068514,0.01065224,0.00009222333,0.0004117241,0.000188303,0.00004945132,0.00003894265,0.00039541742],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9942341,0.0006453317,0.0010267874,0.00041404474,0.002860349,0.0008194069],"domain_scores_gemma":[0.9934638,0.0013972211,0.0004270659,0.00031464925,0.0038087678,0.00058850023],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.006725369,0.00018994855,0.0003480826,0.00032252297,0.0014457096,0.00019484341,0.0007983279,0.00018949846,0.00010127068],"category_scores_gemma":[0.00060303987,0.00016457068,0.00014319726,0.001025145,0.0003738307,0.00056891516,0.0000043323216,0.0012372846,0.000001343437],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002155382,0.00022221066,0.83914685,0.00026872326,0.00016877142,0.000015144318,0.020437825,0.04880673,0.004608571,0.024928955,0.011135869,0.04810497],"study_design_scores_gemma":[0.0013830396,0.0004156318,0.924135,0.00011444068,0.00007405393,1.3952187e-7,0.0079512475,0.0077887145,0.0002266418,0.00068285177,0.056963373,0.00026483912],"about_ca_topic_score_codex":0.8163256,"about_ca_topic_score_gemma":0.99779844,"teacher_disagreement_score":0.18147285,"about_ca_system_score_codex":0.0003629526,"about_ca_system_score_gemma":0.001902207,"threshold_uncertainty_score":0.99985427},"labels":[],"label_agreement":null},{"id":"W1983537108","doi":"10.3141/2168-14","title":"Exclusive Truck Facilities in Toronto, Ontario, Canada","year":2010,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Waterloo; University of Toronto","funders":"Infrastructure Canada","keywords":"Truck; Transport engineering; Traffic flow (computer networking); Travel time; Engineering; Computer science; Automotive engineering","score_opus":0.05206389883305234,"score_gpt":0.3704335640849076,"score_spread":0.3183696652518552,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1983537108","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98899406,0.000097694414,0.00018306315,0.003520698,0.0015261135,0.00077208795,0.00007859142,0.000023083669,0.004804616],"genre_scores_gemma":[0.9911382,0.00036463415,0.0012406178,0.000055389075,0.00021409786,0.00004940294,0.000030008603,0.000030004045,0.0068776],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.991864,0.0011468938,0.0013209097,0.00036832056,0.0042951372,0.0010047343],"domain_scores_gemma":[0.9951491,0.0011465903,0.00038041317,0.00035648406,0.0025030172,0.0004644256],"candidate_categories":["research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0044000726,0.00021641901,0.00039447373,0.0004911697,0.0011425529,0.00015395705,0.0011205204,0.00026718184,0.0035158596],"category_scores_gemma":[0.00030880474,0.00018460123,0.00021404668,0.0013913346,0.0008112942,0.0010444298,0.000003663859,0.002950871,0.0000051351562],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00073635683,0.00017367788,0.9081958,0.00008025309,0.000060270046,0.00015081475,0.058373857,0.001991071,0.0006462835,0.020646164,0.0063681384,0.0025773065],"study_design_scores_gemma":[0.0009153379,0.00011008391,0.853837,0.00011361829,0.000015032692,1.6530983e-7,0.030875659,0.00002437324,0.0001535968,0.0014262581,0.1123508,0.00017803999],"about_ca_topic_score_codex":0.99695,"about_ca_topic_score_gemma":0.99998194,"teacher_disagreement_score":0.10598266,"about_ca_system_score_codex":0.0013298736,"about_ca_system_score_gemma":0.007402693,"threshold_uncertainty_score":0.99934936},"labels":[],"label_agreement":null},{"id":"W1983586045","doi":"10.1109/itsc.2011.6082906","title":"Distributed graphs for solving co-modal transport problems","year":2011,"lang":"en","type":"preprint","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Modal; Computer science; Order (exchange); Distributed computing; Distributed algorithm; Distributed Computing Environment; Service (business); Business; Economics; Economy; Finance","score_opus":0.047791167634826145,"score_gpt":0.30795434089411294,"score_spread":0.2601631732592868,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1983586045","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025104655,0.00011937938,0.9052214,0.0006425708,0.0012578616,0.0023461655,0.0015810891,0.0009968773,0.062729985],"genre_scores_gemma":[0.9814336,0.00014438426,0.009730366,0.000058089678,0.00011302992,0.00023060228,0.0062361024,0.00002597521,0.002027869],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99864316,0.00003006417,0.0003490232,0.00038381224,0.0002704498,0.0003234914],"domain_scores_gemma":[0.9992526,0.00006213123,0.0001836388,0.0001629147,0.00021355612,0.00012515573],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00053169014,0.00017870804,0.00024222658,0.00009351518,0.0004342921,0.000059134745,0.00027461426,0.00039470507,0.0002907788],"category_scores_gemma":[0.00003167703,0.00018068624,0.0001951459,0.0001251617,0.00013167472,0.00011552419,0.0000060581388,0.00021833784,0.0000059906756],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002446908,0.0005800791,0.18227655,0.0012907224,0.00047522524,0.0000127353105,0.15134601,0.10851284,0.00006612298,0.53551114,0.016859142,0.0028247377],"study_design_scores_gemma":[0.005519674,0.00042203543,0.22808282,0.0018485957,0.0015637464,0.00000200352,0.017402273,0.011259846,0.001167586,0.44005254,0.28631145,0.0063674278],"about_ca_topic_score_codex":0.0036094806,"about_ca_topic_score_gemma":0.003943142,"teacher_disagreement_score":0.9563289,"about_ca_system_score_codex":0.00004206403,"about_ca_system_score_gemma":0.0003333454,"threshold_uncertainty_score":0.7368171},"labels":[],"label_agreement":null},{"id":"W1984000720","doi":"10.3141/2135-11","title":"Location Choice Modeling for Shopping and Leisure Activities with MATSim","year":2009,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":59,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Scale (ratio); Scheduling (production processes); Space (punctuation); Process (computing); Competition (biology); Mathematical optimization; Operations research; Geography; Mathematics","score_opus":0.11385935927565304,"score_gpt":0.41348649132624854,"score_spread":0.2996271320505955,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1984000720","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93431973,0.00028629508,0.05542986,0.008330015,0.00019308881,0.001188651,0.0000245859,0.00004739825,0.00018037508],"genre_scores_gemma":[0.9924799,0.0006390879,0.0059559382,0.000054700322,0.00028341773,0.000052846593,0.00002383092,0.00003106659,0.00047921186],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99496675,0.0006773647,0.0008201954,0.00034462896,0.0024815656,0.0007095013],"domain_scores_gemma":[0.99433166,0.0010783012,0.0003374313,0.00021840016,0.0037306712,0.00030353607],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0041972357,0.00019116426,0.00033722143,0.00070496,0.0017213419,0.00025215917,0.0004977562,0.00019044903,0.00003319239],"category_scores_gemma":[0.00022616572,0.00014942413,0.00014286255,0.0016159045,0.00051246816,0.0011735227,0.0000012695554,0.0010215365,0.0000011416793],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0053898357,0.0004632703,0.5212971,0.00082336616,0.00029993974,0.000046790396,0.0614783,0.31420588,0.001749735,0.060205963,0.0021928444,0.031846974],"study_design_scores_gemma":[0.0033431035,0.001403735,0.9345617,0.0013891513,0.00015300607,6.1073325e-7,0.028207503,0.008536473,0.0002755252,0.0057107774,0.015944444,0.00047393134],"about_ca_topic_score_codex":0.0057973163,"about_ca_topic_score_gemma":0.06710229,"teacher_disagreement_score":0.41326463,"about_ca_system_score_codex":0.00016969637,"about_ca_system_score_gemma":0.00088423525,"threshold_uncertainty_score":0.9995783},"labels":[],"label_agreement":null},{"id":"W1984758736","doi":"10.3141/1807-12","title":"Nested Logit Models and Artificial Neural Networks for Predicting Household Automobile Choices: Comparison of Performance","year":2002,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":73,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; McMaster University","funders":"","keywords":"Artificial neural network; Categorical variable; Computer science; Nested logit; Artificial intelligence; Discrete choice; Machine learning; Perceptron; Logit; Logistic regression; Multilayer perceptron; Econometrics; Mathematics","score_opus":0.24216754205822566,"score_gpt":0.4078085481676971,"score_spread":0.16564100610947147,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1984758736","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9792164,0.00042615063,0.017187694,0.0012058398,0.00043582663,0.0012198341,0.00007682922,0.00005060538,0.00018079834],"genre_scores_gemma":[0.9965677,0.0010649558,0.0016995639,0.000019221314,0.00024129293,0.00007118291,0.00002723713,0.00003778669,0.00027103885],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99427426,0.0008001969,0.001479772,0.00033128198,0.0023668837,0.0007476316],"domain_scores_gemma":[0.99470884,0.0015053543,0.00068493164,0.0002336334,0.002564176,0.0003030552],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0041110096,0.00018882315,0.00045518854,0.0006115018,0.0013498791,0.0001418688,0.00063038024,0.0002461202,0.00007903389],"category_scores_gemma":[0.00019024817,0.00015932806,0.0002254356,0.0015597062,0.00090049516,0.0010392588,0.000003189913,0.0011891982,7.5264177e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007784987,0.00028390938,0.42731908,0.00024453818,0.000074835116,0.0000058217024,0.020234078,0.53326386,0.00014791077,0.0041526854,0.0008313737,0.012663413],"study_design_scores_gemma":[0.0017730813,0.0010616375,0.48807174,0.0004787481,0.00012214787,2.714169e-7,0.010513839,0.49163273,0.0003255907,0.0011202813,0.004605968,0.0002939752],"about_ca_topic_score_codex":0.004012606,"about_ca_topic_score_gemma":0.026141286,"teacher_disagreement_score":0.06075267,"about_ca_system_score_codex":0.00010044179,"about_ca_system_score_gemma":0.00020717969,"threshold_uncertainty_score":0.99995023},"labels":[],"label_agreement":null},{"id":"W1985165705","doi":"10.5038/2375-0901.9.5.6","title":"Development, Evaluation, and Selection of Advanced Transit Signal Priority Concept Directions","year":2006,"lang":"en","type":"article","venue":"Journal of Public Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto","funders":"Transport Canada","keywords":"Selection (genetic algorithm); Computer science; Ranking (information retrieval); Listing (finance); Process (computing); Transit (satellite); Operations research; Control (management); Set (abstract data type); Travelling salesman problem; Work (physics); Transport engineering; Management science; Systems engineering; Process management; Public transport; Engineering; Artificial intelligence; Business","score_opus":0.021750502129089168,"score_gpt":0.29787090121581017,"score_spread":0.276120399086721,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1985165705","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93026644,0.00032473364,0.067953065,0.00043902485,0.00016703478,0.00020342796,0.000011486852,0.000032038348,0.0006027451],"genre_scores_gemma":[0.98646235,0.000068827234,0.013202251,0.00000891412,0.000095232725,0.000004757858,0.00007012003,0.0000066319103,0.000080914084],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9983798,0.00013850356,0.0006332708,0.00010280317,0.0006144588,0.00013115711],"domain_scores_gemma":[0.99803346,0.00006268646,0.0005351346,0.000024071047,0.0012697299,0.00007492419],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012640526,0.000077868914,0.00015862873,0.00022701062,0.00027977425,0.0000353994,0.000059233134,0.00008323188,0.0001271036],"category_scores_gemma":[0.000045808218,0.00008093641,0.000054251894,0.000491657,0.00009387175,0.0008255577,2.5490795e-7,0.00010593135,2.5571006e-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":[0.000273611,0.0008753985,0.4083827,0.000119093405,0.00025327803,0.000006100558,0.10548037,0.10395825,0.017246196,0.041452795,0.0005785714,0.3213736],"study_design_scores_gemma":[0.0012776055,0.00006536451,0.97348547,0.000042789903,0.00011669356,0.0000023422363,0.0032348328,0.00027539855,0.0019280813,0.0005053732,0.01893784,0.00012820089],"about_ca_topic_score_codex":0.0002246715,"about_ca_topic_score_gemma":0.0057691853,"teacher_disagreement_score":0.56510276,"about_ca_system_score_codex":0.00007820254,"about_ca_system_score_gemma":0.0006727695,"threshold_uncertainty_score":0.3300491},"labels":[],"label_agreement":null},{"id":"W1985623578","doi":"10.1080/03081060108717671","title":"A line haul transit technology selection model","year":2001,"lang":"en","type":"article","venue":"Transportation Planning and Technology","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Transit (satellite); Selection (genetic algorithm); Transport engineering; Operations research; Event (particle physics); Computer science; Public transport; Value of time; Engineering; Travel time","score_opus":0.019291569232681067,"score_gpt":0.29017815602833374,"score_spread":0.2708865867956527,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1985623578","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7245941,0.00041321298,0.2629358,0.008350751,0.00008607427,0.00022986268,0.000017746615,0.0016327448,0.0017397007],"genre_scores_gemma":[0.9903151,0.0002654179,0.008176899,0.00009024209,0.00003387066,0.00003705833,0.00011210357,0.000018319703,0.00095099706],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998828,0.000021515058,0.00030102956,0.00032950146,0.00018743558,0.00033253047],"domain_scores_gemma":[0.9995179,0.000028906583,0.00010459437,0.00009598075,0.00017606247,0.00007652475],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020337402,0.00014919555,0.00019372838,0.00078595476,0.0005786088,0.00002805883,0.00012347398,0.0004718016,0.000040215684],"category_scores_gemma":[0.000032465323,0.00016838456,0.00002875637,0.0015710692,0.0002830199,0.0002136011,0.0000011792101,0.0002806692,0.000006674993],"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":[0.00018304687,0.0001617434,0.51369834,0.00004010691,0.000079804355,0.0000951705,0.017724711,0.21207422,0.0028154438,0.23084803,0.000340989,0.021938391],"study_design_scores_gemma":[0.012508469,0.0017972613,0.12908728,0.001016602,0.0011317823,0.00023521311,0.061279926,0.36934298,0.008526347,0.19953497,0.21070844,0.004830727],"about_ca_topic_score_codex":0.00012490936,"about_ca_topic_score_gemma":0.0009881769,"teacher_disagreement_score":0.38461107,"about_ca_system_score_codex":0.000026484933,"about_ca_system_score_gemma":0.00010457363,"threshold_uncertainty_score":0.6866523},"labels":[],"label_agreement":null},{"id":"W1986100434","doi":"10.1080/12265934.2013.776288","title":"Modelling traveller response to variable message sign","year":2013,"lang":"en","type":"article","venue":"International Journal of Urban Sciences","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto; University of Calgary; Stantec (Canada)","funders":"Centre for Transportation Engineering and Planning","keywords":"Nested logit; TRIPS architecture; Variable (mathematics); Transport engineering; Computer science; Sign (mathematics); Logistic regression; Set (abstract data type); Variables; Ordered logit; Regression analysis; Estimation; Logit; Operations research; Engineering; Econometrics; Machine learning; Mathematics","score_opus":0.034013669252391035,"score_gpt":0.3075847215307452,"score_spread":0.27357105227835415,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1986100434","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.53156567,0.00012352476,0.42016315,0.015064496,0.0021536748,0.00018172468,0.0000071742406,0.000034698493,0.030705875],"genre_scores_gemma":[0.96487796,0.000021683338,0.03202852,0.0003898697,0.0002570863,0.0000022154634,6.708035e-7,0.0000034806153,0.0024184864],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9983055,0.00013934805,0.00031164067,0.00010758851,0.0009828096,0.00015311743],"domain_scores_gemma":[0.9986158,0.00028611836,0.00019407884,0.000037275957,0.0007318175,0.00013492742],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021436238,0.00005688054,0.00008727162,0.0002904267,0.00023995784,0.00029817066,0.00059317355,0.00003763518,0.0008652179],"category_scores_gemma":[0.00029226445,0.00004786034,0.00004509088,0.00035715406,0.00013892278,0.0008081262,0.000008064091,0.000080391335,0.00004294847],"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":[0.00016940909,0.00006211984,0.0057134326,0.0000010231053,0.000034991263,0.0000128331985,0.02396392,0.934871,0.00090699055,0.01973007,0.012933594,0.0016005996],"study_design_scores_gemma":[0.003395131,0.0014921499,0.03988821,0.0008668513,0.00009873333,0.00007488242,0.061614573,0.18679729,0.0020326138,0.0492879,0.65292007,0.0015316005],"about_ca_topic_score_codex":0.0004763921,"about_ca_topic_score_gemma":0.000011162503,"teacher_disagreement_score":0.7480737,"about_ca_system_score_codex":0.000059932863,"about_ca_system_score_gemma":0.0003674671,"threshold_uncertainty_score":0.9473529},"labels":[],"label_agreement":null},{"id":"W1986972052","doi":"10.1007/s10479-008-0380-8","title":"A hierarchical location-allocation model with travel based on expected referral distances","year":2008,"lang":"en","type":"article","venue":"Annals of Operations Research","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":21,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Irrationality; Irrational number; Travel time; Theory of computation; Computer science; Travel behavior; Referral; Location-allocation; Facility location problem; Operations research; Mathematics; Microeconomics; Economics; Transport engineering; Rationality; Algorithm; Medicine; Engineering","score_opus":0.2675221041792846,"score_gpt":0.45042250610904716,"score_spread":0.18290040192976253,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1986972052","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6874256,0.00008990064,0.25373954,0.022982903,0.00004509521,0.0008720857,0.00005696194,0.0001107513,0.034677137],"genre_scores_gemma":[0.99111074,0.00011739593,0.0060826996,0.0001413549,0.000043279855,0.00008718571,0.00016910439,0.000010164612,0.0022380769],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979479,0.000310253,0.00022777029,0.000221934,0.0010328381,0.00025935614],"domain_scores_gemma":[0.997773,0.00017447882,0.000026549644,0.0001931924,0.0017171998,0.00011559521],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007866934,0.00007553878,0.0001104408,0.00030268112,0.0012396531,0.000051743,0.00018222627,0.00007417089,0.0000651092],"category_scores_gemma":[0.00033898724,0.000066108354,0.00002679706,0.0011150644,0.0005573049,0.00028583637,0.0000033155966,0.00020477013,0.000009821001],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016943322,0.00023781268,0.0012976588,0.000008794932,0.000008239717,0.0000023434227,0.012987532,0.9357717,0.00009184793,0.04805233,0.0011256011,0.0002467312],"study_design_scores_gemma":[0.0006259198,0.00042674923,0.03350594,0.00011880079,0.0000055128708,6.823536e-7,0.0043774345,0.95827234,0.001469604,0.00035424728,0.000620769,0.00022197659],"about_ca_topic_score_codex":0.001155394,"about_ca_topic_score_gemma":0.0031006658,"teacher_disagreement_score":0.3036851,"about_ca_system_score_codex":0.000029658568,"about_ca_system_score_gemma":0.001204061,"threshold_uncertainty_score":0.9534535},"labels":[],"label_agreement":null},{"id":"W1987973803","doi":"10.3141/1895-13","title":"The Trouble with Intercity Travel Demand Models","year":2004,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Nested logit; TRIPS architecture; Transport engineering; Demand forecasting; Mode choice; Travel behavior; Trip generation; Computer science; Traffic congestion; Demand management; Operations research; Mathematical model; Economics; Engineering; Public transport; Econometrics","score_opus":0.10092839926712044,"score_gpt":0.38827562396794624,"score_spread":0.2873472247008258,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1987973803","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9358432,0.0003291745,0.044548396,0.015654402,0.0005602325,0.0013086902,0.000042110663,0.000052908174,0.0016608312],"genre_scores_gemma":[0.9934691,0.0021169875,0.002785165,0.000057366226,0.00020266145,0.00006778829,0.000015312873,0.000045294742,0.0012403326],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99129874,0.0013738697,0.001155707,0.00037132576,0.0047488115,0.0010515605],"domain_scores_gemma":[0.9930931,0.0011629035,0.00047398332,0.00040990778,0.0044038473,0.00045629797],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.007859478,0.00023051158,0.000351198,0.000535695,0.0034633474,0.00037900035,0.0013593127,0.00020827395,0.00008079875],"category_scores_gemma":[0.00017092278,0.00014108741,0.000292128,0.0024111294,0.0017831826,0.0012326725,0.0000033114595,0.0020184855,0.000012101382],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.008354993,0.0009077232,0.18233144,0.00024327381,0.0005826474,0.00028973148,0.13224916,0.32122377,0.0007800782,0.3380522,0.006044642,0.008940345],"study_design_scores_gemma":[0.00537464,0.0011778149,0.8624581,0.0008509899,0.00014227025,0.0000012564302,0.047718316,0.00033497374,0.0010874206,0.0551687,0.025201926,0.00048355674],"about_ca_topic_score_codex":0.027477494,"about_ca_topic_score_gemma":0.30843315,"teacher_disagreement_score":0.68012667,"about_ca_system_score_codex":0.00039237444,"about_ca_system_score_gemma":0.0019476438,"threshold_uncertainty_score":0.997834},"labels":[],"label_agreement":null},{"id":"W1988567495","doi":"10.1177/027046760002000504","title":"The Real Costs of the Automobile: A Report on Recent Research","year":2000,"lang":"en","type":"article","venue":"Bulletin of Science Technology & Society","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Automotive industry; Business; Operations management; Operations research; Marketing; Computer science; Engineering","score_opus":0.02450480351573268,"score_gpt":0.34762325722471893,"score_spread":0.32311845370898623,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1988567495","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.85423696,0.00009610629,0.00000841583,0.06895167,0.00019787594,0.00041678158,0.0000044276762,0.00013710922,0.07595063],"genre_scores_gemma":[0.99149597,0.0027072432,0.001083871,0.000091097856,0.000019545343,0.000024942914,8.8902374e-7,0.000004557616,0.0045718728],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99795824,0.000107748856,0.00027808856,0.00025591676,0.0010495128,0.0003504738],"domain_scores_gemma":[0.9985469,0.0002612831,0.00015943481,0.0004296081,0.0005655749,0.000037225636],"candidate_categories":["sts"],"consensus_categories":["sts"],"category_scores_codex":[0.005836864,0.000057066554,0.00009318661,0.000066966175,0.0023545183,0.000024479232,0.0009957501,0.00015179845,0.00022416603],"category_scores_gemma":[0.00074684375,0.000035760746,0.000065113505,0.0028668887,0.014022533,0.000025715368,0.000035863988,0.00031497746,0.000012976843],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012372225,0.00066192454,0.050595492,0.000023839168,0.000054224238,0.000014893377,0.056726884,0.0031420114,0.0051210625,0.42518982,0.30483505,0.15351106],"study_design_scores_gemma":[0.0002189624,0.00012617836,0.019452726,0.000094750365,0.000008842616,0.000003920684,0.028139897,0.0000689826,0.0051425556,0.0017982547,0.944835,0.000109951085],"about_ca_topic_score_codex":0.00082556356,"about_ca_topic_score_gemma":0.000125962,"teacher_disagreement_score":0.6399999,"about_ca_system_score_codex":0.0001483991,"about_ca_system_score_gemma":0.00061760837,"threshold_uncertainty_score":0.9989443},"labels":[],"label_agreement":null},{"id":"W1988638385","doi":"10.3138/cpp.2013-018","title":"Reducing Automobile Dependency on Campus Using Transport Demand Management: A Case Study for Quebec City","year":2015,"lang":"en","type":"article","venue":"Canadian Public Policy","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"Université Laval","funders":"","keywords":"Demand management; Mode choice; Revealed preference; Dependency (UML); Preference; Mode (computer interface); Transit (satellite); Transport engineering; Modal shift; Business; Travel behavior; Environmental economics; Measure (data warehouse); Public transport; Economics; Computer science; Econometrics; Microeconomics; Engineering","score_opus":0.06893117252175122,"score_gpt":0.34333353068292155,"score_spread":0.2744023581611703,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1988638385","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9796901,0.0000237536,0.0020466768,0.0015142928,0.0002960008,0.0011192232,0.00010823181,0.00012136072,0.015080389],"genre_scores_gemma":[0.9960674,0.0000021741507,0.000626733,0.00032806437,0.00026399977,0.00006606253,0.000050781553,0.000018357128,0.0025764434],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.99859226,0.000092529364,0.00022248583,0.0002864772,0.00025012714,0.000556133],"domain_scores_gemma":[0.9985594,0.00003252349,0.00007759143,0.00018150445,0.00016130885,0.0009876754],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00081005,0.00012282893,0.00013645824,0.00062653824,0.0008758424,0.00014028646,0.0001734292,0.00009605508,0.00003584985],"category_scores_gemma":[0.00014114064,0.00013983548,0.00004736457,0.00069934595,0.00006860345,0.00035292975,0.0000027151716,0.000086282154,0.0000039727743],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008712218,0.00077559607,0.27800953,0.00012080384,0.00044165767,0.0077602956,0.38459024,0.042900797,0.0000019026564,0.20119357,0.009396756,0.07472171],"study_design_scores_gemma":[0.013403623,0.000978655,0.09171793,0.00026385492,0.00079964736,0.0005222176,0.46116388,0.017076442,0.000013864212,0.005417695,0.40505755,0.0035846473],"about_ca_topic_score_codex":0.9674252,"about_ca_topic_score_gemma":0.98862505,"teacher_disagreement_score":0.3956608,"about_ca_system_score_codex":0.0010614471,"about_ca_system_score_gemma":0.0058028433,"threshold_uncertainty_score":0.99983335},"labels":[],"label_agreement":null},{"id":"W1989019060","doi":"10.1016/j.sbspro.2014.01.087","title":"Railway Rapid Transit Timetables with Variable and Elastic Demand","year":2014,"lang":"en","type":"article","venue":"Procedia - Social and Behavioral Sciences","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Transport Canada","funders":"","keywords":"Mode (computer interface); Variable (mathematics); Computer science; Mode choice; Mathematical optimization; Minification; Function (biology); Process (computing); Logit; Sigmoid function; Operations research; Transport engineering; Mathematics; Public transport; Engineering; Artificial intelligence; Artificial neural network","score_opus":0.03151335290980669,"score_gpt":0.2944283448147167,"score_spread":0.26291499190491,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1989019060","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98269314,0.0002557093,0.008134302,0.001183914,0.0001183763,0.0002166889,0.000010046172,0.000115636794,0.0072721746],"genre_scores_gemma":[0.995746,0.00006377213,0.003709415,0.00006429201,0.00012101579,0.000011975419,0.000005558669,0.000004373816,0.00027357487],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9990391,0.000059956055,0.000111051566,0.00023976208,0.00030156906,0.00024854942],"domain_scores_gemma":[0.999694,0.000062368155,0.000056766363,0.00002092028,0.00006164885,0.00010432329],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0006253214,0.000091962414,0.00013042512,0.00004938191,0.0016239311,0.00019948561,0.0000852388,0.00007244225,0.000066644774],"category_scores_gemma":[0.00002357803,0.00006974344,0.000013303811,0.0003767789,0.00086978567,0.00040616354,0.0000052542673,0.000062704916,0.0000015417187],"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":[0.0001535153,0.00037925487,0.5848251,0.00014162473,0.000030401301,0.000005002111,0.14126104,0.0006748097,0.0010736401,0.1745864,0.0006430761,0.096226126],"study_design_scores_gemma":[0.005923547,0.0036253738,0.7833838,0.0003709401,0.0014524733,0.000025677293,0.0561393,0.0050487504,0.0007434961,0.0338469,0.10602756,0.003412203],"about_ca_topic_score_codex":0.0007200612,"about_ca_topic_score_gemma":0.0007961908,"teacher_disagreement_score":0.19855869,"about_ca_system_score_codex":0.0000073286096,"about_ca_system_score_gemma":0.000104656196,"threshold_uncertainty_score":0.9996758},"labels":[],"label_agreement":null},{"id":"W1989533890","doi":"10.3141/1710-18","title":"New Insights into Freeway Capacity at Work Zones: Empirical Case Study","year":2000,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":47,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; Federal Highway Administration; McMaster University","keywords":"Highway Capacity Manual; Downtown; Work (physics); Transport engineering; Work zone; Identification (biology); Environmental science; Geography; Engineering; Ecology; Level of service","score_opus":0.14673942913742294,"score_gpt":0.42786637631644103,"score_spread":0.2811269471790181,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1989533890","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99177647,0.00022380044,0.0014363733,0.0035366185,0.00055827515,0.0015426095,0.000016552012,0.000079789825,0.0008294935],"genre_scores_gemma":[0.9870302,0.00071570976,0.0038558007,0.000070760834,0.0004473247,0.000057937134,0.000019983523,0.000059634767,0.0077426136],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9874737,0.003506459,0.0017427608,0.00061802455,0.005590985,0.0010680726],"domain_scores_gemma":[0.99356455,0.0014467324,0.0004283509,0.00057627854,0.002964721,0.001019391],"candidate_categories":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00542408,0.00032823678,0.0005512409,0.0010050819,0.002780174,0.00026829125,0.0011986576,0.0003449873,0.0028032314],"category_scores_gemma":[0.00022342528,0.00026220293,0.00042122486,0.0047214413,0.00096268783,0.0010699237,0.000005316157,0.0022861979,0.00010586788],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017885453,0.00073425507,0.711714,0.00006257158,0.00019483392,0.0020928723,0.23923641,0.007121567,0.000055021075,0.00067226216,0.020523727,0.015803888],"study_design_scores_gemma":[0.0030285502,0.0009363636,0.8478243,0.0002715104,0.00013663406,0.000004942675,0.04813011,0.000051914096,0.00008226596,0.0024755076,0.096623585,0.0004343151],"about_ca_topic_score_codex":0.15786369,"about_ca_topic_score_gemma":0.61506367,"teacher_disagreement_score":0.4572,"about_ca_system_score_codex":0.00060605304,"about_ca_system_score_gemma":0.0014788158,"threshold_uncertainty_score":0.999983},"labels":[],"label_agreement":null},{"id":"W1989732792","doi":"10.3141/1927-09","title":"Part 2: Management: Performance-Driven Transit Funding Model","year":2005,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Transit (satellite); Transit system; Equity (law); Transport engineering; Per capita; Business; Public transport; Computer science; Operations research; Engineering; Political science","score_opus":0.13360968917848723,"score_gpt":0.401231380917137,"score_spread":0.2676216917386498,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1989732792","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9731264,0.00013970373,0.0099520115,0.008024,0.0006341182,0.001198264,0.00006382892,0.0000970145,0.0067646326],"genre_scores_gemma":[0.97987914,0.0039552995,0.009141095,0.00009738262,0.00046062077,0.00006774688,0.000035108427,0.00005728641,0.0063062957],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9909459,0.0010630577,0.0014377737,0.00044538168,0.004877406,0.0012304807],"domain_scores_gemma":[0.9954129,0.00048700572,0.00043997023,0.00039993133,0.002721069,0.00053916045],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.005922361,0.00025768313,0.0003948712,0.0012216911,0.0022005218,0.00022465528,0.001290572,0.00025416352,0.0005363858],"category_scores_gemma":[0.00007410243,0.00021991479,0.00038361666,0.0026046333,0.0008628791,0.001577691,0.000004128809,0.0018209975,0.000053926353],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016781692,0.00055212865,0.21884188,0.00040910434,0.00033508916,0.00008638177,0.052258473,0.62409955,0.00033964362,0.038677756,0.023088254,0.0396336],"study_design_scores_gemma":[0.0037859129,0.00045923018,0.64346117,0.0011858278,0.00023905108,5.5130204e-7,0.015354255,0.015846621,0.0005024603,0.002191235,0.31629607,0.0006776047],"about_ca_topic_score_codex":0.002205033,"about_ca_topic_score_gemma":0.049640425,"teacher_disagreement_score":0.6082529,"about_ca_system_score_codex":0.0004167538,"about_ca_system_score_gemma":0.0007547816,"threshold_uncertainty_score":0.9990985},"labels":[],"label_agreement":null},{"id":"W1990106667","doi":"10.1007/s13676-012-0007-8","title":"A note on logit choices in strategy transit assignment","year":2012,"lang":"en","type":"article","venue":"EURO Journal on Transportation and Logistics","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Logit; Transit (satellite); Computation; Mathematical optimization; Computer science; Representation (politics); Tree (set theory); Path (computing); Sensitivity (control systems); Centroid; Mixed logit; Operations research; Public transport; Logistic regression; Mathematics; Algorithm; Transport engineering; Engineering; Artificial intelligence; Machine learning; Combinatorics; Computer network","score_opus":0.05427746854326112,"score_gpt":0.3282431087095666,"score_spread":0.2739656401663055,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1990106667","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.60447323,0.0007546813,0.3226752,0.0061630174,0.0027265565,0.0008274249,0.00018670918,0.00025581452,0.061937366],"genre_scores_gemma":[0.9977473,0.00061497337,0.00056246616,0.0005467841,0.00018650113,0.0000025219852,0.000038140646,0.000010169942,0.0002911333],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9987731,0.000132906,0.00032529345,0.00012191161,0.000375123,0.00027165262],"domain_scores_gemma":[0.9993936,0.000166847,0.00013410058,0.00004729554,0.000052750624,0.00020540236],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00059565593,0.000115485316,0.00013130187,0.0001418869,0.00027421556,0.00007257104,0.0000699586,0.00009278633,0.0001267315],"category_scores_gemma":[0.000053150783,0.000105052044,0.000039320377,0.00017505651,0.000084519255,0.00022028793,1.8149622e-7,0.00030563446,0.0000119431825],"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":[0.0006528164,0.0010457365,0.19994935,0.00005212982,0.000040712996,0.00021409505,0.055504445,0.48113468,0.000173643,0.23353435,0.0005386217,0.02715941],"study_design_scores_gemma":[0.0011790078,0.00033928832,0.9617232,0.00009453612,0.000045551427,0.0000027200044,0.0022173263,0.00025155442,0.000062926476,0.00072417565,0.033092573,0.0002671548],"about_ca_topic_score_codex":0.00007031016,"about_ca_topic_score_gemma":0.0005630067,"teacher_disagreement_score":0.7617738,"about_ca_system_score_codex":0.0000462427,"about_ca_system_score_gemma":0.00006398342,"threshold_uncertainty_score":0.4283898},"labels":[],"label_agreement":null},{"id":"W1993039392","doi":"10.1109/itsc.2011.6083130","title":"A combinatorial algorithm and warm start method for dynamic traffic assignment","year":2011,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Cell Transmission Model; Algorithm; Convergence (economics); FIFO (computing and electronics); Queue; Mathematical optimization; Path (computing); Traffic congestion; Mathematics; Computer network; Engineering","score_opus":0.029209437562459972,"score_gpt":0.31442371996751267,"score_spread":0.2852142824050527,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1993039392","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0065430575,0.000019973455,0.98717535,0.00019190056,0.0005022248,0.00028276801,0.000014094562,0.00010532509,0.0051653325],"genre_scores_gemma":[0.48998708,0.000027947466,0.5086853,0.00007323579,0.000050811126,0.000035565175,0.000030465259,0.000008028918,0.0011015895],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994944,0.00004934812,0.00009655976,0.000119658354,0.00011753232,0.00012252643],"domain_scores_gemma":[0.99975723,0.00007392717,0.000031205724,0.000031415453,0.00004358588,0.00006262423],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042323736,0.000047256537,0.000068957546,0.000028580402,0.0002060337,0.000020797535,0.000040508636,0.000052989173,0.00010391452],"category_scores_gemma":[0.000016977527,0.000043918673,0.000021412701,0.00006305235,0.00003808985,0.00008518384,0.0000017301295,0.00002776647,0.0000018207211],"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":[0.00016124905,0.0004422123,0.0011140931,0.000032212807,0.000098344084,0.000004829344,0.17636614,0.0013840923,0.000051184812,0.25001988,0.0019119282,0.56841385],"study_design_scores_gemma":[0.0100999605,0.0014027657,0.017505016,0.00006321579,0.00037028344,0.0000024333494,0.047423232,0.73116064,0.00082576193,0.058842443,0.13077576,0.0015284765],"about_ca_topic_score_codex":0.0004213677,"about_ca_topic_score_gemma":0.0003035513,"teacher_disagreement_score":0.72977656,"about_ca_system_score_codex":0.000022279866,"about_ca_system_score_gemma":0.000046727877,"threshold_uncertainty_score":0.17909515},"labels":[],"label_agreement":null},{"id":"W1993485958","doi":"10.1002/atr.174","title":"Optimizing sustainable feeder bus operation considering realistic networks and heterogeneous demand","year":2011,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Headway; Computer science; Profit (economics); Park and ride; Transit system; Price elasticity of demand; Operations research; Transport engineering; Mathematical optimization; Public transport; Simulation; Engineering; Transit (satellite); Economics; Microeconomics","score_opus":0.018260839795156848,"score_gpt":0.262719266132094,"score_spread":0.24445842633693715,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1993485958","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6580545,0.0010003521,0.33934054,0.00012306175,0.00034562146,0.00024019339,0.0000029936175,0.000039891023,0.00085286127],"genre_scores_gemma":[0.9735598,0.0008601459,0.025328016,0.000049326773,0.00007934353,0.0000033379047,0.000019498446,0.000012440846,0.00008811056],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99899095,0.00005972295,0.00043344157,0.00011616525,0.00020053508,0.00019918065],"domain_scores_gemma":[0.99911,0.00005603951,0.00032559907,0.000048070237,0.0003464211,0.000113873684],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039943782,0.00009412274,0.00016332722,0.00011239849,0.00034966494,0.000051164785,0.00005832089,0.00007918457,0.00004712121],"category_scores_gemma":[0.000040239436,0.00009568162,0.00004430878,0.00014248968,0.00007617915,0.0008193211,7.851261e-7,0.0001178905,3.5216942e-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":[0.00014480975,0.000024740137,0.003857263,0.000021971948,0.000023387425,0.000077983306,0.037787586,0.95295805,0.00007973812,0.004264925,0.000009931013,0.0007495892],"study_design_scores_gemma":[0.013985701,0.0022149705,0.6736012,0.0013352106,0.0016906983,0.00022696302,0.25254238,0.02468554,0.0041549546,0.011799968,0.01107565,0.0026867378],"about_ca_topic_score_codex":0.00020392211,"about_ca_topic_score_gemma":0.00059979,"teacher_disagreement_score":0.92827255,"about_ca_system_score_codex":0.000041030635,"about_ca_system_score_gemma":0.000084749474,"threshold_uncertainty_score":0.39017832},"labels":[],"label_agreement":null},{"id":"W1993898662","doi":"10.1061/9780784413210.016","title":"Opportunities and Operational Difficulties of Introducing Bus Rapid Transit (BRT) in Dhaka","year":2013,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Headway; Bus rapid transit; Public transport; Occupancy; Transport engineering; Transit (satellite); Service (business); Population; Intersection (aeronautics); Business; Computer science; Engineering; Civil engineering","score_opus":0.029514123622158876,"score_gpt":0.2526811929875757,"score_spread":0.2231670693654168,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1993898662","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97886384,0.00014903596,0.0020933088,0.0052874074,0.000073710595,0.00017720638,0.000006749361,0.000039937186,0.013308782],"genre_scores_gemma":[0.99545616,0.0002914315,0.002752917,0.000092720715,0.000031062413,0.000008804638,0.000030204998,0.0000024403535,0.0013342527],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.9995233,0.000042303203,0.00014975235,0.00008007321,0.00012429841,0.000080268444],"domain_scores_gemma":[0.99977356,0.000054982163,0.000025740941,0.000030216905,0.0000829149,0.000032592077],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016462794,0.00003975769,0.000074003474,0.000067450615,0.00009694154,0.000035818786,0.00003496728,0.00002972104,0.0007471354],"category_scores_gemma":[0.000027037531,0.000035305304,0.000010432277,0.00008699137,0.000102961065,0.0002830382,0.0000015384067,0.00003055006,0.0000019090146],"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":[0.000051120573,0.00028275003,0.1354638,0.00012093204,0.00005558768,0.0000059522918,0.43211693,0.07187473,0.0025412622,0.24326043,0.014851589,0.09937492],"study_design_scores_gemma":[0.0010334863,0.00007728366,0.83568376,0.000071674316,0.000018312307,0.000001055581,0.14763138,0.007055672,0.0007096742,0.00070563145,0.006661136,0.00035091996],"about_ca_topic_score_codex":0.0032471102,"about_ca_topic_score_gemma":0.0011647359,"teacher_disagreement_score":0.70022,"about_ca_system_score_codex":0.000006791391,"about_ca_system_score_gemma":0.000051084353,"threshold_uncertainty_score":0.8180608},"labels":[],"label_agreement":null},{"id":"W1993930008","doi":"10.1002/atr.5670420202","title":"Modeling the evolutions of day‐to‐day route choice and year‐to‐year ATIS adoption with stochastic user equilibrium","year":2008,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":53,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Advice (programming); Logit; Travel time; Transport engineering; Computer science; Perfect information; Travel behavior; Operations research; Information system; Path (computing); Imperfect; Logistic regression; Compliance (psychology); Simulation; Engineering; Economics; Computer network; Microeconomics; Psychology","score_opus":0.017229438396149568,"score_gpt":0.27135084521414027,"score_spread":0.2541214068179907,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1993930008","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6871693,0.00004893803,0.31199202,0.00045241695,0.00010839061,0.00016761385,0.0000165794,0.000013092575,0.000031642336],"genre_scores_gemma":[0.97978115,0.000081181955,0.019898804,0.00004289118,0.00009060634,0.0000053585863,0.00001719353,0.000012223685,0.000070570895],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99883974,0.000056865763,0.00038959974,0.00011778167,0.00045147564,0.00014454413],"domain_scores_gemma":[0.99900866,0.00010707739,0.00022705855,0.0000765907,0.00045826455,0.00012236103],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034337697,0.00008767366,0.00015633497,0.00014236811,0.00023237689,0.000015169838,0.00009964146,0.000047053745,0.000008255644],"category_scores_gemma":[0.000106130516,0.000069469825,0.000043791322,0.00043684864,0.00007275902,0.0005658414,9.702269e-7,0.00012390685,0.0000013374075],"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":[0.0000931659,0.000021484691,0.011100996,0.000008051627,0.000016100805,0.0000017824968,0.025511684,0.96179914,0.00035566892,0.0008568956,0.000012946405,0.00022207943],"study_design_scores_gemma":[0.0014964477,0.00041977054,0.98265004,0.000419463,0.00017329655,0.0000049614973,0.00977682,0.004013011,0.000071640236,0.00023840704,0.00049472007,0.00024139312],"about_ca_topic_score_codex":0.00017172926,"about_ca_topic_score_gemma":0.0016407896,"teacher_disagreement_score":0.9715491,"about_ca_system_score_codex":0.00004114793,"about_ca_system_score_gemma":0.00017493447,"threshold_uncertainty_score":0.28328973},"labels":[],"label_agreement":null},{"id":"W1994013888","doi":"10.1016/j.trc.2013.05.009","title":"Unified estimator for excess journey time under heterogeneous passenger incidence behavior using smartcard data","year":2013,"lang":"en","type":"article","venue":"Transportation Research Part C Emerging Technologies","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":48,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Estimator; Public transport; Computer science; Quality of service; Smart card; Incidence (geometry); Service (business); Quality (philosophy); Aggregate (composite); Service quality; Computer security; Statistics; Transport engineering; Engineering; Computer network; Mathematics; Business","score_opus":0.2412176020426375,"score_gpt":0.4524134054501573,"score_spread":0.21119580340751978,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1994013888","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94558007,0.00027477724,0.044132113,0.0053035407,0.00027432077,0.0021970775,0.00027297312,0.0018326874,0.00013244827],"genre_scores_gemma":[0.97301054,0.00026032678,0.02497329,0.00002612132,0.00006356875,0.0004609522,0.00073780183,0.000039449227,0.00042796138],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971359,0.0001663113,0.0005004633,0.0005714363,0.00089810166,0.00072783284],"domain_scores_gemma":[0.99782974,0.00042535368,0.00016626538,0.00062213995,0.0008366131,0.00011989637],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0014935909,0.00018523903,0.00024142295,0.00041856384,0.0014116637,0.0002407537,0.0010049017,0.0002834358,0.00026757497],"category_scores_gemma":[0.0004650992,0.0001900528,0.00006329818,0.0008929996,0.0005967462,0.0010541753,0.00002463358,0.00035594366,0.00005134481],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005331673,0.0017393789,0.31547657,0.0009734571,0.00085317733,0.00030233318,0.035997786,0.3832381,0.03507463,0.07174244,0.08277732,0.07129164],"study_design_scores_gemma":[0.0071026,0.0010542119,0.21905035,0.0021590954,0.0012478122,0.00001685765,0.1795253,0.3438425,0.020230118,0.059222084,0.1600973,0.006451778],"about_ca_topic_score_codex":0.003049433,"about_ca_topic_score_gemma":0.0026639157,"teacher_disagreement_score":0.14352751,"about_ca_system_score_codex":0.0000939118,"about_ca_system_score_gemma":0.00031253617,"threshold_uncertainty_score":0.99988836},"labels":[],"label_agreement":null},{"id":"W1994074689","doi":"10.1590/s2238-10312013000400006","title":"Estimando valor de tempo de viagem com diferentes fontes de dados utilizando modelos logit","year":2013,"lang":"pt","type":"article","venue":"Journal of Transport Literature","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Transport Canada","funders":"","keywords":"Humanities; Mathematics; Philosophy","score_opus":0.02201611816229502,"score_gpt":0.2837009532087002,"score_spread":0.26168483504640516,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1994074689","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6300425,0.009081282,0.3470309,0.010478367,0.0009913801,0.00075305573,0.00033867077,0.00013581805,0.0011480566],"genre_scores_gemma":[0.98754203,0.0013795898,0.0065679536,0.0006016471,0.00039321184,0.000012160769,0.00012628439,0.00004502666,0.0033321155],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99697113,0.0002817102,0.0010367635,0.0002902592,0.00067605736,0.0007440997],"domain_scores_gemma":[0.9975359,0.00021873372,0.0007046111,0.00019589419,0.0007360392,0.0006088354],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0010097479,0.00039528712,0.0006352661,0.0002741165,0.00042572062,0.00044209944,0.0005641022,0.0005707579,0.00036651615],"category_scores_gemma":[0.00010042923,0.00033721197,0.00049770967,0.0005659461,0.00020749473,0.0010132863,0.0000051597017,0.0010823836,0.0000096064605],"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":[0.00095400726,0.0008902043,0.68636155,0.00066699466,0.0006128697,0.0009105284,0.19375028,0.096260205,0.0012734996,0.0021451882,0.013312685,0.0028620018],"study_design_scores_gemma":[0.01428198,0.0018760705,0.6985151,0.012836255,0.0033045078,0.0007765572,0.027627349,0.20721021,0.0018460765,0.01155446,0.01716896,0.0030024694],"about_ca_topic_score_codex":0.00036897653,"about_ca_topic_score_gemma":0.00014387799,"teacher_disagreement_score":0.35749954,"about_ca_system_score_codex":0.00018737502,"about_ca_system_score_gemma":0.00071273075,"threshold_uncertainty_score":0.999908},"labels":[],"label_agreement":null},{"id":"W1994092630","doi":"10.3141/2283-11","title":"Demand and Supply Calibration of Dynamic Traffic Assignment Models","year":2012,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Calibration; Computer science; Robustness (evolution); Context (archaeology); Supply and demand; Queue; Operations research; Queueing theory; Traffic simulation; Mathematical optimization; Simulation; Industrial engineering; Transport engineering; Microsimulation; Engineering; Economics; Mathematics","score_opus":0.08088738083550363,"score_gpt":0.38751252166142064,"score_spread":0.306625140825917,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1994092630","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9803905,0.0007857102,0.0149368085,0.00237192,0.0004181351,0.00080352556,0.00006455546,0.000026874395,0.0002019794],"genre_scores_gemma":[0.99394244,0.00277974,0.002585734,0.00002366414,0.00012587201,0.00003348679,0.00003279985,0.00003301493,0.0004432248],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99265355,0.0016881276,0.001214497,0.00025463282,0.0033876935,0.0008015031],"domain_scores_gemma":[0.9959073,0.0009351795,0.0005200865,0.00021607101,0.0019378182,0.00048352548],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007182153,0.00018338358,0.00037151287,0.00077848235,0.00088051683,0.00009563347,0.00048348543,0.00022625913,0.00017400266],"category_scores_gemma":[0.000142665,0.00014965955,0.00020953843,0.0015126665,0.00095630967,0.0016331624,0.0000032121898,0.0010109716,0.0000022542965],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0021080044,0.0011098655,0.67572695,0.00060569186,0.00034690637,0.000031489988,0.15840498,0.08902122,0.0031728486,0.041021727,0.0038721631,0.024578137],"study_design_scores_gemma":[0.0015103337,0.00036748254,0.97206557,0.0003997131,0.00010799816,3.9990502e-7,0.013859884,0.004584731,0.0004953561,0.0028447018,0.0035125352,0.0002513176],"about_ca_topic_score_codex":0.0061507383,"about_ca_topic_score_gemma":0.032037947,"teacher_disagreement_score":0.2963386,"about_ca_system_score_codex":0.00018961397,"about_ca_system_score_gemma":0.0006277148,"threshold_uncertainty_score":0.98562485},"labels":[],"label_agreement":null},{"id":"W1995917660","doi":"10.1016/j.aap.2012.06.012","title":"Safety models incorporating graph theory based transit indicators","year":2012,"lang":"en","type":"article","venue":"Accident Analysis & Prevention","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":36,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Macro; Graph; Transit (satellite); Public transport; Computer science; Collision; Network planning and design; Transport engineering; Graph theory; Network analysis; Engineering; Mathematics; Computer network; Theoretical computer science; Computer security","score_opus":0.018596381118614662,"score_gpt":0.2956384000740306,"score_spread":0.2770420189554159,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1995917660","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1962006,0.00015115955,0.79878956,0.00016572827,0.00009447478,0.00016966992,7.662506e-7,0.00012129554,0.0043067643],"genre_scores_gemma":[0.9949395,0.00003976319,0.0038957316,0.0000684144,0.00008005824,0.000016342423,0.00038697585,0.000008980433,0.00056422706],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99820894,0.0005467635,0.00037180338,0.00018165288,0.000448726,0.00024208565],"domain_scores_gemma":[0.99927145,0.00009989394,0.0002860446,0.00014497299,0.00006919717,0.00012842982],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025199314,0.00010347112,0.00018068816,0.0006219936,0.0005322465,0.000061898754,0.00014608029,0.00010715051,0.0005292561],"category_scores_gemma":[0.000041488525,0.00010766939,0.00030292544,0.0021898358,0.000056771914,0.0009962254,0.0000042683023,0.0000924427,0.0000117294],"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":[0.000028816981,0.00009876085,0.7762338,0.0000012709055,0.00032334455,4.1045038e-7,0.0059863855,0.1543148,0.000008949806,0.05695041,0.000025569705,0.0060274745],"study_design_scores_gemma":[0.00091073196,0.000037252907,0.92529523,0.00006277661,0.0056230198,1.723951e-7,0.0063575828,0.02075574,0.00020274245,0.03930267,0.0008075946,0.00064447126],"about_ca_topic_score_codex":0.00031215374,"about_ca_topic_score_gemma":0.0042289277,"teacher_disagreement_score":0.7987389,"about_ca_system_score_codex":0.00006126811,"about_ca_system_score_gemma":0.00006967922,"threshold_uncertainty_score":0.57949835},"labels":[],"label_agreement":null},{"id":"W1996460647","doi":"10.1002/atr.5670430402","title":"Multi‐criteria analysis for evaluating the impacts of intelligent speed adaptation","year":2009,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"European Commission","keywords":"Computer science; Adaptation (eye); Transport engineering; Exploratory analysis; Intelligent transportation system; Throughput; Risk analysis (engineering); Operations research; Engineering; Business; Data science","score_opus":0.06379249133076621,"score_gpt":0.40114125017601726,"score_spread":0.33734875884525106,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1996460647","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.63855875,0.00014163222,0.36036682,0.0004914063,0.00017461888,0.00022127513,0.00001670785,0.000009910828,0.000018884268],"genre_scores_gemma":[0.9208606,0.00015025506,0.07877332,0.00004883114,0.00006094372,0.0000014442874,0.00007480513,0.0000046946066,0.000025144698],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9986092,0.000082836596,0.00068596,0.0000872765,0.00040906476,0.00012568434],"domain_scores_gemma":[0.9979673,0.00018520327,0.00088349055,0.00006500537,0.0008404497,0.00005854242],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011255619,0.000076597615,0.00020931345,0.00020853967,0.000174868,0.000022657316,0.000113168055,0.000049117265,0.000021092557],"category_scores_gemma":[0.0002126243,0.00006014646,0.00021676706,0.00064010004,0.000041170995,0.0004343614,1.7554815e-7,0.000076554694,1.9703312e-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":[0.0003929077,0.00006702102,0.003471044,0.000011121966,0.00010665295,8.0543816e-7,0.054763954,0.921997,0.0070947823,0.0005493341,0.0000067697615,0.011538612],"study_design_scores_gemma":[0.0015926951,0.00088341074,0.937641,0.00010968002,0.0012372915,4.81698e-7,0.027070073,0.02759386,0.0022153847,0.0012132543,0.000282315,0.00016050857],"about_ca_topic_score_codex":0.00006383587,"about_ca_topic_score_gemma":0.00075287296,"teacher_disagreement_score":0.93417,"about_ca_system_score_codex":0.000040598945,"about_ca_system_score_gemma":0.00013235213,"threshold_uncertainty_score":0.24527013},"labels":[],"label_agreement":null},{"id":"W1996480852","doi":"10.1061/40952(317)59","title":"Serve Sequence Optimization Approach for Multiple Bus Priority Requests Based on Decision Tree","year":2008,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Tongji University; York University","keywords":"Computer science; Sequence (biology); Decision tree; Reliability (semiconductor); Tree (set theory); Key (lock); Service (business); Operations research; Computer network; Data mining; Engineering; Computer security; Mathematics","score_opus":0.06380306764717081,"score_gpt":0.3163083202687938,"score_spread":0.252505252621623,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1996480852","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0155245615,0.0000072341445,0.9728587,0.00020507604,0.0001204925,0.00052004,0.000020040141,0.00022995273,0.010513846],"genre_scores_gemma":[0.5413168,0.000019883459,0.4577566,0.0001452808,0.000048051632,0.000029915722,0.00022665068,0.000008076999,0.0004486947],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99887866,0.00007522029,0.00020454547,0.00027583764,0.0003659346,0.00019978528],"domain_scores_gemma":[0.9991093,0.00034005908,0.00008365896,0.00014269003,0.00022890771,0.000095358526],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004261446,0.00009859568,0.00011304221,0.00008574438,0.0006859382,0.00003609136,0.00013492058,0.00012813957,0.000041633397],"category_scores_gemma":[0.00043986138,0.000093179726,0.00005732699,0.0003188664,0.00008463627,0.00026316935,0.0000023718756,0.00006378974,0.0000030738001],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010196529,0.000085018524,0.014406248,0.000006235024,0.000002108649,0.000001436207,0.0012424085,0.98126274,0.0000071656445,0.001170211,0.00031818007,0.0013963049],"study_design_scores_gemma":[0.0008374993,0.000066447334,0.0064059133,0.000016026226,0.000007439628,3.8713608e-7,0.00021645827,0.9915389,0.00008031923,0.000051662726,0.0006332508,0.00014570494],"about_ca_topic_score_codex":0.00050021417,"about_ca_topic_score_gemma":0.00051760586,"teacher_disagreement_score":0.52579224,"about_ca_system_score_codex":0.00008099695,"about_ca_system_score_gemma":0.00019320988,"threshold_uncertainty_score":0.52757514},"labels":[],"label_agreement":null},{"id":"W1996586723","doi":"10.1002/atr.5670370305","title":"Retrospect and prospect of public transit privatization in China","year":2003,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","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":"","keywords":"China; Pace; General partnership; Government (linguistics); Business; Public–private partnership; Public transport; Economic policy; Market economy; Economics; Economic growth; Finance; Political science; Law","score_opus":0.011018511155977866,"score_gpt":0.26443299978335594,"score_spread":0.2534144886273781,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1996586723","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.969027,0.00026052078,0.029266607,0.0003276554,0.00014564263,0.00016641758,0.000004866978,0.000012069203,0.0007892202],"genre_scores_gemma":[0.9873427,0.0006262316,0.011949661,0.000009402049,0.000019628074,0.0000018095576,0.000014615982,0.000007861111,0.000028055883],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9988046,0.00009391681,0.0005523633,0.00009583781,0.0003265771,0.00012669685],"domain_scores_gemma":[0.9991895,0.000041626787,0.0004323933,0.000044597433,0.00022685369,0.000065062355],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00073364924,0.00007243147,0.00018872238,0.00022324722,0.00007666298,0.000016134712,0.000055949673,0.0000645365,0.000027526597],"category_scores_gemma":[0.00015357556,0.00007280245,0.000045019533,0.00055266905,0.000071622824,0.0007350258,1.4485137e-7,0.00011799348,1.1888902e-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":[0.00012225095,0.00016967117,0.72970873,0.00007415542,0.000023902607,0.000013998036,0.058550816,0.09416866,0.002415609,0.10986735,0.000006794915,0.004878079],"study_design_scores_gemma":[0.0011317804,0.00011141465,0.991627,0.000083213556,0.000020706175,0.0000014759761,0.0024896367,0.000020691665,0.0007443711,0.002920437,0.0007647387,0.00008454701],"about_ca_topic_score_codex":0.000030529318,"about_ca_topic_score_gemma":0.0008881394,"teacher_disagreement_score":0.26191828,"about_ca_system_score_codex":0.00004333961,"about_ca_system_score_gemma":0.00014074963,"threshold_uncertainty_score":0.2968798},"labels":[],"label_agreement":null},{"id":"W1996849344","doi":"10.1007/s40534-013-0005-z","title":"Road traffic congestion measurement considering impacts on travelers","year":2013,"lang":"en","type":"article","venue":"Journal of Modern Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ministry of Transportation of Ontario","funders":"National Natural Science Foundation of China","keywords":"Traffic congestion; Transport engineering; Feeling; Regression analysis; Variables; Logit; Econometrics; Statistics; Economics; Engineering; Psychology; Mathematics; Social psychology","score_opus":0.04366804696731401,"score_gpt":0.2739622429850059,"score_spread":0.23029419601769185,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1996849344","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98513424,0.00015066718,0.012315478,0.0013012991,0.0003258422,0.00022767662,0.000004606195,0.00004356847,0.0004965955],"genre_scores_gemma":[0.9984068,0.00014947633,0.0011664284,0.000111893016,0.00010463633,0.0000050577146,0.000014196873,0.000012842053,0.000028640772],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9981966,0.00008974754,0.00051691534,0.000110251356,0.0008954387,0.00019107321],"domain_scores_gemma":[0.9986709,0.000046406123,0.0004055783,0.000057323316,0.0006509813,0.00016881307],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00073439575,0.00010912808,0.0001723316,0.00017196305,0.00018886344,0.00006913971,0.00008712969,0.00009824846,0.0001104557],"category_scores_gemma":[0.00008028698,0.00010314337,0.0001002284,0.0001422001,0.000055065342,0.0006006428,1.7286705e-7,0.00017242527,0.000010848934],"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":[0.00017942335,0.00019288767,0.0058073625,0.000037442223,0.0000950813,0.000022265518,0.051675852,0.8416008,0.0031500026,0.00095344847,0.000600716,0.09568472],"study_design_scores_gemma":[0.0017691413,0.00031925822,0.98723924,0.00036295413,0.0001347649,0.0000038548005,0.004069571,0.0034951486,0.0005788617,0.0010339295,0.0007023134,0.00029095425],"about_ca_topic_score_codex":0.00024645147,"about_ca_topic_score_gemma":0.0011224907,"teacher_disagreement_score":0.9814319,"about_ca_system_score_codex":0.00013517572,"about_ca_system_score_gemma":0.00024930443,"threshold_uncertainty_score":0.42060646},"labels":[],"label_agreement":null},{"id":"W1996877768","doi":"10.1109/itsc.2013.6728573","title":"Simultaneous calibration of microscopic traffic simulation model and estimation of origin/destination (OD) flows based on genetic algorithms in a high-performance computer","year":2013,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Calibration; Computer science; Genetic algorithm; Nonlinear system; Real-time computing; Algorithm; Traffic simulation; Simulation; Mathematical optimization; Engineering; Microsimulation; Mathematics; Machine learning","score_opus":0.011986400911902076,"score_gpt":0.2624873881415994,"score_spread":0.2505009872296973,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1996877768","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.62986696,0.0000029440037,0.36975053,0.000046787423,0.00002984154,0.00024411862,0.0000030257825,0.000025355252,0.000030463936],"genre_scores_gemma":[0.8881381,0.0000064652736,0.11171011,0.000029189498,0.000013014058,0.000010004938,0.00005716654,0.000006050394,0.000029870826],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99914455,0.000057788835,0.00032257332,0.00014626182,0.0002268633,0.000101932346],"domain_scores_gemma":[0.9994033,0.0002256588,0.000137572,0.000066109,0.00013497833,0.000032411794],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014385505,0.00007910168,0.00012222727,0.0001681038,0.000072249524,0.00002476317,0.000047105816,0.00008266857,0.000021479707],"category_scores_gemma":[0.00004084476,0.00007900866,0.000012309382,0.0002503588,0.000052908028,0.00028952752,0.0000018624978,0.00004404919,0.0000011750474],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012658137,0.000051782306,0.0038035822,0.00003537072,0.0000013082304,1.5047777e-7,0.0014596857,0.96950024,0.00010727776,0.00011517896,0.0000023283383,0.02491046],"study_design_scores_gemma":[0.0004638842,0.000090201225,0.031353675,0.000059501715,0.0000064925052,4.5123436e-8,0.000042704374,0.96770203,0.00015154062,0.00005454022,8.0256245e-7,0.00007460751],"about_ca_topic_score_codex":0.000540918,"about_ca_topic_score_gemma":0.00019682421,"teacher_disagreement_score":0.2582712,"about_ca_system_score_codex":0.00003468951,"about_ca_system_score_gemma":0.000075624805,"threshold_uncertainty_score":0.322188},"labels":[],"label_agreement":null},{"id":"W1996974248","doi":"10.1080/713643722","title":"A Multi-Attribute Performance Measurement Model for Advanced Public Transit Systems","year":2002,"lang":"en","type":"article","venue":"Journal of Intelligent Transportation Systems","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Headway; Public transport; Schedule; Novelty; Transit (satellite); Transport engineering; Computer science; Control (management); Engineering; Service (business); Work (physics); Automatic train control; Operations research","score_opus":0.16672546232353172,"score_gpt":0.3038336016419592,"score_spread":0.1371081393184275,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1996974248","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.056755804,0.002494071,0.9378378,0.0003945287,0.0014412838,0.00083927705,0.00007818745,0.000064141816,0.00009492498],"genre_scores_gemma":[0.99432313,0.0013877256,0.0031968884,0.000028036206,0.0001669315,0.00006280918,0.000036514848,0.00002515367,0.0007728251],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968177,0.00011287424,0.0013526011,0.00019256973,0.0011813992,0.00034284062],"domain_scores_gemma":[0.9963732,0.00007157458,0.00081685814,0.000114123606,0.002384907,0.00023935102],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019347486,0.00018219044,0.00039061016,0.00028035484,0.00037940484,0.00014995273,0.00025418724,0.00014540285,0.000021733427],"category_scores_gemma":[0.00007883977,0.00016983198,0.00022013274,0.00034329112,0.000056870897,0.0008083339,4.084991e-7,0.00016465742,0.0000072067164],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000060412574,0.00017235702,0.004334157,0.0002188423,0.0000992789,0.000003862988,0.022135453,0.96983254,0.00023247096,0.0014523914,0.00045470177,0.0010035128],"study_design_scores_gemma":[0.002307903,0.00028419675,0.0024187665,0.00077321246,0.0002078958,0.000008305237,0.015922576,0.94599414,0.0002197334,0.0000058426363,0.0313998,0.00045760584],"about_ca_topic_score_codex":0.000064247615,"about_ca_topic_score_gemma":0.00027140975,"teacher_disagreement_score":0.9375673,"about_ca_system_score_codex":0.00024445972,"about_ca_system_score_gemma":0.00019983605,"threshold_uncertainty_score":0.6925547},"labels":[],"label_agreement":null},{"id":"W1997648546","doi":"10.1007/s11518-011-5159-9","title":"Urban transit assignment model based on augmented network with in-vehicle congestion and transfer congestion","year":2011,"lang":"en","type":"article","venue":"Journal of Systems Science and Systems Engineering","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Transit (satellite); Computer science; Urban transit; Transfer (computing); Traffic congestion; Urban rail transit; Transport engineering; Shortest path problem; Path (computing); Traffic network; Mathematical optimization; Simulation; Computer network; Public transport; Engineering; Graph; Mathematics","score_opus":0.022287974640882776,"score_gpt":0.2161313509847492,"score_spread":0.19384337634386642,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1997648546","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.72205645,0.0005560811,0.27564278,0.000097083204,0.0005504372,0.00038300583,0.000002768478,0.000033886474,0.0006774782],"genre_scores_gemma":[0.99948657,0.000034936384,0.00036689115,0.0000095172945,0.00006445112,0.0000070838655,4.4596752e-7,0.0000065235645,0.000023598037],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987019,0.00005571026,0.0003311447,0.0001332191,0.00057550473,0.0002025736],"domain_scores_gemma":[0.9994521,0.000048676422,0.00009596879,0.000050584622,0.00020776188,0.00014487743],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021660433,0.0000910833,0.000185935,0.00022178412,0.00019273678,0.000105846404,0.00007877893,0.000058740916,7.0455224e-7],"category_scores_gemma":[0.000025866035,0.000073309755,0.000013525115,0.0003649692,0.00010025011,0.00045157145,0.00000110768,0.00011270678,1.3683551e-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":[0.00004304487,0.000016255097,0.011182114,0.000047345096,0.0000055164533,0.000009043813,0.0059865834,0.9790615,0.00041044242,0.003146715,0.000011345076,0.000080100785],"study_design_scores_gemma":[0.0006711708,0.00021101734,0.023525119,0.0010549437,0.000020748932,0.000011679738,0.0019974797,0.9722719,0.000030116871,0.0000021541184,0.000083524734,0.000120127595],"about_ca_topic_score_codex":0.00022064288,"about_ca_topic_score_gemma":0.000034562338,"teacher_disagreement_score":0.2774301,"about_ca_system_score_codex":0.00010687894,"about_ca_system_score_gemma":0.00016506245,"threshold_uncertainty_score":0.2989485},"labels":[],"label_agreement":null},{"id":"W1998245585","doi":"10.1504/ijsem.2011.042784","title":"Modelling and discrete-event simulation for ski lifts service operations design","year":2011,"lang":"en","type":"article","venue":"International Journal of Services Economics and Management","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Université de Moncton","funders":"","keywords":"Lift (data mining); Alpine skiing; Discrete event simulation; Population; Event (particle physics); Computer science; Engineering; Operations research; Simulation; Geography","score_opus":0.04590862517666063,"score_gpt":0.2961655710021999,"score_spread":0.2502569458255393,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1998245585","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15143783,0.00010405896,0.8451671,0.001647319,0.00041018377,0.00027940312,0.000012363303,0.000008751058,0.0009329605],"genre_scores_gemma":[0.9425882,0.002758915,0.05408671,0.00036938104,0.000106196974,0.000006919472,0.000012896039,0.0000056694676,0.00006510195],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995112,0.000016367629,0.00025092904,0.00008266626,0.00007421796,0.00006462465],"domain_scores_gemma":[0.99952096,0.000038239235,0.00014698194,0.000028153232,0.00021535928,0.00005030659],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003373056,0.000051777748,0.00006889113,0.000097178156,0.00014077805,0.00012754767,0.00012016803,0.000025892008,0.0000107979295],"category_scores_gemma":[0.0000019907482,0.00005250043,0.000022965072,0.000024804951,0.000013579129,0.0004396645,0.000012884683,0.000023985303,5.619076e-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":[0.000081447484,0.000019115649,0.00018196586,0.000015352656,0.000085146414,9.664332e-7,0.007705879,0.9535241,4.0765997e-7,0.03675916,0.000003998952,0.0016224226],"study_design_scores_gemma":[0.00071701873,0.00003448593,0.0007788385,0.000048329657,0.000049793172,8.835783e-7,0.002758177,0.9818612,0.0000064230994,0.004537416,0.009131179,0.00007628934],"about_ca_topic_score_codex":0.00030736398,"about_ca_topic_score_gemma":0.0004160074,"teacher_disagreement_score":0.7911504,"about_ca_system_score_codex":0.000027019007,"about_ca_system_score_gemma":0.000018242423,"threshold_uncertainty_score":0.21409054},"labels":[],"label_agreement":null},{"id":"W1998276853","doi":"10.3141/1898-13","title":"Incremental Modeling Developments in Sacramento, California: Toward Advanced Integrated Land Use-Transport Model","year":2004,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Microsimulation; TRIPS architecture; Land use; Transport engineering; Work (physics); Computer science; Process (computing); Agency (philosophy); Transportation planning; Operations research; Simulation modeling; Land-use planning; Regional planning; Urban planning; Engineering; Economics; Civil engineering","score_opus":0.12735443390201526,"score_gpt":0.3994544251267315,"score_spread":0.2720999912247163,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1998276853","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9683034,0.00009756613,0.027545964,0.0021573154,0.0003351475,0.0011485333,0.00015752393,0.000049602313,0.0002049689],"genre_scores_gemma":[0.9818062,0.0013628447,0.016191665,0.000077356155,0.000069593014,0.00007844821,0.00010216262,0.00005877871,0.00025293874],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99116594,0.0008502956,0.0018406311,0.0005183716,0.004430214,0.0011945479],"domain_scores_gemma":[0.99532133,0.00038037822,0.00040005418,0.0002919454,0.00311314,0.0004931616],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0053192484,0.00031289636,0.00051084335,0.0013844927,0.0010153722,0.00018978473,0.0009974166,0.0003232019,0.00009997955],"category_scores_gemma":[0.00026051505,0.0002669627,0.00028598533,0.003057511,0.00057694176,0.001325657,0.0000047161657,0.002240959,0.000015833828],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00097385695,0.0002562486,0.37013417,0.00006799506,0.00005886834,0.000085769236,0.017402314,0.60698426,0.000545359,0.0023913062,0.000072175004,0.0010276604],"study_design_scores_gemma":[0.008438912,0.00042388026,0.94022775,0.001879928,0.00010149121,7.018898e-7,0.02261423,0.0107794665,0.0007483798,0.00822181,0.0057699806,0.000793491],"about_ca_topic_score_codex":0.07342643,"about_ca_topic_score_gemma":0.35241455,"teacher_disagreement_score":0.5962048,"about_ca_system_score_codex":0.0010937317,"about_ca_system_score_gemma":0.0028610544,"threshold_uncertainty_score":0.99997824},"labels":[],"label_agreement":null},{"id":"W1999191784","doi":"10.1007/s10732-013-9225-y","title":"A three-phase matheuristic for capacitated multi-commodity fixed-cost network design with design-balance constraints","year":2013,"lang":"en","type":"article","venue":"Journal of Heuristics","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":36,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal; Université de Montréal","funders":"","keywords":"Tabu search; Heuristics; Mathematical optimization; Solver; Benchmark (surveying); Computer science; Neighbourhood (mathematics); Network planning and design; Mathematics","score_opus":0.07108954159709518,"score_gpt":0.319917577250896,"score_spread":0.2488280356538008,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1999191784","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0043527493,0.000081815655,0.9941369,0.0002694776,0.00031882038,0.00070310273,0.000050142575,0.000034795255,0.000052155676],"genre_scores_gemma":[0.51178384,0.000040228595,0.48776844,0.000084208135,0.00022095503,0.000018979861,0.000013659276,0.000016530881,0.00005315966],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985788,0.00016949972,0.00047527533,0.000119835604,0.0003281677,0.00032843728],"domain_scores_gemma":[0.9971685,0.0010345391,0.0005954448,0.0000942165,0.0009080735,0.00019924724],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001097379,0.00013725673,0.00027104598,0.00006334863,0.00042091726,0.00012243084,0.00020149039,0.000096103206,0.00007820428],"category_scores_gemma":[0.0006666422,0.00011177005,0.000060683542,0.00020756373,0.00030709722,0.0002112896,0.0000024811927,0.00020914312,0.0000100486395],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012547991,0.0010038549,0.013709664,0.00008626616,0.00027227166,0.00017447765,0.008674177,0.8706491,0.00010146837,0.0032158745,0.09004757,0.01081048],"study_design_scores_gemma":[0.018905949,0.0032179486,0.02137486,0.0010417844,0.0008796504,0.00015178026,0.0035289552,0.9196232,0.00023363797,0.019034263,0.010577369,0.0014306343],"about_ca_topic_score_codex":0.00006277751,"about_ca_topic_score_gemma":0.00007471417,"teacher_disagreement_score":0.5074311,"about_ca_system_score_codex":0.00006085838,"about_ca_system_score_gemma":0.00037241707,"threshold_uncertainty_score":0.45578504},"labels":[],"label_agreement":null},{"id":"W2000116443","doi":"10.1002/atr.5670350102","title":"Evaluating commuter opinion of congestion pricing in metropolitan Seattle","year":2001,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","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":"Metropolitan area; Occupancy; Singapore Area Licensing Scheme; Transport engineering; Traffic congestion; Congestion pricing; Transit (satellite); Road pricing; Mode (computer interface); Business; Traffic flow (computer networking); Flow network; Computer science; Public transport; Geography; Engineering; Computer security; Civil engineering","score_opus":0.04515833759937181,"score_gpt":0.38590347905602174,"score_spread":0.3407451414566499,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2000116443","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98464197,0.00021276227,0.013708912,0.0004303518,0.00038818273,0.00012519133,0.000004070878,0.000012561164,0.00047597842],"genre_scores_gemma":[0.980141,0.0005048841,0.019194702,0.000019305175,0.00007356436,0.0000017255652,0.000031896394,0.000007159329,0.000025777224],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9986552,0.00011443916,0.000613326,0.00007601838,0.00041426645,0.00012675786],"domain_scores_gemma":[0.9988312,0.00013671868,0.00054925587,0.000051058785,0.00038131388,0.0000504409],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008406484,0.00006502392,0.00017711018,0.000238715,0.00008231088,0.00000978385,0.00008108284,0.00005880689,0.000032218064],"category_scores_gemma":[0.00011039631,0.00006633308,0.000058645932,0.0004404439,0.00007207437,0.00061031024,3.8546452e-7,0.00013076846,4.891534e-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":[0.00038465424,0.00011776499,0.18876344,0.000041155105,0.000019774805,0.000010984069,0.031063883,0.75544226,0.0023246133,0.0068344856,0.000011268552,0.014985701],"study_design_scores_gemma":[0.0021918397,0.0003238131,0.96349293,0.0004610785,0.00004562647,0.000002893305,0.030110933,0.0005017153,0.0005689654,0.0012623499,0.0008953925,0.0001424681],"about_ca_topic_score_codex":0.00040063146,"about_ca_topic_score_gemma":0.0014559879,"teacher_disagreement_score":0.7747295,"about_ca_system_score_codex":0.00013514058,"about_ca_system_score_gemma":0.00010875985,"threshold_uncertainty_score":0.27049845},"labels":[],"label_agreement":null},{"id":"W2000305943","doi":"10.1002/atr.5670420103","title":"Evaluation of driver satisfaction of travel information on variable message signs using fuzzy aggregation","year":2008,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":21,"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":"Variable (mathematics); Fuzzy logic; Fidelity; Computer science; Service quality; Perception; Data mining; Transport engineering; Operations research; Service (business); Simulation; Artificial intelligence; Mathematics; Engineering; Psychology; Marketing; Business; Telecommunications","score_opus":0.033161994737281514,"score_gpt":0.3028212718096228,"score_spread":0.2696592770723413,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2000305943","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92881924,0.0000534941,0.06960656,0.000033236523,0.0003750465,0.0002775729,0.000030042425,0.000011718104,0.0007931159],"genre_scores_gemma":[0.98347104,0.00021987662,0.016147193,0.000014099654,0.000052984906,0.0000029216444,0.00007544322,0.000008065242,0.000008361061],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99709237,0.00018809737,0.00095306203,0.00008500617,0.0015612848,0.00012019495],"domain_scores_gemma":[0.9957758,0.000099217934,0.0016446108,0.00007832687,0.0023425783,0.000059422142],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001513908,0.00010421243,0.0002435393,0.00036630483,0.00019221548,0.000008409133,0.000073696436,0.0001156956,0.000059421196],"category_scores_gemma":[0.00020091506,0.000108280066,0.00009650875,0.0005255474,0.00009341154,0.0021428182,3.9772598e-7,0.0001267175,7.987877e-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":[0.00021194664,0.00006512139,0.009738098,0.000033153745,0.000042074535,9.795481e-7,0.02580871,0.94302,0.009714801,0.002706205,0.000011589998,0.008647327],"study_design_scores_gemma":[0.0041520344,0.00055180234,0.9605785,0.00051652873,0.0006413007,0.000005444322,0.011091018,0.0042202002,0.014685824,0.0031079934,0.0001808545,0.00026850926],"about_ca_topic_score_codex":0.00028024556,"about_ca_topic_score_gemma":0.00015546917,"teacher_disagreement_score":0.9508404,"about_ca_system_score_codex":0.00016630327,"about_ca_system_score_gemma":0.0005809915,"threshold_uncertainty_score":0.4415533},"labels":[],"label_agreement":null},{"id":"W2000826897","doi":"10.1002/atr.5670410302","title":"Dispatching control at transfer stations in multi‐hub transit networks","year":2007,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Transfer station; Transfer (computing); Transit (satellite); Control (management); Transport engineering; Downstream (manufacturing); Heuristic; Computer science; Operations research; Public transport; Engineering; Operations management","score_opus":0.012176127135594133,"score_gpt":0.2943608299159258,"score_spread":0.2821847027803317,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2000826897","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4467811,0.00013491762,0.5523216,0.0002342281,0.00027371183,0.00014939954,0.000012765821,0.000019365942,0.00007291237],"genre_scores_gemma":[0.99013853,0.00029344737,0.009215673,0.000109631925,0.00009083342,0.0000032554058,0.00006577858,0.000016977361,0.00006585307],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99819,0.000080905316,0.00085745973,0.00014001766,0.0004276651,0.000303945],"domain_scores_gemma":[0.99906415,0.00024195384,0.00022629512,0.000056618745,0.00025726054,0.00015374068],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011144222,0.000123409,0.00023869985,0.0002574608,0.00025128602,0.000020523394,0.00010947784,0.00011970732,0.000056884757],"category_scores_gemma":[0.000033922537,0.0001271092,0.0001277702,0.000452098,0.00006926313,0.0007682278,1.9196032e-7,0.00026515377,0.0000011161231],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005216714,0.0000973365,0.054029472,0.000009741496,0.000019399427,0.000055972272,0.03826038,0.90132385,0.00070686004,0.0010035114,0.0000052068744,0.003966563],"study_design_scores_gemma":[0.0044311886,0.00008523117,0.98490787,0.00011618691,0.0000788997,0.0000020469001,0.0074088275,0.001085209,0.00014042734,0.00013623228,0.0014104751,0.00019738261],"about_ca_topic_score_codex":0.00014826944,"about_ca_topic_score_gemma":0.03553498,"teacher_disagreement_score":0.9308784,"about_ca_system_score_codex":0.00014948972,"about_ca_system_score_gemma":0.00009447532,"threshold_uncertainty_score":0.982064},"labels":[],"label_agreement":null},{"id":"W2001092482","doi":"10.1002/atr.5670420302","title":"Evaluation and enhancement of transport network reliability: Network equilibrium based approach","year":2008,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":6,"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":"Citation; Library science; Computer science; Reliability (semiconductor); Operations research; Engineering","score_opus":0.024202129433536904,"score_gpt":0.29046517792089915,"score_spread":0.26626304848736226,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2001092482","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8448723,0.0007327351,0.15258361,0.00019279565,0.00052408053,0.00041502868,0.000007506475,0.000022708035,0.0006492311],"genre_scores_gemma":[0.9268674,0.00041791782,0.07230328,0.00004085191,0.00021042732,0.00001030648,0.00009907607,0.000012351626,0.000038343092],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99739295,0.00019109253,0.00089919346,0.0001791444,0.0011069211,0.0002306999],"domain_scores_gemma":[0.9978395,0.00011968057,0.00078720174,0.000101580496,0.0010364135,0.00011560897],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024925782,0.00012589207,0.00031788487,0.000089088826,0.00021431097,0.000006531264,0.00010102972,0.00010332457,0.000047514688],"category_scores_gemma":[0.00006727181,0.00012571392,0.00011451694,0.00046999645,0.00018453183,0.00056053215,4.4701366e-7,0.0001491526,2.1514633e-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":[0.00026961946,0.00010179297,0.049061783,0.00004050061,0.000028864011,0.0000040053774,0.008226588,0.94013864,0.00018268195,0.00031105932,0.00007075359,0.0015636862],"study_design_scores_gemma":[0.0054297037,0.000764413,0.9666996,0.0004498829,0.000869702,0.0000062271074,0.003357419,0.012736498,0.0007307904,0.0031377925,0.0053028986,0.0005150558],"about_ca_topic_score_codex":0.00004130943,"about_ca_topic_score_gemma":0.0001779489,"teacher_disagreement_score":0.92740214,"about_ca_system_score_codex":0.00006624903,"about_ca_system_score_gemma":0.0005052302,"threshold_uncertainty_score":0.5126465},"labels":[],"label_agreement":null},{"id":"W2002238061","doi":"10.3141/2256-10","title":"Future Scenarios for Traffic Information and Management","year":2011,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ministry of Transportation of Ontario","funders":"","keywords":"Regret; Risk analysis (engineering); Transport engineering; Computer science; Process management; Operations research; Business; Engineering","score_opus":0.08537183044919099,"score_gpt":0.3709179685180203,"score_spread":0.2855461380688293,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2002238061","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9831795,0.00025412985,0.007021877,0.004683167,0.0010774342,0.0024157108,0.00009235413,0.00006527767,0.001210518],"genre_scores_gemma":[0.97874284,0.0048163636,0.015165862,0.000094204755,0.00029472736,0.00014995466,0.000047992264,0.00003185121,0.00065623084],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.99500203,0.0006831606,0.0010550345,0.00024329686,0.0023311803,0.00068528083],"domain_scores_gemma":[0.9955441,0.00040216674,0.00041210765,0.00023156828,0.0030658187,0.00034424485],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0050713387,0.00017288025,0.00026740847,0.0009449702,0.001427713,0.00015955027,0.0006572111,0.00021003402,0.00016268021],"category_scores_gemma":[0.000094853975,0.00014059994,0.00021920653,0.0014867873,0.00064445194,0.001456574,0.0000028086185,0.00085346395,0.000008940593],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.007233817,0.00080326904,0.2568559,0.0017773787,0.00064710027,0.000096490694,0.3028104,0.009269972,0.00010072817,0.21263236,0.03215319,0.17561938],"study_design_scores_gemma":[0.0020083147,0.00042005308,0.759569,0.00026559577,0.000095027535,2.40427e-7,0.03446713,0.0001832729,0.000062486164,0.002576891,0.20012736,0.00022465963],"about_ca_topic_score_codex":0.0043796417,"about_ca_topic_score_gemma":0.04184681,"teacher_disagreement_score":0.5027131,"about_ca_system_score_codex":0.00013846213,"about_ca_system_score_gemma":0.00044335498,"threshold_uncertainty_score":0.99987227},"labels":[],"label_agreement":null},{"id":"W2002763026","doi":"10.1002/atr.5670400306","title":"Evacuee behaviors and factors affecting the tsunami trip generation model: A case study in phang-nga, Thailand","year":2006,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Respondent; Logistic regression; Residence; Geography; Transport engineering; Engineering; Demography; Statistics; Mathematics","score_opus":0.02552215165005726,"score_gpt":0.3177610007692707,"score_spread":0.29223884911921344,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2002763026","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9935988,0.00012164044,0.0055978564,0.00010831768,0.00017627531,0.0003481862,0.0000071968925,0.000017162927,0.0000245421],"genre_scores_gemma":[0.99859834,0.000045567947,0.0011767169,0.000009780011,0.00009568682,0.0000052432324,0.000027483744,0.000010359742,0.00003082458],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99879503,0.0001273153,0.0004817746,0.00012435792,0.0003335637,0.00013793155],"domain_scores_gemma":[0.9992869,0.00010675707,0.00036109783,0.000051563686,0.00014844857,0.00004522795],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00071167893,0.0001017894,0.00015688644,0.00015737362,0.00032025692,0.000051696526,0.00005753128,0.000057711426,0.0000042976344],"category_scores_gemma":[0.00002666301,0.00007870197,0.00004888057,0.00029117102,0.000041626576,0.00064807385,4.6936287e-7,0.00016708065,8.232692e-8],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000034160337,0.00010665433,0.28786623,0.000003771395,0.000005944097,0.00014942995,0.084646136,0.62608564,0.00032038605,0.000055528642,0.0000051518227,0.0007209791],"study_design_scores_gemma":[0.0023660017,0.00022769089,0.83194596,0.000042016305,0.00017022513,0.000028333634,0.15844509,0.006194543,0.00018572406,0.00017625054,0.000027245429,0.00019089902],"about_ca_topic_score_codex":0.0015460568,"about_ca_topic_score_gemma":0.055782683,"teacher_disagreement_score":0.61989105,"about_ca_system_score_codex":0.000053997137,"about_ca_system_score_gemma":0.00007528274,"threshold_uncertainty_score":0.9614468},"labels":[],"label_agreement":null},{"id":"W2003131933","doi":"10.1002/atr.5670420306","title":"Evaluating critical lines and stations considering the impact of the consequence using transit assignment model ‐case study of London's underground network","year":2008,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Imperial College London","keywords":"Overcrowding; Crowding; Transit (satellite); Transport engineering; Computer science; Function (biology); Service (business); Quality (philosophy); Transit system; Operations research; Service quality; Rush hour; Public transport; Engineering; Business; Economics; Marketing","score_opus":0.11774941796103112,"score_gpt":0.42107035582781194,"score_spread":0.3033209378667808,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2003131933","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.91466486,0.00016304452,0.08467365,0.00013622241,0.00008881081,0.00024897157,0.000011408541,0.00000516523,0.000007859949],"genre_scores_gemma":[0.9841441,0.000101591024,0.015696105,0.00001073523,0.0000322887,0.0000023247385,0.0000016446236,0.0000073767665,0.000003820336],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9984725,0.00020403104,0.00064544624,0.000092039,0.00045787008,0.00012810169],"domain_scores_gemma":[0.9982185,0.00054306857,0.00056028087,0.000077235585,0.0005476687,0.00005325854],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000624106,0.00008879875,0.00019976035,0.00005090282,0.0005902005,0.000013826196,0.00007732028,0.000038679445,0.0000057448206],"category_scores_gemma":[0.00013851856,0.000059478578,0.00009198849,0.00028728152,0.00032378768,0.000412102,9.794428e-7,0.00014594462,1.21780275e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007507132,0.000090456946,0.020301439,0.00001207544,0.000042840235,0.000056124303,0.053867538,0.9237507,0.0013471129,0.0002825584,0.000001521401,0.00017256409],"study_design_scores_gemma":[0.006211782,0.0018597662,0.48204368,0.00080619304,0.0013468739,0.0009196645,0.23056705,0.26784465,0.00056961336,0.007330678,0.000004929423,0.0004951462],"about_ca_topic_score_codex":0.0006694786,"about_ca_topic_score_gemma":0.0013132722,"teacher_disagreement_score":0.6559061,"about_ca_system_score_codex":0.00004897907,"about_ca_system_score_gemma":0.00046649922,"threshold_uncertainty_score":0.45394045},"labels":[],"label_agreement":null},{"id":"W2003428518","doi":"10.5038/2375-0901.14.3.4","title":"The Effects of Articulated Buses on Dwell and Running Times","year":2011,"lang":"en","type":"article","venue":"Journal of Public Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":48,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"McGill University","funders":"","keywords":"Dwell time; Acceleration; Automotive engineering; Passenger information; Transit (satellite); Transport engineering; Measure (data warehouse); Simulation; Computer science; Running time; Real-time computing; Engineering; Public transport","score_opus":0.022697957461429608,"score_gpt":0.254318389757065,"score_spread":0.2316204322956354,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2003428518","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9955832,0.00028278457,0.002389888,0.00038882217,0.0002757852,0.00008887074,0.0000015081542,0.00001657134,0.00097260514],"genre_scores_gemma":[0.9987676,0.00034748993,0.0007281415,0.000025284737,0.00003523608,0.0000011566977,0.0000041231915,0.0000050721455,0.00008591092],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99915767,0.000072923875,0.00032550454,0.00005435878,0.00028043165,0.00010913984],"domain_scores_gemma":[0.9990362,0.00021972231,0.00036729831,0.00004191201,0.00026292878,0.0000719605],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000620395,0.000053409025,0.00009950376,0.00009669587,0.00020705984,0.000030380477,0.00007918868,0.000049752285,0.000019993337],"category_scores_gemma":[0.00014482894,0.000037780104,0.000045709636,0.00019762192,0.00011285665,0.00031934475,3.1211513e-7,0.000085213505,6.493122e-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":[0.0004619542,0.00046112767,0.51786345,0.0001765865,0.00032822354,0.00006172426,0.23049007,0.0017063955,0.0019794619,0.20876367,0.000817569,0.03688974],"study_design_scores_gemma":[0.00060695614,0.0002604667,0.98999923,0.000117824464,0.00007947547,0.0000010053259,0.003486051,0.000044850633,0.001925548,0.0012481363,0.002153394,0.00007708608],"about_ca_topic_score_codex":0.000105691324,"about_ca_topic_score_gemma":0.00028270707,"teacher_disagreement_score":0.47213572,"about_ca_system_score_codex":0.00001071839,"about_ca_system_score_gemma":0.00008507839,"threshold_uncertainty_score":0.15925579},"labels":[],"label_agreement":null},{"id":"W2003493605","doi":"10.3141/2176-08","title":"Changing Assignment Algorithms","year":2010,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Inro Consultants (Canada)","funders":"","keywords":"Algorithm; Computer science; Task (project management); Convergence (economics); Uniqueness; Measure (data warehouse); Adaptation (eye); Mathematical optimization; Mathematics; Data mining; Engineering","score_opus":0.08733207856971496,"score_gpt":0.41260673256739216,"score_spread":0.3252746539976772,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2003493605","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97886693,0.00008208687,0.008150465,0.0076070074,0.002430547,0.0010292513,0.000054782304,0.000075851094,0.0017030637],"genre_scores_gemma":[0.99037105,0.0004499709,0.0055680564,0.00007272272,0.0006858956,0.000059896123,0.000025568499,0.000049744674,0.0027171217],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99052924,0.0013707749,0.0012052538,0.00039264653,0.0052922745,0.001209839],"domain_scores_gemma":[0.99318975,0.0011171123,0.0004908052,0.0004134727,0.0042187055,0.0005701649],"candidate_categories":["sts","research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.010699164,0.00021820705,0.00035344958,0.001439769,0.002293818,0.00026129247,0.0012592521,0.00031351537,0.00092559267],"category_scores_gemma":[0.0004065194,0.00017668853,0.00035737403,0.0033998494,0.0011768074,0.000894018,0.000004544103,0.0032068118,0.000038134724],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014337378,0.0010192585,0.69140583,0.0002666061,0.00036556562,0.00033418174,0.1254446,0.006569491,0.011395573,0.09835405,0.017914265,0.045496844],"study_design_scores_gemma":[0.001729431,0.000347089,0.80798066,0.00029300817,0.000074826516,5.0621446e-7,0.021530049,0.00021342214,0.0019366322,0.004992378,0.16052467,0.0003773648],"about_ca_topic_score_codex":0.011198698,"about_ca_topic_score_gemma":0.11323984,"teacher_disagreement_score":0.1426104,"about_ca_system_score_codex":0.00019688356,"about_ca_system_score_gemma":0.0012407223,"threshold_uncertainty_score":0.9999877},"labels":[],"label_agreement":null},{"id":"W2003745438","doi":"10.3141/1807-04","title":"Comparison of Morning and Evening Commutes in the Vickrey Bottleneck Model","year":2002,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":73,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Evening; Morning; Bottleneck; Work (physics); Econometrics; Mathematics; Computer science; Economics; Engineering; Operations management; Physics","score_opus":0.22426884898268715,"score_gpt":0.45626898409149375,"score_spread":0.2320001351088066,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2003745438","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9905713,0.00065263524,0.0019579472,0.0052424567,0.00013566489,0.00062362675,0.000026175952,0.000016322425,0.0007738286],"genre_scores_gemma":[0.99505144,0.0015590849,0.0029259035,0.000042743446,0.00006523593,0.000028689281,0.000010496183,0.000022587537,0.00029379153],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9926085,0.0019516406,0.0013209288,0.00024918016,0.0032550194,0.0006147687],"domain_scores_gemma":[0.99551123,0.0018543696,0.00050826446,0.00027484132,0.001670647,0.00018065417],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007365553,0.0001600576,0.0003999341,0.0007668938,0.0010343456,0.00012246234,0.00093813037,0.00017028222,0.00012548889],"category_scores_gemma":[0.00032498757,0.00011651569,0.00017438357,0.002069121,0.0009433547,0.0006267037,0.000003431021,0.0017287314,0.0000032148998],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002658403,0.00030060898,0.778992,0.0001207247,0.000042216776,0.000019401814,0.14718741,0.05963208,0.000469788,0.007250217,0.001778971,0.003940761],"study_design_scores_gemma":[0.0012792512,0.00030303717,0.93208313,0.00049418164,0.00004903217,2.6498506e-7,0.04124601,0.016880069,0.00026853048,0.002591102,0.00461992,0.00018548223],"about_ca_topic_score_codex":0.007977934,"about_ca_topic_score_gemma":0.070759475,"teacher_disagreement_score":0.15309113,"about_ca_system_score_codex":0.000097377604,"about_ca_system_score_gemma":0.00026465513,"threshold_uncertainty_score":0.998628},"labels":[],"label_agreement":null},{"id":"W2004103537","doi":"10.1016/s1874-8651(10)60014-2","title":"Path Optimization of Stochastic Transportation Network Based on Frequency-Domain Spanning Graph Model","year":2009,"lang":"en","type":"article","venue":"Systems Engineering - Theory & Practice","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Adjacency matrix; Computer science; Mathematical optimization; Probability distribution; Node (physics); Path (computing); Graph; Adjacency list; Domain (mathematical analysis); Frequency domain; Transformation (genetics); Algorithm; Mathematics; Theoretical computer science; Engineering","score_opus":0.008711621415275149,"score_gpt":0.24368748039867577,"score_spread":0.23497585898340062,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2004103537","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0022518323,0.00018755069,0.9939895,0.00016835009,0.00036354142,0.0003555288,0.00001720022,0.00024434016,0.002422113],"genre_scores_gemma":[0.87961674,0.00001189201,0.119994044,0.0000817149,0.0001255435,0.00001883975,0.000089759706,0.000022535127,0.000038942948],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983431,0.00026842105,0.00042023952,0.0002284619,0.00046499306,0.00027479985],"domain_scores_gemma":[0.99860156,0.0005931961,0.0003305464,0.00017838182,0.00020421634,0.000092102244],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020091943,0.00016415725,0.00022185549,0.00016019639,0.00022690876,0.000053099957,0.0001315571,0.00013645751,0.00000969476],"category_scores_gemma":[0.0004546702,0.00018146614,0.000065460525,0.0005335711,0.000033710756,0.000522105,6.482103e-7,0.0001781308,0.0000017710502],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000079045516,0.00003750087,0.000023871042,0.00002731576,0.0000138783835,0.00000328435,0.0035522112,0.7788195,0.00005139264,0.21733733,0.000013566551,0.000041114345],"study_design_scores_gemma":[0.00033486212,0.00009644313,0.00028602683,0.00035995847,0.00008495321,6.680744e-7,0.0016062702,0.9948264,0.000003956984,0.0021117362,0.00006324373,0.00022549131],"about_ca_topic_score_codex":0.00011801793,"about_ca_topic_score_gemma":0.000005980157,"teacher_disagreement_score":0.8773649,"about_ca_system_score_codex":0.00006778221,"about_ca_system_score_gemma":0.00013328438,"threshold_uncertainty_score":0.7399974},"labels":[],"label_agreement":null},{"id":"W2004372707","doi":"10.1002/atr.5670340105","title":"Transport economics: A personal view","year":2000,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Theme (computing); Destiny (ISS module); Field (mathematics); Transport policy; Economics; Transportation planning; Key (lock); Public relations; Business; Marketing; Management science; Political science; Transport engineering; Engineering; Computer science; Public transport","score_opus":0.011179324264110571,"score_gpt":0.267585335641944,"score_spread":0.2564060113778334,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2004372707","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9933476,0.0003248196,0.0019178083,0.0007608785,0.0003207172,0.00012176815,0.000022389255,0.000035858786,0.0031482026],"genre_scores_gemma":[0.98938286,0.0033440455,0.006118815,0.00014851837,0.00020659801,0.0000029148914,0.000057070934,0.000013498026,0.00072570384],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9988574,0.000040073257,0.0005563538,0.00011562886,0.00025744337,0.00017309996],"domain_scores_gemma":[0.99932617,0.000041721796,0.00026794037,0.00004791584,0.00018430904,0.00013193178],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003565712,0.00009811347,0.00020131547,0.00010042303,0.00018971253,0.000016810012,0.00011309586,0.000075181044,0.0013085324],"category_scores_gemma":[0.000007790333,0.00010083903,0.00015240799,0.00019681985,0.00007387773,0.000845924,7.505147e-8,0.00013979201,0.000011981225],"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":[0.00094835047,0.00023729753,0.01965157,0.00005492299,0.00010970602,0.00008061545,0.1522056,0.52519107,0.00015916157,0.005392322,0.00015607601,0.29581332],"study_design_scores_gemma":[0.002961986,0.00028723816,0.5630272,0.00023449326,0.00021806259,0.000011778079,0.010970679,0.00010174628,0.00012432845,0.0018355467,0.41977614,0.00045079424],"about_ca_topic_score_codex":0.00005494359,"about_ca_topic_score_gemma":0.0007270214,"teacher_disagreement_score":0.5433756,"about_ca_system_score_codex":0.000051780982,"about_ca_system_score_gemma":0.00020523707,"threshold_uncertainty_score":0.9996044},"labels":[],"label_agreement":null},{"id":"W2004623316","doi":"10.1061/(asce)0733-9488(2005)131:2(87)","title":"Network Modeling Approach to Transit Network Design","year":2005,"lang":"en","type":"article","venue":"Journal of Urban Planning and Development","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Transit (satellite); Component (thermodynamics); Computer science; Process (computing); Rail transit; Network planning and design; Transit system; Public transport; Transport engineering; Engineering; Computer network","score_opus":0.06045667372558772,"score_gpt":0.2721835126436701,"score_spread":0.21172683891808236,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2004623316","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0142389545,0.0013029615,0.982004,0.0003609967,0.00016970358,0.0000931079,2.547815e-7,0.000027869439,0.0018021683],"genre_scores_gemma":[0.5361788,0.000023405655,0.46263605,0.000331227,0.0006504446,0.0000019438692,0.0000027477288,0.0000069336425,0.00016841234],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99883443,0.00007956137,0.0003929069,0.000107718566,0.0003161688,0.00026919314],"domain_scores_gemma":[0.9994615,0.000064055486,0.00013052221,0.000034482637,0.00011022871,0.00019923865],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014187533,0.00009131516,0.00016988991,0.0000770622,0.00047466683,0.00007542522,0.00010012584,0.000066162895,0.0000055201444],"category_scores_gemma":[0.000023230145,0.00008377797,0.000023586237,0.00019265912,0.000016875907,0.00015903701,0.0000038876256,0.00013847463,0.0000024964459],"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":[0.000063331056,0.000017671746,0.004773551,0.0000033489487,0.000021074822,0.0000035532817,0.04755259,0.9335714,7.8845386e-7,0.00016450943,0.010318921,0.0035092796],"study_design_scores_gemma":[0.0026529965,0.00032294722,0.017174855,0.0013608338,0.00015667267,0.00007469883,0.022348138,0.354899,0.000031619446,0.00057236245,0.5990153,0.0013905991],"about_ca_topic_score_codex":0.000008809001,"about_ca_topic_score_gemma":0.000007061696,"teacher_disagreement_score":0.58869636,"about_ca_system_score_codex":0.000054578297,"about_ca_system_score_gemma":0.000251455,"threshold_uncertainty_score":0.36508015},"labels":[],"label_agreement":null},{"id":"W2004666124","doi":"10.1002/atr.5670360306","title":"Influence of peak spreading and departure time choice on evaluation of its measures","year":2002,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","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":"Travel time; Value of time; Operations research; Value (mathematics); Computer science; Time value of money; Transport engineering; Econometrics; Economics; Mathematics; Engineering","score_opus":0.028487024350898497,"score_gpt":0.30858064841783117,"score_spread":0.28009362406693267,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2004666124","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9984718,0.0005020245,0.0002518413,0.00017315948,0.000062122206,0.00014748763,0.000011661591,0.000008537173,0.00037134867],"genre_scores_gemma":[0.99837554,0.0005406732,0.0009636826,0.000016256185,0.000039342918,0.0000017257137,0.000009117214,0.0000061303813,0.00004752923],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9984373,0.00009947228,0.00047131506,0.000085647014,0.000819379,0.00008693073],"domain_scores_gemma":[0.9980263,0.00015015082,0.0006698049,0.000047685404,0.0010486023,0.000057470646],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007585234,0.000071430324,0.00017335557,0.00014649193,0.00008029366,0.000007197652,0.00006954055,0.000064771746,0.000041595296],"category_scores_gemma":[0.00033197162,0.00006262249,0.000048368653,0.00023969833,0.0000555124,0.0005865673,2.6892434e-7,0.00009422669,9.2348057e-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":[0.00007352822,0.000055267672,0.008867672,0.000034024353,0.000022314794,0.0000011700148,0.015955672,0.95741105,0.009036278,0.0003249231,0.000016563381,0.008201533],"study_design_scores_gemma":[0.0016006826,0.00026877865,0.9875122,0.00051625044,0.00026486747,0.0000012086781,0.001234292,0.0016343801,0.005591592,0.00028313688,0.00095594453,0.00013671619],"about_ca_topic_score_codex":0.000018608955,"about_ca_topic_score_gemma":0.00017013536,"teacher_disagreement_score":0.9786445,"about_ca_system_score_codex":0.000029951394,"about_ca_system_score_gemma":0.00005413943,"threshold_uncertainty_score":0.2553671},"labels":[],"label_agreement":null},{"id":"W2005411779","doi":"10.3141/2418-14","title":"Examining the Characteristics of Intercity Bus Passengers in Michigan","year":2014,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Michigan Department of Transportation","keywords":"Transport engineering; Service (business); Public transport; Quarter (Canadian coin); Engineering; Business; Geography; Marketing","score_opus":0.11021602330887009,"score_gpt":0.3895090313540492,"score_spread":0.2792930080451791,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2005411779","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9920588,0.00006009258,0.002358044,0.003733792,0.0005440466,0.0006139793,0.000032896176,0.000019053281,0.0005792645],"genre_scores_gemma":[0.99773544,0.00067124044,0.0009337179,0.000050697858,0.0001916863,0.000028053782,0.000019430063,0.000029455405,0.00034026976],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99130636,0.0030239238,0.0015403526,0.00029113356,0.0031368164,0.000701421],"domain_scores_gemma":[0.99348706,0.002540511,0.0006829241,0.00036365815,0.0026931611,0.0002326807],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012483149,0.00017517283,0.00042563007,0.0008268464,0.00076460646,0.00010978819,0.0011606934,0.00019742629,0.0001489201],"category_scores_gemma":[0.00087242684,0.00012476502,0.00021607093,0.0024090118,0.0011968138,0.0005034031,0.0000040841223,0.0018253232,0.0000059602426],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00070729165,0.00019933206,0.92699337,0.00013775066,0.000060955266,0.000021302176,0.04806698,0.0023979722,0.0009690023,0.0095919855,0.000651073,0.010202982],"study_design_scores_gemma":[0.00078119134,0.00024094795,0.97333956,0.00038753642,0.000029354464,1.0514581e-7,0.013589075,0.00018142024,0.0003031799,0.0012554687,0.009768503,0.00012366625],"about_ca_topic_score_codex":0.019403638,"about_ca_topic_score_gemma":0.18271522,"teacher_disagreement_score":0.16331159,"about_ca_system_score_codex":0.00012932856,"about_ca_system_score_gemma":0.0006561356,"threshold_uncertainty_score":0.98712623},"labels":[],"label_agreement":null},{"id":"W2005828303","doi":"10.4102/jtscm.v8i1.134","title":"Public transport policy implementation in South Africa: &lt;i&gt;Quo vadis&lt;/i&gt;?","year":2014,"lang":"en","type":"article","venue":"Journal of Transport and Supply Chain Management","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Transport Canada","funders":"","keywords":"Public transport; Status quo; Business; Government (linguistics); Public policy; Psychological intervention; Order (exchange); Transport policy; Service (business); Transport engineering; Economic growth; Economics; Finance; Engineering; Marketing","score_opus":0.018512678229174635,"score_gpt":0.2636168660998295,"score_spread":0.24510418787065485,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2005828303","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.85502553,0.0009068135,0.08082739,0.016027307,0.0007933311,0.0012661053,0.00009660606,0.00013767554,0.044919237],"genre_scores_gemma":[0.99567777,0.0006684571,0.0021111283,0.0001815448,0.00024321047,0.000014454079,0.00009335285,0.000020209807,0.0009898624],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99755555,0.00013196336,0.00085821754,0.00024492064,0.0007393396,0.00046998105],"domain_scores_gemma":[0.9990962,0.00004201111,0.00034622787,0.00012463084,0.00013515653,0.00025573102],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002090476,0.00019757131,0.00034133936,0.00082312,0.00027958566,0.000060772734,0.00025823351,0.00010351294,0.0001776578],"category_scores_gemma":[0.000012160635,0.00019208019,0.00014956371,0.00070433464,0.000116935,0.00051067193,0.0000040624295,0.00016150861,0.00000274963],"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":[0.00022520337,0.00038116096,0.56982505,0.00021796551,0.00027456763,0.00019585961,0.16083102,0.0055190013,0.0001346223,0.23814587,0.0011429013,0.02310675],"study_design_scores_gemma":[0.0027526114,0.00016484372,0.74230963,0.00009642531,0.00016511406,0.0000025746526,0.0076194233,0.00018236731,0.00000815061,0.001903651,0.24445154,0.0003436726],"about_ca_topic_score_codex":0.000080458056,"about_ca_topic_score_gemma":0.003321758,"teacher_disagreement_score":0.24330863,"about_ca_system_score_codex":0.00010655997,"about_ca_system_score_gemma":0.00014465301,"threshold_uncertainty_score":0.7832803},"labels":[],"label_agreement":null},{"id":"W2006007303","doi":"10.5038/2375-0901.8.2.5","title":"Can Trip Planner Log Files Analysis Help in Transit Service Planning?","year":2005,"lang":"en","type":"article","venue":"Journal of Public Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Planner; Transit (satellite); The Internet; Computer science; Service (business); Destinations; Transport engineering; Point (geometry); World Wide Web; Public transport; Geography; Business; Engineering; Tourism","score_opus":0.030304720037349457,"score_gpt":0.29447238933721526,"score_spread":0.2641676692998658,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2006007303","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95784456,0.00027351317,0.022986209,0.017685395,0.00018747334,0.0001319811,0.000081726575,0.000049139504,0.00076002034],"genre_scores_gemma":[0.99648386,0.00008087562,0.0023660704,0.0003907892,0.00022153153,0.000003794808,0.0002914823,0.000011034387,0.00015055617],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9979908,0.00016159545,0.00079289544,0.00015477388,0.0006159985,0.00028396485],"domain_scores_gemma":[0.9987368,0.00010706042,0.00045373535,0.00007865524,0.00043227832,0.00019149474],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012021754,0.00012590725,0.00031178445,0.00095370325,0.00016282733,0.000086505395,0.00021547123,0.00015559839,0.00028038994],"category_scores_gemma":[0.00004672587,0.00012417874,0.000170783,0.0018725718,0.00004479861,0.00089693215,3.8949273e-7,0.00024308942,0.0000028104484],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010662954,0.00018492165,0.37825716,0.00002055266,0.00023231331,0.000048397287,0.0822032,0.5314725,0.00005273716,0.0024047953,0.0004961294,0.004520654],"study_design_scores_gemma":[0.0013580493,0.00005610571,0.96784216,0.000056327324,0.00039508403,0.000001960835,0.009497813,0.0017126587,0.00004129762,0.00021971317,0.018593427,0.0002254228],"about_ca_topic_score_codex":0.0010681618,"about_ca_topic_score_gemma":0.070344925,"teacher_disagreement_score":0.589585,"about_ca_system_score_codex":0.00011074548,"about_ca_system_score_gemma":0.00034730177,"threshold_uncertainty_score":0.94661885},"labels":[],"label_agreement":null},{"id":"W2006155966","doi":"10.3141/1804-10","title":"Time-Use Metadata","year":2002,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Saint Mary's University","funders":"","keywords":"Metadata; Comparability; Computer science; Task (project management); Work (physics); Metadata repository; Field (mathematics); Data science; Transport engineering; World Wide Web; Engineering","score_opus":0.1665035584748314,"score_gpt":0.4043798956268431,"score_spread":0.23787633715201173,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2006155966","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98172086,0.0004329909,0.0023401026,0.011105671,0.0009113806,0.001228865,0.0001559219,0.000104046456,0.0020001316],"genre_scores_gemma":[0.9700856,0.0029429886,0.0036222865,0.00012191775,0.00038476515,0.000043717217,0.00004679139,0.00006566369,0.02268622],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9890632,0.002581366,0.0013966687,0.00043307274,0.0054475707,0.0010781124],"domain_scores_gemma":[0.9917041,0.0019289274,0.0005216932,0.00053539954,0.0047360556,0.00057383097],"candidate_categories":["sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0066057364,0.0002394264,0.00043606517,0.0011226883,0.001925972,0.00051198684,0.0014362679,0.00027584637,0.0025011045],"category_scores_gemma":[0.0007375092,0.00019362371,0.00041415906,0.0034073568,0.0015082418,0.0029511456,0.0000044960757,0.0020162791,0.00016128166],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018098297,0.0015694979,0.5456209,0.00029495542,0.00070507906,0.0005122183,0.091916025,0.016285634,0.0021884358,0.05130014,0.2664529,0.02134435],"study_design_scores_gemma":[0.0017091585,0.00038099082,0.5455737,0.00034998063,0.00012321316,4.476108e-7,0.006649626,0.0006671371,0.00026646274,0.0016102811,0.4422864,0.00038257474],"about_ca_topic_score_codex":0.020973664,"about_ca_topic_score_gemma":0.071801156,"teacher_disagreement_score":0.17583346,"about_ca_system_score_codex":0.000259902,"about_ca_system_score_gemma":0.0005010477,"threshold_uncertainty_score":0.9993734},"labels":[],"label_agreement":null},{"id":"W2006407959","doi":"10.1016/j.sbspro.2014.01.1457","title":"Truck-only Lanes on Urban Arterials: A Value of Time Approach","year":2014,"lang":"en","type":"article","venue":"Procedia - Social and Behavioral Sciences","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Truck; Value of time; Travel time; Transport engineering; Value (mathematics); Time value of money; Traffic volume; Computer science; Business; Statistics; Engineering; Mathematics; Finance; Automotive engineering","score_opus":0.04856658084089273,"score_gpt":0.33191496963631445,"score_spread":0.28334838879542174,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2006407959","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98621434,0.000017038505,0.00011868258,0.00037053908,0.00012127255,0.00013298015,0.000011767866,0.00006194088,0.012951415],"genre_scores_gemma":[0.99820304,0.000010188292,0.0010705129,0.000050400708,0.00026716103,0.000008636088,0.000012828775,0.0000037789857,0.00037347907],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9989427,0.00009844825,0.00016283127,0.00021090465,0.00038844562,0.00019667398],"domain_scores_gemma":[0.9996849,0.000050704202,0.000113391776,0.000029479426,0.00005450848,0.00006702242],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007123605,0.00008263384,0.0001537879,0.000060008984,0.00075237977,0.00008329509,0.00014646967,0.00008557325,0.000041306685],"category_scores_gemma":[0.00004701567,0.000067913694,0.00003480313,0.00030249153,0.0006729304,0.00019001486,0.000006410068,0.00005027367,0.000006043973],"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":[0.00010623397,0.0008611306,0.5118877,0.000088636465,0.000013274098,0.0000012840413,0.2139713,0.00025720647,0.0019945153,0.22131935,0.0020836038,0.04741572],"study_design_scores_gemma":[0.0025889839,0.0028808466,0.8983536,0.00019927834,0.0003912877,0.000005384886,0.045929465,0.0037681295,0.0014545133,0.016227875,0.026163239,0.0020374022],"about_ca_topic_score_codex":0.0007652183,"about_ca_topic_score_gemma":0.00009894036,"teacher_disagreement_score":0.38646585,"about_ca_system_score_codex":0.000010594439,"about_ca_system_score_gemma":0.000108692686,"threshold_uncertainty_score":0.5786773},"labels":[],"label_agreement":null},{"id":"W2006492857","doi":"10.2139/ssrn.1466523","title":"Highway Toll Pricing","year":2009,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Toll; Road pricing; Business; Transport engineering; Economics; Engineering; Traffic congestion; Medicine","score_opus":0.008135064933930179,"score_gpt":0.26705104869004553,"score_spread":0.25891598375611535,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2006492857","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6035837,0.004004937,0.21390063,0.028005423,0.001212998,0.00034894506,0.0000023879713,0.00058120495,0.14835975],"genre_scores_gemma":[0.99356705,0.0014514417,0.00035532622,0.0001975195,0.00027986584,3.6055013e-7,0.0000030447065,0.0000039668275,0.004141423],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9985094,0.000059140373,0.00013145861,0.00007780543,0.00022624308,0.000995916],"domain_scores_gemma":[0.9997255,0.000020217074,0.000075485965,0.00004026413,0.00007728264,0.000061215746],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011126723,0.00005121451,0.00006405925,0.00006297019,0.0005324011,0.00005940376,0.00011683073,0.000047550595,0.000032955435],"category_scores_gemma":[0.00005177288,0.000048229424,0.000035046807,0.00022051489,0.000021205016,0.0002085819,8.05078e-7,0.0005151585,0.000016163427],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019610303,0.000035124256,0.0026393116,5.835293e-7,0.000018330535,0.0000034783036,0.0053694593,0.0021618803,0.00006991251,0.9463245,0.00035997832,0.042997807],"study_design_scores_gemma":[0.0019249171,0.00079678075,0.037287183,0.00008024034,0.00010056927,0.00010620817,0.03426615,0.0005636233,0.00014572087,0.80248016,0.121498264,0.00075020245],"about_ca_topic_score_codex":0.00011958096,"about_ca_topic_score_gemma":0.0012813857,"teacher_disagreement_score":0.38998333,"about_ca_system_score_codex":0.00032473548,"about_ca_system_score_gemma":0.001390276,"threshold_uncertainty_score":0.40948525},"labels":[],"label_agreement":null},{"id":"W2007549677","doi":"10.3141/2006-01","title":"Development of Control Strategy for Intermodal Connection Protection of Timed-Transfer Transit Routes","year":2007,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Transit (satellite); Transfer (computing); Rail transit; Unit (ring theory); Control (management); Transport engineering; Public transport; Transfer station; Computer science; Engineering","score_opus":0.12088933335902245,"score_gpt":0.40635428985675187,"score_spread":0.2854649564977294,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2007549677","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.707221,0.00006936114,0.29004622,0.00050791434,0.00032625327,0.0015740985,0.00007398725,0.000022284066,0.0001589024],"genre_scores_gemma":[0.99303085,0.0001292661,0.0063097435,0.000011171045,0.00014326393,0.00007158374,0.000036319987,0.00003717394,0.0002306391],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.99291223,0.00078113016,0.0022302875,0.00032105463,0.0030165552,0.00073872454],"domain_scores_gemma":[0.99151194,0.001174254,0.00060236093,0.00022055978,0.0062122094,0.00027865262],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011726667,0.00020869175,0.000520541,0.0011446407,0.0008517225,0.000053137082,0.00063208427,0.00029708902,0.00014065475],"category_scores_gemma":[0.00024253152,0.00017734773,0.00041050185,0.0017923277,0.0008079219,0.0006193933,0.0000012059847,0.00095930474,0.0000019610004],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.04093799,0.0029646691,0.20412293,0.0034772574,0.0019190063,0.00007233692,0.24968924,0.075623356,0.17936389,0.06921666,0.0011442604,0.1714684],"study_design_scores_gemma":[0.005879312,0.001474317,0.908301,0.0008798533,0.0001932737,4.5296406e-7,0.028798059,0.00043501044,0.039939664,0.002135612,0.011585368,0.0003780655],"about_ca_topic_score_codex":0.00661989,"about_ca_topic_score_gemma":0.1091109,"teacher_disagreement_score":0.7041781,"about_ca_system_score_codex":0.00022651703,"about_ca_system_score_gemma":0.0014300104,"threshold_uncertainty_score":0.9999951},"labels":[],"label_agreement":null},{"id":"W2007908002","doi":"10.1061/40717(148)32","title":"Transit in the Greater Toronto Area: Overview and Challenges","year":2004,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Transit (satellite); Geography; Government (linguistics); Regional science; Population; Public transport; Transport engineering; Engineering; Demography; Sociology","score_opus":0.08462856579631088,"score_gpt":0.30075994197886413,"score_spread":0.21613137618255324,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2007908002","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3588725,0.035463057,0.0026480975,0.09468016,0.0001676471,0.00059173105,0.00000537034,0.00018872038,0.5073827],"genre_scores_gemma":[0.98798555,0.011293868,0.00024877844,0.000316679,0.000015860607,0.0000044112317,0.0000017606253,0.00000144192,0.0001316311],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.99970144,0.000030483103,0.000050056486,0.000062991996,0.00008797509,0.000067075554],"domain_scores_gemma":[0.99991816,0.000016491595,0.0000075660278,0.00003330127,0.000008879664,0.000015612632],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019730805,0.000028402901,0.000036313755,0.0000069161174,0.00007877568,0.000018797493,0.00004156049,0.000026953583,0.000091785754],"category_scores_gemma":[0.0000036957053,0.00001842506,0.000008987762,0.000033121378,0.000027295804,0.00012802369,5.1405834e-7,0.000018219855,0.000001700752],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010315777,0.00004947227,0.0064452267,0.000027112483,0.000006661875,0.000009827828,0.58283037,0.0015647341,0.0000026580985,0.39490494,0.0000763294,0.014072363],"study_design_scores_gemma":[0.00094199635,0.000054425465,0.8281347,0.00007767506,0.000018570272,0.0000017235545,0.10681196,0.000028034978,0.000010727652,0.0051097074,0.05861503,0.00019545184],"about_ca_topic_score_codex":0.0056746,"about_ca_topic_score_gemma":0.15094645,"teacher_disagreement_score":0.8216895,"about_ca_system_score_codex":0.000015650732,"about_ca_system_score_gemma":0.000014334322,"threshold_uncertainty_score":0.86454654},"labels":[],"label_agreement":null},{"id":"W2007922317","doi":"10.1002/atr.122","title":"A risk‐averse user equilibrium model for route choice problem in signal‐controlled networks","year":2010,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Variational inequality; Path (computing); Computer science; Mathematical optimization; Sensitivity (control systems); Accident (philosophy); Travel time; Simulation; Operations research; Transport engineering; Engineering; Mathematics; Computer network","score_opus":0.010381801777638994,"score_gpt":0.2795068703442811,"score_spread":0.26912506856664214,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2007922317","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.65338343,0.00005125526,0.3449432,0.00032606226,0.00044047818,0.00064921024,0.000029068171,0.00003147476,0.0001458378],"genre_scores_gemma":[0.9272389,0.000109820765,0.07201642,0.000048923517,0.00021468505,0.000031482436,0.000054407556,0.000017873477,0.00026748754],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99846524,0.00005674777,0.00075026386,0.00015078468,0.00032358585,0.000253374],"domain_scores_gemma":[0.9982834,0.00028992398,0.000801556,0.00006854023,0.00043826306,0.00011835715],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009482137,0.00012298624,0.00030863288,0.00017852303,0.00014617256,0.00003823634,0.00014458613,0.00015193902,0.000022441907],"category_scores_gemma":[0.00011769028,0.000117033305,0.00017959873,0.0002773274,0.000051001767,0.0010152934,4.4934833e-7,0.00037920265,4.4844316e-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":[0.0009452496,0.000074981144,0.021665096,0.000010436865,0.00002259616,0.0000034746222,0.008461029,0.9655005,0.00067951623,0.0012892414,0.000034952096,0.0013129547],"study_design_scores_gemma":[0.032477915,0.0003852556,0.25861248,0.0002372538,0.00051139377,0.0000017068382,0.0035574834,0.6900486,0.0001483829,0.006753602,0.006587804,0.00067812746],"about_ca_topic_score_codex":0.00012104042,"about_ca_topic_score_gemma":0.010953102,"teacher_disagreement_score":0.27545187,"about_ca_system_score_codex":0.00004162098,"about_ca_system_score_gemma":0.00023035861,"threshold_uncertainty_score":0.61120886},"labels":[],"label_agreement":null},{"id":"W2008544847","doi":"10.1007/s11116-004-7962-3","title":"A tour-based model of travel mode choice","year":2005,"lang":"en","type":"article","venue":"Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":178,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Mode choice; Microsimulation; Constraint (computer-aided design); Mode (computer interface); Operations research; Computer science; Travel behavior; Scheduling (production processes); Discrete choice; Transport engineering; Engineering; Public transport; Operations management","score_opus":0.0317138223645952,"score_gpt":0.30554991302387247,"score_spread":0.2738360906592773,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2008544847","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5810035,0.000025358206,0.41218936,0.0008660314,0.000050326682,0.00019972882,0.00010653828,0.00011236789,0.005446772],"genre_scores_gemma":[0.98265064,0.00001274139,0.0164237,0.0001282404,0.000050756193,0.000010846563,0.00023876286,0.000009521511,0.00047479672],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99915653,0.00002379001,0.00025647788,0.0001283585,0.00029627307,0.00013859004],"domain_scores_gemma":[0.9995957,0.000046399888,0.00010447655,0.0000738275,0.00011854671,0.000061036866],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015109584,0.00007051872,0.00010420955,0.00007851074,0.00012347633,0.000009128275,0.00008306411,0.00007986046,0.00011226873],"category_scores_gemma":[0.000014196439,0.00007915244,0.000056423436,0.00019185647,0.00006238454,0.0002421223,1.16389806e-7,0.000055540546,0.0000048820402],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026518588,0.00006282266,0.0063932496,0.0000143225325,0.0000047916683,2.0959234e-7,0.014722944,0.9654459,0.000996063,0.010915381,0.000094343624,0.0013234537],"study_design_scores_gemma":[0.001816486,0.000052967876,0.15502295,0.00007270062,0.00012487723,3.60286e-8,0.0018213959,0.8303751,0.0068644825,0.00084679597,0.0026120332,0.0003901817],"about_ca_topic_score_codex":0.0012195125,"about_ca_topic_score_gemma":0.024313001,"teacher_disagreement_score":0.40164712,"about_ca_system_score_codex":0.000025185542,"about_ca_system_score_gemma":0.00016979205,"threshold_uncertainty_score":0.99349076},"labels":[],"label_agreement":null},{"id":"W2008971185","doi":"10.3141/2090-02","title":"A New Look at Projected Gradient Method for Equilibrium Assignment","year":2009,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":99,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Inro Consultants (Canada); Université de Montréal","funders":"","keywords":"Mathematical optimization; Computer science; Algorithm; Path (computing); Decomposition; Applied mathematics; Mathematics","score_opus":0.11419424887259236,"score_gpt":0.4426854882501239,"score_spread":0.32849123937753155,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2008971185","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6916375,0.00048492738,0.25974503,0.038503017,0.0016646854,0.005744082,0.00019251305,0.0001831178,0.0018450841],"genre_scores_gemma":[0.9024916,0.0007050235,0.08290853,0.00024599678,0.00078793813,0.00013820673,0.00012203569,0.000077439065,0.012523215],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9906161,0.0017438519,0.0014575919,0.00051749405,0.0044531054,0.001211851],"domain_scores_gemma":[0.99291056,0.0014709,0.000612628,0.00038940358,0.0038876846,0.0007288445],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.007942312,0.00026705043,0.00047855193,0.00097651454,0.0015022834,0.00020998671,0.0010855064,0.0002835968,0.00041323324],"category_scores_gemma":[0.00039299196,0.00021647646,0.0005164289,0.0024501283,0.00038247657,0.000741578,0.000003411619,0.0012183401,0.0000145350605],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.017813297,0.0018894214,0.21727853,0.00059774163,0.00081701396,0.00025299986,0.15395708,0.04907622,0.025620902,0.11287725,0.29567674,0.1241428],"study_design_scores_gemma":[0.005031283,0.0022112918,0.7580271,0.00054503867,0.00019727534,7.435937e-7,0.009380439,0.0007442873,0.0036219778,0.01693449,0.20274703,0.0005590791],"about_ca_topic_score_codex":0.011753319,"about_ca_topic_score_gemma":0.06132654,"teacher_disagreement_score":0.54074854,"about_ca_system_score_codex":0.0006919923,"about_ca_system_score_gemma":0.0020693142,"threshold_uncertainty_score":0.99979764},"labels":[],"label_agreement":null},{"id":"W2010206951","doi":"10.1002/atr.5670370103","title":"A system dynamics approach to land use / transportation system performance modeling Part II: Application","year":2003,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":35,"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":"Causal loop diagram; System dynamics; Population; Robustness (evolution); Computer science; Land use; Econometrics; Population growth; Operations research; Simulation; Transport engineering; Engineering; Civil engineering; Economics","score_opus":0.016058141057472292,"score_gpt":0.24660028759022207,"score_spread":0.23054214653274976,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2010206951","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.56813043,0.00003418388,0.4305241,0.000033864395,0.00033314587,0.00033873305,0.000034106197,0.000081622944,0.0004897852],"genre_scores_gemma":[0.96603465,0.00008274303,0.033316754,0.000021231326,0.000103726336,0.0000379135,0.00030254503,0.00002764905,0.00007277992],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977511,0.0000992572,0.00095545925,0.0002497638,0.00067222957,0.00027217806],"domain_scores_gemma":[0.9983212,0.000044161694,0.00058480655,0.00013756623,0.00070136995,0.00021092029],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007743586,0.00018072163,0.00030916353,0.00024802907,0.00055225624,0.000057722373,0.00014825402,0.00014095324,0.0000021976691],"category_scores_gemma":[0.000030272278,0.00018514837,0.00011688507,0.00063245156,0.000030266921,0.0013015212,3.2194828e-7,0.00019069707,0.0000024938124],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014109579,0.00006872744,0.013813418,0.00016585743,0.000023023764,0.0000033387514,0.0120165385,0.93241996,0.000040565403,0.040485848,0.0000072178123,0.0008143898],"study_design_scores_gemma":[0.0075754006,0.00088828633,0.18101999,0.0028388726,0.0011794802,0.000045330908,0.15452583,0.6334781,0.000492734,0.00019758834,0.015649337,0.0021090254],"about_ca_topic_score_codex":0.0001375497,"about_ca_topic_score_gemma":0.0006135368,"teacher_disagreement_score":0.39790422,"about_ca_system_score_codex":0.00034733934,"about_ca_system_score_gemma":0.00018345505,"threshold_uncertainty_score":0.7550131},"labels":[],"label_agreement":null},{"id":"W2011461298","doi":"10.1002/atr.5670430103","title":"Private road competition and equilibrium with traffic equilibrium constraints","year":2009,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China; National Science Foundation","keywords":"Toll; Competition (biology); Competitor analysis; Variational inequality; Road pricing; Industrial organization; Economics; Microeconomics; Supply and demand; Heuristic; Symmetric equilibrium; Toll road; Welfare; Economic surplus; Transport engineering; Computer science; Game theory; Engineering; Mathematical optimization; Traffic congestion; Market economy; Mathematics; Repeated game","score_opus":0.00961061390696832,"score_gpt":0.2633980318850039,"score_spread":0.25378741797803556,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2011461298","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9907383,0.00013575846,0.0071803625,0.0010973547,0.00015698005,0.00015548778,0.000010232392,0.000049050122,0.00047647988],"genre_scores_gemma":[0.9878644,0.00013760706,0.011709051,0.00009768883,0.00009513455,0.0000010249978,0.0000508455,0.000008414612,0.00003587812],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9988189,0.000050761144,0.0004236994,0.00012987491,0.00039574862,0.00018105525],"domain_scores_gemma":[0.99910283,0.000040848576,0.0004091187,0.000052868367,0.00025855555,0.00013578575],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028932552,0.0001102662,0.00019491586,0.00012858938,0.00011066252,0.000045323384,0.00007751829,0.00006863341,0.000032854754],"category_scores_gemma":[0.000020414696,0.00010003812,0.00004854277,0.00024375599,0.00018334479,0.00096583547,2.5833705e-7,0.00014326276,8.2830456e-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":[0.0014230293,0.00026788426,0.0072757364,0.000059704187,0.0000711588,0.0001465114,0.043975227,0.8154774,0.012057562,0.020919481,0.00004019308,0.098286144],"study_design_scores_gemma":[0.0044298694,0.0013419945,0.9801162,0.00048071102,0.0001871929,0.000033230765,0.007787796,0.00029651538,0.0008101255,0.0015116239,0.002562257,0.0004425148],"about_ca_topic_score_codex":0.000005243916,"about_ca_topic_score_gemma":0.00007815375,"teacher_disagreement_score":0.9728404,"about_ca_system_score_codex":0.000031272164,"about_ca_system_score_gemma":0.00011897841,"threshold_uncertainty_score":0.40794364},"labels":[],"label_agreement":null},{"id":"W2011725305","doi":"10.3141/2191-22","title":"Macroscopic Model of Greenhouse Gas Emissions for Municipalities","year":2010,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Per capita; Greenhouse gas; Gross domestic product; Transport engineering; Public transport; Population; Environmental economics; Land use; Business; Environmental science; Economics; Engineering; Economic growth; Civil engineering","score_opus":0.13625053715048396,"score_gpt":0.43423493487836534,"score_spread":0.2979843977278814,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2011725305","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98700356,0.0000699378,0.0070977504,0.0032813046,0.00067836064,0.0011106601,0.00025975896,0.00003883205,0.0004598506],"genre_scores_gemma":[0.98209757,0.00081499584,0.013798201,0.000029923787,0.00020531846,0.00008728684,0.0000375939,0.000048998976,0.002880097],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9935911,0.00073817483,0.0013952015,0.00031171436,0.0031387543,0.000825031],"domain_scores_gemma":[0.99178874,0.0016104174,0.000589799,0.0004293711,0.0051517426,0.00042994836],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0063581127,0.0001927308,0.00042026656,0.00084125606,0.0014498142,0.000106107,0.0011879449,0.00030332475,0.00025306846],"category_scores_gemma":[0.00053412456,0.0001573962,0.0004189572,0.0014964037,0.0014368679,0.0006472488,0.0000036705167,0.001785169,0.000003182499],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0045237835,0.001460447,0.3696943,0.0011797748,0.00042217821,0.00003540313,0.20404272,0.08079403,0.055084005,0.2522955,0.018279076,0.012188797],"study_design_scores_gemma":[0.0107572,0.0024471746,0.6406999,0.0019431367,0.00052190345,6.900233e-7,0.094080366,0.013708524,0.018700974,0.095953554,0.11980369,0.0013828756],"about_ca_topic_score_codex":0.023031844,"about_ca_topic_score_gemma":0.2629794,"teacher_disagreement_score":0.27100563,"about_ca_system_score_codex":0.00009183286,"about_ca_system_score_gemma":0.0017570202,"threshold_uncertainty_score":0.99985015},"labels":[],"label_agreement":null},{"id":"W2011748091","doi":"10.3141/2006-04","title":"Success and Challenges in Modernizing Streetcar Systems","year":2007,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":51,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto","funders":"Monash University","keywords":"Transport engineering; Transit system; Light rail transit; Public transport; Light rail; Business; Quality (philosophy); Telecommunications; Transit (satellite); Public administration; Engineering; Political science","score_opus":0.21601121991332178,"score_gpt":0.42405103560068735,"score_spread":0.20803981568736557,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2011748091","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98794657,0.0033342692,0.0018364784,0.0043657343,0.0005685341,0.00080747524,0.000020739668,0.000033770648,0.0010864196],"genre_scores_gemma":[0.98202556,0.016571315,0.0008193817,0.0000144457845,0.0002100436,0.000021898604,0.000009079166,0.00003312261,0.00029512425],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99242616,0.0014360363,0.0013076462,0.00037755998,0.0034900978,0.00096251303],"domain_scores_gemma":[0.9948472,0.0016319765,0.00038758822,0.00025125925,0.0024693971,0.0004125942],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014909863,0.00018343676,0.00037286998,0.0014035691,0.0008809667,0.00017691468,0.0007002404,0.00026163994,0.000046397156],"category_scores_gemma":[0.0002694848,0.00015503645,0.00014040734,0.0018726839,0.00076777645,0.0008974692,0.0000033651158,0.0015816488,0.0000040025975],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00088887196,0.00020271035,0.889266,0.00035323124,0.00006520648,0.00020011373,0.040491648,0.010116955,0.00023092546,0.04075166,0.00031604982,0.017116599],"study_design_scores_gemma":[0.0010155557,0.00016186303,0.9451669,0.0004803189,0.000020436684,3.0005185e-7,0.03450598,0.00019954893,0.000065291846,0.0011348849,0.01708295,0.00016595957],"about_ca_topic_score_codex":0.03721545,"about_ca_topic_score_gemma":0.40878996,"teacher_disagreement_score":0.37157452,"about_ca_system_score_codex":0.0002608287,"about_ca_system_score_gemma":0.0005735288,"threshold_uncertainty_score":0.96919584},"labels":[],"label_agreement":null},{"id":"W2012428429","doi":"10.1002/atr.5670380202","title":"A multiobjective planning model for intercity train seat allocation","year":2004,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Science Council","keywords":"Operations research; Plan (archaeology); Revenue; Transport engineering; Computer science; Engineering; Economics","score_opus":0.027835904772909894,"score_gpt":0.3312743014206672,"score_spread":0.30343839664775735,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2012428429","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3520086,0.000046981713,0.6470145,0.00041108677,0.00019320702,0.00020846115,0.000018738787,0.000027123608,0.000071309696],"genre_scores_gemma":[0.9001499,0.000048360926,0.09948228,0.00006902489,0.00009797766,0.000013049797,0.00007306253,0.000011674303,0.000054677752],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99896127,0.000025725936,0.0004436322,0.000118839496,0.0002933054,0.0001572125],"domain_scores_gemma":[0.9988513,0.00007187043,0.0004010054,0.000043773307,0.0005448357,0.000087230896],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004254253,0.00009150425,0.00015916613,0.00014475921,0.00023284476,0.000024438576,0.00009270517,0.000081858554,0.0000037874775],"category_scores_gemma":[0.00009101743,0.00009441339,0.00011599396,0.00018649948,0.000051440154,0.00087991654,2.4841438e-7,0.00012261952,4.4361855e-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":[0.000283364,0.000053590447,0.0005071394,0.000014729146,0.000017504224,0.000002731499,0.10547005,0.8867663,0.0008372722,0.003911603,0.000009281082,0.0021263987],"study_design_scores_gemma":[0.03910508,0.0023116006,0.5013779,0.0026458849,0.0010967423,0.000016627378,0.2109007,0.096812725,0.011278155,0.12757482,0.0046756794,0.0022040938],"about_ca_topic_score_codex":0.00004248546,"about_ca_topic_score_gemma":0.0007163084,"teacher_disagreement_score":0.7899536,"about_ca_system_score_codex":0.00015585957,"about_ca_system_score_gemma":0.00028754125,"threshold_uncertainty_score":0.38500664},"labels":[],"label_agreement":null},{"id":"W2013015254","doi":"10.1002/atr.5670360305","title":"A traffic flow simulator for short‐term travel time forecasting","year":2002,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":33,"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":"Term (time); Traffic flow (computer networking); Demand forecasting; Computer science; Travel time; Simulation; Line (geometry); Flow network; Operations research; Real-time computing; Transport engineering; Engineering; Mathematics; Mathematical optimization","score_opus":0.029887955097480445,"score_gpt":0.2816833730445818,"score_spread":0.25179541794710136,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2013015254","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9184267,0.00021679573,0.07968125,0.0003787988,0.00050533545,0.0003895224,0.00005635552,0.000053915188,0.00029136727],"genre_scores_gemma":[0.9677185,0.00009810712,0.0315197,0.000037400863,0.00025769914,0.0000066401744,0.00006179042,0.000019244713,0.0002809161],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99869823,0.00003057609,0.0005720491,0.00012537387,0.00035813477,0.00021565254],"domain_scores_gemma":[0.99897295,0.00017450732,0.00030648764,0.000052136118,0.00036804038,0.00012586087],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034095734,0.000109800174,0.00021722089,0.00013722177,0.00029612007,0.00003254241,0.000107673135,0.00008933681,0.0001284299],"category_scores_gemma":[0.000077111246,0.00011069716,0.00017003885,0.0002269254,0.00004681564,0.00066482363,1.924095e-7,0.00011347481,0.000002817925],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012135532,0.000070159396,0.0006611631,0.000023161114,0.000025496853,0.00001144925,0.026871858,0.93092424,0.00044277558,0.00010943899,0.00012219022,0.04061669],"study_design_scores_gemma":[0.015720094,0.002525221,0.20467788,0.0012810794,0.0013033629,0.000033168206,0.026333813,0.7157133,0.0013721592,0.0012953625,0.027640305,0.0021042319],"about_ca_topic_score_codex":0.0000020917653,"about_ca_topic_score_gemma":0.00008721314,"teacher_disagreement_score":0.21521094,"about_ca_system_score_codex":0.00004925872,"about_ca_system_score_gemma":0.00005027123,"threshold_uncertainty_score":0.45140994},"labels":[],"label_agreement":null},{"id":"W2013304779","doi":"10.5539/cis.v4n4p72","title":"An Agent-Based Simulation Model for Urban Traffic System","year":2011,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"Transportation Planning and Optimization","field":"Social Sciences","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":"Computer science; Traffic system; Traffic flow (computer networking); Traffic model; Population; Simulation; Distributed computing; Transport engineering; Computer security; Computer network","score_opus":0.04774825512470108,"score_gpt":0.2934642801428338,"score_spread":0.24571602501813275,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2013304779","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06205245,0.0000021790104,0.9360168,0.000017738437,0.00016611807,0.00018820999,0.0000068835834,0.00012711759,0.0014224956],"genre_scores_gemma":[0.95491356,0.0000010564918,0.044833984,0.00017894509,0.000029708532,0.00000762961,0.000025610321,0.0000015295614,0.000007983431],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993735,0.00001440371,0.00016997021,0.00009207202,0.00022103572,0.00012904621],"domain_scores_gemma":[0.9994661,0.000024509167,0.00008013787,0.00007358586,0.00025830808,0.00009731653],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060666323,0.000046437275,0.000048974147,0.00015287043,0.000642512,0.00017121776,0.00013937737,0.00003137782,0.0000028227023],"category_scores_gemma":[0.000013414234,0.00004480355,0.000014630962,0.0002628404,0.00014717455,0.004637906,0.0000028287407,0.000020385804,0.0000034887048],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008391494,0.000007212416,0.00029815838,0.000012557686,4.6473986e-7,2.2553948e-8,0.037109677,0.9344855,0.0000012079425,0.01903999,0.00002815373,0.009008643],"study_design_scores_gemma":[0.0001770931,0.00003270216,0.0032796029,0.000009729305,0.0000031959207,6.816523e-8,0.0007336465,0.9950466,0.000010109648,0.000013842426,0.0006293397,0.000064046624],"about_ca_topic_score_codex":0.000015567406,"about_ca_topic_score_gemma":0.0000055784994,"teacher_disagreement_score":0.8928611,"about_ca_system_score_codex":0.000031254865,"about_ca_system_score_gemma":0.00015449774,"threshold_uncertainty_score":0.49417478},"labels":[],"label_agreement":null},{"id":"W2014193427","doi":"10.1061/41039(345)458","title":"System Analysis of Air-Rail Inter-Modality Service in Yangtze Delta Area","year":2009,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ministry of Education and Child Care","funders":"","keywords":"China; Transport engineering; Service (business); Aviation; Delta; Yangtze river; Key (lock); Business; Computer science; Engineering; Geography","score_opus":0.021726437870789917,"score_gpt":0.2918455588125181,"score_spread":0.2701191209417282,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2014193427","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9234537,0.000016646967,0.024684727,0.0020811425,0.00006132066,0.00011478836,0.000025020687,0.000130413,0.049432248],"genre_scores_gemma":[0.9986996,0.0000032831556,0.00073867507,0.00024072701,0.000007921477,0.0000013837816,0.000061205625,0.0000014401373,0.00024573092],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99930996,0.00007181843,0.00022891599,0.00011521199,0.00017177865,0.000102294085],"domain_scores_gemma":[0.9996269,0.000042007545,0.00007592655,0.00009401853,0.00012164051,0.000039561262],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038443517,0.00004612122,0.00015436979,0.0001862485,0.00005515656,0.000011700055,0.000105593004,0.00005627653,0.00008488562],"category_scores_gemma":[0.000016871338,0.000043555836,0.0000502709,0.0016448566,0.000017481083,0.000116811694,0.0000018230811,0.000034977995,0.000002769166],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000613684,0.00020697045,0.62533927,0.000059826772,0.00019946648,0.0000066092166,0.07136641,0.22465381,0.000059968104,0.07498258,0.00024736454,0.0028163728],"study_design_scores_gemma":[0.0002201704,0.00001800268,0.9371407,0.00005996753,0.00019031776,6.0483764e-8,0.021075232,0.04066598,0.000045737284,0.00010183732,0.00036695498,0.00011501861],"about_ca_topic_score_codex":0.016096605,"about_ca_topic_score_gemma":0.068488695,"teacher_disagreement_score":0.31180146,"about_ca_system_score_codex":0.00004601386,"about_ca_system_score_gemma":0.00003935638,"threshold_uncertainty_score":0.99045527},"labels":[],"label_agreement":null},{"id":"W2014285129","doi":"10.2495/978-1-84564-908-1/01","title":"Inserting bus rapid transit into an existing transportation system: the Mexico City experience","year":2014,"lang":"en","type":"article","venue":"WIT Transactions on State-of-the-art in Science and Engineering","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Bus rapid transit; TRIPS architecture; Public transport; Transport engineering; Train; Capital city; Business; Transit (satellite); Transit system; Service (business); Quarter (Canadian coin); Geography; Engineering; Marketing","score_opus":0.018756421668005483,"score_gpt":0.26063911090622127,"score_spread":0.2418826892382158,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2014285129","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.81314474,0.00001601357,0.18589829,0.00021346718,0.00029950347,0.00018982092,0.000005436968,0.00004255241,0.0001901942],"genre_scores_gemma":[0.99878526,0.000014704044,0.0011143603,0.000026549395,0.000009893747,0.000021722615,9.640094e-7,0.0000070210076,0.000019513553],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985941,0.00007116756,0.00034898726,0.00021203862,0.00055194227,0.0002217409],"domain_scores_gemma":[0.9993861,0.0001226154,0.000106575346,0.00018920691,0.00013214034,0.00006337888],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013242405,0.00009943445,0.00012682937,0.00011949058,0.00084836106,0.00004791423,0.00033845517,0.00003907188,0.0000036568829],"category_scores_gemma":[0.00007316569,0.000069231864,0.00004395136,0.001390158,0.0004706984,0.00050176604,0.0000011523805,0.00016302639,2.6200183e-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":[0.000015923215,0.000031001593,0.0040125414,0.00006703429,0.0000039432016,3.0350324e-7,0.12364542,0.85486597,0.004621122,0.0013680648,7.371752e-7,0.011367965],"study_design_scores_gemma":[0.0013517699,0.00024170148,0.290893,0.0017503581,0.00010318466,0.000006769735,0.055148583,0.5787209,0.06735278,0.00028841832,0.0032562893,0.00088620355],"about_ca_topic_score_codex":0.0008165284,"about_ca_topic_score_gemma":0.0020931733,"teacher_disagreement_score":0.28688043,"about_ca_system_score_codex":0.00006044329,"about_ca_system_score_gemma":0.00010938398,"threshold_uncertainty_score":0.6524993},"labels":[],"label_agreement":null},{"id":"W2015299857","doi":"10.1002/atr.5670350306","title":"Bus routing strategies in a transit market: A case study of Hong Kong","year":2001,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"University of Hong Kong","keywords":"Profit maximization; Transport engineering; Profit (economics); Routing (electronic design automation); Computer science; Service provider; Transit (satellite); Operations research; Government (linguistics); Quality (philosophy); Business; Public transport; Service (business); Engineering; Computer network; Economics; Microeconomics; Marketing","score_opus":0.017070816606813696,"score_gpt":0.3075760970124529,"score_spread":0.2905052804056392,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2015299857","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98977655,0.00009397147,0.009182513,0.00007746054,0.00016067494,0.00022501679,0.0000033613278,0.000016174565,0.0004642969],"genre_scores_gemma":[0.9973766,0.00017310133,0.0023460444,0.0000071040054,0.000048384907,0.000002920571,0.000004658526,0.0000098165265,0.000031326847],"study_design_codex":"simulation_or_modeling","study_design_gemma":"qualitative","domain_scores_codex":[0.9984243,0.000117319505,0.0007862022,0.00011345811,0.00039484657,0.00016390684],"domain_scores_gemma":[0.9989637,0.000105075385,0.0005281917,0.00005706773,0.0002813098,0.00006466707],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006488484,0.00009564488,0.00024135815,0.00025331392,0.00012795156,0.000025881454,0.000085617336,0.00006100754,0.000039421924],"category_scores_gemma":[0.000029313016,0.00009759971,0.00006836804,0.00068363006,0.000041732863,0.001031297,2.6988567e-7,0.00017009919,1.480955e-7],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036523,0.00035071973,0.15700564,0.0000206158,0.000027284746,0.0026856232,0.22405194,0.61015576,0.00021439519,0.00026780617,0.0000024713768,0.0048525217],"study_design_scores_gemma":[0.0023669424,0.0002966649,0.4919392,0.00014148532,0.00007047702,0.000063632695,0.5045596,0.00017895494,0.000022626733,0.00016397539,0.00007823798,0.00011822377],"about_ca_topic_score_codex":0.0014185882,"about_ca_topic_score_gemma":0.040287517,"teacher_disagreement_score":0.6099768,"about_ca_system_score_codex":0.00004272601,"about_ca_system_score_gemma":0.00016130744,"threshold_uncertainty_score":0.9772247},"labels":[],"label_agreement":null},{"id":"W2015728112","doi":"10.1080/15472450701649398","title":"Expected Time of Arrival Model for School Bus Transit Using Real-Time Global Positioning System-Based Automatic Vehicle Location Data","year":2007,"lang":"en","type":"article","venue":"Journal of Intelligent Transportation Systems","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Automatic vehicle location; Arrival time; Transport engineering; Transit (satellite); Real-time data; Mode (computer interface); School bus; Location data; Real-time computing; Computer science; Travel time; Service (business); Simulation; Operations research; Public transport; Global Positioning System; Engineering; Telecommunications","score_opus":0.04985802164247499,"score_gpt":0.3346928987928159,"score_spread":0.2848348771503409,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2015728112","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.31888574,0.000116455565,0.6799239,0.000032672797,0.00024456446,0.00044383373,0.00019602594,0.00006649274,0.000090301306],"genre_scores_gemma":[0.97765684,0.000012687455,0.021610873,0.000010927958,0.00016637078,0.000004457016,0.0004666142,0.000021568516,0.000049661827],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9967144,0.00014420292,0.0017856157,0.00022185587,0.00086592423,0.00026797218],"domain_scores_gemma":[0.99640363,0.00020459158,0.001358435,0.00021825572,0.0016018962,0.00021316303],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024634341,0.00016854683,0.0004390189,0.0002566699,0.00027203813,0.00007679471,0.00035518405,0.00017269513,0.000023391403],"category_scores_gemma":[0.00009735279,0.00017347613,0.00014498843,0.00064266444,0.00007290004,0.00066016545,0.0000010649989,0.00009968382,0.000005045388],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029063362,0.00010217411,0.0031387766,0.00037534302,0.00010181245,0.0000088288625,0.0064533446,0.98424524,0.004182147,0.0008176386,0.00006348786,0.0002205657],"study_design_scores_gemma":[0.00076467247,0.00009238241,0.0038809455,0.0011923158,0.00030473978,0.000005888637,0.0065474333,0.98624766,0.00074115174,0.000018211993,0.000023087374,0.00018153724],"about_ca_topic_score_codex":0.00077399495,"about_ca_topic_score_gemma":0.00020700143,"teacher_disagreement_score":0.6587711,"about_ca_system_score_codex":0.0004071678,"about_ca_system_score_gemma":0.00079164846,"threshold_uncertainty_score":0.7074151},"labels":[],"label_agreement":null},{"id":"W2015742643","doi":"10.1016/j.jtrangeo.2011.05.007","title":"Policy frames of Park-and-Ride in Europe","year":2011,"lang":"en","type":"article","venue":"Journal of Transport Geography","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":57,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Software deployment; Quarter (Canadian coin); Urban policy; Traffic congestion; Diversity (politics); Regional science; Variance (accounting); Business; Public economics; Urban planning; Political science; Economics; Geography; Transport engineering; Engineering","score_opus":0.018228575837822666,"score_gpt":0.26711507010335933,"score_spread":0.24888649426553666,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2015742643","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99025506,0.00030837045,0.0010027963,0.00032987652,0.00011147024,0.000054979555,0.000007631221,0.000010571479,0.007919241],"genre_scores_gemma":[0.9951133,0.0014729279,0.0032692107,0.000060584647,0.000051649993,4.803236e-7,0.0000020707944,0.0000054793854,0.000024284236],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9990818,0.00004963078,0.0004305696,0.000065559536,0.0002416687,0.00013078177],"domain_scores_gemma":[0.999362,0.0000296714,0.00026357413,0.000048076494,0.00020796256,0.0000887017],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000536076,0.00006133087,0.00017579005,0.00052557315,0.000051510713,0.0000051399925,0.00012192744,0.000058894064,0.000045106466],"category_scores_gemma":[0.000035125693,0.000055356388,0.00009037871,0.0008338984,0.0001619835,0.00020954343,8.9962236e-7,0.00012850853,3.2254647e-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":[0.00006433635,0.00006942265,0.97764295,0.000011336292,0.000021931965,0.000023884437,0.017278416,0.0001982861,0.000017220073,0.0036554204,0.000013590954,0.0010032273],"study_design_scores_gemma":[0.00029907477,0.000070537084,0.99545896,0.00006411904,0.000029651323,0.0000018823903,0.0008828318,0.0000019058565,0.00006877616,0.0009538771,0.002108966,0.000059395545],"about_ca_topic_score_codex":0.0019071611,"about_ca_topic_score_gemma":0.0006091412,"teacher_disagreement_score":0.017816056,"about_ca_system_score_codex":0.00000405016,"about_ca_system_score_gemma":0.00013660887,"threshold_uncertainty_score":0.28830698},"labels":[],"label_agreement":null},{"id":"W2016057461","doi":"10.3141/2143-14","title":"Anxiety-Based Formulation to Estimate Generalized Cost of Transit Travel Time","year":2010,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Anxiety; Transit (satellite); Reliability (semiconductor); Mode (computer interface); Function (biology); Service (business); Econometrics; Operations research; Public transport; Computer science; Transport engineering; Engineering; Economics; Psychology; Marketing; Business","score_opus":0.06647666086540845,"score_gpt":0.41752634271503297,"score_spread":0.35104968184962454,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2016057461","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9613683,0.000027971388,0.028716894,0.0066071455,0.000635848,0.0018215704,0.00015549427,0.00004884027,0.00061790907],"genre_scores_gemma":[0.9814515,0.0001089479,0.017135587,0.00007537098,0.00020111144,0.00007746103,0.00008614276,0.000056495028,0.0008073738],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9915204,0.0011532564,0.0016182462,0.0004113223,0.0043813423,0.0009154031],"domain_scores_gemma":[0.9918782,0.0009681208,0.00057507755,0.00043714323,0.005500355,0.0006410676],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0074608875,0.0002549318,0.0005277594,0.0013038651,0.0011063582,0.00014160774,0.0010992261,0.00033729555,0.0007810974],"category_scores_gemma":[0.0004185979,0.00021691326,0.00043718072,0.003164179,0.00068130565,0.000716442,0.0000024673825,0.0017817331,0.000029007868],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.006833598,0.001219495,0.45864102,0.00053682533,0.0002986534,0.000076510034,0.05080604,0.24048214,0.15898018,0.049536444,0.009216775,0.023372319],"study_design_scores_gemma":[0.0027749524,0.00046263373,0.9508739,0.0003239258,0.00010452787,2.8004004e-7,0.0016930929,0.0023445152,0.011088749,0.001959136,0.028024752,0.0003495326],"about_ca_topic_score_codex":0.011625658,"about_ca_topic_score_gemma":0.11431061,"teacher_disagreement_score":0.4922329,"about_ca_system_score_codex":0.00015021788,"about_ca_system_score_gemma":0.0015489485,"threshold_uncertainty_score":0.994956},"labels":[],"label_agreement":null},{"id":"W2016083649","doi":"10.1002/net.20456","title":"Progressive hedging‐based metaheuristics for stochastic network design","year":2011,"lang":"en","type":"article","venue":"Networks","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":138,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Computer Research Institute of Montréal; Université de Montréal; École de Technologie Supérieure; Center for Interuniversity Research and Analysis on Organizations; Université du Québec à Montréal","funders":"Engineering and Physical Sciences Research Council","keywords":"Mathematical optimization; Stochastic programming; Computer science; Metaheuristic; Network planning and design; Decomposition; Mathematics","score_opus":0.07614022353364325,"score_gpt":0.3012108241517992,"score_spread":0.22507060061815592,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2016083649","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00010361351,0.00032992635,0.9969143,0.00005624566,0.0005612957,0.00054353476,0.0000032411347,0.00016266895,0.0013251662],"genre_scores_gemma":[0.79600567,0.000006328581,0.20270461,0.00017960985,0.0006697875,0.00012788356,0.000050136598,0.00001969167,0.00023631078],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990758,0.00010859859,0.00016345608,0.00016582459,0.00014532343,0.00034101488],"domain_scores_gemma":[0.99920297,0.0003329987,0.000120574216,0.000093415765,0.00015277216,0.000097271506],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005695075,0.00009060854,0.00011948587,0.00002826585,0.0004850014,0.000037747475,0.00012914292,0.00011694245,0.00009479132],"category_scores_gemma":[0.00011079677,0.00009066812,0.00005274866,0.0002293013,0.00010673997,0.000071114155,0.0000025540346,0.000085324275,0.000004154187],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007975488,0.000025883752,0.00041409073,0.000003394452,0.000016096128,0.000003118927,0.0018825354,0.9834802,6.283925e-8,0.008389538,0.0037345777,0.0019707426],"study_design_scores_gemma":[0.0006170698,0.00014337542,0.0014965287,0.00008142537,0.00016532147,3.7621143e-7,0.0003355333,0.9887206,0.0000047014673,0.00408266,0.004055074,0.00029732613],"about_ca_topic_score_codex":0.00003832684,"about_ca_topic_score_gemma":0.000043776996,"teacher_disagreement_score":0.795902,"about_ca_system_score_codex":0.0000207615,"about_ca_system_score_gemma":0.00011256436,"threshold_uncertainty_score":0.37302876},"labels":[],"label_agreement":null},{"id":"W2016558995","doi":"10.1177/1087724x0044001","title":"Using Technology in Surface Transportation to Save Lives, Time, and Money","year":2000,"lang":"en","type":"article","venue":"Public Works Management & Policy","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lockheed Martin (Canada)","funders":"","keywords":"Gridlock; Software deployment; Traffic congestion; Bureaucracy; Legislation; Business; Public transport; Transport engineering; Intelligent transportation system; Engineering; Political science; Politics","score_opus":0.01689360294104173,"score_gpt":0.2995809035186368,"score_spread":0.28268730057759506,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2016558995","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.927407,0.000163245,0.003092945,0.023755033,0.00004334122,0.00062880234,0.000010544535,0.0002721079,0.044626977],"genre_scores_gemma":[0.98524773,0.000458189,0.010149962,0.00039886637,0.000042873373,0.000012386234,0.000043063377,0.0000118463,0.003635093],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9990467,0.000045588236,0.00019223802,0.00022440556,0.0001754726,0.00031560284],"domain_scores_gemma":[0.9997115,0.000017638173,0.000041103664,0.00010182695,0.000029236331,0.00009870941],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002602115,0.000090064656,0.00010601918,0.0004965025,0.00019894712,0.00012312087,0.00013078353,0.00007991644,0.00022445244],"category_scores_gemma":[0.000017965624,0.0001063792,0.000016422262,0.0018254159,0.00007128184,0.00034646704,0.0000045147185,0.00007897929,0.000032873817],"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":[0.000066740446,0.0002291138,0.05996982,0.000042346965,0.0000739101,0.00004052508,0.036602527,0.2905343,0.000039961316,0.22993328,0.0012238447,0.38124365],"study_design_scores_gemma":[0.0033026042,0.00013827447,0.53645897,0.00064989494,0.00015043393,0.0000022196764,0.023115883,0.02008424,0.000035796656,0.0141902305,0.40010518,0.001766285],"about_ca_topic_score_codex":0.0027613689,"about_ca_topic_score_gemma":0.0015895112,"teacher_disagreement_score":0.47648913,"about_ca_system_score_codex":0.000081670456,"about_ca_system_score_gemma":0.000046254427,"threshold_uncertainty_score":0.4338018},"labels":[],"label_agreement":null},{"id":"W2016771359","doi":"10.1002/atr.5670380304","title":"Sensitivity analysis on stochastic equilibrium transportation networks using genetic algorithm","year":2004,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Sensitivity (control systems); Estimator; Computer science; Genetic algorithm; Mathematical optimization; Traffic flow (computer networking); Path (computing); Flow network; SIGNAL (programming language); Algorithm; Mathematics; Engineering; Statistics","score_opus":0.012873161733611124,"score_gpt":0.28327764882063283,"score_spread":0.2704044870870217,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2016771359","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4399544,0.000059255366,0.5594194,0.00009147734,0.00030193408,0.00010965178,0.000024682726,0.000028323184,0.000010884134],"genre_scores_gemma":[0.92391,0.00006588813,0.0755483,0.00006426392,0.00022767016,0.0000020133027,0.00015188826,0.000019429332,0.000010536657],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9979654,0.000104825944,0.00074568146,0.00022171282,0.0006836749,0.0002786904],"domain_scores_gemma":[0.9983987,0.000117047086,0.00071917765,0.000105225074,0.00048187395,0.00017792988],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049719296,0.00017480252,0.0003572026,0.00044843598,0.00024238632,0.000041033065,0.00009141569,0.0001375852,0.000021337773],"category_scores_gemma":[0.00003163291,0.0001843505,0.0002864168,0.0012549744,0.000093147326,0.000664778,2.4855063e-7,0.00024306201,0.0000011860834],"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":[0.00014034337,0.00009051754,0.0014791398,0.0000061424967,0.00015621938,0.00007516769,0.0074642417,0.9861274,0.0002404322,0.00039039788,0.0000012793462,0.003828734],"study_design_scores_gemma":[0.0031560184,0.00040161252,0.946041,0.00024689102,0.002566127,0.0000062068634,0.004803178,0.040670685,0.00027116362,0.0011737611,0.0001009589,0.0005623976],"about_ca_topic_score_codex":0.00034461851,"about_ca_topic_score_gemma":0.0015752942,"teacher_disagreement_score":0.9454567,"about_ca_system_score_codex":0.00017265056,"about_ca_system_score_gemma":0.00023261632,"threshold_uncertainty_score":0.7517595},"labels":[],"label_agreement":null},{"id":"W2017202498","doi":"10.1002/atr.5670340304","title":"Estimating time dependent O‐D trip tables during peak periods","year":2000,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"TRIPS architecture; Metropolitan area; Transport engineering; Trip distribution; Work (physics); Las vegas; Travel time; Traffic congestion; Computer science; Journey to work; Trip generation; Statistics; Simulation; Operations research; Public transport; Geography; Mathematics; Engineering","score_opus":0.00717924834699091,"score_gpt":0.2669106431317201,"score_spread":0.2597313947847292,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2017202498","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99439996,0.00015060088,0.003641437,0.0002222465,0.0002899726,0.000108799766,0.000013518692,0.00004042302,0.0011330581],"genre_scores_gemma":[0.96686745,0.00019282404,0.031130044,0.000024623849,0.00026625462,0.0000027662863,0.00003924761,0.0000145865415,0.0014622243],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9985741,0.00005944265,0.0005621789,0.0001190246,0.00048932555,0.00019597965],"domain_scores_gemma":[0.99921364,0.000053554195,0.00034738248,0.000059870308,0.00021355867,0.00011198797],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003856845,0.000101147016,0.00018743942,0.00011486349,0.00037828146,0.00004793949,0.000120013356,0.00006835294,0.0011659844],"category_scores_gemma":[0.000043157954,0.0001027089,0.00009339774,0.00024752272,0.000049393457,0.0007219143,2.5393058e-7,0.00015003361,0.000014935306],"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":[0.00020387572,0.000048963237,0.0027041167,0.000020301406,0.000019799845,0.000036729307,0.022012461,0.95743835,0.002926561,0.00009164253,0.000017330056,0.0144799],"study_design_scores_gemma":[0.006543705,0.00038399946,0.9572431,0.00075187464,0.00034866182,0.000036073507,0.012801348,0.0026199415,0.0020710716,0.0012144558,0.015123855,0.0008619352],"about_ca_topic_score_codex":0.00006003198,"about_ca_topic_score_gemma":0.00021912163,"teacher_disagreement_score":0.95481837,"about_ca_system_score_codex":0.00006965959,"about_ca_system_score_gemma":0.00013098004,"threshold_uncertainty_score":0.9997471},"labels":[],"label_agreement":null},{"id":"W2017712805","doi":"10.1007/s11431-010-4244-5","title":"Multi-criterion system optimization model for urban multimodal traffic network","year":2011,"lang":"en","type":"article","venue":"Science in China. Series E, Technological sciences/Science in China. Series E, Technological Sciences","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Computer science; Mathematical optimization; Optimization problem; Mode (computer interface); Programming paradigm; Energy consumption; Simple (philosophy); Bilevel optimization; Engineering; Algorithm; Mathematics","score_opus":0.04436926087847434,"score_gpt":0.3010676006735444,"score_spread":0.25669833979507006,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2017712805","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.89835733,0.00024840957,0.081125826,0.004051437,0.001448259,0.0032049995,0.00005578991,0.0034100923,0.008097883],"genre_scores_gemma":[0.7501982,0.0003012096,0.24876745,0.00007886719,0.000054535583,0.00038650207,0.0000070156684,0.000020124366,0.0001861327],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9865429,0.00031457332,0.0017878703,0.0037599865,0.0034384832,0.004156212],"domain_scores_gemma":[0.9969543,0.0002977403,0.0008481592,0.00088006083,0.00043388052,0.0005858305],"candidate_categories":["metaepi_narrow","bibliometrics","sts","scholarly_communication","open_science"],"consensus_categories":["sts"],"category_scores_codex":[0.020456968,0.0009022043,0.0010345682,0.002584091,0.01013204,0.0014715202,0.00931764,0.0010882483,0.000097402655],"category_scores_gemma":[0.0053612767,0.0007011661,0.00022253959,0.030706046,0.07855078,0.00903814,0.0008466758,0.0011865395,0.000011463626],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013747066,0.00057625637,0.038737155,0.00004363507,0.0000028223803,0.000037943675,0.00660696,0.7409429,0.00090380816,0.20336647,0.000029863311,0.008614725],"study_design_scores_gemma":[0.00079686026,0.0012875447,0.045020178,0.00036650852,0.000017529359,0.000046210094,0.023124138,0.91207993,0.0008466859,0.014674322,0.000234217,0.0015058646],"about_ca_topic_score_codex":0.0009496261,"about_ca_topic_score_gemma":0.0032714976,"teacher_disagreement_score":0.18869215,"about_ca_system_score_codex":0.0011873485,"about_ca_system_score_gemma":0.002242999,"threshold_uncertainty_score":0.99956506},"labels":[],"label_agreement":null},{"id":"W2017790523","doi":"10.3141/1971-08","title":"Empirical Analysis of Transit Network Evolution: Case Study of Mississauga, Ontario, Canada, Bus Network","year":2006,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Socioeconomic status; Transit (satellite); Regression analysis; Transport engineering; Population; Variables; Supply and demand; Geography; Public transport; Econometrics; Computer science; Statistics; Demography; Mathematics; Engineering; Economics; Sociology","score_opus":0.08123131375242232,"score_gpt":0.38857535633154533,"score_spread":0.307344042579123,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2017790523","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99388576,0.00024975074,0.0030424106,0.0008854104,0.00046508352,0.0010331892,0.00008371546,0.000019171572,0.0003354971],"genre_scores_gemma":[0.9975288,0.000073543255,0.0012206279,0.000017107825,0.0002718132,0.00003019339,0.00005356801,0.000031365853,0.0007729853],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9880581,0.0025414126,0.0025179114,0.00044258038,0.0054350407,0.0010049699],"domain_scores_gemma":[0.99104595,0.001559817,0.0010652217,0.00046748336,0.0054772682,0.00038427277],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0066566058,0.00026274077,0.0009051757,0.0010860234,0.0013653897,0.00006822711,0.00086295296,0.00024807118,0.0006042319],"category_scores_gemma":[0.00009604087,0.00022675608,0.00053508335,0.008643325,0.00071621843,0.0004290959,0.0000035871578,0.0014862774,7.157321e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005183939,0.0003740116,0.5890517,0.00003863769,0.00049609534,0.0003675751,0.012898472,0.39085263,0.000013106964,0.00065072154,0.00457025,0.00016836976],"study_design_scores_gemma":[0.00129641,0.00047267842,0.9620352,0.00013789996,0.0008711139,0.0000011261068,0.03022495,0.00031565526,0.000014620503,0.000506079,0.003931167,0.00019313235],"about_ca_topic_score_codex":0.9936265,"about_ca_topic_score_gemma":0.99980354,"teacher_disagreement_score":0.390537,"about_ca_system_score_codex":0.0007317378,"about_ca_system_score_gemma":0.0051090275,"threshold_uncertainty_score":0.9999347},"labels":[],"label_agreement":null},{"id":"W2017872791","doi":"10.3141/2218-02","title":"Introduction of Reserved Bus Lane","year":2011,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":66,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Ministère des Transports","funders":"","keywords":"Transport engineering; Reliability (semiconductor); Service (business); Traffic congestion; TRIPS architecture; Computer science; Public transport; Duration (music); Engineering; Business","score_opus":0.1551262196391985,"score_gpt":0.39668980319500596,"score_spread":0.24156358355580745,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2017872791","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98819196,0.00019073005,0.0024884506,0.0049326573,0.0009665571,0.00084333966,0.00005585309,0.000048443013,0.0022820013],"genre_scores_gemma":[0.99073374,0.0012158345,0.0054301876,0.000021299637,0.00052197196,0.000030467721,0.000037111982,0.00004030661,0.0019690995],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.991167,0.002115457,0.0015448435,0.0003609867,0.0040370594,0.0007746153],"domain_scores_gemma":[0.99159443,0.00063149404,0.00073992676,0.00044978337,0.006224023,0.00036033292],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00843879,0.00019040785,0.00042412317,0.0011345946,0.0009311778,0.00006255133,0.0011362534,0.00025680204,0.0015306285],"category_scores_gemma":[0.00052930077,0.00015563903,0.00033999718,0.0031256685,0.0013232613,0.0008878952,0.000003819784,0.0015395841,0.000022301256],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.004352214,0.0009638061,0.7679281,0.000393192,0.00032224914,0.00011115125,0.0954548,0.0023013696,0.0028841943,0.08566285,0.03261669,0.0070093945],"study_design_scores_gemma":[0.0010902612,0.0005067601,0.93196404,0.0001904721,0.00006680768,2.4309776e-7,0.012017852,0.000023254814,0.0023814712,0.0051631005,0.046413712,0.00018202682],"about_ca_topic_score_codex":0.052495707,"about_ca_topic_score_gemma":0.106362194,"teacher_disagreement_score":0.16403595,"about_ca_system_score_codex":0.00018078313,"about_ca_system_score_gemma":0.00095394376,"threshold_uncertainty_score":0.9993821},"labels":[],"label_agreement":null},{"id":"W2017928262","doi":"10.5038/2375-0901.10.3.4","title":"A New Performance Index for Evaluating Transit Quality of Service","year":2007,"lang":"en","type":"article","venue":"Journal of Public Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":72,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Stantec (Canada); University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; New York Community Trust","keywords":"Service quality; Service (business); Index (typography); Measure (data warehouse); Transport engineering; Level of service; Transit (satellite); Quality (philosophy); Computer science; Service level; Quality of service; Sensitivity (control systems); Operations research; Reliability engineering; Public transport; Statistics; Data mining; Business; Engineering; Mathematics; Telecommunications; Marketing","score_opus":0.11622284238155382,"score_gpt":0.40425772198926785,"score_spread":0.288034879607714,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2017928262","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5378307,0.000036776833,0.46070343,0.00094961503,0.00015671413,0.00011246874,0.000007799228,0.000012000023,0.00019047556],"genre_scores_gemma":[0.9740118,0.000024906025,0.025586432,0.00009135736,0.0001496647,0.0000012704977,0.000039999828,0.000008305518,0.000086256434],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99801636,0.00007247139,0.0009491432,0.000086856904,0.0006799752,0.00019522339],"domain_scores_gemma":[0.99734986,0.00023320236,0.0008600358,0.00005542222,0.0013512113,0.00015027363],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005438927,0.000074106814,0.0002003399,0.00020882706,0.00015398255,0.00002541974,0.00014850672,0.00010178422,0.00005521594],"category_scores_gemma":[0.00014351719,0.00007397856,0.000112804155,0.0005573885,0.000029992509,0.00078902836,2.3991856e-7,0.000110165354,3.6417222e-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":[0.0015341011,0.0002525053,0.5670233,0.0005141846,0.0001714324,0.0000027569436,0.1390902,0.059288915,0.004685001,0.025097476,0.00024927888,0.20209084],"study_design_scores_gemma":[0.0017595583,0.00017262073,0.9890162,0.00007097623,0.00006168034,5.685677e-7,0.005608677,0.0005343687,0.00048736998,0.00045404903,0.0017229256,0.000111043686],"about_ca_topic_score_codex":0.0006524096,"about_ca_topic_score_gemma":0.008972925,"teacher_disagreement_score":0.43618107,"about_ca_system_score_codex":0.000045180455,"about_ca_system_score_gemma":0.00068045466,"threshold_uncertainty_score":0.5007103},"labels":[],"label_agreement":null},{"id":"W2018453202","doi":"10.3141/2357-01","title":"Diagnosing Transportation","year":2013,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Benchmarking; Context (archaeology); Transportation planning; Transport engineering; Process (computing); Benchmark (surveying); Performance indicator; Computer science; Best practice; Business; Operations research; Engineering; Economics; Geography; Marketing","score_opus":0.09867125768422692,"score_gpt":0.40820094637405413,"score_spread":0.3095296886898272,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2018453202","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98080134,0.00029046444,0.0035123662,0.010887379,0.0012420266,0.0018216298,0.00007070558,0.000102647326,0.0012714663],"genre_scores_gemma":[0.9920623,0.0016379227,0.0035260008,0.00013781388,0.00046243786,0.00018006512,0.00006731825,0.00007390788,0.0018522369],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.98854333,0.0021656824,0.0019175007,0.0005180385,0.0055233166,0.0013321402],"domain_scores_gemma":[0.9891024,0.0018536344,0.00073047995,0.0004708565,0.00714465,0.00069796015],"candidate_categories":["metaepi_narrow","sts","research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0058071553,0.00031491,0.00052738964,0.001267489,0.0022561261,0.00043480718,0.0013725975,0.0003775381,0.0017222671],"category_scores_gemma":[0.0004365455,0.00026088604,0.00050549547,0.0036730596,0.0013584355,0.0021537393,0.0000024886895,0.002304636,0.00012727478],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00063407986,0.00062884757,0.83167624,0.00026318757,0.00023621439,0.00011963668,0.056185707,0.008577711,0.0019146418,0.032365803,0.047745362,0.019652538],"study_design_scores_gemma":[0.0011892676,0.00026637776,0.91650003,0.000379283,0.00006642971,1.6050025e-7,0.013108354,0.00008153519,0.00047530903,0.0056927176,0.061949402,0.00029113036],"about_ca_topic_score_codex":0.07995588,"about_ca_topic_score_gemma":0.16112201,"teacher_disagreement_score":0.08482376,"about_ca_system_score_codex":0.0003770539,"about_ca_system_score_gemma":0.0012467586,"threshold_uncertainty_score":0.9999971},"labels":[],"label_agreement":null},{"id":"W2018846914","doi":"10.1007/s10957-008-9440-0","title":"EP Theorem for Dual Linear Complementarity Problems","year":2008,"lang":"en","type":"article","venue":"Journal of Optimization Theory and Applications","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":24,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Complementarity (molecular biology); Dual (grammatical number); Linear complementarity problem; Mathematics; Matrix (chemical analysis); Complementarity theory; Time complexity; Property (philosophy); Pure mathematics; Discrete mathematics; Combinatorics; Algebra over a field; Nonlinear system; Physics","score_opus":0.03453408348496048,"score_gpt":0.31512657208195927,"score_spread":0.2805924885969988,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2018846914","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0027136367,0.0001166521,0.9944581,0.0006364095,0.000053143216,0.00038108963,0.000023702018,0.00002439126,0.0015928679],"genre_scores_gemma":[0.8097323,0.0014151087,0.1862656,0.0003736577,0.0006499971,0.00010236003,0.00015884443,0.000020946209,0.0012811645],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99922776,0.00012137207,0.00031540633,0.000084146304,0.00015024582,0.00010104325],"domain_scores_gemma":[0.9988921,0.0003051812,0.00029316475,0.000056973764,0.00036986306,0.00008267847],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00095177593,0.000060225582,0.00010959956,0.00007702257,0.00084545906,0.000023487026,0.00008052327,0.00004856829,0.0000967901],"category_scores_gemma":[0.00008679095,0.000055899774,0.0000488053,0.00017759179,0.00019425483,0.00023311589,0.00000371235,0.00007134587,9.837557e-7],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007542835,0.00010010648,0.0012939094,0.00001159574,0.000027115633,5.2267154e-7,0.0056907134,0.46718028,0.000015815476,0.5243713,0.00041408173,0.00081907766],"study_design_scores_gemma":[0.0073696277,0.00073916535,0.00537201,0.00017334992,0.0005821268,0.00017446403,0.02259263,0.05868508,0.00047201134,0.23975015,0.66295373,0.0011356559],"about_ca_topic_score_codex":0.0000073425063,"about_ca_topic_score_gemma":0.000008764074,"teacher_disagreement_score":0.8081925,"about_ca_system_score_codex":0.000019328725,"about_ca_system_score_gemma":0.00008633759,"threshold_uncertainty_score":0.6502673},"labels":[],"label_agreement":null},{"id":"W2019458693","doi":"10.1002/atr.5670380206","title":"Real time traffic information and its impacts on route switching behavior of expressway drivers","year":2004,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":35,"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":"Structural equation modeling; Latent variable; Computer science; Transport engineering; Predictive modelling; Travel behavior; Travel time; Engineering; Machine learning","score_opus":0.00781559428450887,"score_gpt":0.26705519239626907,"score_spread":0.2592395981117602,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2019458693","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99830043,0.000026928428,0.0010381045,0.00013377238,0.00014472542,0.0001538756,0.000021694585,0.000023634559,0.0001568376],"genre_scores_gemma":[0.9951455,0.00049388054,0.004239884,0.000016698861,0.00003507354,0.0000019386903,0.000050874012,0.000006161266,0.000010000739],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99888235,0.000025632502,0.00050715066,0.0000639659,0.00040360776,0.00011729971],"domain_scores_gemma":[0.99892193,0.000042561704,0.0006296459,0.000037398942,0.00026833446,0.00010011689],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026047704,0.000083703606,0.00015856525,0.00018917405,0.00013325394,0.000021992608,0.00006762045,0.00007231414,0.000011274205],"category_scores_gemma":[0.00003680331,0.00008158455,0.000059878035,0.00017965928,0.000026883772,0.0019004416,3.2779406e-7,0.00011742471,0.000001837445],"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":[0.00025976877,0.00007442337,0.0020019456,0.000029244922,0.00001766199,0.000009720039,0.065840386,0.91253024,0.011046388,0.0019155814,0.00000400396,0.0062706466],"study_design_scores_gemma":[0.005760455,0.00089787587,0.9687929,0.0006965638,0.0002655714,0.000005351197,0.013591027,0.00018707533,0.00857109,0.00030156234,0.0005713075,0.00035918236],"about_ca_topic_score_codex":0.00005193934,"about_ca_topic_score_gemma":0.00012029045,"teacher_disagreement_score":0.966791,"about_ca_system_score_codex":0.00006614194,"about_ca_system_score_gemma":0.00016098535,"threshold_uncertainty_score":0.33269212},"labels":[],"label_agreement":null},{"id":"W2019576575","doi":"10.3141/1994-13","title":"Costs of Urban Congestion in Canada","year":2007,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Inro Consultants (Canada); St. Michael's Hospital; Transport Canada; Communications Research Centre Canada","funders":"Abu Dhabi Education Council; U.S. Department of Transportation","keywords":"Metropolitan area; Traffic congestion; Transport engineering; Transportation planning; Urban planning; Computer science; Operations research; Business; Environmental economics; Environmental planning; Economics; Environmental science; Engineering; Geography; Civil engineering","score_opus":0.06854301273091368,"score_gpt":0.38721809918031014,"score_spread":0.31867508644939646,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2019576575","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99397206,0.0001932741,0.0015122107,0.0019855616,0.0006515195,0.0006951839,0.000046996753,0.00001298675,0.0009302278],"genre_scores_gemma":[0.9979085,0.0006111788,0.0009253948,0.00003412024,0.0001297831,0.000012432491,0.000021223754,0.000025636748,0.0003317626],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99148524,0.001259183,0.0016617326,0.00027080718,0.004453626,0.0008694263],"domain_scores_gemma":[0.99300045,0.0018940305,0.00058927113,0.0002564291,0.0038902883,0.00036952746],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010145298,0.0001595739,0.0003766332,0.0010794756,0.00050748535,0.000045268232,0.00080219656,0.00017523585,0.00019801994],"category_scores_gemma":[0.0004399361,0.00013873972,0.00016860386,0.0032114766,0.00064363773,0.000527791,0.0000021899805,0.00148716,0.0000029708694],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009206159,0.00012662994,0.9637931,0.00007224269,0.0000382089,0.00010472348,0.010260942,0.0050975494,0.00046046855,0.011983505,0.0024029217,0.004739067],"study_design_scores_gemma":[0.0010262755,0.00014019058,0.9720966,0.00037888397,0.000019198225,9.4350746e-8,0.01618929,0.000046307752,0.0006139927,0.00053512526,0.008824041,0.00012998054],"about_ca_topic_score_codex":0.95912427,"about_ca_topic_score_gemma":0.9984977,"teacher_disagreement_score":0.03937345,"about_ca_system_score_codex":0.0013326278,"about_ca_system_score_gemma":0.005095724,"threshold_uncertainty_score":0.9039596},"labels":[],"label_agreement":null},{"id":"W2019797290","doi":"10.2495/ut110221","title":"Analysis of flows and speeds of urban transit systems for consideration of modal transition in a corridor","year":2011,"lang":"en","type":"article","venue":"WIT transactions on the built environment","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Modal; Modal shift; Computer science; Transition (genetics); Transport engineering; Transit (satellite); Public transport; Engineering","score_opus":0.03558924391730517,"score_gpt":0.23780623033039433,"score_spread":0.20221698641308916,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2019797290","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5296393,0.00006262864,0.4694146,0.00013224219,0.00003304446,0.000413956,0.00014351521,0.0000061639203,0.00015458371],"genre_scores_gemma":[0.99881256,0.00017496124,0.0008845868,0.000009036665,0.000004285123,0.000039554212,0.00001902177,0.0000052415717,0.000050756662],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991661,0.00009928861,0.00034296175,0.000119154916,0.00018999807,0.00008251779],"domain_scores_gemma":[0.99959993,0.0001197642,0.00012306243,0.00010638826,0.000024103618,0.000026762778],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037763926,0.00006927415,0.00020817033,0.00020285478,0.0000888485,0.0000041051085,0.000052047984,0.00006409721,0.00012455226],"category_scores_gemma":[0.000004157468,0.000060700844,0.00009100789,0.00023657395,0.00015617859,0.00007350714,2.1238418e-7,0.00004505273,2.3127747e-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":[0.00027206156,0.00033601446,0.002353655,0.000052151987,0.00029247106,2.9578345e-7,0.07119673,0.92020893,0.0015733101,0.003116447,0.0000046032733,0.0005933054],"study_design_scores_gemma":[0.004715422,0.0014667156,0.22011685,0.00038693758,0.0065913955,0.0000016353667,0.06551343,0.6709447,0.02811626,0.0009153158,0.00047891008,0.00075241714],"about_ca_topic_score_codex":0.001906659,"about_ca_topic_score_gemma":0.0011085222,"teacher_disagreement_score":0.46917325,"about_ca_system_score_codex":0.00002509644,"about_ca_system_score_gemma":0.00002395843,"threshold_uncertainty_score":0.28823107},"labels":[],"label_agreement":null},{"id":"W2020093709","doi":"10.1139/l07-013","title":"A class of multicriteria shortest path problems for real-time in-vehicle routing","year":2007,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Mathematical optimization; Nondeterministic algorithm; Computer science; Heuristic; Routing (electronic design automation); Path (computing); Context (archaeology); Mathematics; Algorithm","score_opus":0.011514398118651407,"score_gpt":0.23525397417145352,"score_spread":0.22373957605280212,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2020093709","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.927487,0.00017521056,0.06709106,0.00025946798,0.00052097137,0.00027127,0.000024782283,0.000024855595,0.0041453377],"genre_scores_gemma":[0.9973284,0.0000093306335,0.0025070999,0.000007210428,0.00009011473,8.767757e-7,0.0000032424196,0.000009693122,0.00004402392],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9992687,0.000010318691,0.0003304708,0.000050406776,0.00010011096,0.000239982],"domain_scores_gemma":[0.9993807,0.00011035894,0.0001121649,0.000033748038,0.00014404733,0.0002189482],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010095936,0.000050963252,0.00012179309,0.00022999536,0.000059621867,0.000021033315,0.00008519341,0.000054447737,0.000025114066],"category_scores_gemma":[0.00021966113,0.000057636462,0.000039748895,0.00020903631,0.00002228594,0.000138172,6.9219135e-7,0.00007517653,2.658404e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018169174,0.000017963948,0.07875437,0.00006234719,0.000025672223,0.000059585327,0.035716552,0.87282556,0.008741403,0.0025780874,0.00021447812,0.0009858055],"study_design_scores_gemma":[0.0040563513,0.0004551807,0.59689814,0.0033876484,0.0001271945,0.000030134377,0.010664969,0.35433277,0.0015579144,0.00035629264,0.027041221,0.0010921631],"about_ca_topic_score_codex":0.0054082335,"about_ca_topic_score_gemma":0.3428818,"teacher_disagreement_score":0.51849276,"about_ca_system_score_codex":0.00010496465,"about_ca_system_score_gemma":0.00036253032,"threshold_uncertainty_score":0.81756675},"labels":[],"label_agreement":null},{"id":"W2020257725","doi":"10.1016/j.disopt.2006.05.006","title":"Algorithms for time-dependent bicriteria shortest path problems","year":2006,"lang":"en","type":"article","venue":"Discrete Optimization","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":54,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Shortest path problem; K shortest path routing; Yen's algorithm; Constrained Shortest Path First; Path (computing); Mathematical optimization; Algorithm; Computer science; Euclidean shortest path; Shortest Path Faster Algorithm; Travel time; Mathematics; Dijkstra's algorithm; Theoretical computer science; Graph","score_opus":0.014338220414387697,"score_gpt":0.2744137807614725,"score_spread":0.2600755603470848,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2020257725","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010052926,0.00006947979,0.9847389,0.0005828374,0.00027507255,0.0007669163,0.00013921305,0.00030520247,0.012117128],"genre_scores_gemma":[0.67414993,0.00016793754,0.29330346,0.0001967869,0.0010737034,0.00038566912,0.009865349,0.00011219363,0.020744959],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988123,0.00006566128,0.0002882988,0.00027142023,0.00028772876,0.00027459167],"domain_scores_gemma":[0.9994003,0.000054855744,0.0001286208,0.000110863955,0.0002385134,0.000066819106],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037509133,0.0001251364,0.00012937633,0.00008845861,0.0005085746,0.00019543181,0.00012160723,0.00011879132,0.00023804483],"category_scores_gemma":[0.000052103114,0.00013006815,0.00006002594,0.00026058315,0.00006889451,0.00042049983,0.0000050837652,0.000047012374,0.000017082286],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013340914,0.000039309045,0.0011880851,0.0000075490766,0.0000068415916,7.350832e-7,0.0011478631,0.9932056,0.00008134489,0.0025771589,0.0012796883,0.00045246846],"study_design_scores_gemma":[0.0009106224,0.00009024787,0.0012588467,0.00005973823,0.00007215713,7.941072e-7,0.00059199973,0.9790028,0.00012397589,0.00092676,0.016499484,0.0004625884],"about_ca_topic_score_codex":0.00062271795,"about_ca_topic_score_gemma":0.0002021682,"teacher_disagreement_score":0.6914354,"about_ca_system_score_codex":0.0000560503,"about_ca_system_score_gemma":0.000076941746,"threshold_uncertainty_score":0.53040254},"labels":[],"label_agreement":null},{"id":"W2020666416","doi":"10.1016/j.trb.2008.05.003","title":"An integrated model of vehicle transactions, activity scheduling and mode choice","year":2008,"lang":"en","type":"article","venue":"Transportation Research Part B Methodological","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":89,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Transport Canada; McLean Foundation","keywords":"Purchasing; Operationalization; Database transaction; Mode choice; Car ownership; Mode (computer interface); Microsimulation; Computer science; Nested logit; Fleet management; Econometrics; Operations research; Transport engineering; Economics; Operations management; Engineering; Public transport","score_opus":0.516046839134684,"score_gpt":0.5187783194728048,"score_spread":0.002731480338120873,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2020666416","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5897771,0.000022343793,0.4095546,0.00022077016,0.00002237916,0.00016446154,0.00004362003,0.00007423257,0.00012049892],"genre_scores_gemma":[0.88524103,0.0004731419,0.11397742,0.000020357029,0.00002848085,0.00003335395,0.000058407455,0.000009179159,0.00015863188],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9965926,0.0017704439,0.00030483727,0.00033081239,0.00067641324,0.00032492346],"domain_scores_gemma":[0.99788916,0.0012214244,0.000079666155,0.00013399041,0.00044895406,0.00022677435],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032040714,0.00010383034,0.00023750788,0.00016965598,0.000786658,0.000020795895,0.00015080672,0.0002129793,0.00013844194],"category_scores_gemma":[0.0003701536,0.000096984164,0.000054927164,0.0005735457,0.0007412939,0.00046964205,7.612448e-7,0.00043284628,0.0000017475878],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000523169,0.00043698333,0.04400632,0.000047856367,0.00003233271,0.0000103505945,0.030438844,0.8921665,0.016650125,0.005856573,0.000031537795,0.00979939],"study_design_scores_gemma":[0.0015191064,0.00046661167,0.40075174,0.00007600069,0.000061872466,0.0000010528785,0.006649899,0.57592154,0.010154114,0.0022183198,0.0016824384,0.0004973101],"about_ca_topic_score_codex":0.0033564256,"about_ca_topic_score_gemma":0.0025344181,"teacher_disagreement_score":0.35674542,"about_ca_system_score_codex":0.000029869008,"about_ca_system_score_gemma":0.00026818397,"threshold_uncertainty_score":0.6050417},"labels":[],"label_agreement":null},{"id":"W2021655490","doi":"10.1002/atr.134","title":"Analyzing transit service reliability using detailed data from automatic vehicular locator systems","year":2010,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Portland State University; Minnesota Department of Transportation; U.S. Department of Transportation","keywords":"Schedule; Automatic vehicle location; Reliability (semiconductor); Transit (satellite); Transport engineering; Computer science; Transit system; Service (business); Reliability engineering; Engineering; Public transport; Telecommunications; Business","score_opus":0.02477076312958778,"score_gpt":0.30314050708510093,"score_spread":0.27836974395551317,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2021655490","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.897089,0.00019577524,0.10102638,0.00028442286,0.0010567681,0.00018860342,0.000089464054,0.000053108466,0.000016480304],"genre_scores_gemma":[0.94963944,0.000052093877,0.049693238,0.00003140425,0.00024015001,0.0000016755411,0.0003199202,0.000016924036,0.0000051786915],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99805075,0.0001277556,0.0008452431,0.00023516284,0.0005488116,0.00019226177],"domain_scores_gemma":[0.99797326,0.00017543022,0.0007077639,0.00030760033,0.000673995,0.00016192145],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010662582,0.00013348133,0.0003045218,0.00012932815,0.0002821228,0.0000757147,0.00039512126,0.00014797477,0.000068163514],"category_scores_gemma":[0.00012647883,0.00013079052,0.000077617406,0.00057152775,0.000061825085,0.0016333628,0.0000012192007,0.0003485423,0.0000030780159],"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":[0.0000849188,0.00007786502,0.041573584,0.000076026576,0.000063375366,0.000023932265,0.012067345,0.9336089,0.011158938,0.00015169659,0.000012964522,0.0011004469],"study_design_scores_gemma":[0.003549628,0.00009723843,0.78593916,0.0007712677,0.0013609347,0.000008279287,0.019179694,0.18215351,0.0007351566,0.0007163204,0.0047205826,0.0007682225],"about_ca_topic_score_codex":0.0010515552,"about_ca_topic_score_gemma":0.0075283484,"teacher_disagreement_score":0.75145537,"about_ca_system_score_codex":0.000052856227,"about_ca_system_score_gemma":0.00034816138,"threshold_uncertainty_score":0.53334826},"labels":[],"label_agreement":null},{"id":"W2021665984","doi":"10.1007/s10479-005-2038-0","title":"Maximizing Trip Coverage in the Location of a Single Rapid Transit Alignment","year":2005,"lang":"en","type":"article","venue":"Annals of Operations Research","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":80,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Heuristics; Theory of computation; Heuristic; Maximization; Computer science; Greedy algorithm; Mathematical optimization; Extension (predicate logic); Upper and lower bounds; Simple (philosophy); Computational complexity theory; Transit (satellite); Algorithm; Mathematics; Public transport; Transport engineering; Engineering","score_opus":0.2356324665256801,"score_gpt":0.4466647061607565,"score_spread":0.2110322396350764,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2021665984","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9130157,0.0010150435,0.014448226,0.04584089,0.000046707853,0.0009787537,0.000024107685,0.000020892636,0.02460968],"genre_scores_gemma":[0.99821013,0.0005050338,0.0007712635,0.00013341873,0.00004005018,0.000025486046,0.000027408389,0.0000034801838,0.00028371855],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99834424,0.0005001883,0.000269539,0.00009798696,0.0006182819,0.00016976173],"domain_scores_gemma":[0.9989729,0.00018904988,0.000024651486,0.00012014056,0.00066487706,0.000028380622],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002988445,0.00003833937,0.000080133104,0.00020105076,0.00028239767,0.00004904943,0.00017904393,0.00004486274,0.00010369036],"category_scores_gemma":[0.0002676725,0.000032523196,0.00002529277,0.0010399749,0.00014904574,0.00026981224,0.000003197333,0.000096537246,0.0000053733884],"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":[0.00007259577,0.0007509284,0.0010668347,0.000032898417,0.000017881268,0.0000016624365,0.1526456,0.772374,0.003789108,0.040947627,0.0017838385,0.02651705],"study_design_scores_gemma":[0.0072716596,0.0028789241,0.17823961,0.0015717897,0.00007967168,0.000005007806,0.26048285,0.10422957,0.1759715,0.003194711,0.26464936,0.0014253571],"about_ca_topic_score_codex":0.0016733542,"about_ca_topic_score_gemma":0.004664484,"teacher_disagreement_score":0.6681444,"about_ca_system_score_codex":0.000021691969,"about_ca_system_score_gemma":0.00021778149,"threshold_uncertainty_score":0.26028916},"labels":[],"label_agreement":null},{"id":"W2021812614","doi":"10.1109/icmse.2013.6586547","title":"Study on the monthly and seasonal variation characteristics of passenger volume of Pingdingshan City public transport","year":2013,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Seasonality; Public transport; Quarter (Canadian coin); Trough (economics); Index (typography); Passenger transport; Environmental science; Geography; Volume (thermodynamics); Variation (astronomy); Meteorology; Transport engineering; Statistics; Computer science; Engineering; Mathematics; Economics","score_opus":0.03400259264474761,"score_gpt":0.25899574833357647,"score_spread":0.22499315568882886,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2021812614","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99348664,0.0000020768532,0.0010135506,0.0016151278,0.000043869713,0.0002844614,0.000019850766,0.000022649765,0.003511749],"genre_scores_gemma":[0.9993762,0.0000040295313,0.00016684197,0.00004367884,0.000022513888,0.000012391165,0.00001950618,0.0000033657382,0.00035150364],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99928814,0.000071651,0.00019442926,0.000092911265,0.0002673281,0.00008554696],"domain_scores_gemma":[0.9994619,0.00009362843,0.00013349175,0.00006487935,0.0002069793,0.00003915344],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004619654,0.000049384034,0.00009764067,0.00003642439,0.00013742008,0.000025391835,0.00007067281,0.000039644925,0.00039786042],"category_scores_gemma":[0.000087651715,0.00003629513,0.000020117544,0.00015272493,0.000069074114,0.00017321923,0.0000018932896,0.00004863557,0.0000020590426],"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":[0.0000057059033,0.00014007217,0.9696581,0.0000046993887,0.00001722812,1.7939199e-7,0.020780034,0.000025616213,0.0000484605,0.008881364,0.00009937857,0.00033918687],"study_design_scores_gemma":[0.00010204881,0.000054133106,0.9945896,0.0000070536544,0.0000133825015,1.7848093e-8,0.0043885466,0.000573374,0.000010905519,0.00007925267,0.0001408485,0.000040811115],"about_ca_topic_score_codex":0.0018234549,"about_ca_topic_score_gemma":0.00038771727,"teacher_disagreement_score":0.024931552,"about_ca_system_score_codex":0.00000838028,"about_ca_system_score_gemma":0.000045898723,"threshold_uncertainty_score":0.43562925},"labels":[],"label_agreement":null},{"id":"W2021934181","doi":"10.1002/atr.5670350206","title":"A simulation model of reliable schedule design for a fixed transit route","year":2001,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":53,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Schedule; Selection (genetic algorithm); Computer science; Transit (satellite); Mathematical optimization; Heuristic; Process (computing); Ranking (information retrieval); Population; Point (geometry); Operations research; Simulation; Engineering; Public transport; Transport engineering; Mathematics","score_opus":0.042223361993097536,"score_gpt":0.31869621884922233,"score_spread":0.27647285685612477,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2021934181","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22900575,0.000058151323,0.77025473,0.00020037645,0.00012379387,0.0002555446,0.000018367446,0.00001883155,0.0000644316],"genre_scores_gemma":[0.78823584,0.00013625062,0.21138051,0.000023938726,0.000049111073,0.000006217834,0.0000372673,0.00001123076,0.00011961757],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987327,0.000035363857,0.0006202828,0.00010443443,0.00035283636,0.00015437606],"domain_scores_gemma":[0.9983475,0.00020623032,0.0005611805,0.00005913082,0.0007504669,0.00007546694],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005646859,0.00008802313,0.00021423413,0.00014794998,0.0001532393,0.000013473442,0.000094597985,0.000090703994,0.000015109274],"category_scores_gemma":[0.00009218507,0.00009042387,0.00013151964,0.00029281434,0.000042101958,0.00080680783,1.5793458e-7,0.00008313709,3.187611e-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":[0.00081097067,0.00006570374,0.0011484812,0.000023189328,0.000020376681,0.0000018044112,0.011794464,0.98144376,0.0020195656,0.0013975392,0.000011477395,0.0012626391],"study_design_scores_gemma":[0.0078097917,0.0008675037,0.023246197,0.00043994596,0.0005094782,0.0000014267482,0.0075120092,0.93862015,0.004499449,0.011752757,0.004242247,0.00049906777],"about_ca_topic_score_codex":0.000020705646,"about_ca_topic_score_gemma":0.00013623609,"teacher_disagreement_score":0.5592301,"about_ca_system_score_codex":0.000044702745,"about_ca_system_score_gemma":0.00023597784,"threshold_uncertainty_score":0.36873782},"labels":[],"label_agreement":null},{"id":"W2022191260","doi":"10.3141/2076-23","title":"Toll Demand Model for the Delaware Department of Transportation Travel Demand Model","year":2008,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Fields Institute for Research in Mathematical Sciences","funders":"","keywords":"Toll; Toll road; Revenue; Business; Transport engineering; Finance; Economics; Engineering","score_opus":0.14723873463301254,"score_gpt":0.40263498865024877,"score_spread":0.2553962540172362,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2022191260","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6917033,0.0004884149,0.30096567,0.003329309,0.00033517886,0.0023129485,0.0006839317,0.00003674557,0.00014451229],"genre_scores_gemma":[0.9842784,0.004152298,0.009338206,0.00007329498,0.00016433974,0.00024431496,0.00017546487,0.000068242756,0.001505429],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99099165,0.0009026342,0.0020401673,0.0004980352,0.0045199515,0.0010475314],"domain_scores_gemma":[0.9908915,0.0019403351,0.00087037624,0.00047599978,0.005383731,0.00043803378],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.006439549,0.0003306186,0.0006220555,0.0007050027,0.0027554503,0.00008836661,0.0013025239,0.00034233372,0.00008896884],"category_scores_gemma":[0.0002275816,0.00024118632,0.00069335604,0.0017097483,0.0015352943,0.00089895114,0.0000025626605,0.0013024482,0.0000034121815],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0027969242,0.00047347936,0.0885374,0.00030656508,0.00031058272,0.00003470441,0.062664814,0.8179636,0.0008142571,0.015640432,0.00927185,0.0011853933],"study_design_scores_gemma":[0.006147271,0.0008932816,0.7861556,0.00062017294,0.00056973257,0.0000011947744,0.017896589,0.16654235,0.0025325639,0.009635844,0.008280692,0.0007247415],"about_ca_topic_score_codex":0.0049718693,"about_ca_topic_score_gemma":0.091836035,"teacher_disagreement_score":0.6976182,"about_ca_system_score_codex":0.00023867533,"about_ca_system_score_gemma":0.0022361395,"threshold_uncertainty_score":0.99854285},"labels":[],"label_agreement":null},{"id":"W2022894424","doi":"10.1145/2728732.2728737","title":"Network improvement for equilibrium routing","year":2015,"lang":"en","type":"article","venue":"ACM SIGecom Exchanges","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Routing (electronic design automation); Mathematical optimization; Work (physics); Network planning and design; Traffic network; Series (stratigraphy); Static routing; Routing protocol; Computer network; Mathematics","score_opus":0.08679214533124972,"score_gpt":0.3312607305876934,"score_spread":0.24446858525644366,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2022894424","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6747608,0.0036573475,0.13867864,0.08131558,0.013402736,0.0049513564,0.00014824068,0.00259719,0.08048808],"genre_scores_gemma":[0.9809839,0.000023584189,0.013518212,0.00071722636,0.0013319184,0.00007840194,0.0000880704,0.000016275528,0.003242395],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992142,0.000037059224,0.00012722265,0.0001454992,0.00018019279,0.0002958247],"domain_scores_gemma":[0.9993752,0.00017608699,0.00007712806,0.00013641395,0.0001236016,0.00011157071],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007447508,0.00006749529,0.00008974999,0.00002541488,0.00021185198,0.00005844327,0.00019493759,0.00006298181,0.000033208744],"category_scores_gemma":[0.0003701042,0.000069040456,0.000029930268,0.00013423168,0.00003525423,0.00014664719,0.000018215525,0.000037764188,0.000011263814],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031196835,0.00021285695,0.13085529,0.00010994516,0.00013029073,0.000008971193,0.234824,0.052360598,0.00038683054,0.06858196,0.41005418,0.10216311],"study_design_scores_gemma":[0.0030400597,0.0008035927,0.011058193,0.0001350422,0.00012642411,5.967266e-7,0.04122943,0.005228728,0.0006497668,0.05175029,0.88490856,0.0010693196],"about_ca_topic_score_codex":0.0003755922,"about_ca_topic_score_gemma":0.00088961446,"teacher_disagreement_score":0.47485438,"about_ca_system_score_codex":0.00004414417,"about_ca_system_score_gemma":0.0000837015,"threshold_uncertainty_score":0.2815388},"labels":[],"label_agreement":null},{"id":"W2023245499","doi":"10.2495/sdp-v1-n2-170-191","title":"Risk evaluation in a transportation system","year":2006,"lang":"en","type":"article","venue":"International Journal of Sustainable Development and Planning","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":83,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Vulnerability (computing); Transport engineering; Emergency evacuation; Systems design; Risk analysis (engineering); Flow network; Operations research; Business; Engineering; Computer security; Geography; Mathematics","score_opus":0.013055720032858093,"score_gpt":0.2806415540075747,"score_spread":0.2675858339747166,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2023245499","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9899477,0.00031255328,0.0058770957,0.0001199778,0.00027993548,0.00010601364,0.0000017614733,0.000013620039,0.0033413563],"genre_scores_gemma":[0.9965751,0.000022194745,0.0029526658,0.000009419661,0.00015553099,0.0000047045523,0.000044632987,0.0000039631004,0.00023179964],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9987266,0.00008363215,0.0004030323,0.00007370862,0.00058178673,0.00013122075],"domain_scores_gemma":[0.9987285,0.000069816604,0.00031476683,0.000016562255,0.0008374355,0.000032897813],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017895995,0.000057084188,0.00008790354,0.00039150933,0.00015701729,0.00008170286,0.00009061914,0.000049004255,0.000012594717],"category_scores_gemma":[0.00009061985,0.000058097652,0.000019311958,0.00016855763,0.000021219941,0.00042569652,0.0000017648133,0.00009469364,5.770036e-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":[0.00019472424,0.000057774687,0.7258088,0.000032536445,0.000056993737,0.0008786888,0.09661275,0.1330578,0.000008153147,0.03335292,0.00032051146,0.009618347],"study_design_scores_gemma":[0.0011474631,0.000013208175,0.81467766,0.00021068183,0.00002693937,0.0000059814292,0.16788052,0.0012313364,0.000057682875,0.00079027924,0.013835444,0.00012282793],"about_ca_topic_score_codex":0.0009067293,"about_ca_topic_score_gemma":0.00031334718,"teacher_disagreement_score":0.13182646,"about_ca_system_score_codex":0.0002877349,"about_ca_system_score_gemma":0.000364761,"threshold_uncertainty_score":0.23691535},"labels":[],"label_agreement":null},{"id":"W2023452542","doi":"10.3141/1719-09","title":"Toronto Area Car Ownership Study: A Retrospective Interview and Its Applications","year":2000,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto; Procter & Gamble (Canada)","funders":"","keywords":"Microsimulation; Univariate; Car ownership; Loyalty; Computer science; Transport engineering; Survey data collection; Telephone survey; Work (physics); Travel behavior; Data collection; Operations research; Engineering; Marketing; Business; Public transport; Statistics; Multivariate statistics","score_opus":0.12214194936543102,"score_gpt":0.41478849037356935,"score_spread":0.29264654100813836,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2023452542","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9896525,0.0010069751,0.0006906055,0.0036142166,0.00022109588,0.0024632248,0.00007825915,0.00006427602,0.0022088077],"genre_scores_gemma":[0.99029297,0.005283111,0.00043526778,0.000052480205,0.0002232269,0.00022503464,0.000020631513,0.00004049255,0.0034267786],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9917307,0.0023980509,0.0011681113,0.00048561374,0.0034464528,0.00077108014],"domain_scores_gemma":[0.99461526,0.0007697567,0.00033921114,0.0003509066,0.0034273877,0.00049750163],"candidate_categories":["sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0071696253,0.00023110243,0.00043409027,0.00040903475,0.0017124162,0.00022198953,0.0009065785,0.00019984166,0.0019970913],"category_scores_gemma":[0.00017529067,0.00018528444,0.0002369491,0.0018579593,0.0005941858,0.0011342531,0.0000033469346,0.0013639707,0.000025399086],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017770127,0.0015631554,0.7421051,0.0003282285,0.00049839995,0.0001361412,0.16707897,0.002352408,0.00033170587,0.020804515,0.004934127,0.058090188],"study_design_scores_gemma":[0.0013532671,0.00060793024,0.90526056,0.00025417475,0.000099103025,3.2073604e-7,0.029914256,0.00007315548,0.000046123798,0.001727496,0.060427703,0.00023591494],"about_ca_topic_score_codex":0.03056486,"about_ca_topic_score_gemma":0.3445328,"teacher_disagreement_score":0.3139679,"about_ca_system_score_codex":0.00046791745,"about_ca_system_score_gemma":0.0006455077,"threshold_uncertainty_score":0.99958724},"labels":[],"label_agreement":null},{"id":"W2024182233","doi":"10.1002/atr.117","title":"Dynamic OD estimation using three phase traffic flow theory","year":2010,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"University of Seoul","keywords":"Traffic flow (computer networking); Three-phase traffic theory; Microscopic traffic flow model; Computer science; Queue; Traffic congestion reconstruction with Kerner's three-phase theory; Traffic generation model; Nonlinear system; Interval (graph theory); Flow (mathematics); Queueing theory; Process (computing); Mathematical optimization; Traffic congestion; Real-time computing; Engineering; Mathematics; Transport engineering; Computer network","score_opus":0.011564888097792367,"score_gpt":0.31944802671645917,"score_spread":0.3078831386186668,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2024182233","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6789968,0.00004557338,0.31983793,0.00013097088,0.00078458583,0.00010909694,0.000013106741,0.000035279212,0.000046666446],"genre_scores_gemma":[0.8402451,0.000039887702,0.15950572,0.000021304068,0.00008296218,0.0000016086025,0.000068604015,0.000013883597,0.00002089048],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99869955,0.00005374238,0.00053035695,0.000118141324,0.00042799077,0.00017023455],"domain_scores_gemma":[0.9988257,0.00010763757,0.0005273573,0.000079281446,0.00034643256,0.000113562215],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007333418,0.00010818922,0.0001724131,0.00018783969,0.00032253272,0.00003935311,0.00012586868,0.00011255037,0.00010438174],"category_scores_gemma":[0.00009791559,0.00010756743,0.00010741997,0.00030581103,0.00009970335,0.0010530627,2.9526626e-7,0.00029286067,0.0000021489518],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021045115,0.000101547346,0.00026846016,0.000010981089,0.000018071298,0.000012888625,0.011476023,0.8940078,0.0041401703,0.0019593437,0.0000039287947,0.08779033],"study_design_scores_gemma":[0.016904475,0.0009875658,0.1788373,0.0005736335,0.0011797512,0.000048530746,0.023524337,0.7379437,0.0014140715,0.032549135,0.0046174056,0.0014200793],"about_ca_topic_score_codex":0.000012397783,"about_ca_topic_score_gemma":0.0017556679,"teacher_disagreement_score":0.17856884,"about_ca_system_score_codex":0.000046200174,"about_ca_system_score_gemma":0.00022684183,"threshold_uncertainty_score":0.43864724},"labels":[],"label_agreement":null},{"id":"W2024616421","doi":"10.1002/atr.5670350205","title":"Operational objective functions in designing public transport routes","year":2001,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":39,"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":"Operator (biology); Integer programming; Public transport; Computer science; Work (physics); Operations research; Mathematical optimization; Nonlinear programming; Linear programming; Philosophy of design; Management science; Nonlinear system; Transport engineering; Engineering; Mathematics; Algorithm","score_opus":0.020609699181483865,"score_gpt":0.2850057747736366,"score_spread":0.2643960755921527,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2024616421","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8511014,0.00017443731,0.14468409,0.0014581819,0.00053612574,0.00020333857,0.000013731306,0.000044493674,0.001784215],"genre_scores_gemma":[0.9875112,0.00027694632,0.011586797,0.00008011617,0.00015570586,0.000009529083,0.00011139934,0.000012442998,0.0002558202],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9984252,0.00008836636,0.00064413465,0.00014182639,0.00048152907,0.00021897387],"domain_scores_gemma":[0.99887437,0.00011412624,0.00031464707,0.000052480606,0.00052761304,0.00011677386],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062652846,0.000106921856,0.00019077669,0.00036728458,0.00027176982,0.000033500353,0.00011090593,0.00009074413,0.00013418189],"category_scores_gemma":[0.00007706912,0.0001113684,0.00009773575,0.00077803613,0.0000692169,0.0017655053,2.4548314e-7,0.00021975687,0.0000032453638],"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":[0.00024604928,0.00016952709,0.43473572,0.000007445486,0.000031384126,0.000078653364,0.033483893,0.52124506,0.00060117745,0.005396165,0.000035195288,0.0039697145],"study_design_scores_gemma":[0.0014783486,0.00011606394,0.9690777,0.000085755804,0.000042616768,0.000006780775,0.019534089,0.00007335898,0.00008485304,0.0007952909,0.008524231,0.00018090522],"about_ca_topic_score_codex":0.00014352784,"about_ca_topic_score_gemma":0.0057956204,"teacher_disagreement_score":0.534342,"about_ca_system_score_codex":0.00012570758,"about_ca_system_score_gemma":0.00040518632,"threshold_uncertainty_score":0.45414716},"labels":[],"label_agreement":null},{"id":"W2024722550","doi":"10.2333/bhmk.42.63","title":"An Alternative Estimation Procedure for Partial Least Squares Path Modeling","year":2015,"lang":"en","type":"article","venue":"Behaviormetrika","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria; McGill University","funders":"","keywords":"Least-squares function approximation; Component (thermodynamics); Path (computing); Mathematical optimization; Partial least squares regression; Mode (computer interface); Computer science; Generalized least squares; Algorithm; Non-linear least squares; Mathematics; Estimation theory; Statistics","score_opus":0.09593598225236533,"score_gpt":0.3838157863127113,"score_spread":0.28787980406034597,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2024722550","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.567892,0.000041099458,0.43072894,0.00012030544,0.00027588152,0.00041823348,0.00004388355,0.00015864175,0.0003210017],"genre_scores_gemma":[0.9824923,0.0000047583844,0.01685647,0.000025080946,0.00017686855,0.00007821845,0.00024543586,0.000011968276,0.00010888413],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990725,0.00004699597,0.00017187491,0.0001826461,0.00033799838,0.0001880156],"domain_scores_gemma":[0.9993785,0.000025406805,0.00007707611,0.00007730592,0.00027964212,0.00016209169],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004670895,0.00007769032,0.00008880332,0.00011429138,0.00027886813,0.0000962033,0.00011455573,0.00008076057,0.00001268229],"category_scores_gemma":[0.00020053092,0.00007976126,0.000033254084,0.00027710962,0.000035814865,0.0005353119,0.0000021824403,0.000053893662,0.0000059135155],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011228047,0.00023444084,0.027273526,0.000011768162,0.000007471376,0.0000023122163,0.042476624,0.90611863,0.000041605876,0.0065153493,0.0003579948,0.016848022],"study_design_scores_gemma":[0.0009454679,0.0002517517,0.0023770742,0.000030739764,0.000086528074,5.670008e-7,0.010663228,0.9832633,0.00022845167,0.00095432467,0.0009447775,0.00025376014],"about_ca_topic_score_codex":0.0009053011,"about_ca_topic_score_gemma":0.00031970165,"teacher_disagreement_score":0.41460028,"about_ca_system_score_codex":0.000061968414,"about_ca_system_score_gemma":0.00023236425,"threshold_uncertainty_score":0.32525697},"labels":[],"label_agreement":null},{"id":"W2025537608","doi":"10.3141/2429-13","title":"Application of Travel Activity Scheduler for Household Agents in a Chinese City","year":2014,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Replicate; Scheduling (production processes); Duration (music); Travel time; Computer science; Travel behavior; Simulation; Transport engineering; Operations research; Statistics; Econometrics; Engineering; Operations management; Mathematics","score_opus":0.1372187950826065,"score_gpt":0.4302190057941691,"score_spread":0.29300021071156257,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2025537608","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9326508,0.00003563917,0.06310412,0.0021337017,0.00028640672,0.0014041108,0.00009239834,0.000021326856,0.00027149188],"genre_scores_gemma":[0.99567264,0.00028034332,0.0033854332,0.000029474522,0.00017149693,0.00013315403,0.000030880157,0.000037213824,0.0002593577],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9934248,0.0014428581,0.001215395,0.0003696906,0.0028478631,0.00069944665],"domain_scores_gemma":[0.9944155,0.0016266927,0.0006528768,0.0003456102,0.0026812307,0.00027807488],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009304278,0.0001938521,0.00047755564,0.0010542625,0.0006500416,0.00006867996,0.00086636224,0.00025891635,0.00005335076],"category_scores_gemma":[0.00061746954,0.00015993987,0.0003365882,0.0026426255,0.0006822384,0.00067008473,0.0000027134713,0.0011365319,0.000002216008],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016720216,0.0005156058,0.9511447,0.00025290367,0.000060218,0.0000041704766,0.0104495585,0.014374574,0.0021376386,0.010369538,0.0005850664,0.008434029],"study_design_scores_gemma":[0.0018767199,0.0002586245,0.98541504,0.0001744897,0.00002800124,4.8419587e-8,0.0015480515,0.0016660348,0.0006137985,0.0040651034,0.004205497,0.00014861178],"about_ca_topic_score_codex":0.025190942,"about_ca_topic_score_gemma":0.1324266,"teacher_disagreement_score":0.10723566,"about_ca_system_score_codex":0.00021224836,"about_ca_system_score_gemma":0.0006445172,"threshold_uncertainty_score":0.9813004},"labels":[],"label_agreement":null},{"id":"W2025851246","doi":"10.1057/jors.2010.178","title":"A spreadsheet decision support optimization model for railcar storage at Canadian Pacific Railway","year":2011,"lang":"en","type":"article","venue":"Journal of the Operational Research Society","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Calgary; Canadian Pacific Railway (Canada)","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Purchasing; Operations research; Decision support system; Product (mathematics); Computer science; Operations management; Business; Engineering","score_opus":0.10633479089231937,"score_gpt":0.3669484108489791,"score_spread":0.26061361995665977,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2025851246","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09572055,0.00019694761,0.8544497,0.016977182,0.0017201231,0.002290319,0.00045731102,0.000044307446,0.02814356],"genre_scores_gemma":[0.82727396,0.00027367892,0.15765963,0.00034029226,0.00030867645,0.000024042694,0.000055620836,0.000022054759,0.014042029],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99782634,0.00018260365,0.00034714508,0.00013536605,0.0011745987,0.00033392306],"domain_scores_gemma":[0.9973126,0.00028881629,0.00014248627,0.0001272154,0.0018426911,0.00028621382],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0046199257,0.00007842282,0.00011888578,0.00010592322,0.0020667277,0.00011972606,0.00040786533,0.0001373559,0.00057097926],"category_scores_gemma":[0.0007735835,0.000058271024,0.00022859874,0.00036291947,0.00021677608,0.0004979774,0.00001937526,0.0002683471,0.000006371146],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012751111,0.00005115997,0.0017239753,0.0000062847193,0.00003963156,0.0000022259221,0.04378498,0.8351111,0.000057902853,0.005040294,0.113848455,0.00020647679],"study_design_scores_gemma":[0.0012798925,0.00016145923,0.0038193658,0.00008193894,0.00004038289,0.000010297362,0.007621049,0.9441177,0.00009353939,0.0024495216,0.04009931,0.00022548951],"about_ca_topic_score_codex":0.0026204204,"about_ca_topic_score_gemma":0.015302327,"teacher_disagreement_score":0.73155344,"about_ca_system_score_codex":0.0006925074,"about_ca_system_score_gemma":0.0031395482,"threshold_uncertainty_score":0.9992325},"labels":[],"label_agreement":null},{"id":"W2026050847","doi":"10.1016/j.tranpol.2005.11.006","title":"Research challenges in modelling urban road pricing: An overview","year":2006,"lang":"en","type":"article","venue":"Transport Policy","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":57,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"European Commission","keywords":"Road pricing; Equity (law); Singapore Area Licensing Scheme; Transport engineering; Public transport; Business; Economics; Regional science; Traffic congestion; Engineering; Political science; Geography","score_opus":0.25840716263278196,"score_gpt":0.4262449067094241,"score_spread":0.16783774407664215,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2026050847","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7764572,0.007877647,0.002753904,0.006924598,0.00011124132,0.00060977816,0.000022772909,0.00039037425,0.20485252],"genre_scores_gemma":[0.9931552,0.004605786,0.0006882007,0.00003999284,0.00042277394,0.00001678807,0.000070767,0.000017378261,0.0009831524],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9982817,0.0001796931,0.0002843297,0.00027361454,0.0005052551,0.00047538438],"domain_scores_gemma":[0.99955124,0.000040269868,0.000040595107,0.0001604299,0.000100890415,0.000106583604],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013278305,0.000095952535,0.00014741377,0.00042146238,0.00029612487,0.00002563539,0.00019359325,0.00012986064,0.000045610366],"category_scores_gemma":[0.00000769913,0.00010494343,0.00004013726,0.0008667022,0.00012010088,0.0003328829,0.0000014469108,0.00020841458,0.000013850298],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027283017,0.00022320508,0.027617706,0.0000558045,0.0000044020694,0.000021910717,0.07920388,0.2697109,0.000010278151,0.6185735,0.00006703139,0.0044840896],"study_design_scores_gemma":[0.0014589813,0.00016168864,0.7989819,0.0003733431,0.000028427994,0.0000013597837,0.021512013,0.012603435,0.000062982384,0.026112221,0.13784222,0.00086140697],"about_ca_topic_score_codex":0.08752662,"about_ca_topic_score_gemma":0.043019183,"teacher_disagreement_score":0.7713642,"about_ca_system_score_codex":0.00009758738,"about_ca_system_score_gemma":0.0003181383,"threshold_uncertainty_score":0.9744432},"labels":[],"label_agreement":null},{"id":"W2026634277","doi":"10.1061/(asce)0733-947x(2007)133:10(549)","title":"G-EMME/2: Automatic Calibration Tool of the EMME/2 Transit Assignment Using Genetic Algorithms","year":2007,"lang":"en","type":"article","venue":"Journal of Transportation Engineering","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Set (abstract data type); Calibration; Process (computing); Genetic algorithm; Transit (satellite); Computer science; Algorithm; Software; Mathematical optimization; Engineering; Public transport; Machine learning; Transport engineering; Mathematics; Programming language; Statistics","score_opus":0.01486237434063044,"score_gpt":0.2564245515283288,"score_spread":0.24156217718769837,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2026634277","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.46030632,0.000053793596,0.53909975,0.000085559244,0.00032898874,0.000085835396,0.000005518594,0.00001611455,0.00001812875],"genre_scores_gemma":[0.95859784,0.000021966443,0.041204065,0.00002124159,0.00011884869,7.622988e-7,0.0000053370304,0.000013069519,0.000016883148],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99839294,0.00003400284,0.0007539711,0.00007409563,0.000578126,0.00016688093],"domain_scores_gemma":[0.9991998,0.00008088309,0.0004115676,0.00006960625,0.00016972082,0.000068442496],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00074608944,0.00009374911,0.00016504443,0.0001497688,0.00012095821,0.000021883043,0.00013705943,0.00008237103,0.000035194946],"category_scores_gemma":[0.000032898275,0.00008042702,0.000132534,0.00039349336,0.00003571594,0.000322769,5.102925e-7,0.0001345011,1.7564152e-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":[0.000011737557,0.00003195491,0.009359955,0.000044016535,0.000034310255,0.0000084579915,0.009713562,0.97434306,0.004084408,0.000481064,0.000008546227,0.0018789009],"study_design_scores_gemma":[0.0011413094,0.000088155524,0.7631886,0.000463723,0.00032420008,0.000010837112,0.0028618441,0.21892966,0.011674423,0.0000880854,0.0009166852,0.00031246603],"about_ca_topic_score_codex":0.00008464883,"about_ca_topic_score_gemma":0.000106834465,"teacher_disagreement_score":0.7554134,"about_ca_system_score_codex":0.00007873603,"about_ca_system_score_gemma":0.00014414497,"threshold_uncertainty_score":0.32797185},"labels":[],"label_agreement":null},{"id":"W2026709669","doi":"10.1007/s12469-011-0048-7","title":"Public transport: case studies and applications","year":2011,"lang":"en","type":"article","venue":"Public Transport","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Victoria Hospital","funders":"","keywords":"Public transport; Computer science; Transport engineering; Engineering","score_opus":0.11701443716488646,"score_gpt":0.3074416625240259,"score_spread":0.19042722535913942,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2026709669","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.64088243,0.0049071712,0.1388348,0.01024498,0.00089475396,0.0028103548,0.0003263743,0.0026705868,0.19842851],"genre_scores_gemma":[0.99509674,0.0007286404,0.0027813374,0.00016919039,0.00011372771,0.0002079014,0.000096000374,0.000020427371,0.0007860104],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.99850684,0.000057611684,0.00036441124,0.00036231262,0.00029405238,0.0004147766],"domain_scores_gemma":[0.99902207,0.00005435297,0.000100030404,0.00019535527,0.00030006067,0.00032811923],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00065707375,0.00016340273,0.00021525593,0.00019571034,0.00085802295,0.000035043533,0.00017280436,0.00013076329,0.00027345953],"category_scores_gemma":[0.00002589138,0.00016353335,0.0000712534,0.0006243642,0.0004912471,0.00075888506,0.0000025040638,0.00014129326,0.000013197588],"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":[0.000018337216,0.00022883723,0.52899116,0.00010549797,0.0001687616,0.00048352525,0.09262341,0.00001497743,0.0000034823788,0.35916921,0.00015387712,0.018038953],"study_design_scores_gemma":[0.0016890465,0.00011002973,0.44184688,0.000050843544,0.00039119727,0.00030031265,0.053250782,0.000029120556,0.000030705,0.009042948,0.49199092,0.0012672124],"about_ca_topic_score_codex":0.0010567935,"about_ca_topic_score_gemma":0.012472035,"teacher_disagreement_score":0.49183702,"about_ca_system_score_codex":0.000033283428,"about_ca_system_score_gemma":0.00021127134,"threshold_uncertainty_score":0.6959688},"labels":[],"label_agreement":null},{"id":"W2026873606","doi":"10.3141/1825-01","title":"Pricing Commuter, Intercity, and Freight Trains in a Terminal Railway Context: An Approach","year":2003,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Operations research; Train; Context (archaeology); Transport engineering; Track (disk drive); Cost allocation; Computer science; Rail freight transport; Resource (disambiguation); Capital cost; Economics; Engineering","score_opus":0.11938505574782793,"score_gpt":0.40214588022939884,"score_spread":0.2827608244815709,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2026873606","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9910642,0.00026409974,0.0045974306,0.0011520551,0.00028897976,0.0009179973,0.000027647475,0.00003158429,0.0016560327],"genre_scores_gemma":[0.9932663,0.0013737358,0.004663347,0.00007072913,0.00009378217,0.00005062327,0.000025244455,0.000038281047,0.00041794922],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.991096,0.0036227647,0.0012599109,0.00043223542,0.002710511,0.00087856303],"domain_scores_gemma":[0.9963135,0.0010776686,0.00036787288,0.00031930418,0.0014308747,0.0004907727],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0098407855,0.000228401,0.00044272002,0.0012184276,0.00110892,0.0002539177,0.0008052922,0.00025543524,0.0001839793],"category_scores_gemma":[0.0003933135,0.00019022709,0.00018529377,0.0021090559,0.0011282221,0.0012615807,0.0000030222511,0.0020585475,0.0000033028152],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009385877,0.00084590394,0.7959789,0.00023045608,0.00008344243,0.00015342188,0.13556421,0.0020599968,0.00042460815,0.04578854,0.00061659835,0.017315347],"study_design_scores_gemma":[0.0025180222,0.0004337036,0.9191469,0.00037943607,0.000038969694,0.0000015075042,0.051604614,0.00032797153,0.00021277338,0.004039096,0.021003807,0.00029321117],"about_ca_topic_score_codex":0.021688756,"about_ca_topic_score_gemma":0.22924761,"teacher_disagreement_score":0.20755886,"about_ca_system_score_codex":0.00024032775,"about_ca_system_score_gemma":0.00087312254,"threshold_uncertainty_score":0.9848259},"labels":[],"label_agreement":null},{"id":"W2026964874","doi":"10.1002/atr.5670380104","title":"A simulation‐based reliability assessment approach for congested transit network","year":2004,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":46,"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":"Hong Kong Polytechnic University","keywords":"Reliability (semiconductor); Transit (satellite); Schedule; Computer science; Reliability engineering; Monte Carlo method; Transport engineering; Service (business); Bus rapid transit; Level of service; Public transport; Simulation; Engineering","score_opus":0.021104365269237844,"score_gpt":0.32936272870356614,"score_spread":0.3082583634343283,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2026964874","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.117990434,0.0000431352,0.88054246,0.0004910751,0.00028381092,0.0004529919,0.000021306432,0.00004296905,0.00013184198],"genre_scores_gemma":[0.68749845,0.000014730267,0.3120984,0.00007138238,0.00013219244,0.000012378363,0.00015198965,0.000010156601,0.0000103325665],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9985163,0.00006433498,0.00062358857,0.0001529231,0.00043994098,0.00020288108],"domain_scores_gemma":[0.9982143,0.00034652845,0.0004870582,0.00007966595,0.0007607826,0.000111641304],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00081360334,0.00010875998,0.00022597493,0.00008927486,0.0003058632,0.000029447663,0.00010638731,0.00010344897,0.000012562588],"category_scores_gemma":[0.000105362,0.000108410844,0.00016628683,0.00036124367,0.00007208392,0.0005542727,1.6109946e-7,0.00014490928,2.0372231e-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":[0.00029296585,0.00013213285,0.0027692867,0.00003557542,0.000017638453,0.0000020387674,0.005324786,0.9879279,0.000044149645,0.0024460668,0.0000049931155,0.0010024369],"study_design_scores_gemma":[0.030788627,0.0018955711,0.76922446,0.0006289307,0.0010698735,0.000001360404,0.022458319,0.13760418,0.00035937244,0.018338151,0.016301656,0.0013295059],"about_ca_topic_score_codex":0.000030859355,"about_ca_topic_score_gemma":0.00022319803,"teacher_disagreement_score":0.85032374,"about_ca_system_score_codex":0.00014152362,"about_ca_system_score_gemma":0.00047641754,"threshold_uncertainty_score":0.44208658},"labels":[],"label_agreement":null},{"id":"W2027429121","doi":"10.3141/2054-02","title":"Analysis of Activity Conflict Resolution Strategies","year":2008,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Wilfrid Laurier University","funders":"","keywords":"Conflict resolution; Scheduling (production processes); Computer science; Operations research; Mathematical optimization; Engineering; Mathematics","score_opus":0.16103783988181994,"score_gpt":0.4315814768480411,"score_spread":0.2705436369662212,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2027429121","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9886132,0.0001750674,0.0073278905,0.0016791151,0.0004030082,0.0006054284,0.00008322365,0.000037872196,0.0010752059],"genre_scores_gemma":[0.9951622,0.0024688905,0.001187704,0.000016850181,0.00012817375,0.000025083504,0.000037627124,0.000025970774,0.0009474948],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99049723,0.0022614792,0.0013101635,0.00035386247,0.004823417,0.00075387507],"domain_scores_gemma":[0.9914221,0.0013498605,0.00079698657,0.00040038925,0.0056855856,0.00034507655],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.006009925,0.00019657615,0.00060179096,0.0022558789,0.0015922406,0.000090348025,0.00092434423,0.00026547993,0.00039702334],"category_scores_gemma":[0.0002967925,0.00016446502,0.0006296109,0.007305858,0.0019420296,0.0011826459,0.0000031495438,0.001432482,0.0000063527946],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0020413273,0.0005287132,0.8265096,0.00013358016,0.0011424007,0.00009970271,0.06091301,0.081143506,0.0020043133,0.02006308,0.003391599,0.0020291395],"study_design_scores_gemma":[0.0007487761,0.00025830264,0.9754869,0.00010708425,0.0002922435,1.4683539e-7,0.00904061,0.0005125314,0.0003815403,0.00041859515,0.012598644,0.00015464553],"about_ca_topic_score_codex":0.056762036,"about_ca_topic_score_gemma":0.1626931,"teacher_disagreement_score":0.14897725,"about_ca_system_score_codex":0.00023302333,"about_ca_system_score_gemma":0.0019704557,"threshold_uncertainty_score":0.9997076},"labels":[],"label_agreement":null},{"id":"W2027596739","doi":"10.3141/2269-16","title":"Simulating Sustainable Urban Gateway Development","year":2012,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Windsor; McMaster University","funders":"","keywords":"Gateway (web page); Transport engineering; Sustainable development; Public transport; Port (circuit theory); Work (physics); Promotion (chess); Sustainable city; Sustainable transport; Business; Engineering; Urban planning; Sustainability; Computer science; Civil engineering; Political science","score_opus":0.09906270987467616,"score_gpt":0.4087189896657205,"score_spread":0.3096562797910444,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2027596739","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98981935,0.0004393196,0.0034364508,0.00209761,0.0007650007,0.00102049,0.000012974147,0.00006872366,0.0023400818],"genre_scores_gemma":[0.9883961,0.0003030867,0.004653416,0.00005154472,0.0005519236,0.00005373033,0.000028257264,0.000051875064,0.005910068],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.98975974,0.001932659,0.0014437939,0.00030578408,0.0047843233,0.0017737144],"domain_scores_gemma":[0.9920672,0.0013121718,0.00054453633,0.00032119447,0.005016329,0.0007385424],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.012544839,0.00023448578,0.0003733993,0.0010351504,0.002895534,0.00022374079,0.00096514606,0.00025783738,0.00054603664],"category_scores_gemma":[0.000550364,0.00019268139,0.00027455986,0.002951669,0.0007443062,0.0017409351,0.000005671732,0.0017579591,0.00004268079],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00049564685,0.00036268143,0.8525018,0.00019003269,0.00011438572,0.000047562393,0.09804977,0.005445905,0.00023471787,0.031293422,0.007901636,0.0033624626],"study_design_scores_gemma":[0.0008401452,0.00011483569,0.71889746,0.00020356692,0.000035764082,1.3272376e-7,0.031165373,0.000039354058,0.0005515038,0.0006539374,0.2472766,0.00022131162],"about_ca_topic_score_codex":0.013101057,"about_ca_topic_score_gemma":0.02429114,"teacher_disagreement_score":0.23937497,"about_ca_system_score_codex":0.00055766053,"about_ca_system_score_gemma":0.0016907179,"threshold_uncertainty_score":0.99840254},"labels":[],"label_agreement":null},{"id":"W2028365238","doi":"10.1007/s11116-009-9225-9","title":"A multi-instrumented approach to observing the activity rescheduling decision process","year":2009,"lang":"en","type":"article","venue":"Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":20,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Wilfrid Laurier University","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Computer science; Operations research; Schedule; Scheduling (production processes); Coding (social sciences); Data collection; Process (computing); Operations management; Engineering","score_opus":0.04166319830382897,"score_gpt":0.33270462783619803,"score_spread":0.29104142953236906,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2028365238","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7150121,0.000011835397,0.28259638,0.0007917569,0.000079077196,0.00037591514,0.0000069476487,0.00014467411,0.0009813525],"genre_scores_gemma":[0.9634648,0.000013457653,0.036015995,0.00025725848,0.00004699515,0.000022451239,0.00007303154,0.000007837524,0.00009819477],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9988785,0.000058196758,0.00020300054,0.00023792074,0.00041041014,0.00021198498],"domain_scores_gemma":[0.99951094,0.000055147466,0.00008480403,0.000119428165,0.00013475984,0.0000949188],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046048104,0.000099570214,0.00010011832,0.00006882791,0.000608727,0.00006858042,0.0001859886,0.000078732286,0.000011862607],"category_scores_gemma":[0.00006350321,0.00008317455,0.00004516597,0.0006206616,0.00003127366,0.00036147973,4.633751e-7,0.00011913745,0.00000826814],"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":[0.0005929697,0.00070910424,0.044276327,0.00004843387,0.000030893683,0.000004968649,0.30638143,0.5370056,0.0033452087,0.007839881,0.00013740164,0.09962775],"study_design_scores_gemma":[0.00062138337,0.00004462366,0.97683024,0.00007556999,0.000038705224,1.8020398e-7,0.0068286313,0.013796446,0.00052396767,0.00034262353,0.00067948067,0.00021814984],"about_ca_topic_score_codex":0.00031577074,"about_ca_topic_score_gemma":0.0013887308,"teacher_disagreement_score":0.9325539,"about_ca_system_score_codex":0.00003689923,"about_ca_system_score_gemma":0.00008091427,"threshold_uncertainty_score":0.46818975},"labels":[],"label_agreement":null},{"id":"W2028417331","doi":"10.3141/2350-13","title":"Users’ Evaluation of Rail Systems in Mass Events","year":2013,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Milestone; Pilgrimage; Hajj; Ninth; Transport engineering; Pathfinder; Line (geometry); Quality (philosophy); Islam; Geography; Business; Engineering; Computer science; Archaeology; Library science","score_opus":0.15825527589653726,"score_gpt":0.4327138435299289,"score_spread":0.27445856763339166,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2028417331","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99229896,0.00036702244,0.000978449,0.0025593585,0.00077390234,0.002184864,0.000034417928,0.000021162252,0.00078183867],"genre_scores_gemma":[0.9972503,0.0007970278,0.0009802108,0.000015171474,0.00013080245,0.00017686676,0.000027864728,0.0000343482,0.0005874151],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9848187,0.0044049625,0.0018277229,0.00034098382,0.0077980175,0.0008095917],"domain_scores_gemma":[0.98751086,0.001083089,0.0007385452,0.00035348051,0.009991034,0.00032298185],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.017194318,0.00019266758,0.0004506145,0.001469364,0.0005704403,0.000120706776,0.00097263436,0.00026865298,0.0006870991],"category_scores_gemma":[0.0006058028,0.00016042653,0.00025998172,0.0032474848,0.00061549764,0.0012652443,0.0000024656824,0.0013518668,0.000027958977],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041521084,0.00031651065,0.8923464,0.00020555819,0.00010728161,0.000014296415,0.022784406,0.067582816,0.0016608767,0.006659625,0.0028514036,0.005055624],"study_design_scores_gemma":[0.0018963859,0.00017721768,0.97127116,0.0005337161,0.00005807133,7.996227e-8,0.017315136,0.0013115233,0.0001948523,0.00426122,0.0028229698,0.0001576927],"about_ca_topic_score_codex":0.10046344,"about_ca_topic_score_gemma":0.09329893,"teacher_disagreement_score":0.078924745,"about_ca_system_score_codex":0.0004834673,"about_ca_system_score_gemma":0.0017088413,"threshold_uncertainty_score":0.923246},"labels":[],"label_agreement":null},{"id":"W2028683580","doi":"10.1002/atr.5670340302","title":"ITS applications in public transport: Improving the service to the transport system","year":2000,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":14,"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":"Interurban; Public transport; Telematics; Scope (computer science); Transport engineering; Flexibility (engineering); Service (business); Computer science; Economies of scope; Information system; Passenger information; Train; Taxis; Intelligent transportation system; Economies of scale; Telecommunications; Business; Engineering; Marketing","score_opus":0.013538699438088575,"score_gpt":0.26305047644613194,"score_spread":0.24951177700804336,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2028683580","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9662847,0.00031731985,0.019854821,0.010529983,0.00032763177,0.0011987509,0.00006081215,0.00008545985,0.0013405546],"genre_scores_gemma":[0.9977251,0.00019433675,0.0012483986,0.00029921756,0.00019216706,0.00007245365,0.00006742697,0.000019557578,0.00018128529],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.997902,0.000107227694,0.0008601613,0.00018904764,0.00064058224,0.0003010293],"domain_scores_gemma":[0.9987424,0.00014622648,0.00033626475,0.00017037302,0.00045238048,0.00015234997],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010591522,0.00014992594,0.00022007526,0.00015810902,0.0005694453,0.000037225254,0.00047266713,0.00009178611,0.00009165473],"category_scores_gemma":[0.0000169254,0.000101878635,0.00012331082,0.0014878117,0.000047234673,0.00078252057,3.5394362e-7,0.00030091105,0.000012345387],"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":[0.00020991503,0.00011454201,0.011767258,0.00009404298,0.000033062515,0.000019927782,0.10048675,0.8538812,0.00028251763,0.009595285,0.000021229413,0.023494307],"study_design_scores_gemma":[0.0014001537,0.0000979084,0.80146414,0.0002347203,0.00018136932,0.0000068285312,0.055092152,0.0002986309,0.00014922557,0.00011940801,0.1406036,0.0003518548],"about_ca_topic_score_codex":0.0003448299,"about_ca_topic_score_gemma":0.016446637,"teacher_disagreement_score":0.85358256,"about_ca_system_score_codex":0.00010749721,"about_ca_system_score_gemma":0.00027851612,"threshold_uncertainty_score":0.9177609},"labels":[],"label_agreement":null},{"id":"W2029074290","doi":"10.1007/s12469-013-0061-0","title":"Variation in bus transit service: understanding the impacts of various improvement strategies on transit service reliability","year":2013,"lang":"en","type":"article","venue":"Public Transport","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":60,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Service (business); Schedule; Reliability (semiconductor); Transport engineering; Variation (astronomy); Smart card; Transit (satellite); Bus rapid transit; Public transport; Level of service; Automatic vehicle location; Engineering; Computer science; Telecommunications; Computer security; Business","score_opus":0.028686061934661087,"score_gpt":0.26274568086608024,"score_spread":0.23405961893141916,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2029074290","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.91188407,0.000025275964,0.051864177,0.028506938,0.0002197056,0.0011303525,0.00004717556,0.00015454658,0.0061677545],"genre_scores_gemma":[0.9987692,0.000037367256,0.00024310472,0.0007105827,0.000038548602,0.00006127441,0.000094006966,0.000017237879,0.000028649605],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.9978968,0.00013859072,0.00057428563,0.000324318,0.00063672935,0.00042929035],"domain_scores_gemma":[0.99896353,0.000151427,0.00016825683,0.00026081078,0.00032066132,0.00013531865],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011891533,0.00018522762,0.00023868657,0.00016280926,0.0003234496,0.00012468036,0.00031494405,0.0001822457,0.00031805882],"category_scores_gemma":[0.000020326626,0.00015296563,0.00007624966,0.001285578,0.0000913474,0.0010456942,0.0000016431968,0.00024421426,0.0000087904355],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029540466,0.0011217368,0.07864135,0.0006640569,0.00015746913,0.00000904553,0.5031537,0.24956799,0.0025985825,0.16122232,0.00007783244,0.0024905547],"study_design_scores_gemma":[0.0011256087,0.00011125306,0.96226156,0.000078996985,0.000057749345,3.9756148e-7,0.02672965,0.0021809202,0.000049894574,0.006719589,0.00040648112,0.0002779152],"about_ca_topic_score_codex":0.046455946,"about_ca_topic_score_gemma":0.09416543,"teacher_disagreement_score":0.8836202,"about_ca_system_score_codex":0.00022680206,"about_ca_system_score_gemma":0.0005752678,"threshold_uncertainty_score":0.95989376},"labels":[],"label_agreement":null},{"id":"W2029913608","doi":"10.1002/atr.5670410205","title":"Development of a probabilistic model to optimize disseminated real‐time bus arrival information for pre‐trip passengers","year":2007,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Arrival time; Probabilistic logic; Time of arrival; Computer science; Real-time computing; Transport engineering; Automatic vehicle location; Simulation; Engineering; Telecommunications; Global Positioning System; Wireless","score_opus":0.013423224131017444,"score_gpt":0.3065148072172606,"score_spread":0.29309158308624317,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2029913608","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.54556197,0.000006089545,0.4536005,0.00009365896,0.000119702556,0.0004148915,0.000022901982,0.000024290086,0.00015602128],"genre_scores_gemma":[0.6122614,0.0000205556,0.3874715,0.000015379637,0.00002636901,0.000010333176,0.00013441723,0.000007858332,0.00005219301],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9981231,0.000017181472,0.0010764028,0.00009362123,0.0004802509,0.00020946162],"domain_scores_gemma":[0.9979138,0.00014735489,0.0007667337,0.00005970996,0.00095891685,0.00015348724],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001090211,0.000108585,0.00022481436,0.00025783942,0.00016842081,0.000018542125,0.00012120104,0.00008973917,0.000008149959],"category_scores_gemma":[0.00019367362,0.000108529464,0.00008416377,0.00037798545,0.000036483023,0.0009273793,5.853259e-7,0.00008015282,8.6168467e-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":[0.0010463081,0.000058677073,0.00019742077,0.00006538245,0.00002375515,0.0000011196724,0.08135837,0.90194815,0.00478819,0.00088654936,0.000032560467,0.009593521],"study_design_scores_gemma":[0.019969506,0.0023107505,0.74298894,0.0030434586,0.0012196,0.0000075005582,0.09583665,0.055446398,0.051434506,0.005594888,0.019333951,0.0028138715],"about_ca_topic_score_codex":0.0000135543805,"about_ca_topic_score_gemma":0.0002194963,"teacher_disagreement_score":0.84650177,"about_ca_system_score_codex":0.0001286968,"about_ca_system_score_gemma":0.00036979988,"threshold_uncertainty_score":0.4425703},"labels":[],"label_agreement":null},{"id":"W2030107351","doi":"","title":"Effects of Articulated Buses on Dwell and Running Times","year":2011,"lang":"en","type":"article","venue":"Digital Commons - University of South Florida (University of South Florida)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Dwell time; Passenger information; Doors; Transit (satellite); Public transport; Transport engineering; Automotive engineering; Acceleration; Running time; Train; Engineering; Simulation; Computer science; Geography; Operating system","score_opus":0.017356282937800303,"score_gpt":0.1860011675439038,"score_spread":0.1686448846061035,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2030107351","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9798137,0.00003210262,0.002338702,0.000038241877,0.00011904788,0.00019350188,0.00014567276,0.00009212276,0.017226942],"genre_scores_gemma":[0.9985278,0.000019556059,0.00079501607,0.00000522507,0.000015736785,2.6878494e-8,0.00003292859,0.000009535572,0.0005941515],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.9989434,0.000052915748,0.00014623607,0.00025787327,0.00036683463,0.00023276915],"domain_scores_gemma":[0.99897933,0.0001406684,0.00030968842,0.00017650386,0.00022354495,0.00017024086],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013854665,0.0001559998,0.00034066642,0.00029171383,0.00047434494,0.000016690383,0.00032661305,0.0001512065,0.000087276894],"category_scores_gemma":[0.00004626534,0.00021053405,0.00013973094,0.0004659063,0.0008579872,0.00058147724,0.00006329664,0.00014161214,0.000016033453],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015052095,0.00030190696,0.3843794,0.0003142227,0.0003881213,0.00012176406,0.5970298,0.0019201479,0.00012399431,0.012753135,0.00024419583,0.00091809494],"study_design_scores_gemma":[0.0043725194,0.00073172315,0.5953322,0.0006913072,0.00063668954,0.0000011634002,0.39355853,0.0007590421,0.0011936412,0.00045084965,0.0014280697,0.00084429316],"about_ca_topic_score_codex":0.0009449634,"about_ca_topic_score_gemma":0.0001397992,"teacher_disagreement_score":0.21095279,"about_ca_system_score_codex":0.000031708147,"about_ca_system_score_gemma":0.00008964916,"threshold_uncertainty_score":0.8585329},"labels":[],"label_agreement":null},{"id":"W2030507653","doi":"10.3141/2197-14","title":"Comparison of MATSim and EMME/2 on Greater Toronto and Hamilton Area Network, Canada","year":2010,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":58,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Microsimulation; Computer science; Traffic flow (computer networking); Transportation planning; Transport engineering; Traffic simulation; Flow network; Travel behavior; Point (geometry); Street network; Operations research; Simulation; Mathematical optimization; Engineering; Mathematics","score_opus":0.0959454661896295,"score_gpt":0.40751117289689653,"score_spread":0.311565706707267,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2030507653","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9953209,0.00019109207,0.00025214042,0.002232814,0.00058564456,0.0005355009,0.000055006294,0.000014789259,0.0008120971],"genre_scores_gemma":[0.997154,0.0008134857,0.0012299961,0.000050472016,0.00016403035,0.000017284372,0.000017535998,0.000025633388,0.0005275434],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9945729,0.0007212225,0.0010063018,0.0003014826,0.0027724125,0.0006256962],"domain_scores_gemma":[0.9960979,0.00087178044,0.0004316569,0.00025022498,0.0019416566,0.0004067751],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003412472,0.00017956941,0.00041150337,0.00024210528,0.0009154962,0.000101435566,0.00046724788,0.00019958905,0.0002570717],"category_scores_gemma":[0.00015075116,0.00014235543,0.00010450777,0.0006974058,0.0008498243,0.0004513169,0.0000026979235,0.0013963535,8.711443e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008433246,0.00011087458,0.9566608,0.00011296928,0.000075413634,0.000024660218,0.019642796,0.0023141322,0.0006227317,0.009758887,0.00631328,0.003520136],"study_design_scores_gemma":[0.0008002316,0.000317824,0.9630925,0.00021501987,0.000039461807,1.8551128e-7,0.008498916,0.0002370459,0.00036782358,0.00055934,0.025719108,0.00015251822],"about_ca_topic_score_codex":0.6147357,"about_ca_topic_score_gemma":0.9873367,"teacher_disagreement_score":0.37260094,"about_ca_system_score_codex":0.00014401496,"about_ca_system_score_gemma":0.0009665998,"threshold_uncertainty_score":0.70413494},"labels":[],"label_agreement":null},{"id":"W2030738966","doi":"10.3141/2279-03","title":"Path-Based Algorithms to Solve C-Logit Stochastic User Equilibrium Assignment Problem","year":2012,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":40,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Multinomial logistic regression; Logit; Computer science; Mathematical optimization; Path (computing); Line search; Line (geometry); Algorithm; Flow (mathematics); Flow network; Mathematics; Machine learning","score_opus":0.10755483940230054,"score_gpt":0.4051072927315346,"score_spread":0.2975524533292341,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2030738966","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8162501,0.0002733818,0.16345598,0.014193867,0.0017401208,0.003003436,0.00016315216,0.00012888442,0.00079110067],"genre_scores_gemma":[0.976786,0.00008250738,0.020953927,0.00018515458,0.0005699424,0.00017628296,0.000049881422,0.00007057199,0.0011256922],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.98867357,0.0014665247,0.0014820868,0.00044781138,0.00618124,0.0017487392],"domain_scores_gemma":[0.9925167,0.0013721687,0.00051096885,0.00043929127,0.0039482485,0.0012126385],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0103277005,0.0003053328,0.00046978454,0.0011774654,0.0013373047,0.00025723458,0.0012182723,0.00029594032,0.00058224995],"category_scores_gemma":[0.00038562968,0.00024876453,0.0003933421,0.003176045,0.0007554443,0.0012440885,0.000006275158,0.0017545181,0.00007924739],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.003994693,0.00247547,0.55315024,0.00060438894,0.0004547717,0.000133599,0.09540446,0.24054925,0.004401976,0.043563563,0.038302865,0.01696472],"study_design_scores_gemma":[0.0027150367,0.001213889,0.9047279,0.0009025523,0.00015594006,3.8791217e-7,0.014970664,0.0004421895,0.0009811828,0.0023483844,0.07086325,0.00067864236],"about_ca_topic_score_codex":0.0103346575,"about_ca_topic_score_gemma":0.01992363,"teacher_disagreement_score":0.35157764,"about_ca_system_score_codex":0.00054620695,"about_ca_system_score_gemma":0.0015730159,"threshold_uncertainty_score":0.9999965},"labels":[],"label_agreement":null},{"id":"W2031281561","doi":"10.1257/aer.99.5.2149","title":"Departure Times in Y-Shaped Traffic Networks with Multiple Bottlenecks","year":2009,"lang":"en","type":"article","venue":"American Economic Review","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":56,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Counterintuitive; Bottleneck; Upstream (networking); Traverse; Nash equilibrium; Computer science; Travel time; Economics; Traffic network; Microeconomics; Operations research; Econometrics; Transport engineering; Computer network; Mathematical optimization; Operations management; Mathematics; Physics; Geography; Engineering","score_opus":0.008982173112068975,"score_gpt":0.26904199006218277,"score_spread":0.2600598169501138,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2031281561","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6329423,0.16466528,0.013090768,0.05499714,0.0007778053,0.00567131,0.000052273404,0.0013233066,0.12647977],"genre_scores_gemma":[0.9508516,0.045285102,0.0013226164,0.0022428327,0.000058108042,0.000014581955,0.000038294344,0.000007132836,0.00017971719],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992729,0.00008409166,0.00022271732,0.00017961758,0.00005167719,0.00018898427],"domain_scores_gemma":[0.9995888,0.000071159055,0.00015839055,0.00010185941,0.000012280164,0.00006751534],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024358774,0.00008713835,0.00026644045,0.00003263886,0.00007600171,0.000021248467,0.00010921594,0.000025856692,0.000107205604],"category_scores_gemma":[0.000021854223,0.00007787661,0.000039991006,0.00019069774,0.00010597216,0.00012435883,0.0000011504044,0.000077981895,0.000030701685],"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":[0.000040797448,0.00006669895,0.04897459,0.000052588282,0.000022606593,0.00001096656,0.0017577588,0.28952742,3.6915634e-7,0.00093413255,0.005501065,0.65311104],"study_design_scores_gemma":[0.0014027631,0.00044955063,0.35872358,0.0027346811,0.00014706206,0.0000067895294,0.0018095147,0.06376502,0.0000010296342,0.000049474278,0.5697556,0.0011549435],"about_ca_topic_score_codex":0.00041384707,"about_ca_topic_score_gemma":0.006149201,"teacher_disagreement_score":0.6519561,"about_ca_system_score_codex":0.00005848749,"about_ca_system_score_gemma":0.00008803976,"threshold_uncertainty_score":0.34313986},"labels":[],"label_agreement":null},{"id":"W2031559113","doi":"10.1002/atr.155","title":"On the allocation of new lines in a competitive transit network with uncertain demand and scale economies","year":2011,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Economies of scale; Bidding; Transit (satellite); Computer science; Operator (biology); Profit (economics); Probabilistic logic; Operations research; Economics; Public transport; Resource allocation; Microeconomics; Transport engineering; Engineering","score_opus":0.01764778073998993,"score_gpt":0.2513610805437878,"score_spread":0.23371329980379787,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2031559113","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98559576,0.00012051706,0.0130107505,0.00057311653,0.000065177526,0.00014874787,0.000003372398,0.000005763628,0.00047676917],"genre_scores_gemma":[0.99039066,0.0003553022,0.009118286,0.00005067419,0.000041029147,0.0000021392354,0.000007660054,0.000005138815,0.000029093539],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99932796,0.000050630104,0.0003202678,0.00007161258,0.00014312964,0.00008640998],"domain_scores_gemma":[0.9993484,0.00013011754,0.00030888023,0.00003528351,0.00013377286,0.00004354997],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032685618,0.000063775995,0.00014062374,0.00006784383,0.00007702508,0.000007549407,0.0000567131,0.000037446724,0.000021881715],"category_scores_gemma":[0.000013867457,0.000045815188,0.000027156384,0.00018751185,0.000093376046,0.0002940122,1.6477745e-7,0.00008595309,1.6231388e-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":[0.0011785685,0.00006475665,0.06235772,0.00001897039,0.00003059699,0.000005911341,0.1575834,0.7374076,0.00007113145,0.038343437,0.000019066052,0.0029188383],"study_design_scores_gemma":[0.0014116872,0.0004043932,0.9593954,0.00040512212,0.00006345784,0.0000012174339,0.030764172,0.000114313414,0.0004831555,0.006551764,0.0002928608,0.0001124531],"about_ca_topic_score_codex":0.0002839717,"about_ca_topic_score_gemma":0.015241693,"teacher_disagreement_score":0.8970377,"about_ca_system_score_codex":0.000016002972,"about_ca_system_score_gemma":0.00010730109,"threshold_uncertainty_score":0.8505222},"labels":[],"label_agreement":null},{"id":"W2033254839","doi":"10.1002/atr.143","title":"A comparative study of a hybrid Logit–Fratar and neural network models for trip distribution: case of the city of Isfahan","year":2010,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","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":"Artificial neural network; Logit; Logistic regression; Mixed logit; Econometrics; Computer science; Feature (linguistics); Distribution (mathematics); Operations research; Engineering; Economics; Artificial intelligence; Mathematics; Machine learning","score_opus":0.03239032381930722,"score_gpt":0.32233550007181805,"score_spread":0.2899451762525108,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2033254839","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97468615,0.000053881395,0.024355078,0.00007171951,0.00024763195,0.0003987974,0.00017260807,0.0000039142146,0.000010227488],"genre_scores_gemma":[0.99717295,0.00001931787,0.0027134395,0.000004645342,0.00004720355,0.0000044358885,0.00002973088,0.0000035274654,0.000004760325],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99893224,0.000064927524,0.0006024287,0.000081491875,0.00022815801,0.00009075323],"domain_scores_gemma":[0.99814266,0.00016121547,0.00095524156,0.00006946772,0.00062667404,0.00004475325],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041712294,0.00007103315,0.00027417653,0.00003482251,0.00015587184,0.0000058674264,0.0000867079,0.00003313191,0.0000036462727],"category_scores_gemma":[0.000036749043,0.000055272358,0.000084204075,0.00022738107,0.00014587479,0.0003605624,8.2007506e-7,0.00013315248,5.0015205e-9],"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":[0.00059819507,0.00023641069,0.028426271,0.00003444942,0.000047196423,0.000010304427,0.033489414,0.9342803,0.00046371942,0.001910065,0.000016103864,0.0004876137],"study_design_scores_gemma":[0.009928416,0.0025330829,0.86837435,0.00025807324,0.00089537125,0.00005391388,0.08553406,0.016204163,0.0049460204,0.010704168,0.00020274418,0.0003656621],"about_ca_topic_score_codex":0.00016565787,"about_ca_topic_score_gemma":0.0040098466,"teacher_disagreement_score":0.9180761,"about_ca_system_score_codex":0.0000087041535,"about_ca_system_score_gemma":0.000083152925,"threshold_uncertainty_score":0.22539413},"labels":[],"label_agreement":null},{"id":"W2034262362","doi":"10.1002/atr.5670380106","title":"Walking infrastructure design assessment by continuous space dynamic assignment modeling","year":2004,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Space (punctuation); Heuristic; Set (abstract data type); Countable set; Sequence (biology); Traffic flow (computer networking); Flow network; Flow (mathematics); Mathematical optimization; Simulation; Mathematics; Artificial intelligence; Computer network","score_opus":0.008116915105766412,"score_gpt":0.2862230495861196,"score_spread":0.2781061344803532,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2034262362","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23606075,0.00017488546,0.76270545,0.0004510248,0.0003219363,0.00015587632,0.000009247228,0.000030838004,0.000089962516],"genre_scores_gemma":[0.8481863,0.00037327028,0.15125096,0.000045224304,0.00004471609,0.0000034254829,0.000052509,0.000013661319,0.000029927744],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99845886,0.000072656956,0.0005287966,0.0001363871,0.0005993251,0.0002039879],"domain_scores_gemma":[0.9989829,0.00005084877,0.000495602,0.00005925104,0.00029535836,0.00011605316],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000502842,0.00012355074,0.00020613371,0.000110876266,0.00025518812,0.000048501657,0.00012063319,0.00009498219,0.000030982097],"category_scores_gemma":[0.000026452413,0.00012182175,0.000082256985,0.00022004804,0.00003933754,0.0007232471,4.5945694e-7,0.00023283031,6.24176e-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":[0.00006470627,0.000044040746,0.00057481305,0.000007588332,0.000023442737,0.00001479512,0.012361886,0.9792243,0.0030828933,0.0011114797,0.000034604825,0.0034554268],"study_design_scores_gemma":[0.04843608,0.005022422,0.36131278,0.004511529,0.0023678897,0.00006794568,0.3060879,0.09350103,0.009598628,0.14164275,0.022107394,0.0053436426],"about_ca_topic_score_codex":0.000046856705,"about_ca_topic_score_gemma":0.00014636606,"teacher_disagreement_score":0.8857233,"about_ca_system_score_codex":0.00030620018,"about_ca_system_score_gemma":0.00032824965,"threshold_uncertainty_score":0.49677467},"labels":[],"label_agreement":null},{"id":"W2034564784","doi":"10.5430/jms.v1n1p93","title":"Institutional Aspects on Bus Rapid Transit Systems Implementation in Mexico City, Estado de Mexico and León Guanajuato","year":2010,"lang":"en","type":"article","venue":"Journal of Management and Strategy","topic":"Transportation Planning and Optimization","field":"Social Sciences","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":"Public transport; Mexico city; State (computer science); Political science; Public administration; Business; Sociology; Computer science; Law","score_opus":0.021586140081426935,"score_gpt":0.3037913225468875,"score_spread":0.28220518246546056,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2034564784","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97763294,0.00020637555,0.0032257505,0.00086895714,0.000270658,0.00018465145,0.000005516075,0.000011283534,0.01759387],"genre_scores_gemma":[0.9988387,0.00049891,0.00043727778,0.000058567846,0.000073657,0.0000025216655,0.000008029205,0.0000032656194,0.00007903913],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9991594,0.00006491015,0.0002675659,0.00009213753,0.00027915384,0.00013682377],"domain_scores_gemma":[0.9996674,0.00003118937,0.0001357588,0.000033430777,0.000050548762,0.00008164589],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00083983503,0.000069623544,0.00010955158,0.00020556561,0.0001845249,0.00013446032,0.00006597705,0.000051407093,0.000043793676],"category_scores_gemma":[0.000007757639,0.00006480892,0.00002204946,0.00014220546,0.00006516719,0.00029092704,0.0000030144793,0.00015003466,4.9009753e-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":[0.0001962238,0.00016859005,0.20035316,0.000094338146,0.00011338139,0.00023556367,0.008563979,0.03745654,0.00014984459,0.7099189,0.0006604608,0.042089004],"study_design_scores_gemma":[0.0025451833,0.00029551957,0.9633167,0.00012217042,0.00008445306,0.000022633074,0.017255072,0.0013429458,0.0000752347,0.004873632,0.009847639,0.00021882611],"about_ca_topic_score_codex":0.00032577445,"about_ca_topic_score_gemma":0.0024986074,"teacher_disagreement_score":0.76296353,"about_ca_system_score_codex":0.000033314464,"about_ca_system_score_gemma":0.000083102095,"threshold_uncertainty_score":0.26428312},"labels":[],"label_agreement":null},{"id":"W2035225386","doi":"10.1016/j.jue.2008.02.005","title":"The merits of separating cars and trucks","year":2008,"lang":"en","type":"article","venue":"Journal of Urban Economics","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":46,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Truck; Toll; Transport engineering; Traffic congestion; Vehicle type; Toll road; Road pricing; Computer science; Business; Automotive engineering; Engineering","score_opus":0.0190642222393632,"score_gpt":0.25196140005925827,"score_spread":0.23289717781989505,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2035225386","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99633646,0.00037434197,0.00017130419,0.00038786136,0.0001545915,0.000018687422,0.0000016090685,0.00000197133,0.0025531654],"genre_scores_gemma":[0.99666816,0.0015564742,0.0013935197,0.000021753618,0.00008752054,1.2110347e-7,3.0590607e-7,0.000002183125,0.0002699336],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99963665,0.000023631277,0.00022120228,0.000025299589,0.00004085405,0.000052377058],"domain_scores_gemma":[0.99949217,0.000098574616,0.00029250162,0.00002380888,0.000059320475,0.000033604574],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000304097,0.000023469978,0.00007402476,0.00002201903,0.00023030493,0.000014335961,0.000056268564,0.000022504328,0.0000033407082],"category_scores_gemma":[0.000045418896,0.000018703757,0.000026707372,0.000029023235,0.00010584323,0.00012843321,0.0000013699985,0.000047096473,2.2917108e-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":[0.000106408974,0.0000386772,0.76809853,0.000009492055,0.00012600943,0.000010293445,0.1481359,0.0249342,0.00007528461,0.042097405,0.006615381,0.009752442],"study_design_scores_gemma":[0.0026015025,0.0004635719,0.60392123,0.00011172631,0.00013584031,0.00015091439,0.030945359,0.0048182593,0.00081064587,0.0045529515,0.3510448,0.0004431707],"about_ca_topic_score_codex":0.000039252165,"about_ca_topic_score_gemma":0.00012722009,"teacher_disagreement_score":0.34442943,"about_ca_system_score_codex":0.000015035502,"about_ca_system_score_gemma":0.00010364798,"threshold_uncertainty_score":0.17713426},"labels":[],"label_agreement":null},{"id":"W2035264105","doi":"10.1002/atr.5670400206","title":"Equilibrium analysis of the scheduling of tours in congested networks","year":2006,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Joins; Travel time; Scheduling (production processes); Congestion pricing; Computer science; Operations research; Travel behavior; Value of time; Transport engineering; Simulation; Traffic congestion; Economics; Operations management; Engineering","score_opus":0.009037248649553955,"score_gpt":0.27156992864542134,"score_spread":0.2625326799958674,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2035264105","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.987053,0.0001921455,0.012180792,0.00015869737,0.00018848505,0.00007526314,0.000008292295,0.000004832441,0.0001384912],"genre_scores_gemma":[0.99656385,0.00005810554,0.0032796322,0.000009010881,0.000035346875,6.7272845e-7,0.000027182014,0.0000043039363,0.0000219225],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9987611,0.000066530716,0.0006872596,0.00006277169,0.00032399833,0.00009834693],"domain_scores_gemma":[0.998641,0.000102194754,0.0008536926,0.000058679045,0.00032087954,0.000023517956],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040472872,0.000054327724,0.00023403818,0.0002531057,0.000033172957,0.000005601781,0.00011179339,0.00006124039,0.000012843314],"category_scores_gemma":[0.00003371594,0.00004538437,0.00015780413,0.0014698773,0.00008091242,0.00027767842,3.8845243e-7,0.00010476952,2.6335751e-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":[0.00005721158,0.000033355638,0.21744107,0.000005996453,0.000039927207,0.0000013127305,0.0026938724,0.77776915,0.000747951,0.0009403746,0.000001611674,0.00026819383],"study_design_scores_gemma":[0.00047520953,0.000024084828,0.9927462,0.000082279126,0.00031769747,9.229852e-8,0.002168631,0.003452553,0.00046396017,0.0001836255,0.00003823882,0.00004743952],"about_ca_topic_score_codex":0.00034445687,"about_ca_topic_score_gemma":0.0057691354,"teacher_disagreement_score":0.7753051,"about_ca_system_score_codex":0.00002613303,"about_ca_system_score_gemma":0.000091325644,"threshold_uncertainty_score":0.3219313},"labels":[],"label_agreement":null},{"id":"W2035893651","doi":"10.1287/trsc.35.4.345.10433","title":"A Bilevel Model for Toll Optimization on a Multicommodity Transportation Network","year":2001,"lang":"en","type":"article","venue":"Transportation Science","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":199,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis; Université de Montréal","funders":"","keywords":"Toll; Bilevel optimization; Mathematical optimization; Flow network; Revenue; Set (abstract data type); Computer science; Mathematics; Operations research; Optimization problem; Economics","score_opus":0.06522803791999959,"score_gpt":0.34045945924746973,"score_spread":0.27523142132747014,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2035893651","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09750892,0.000012646142,0.8984694,0.000839352,0.0003469333,0.00085524964,0.00014793056,0.0002880541,0.0015315524],"genre_scores_gemma":[0.9062107,0.0000857717,0.091962,0.00041335137,0.00012656907,0.0001319951,0.0004815321,0.000021504025,0.000566526],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974248,0.0000434425,0.00050241774,0.00055212266,0.0009053814,0.00057183026],"domain_scores_gemma":[0.99859035,0.00017329681,0.00022856578,0.0002090782,0.0005583119,0.00024041743],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0012566028,0.00019009219,0.00019905927,0.00023529315,0.0015727289,0.00012964218,0.0003500879,0.00012926957,0.00009105042],"category_scores_gemma":[0.000099190846,0.00020319784,0.00010464757,0.0013978381,0.00042506863,0.0010689585,6.013414e-7,0.000115644594,0.000008552394],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001492576,0.0000791216,0.0071213497,0.000008830813,0.0000044853773,0.000001640119,0.011144287,0.9549472,0.000037623737,0.024700886,0.00018119432,0.0016241344],"study_design_scores_gemma":[0.0010145323,0.000072270625,0.062245287,0.00004944994,0.000047849146,1.7788439e-7,0.0007327331,0.9311576,0.00004344068,0.0019357135,0.0023577206,0.0003432127],"about_ca_topic_score_codex":0.00027301733,"about_ca_topic_score_gemma":0.0036404626,"teacher_disagreement_score":0.8087018,"about_ca_system_score_codex":0.00011539993,"about_ca_system_score_gemma":0.0005326755,"threshold_uncertainty_score":0.9997271},"labels":[],"label_agreement":null},{"id":"W2035954900","doi":"10.2495/sdp-v2-n3-363-374","title":"Integration of public transport through a structured analysis technique","year":2007,"lang":"en","type":"article","venue":"International Journal of Sustainable Development and Planning","topic":"Transportation Planning and Optimization","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":"Public transport; Transport engineering; Service (business); Order (exchange); Transport system; System integration; Passenger transport; Engineering; Computer science; Business; Finance","score_opus":0.023536010730643483,"score_gpt":0.31211343320080187,"score_spread":0.2885774224701584,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2035954900","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.54433274,0.00010526426,0.4531881,0.00014336329,0.00011019287,0.00006441426,0.0000013944414,0.0000095066725,0.0020450137],"genre_scores_gemma":[0.9564351,0.000034698453,0.04309507,0.000028491335,0.00007270119,0.0000014710984,0.000042039537,0.0000037517375,0.0002867097],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99870294,0.00003537611,0.00051263336,0.00007795331,0.00050754374,0.00016352297],"domain_scores_gemma":[0.99811023,0.00008719863,0.00041840394,0.000027694063,0.0012950195,0.00006146695],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017938282,0.00007069021,0.00015548208,0.0006964501,0.00014534092,0.00004537945,0.00016464574,0.000075126976,0.000036470385],"category_scores_gemma":[0.00014886462,0.00006540766,0.000057310153,0.000569603,0.000059345868,0.00056529976,0.0000053221097,0.00011298182,8.702329e-8],"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":[0.00041002186,0.00011384652,0.5148934,0.0000442127,0.0016758444,0.0010728849,0.3640627,0.0066378294,0.0009152264,0.0917804,0.00011604231,0.018277584],"study_design_scores_gemma":[0.00078229414,0.000054712462,0.6646129,0.00015897816,0.00020561651,0.000020758145,0.28900975,0.000065918124,0.007242949,0.0028495984,0.034723513,0.00027303686],"about_ca_topic_score_codex":0.00015832503,"about_ca_topic_score_gemma":0.00015111086,"teacher_disagreement_score":0.4121023,"about_ca_system_score_codex":0.00010628821,"about_ca_system_score_gemma":0.0003173408,"threshold_uncertainty_score":0.26672468},"labels":[],"label_agreement":null},{"id":"W2036201060","doi":"10.3141/2429-11","title":"Spatial Transferability of Tour-Based Time-of-Day Choice Models","year":2014,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Federal Highway Administration; National Science Foundation","keywords":"Transferability; Pooling; Context (archaeology); Computer science; Estimation; Geospatial analysis; Econometrics; Geography; Machine learning; Cartography; Artificial intelligence; Mathematics; Engineering","score_opus":0.08956701443126719,"score_gpt":0.38656919962776076,"score_spread":0.2970021851964936,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2036201060","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.91903275,0.00007718909,0.07612768,0.0023759082,0.00038593708,0.0009782049,0.00012784,0.000036317288,0.000858184],"genre_scores_gemma":[0.99600196,0.00018841997,0.0031164102,0.00003103655,0.00018351089,0.000029756542,0.000041846277,0.00004128826,0.00036575535],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9882719,0.0036763526,0.001969712,0.00039369278,0.004926137,0.00076222967],"domain_scores_gemma":[0.9887141,0.0035164733,0.0007479052,0.000475367,0.006150359,0.00039578622],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012633797,0.00023438493,0.0006691569,0.0009764151,0.00072255515,0.00006371567,0.0011962511,0.00029926948,0.0005961153],"category_scores_gemma":[0.00075916294,0.00019594672,0.00053931476,0.002285643,0.001552852,0.00072912837,0.0000024208198,0.0013369786,0.0000066478206],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.003703934,0.0015392951,0.66988075,0.001053722,0.0003221198,0.00001992064,0.048171196,0.21327442,0.009094737,0.022874672,0.0029190138,0.027146243],"study_design_scores_gemma":[0.0029010372,0.0010264025,0.9616727,0.0006995641,0.0001388079,6.6353614e-8,0.0025023704,0.008758672,0.0068402793,0.0071874773,0.007946243,0.000326357],"about_ca_topic_score_codex":0.04551828,"about_ca_topic_score_gemma":0.090316184,"teacher_disagreement_score":0.29179198,"about_ca_system_score_codex":0.00015719923,"about_ca_system_score_gemma":0.0014732737,"threshold_uncertainty_score":0.9608377},"labels":[],"label_agreement":null},{"id":"W2036361689","doi":"10.1016/j.tra.2006.12.004","title":"Alternative methods for estimating full marginal costs of highway transportation","year":2007,"lang":"en","type":"article","venue":"Transportation Research Part A Policy and Practice","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":41,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Software; Transport engineering; Marginal cost; Computer science; Estimation; Flow network; Computation; Operations research; Engineering; Mathematical optimization; Mathematics; Algorithm; Systems engineering","score_opus":0.15851283406291403,"score_gpt":0.5475293950182063,"score_spread":0.38901656095529225,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2036361689","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019058343,0.0001470082,0.97047037,0.003640973,0.00020410964,0.0008802541,0.00018748447,0.000073056835,0.005338373],"genre_scores_gemma":[0.4936963,0.00035708037,0.50435424,0.0001698817,0.00045810343,0.000082954815,0.0004262511,0.000023251143,0.00043192352],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99736416,0.00072050997,0.0005515077,0.00028854777,0.000604944,0.0004703339],"domain_scores_gemma":[0.98943686,0.00879204,0.00030647745,0.0001038098,0.0011487403,0.00021206363],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011050968,0.000120085504,0.00019808215,0.00038775694,0.0006663479,0.000056680627,0.00013018853,0.00013457182,0.00003935566],"category_scores_gemma":[0.0029555834,0.00013008891,0.000059524984,0.00083760003,0.00036864946,0.0008164192,8.9215416e-7,0.0002452593,0.0000022298948],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0033189228,0.0002416101,0.0013602903,0.00023365318,0.00012113481,0.000014684959,0.10174587,0.00739656,0.0014598999,0.79927343,0.00058691064,0.08424701],"study_design_scores_gemma":[0.005893382,0.0015564961,0.053021435,0.00064097834,0.00046135182,0.0000056145586,0.061510652,0.01451433,0.0056818346,0.026911223,0.8286715,0.0011311924],"about_ca_topic_score_codex":0.01204426,"about_ca_topic_score_gemma":0.005424619,"teacher_disagreement_score":0.8280846,"about_ca_system_score_codex":0.00008091149,"about_ca_system_score_gemma":0.00045242542,"threshold_uncertainty_score":0.9945346},"labels":[],"label_agreement":null},{"id":"W2037575195","doi":"10.1061/(asce)0733-9488(2005)131:2(71)","title":"Review of Optimal Transit Subsidies: Comparison between Models","year":2005,"lang":"en","type":"article","venue":"Journal of Urban Planning and Development","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Subsidy; Externality; Economics; Investment (military); Argument (complex analysis); Transit (satellite); Microeconomics; Public economics; Scale (ratio); Econometrics; Public transport; Transport engineering; Engineering; Market economy; Geography","score_opus":0.06645365490151184,"score_gpt":0.33025407870047335,"score_spread":0.2638004237989615,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2037575195","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4445938,0.23167877,0.31313795,0.0034628273,0.00032617047,0.00035279072,0.00001604811,0.000071003895,0.006360651],"genre_scores_gemma":[0.95319366,0.0037113614,0.042669114,0.00016332009,0.00013423547,8.2132516e-7,0.00001152656,0.0000055638184,0.000110399305],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9988074,0.000053096497,0.00062431867,0.00006971795,0.00032072945,0.00012476738],"domain_scores_gemma":[0.999221,0.00008506103,0.0003795752,0.000032333082,0.00017856674,0.000103456885],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00089414313,0.00007387352,0.0002919657,0.00009341469,0.0001515567,0.00001717643,0.00009036864,0.00004868346,0.0000169119],"category_scores_gemma":[0.000030156058,0.0000653768,0.000037465208,0.0001053948,0.000047146736,0.00021230336,0.0000029619987,0.0001158146,8.8922263e-7],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013459283,0.000163803,0.38412303,0.0016004222,0.00029923377,0.000015651845,0.37977543,0.1394682,0.000016777018,0.00044359837,0.044393007,0.049566276],"study_design_scores_gemma":[0.0020321535,0.00029314295,0.10785892,0.025432615,0.0002985745,0.000018783401,0.016157677,0.0019668636,0.00071652356,0.000070914335,0.84445006,0.00070375897],"about_ca_topic_score_codex":0.000012340461,"about_ca_topic_score_gemma":0.000007487412,"teacher_disagreement_score":0.80005705,"about_ca_system_score_codex":0.000035004392,"about_ca_system_score_gemma":0.00024259361,"threshold_uncertainty_score":0.26659885},"labels":[],"label_agreement":null},{"id":"W2037637901","doi":"10.1007/s10626-005-2865-9","title":"Ghost Simulation Model for the Optimization of an Urban Subway System","year":2005,"lang":"en","type":"article","venue":"Discrete Event Dynamic Systems","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":24,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Bishop's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Headway; Overhead (engineering); Computer science; Function (biology); Event (particle physics); Control (management); Filter (signal processing); Service (business); Mathematical optimization; Engineering; Simulation; Control theory (sociology); Mathematics; Artificial intelligence","score_opus":0.016488809153549742,"score_gpt":0.30882021994499165,"score_spread":0.2923314107914419,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2037637901","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012407347,0.00018892436,0.9852009,0.00016648896,0.00036107973,0.00095418625,0.00013459934,0.00011850029,0.00046800898],"genre_scores_gemma":[0.99540406,0.000013057766,0.0028801123,0.000010047179,0.00015573706,0.00007805268,0.00038013625,0.000018898063,0.0010598793],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987496,0.00013134546,0.0004133386,0.00018214567,0.00034444605,0.00017909192],"domain_scores_gemma":[0.99908024,0.00017759064,0.00028880851,0.00018699346,0.00020739036,0.00005898226],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007861903,0.00010022247,0.0001520239,0.000063796564,0.0004234243,0.000063245796,0.00018119189,0.00009568701,0.0000044391213],"category_scores_gemma":[0.00004433904,0.00008030349,0.00007437059,0.00017185164,0.000055302397,0.00034508077,0.000003341304,0.00004152451,0.0000016918602],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024768733,0.000015464688,0.00038669992,0.000049382557,0.000015842741,7.45972e-8,0.007884152,0.9817518,0.0000046972955,0.00959688,0.000022871911,0.000247396],"study_design_scores_gemma":[0.00026650474,0.00002067005,0.00022647005,0.0000794581,0.00004754517,2.0678438e-7,0.0061141485,0.99276036,0.00000142984,0.000007905297,0.00037447643,0.00010084561],"about_ca_topic_score_codex":0.00040842788,"about_ca_topic_score_gemma":0.0004776639,"teacher_disagreement_score":0.98299676,"about_ca_system_score_codex":0.00014636689,"about_ca_system_score_gemma":0.00008548068,"threshold_uncertainty_score":0.32746816},"labels":[],"label_agreement":null},{"id":"W2037995348","doi":"10.5038/2375-0901.6.2.5","title":"Optimization of Travel in Bus Rapid Transit-Based Multimodal Corridors","year":2003,"lang":"en","type":"article","venue":"Journal of Public Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Public transport; Fuel efficiency; Key (lock); Transport engineering; Transit (satellite); Travel time; Greenhouse gas; Modal; Identification (biology); Computer science; Engineering; Automotive engineering","score_opus":0.02518499913558916,"score_gpt":0.27070084777727893,"score_spread":0.24551584864168977,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2037995348","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.34396723,0.00014907138,0.65343654,0.00070974353,0.00042263992,0.00021311299,0.00003308076,0.000020890002,0.001047708],"genre_scores_gemma":[0.9744415,0.00011130506,0.0252396,0.000043846987,0.00003230497,0.0000031725172,0.00009092318,0.000013120187,0.000024199475],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9979009,0.0002860484,0.0008997964,0.0001222747,0.0005781432,0.00021284501],"domain_scores_gemma":[0.9984792,0.00012359761,0.00061765214,0.000069182584,0.00057783176,0.00013252007],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016210516,0.00011112933,0.00026702866,0.0005320337,0.00009927173,0.000029993353,0.00013264488,0.00014748843,0.00024093228],"category_scores_gemma":[0.00018778793,0.00011499884,0.00013331202,0.00083866785,0.000105198196,0.00068303646,1.2317291e-7,0.00017308808,4.9384505e-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":[0.00008319692,0.0002504796,0.047731787,0.000028870527,0.00001809814,0.000011483317,0.010997713,0.9342718,0.00021609674,0.0044702776,0.00002697183,0.0018932329],"study_design_scores_gemma":[0.013479756,0.0006880194,0.90397364,0.00039686158,0.0002758018,0.0000064278233,0.027716879,0.044111226,0.003632415,0.0005641689,0.0043641836,0.0007906175],"about_ca_topic_score_codex":0.00024072024,"about_ca_topic_score_gemma":0.0015038132,"teacher_disagreement_score":0.89016056,"about_ca_system_score_codex":0.000069506954,"about_ca_system_score_gemma":0.0007194752,"threshold_uncertainty_score":0.46895167},"labels":[],"label_agreement":null},{"id":"W2038723508","doi":"10.1002/atr.5670400104","title":"Effect of variable message signs on driver speed behavior on a section of expressway under adverse fog conditions—A driving simulator approach","year":2006,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Oceanic and Atmospheric Administration","keywords":"Driving simulator; Variable (mathematics); Simulation; Section (typography); Computer science; Variance (accounting); Driving simulation; Representation (politics); Engineering","score_opus":0.008511157442775896,"score_gpt":0.27920661530778396,"score_spread":0.27069545786500804,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2038723508","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9685731,0.000014617221,0.029736305,0.000015624355,0.00034474014,0.0003524314,0.000035988134,0.00002576104,0.0009014213],"genre_scores_gemma":[0.9964953,0.00001558421,0.003068488,0.000010947463,0.00011394536,0.0000070375777,0.0001481174,0.000017270093,0.00012329772],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99822146,0.0001716957,0.00067193434,0.00015520435,0.0006260347,0.00015367678],"domain_scores_gemma":[0.9982807,0.00032981602,0.0009006409,0.00009234403,0.00033168212,0.00006480596],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005496913,0.00014161594,0.00031485118,0.00026102533,0.00016181251,0.000009443896,0.00009543275,0.00013050127,0.000055226228],"category_scores_gemma":[0.000043890803,0.0001329137,0.00015874027,0.00037693337,0.00008691034,0.0005443663,4.974872e-7,0.00018872805,5.919903e-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":[0.0004376178,0.0003026497,0.015246866,0.00005259498,0.000027307522,0.000005003042,0.001946562,0.9467675,0.031266883,0.0038103096,0.000019588262,0.00011710414],"study_design_scores_gemma":[0.009502724,0.004053788,0.89539254,0.0010247057,0.0010387923,0.00000290925,0.0051832977,0.00137822,0.08045504,0.0008990746,0.00048238874,0.0005865173],"about_ca_topic_score_codex":0.00009397636,"about_ca_topic_score_gemma":0.00006598918,"teacher_disagreement_score":0.9453893,"about_ca_system_score_codex":0.00007767191,"about_ca_system_score_gemma":0.000087934495,"threshold_uncertainty_score":0.5420063},"labels":[],"label_agreement":null},{"id":"W2038840139","doi":"10.3141/2064-09","title":"Forecasting Travel Supply and Demand by Modeling Multiscale Urban Environments","year":2008,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Université Laval","funders":"Ministère des Transports","keywords":"Metropolitan area; Transport engineering; Computer science; Population; Task (project management); Supply and demand; Scale (ratio); Traffic simulation; Simulation; Operations research; Engineering; Geography; Microsimulation; Economics","score_opus":0.11328754086651494,"score_gpt":0.3580074631772708,"score_spread":0.24471992231075584,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2038840139","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97839016,0.0005628468,0.017831024,0.0018596945,0.0002509537,0.00072933163,0.000084883424,0.00002267416,0.000268416],"genre_scores_gemma":[0.9905863,0.004169852,0.0031279484,0.000037962476,0.00016596078,0.000036546055,0.000046799254,0.000044408054,0.001784257],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99295276,0.0011947057,0.0011321255,0.00040588283,0.0034461154,0.000868395],"domain_scores_gemma":[0.99709874,0.0007481326,0.00034514905,0.00022970488,0.0010588603,0.00051942596],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0041669095,0.00021795064,0.00036747055,0.00056582893,0.002463458,0.00011171106,0.0006185055,0.00023493801,0.00014538552],"category_scores_gemma":[0.00020582846,0.00018584114,0.00020429255,0.0011290713,0.0011423932,0.0009117301,0.0000037935752,0.0014404863,0.000006099653],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00088520074,0.00034497734,0.8929898,0.00011494317,0.0001377355,0.00015126118,0.06603103,0.021653367,0.0025597396,0.0013488603,0.010291959,0.0034911039],"study_design_scores_gemma":[0.004059776,0.0005928487,0.9262348,0.0005656273,0.00011175529,0.0000028721026,0.021910798,0.014626153,0.0012061376,0.0013858704,0.028718323,0.0005850332],"about_ca_topic_score_codex":0.016125672,"about_ca_topic_score_gemma":0.022650845,"teacher_disagreement_score":0.04412023,"about_ca_system_score_codex":0.0001779555,"about_ca_system_score_gemma":0.00046877834,"threshold_uncertainty_score":0.9988352},"labels":[],"label_agreement":null},{"id":"W2039045033","doi":"10.1061/(asce)1076-0342(2007)13:1(43)","title":"System Risk Curves: Probabilistic Performance Scenarios for Highway Networks Subject to Earthquake Damage","year":2007,"lang":"en","type":"article","venue":"Journal of Infrastructure Systems","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":116,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Engineering Research Centers; U.S. Department of Transportation; National Science Foundation","keywords":"Bridge (graph theory); Fragility; Seismic hazard; Computer science; Probabilistic logic; Monte Carlo method; Earthquake scenario; Transport engineering; Engineering; Civil engineering; Forensic engineering; Statistics; Mathematics","score_opus":0.008486943629711308,"score_gpt":0.25500932354478506,"score_spread":0.24652237991507375,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2039045033","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.70331794,0.0006129925,0.28952798,0.0001142571,0.004083547,0.0011444918,0.000037445086,0.000087124434,0.0010742395],"genre_scores_gemma":[0.9957067,0.00008548335,0.0026836232,0.000055739107,0.0012683341,0.0000075679327,0.000010601085,0.00001791788,0.00016402731],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9979504,0.0001710596,0.0008386541,0.00014812557,0.0005352579,0.00035651796],"domain_scores_gemma":[0.9976492,0.0002846504,0.0008854043,0.00013303825,0.00079298817,0.0002547502],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036228974,0.00014626025,0.00034304746,0.00019172335,0.00046122476,0.000108767184,0.00026429055,0.00015979029,0.0000085432175],"category_scores_gemma":[0.0003586796,0.00011786045,0.00010939906,0.00045095696,0.00005515594,0.0002761794,0.0000047873978,0.00027710755,0.0000023544394],"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":[0.00016182497,0.000010534706,0.04660643,0.00050603703,0.00004399906,0.000009522257,0.0043230387,0.9418176,0.000008890879,0.001004552,0.004318473,0.0011891075],"study_design_scores_gemma":[0.0027835737,0.0012876178,0.81368065,0.009014861,0.00043857173,0.00016289343,0.023917744,0.07981377,0.000040407558,0.000051901217,0.06781582,0.0009921817],"about_ca_topic_score_codex":0.00015546568,"about_ca_topic_score_gemma":0.00027305304,"teacher_disagreement_score":0.8620038,"about_ca_system_score_codex":0.00021115494,"about_ca_system_score_gemma":0.00023580955,"threshold_uncertainty_score":0.48062095},"labels":[],"label_agreement":null},{"id":"W2039291293","doi":"10.7202/051054ar","title":"Analyse de l'activité et coopération des acteurs de la conception","year":2005,"lang":"fr","type":"article","venue":"Relations industrielles","topic":"Transportation Planning and Optimization","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":"Humanities; Political science; Philosophy","score_opus":0.03343823418228802,"score_gpt":0.3190498285730558,"score_spread":0.2856115943907678,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2039291293","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8199728,0.0015874511,0.1272449,0.009051091,0.00021300754,0.00019857135,0.00005144599,0.00015952115,0.04152121],"genre_scores_gemma":[0.9629672,0.0014517478,0.011135066,0.00018186819,0.00038950876,0.000014146464,0.00015263313,0.00001722596,0.023690566],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99811643,0.0009065816,0.00028102816,0.0001916717,0.00021168959,0.00029260613],"domain_scores_gemma":[0.9988183,0.0006182309,0.00018354565,0.00010496083,0.00012984482,0.00014511112],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010684206,0.00013512609,0.00012211442,0.00013841878,0.00070997013,0.00013926822,0.0000946283,0.0006173876,0.0007008657],"category_scores_gemma":[0.0003630055,0.00016581625,0.000070241884,0.00089214277,0.00058449147,0.00093264563,0.0000050953713,0.000490735,0.00009907808],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013853396,0.00018310113,0.16761458,0.0000030268143,0.000055586595,0.0000033701401,0.03629379,0.6389319,0.00018598724,0.069355786,0.0056085144,0.081750505],"study_design_scores_gemma":[0.0009332335,0.00007423468,0.20864965,0.0002487142,0.00040672152,0.00001436527,0.018211642,0.055123296,0.0009877282,0.0024297768,0.71240634,0.00051429984],"about_ca_topic_score_codex":0.0011022529,"about_ca_topic_score_gemma":0.0014357874,"teacher_disagreement_score":0.70679784,"about_ca_system_score_codex":0.00037636276,"about_ca_system_score_gemma":0.00078574196,"threshold_uncertainty_score":0.7673988},"labels":[],"label_agreement":null},{"id":"W2041121043","doi":"10.1002/atr.5670400205","title":"Drivers' route choice and learning mechanism under bounded information environment","year":2006,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","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":"","keywords":"Order (exchange); Bounded function; Bounded rationality; Mechanism (biology); Rational choice theory (criminology); Rational planning model; Computer science; Term (time); Rational analysis; Psychology; Mathematics; Artificial intelligence; Economics; Cognition; Epistemology","score_opus":0.00528626760456735,"score_gpt":0.22806929008765842,"score_spread":0.22278302248309106,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2041121043","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8474822,0.00006505055,0.15113278,0.0004613143,0.00020521249,0.00011195906,0.000005677814,0.00003174225,0.00050408026],"genre_scores_gemma":[0.9846849,0.00028520942,0.014637208,0.000052811047,0.00007954005,0.0000016805349,0.00009430359,0.000005542064,0.00015880915],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9990215,0.000051419727,0.00038314113,0.000065152024,0.0003606274,0.000118181735],"domain_scores_gemma":[0.9992977,0.00006577566,0.00043472214,0.000029532484,0.00011456332,0.000057710553],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029412858,0.00007417859,0.000108722095,0.00011726804,0.00031927408,0.00004943238,0.000048096466,0.000065717235,0.000029568655],"category_scores_gemma":[0.000019490095,0.00007723171,0.00004292125,0.000106315696,0.000047513546,0.0016345525,4.5160792e-7,0.00016157846,0.0000025058082],"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":[0.000049097653,0.00003255111,0.017889118,0.000015055546,0.000018610324,0.00000332233,0.016027058,0.9189189,0.00056258537,0.042521108,0.000021043576,0.003941548],"study_design_scores_gemma":[0.001827011,0.00014014101,0.9325179,0.000058149904,0.00009749479,0.0000020172124,0.0125970915,0.00023521131,0.00023685429,0.009625671,0.042460073,0.00020240332],"about_ca_topic_score_codex":0.00023602732,"about_ca_topic_score_gemma":0.00044312654,"teacher_disagreement_score":0.9186837,"about_ca_system_score_codex":0.000072645,"about_ca_system_score_gemma":0.00005722683,"threshold_uncertainty_score":0.31494176},"labels":[],"label_agreement":null},{"id":"W2041197059","doi":"10.3917/reru.134.0737","title":"La variété des coûts de congestion routière. Le cas du boulevard Périphérique parisien","year":2014,"lang":"fr","type":"article","venue":"Revue d’Économie Régionale & Urbaine","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ministère des Transports","funders":"","keywords":"Humanities; Physics; Art","score_opus":0.014797013003834188,"score_gpt":0.24695117645101927,"score_spread":0.2321541634471851,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2041197059","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7323965,0.006378607,0.11718265,0.06747535,0.002352355,0.00087705883,0.00024471403,0.00047509678,0.07261769],"genre_scores_gemma":[0.9688013,0.0015474382,0.0062402072,0.0006244015,0.0012508844,0.00004909974,0.00029996922,0.00004873339,0.021137942],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9974401,0.0007552248,0.00056571316,0.0004993942,0.0001754015,0.000564179],"domain_scores_gemma":[0.9978374,0.0010083712,0.0003313971,0.00025138777,0.0002274838,0.0003439164],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001703132,0.00029268782,0.00042195362,0.00011576555,0.00060852314,0.00014718535,0.00027495783,0.00050048885,0.0002405623],"category_scores_gemma":[0.0005414749,0.0003665833,0.00018594843,0.00033594825,0.00069372496,0.00052412297,0.00002191696,0.0003397068,0.0001767205],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011494136,0.00040404013,0.10621826,0.00024605126,0.0001404785,0.000054589345,0.024555575,0.13489576,0.000050181843,0.7024919,0.0059155286,0.02491271],"study_design_scores_gemma":[0.0015392659,0.00016826886,0.23478207,0.00047036077,0.0001852909,0.000102681806,0.0009706562,0.052695096,0.0000527495,0.017506456,0.6908293,0.0006978273],"about_ca_topic_score_codex":0.007869219,"about_ca_topic_score_gemma":0.0060612033,"teacher_disagreement_score":0.68498546,"about_ca_system_score_codex":0.00066761905,"about_ca_system_score_gemma":0.00064486975,"threshold_uncertainty_score":0.9998786},"labels":[],"label_agreement":null},{"id":"W2042089672","doi":"10.1016/j.trb.2005.05.006","title":"A frequency-based assignment model for congested transit networks with strict capacity constraints: characterization and computation of equilibria","year":2005,"lang":"en","type":"article","venue":"Transportation Research Part B Methodological","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":238,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computation; Transit (satellite); Mathematical optimization; Computer science; Generalization; Flow network; Computable general equilibrium; Function (biology); Mathematical economics; Public transport; Mathematics; Transport engineering; Economics; Algorithm; Engineering","score_opus":0.4047146639603632,"score_gpt":0.4536928578286799,"score_spread":0.0489781938683167,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2042089672","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.33279216,0.000014979968,0.66540366,0.00076435035,0.00002086807,0.00072714395,0.00016507422,0.000059232974,0.000052524003],"genre_scores_gemma":[0.8148425,0.00003889937,0.18405901,0.000062488325,0.00004704699,0.0001186756,0.0007910208,0.000011077823,0.000029271505],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99685836,0.0013242103,0.00048155556,0.0003490447,0.00062253367,0.00036430178],"domain_scores_gemma":[0.99724275,0.0016620953,0.00018434893,0.000083342195,0.0006566856,0.00017078633],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004816552,0.00013521797,0.00028908552,0.00015583375,0.00035691273,0.000044480366,0.0001004396,0.00021428832,0.000058994818],"category_scores_gemma":[0.0002594921,0.00011967524,0.000054368888,0.00043663953,0.00085482263,0.00027018544,9.049604e-7,0.0002177692,4.1805498e-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":[0.0008331638,0.00020592923,0.015177348,0.000095251555,0.000046945028,0.0000023766947,0.0072778054,0.9360076,0.00497156,0.025017526,0.000030769985,0.01033368],"study_design_scores_gemma":[0.0021450166,0.00039180904,0.11133281,0.00011948648,0.000077594064,3.0607566e-7,0.00083760556,0.8831867,0.00067792216,0.00071395375,0.0002578192,0.0002589598],"about_ca_topic_score_codex":0.00008965427,"about_ca_topic_score_gemma":0.0007689675,"teacher_disagreement_score":0.48205033,"about_ca_system_score_codex":0.000052524458,"about_ca_system_score_gemma":0.0002663823,"threshold_uncertainty_score":0.48802146},"labels":[],"label_agreement":null},{"id":"W2042169728","doi":"10.1007/s11116-006-0012-6","title":"Special issue on activity-travel decision processes","year":2006,"lang":"en","type":"article","venue":"Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Wilfrid Laurier University","funders":"","keywords":"General partnership; Dissemination; State (computer science); Operations research; Travel behavior; Transport engineering; Research council; Engineering; Political science; Public relations; Government (linguistics); Computer science; Telecommunications; Law","score_opus":0.013850923109743778,"score_gpt":0.28623323005135193,"score_spread":0.27238230694160814,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2042169728","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.76913106,0.000024860043,0.031236894,0.0010867445,0.0013930061,0.0005280736,0.000093449096,0.00034435207,0.19616157],"genre_scores_gemma":[0.991067,0.00004764471,0.0014359617,0.000068869784,0.004404728,0.000014382452,0.0002871088,0.000014658855,0.0026596598],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99890167,0.000031664964,0.00019767192,0.000217329,0.00046137016,0.00019029113],"domain_scores_gemma":[0.9995145,0.0001240666,0.000092274844,0.00007410925,0.00014200101,0.000053042277],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017314183,0.00010391183,0.000107395645,0.00010555778,0.00037150356,0.000050243823,0.00008573553,0.000105385785,0.0005914364],"category_scores_gemma":[0.000032360087,0.00010927786,0.000038921233,0.00043282946,0.00006305605,0.00032118204,2.212163e-7,0.00008465762,0.00009869828],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00244002,0.0021556767,0.1435003,0.00033354425,0.000060191844,0.00011606398,0.09232853,0.19928555,0.0011740471,0.17646195,0.11521398,0.26693013],"study_design_scores_gemma":[0.0007137494,0.00007765661,0.77151895,0.000080175814,0.000036577716,1.4237114e-7,0.001074708,0.000051813222,0.0017469538,0.002041771,0.2223665,0.0002910128],"about_ca_topic_score_codex":0.0015632419,"about_ca_topic_score_gemma":0.018250339,"teacher_disagreement_score":0.6280186,"about_ca_system_score_codex":0.000038432932,"about_ca_system_score_gemma":0.00013379185,"threshold_uncertainty_score":0.999664},"labels":[],"label_agreement":null},{"id":"W2042550318","doi":"10.5038/2375-0901.7.3.4","title":"Impacts of Various Parameters on Transit Signal Priority Effectiveness","year":2004,"lang":"en","type":"article","venue":"Journal of Public Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":80,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; University of British Columbia","keywords":"VisSim; Headway; Transit (satellite); SIGNAL (programming language); Bus priority; Traffic volume; Traffic simulation; Volume (thermodynamics); Traffic signal; Computer science; Detector; Simulation; Simulation software; Transport engineering; Software; Real-time computing; Automotive engineering; Engineering; Microsimulation; Public transport; Telecommunications; Operating system; Physics","score_opus":0.022669387919213437,"score_gpt":0.2905317192500215,"score_spread":0.2678623313308081,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2042550318","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.89601386,0.0000465175,0.102718554,0.00062399765,0.00023610082,0.00015132732,0.000019672103,0.000023310367,0.00016666281],"genre_scores_gemma":[0.9969772,0.000064983804,0.002823485,0.000037960694,0.00005222519,0.000001833576,0.000028833814,0.000009679157,0.000003786633],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99830914,0.00019531407,0.00053490093,0.0001086851,0.0006581385,0.00019385139],"domain_scores_gemma":[0.99859667,0.0001848781,0.0005117561,0.00006220832,0.00047330477,0.00017120248],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015051165,0.00010294409,0.0002447818,0.0002714444,0.0001410886,0.000039731578,0.00014056795,0.000118912656,0.00002691551],"category_scores_gemma":[0.00008577133,0.000096078715,0.00016367722,0.00046205556,0.00010810456,0.00064657314,2.2568892e-7,0.00019297547,0.0000012649962],"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":[0.0013899888,0.0015268287,0.22759847,0.00029340922,0.00037823844,0.0001328097,0.09511756,0.5624546,0.0059439563,0.08275427,0.000034256253,0.022375602],"study_design_scores_gemma":[0.0025283017,0.0006429068,0.98847884,0.00022658102,0.000111427486,0.0000029129562,0.0013087153,0.000014502841,0.0037426264,0.0024930676,0.00029333294,0.00015675793],"about_ca_topic_score_codex":0.0007955202,"about_ca_topic_score_gemma":0.0016959264,"teacher_disagreement_score":0.7608804,"about_ca_system_score_codex":0.0001253424,"about_ca_system_score_gemma":0.00063273084,"threshold_uncertainty_score":0.39179763},"labels":[],"label_agreement":null},{"id":"W2042557607","doi":"10.1016/j.ejor.2008.08.015","title":"Location with acceleration–deceleration distance","year":2008,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":22,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Acceleration; Heuristic; Point (geometry); Mathematical optimization; Travel time; Mathematics; Zero (linguistics); Computer science; Algorithm; Geometry; Physics","score_opus":0.1353533354940566,"score_gpt":0.38323387947957693,"score_spread":0.24788054398552034,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2042557607","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.46660647,0.0005236702,0.4222776,0.011487915,0.00036439628,0.00046705749,0.000006288267,0.0000576102,0.09820901],"genre_scores_gemma":[0.9894214,0.00022297914,0.0069127646,0.00009637794,0.0005273589,0.0000022802858,0.00002427886,0.000013237984,0.0027793078],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99687165,0.0010739067,0.00035404714,0.00011777604,0.0014026801,0.00017993049],"domain_scores_gemma":[0.99615675,0.00016913089,0.00013539151,0.000081240105,0.0033280742,0.00012942917],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.003577602,0.000063616746,0.00008662249,0.00020895226,0.0014189713,0.0001642506,0.00020733406,0.000021053937,0.00018278831],"category_scores_gemma":[0.00047600025,0.00005286849,0.000024855251,0.0006366614,0.00028592814,0.0008651714,0.000005268822,0.00028037754,0.000056200803],"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":[0.0015395887,0.00082216103,0.07199116,0.000056169494,0.00015651455,0.0010693143,0.12378077,0.4753983,0.0021374659,0.24609658,0.065850124,0.01110185],"study_design_scores_gemma":[0.003978183,0.0017673003,0.5257623,0.00047427806,0.000030484147,0.0002682946,0.009316118,0.003919577,0.0011318249,0.0003780258,0.45232,0.00065360114],"about_ca_topic_score_codex":0.000029295667,"about_ca_topic_score_gemma":0.00010029846,"teacher_disagreement_score":0.5228149,"about_ca_system_score_codex":0.00010183246,"about_ca_system_score_gemma":0.0009928438,"threshold_uncertainty_score":0.999881},"labels":[],"label_agreement":null},{"id":"W2042776763","doi":"10.1002/atr.5670350207","title":"A schedule‐based dynamic transit network model — Recent advances and prospective future research","year":2001,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":33,"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":"Headway; Schedule; Transit (satellite); Computer science; Operations research; Path (computing); Transport engineering; Public transport; Travel time; Simulation; Mathematical optimization; Engineering; Mathematics","score_opus":0.02237585426833159,"score_gpt":0.3496214484000707,"score_spread":0.3272455941317391,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2042776763","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.67555773,0.014046377,0.29971153,0.0076858103,0.0009369411,0.00083253504,0.000028516528,0.00010451191,0.0010960674],"genre_scores_gemma":[0.92744166,0.023639,0.04836303,0.0000767443,0.0003398975,0.000011238955,0.00003800966,0.000017985014,0.00007245221],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9982092,0.00012457723,0.00045477282,0.00019211083,0.0007115757,0.00030779245],"domain_scores_gemma":[0.99844205,0.00010058937,0.0002795495,0.0000712374,0.00095210294,0.00015443796],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009879762,0.00011636579,0.00020763092,0.00018555266,0.00049958436,0.00004166168,0.00011520541,0.00011260505,0.000027197731],"category_scores_gemma":[0.000039493454,0.00011165681,0.000065343476,0.0007965886,0.00015289552,0.00096578826,4.5099242e-7,0.0003731336,7.067849e-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":[0.00069422385,0.00006747493,0.0077897944,0.000025335044,0.000016149274,0.000023541474,0.013274539,0.95259124,0.000102401085,0.0017932354,0.000023236227,0.02359885],"study_design_scores_gemma":[0.0133304,0.0018003142,0.54528105,0.0013000291,0.0004490677,0.00002392327,0.10658389,0.04719871,0.00030159403,0.053340305,0.22895223,0.0014385057],"about_ca_topic_score_codex":0.000009080473,"about_ca_topic_score_gemma":0.0019388783,"teacher_disagreement_score":0.9053925,"about_ca_system_score_codex":0.00010706187,"about_ca_system_score_gemma":0.00031448336,"threshold_uncertainty_score":0.45532325},"labels":[],"label_agreement":null},{"id":"W2042833945","doi":"10.1002/atr.5670420308","title":"An analysis of instability in a departure time choice problem","year":2008,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Japan Society for the Promotion of Science","keywords":"Monotonic function; Instability; Schedule; Stability (learning theory); Function (biology); Reliability (semiconductor); Travel time; Mathematics; Computer science; Mathematical optimization; Applied mathematics; Mathematical economics; Engineering; Thermodynamics; Mechanics; Physics; Mathematical analysis","score_opus":0.011432602024221236,"score_gpt":0.2919427233222304,"score_spread":0.2805101212980092,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2042833945","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9959711,0.00005426821,0.0035385878,0.00009850517,0.000045447898,0.00010296662,0.000019143852,0.000013316998,0.00015666064],"genre_scores_gemma":[0.9880173,0.000121729856,0.0116992565,0.00001672157,0.000025172872,0.0000018290258,0.00009188039,0.000004830231,0.000021276403],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.998709,0.000092461065,0.0006131259,0.000103229264,0.00037225147,0.0001099124],"domain_scores_gemma":[0.9989039,0.00009093991,0.0004935245,0.00007092046,0.0003650341,0.000075683616],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004355731,0.000066388726,0.00026774217,0.0003755426,0.00008502445,0.0000051883967,0.00010244507,0.00007159914,0.00005439699],"category_scores_gemma":[0.000048680657,0.000065411055,0.0001130101,0.0012326747,0.00007770677,0.00080849166,1.6942491e-7,0.000115038,3.397719e-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":[0.00009178523,0.000120048804,0.38281748,0.000007179739,0.00003850967,0.000007969119,0.02709579,0.5886152,0.00058964244,0.0001367123,0.0000021947658,0.00047751513],"study_design_scores_gemma":[0.00049570645,0.00008594376,0.9966404,0.000023842402,0.00016997872,3.7587907e-7,0.0015131145,0.00045279125,0.00010470039,0.000097420045,0.0003489657,0.00006675608],"about_ca_topic_score_codex":0.0003051332,"about_ca_topic_score_gemma":0.009560919,"teacher_disagreement_score":0.61382294,"about_ca_system_score_codex":0.000050327897,"about_ca_system_score_gemma":0.00016766465,"threshold_uncertainty_score":0.5335217},"labels":[],"label_agreement":null},{"id":"W2042874524","doi":"10.3141/2261-11","title":"Expanding Alaska–Canada Rail","year":2011,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Revenue; Resource (disambiguation); Investment (military); Commodity; Net present value; Index (typography); Mineral resource classification; Present value; Economic impact analysis; Cost estimate; Capital expenditure; Environmental economics; Transport engineering; Business; Environmental resource management; Computer science; Environmental science; Engineering; Finance; Economics; Civil engineering; Production (economics); Geology","score_opus":0.13723008993724695,"score_gpt":0.39231357222620566,"score_spread":0.2550834822889587,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2042874524","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98732424,0.0001954832,0.002260489,0.0030163454,0.0014606674,0.00079762615,0.00005904016,0.000053588406,0.004832532],"genre_scores_gemma":[0.993548,0.0008818766,0.00269151,0.00008678965,0.00027916217,0.000040030856,0.000019841676,0.00004655079,0.0024062898],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99028,0.0017963154,0.0013452916,0.0003801239,0.0051220297,0.0010762903],"domain_scores_gemma":[0.9933753,0.00096449914,0.0005370721,0.00037618732,0.0041670147,0.00057990255],"candidate_categories":["sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.006182035,0.0002246795,0.00038510837,0.00078647176,0.0018326008,0.00012852675,0.00130412,0.00021793201,0.0012600285],"category_scores_gemma":[0.0003574429,0.00018485023,0.00029039,0.0026621416,0.0009145387,0.00089963723,0.000003837897,0.0018173238,0.000017568665],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013620438,0.0003196449,0.8767728,0.00015217623,0.00020019402,0.00027817828,0.06588233,0.0014164111,0.00037942646,0.017277561,0.029515037,0.0064441673],"study_design_scores_gemma":[0.0010426722,0.00021634408,0.9178746,0.00027934797,0.000050065035,2.4767053e-7,0.02204606,0.000017806211,0.00085920823,0.0024475125,0.05492688,0.00023930802],"about_ca_topic_score_codex":0.6987812,"about_ca_topic_score_gemma":0.9543479,"teacher_disagreement_score":0.25556675,"about_ca_system_score_codex":0.0004731279,"about_ca_system_score_gemma":0.0036370726,"threshold_uncertainty_score":0.999653},"labels":[],"label_agreement":null},{"id":"W2043145318","doi":"10.1002/atr.5670370304","title":"Network equilibrium for congested multi‐mode networks with elastic demand","year":2003,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":21,"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":"Hong Kong Polytechnic University","keywords":"Variational inequality; Price elasticity of demand; Constraint (computer-aided design); Mode (computer interface); Mathematical optimization; Computer science; Mode choice; Transit (satellite); Logit; Public transport; Mathematics; Economics; Transport engineering; Engineering; Microeconomics","score_opus":0.01479621600579807,"score_gpt":0.28471632184018164,"score_spread":0.2699201058343836,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2043145318","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18370903,0.00033762574,0.8146507,0.000125447,0.00072320143,0.00030725668,0.000008455997,0.000033031374,0.00010526065],"genre_scores_gemma":[0.8808223,0.000097514974,0.11863228,0.000064712185,0.00016380018,0.000010019872,0.000053523912,0.000018464187,0.00013736627],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9988297,0.0000757286,0.00045070765,0.00012344806,0.0002647434,0.0002556363],"domain_scores_gemma":[0.99860984,0.00022317267,0.00048958935,0.000055004763,0.00050058065,0.00012182774],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044661402,0.000111276466,0.0002095096,0.00006509676,0.00020907777,0.00003068313,0.000077362514,0.00008942663,0.000014399484],"category_scores_gemma":[0.00007205778,0.00009872937,0.000072348754,0.00029177053,0.00007050207,0.0005221272,1.4506327e-7,0.00012880148,3.0544172e-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":[0.00039335585,0.000040312447,0.015161617,0.0000139158155,0.000031230342,0.0000075983744,0.0034733994,0.97780544,0.00006460672,0.0026474113,0.00007297894,0.000288119],"study_design_scores_gemma":[0.041199848,0.00411451,0.79731387,0.0020944565,0.0023802852,0.000044214998,0.026419692,0.03618004,0.0008630718,0.007698058,0.079150416,0.002541554],"about_ca_topic_score_codex":0.000009007077,"about_ca_topic_score_gemma":0.0008870417,"teacher_disagreement_score":0.9416254,"about_ca_system_score_codex":0.000035854733,"about_ca_system_score_gemma":0.00017639139,"threshold_uncertainty_score":0.40260667},"labels":[],"label_agreement":null},{"id":"W2043258378","doi":"10.3141/1831-19","title":"Transportation’s Influence on Land Use Development: Historical Spatial-Temporal Approach","year":2003,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Land use; Traffic congestion; Geographic information system; Spatial analysis; Transport engineering; Information system; Visualization; Environmental resource management; Computer science; Operations research; Geography; Data science; Cartography; Data mining; Environmental science; Civil engineering; Engineering","score_opus":0.12091042674703363,"score_gpt":0.3721223897913364,"score_spread":0.25121196304430277,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2043258378","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98378575,0.0001359301,0.011433543,0.0015632157,0.0007865778,0.0012414734,0.00006419532,0.000085458756,0.0009038586],"genre_scores_gemma":[0.98731625,0.0004840004,0.0087667685,0.00009732756,0.00016768779,0.000102218204,0.00010109565,0.00007279345,0.0028918542],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.98763543,0.0026676347,0.0019558962,0.00064722047,0.0058818646,0.0012119435],"domain_scores_gemma":[0.99214053,0.0014818913,0.00072788703,0.0004815728,0.004449538,0.0007185529],"candidate_categories":["metaepi_narrow","sts","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0069063087,0.000374959,0.0005807269,0.0013039645,0.0022112431,0.0003196004,0.0010571766,0.00040578257,0.0002863567],"category_scores_gemma":[0.0007467455,0.00031343495,0.00039127306,0.0029468897,0.000722015,0.0013659709,0.0000017118913,0.002366421,0.000033183765],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010806319,0.000693159,0.92797303,0.00015403563,0.0001402676,0.000102596576,0.026169388,0.021910572,0.00013051376,0.015121546,0.0043074526,0.0022168246],"study_design_scores_gemma":[0.0014311506,0.00027839234,0.8051579,0.00024303095,0.000048837934,2.775379e-7,0.0024058027,0.00003628244,0.0002888116,0.00076714007,0.18901831,0.00032405587],"about_ca_topic_score_codex":0.056725778,"about_ca_topic_score_gemma":0.20186602,"teacher_disagreement_score":0.18471086,"about_ca_system_score_codex":0.0010324654,"about_ca_system_score_gemma":0.0023603048,"threshold_uncertainty_score":0.99993515},"labels":[],"label_agreement":null},{"id":"W2044106100","doi":"10.1002/atr.5670390203","title":"Estimation of origin-destination matrices for a multimodal public transit network","year":2010,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Public transport; Transit (satellite); Modal; Mode (computer interface); Computer science; Multimodal transport; Entropy maximization; Flow network; Maximization; Trip distribution; Transport engineering; Entropy (arrow of time); Process (computing); Estimation; Set (abstract data type); Data mining; Principle of maximum entropy; Mathematical optimization; Mathematics; Engineering; Artificial intelligence; Physics","score_opus":0.015038487635458422,"score_gpt":0.3085768593815098,"score_spread":0.2935383717460514,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2044106100","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.67564636,0.00005371253,0.3227164,0.0004549451,0.00074023457,0.00025921772,0.000020781965,0.000028321287,0.000080014834],"genre_scores_gemma":[0.82008046,0.00007254651,0.1794805,0.0000149474945,0.0002105592,0.000008192371,0.00010332527,0.000011001001,0.000018474451],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.998586,0.000039063852,0.00067420176,0.00011036717,0.0004099144,0.00018045065],"domain_scores_gemma":[0.99792534,0.00023631261,0.0008659093,0.000062664716,0.00081530923,0.000094462026],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008155259,0.000095485695,0.00020496518,0.0001846488,0.00021722126,0.000030467569,0.0001286555,0.00011414077,0.000027748847],"category_scores_gemma":[0.00018335099,0.00009631505,0.00012543128,0.00043623202,0.00007845641,0.001102408,2.3703713e-7,0.00016491566,4.7443763e-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":[0.00024235217,0.00009212344,0.0073629827,0.00006311202,0.000026569904,0.0000018245365,0.010942281,0.9081155,0.0019783054,0.01926268,0.000040465555,0.051871784],"study_design_scores_gemma":[0.012438141,0.0013715602,0.864805,0.000637749,0.0009119194,0.000012858005,0.014932823,0.03955359,0.0036549973,0.027776232,0.032771613,0.0011335629],"about_ca_topic_score_codex":0.000044720506,"about_ca_topic_score_gemma":0.001313294,"teacher_disagreement_score":0.8685619,"about_ca_system_score_codex":0.00002577345,"about_ca_system_score_gemma":0.00024754432,"threshold_uncertainty_score":0.39276138},"labels":[],"label_agreement":null},{"id":"W2045625805","doi":"10.3141/2176-01","title":"Integrating an Activity-Based Travel Demand Model with Dynamic Traffic Assignment and Emission Models","year":2010,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":94,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Microsimulation; Agent-based model; Computer science; Sensitivity (control systems); Process (computing); Linkage (software); Traffic simulation; Traffic congestion; Travel behavior; Vehicle miles of travel; Simulation modeling; Transport engineering; Simulation; Operations research; Engineering; Economics; Artificial intelligence","score_opus":0.07153097176663609,"score_gpt":0.3911466279606541,"score_spread":0.319615656194018,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2045625805","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8960596,0.0000341292,0.09986413,0.0028029794,0.0001736677,0.0007995399,0.00004468828,0.000042901425,0.00017836309],"genre_scores_gemma":[0.9837943,0.000247785,0.01529853,0.00003572264,0.000076186225,0.000053553136,0.00003147386,0.000052503263,0.000409987],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9930768,0.0011995682,0.0008300603,0.0004806512,0.0036277678,0.0007851285],"domain_scores_gemma":[0.9953748,0.0007328719,0.00043846626,0.00030904426,0.0024815132,0.00066330476],"candidate_categories":["sts","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0060360697,0.00026167388,0.00038310114,0.000800039,0.0017741845,0.0002787271,0.0006598768,0.00030228082,0.00007602726],"category_scores_gemma":[0.00011595394,0.00019071787,0.00016279073,0.0013399499,0.0010886743,0.0014806943,0.0000022340505,0.0026800586,0.000001194686],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00437707,0.0010493699,0.06940236,0.00030133754,0.00014777639,0.00010165395,0.062785335,0.79261124,0.02971883,0.0108842645,0.00043217212,0.028188575],"study_design_scores_gemma":[0.004043196,0.0013795386,0.451534,0.00072953996,0.0001572787,9.724855e-7,0.016237294,0.5178648,0.001828618,0.004758988,0.0008337437,0.0006320035],"about_ca_topic_score_codex":0.004928083,"about_ca_topic_score_gemma":0.1787125,"teacher_disagreement_score":0.38213164,"about_ca_system_score_codex":0.00016032827,"about_ca_system_score_gemma":0.0017326721,"threshold_uncertainty_score":0.9996208},"labels":[],"label_agreement":null},{"id":"W2046076181","doi":"10.1287/inte.1070.0322","title":"Optimizing Highway Transportation at the United States Postal Service","year":2007,"lang":"en","type":"article","venue":"INFORMS Journal on Applied Analytics","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Postal service; Service (business); Transport engineering; Business; Finance; Engineering; Marketing","score_opus":0.019345960917214966,"score_gpt":0.2791708987249374,"score_spread":0.2598249378077224,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2046076181","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9046055,0.000030465939,0.0575869,0.004723721,0.0004392151,0.00030779128,0.00004503326,0.00018571346,0.032075662],"genre_scores_gemma":[0.99303263,0.00024684888,0.0015174755,0.0036313909,0.00019012438,0.0000016074446,0.00048036338,0.00001684162,0.0008827007],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99843574,0.000015941952,0.00047604015,0.00010192325,0.00063033035,0.00034001825],"domain_scores_gemma":[0.9989095,0.00019973694,0.00032378014,0.000101788566,0.00027853416,0.0001867075],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012244096,0.0001285124,0.000121047255,0.00023706123,0.00116649,0.00017032833,0.00020002661,0.000106420244,0.00012938639],"category_scores_gemma":[0.000020110734,0.000088889195,0.00006675732,0.0009191602,0.000092471164,0.00020691504,0.000002977166,0.00032843126,0.00004966971],"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":[0.00028900141,0.00004080137,0.0013181574,0.000009521165,0.00006834588,0.000022272226,0.05017513,0.9192377,0.000049936953,0.02526229,0.0012786094,0.0022482611],"study_design_scores_gemma":[0.0034962925,0.00024535015,0.038986605,0.00016448692,0.0003611101,0.000021242366,0.121067815,0.018899025,0.0016384257,0.0028562436,0.8110922,0.0011712058],"about_ca_topic_score_codex":0.0002691842,"about_ca_topic_score_gemma":0.0026810102,"teacher_disagreement_score":0.90033865,"about_ca_system_score_codex":0.00017513285,"about_ca_system_score_gemma":0.00012000685,"threshold_uncertainty_score":0.89718163},"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,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_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","score_opus":0.025923756968370622,"score_gpt":0.28853186610072895,"score_spread":0.2626081091323583,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2046533071","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13678472,0.00086733856,0.8595464,0.0005807543,0.0005652359,0.0010847728,0.000078739205,0.00008127018,0.0004107547],"genre_scores_gemma":[0.8286498,0.0015400437,0.16930664,0.000050873925,0.00010078293,0.00003896677,0.00014546048,0.000020781354,0.00014662843],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99871355,0.000036265013,0.00056137523,0.00016961787,0.00033636193,0.00018284],"domain_scores_gemma":[0.99883884,0.00009452005,0.00051292515,0.0000491955,0.00040660283,0.00009792023],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032154872,0.0001224642,0.00018631297,0.0002289123,0.00030841405,0.00003823653,0.00007455768,0.000086404376,0.00003655031],"category_scores_gemma":[0.000025630423,0.00012713164,0.00008034329,0.00027821423,0.00006204849,0.00092253176,2.293546e-7,0.000094836556,4.4092795e-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":[0.00011832286,0.00006742373,0.0029999658,0.00009917946,0.00004512248,0.000006931113,0.04373552,0.93989915,0.000055244356,0.001997766,0.000067136716,0.01090826],"study_design_scores_gemma":[0.021189231,0.0016725146,0.7897793,0.0021785982,0.0014731925,0.000010058558,0.06863354,0.06125533,0.00057141215,0.005339973,0.04618178,0.001715086],"about_ca_topic_score_codex":0.000014075285,"about_ca_topic_score_gemma":0.00013400406,"teacher_disagreement_score":0.8786438,"about_ca_system_score_codex":0.00005211353,"about_ca_system_score_gemma":0.000021593178,"threshold_uncertainty_score":0.5184278},"labels":[],"label_agreement":null},{"id":"W2047255134","doi":"10.3141/2111-04","title":"Assessing a Model for Optimal Bus Stop Spacing with High-Resolution Archived Stop-Level Data","year":2009,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"McGill University","keywords":"Metropolitan area; Transit (satellite); Public transport; Transport engineering; Operating cost; Service (business); Process (computing); Computer science; Engineering; Business; Geography; Operating system","score_opus":0.27512821091345435,"score_gpt":0.4509045113488333,"score_spread":0.17577630043537895,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2047255134","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6326645,0.00008114586,0.3580033,0.007103843,0.00025514612,0.0013586344,0.000291706,0.00006285664,0.00017886981],"genre_scores_gemma":[0.90682423,0.00038275978,0.09109266,0.000076554665,0.00029453516,0.0000452941,0.00030698066,0.00005011062,0.00092687784],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99132097,0.0012172033,0.0012927572,0.0006642418,0.004291431,0.0012133758],"domain_scores_gemma":[0.9926041,0.0012810489,0.0006833474,0.00068866584,0.004245254,0.0004976111],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.008208708,0.00029579032,0.0004930187,0.0010050666,0.002394485,0.000514918,0.0017186321,0.00024608758,0.00005502106],"category_scores_gemma":[0.00042824793,0.00023493318,0.00023064218,0.0015910183,0.0008095754,0.0027199367,0.0000057125794,0.0016998905,0.0000036587733],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.007571222,0.0009811362,0.048217084,0.00031323402,0.00034452666,0.00013221931,0.06967387,0.7966782,0.0030577455,0.036476113,0.0076984996,0.028856168],"study_design_scores_gemma":[0.0048434483,0.0011832889,0.891279,0.0010071653,0.00024885402,8.9889465e-7,0.019233095,0.0685684,0.00033093078,0.0077754543,0.0049262927,0.0006031758],"about_ca_topic_score_codex":0.0088527445,"about_ca_topic_score_gemma":0.06917148,"teacher_disagreement_score":0.8430619,"about_ca_system_score_codex":0.00030851449,"about_ca_system_score_gemma":0.0024305268,"threshold_uncertainty_score":0.9989043},"labels":[],"label_agreement":null},{"id":"W2047959283","doi":"10.1007/s11081-013-9227-5","title":"A surrogate-based multiobjective metaheuristic and network degradation simulation model for robust toll pricing","year":2013,"lang":"en","type":"article","venue":"Optimization and Engineering","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":34,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Mathematical optimization; Computer science; Pareto principle; Metaheuristic; Multi-objective optimization; Toll; Network planning and design; Heuristic; Mathematics","score_opus":0.020200121759238288,"score_gpt":0.24345002852494832,"score_spread":0.22324990676571002,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2047959283","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009144485,0.00005045386,0.9899536,0.0001237219,0.00006932141,0.00043314695,0.0000040208683,0.00012702002,0.000094236944],"genre_scores_gemma":[0.67114645,0.000029847053,0.32850024,0.000032819586,0.000036852798,0.00004720896,0.00007931383,0.000012375564,0.00011491232],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994276,0.00002020111,0.00014982828,0.00015218875,0.000094399584,0.00015574641],"domain_scores_gemma":[0.99948597,0.00019909673,0.00005862812,0.000039990973,0.00014613762,0.000070151036],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019415637,0.00008701725,0.000098647826,0.00007641914,0.00029804866,0.00011002027,0.000024372936,0.00006900083,0.000012563349],"category_scores_gemma":[0.00015117227,0.000095852025,0.0000174918,0.00016089891,0.0000203578,0.00035353715,0.0000026394173,0.000040627747,4.1343276e-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":[0.0000057574644,0.0000055947,0.0007392905,0.00001939487,0.0000062178133,4.7239773e-8,0.0011431689,0.996949,0.000007182487,0.0005864279,0.000014946285,0.00052296475],"study_design_scores_gemma":[0.00035738095,0.000009517281,0.0016182563,0.00002551552,0.000024227142,6.544152e-8,0.000151698,0.99762297,0.0000035073128,0.000025682966,0.000049908296,0.000111276175],"about_ca_topic_score_codex":0.00010576393,"about_ca_topic_score_gemma":0.000044436587,"teacher_disagreement_score":0.66200197,"about_ca_system_score_codex":0.000030256313,"about_ca_system_score_gemma":0.000029190149,"threshold_uncertainty_score":0.39087322},"labels":[],"label_agreement":null},{"id":"W2048675050","doi":"10.1016/j.tra.2010.03.020","title":"Integrated modeling of urban hierarchy and transportation network planning","year":2010,"lang":"en","type":"article","venue":"Transportation Research Part A Policy and Practice","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":56,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Fundação para a Ciência e a Tecnologia","keywords":"Hierarchy; Urban hierarchy; Transport engineering; Urban planning; Context (archaeology); Transportation planning; Computer science; Class (philosophy); Population; Land-use planning; Flow network; Operations research; Network planning and design; Regional planning; Environmental planning; Management science; Geography; Land use; Engineering; Civil engineering; Telecommunications; Artificial intelligence; Political science; Sociology; Mathematics","score_opus":0.110720629814042,"score_gpt":0.43687397478000756,"score_spread":0.32615334496596554,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2048675050","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96368194,0.00050304376,0.015328359,0.00706742,0.00022315253,0.0007283993,0.00016012821,0.00015745041,0.012150129],"genre_scores_gemma":[0.9916514,0.0007419439,0.0065761977,0.00013353088,0.00030887086,0.000029045745,0.00027466455,0.000015895417,0.00026842437],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99790275,0.00044161774,0.00040610036,0.00026859614,0.0005867787,0.0003941358],"domain_scores_gemma":[0.9974009,0.0015337955,0.00014033008,0.000109546185,0.00059094926,0.0002244699],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031851458,0.000115460265,0.00017460433,0.00026813414,0.0007450757,0.00008594657,0.000099316305,0.00017858605,0.00005100466],"category_scores_gemma":[0.0010106756,0.000121001,0.000030151205,0.0009015441,0.00041615166,0.00087117107,0.0000011764383,0.0006062313,0.0000016377766],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013742613,0.00017350777,0.05908102,0.00018298569,0.000107545726,0.00003231304,0.22871597,0.073702216,0.0008521635,0.62891346,0.0014809935,0.005383549],"study_design_scores_gemma":[0.0029597858,0.00047589242,0.13833505,0.00053985533,0.0002843766,0.000006024367,0.059744764,0.04438596,0.00016454722,0.014605916,0.7375125,0.0009853331],"about_ca_topic_score_codex":0.011900141,"about_ca_topic_score_gemma":0.007744075,"teacher_disagreement_score":0.73603153,"about_ca_system_score_codex":0.000013784974,"about_ca_system_score_gemma":0.0004221294,"threshold_uncertainty_score":0.9946797},"labels":[],"label_agreement":null},{"id":"W2049362623","doi":"10.1002/atr.5670380205","title":"The development and implementation of the operation system and data bank for the intelligent transportation system — sitcuo","year":2004,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Global Positioning System; Computer science; Relational database management system; Database; The Internet; Information system; Java; Transport engineering; Relational database; World Wide Web; Telecommunications; Operating system; Engineering","score_opus":0.025193708132248808,"score_gpt":0.32388924557798265,"score_spread":0.29869553744573385,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2049362623","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7299184,0.000879654,0.26587948,0.0014900798,0.00067302823,0.0010364263,0.00008799101,0.000020982421,0.000013907695],"genre_scores_gemma":[0.9911096,0.000375863,0.008302349,0.000014591399,0.0000653078,0.000014423,0.00010046142,0.000009018839,0.000008375424],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9985318,0.000057931553,0.0007555747,0.00013399147,0.00039870958,0.00012196075],"domain_scores_gemma":[0.9986092,0.00021162539,0.0006724554,0.00013490402,0.00032825183,0.00004359883],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011778498,0.000093740855,0.00013507334,0.000042706695,0.0009187365,0.000058939073,0.00022761954,0.000047036152,9.125168e-7],"category_scores_gemma":[0.000022841486,0.000054301734,0.000042748994,0.00016341794,0.000100178324,0.00051100116,0.0000014125554,0.00008550384,9.6684566e-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":[0.0005573572,0.000053283205,0.014592281,0.00066106586,0.00027652475,0.0000030708136,0.25153834,0.48582184,0.0009673971,0.11727599,0.00003897179,0.12821388],"study_design_scores_gemma":[0.0026763834,0.0001516888,0.62773174,0.0005465733,0.00048056443,0.000005065316,0.35486987,0.00084884086,0.0046868534,0.00027434315,0.007517551,0.00021051131],"about_ca_topic_score_codex":0.00018473835,"about_ca_topic_score_gemma":0.011878076,"teacher_disagreement_score":0.61313945,"about_ca_system_score_codex":0.0000962415,"about_ca_system_score_gemma":0.00028600072,"threshold_uncertainty_score":0.70662713},"labels":[],"label_agreement":null},{"id":"W2049647290","doi":"","title":"Development of a Dynamic Transit Signal Priority Strategy","year":2009,"lang":"en","type":"article","venue":"2009 ANNUAL CONFERENCE AND EXHIBITION OF THE TRANSPORTATION ASSOCIATION OF CANADA - TRANSPORTATION IN A CLIMATE OF CHANGE","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":83,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"VisSim; Transit (satellite); Intersection (aeronautics); Signal timing; Computer science; SIGNAL (programming language); Traffic simulation; Intelligent transportation system; Real-time computing; Simulation; Transport engineering; Engineering; Public transport; Traffic signal","score_opus":0.02424437713696235,"score_gpt":0.26595693777228496,"score_spread":0.24171256063532262,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2049647290","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99610096,0.00006718371,0.00069343345,0.0009154802,0.00010427469,0.00044141096,0.001225311,0.000015838115,0.00043611255],"genre_scores_gemma":[0.998365,0.0002467884,0.00089003914,0.000041951575,0.000009985213,0.000012880715,0.00038526437,0.0000071386376,0.000040991123],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.9974179,0.00010698249,0.0011670087,0.00019034153,0.0008745101,0.00024324324],"domain_scores_gemma":[0.9977271,0.00009306478,0.0012002859,0.00009031498,0.00081818993,0.00007103012],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00068296917,0.00014929792,0.0003925408,0.00016720974,0.00013379364,0.0000075002263,0.00014071364,0.0001657126,0.00008054872],"category_scores_gemma":[0.000026069774,0.00015482817,0.00008584563,0.00051428005,0.0000970428,0.00039359537,5.7518497e-7,0.000120876946,8.4131656e-8],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002619161,0.0013078306,0.24235122,0.0020673308,0.00026853482,0.000007384518,0.612242,0.014014821,0.012489793,0.07507584,0.00021961652,0.037336484],"study_design_scores_gemma":[0.0013062526,0.00009410651,0.9830105,0.00032345907,0.00007059656,3.5906442e-8,0.011528563,0.00017645178,0.0027685862,0.00037357328,0.00018365472,0.00016425685],"about_ca_topic_score_codex":0.02570208,"about_ca_topic_score_gemma":0.7507268,"teacher_disagreement_score":0.74065924,"about_ca_system_score_codex":0.000117611766,"about_ca_system_score_gemma":0.00095414335,"threshold_uncertainty_score":0.98078585},"labels":[],"label_agreement":null},{"id":"W2049920895","doi":"10.4028/www.scientific.net/amm.409-410.1366","title":"Study on Trip Generation of Residential Quarters in Suzhou","year":2013,"lang":"en","type":"article","venue":"Applied Mechanics and Materials","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"TRIPS architecture; Quarter (Canadian coin); China; Transport engineering; Trip generation; Sustainable development; Sustainable transport; Engineering; Geography; Sustainability; Political science","score_opus":0.03295039514734275,"score_gpt":0.280024362376842,"score_spread":0.24707396722949923,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2049920895","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9959941,0.00000264706,0.0027708488,0.00008078834,0.00019996085,0.00047836872,0.000004533683,0.000014958725,0.00045377508],"genre_scores_gemma":[0.9993272,0.000018046292,0.0004677913,0.000031301646,0.000049223574,0.00004908584,0.000017512872,0.000004239583,0.00003561132],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994654,0.00005003196,0.00018155196,0.000103779836,0.0001205133,0.00007869127],"domain_scores_gemma":[0.9998204,0.000016673723,0.00006832655,0.000048940405,0.000022833721,0.00002280997],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039409843,0.00004392837,0.00009942199,0.000057234804,0.00006670551,0.000046747737,0.000035296962,0.00004315971,0.0000888709],"category_scores_gemma":[0.000009103737,0.00004265047,0.000005827266,0.000066643894,0.0000068765885,0.000055639164,0.000002671405,0.000016486467,0.0000072276102],"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":[0.00006192217,0.00018369681,0.00016541185,0.000012292202,0.000012462384,0.0000014359304,0.030418795,0.00017907248,0.17470737,0.7926903,0.0004450464,0.0011222184],"study_design_scores_gemma":[0.015745161,0.00241174,0.11431948,0.00019630836,0.00025154147,9.790755e-7,0.24961963,0.00076468225,0.43165946,0.1815665,0.0014646841,0.0019998355],"about_ca_topic_score_codex":0.0012605509,"about_ca_topic_score_gemma":0.00051115046,"teacher_disagreement_score":0.61112374,"about_ca_system_score_codex":0.000007945944,"about_ca_system_score_gemma":0.000017336431,"threshold_uncertainty_score":0.19055843},"labels":[],"label_agreement":null},{"id":"W2052108073","doi":"10.1016/j.tra.2013.01.028","title":"Modelling departure time choices by a Heteroskedastic Generalized Logit (Het-GenL) model: An investigation on home-based commuting trips in the Greater Toronto and Hamilton Area (GTHA)","year":2013,"lang":"en","type":"article","venue":"Transportation Research Part A Policy and Practice","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":28,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Heteroscedasticity; Discrete choice; Mixed logit; Econometrics; Logit; Mode choice; Econometric model; TRIPS architecture; Logistic regression; Computer science; Mathematics; Economics; Statistics; Engineering; Transport engineering; Public transport","score_opus":0.17055333579448892,"score_gpt":0.39935754706628546,"score_spread":0.22880421127179654,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2052108073","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98414063,0.00018934997,0.005376934,0.007861773,0.000018881525,0.0009198302,0.00007950493,0.000075242075,0.0013378435],"genre_scores_gemma":[0.99499375,0.00046898687,0.0019784982,0.0014921036,0.000076897886,0.0001864254,0.00050284364,0.000017192197,0.00028331717],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99676543,0.0014844475,0.00033032344,0.000332374,0.00067725254,0.0004101892],"domain_scores_gemma":[0.99755657,0.0017081845,0.00013554638,0.00016658417,0.00023457604,0.00019856756],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026025234,0.00015651951,0.00016576085,0.00014337941,0.0008378909,0.00037290703,0.00016084356,0.00016020332,0.000053143114],"category_scores_gemma":[0.00025169022,0.0001301106,0.00002597384,0.00036218026,0.00025908428,0.0016859756,0.0000016815342,0.0003317968,0.000005935735],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00080198457,0.0002529527,0.008322373,0.00008891126,0.00003523106,0.000009242844,0.18229496,0.7959439,0.0005331664,0.00907963,0.0015486929,0.0010889132],"study_design_scores_gemma":[0.0017954159,0.00033974793,0.009215931,0.00012555103,0.000056691108,0.0000012145214,0.008607921,0.96859366,0.000078148805,0.0019990073,0.008794365,0.0003923247],"about_ca_topic_score_codex":0.044785935,"about_ca_topic_score_gemma":0.010771256,"teacher_disagreement_score":0.17368704,"about_ca_system_score_codex":0.0000685398,"about_ca_system_score_gemma":0.00016933451,"threshold_uncertainty_score":0.9615749},"labels":[],"label_agreement":null},{"id":"W2052352752","doi":"","title":"Improving the Efficiency of Dynamic Traffic Assignment Through Computational Methods Based on Combinatorial Algorithm","year":2012,"lang":"en","type":"article","venue":"Transportation Research Board 91st Annual MeetingTransportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Algorithm; Cell Transmission Model; Convergence (economics); Queue; Mathematical optimization; FIFO (computing and electronics); Path (computing); Mathematics; Traffic congestion; Engineering","score_opus":0.05754672257237923,"score_gpt":0.4272030779018701,"score_spread":0.36965635532949087,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2052352752","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.52394575,0.00062618026,0.45906797,0.0043913936,0.0019743186,0.004800304,0.0009080235,0.0005450019,0.0037410276],"genre_scores_gemma":[0.94495124,0.00012842314,0.053044047,0.000106928295,0.0002935123,0.0003798599,0.000745829,0.00008335287,0.0002668093],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.98401546,0.0048339884,0.001477286,0.00086214615,0.0068403725,0.0019707277],"domain_scores_gemma":[0.98911726,0.0056430013,0.00047645892,0.0004895136,0.0037070073,0.00056678196],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.020839997,0.00042142769,0.0005463794,0.0009834078,0.002975981,0.00019119811,0.0008960927,0.00043239264,0.00036255477],"category_scores_gemma":[0.0009014479,0.00037742706,0.00032294745,0.0035923126,0.0021809614,0.0010501433,0.000008968347,0.0017249079,0.00004741687],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0025743213,0.0050331806,0.030188795,0.0008866353,0.0003287182,0.00005536525,0.2833904,0.47932163,0.0014704226,0.09952695,0.0034709042,0.09375267],"study_design_scores_gemma":[0.012082035,0.0036694391,0.5282292,0.0013675429,0.00043786285,8.1218343e-7,0.15916845,0.2318724,0.004556495,0.0068387827,0.04885648,0.0029205165],"about_ca_topic_score_codex":0.009716959,"about_ca_topic_score_gemma":0.002516714,"teacher_disagreement_score":0.4980404,"about_ca_system_score_codex":0.00044885793,"about_ca_system_score_gemma":0.0014174622,"threshold_uncertainty_score":0.99986774},"labels":[],"label_agreement":null},{"id":"W2052659776","doi":"10.1504/ijlsm.2013.055561","title":"A hybrid meta-heuristic algorithm for solving real-life transportation network design problems","year":2013,"lang":"en","type":"article","venue":"International Journal of Logistics Systems and Management","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Tabu search; Benchmark (surveying); Computer science; Heuristic; Network planning and design; Mathematical optimization; Genetic algorithm; Flow network; Network packet; Metaheuristic; Meta heuristic; Algorithm; Artificial intelligence; Machine learning; Mathematics","score_opus":0.06295942445119869,"score_gpt":0.29796805496038686,"score_spread":0.2350086305091882,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2052659776","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00018244106,0.00047820472,0.9962935,0.0004511363,0.0013167687,0.0005924311,0.000023179908,0.000020208892,0.0006421585],"genre_scores_gemma":[0.81418234,0.0029697868,0.18058117,0.00013985428,0.0008857365,0.00014996869,0.00006325374,0.000018532757,0.001009334],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99867266,0.00006356993,0.00054796104,0.00011349426,0.0004473321,0.00015495293],"domain_scores_gemma":[0.9984598,0.00018705952,0.00049301615,0.00004393726,0.0007162162,0.00009998442],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000996513,0.00009294659,0.00020651755,0.00010766921,0.00015923739,0.00022636153,0.00017281505,0.000034248027,0.000022722985],"category_scores_gemma":[0.000055317043,0.0000792779,0.000083344516,0.00005290281,0.0000496078,0.00021951564,0.0000038101743,0.000051744682,0.0000017811917],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002966662,0.000068748035,0.00043616942,0.00010010324,0.0019303343,0.000033599492,0.0015216589,0.8772409,0.0000054671123,0.09678574,0.012947784,0.008899818],"study_design_scores_gemma":[0.006470567,0.0009359046,0.01881162,0.0013191736,0.007286126,0.000048258087,0.00952946,0.7525695,0.000017530912,0.05433383,0.1472489,0.0014291435],"about_ca_topic_score_codex":0.00077135774,"about_ca_topic_score_gemma":0.000037373185,"teacher_disagreement_score":0.81571233,"about_ca_system_score_codex":0.000051582385,"about_ca_system_score_gemma":0.000050372146,"threshold_uncertainty_score":0.3232859},"labels":[],"label_agreement":null},{"id":"W2053079299","doi":"10.3934/jimo.2010.6.299","title":"A practical trial-and-error implementation of marginal-cost pricing on networks","year":2010,"lang":"en","type":"article","venue":"Journal of Industrial and Management Optimization","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Mathematical optimization; Observable; Mathematics","score_opus":0.06236035134081435,"score_gpt":0.3661185663224827,"score_spread":0.30375821498166833,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2053079299","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.368237,0.00004626174,0.6097505,0.010192132,0.0037252707,0.002707192,0.0000092102755,0.000050390558,0.005282016],"genre_scores_gemma":[0.96708125,0.0012135837,0.03078008,0.00010097835,0.0006446791,0.000010366844,0.000021196434,0.000010960276,0.00013688442],"study_design_codex":"simulation_or_modeling","study_design_gemma":"randomized_trial","domain_scores_codex":[0.9989593,0.00009809727,0.0004327309,0.00009416891,0.0002983946,0.00011731074],"domain_scores_gemma":[0.9990548,0.00010234731,0.00057879725,0.000045727367,0.0001406611,0.00007768508],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011820314,0.0000722366,0.00014332656,0.00018918145,0.00016971448,0.000068280984,0.000048797352,0.000106865715,0.00007371434],"category_scores_gemma":[0.00011592571,0.00006431747,0.00003203968,0.0002218099,0.00005839196,0.00034073638,0.000008288106,0.00020825032,1.685521e-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":[0.010697567,0.00041169496,0.011160268,0.00004620531,0.00026315244,0.00002568588,0.0044321963,0.77331847,0.000022204224,0.08329068,0.0062024193,0.11012944],"study_design_scores_gemma":[0.37784228,0.011394243,0.043678794,0.0019790565,0.0047923503,0.000084700136,0.11936185,0.27839285,0.00061261456,0.0014374901,0.15757656,0.0028472021],"about_ca_topic_score_codex":0.00006571299,"about_ca_topic_score_gemma":0.00006217828,"teacher_disagreement_score":0.5988443,"about_ca_system_score_codex":0.00001830388,"about_ca_system_score_gemma":0.000062860534,"threshold_uncertainty_score":0.26227903},"labels":[],"label_agreement":null},{"id":"W2053134070","doi":"10.3141/2409-08","title":"Developing and Applying Level-of-Service Framework to Land-Based Port-of-Entry Infrastructure Planning","year":2014,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Research Manitoba","funders":"","keywords":"Port (circuit theory); Flexibility (engineering); Transport engineering; Staffing; Key (lock); Pace; Service (business); Computer science; Operations research; Engineering; Business; Economics","score_opus":0.15578593550002998,"score_gpt":0.42375679716141323,"score_spread":0.2679708616613833,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2053134070","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.84580445,0.000092307615,0.14808396,0.0046862024,0.00031852582,0.00073959865,0.000059127684,0.000022255334,0.00019355683],"genre_scores_gemma":[0.9515358,0.00018283124,0.04782236,0.00016540488,0.00013479004,0.00003418948,0.000024374389,0.000032587748,0.000067644076],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9936926,0.0010650377,0.0012886463,0.00031667372,0.003016369,0.0006206859],"domain_scores_gemma":[0.9930676,0.0019592482,0.0006368214,0.000270871,0.0037003306,0.0003651149],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0055004465,0.00019375051,0.00046348173,0.0010054576,0.00076182897,0.00008479658,0.00075190025,0.00026502795,0.000078918594],"category_scores_gemma":[0.0006867726,0.0001639349,0.00015768985,0.002821157,0.00047294714,0.00039334368,0.0000050913227,0.0012349722,0.0000021258677],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010104188,0.000074519725,0.87099254,0.00053707534,0.00008305442,0.000013574843,0.027569167,0.06259904,0.0005871738,0.02652806,0.000549793,0.009455588],"study_design_scores_gemma":[0.00088729453,0.0002409698,0.9740517,0.0015202909,0.000044032487,1.4102574e-7,0.0066128313,0.00044660943,0.0009885031,0.0056450134,0.009369291,0.00019330237],"about_ca_topic_score_codex":0.010448147,"about_ca_topic_score_gemma":0.01935157,"teacher_disagreement_score":0.105731346,"about_ca_system_score_codex":0.00011622785,"about_ca_system_score_gemma":0.0011306427,"threshold_uncertainty_score":0.9985427},"labels":[],"label_agreement":null},{"id":"W2053687643","doi":"10.3328/tl.2010.02.01.27-37","title":"Travel time reliability on a highway network: estimations using floating car data","year":2010,"lang":"en","type":"article","venue":"Transportation Letters","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Ministère des Transports","keywords":"Reliability (semiconductor); Computer science; Transport engineering; Reliability engineering; Real-time computing; Engineering","score_opus":0.037854711617758316,"score_gpt":0.30679843599607404,"score_spread":0.2689437243783157,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2053687643","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.89536846,0.0000032465161,0.098479465,0.0036888812,0.00062047155,0.00034874456,0.00019599109,0.0002761063,0.0010186431],"genre_scores_gemma":[0.9376316,0.0000028322706,0.059307143,0.00089847884,0.00033986178,0.0000059865665,0.0017115994,0.000023084332,0.00007938501],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.998373,0.00009484526,0.00038759204,0.00039843365,0.00042608118,0.00032004705],"domain_scores_gemma":[0.99900174,0.00020290594,0.00016652244,0.0004113681,0.00009535422,0.00012213086],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008159371,0.00014210002,0.00015677145,0.00007807471,0.00077313,0.00007210018,0.00031497874,0.00011936408,0.00029262385],"category_scores_gemma":[0.000101341546,0.00015754167,0.000050730156,0.00039220476,0.0001378699,0.00058062334,0.0000017988094,0.0002864408,0.000041711348],"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":[0.00005220051,0.00010076382,0.026360307,0.000026988775,0.000032140448,0.000012583357,0.017357994,0.91657233,0.024435999,0.01019527,0.0038914243,0.0009619681],"study_design_scores_gemma":[0.001813935,0.000055750315,0.72809553,0.0001870052,0.00037611264,0.0000015623609,0.0014318258,0.25285527,0.0006827392,0.0007546114,0.012462479,0.001283169],"about_ca_topic_score_codex":0.0018943882,"about_ca_topic_score_gemma":0.0021845682,"teacher_disagreement_score":0.70173526,"about_ca_system_score_codex":0.00003462882,"about_ca_system_score_gemma":0.00013594232,"threshold_uncertainty_score":0.64243627},"labels":[],"label_agreement":null},{"id":"W2054113148","doi":"10.1016/j.trb.2010.01.007","title":"Bayesian flexible modeling of trip durations","year":2010,"lang":"en","type":"article","venue":"Transportation Research Part B Methodological","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"","keywords":"Markov chain Monte Carlo; Flexibility (engineering); Computer science; Bayesian probability; Dimension (graph theory); Prior probability; Markov chain; Bayesian inference; Data mining; Machine learning; Econometrics; Artificial intelligence; Statistics; Mathematics","score_opus":0.49970152970246057,"score_gpt":0.5318408510352947,"score_spread":0.03213932133283409,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2054113148","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3323419,0.000030377973,0.64992106,0.0021104915,0.00036867952,0.00052459136,0.000054275126,0.00022748244,0.014421113],"genre_scores_gemma":[0.9042167,0.00009413346,0.09461367,0.000029564111,0.00014175437,0.00005630318,0.00014887693,0.00000981644,0.0006891663],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99666584,0.0012514505,0.0005055655,0.00029173095,0.0008890363,0.00039639187],"domain_scores_gemma":[0.9973842,0.0014416686,0.00008996555,0.00018135452,0.00069658563,0.00020622613],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0077272663,0.000093528746,0.00021721028,0.00024390612,0.0006684796,0.000040704934,0.00024034859,0.00025433532,0.0014501978],"category_scores_gemma":[0.0015336474,0.000086693384,0.00009552286,0.00089299196,0.0004699224,0.0002491116,0.0000020493856,0.0006111766,0.000014965201],"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":[0.00038446335,0.00041699538,0.047202367,0.00007201808,0.00004917079,0.000019893989,0.029392216,0.16601084,0.010240545,0.7399703,0.0012046177,0.0050365673],"study_design_scores_gemma":[0.0055396804,0.0011346241,0.3591269,0.00029027925,0.00027853987,0.0000032560724,0.0521708,0.20986874,0.016109817,0.1803044,0.17294128,0.0022316948],"about_ca_topic_score_codex":0.0011849946,"about_ca_topic_score_gemma":0.004799922,"teacher_disagreement_score":0.5718748,"about_ca_system_score_codex":0.000014782503,"about_ca_system_score_gemma":0.00028569644,"threshold_uncertainty_score":0.9994626},"labels":[],"label_agreement":null},{"id":"W2055354324","doi":"10.1061/40730(144)43","title":"OptimalCMS: A Decision Support System for Locating Changeable Message Signs","year":2004,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Queueing theory; Genetic algorithm; Software; Decision support system; Geographic information system; Logit; Real-time computing; Data mining; Distributed computing; Operations research; Computer network; Machine learning; Engineering","score_opus":0.03316408130739952,"score_gpt":0.31149713545684365,"score_spread":0.27833305414944415,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2055354324","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008166463,0.000026022264,0.9452861,0.00043416565,0.0002591772,0.00041109734,0.000012054873,0.0003213522,0.045083553],"genre_scores_gemma":[0.88775766,0.000008541442,0.11015867,0.00008728223,0.00009651217,0.000050742936,0.000044941244,0.00001024153,0.0017854067],"study_design_codex":"simulation_or_modeling","study_design_gemma":"qualitative","domain_scores_codex":[0.99918956,0.000017084169,0.00017988506,0.00016453388,0.00022484177,0.00022408806],"domain_scores_gemma":[0.99954295,0.00010630256,0.00006560336,0.000077645265,0.00012639216,0.000081123806],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005871724,0.00006591209,0.00009803344,0.00006005239,0.00050106924,0.00006832775,0.00010575319,0.000073922514,0.00010934784],"category_scores_gemma":[0.00007723719,0.00006205928,0.000044111242,0.00021803782,0.000032994205,0.00021616198,0.000003345918,0.00003202071,0.000033351873],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014306532,0.00016729397,0.0023671822,0.00020977567,0.00004534454,0.000020113228,0.067042455,0.46419996,0.00050455023,0.44660822,0.0037522772,0.014939751],"study_design_scores_gemma":[0.023480477,0.0025667448,0.0050487434,0.003162715,0.0005939589,0.000025878708,0.58769107,0.04760217,0.021056078,0.013784183,0.29042944,0.004558572],"about_ca_topic_score_codex":0.0008248593,"about_ca_topic_score_gemma":0.0009167171,"teacher_disagreement_score":0.8795912,"about_ca_system_score_codex":0.00010162636,"about_ca_system_score_gemma":0.0001673079,"threshold_uncertainty_score":0.385387},"labels":[],"label_agreement":null},{"id":"W2055576367","doi":"10.1007/s12469-015-0112-9","title":"Don’t stop just yet! A simple, effective, and socially responsible approach to bus-stop consolidation","year":2015,"lang":"en","type":"article","venue":"Public Transport","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":16,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Consolidation (business); Local bus; Transport engineering; Engineering; Public transport; Computer science; Operations research; Business; System bus; Control bus; Finance; Operating system","score_opus":0.05528362458847398,"score_gpt":0.3127046634446392,"score_spread":0.25742103885616524,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2055576367","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.77576566,0.00014547119,0.067897856,0.013455611,0.00057436444,0.002018769,0.00014202415,0.00077091047,0.13922936],"genre_scores_gemma":[0.9906869,0.000025858655,0.006904825,0.0003916194,0.000117740026,0.00011099019,0.0003542735,0.000021400465,0.0013863782],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99823225,0.00020344998,0.0002728838,0.00037473123,0.00052642863,0.00039024712],"domain_scores_gemma":[0.9988379,0.000111834255,0.00008678742,0.00014315743,0.00034427675,0.00047603506],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015185593,0.00015217214,0.00021684391,0.00022864873,0.0004283212,0.000113082504,0.00017261918,0.00016759432,0.000043731106],"category_scores_gemma":[0.00023551505,0.00016267462,0.00004747614,0.00078406336,0.00017629347,0.00057381904,0.000005134886,0.00014134998,0.000016121343],"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":[0.00046827598,0.00045405925,0.30606452,0.000084060775,0.00009533017,0.000015173219,0.17986532,0.003863379,0.00010772552,0.4876377,0.008202791,0.013141688],"study_design_scores_gemma":[0.0026731289,0.0001997203,0.6462345,0.000032472537,0.00013156216,0.0000045882607,0.027500734,0.00034900117,0.00007633732,0.0047001513,0.3173041,0.00079366134],"about_ca_topic_score_codex":0.0014496155,"about_ca_topic_score_gemma":0.0034823867,"teacher_disagreement_score":0.48293754,"about_ca_system_score_codex":0.00010619283,"about_ca_system_score_gemma":0.0015149578,"threshold_uncertainty_score":0.66336787},"labels":[],"label_agreement":null},{"id":"W2056159949","doi":"10.1002/atr.5670360105","title":"Optimization of grid bus transit systems with elastic demand","year":2002,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":47,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Grid; Headway; Schedule; Mathematical optimization; Computer science; Profit (economics); Public transport; Operations research; Operator (biology); Price elasticity of demand; Transport engineering; Simulation; Engineering; Economics; Microeconomics; Mathematics","score_opus":0.010845637437726945,"score_gpt":0.2375784227862023,"score_spread":0.22673278534847535,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2056159949","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27834463,0.00091327,0.7186231,0.0002625285,0.0007474559,0.0002896889,0.00003055967,0.00004185759,0.0007468928],"genre_scores_gemma":[0.98053944,0.00081478455,0.018379766,0.000011014869,0.00012065574,0.0000029936027,0.000033986627,0.000013626322,0.00008373863],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.998563,0.000063971,0.0006034133,0.00010205127,0.00052645867,0.0001411308],"domain_scores_gemma":[0.99850404,0.000084393876,0.0006753583,0.000059850227,0.0005833644,0.00009300506],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026052163,0.00009956934,0.00023701398,0.00017257709,0.00013412448,0.000021349122,0.00009316324,0.00007395069,0.00007093736],"category_scores_gemma":[0.000026585461,0.000087428285,0.000065949374,0.00041098602,0.00008398337,0.0007164854,1.456946e-7,0.00011067037,8.574231e-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":[0.00015200418,0.00006707191,0.0027875525,0.000047102076,0.000032396867,0.0000140245675,0.013650706,0.9819813,0.00010781368,0.0006443515,0.000028379814,0.00048733247],"study_design_scores_gemma":[0.038777355,0.0082359435,0.6285136,0.007441675,0.0044379705,0.00015622984,0.12002821,0.15916534,0.0029965313,0.0006560868,0.02603916,0.003551931],"about_ca_topic_score_codex":0.000040627234,"about_ca_topic_score_gemma":0.00015753105,"teacher_disagreement_score":0.82281595,"about_ca_system_score_codex":0.000033975633,"about_ca_system_score_gemma":0.000062875646,"threshold_uncertainty_score":0.3565222},"labels":[],"label_agreement":null},{"id":"W2056193625","doi":"10.3141/1760-14","title":"Potential Effects of Automatic Vehicle Location and Computer-Aided Dispatch Technology on Paratransit Performance: A Simulation Study","year":2001,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Paratransit; Automatic vehicle location; Reliability (semiconductor); CAD; Transport engineering; Public transport; Engineering; Service (business); Productivity; TRIPS architecture; Simulation; Computer science; Telecommunications; Engineering drawing","score_opus":0.039582726226033114,"score_gpt":0.37817834015137947,"score_spread":0.3385956139253464,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2056193625","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98154515,0.000094369534,0.014953551,0.0013099439,0.00037984434,0.0016181825,0.000007726902,0.00005390261,0.000037351376],"genre_scores_gemma":[0.9979746,0.00077337364,0.0008714118,0.000018804461,0.00013488681,0.000053287273,0.000011865785,0.000030910658,0.0001308953],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99347794,0.0013752817,0.0011825582,0.00036517167,0.0030095403,0.00058950554],"domain_scores_gemma":[0.99463564,0.0011358964,0.00051414303,0.00031333085,0.0031654993,0.00023548417],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003357994,0.00020694603,0.00042416638,0.0013139999,0.0010655082,0.00009983205,0.0005633716,0.00022990392,0.000053664622],"category_scores_gemma":[0.0001920489,0.00017257873,0.00013985942,0.0033424217,0.00074274966,0.0006491408,0.0000037049092,0.0010968487,0.0000062194244],"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":[0.0012510665,0.0010419326,0.7484544,0.00047904855,0.00014469199,0.00008861691,0.02241618,0.19972858,0.00052450114,0.0015498475,0.00012106439,0.024200058],"study_design_scores_gemma":[0.0021609734,0.0018441955,0.96863157,0.00058442773,0.000090765796,2.8917563e-7,0.005168561,0.020235742,0.00030394937,0.0005037376,0.00031774887,0.00015803504],"about_ca_topic_score_codex":0.004234194,"about_ca_topic_score_gemma":0.011659561,"teacher_disagreement_score":0.22017716,"about_ca_system_score_codex":0.00014690719,"about_ca_system_score_gemma":0.00047297246,"threshold_uncertainty_score":0.81951356},"labels":[],"label_agreement":null},{"id":"W2056351325","doi":"10.1002/atr.5670400106","title":"Estimation of a time‐dependent origin‐destination matrix for congested highway networks","year":2006,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Matrix (chemical analysis); Scheme (mathematics); Reliability (semiconductor); Set (abstract data type); Mathematical optimization; Computer science; Estimation; Algorithm; Travel time; Quality (philosophy); Mathematics; Engineering; Transport engineering","score_opus":0.00900157799906168,"score_gpt":0.2939752145292385,"score_spread":0.2849736365301768,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2056351325","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.41196194,0.00014885484,0.5866558,0.00022291226,0.0004104029,0.00035140733,0.0000324878,0.000042580894,0.00017361733],"genre_scores_gemma":[0.9595035,0.00007115289,0.03958188,0.000009112248,0.00016509765,0.000008929688,0.00033306653,0.000013616639,0.00031367238],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9985141,0.000054070824,0.0007582288,0.00010878171,0.00041505165,0.00014977057],"domain_scores_gemma":[0.99792993,0.00021654883,0.0010194863,0.00005515652,0.0007264969,0.000052390984],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005953433,0.00009848072,0.00021848817,0.00018097294,0.00014976774,0.000023166218,0.00009219265,0.00010238455,0.000023173201],"category_scores_gemma":[0.00006911335,0.00010106289,0.00010316903,0.0002976111,0.00005667637,0.00066638854,2.8224645e-7,0.00008827546,9.150272e-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":[0.00023585353,0.000070766364,0.0016884571,0.000029489645,0.000014793605,0.0000026792673,0.0014661566,0.98437786,0.00084449194,0.005995418,0.00009722932,0.00517683],"study_design_scores_gemma":[0.019516908,0.0020376246,0.7103762,0.0013761211,0.0015205143,0.000016395414,0.0058887964,0.21617162,0.009897726,0.02056982,0.011127692,0.0015005905],"about_ca_topic_score_codex":0.000102643324,"about_ca_topic_score_gemma":0.00032440925,"teacher_disagreement_score":0.76820624,"about_ca_system_score_codex":0.00007121604,"about_ca_system_score_gemma":0.00014152598,"threshold_uncertainty_score":0.41212252},"labels":[],"label_agreement":null},{"id":"W2056557846","doi":"10.1002/atr.5670360103","title":"Application of genetic algorithm for scheduling and schedule coordination problems","year":2002,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":54,"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":"Schedule; Computer science; Operations research; Scheduling (production processes); Profit (economics); Operator (biology); Genetic algorithm; Mathematical optimization; Job shop scheduling; Engineering; Economics; Mathematics; Machine learning; Microeconomics; Operating system","score_opus":0.013586761692437582,"score_gpt":0.2720677205323505,"score_spread":0.25848095883991296,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2056557846","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19518356,0.0005455402,0.8035443,0.00027633616,0.000113742906,0.000276791,0.0000125540555,0.000013803903,0.000033355078],"genre_scores_gemma":[0.70765555,0.0003130843,0.29189453,0.000009897913,0.00006115019,0.000010547543,0.000020362671,0.0000070312935,0.000027869826],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99907833,0.000023152523,0.0004716045,0.00009430091,0.00023469361,0.00009791347],"domain_scores_gemma":[0.9987394,0.00007245124,0.00056816795,0.00003990212,0.00052372407,0.000056355395],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027613298,0.00006386035,0.00014108434,0.00012559746,0.00013225604,0.000013052696,0.000057422276,0.0000635894,0.000008395761],"category_scores_gemma":[0.000037866386,0.00006719262,0.000051549236,0.00021172293,0.00005867873,0.00042539052,2.6771232e-7,0.000062615865,2.933458e-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":[0.0000413228,0.00013226815,0.0109334355,0.000116165356,0.00003542829,0.0000010613818,0.019455947,0.5780262,0.0032718424,0.003494452,0.000014535789,0.38447738],"study_design_scores_gemma":[0.011469677,0.0015345252,0.5850832,0.00074228056,0.00070032524,0.000010555693,0.020105973,0.34483278,0.0044332324,0.011731878,0.018435515,0.00092006335],"about_ca_topic_score_codex":0.00001772299,"about_ca_topic_score_gemma":0.000055293232,"teacher_disagreement_score":0.5741497,"about_ca_system_score_codex":0.000022788237,"about_ca_system_score_gemma":0.000027598953,"threshold_uncertainty_score":0.27400354},"labels":[],"label_agreement":null},{"id":"W2057493281","doi":"10.4028/www.scientific.net/amm.148-149.1543","title":"Research on the Evaluation Method of Energy-Saving Technology about Traffic and Transportation","year":2011,"lang":"en","type":"article","venue":"Applied Mechanics and Materials","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ministry of Transportation of Ontario","funders":"","keywords":"Analytic hierarchy process; Delphi method; Evaluation methods; Promotion (chess); Transport engineering; Delphi; Index (typography); Fuzzy logic; Energy (signal processing); Environmental economics; Engineering; Operations research; Computer science; Risk analysis (engineering); Business; Reliability engineering; Economics; Mathematics; Artificial intelligence","score_opus":0.09109061850207162,"score_gpt":0.36504404790915296,"score_spread":0.27395342940708134,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2057493281","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9405896,0.00008950395,0.05528565,0.00036286045,0.00015761102,0.0005324143,0.000025882568,0.0000586059,0.0028978863],"genre_scores_gemma":[0.9922333,0.00026025632,0.0073473807,0.000027894626,0.000017191383,0.00007313988,0.000018586214,0.00000637687,0.000015883326],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99916625,0.00015421823,0.00016655374,0.00014720295,0.00024756935,0.00011821177],"domain_scores_gemma":[0.99956304,0.0001331421,0.00008353899,0.00007607822,0.00012006253,0.000024168156],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031876245,0.000052125884,0.00009814767,0.000117290765,0.0002649838,0.000020960904,0.00006433059,0.000107805696,0.000088012166],"category_scores_gemma":[0.000035809073,0.000041083404,0.000007277688,0.00020574853,0.000051102023,0.000034850073,0.0000028722077,0.000044006563,6.5029735e-7],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030136269,0.000012675627,0.0000030151202,0.000007911919,0.0000061151495,1.3927342e-7,0.0058852225,0.000037306843,0.009407346,0.9743593,0.000010558616,0.010240311],"study_design_scores_gemma":[0.0011825702,0.00029804299,0.0049693272,0.00015819487,0.00018951045,9.486214e-7,0.046441857,0.00085168786,0.2224254,0.7208362,0.0022571988,0.00038906117],"about_ca_topic_score_codex":0.00020422417,"about_ca_topic_score_gemma":0.00024989748,"teacher_disagreement_score":0.25352305,"about_ca_system_score_codex":0.000008844026,"about_ca_system_score_gemma":0.00004165094,"threshold_uncertainty_score":0.20380679},"labels":[],"label_agreement":null},{"id":"W2057662664","doi":"10.3141/1964-22","title":"Noniterative Approach to Dynamic Traffic Origin-Destination Estimation with Parallel Evolutionary Algorithms","year":2006,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Dynode; Estimator; Computer science; Computation; Block (permutation group theory); Algorithm; Multiprocessing; Mathematical optimization; Parallel computing; Mathematics","score_opus":0.056200755089024834,"score_gpt":0.3815467299405467,"score_spread":0.3253459748515219,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2057662664","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.87059164,0.000097711534,0.122834176,0.0029560116,0.00033715085,0.0018799012,0.000079589154,0.00009658045,0.0011272312],"genre_scores_gemma":[0.9035671,0.00011500013,0.09375053,0.000033720895,0.0002041858,0.0001742631,0.00020750437,0.000056188102,0.0018915206],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.990634,0.0016070084,0.0012890018,0.00055163866,0.0049170638,0.0010012813],"domain_scores_gemma":[0.9924789,0.0007966591,0.0005483994,0.00034271934,0.0054017794,0.0004315092],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0044807447,0.00030089487,0.00041431576,0.0014633308,0.0020834263,0.00029293614,0.00087770086,0.00025061937,0.00009662406],"category_scores_gemma":[0.00015468821,0.00023721163,0.0002319796,0.0042907703,0.0010284038,0.0013624392,0.0000032097216,0.0014792215,0.000022435534],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015522256,0.0008411837,0.11891221,0.00014711652,0.000115640854,0.000063793836,0.024427377,0.8252262,0.00016520976,0.01683867,0.004706709,0.0070036477],"study_design_scores_gemma":[0.0016423357,0.0005342191,0.96950704,0.00035060386,0.00007272748,8.410294e-7,0.004711054,0.014622274,0.00003818347,0.0020739136,0.006107619,0.00033920148],"about_ca_topic_score_codex":0.01914536,"about_ca_topic_score_gemma":0.07153938,"teacher_disagreement_score":0.8505948,"about_ca_system_score_codex":0.0006394705,"about_ca_system_score_gemma":0.0013630782,"threshold_uncertainty_score":0.9992157},"labels":[],"label_agreement":null},{"id":"W2058183620","doi":"10.1002/atr.5670350304","title":"Fare deregulation of transit services: Winners and losers in a competitive market","year":2001,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Deregulation; Revenue; Business; Government (linguistics); Public transport; Service (business); Transit (satellite); Control (management); Transport engineering; Industrial organization; Public economics; Finance; Economics; Marketing; Market economy; Engineering","score_opus":0.0071382886734749065,"score_gpt":0.25869268498590775,"score_spread":0.25155439631243287,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2058183620","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9916546,0.00019001386,0.0061688665,0.00044936023,0.000104708466,0.0001344538,0.000012231175,0.000010534171,0.0012752669],"genre_scores_gemma":[0.99403065,0.00080327585,0.00502058,0.000035133104,0.000026012867,0.0000012698839,0.00003488952,0.0000066550865,0.000041551],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99900615,0.00006114144,0.0004634912,0.00008818386,0.00027346401,0.00010754395],"domain_scores_gemma":[0.99922335,0.00006918506,0.00036323204,0.00003402623,0.00025149327,0.000058704638],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003078905,0.00007305112,0.0001775013,0.0001950798,0.00006324372,0.000010150133,0.000058039754,0.00006433732,0.00005155952],"category_scores_gemma":[0.000010704599,0.00007560865,0.000046158217,0.00035662254,0.000068233516,0.0006624955,2.1474094e-7,0.00008807621,1.2953693e-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":[0.0013163835,0.00013524298,0.31604153,0.0001698745,0.00004558059,0.000055526853,0.13254185,0.5305861,0.0013215085,0.004580781,0.000009248889,0.013196346],"study_design_scores_gemma":[0.0014434129,0.00008372079,0.9619533,0.0003100586,0.00004187906,0.0000022342751,0.033790857,0.00019821658,0.00011565531,0.0005796678,0.0013873116,0.000093679504],"about_ca_topic_score_codex":0.00015371462,"about_ca_topic_score_gemma":0.0051134545,"teacher_disagreement_score":0.6459118,"about_ca_system_score_codex":0.000031916974,"about_ca_system_score_gemma":0.000062190506,"threshold_uncertainty_score":0.30832312},"labels":[],"label_agreement":null},{"id":"W2058753381","doi":"10.1007/s10107-004-0542-7","title":"Capacitated transit assignment with loading priorities","year":2004,"lang":"en","type":"article","venue":"Mathematical Programming","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":40,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Queue; Position (finance); Transit (satellite); Variational inequality; Mathematical optimization; Set (abstract data type); Node (physics); Residual; Order (exchange); Preference; Computer science; Mathematics; Operations research; Public transport; Transport engineering; Computer network; Algorithm; Engineering","score_opus":0.023764123007246705,"score_gpt":0.27599749463286477,"score_spread":0.25223337162561804,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2058753381","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13113981,0.000039945196,0.8476987,0.0015667462,0.00005773572,0.00055076054,0.0000019166216,0.00051793753,0.018426469],"genre_scores_gemma":[0.9190246,0.0000029507898,0.08061386,0.00003308563,0.000031800977,0.000028464898,0.0000056267354,0.000011017879,0.00024862593],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","domain_scores_codex":[0.99906474,0.000029345481,0.0001655091,0.00013039469,0.00034888144,0.00026111043],"domain_scores_gemma":[0.9996796,0.000060941486,0.00004307203,0.00006408674,0.00005100099,0.00010126749],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030522916,0.0000833215,0.000115038216,0.0000404868,0.00035710953,0.00011937222,0.000072622,0.000054570355,0.00006897651],"category_scores_gemma":[0.00004784365,0.00006888019,0.000029939418,0.00020744321,0.00017942683,0.00016643995,0.0000016882939,0.000086652435,0.000025338724],"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":[0.00005107402,0.0003731674,0.001700899,0.0002476663,0.000080120895,0.000069073016,0.2205346,0.012109036,0.00012949726,0.7353474,0.00002270605,0.029334806],"study_design_scores_gemma":[0.016576331,0.00267208,0.005809264,0.0075195967,0.001219492,0.00010092089,0.4232255,0.0045357654,0.007615819,0.42074385,0.10409072,0.005890668],"about_ca_topic_score_codex":0.00012591359,"about_ca_topic_score_gemma":0.0001664136,"teacher_disagreement_score":0.7878848,"about_ca_system_score_codex":0.00007284784,"about_ca_system_score_gemma":0.000076901786,"threshold_uncertainty_score":0.28088528},"labels":[],"label_agreement":null},{"id":"W2058756757","doi":"10.1007/s11116-011-9375-4","title":"Measuring quality of service in dial-a-ride operations: the case of a Canadian city","year":2011,"lang":"en","type":"article","venue":"Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":60,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"HEC Montréal","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Quality (philosophy); Service quality; Public transport; Service (business); Population; Scale (ratio); Transport engineering; Computer science; Service provider; Quality of service; Marketing; Business; Operations research; Telecommunications; Engineering; Geography; Medicine","score_opus":0.1557730674647822,"score_gpt":0.3143592988473008,"score_spread":0.1585862313825186,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2058756757","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.995691,0.000023145692,0.00082362903,0.00034596425,0.00005448683,0.00020917966,0.000065133114,0.00001416438,0.0027732998],"genre_scores_gemma":[0.998737,0.000026554655,0.0010816788,0.000055670956,0.000010210886,0.0000125880815,0.000054494838,0.000004061927,0.000017766293],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.99915606,0.00012169099,0.00038217733,0.00009287173,0.00013459084,0.00011263513],"domain_scores_gemma":[0.9994879,0.00005501691,0.00009137564,0.000091186834,0.00021917232,0.00005538011],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008466986,0.00005067097,0.00010331213,0.00019475952,0.00015369391,0.0000055839837,0.00008746067,0.00005676205,0.000103060214],"category_scores_gemma":[0.000050965897,0.000046615984,0.00002910968,0.0008461891,0.00006392513,0.00019777879,2.8093058e-7,0.00006046529,9.588491e-7],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007006427,0.000089600275,0.3706644,0.00008311028,0.0000198353,0.00004656884,0.5261332,0.026499562,0.00028310358,0.07517194,0.000008293124,0.0009303616],"study_design_scores_gemma":[0.0002592969,0.000009325966,0.9836583,0.00003332735,0.000020383648,5.824203e-7,0.014579526,0.00021107055,0.00073433504,0.00037206768,0.000045811797,0.0000759494],"about_ca_topic_score_codex":0.9607615,"about_ca_topic_score_gemma":0.99786294,"teacher_disagreement_score":0.61299396,"about_ca_system_score_codex":0.000026510732,"about_ca_system_score_gemma":0.0002718997,"threshold_uncertainty_score":0.19009447},"labels":[],"label_agreement":null},{"id":"W2058763146","doi":"10.1061/(asce)te.1943-5436.0000371","title":"Large-Scale Evacuation Using Subway and Bus Transit: Approach and Application in City of Toronto","year":2011,"lang":"en","type":"article","venue":"Journal of Transportation Engineering","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":47,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Transport engineering; Public transport; Transit (satellite); Vehicle routing problem; Computer science; Routing (electronic design automation); Emergency evacuation; Population; Constraint (computer-aided design); Scheduling (production processes); Traffic congestion; Operations research; Engineering; Geography; Operations management; Computer network","score_opus":0.019157488201095765,"score_gpt":0.2489023837532756,"score_spread":0.22974489555217986,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2058763146","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6150962,0.00025393837,0.38444462,0.00000722045,0.000030865453,0.00007221739,0.000005321196,0.0000071299446,0.0000824396],"genre_scores_gemma":[0.96914977,0.00023344503,0.030569734,0.0000033281397,0.000022402044,0.0000018173977,0.000010669255,0.000006331724,0.0000025112502],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.9992738,0.000020137193,0.00036350798,0.0000778726,0.00017376928,0.00009091595],"domain_scores_gemma":[0.9996056,0.000020562446,0.00017755399,0.000032439104,0.000108353925,0.000055485325],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005237852,0.00006145561,0.00014039435,0.000081616105,0.000048094073,0.0000072100415,0.000040574087,0.000066914494,0.000008710613],"category_scores_gemma":[0.000009795435,0.00006613797,0.000026617843,0.000118485565,0.000023344577,0.00047945243,3.550743e-7,0.000068545705,1.9189685e-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":[0.00021885587,0.00023899556,0.31912813,0.0002665161,0.000049883212,0.0000048618895,0.40037134,0.2643666,0.006443518,0.00595777,0.0000017398804,0.0029518043],"study_design_scores_gemma":[0.00069788564,0.000034463246,0.94511485,0.00007087502,0.000061018825,0.000002218492,0.006461268,0.04686909,0.0004469694,0.000052273885,0.00008087115,0.00010820569],"about_ca_topic_score_codex":0.0011109869,"about_ca_topic_score_gemma":0.0029849801,"teacher_disagreement_score":0.62598675,"about_ca_system_score_codex":0.00003460695,"about_ca_system_score_gemma":0.00002951597,"threshold_uncertainty_score":0.2697028},"labels":[],"label_agreement":null},{"id":"W2059228987","doi":"10.1002/atr.5670360107","title":"Deploying the advanced public transportation systems (APTS) technologies in Los Angeles: A summary and analysis","year":2002,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","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":"California Department of Transportation; U.S. Department of Transportation","keywords":"Software deployment; Metropolitan area; Intelligent transportation system; Transport engineering; Public transport; Key (lock); Engineering; Computer science; Business; Computer security; Geography; Operating system","score_opus":0.018972801839751315,"score_gpt":0.26436063919919095,"score_spread":0.24538783735943964,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2059228987","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97620755,0.005781747,0.01575226,0.0011642271,0.00029306315,0.00040241182,0.00007471473,0.00013378754,0.00019024726],"genre_scores_gemma":[0.9936137,0.003357064,0.0028142757,0.000024571014,0.000038068363,0.000018733312,0.000069821355,0.0000137213065,0.000050022583],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9981723,0.00010866625,0.00077766174,0.00019228474,0.00050120865,0.00024791574],"domain_scores_gemma":[0.99871194,0.00019244953,0.00062019326,0.000110743385,0.00029445475,0.000070236696],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00058627216,0.00014331192,0.00031872734,0.0006183863,0.0002822509,0.00007814801,0.00018616549,0.0001290526,0.000015898642],"category_scores_gemma":[0.00008672961,0.00011805028,0.00012289987,0.0017773797,0.00015005529,0.0012963413,5.1972324e-7,0.0002684985,3.7980067e-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":[0.0001367508,0.00014375636,0.27632186,0.00008154333,0.00022976035,0.00006867288,0.03897246,0.64303225,0.0003735437,0.0053081824,0.00012098479,0.03521025],"study_design_scores_gemma":[0.0023341943,0.00016057245,0.8626595,0.00034738312,0.00076502736,0.00000336217,0.120499745,0.0030936587,0.00008421348,0.00062160246,0.008988719,0.00044198186],"about_ca_topic_score_codex":0.00009592504,"about_ca_topic_score_gemma":0.009450153,"teacher_disagreement_score":0.6399386,"about_ca_system_score_codex":0.000073654075,"about_ca_system_score_gemma":0.000047605456,"threshold_uncertainty_score":0.5273407},"labels":[],"label_agreement":null},{"id":"W2059457987","doi":"10.1002/atr.5670420305","title":"Modelling rain effects on risk‐taking behaviours of multi‐user classes in road networks with uncertainty","year":2008,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":46,"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":"China University of Mining and Technology; Hong Kong Polytechnic University","keywords":"Travel time; Reliability (semiconductor); Path (computing); Variational inequality; Function (biology); Computer science; Logit; Econometrics; Transport engineering; Operations research; Simulation; Mathematical optimization; Mathematics; Engineering","score_opus":0.018613090345180185,"score_gpt":0.28439295144023097,"score_spread":0.2657798610950508,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2059457987","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.83595425,0.00012792999,0.16351555,0.00003770276,0.00015171335,0.00015998595,0.0000058823466,0.000015303005,0.000031669766],"genre_scores_gemma":[0.9757928,0.0006832847,0.02337475,0.000018114764,0.000059245947,0.0000038662834,0.000029890496,0.000014556835,0.000023456134],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9986047,0.000120497265,0.0005285349,0.0001306326,0.0004329786,0.00018264532],"domain_scores_gemma":[0.99815834,0.00019373887,0.0012422805,0.00006195747,0.0002697087,0.00007396579],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040679853,0.00011708375,0.00025668798,0.00021072327,0.00017815724,0.000008337721,0.0000939756,0.00009462966,0.00000492885],"category_scores_gemma":[0.000052778414,0.00010373706,0.00007920818,0.0004221861,0.00007968454,0.0004318837,2.7040542e-7,0.00028964016,1.5065586e-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":[0.0004061429,0.00008333835,0.15668447,0.000010311482,0.000013470396,0.00004632952,0.014256121,0.82604885,0.0000237905,0.00013638986,0.0000019026944,0.002288873],"study_design_scores_gemma":[0.0031584892,0.00039997057,0.9679233,0.00066643936,0.00009172411,0.0000023041528,0.0033326221,0.023862101,0.00024300779,0.00005161584,0.00006818443,0.00020023415],"about_ca_topic_score_codex":0.00096868456,"about_ca_topic_score_gemma":0.0035338148,"teacher_disagreement_score":0.8112388,"about_ca_system_score_codex":0.00006597923,"about_ca_system_score_gemma":0.00013360496,"threshold_uncertainty_score":0.42302746},"labels":[],"label_agreement":null},{"id":"W2059606555","doi":"10.3141/2415-11","title":"Transitory Optimism","year":2014,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Attractiveness; Descriptive statistics; Perception; Service quality; Transport engineering; Service (business); Optimism; Public transport; Term (time); Marketing; Computer science; Business; Applied psychology; Psychology; Engineering; Statistics; Mathematics; Social psychology","score_opus":0.087478025927429,"score_gpt":0.40256147374858314,"score_spread":0.31508344782115416,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2059606555","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95960134,0.0002267007,0.019646062,0.012014528,0.001862207,0.0011150661,0.00004312491,0.00011074324,0.0053802375],"genre_scores_gemma":[0.9912779,0.0009180557,0.0044395844,0.000119960554,0.000647757,0.00004272772,0.00002220804,0.000056489305,0.0024753332],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.98864365,0.0033039213,0.0013816169,0.00042270878,0.0051738606,0.0010742759],"domain_scores_gemma":[0.99216336,0.0016842056,0.00048023686,0.00045033888,0.004622551,0.0005992959],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0106762545,0.00024073651,0.00044161003,0.0010474949,0.0020570601,0.00022402182,0.0013707185,0.00032146988,0.00051684823],"category_scores_gemma":[0.00046785932,0.00019758164,0.00044828845,0.0025998515,0.0013733037,0.0009192692,0.000002753758,0.0021854802,0.000042450192],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.003066227,0.0011246465,0.5325316,0.0005352591,0.00045284457,0.00017441218,0.118745476,0.052970998,0.0030511483,0.1840241,0.05857832,0.044744972],"study_design_scores_gemma":[0.0017248151,0.00042980563,0.68955296,0.0003438807,0.000074476404,2.4815392e-7,0.008133812,0.0002743645,0.00045000832,0.005468733,0.29324052,0.00030634794],"about_ca_topic_score_codex":0.01708173,"about_ca_topic_score_gemma":0.07891958,"teacher_disagreement_score":0.2346622,"about_ca_system_score_codex":0.00025317603,"about_ca_system_score_gemma":0.0010678975,"threshold_uncertainty_score":0.9992421},"labels":[],"label_agreement":null},{"id":"W2060633232","doi":"10.1002/atr.5670420403","title":"General stochastic user equilibrium traffic assignment problem with link capacity constraints","year":2008,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":60,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National University of Singapore","keywords":"Subroutine; Mathematical optimization; Dual (grammatical number); Lagrangian; Minification; Path (computing); Sequence (biology); Lagrangian relaxation; Computer science; Basis (linear algebra); Benchmark (surveying); Link (geometry); Function (biology); Trajectory; Applied mathematics; Mathematics","score_opus":0.01937116034573813,"score_gpt":0.2573713804144278,"score_spread":0.23800022006868968,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2060633232","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8824261,0.000039407256,0.116405986,0.00043456064,0.00022090976,0.00021510173,0.000016256077,0.000042388812,0.00019928624],"genre_scores_gemma":[0.95806056,0.00004611756,0.041426443,0.000048136393,0.00018857727,0.0000057315565,0.000039468472,0.000015101624,0.00016987987],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99839944,0.00006414021,0.00052008266,0.00014515217,0.0006426189,0.00022859577],"domain_scores_gemma":[0.99880606,0.00006115359,0.00050235377,0.000055051798,0.0004092396,0.00016615946],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027815314,0.00013003504,0.00021966152,0.000120871046,0.00023968249,0.000020370622,0.000099475714,0.00008374677,0.00006397546],"category_scores_gemma":[0.00001827802,0.00011376285,0.00008147485,0.00026344054,0.00029531334,0.0007262989,3.1961116e-7,0.00020853842,0.000001995128],"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":[0.00020231323,0.000071217255,0.0024698814,0.000012648669,0.00003619941,0.000050748982,0.020441703,0.97285545,0.0005663675,0.0009615712,0.000031182644,0.0023006874],"study_design_scores_gemma":[0.018198507,0.00315776,0.9390502,0.0012409213,0.00082962506,0.00025479615,0.020467067,0.001841993,0.0018287892,0.0016446557,0.009469986,0.0020157124],"about_ca_topic_score_codex":0.000022979972,"about_ca_topic_score_gemma":0.00026069125,"teacher_disagreement_score":0.9710135,"about_ca_system_score_codex":0.00008186736,"about_ca_system_score_gemma":0.00035184174,"threshold_uncertainty_score":0.46391144},"labels":[],"label_agreement":null},{"id":"W2060895377","doi":"10.1007/s11431-009-0260-8","title":"Equilibrium model and algorithm of urban transit assignment based on augmented network","year":2009,"lang":"en","type":"article","venue":"Science in China. Series E, Technological sciences/Science in China. Series E, Technological Sciences","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Urban rail transit; Assignment problem; Transit (satellite); Flow network; Computer science; Line (geometry); Simple (philosophy); Network planning and design; Transport engineering; Transit system; Mathematical optimization; Flow (mathematics); Urban transit; Public transport; Operations research; Computer network; Engineering; Mathematics","score_opus":0.019014306175704473,"score_gpt":0.28699552313661586,"score_spread":0.2679812169609114,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2060895377","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94545674,0.00021551288,0.008940867,0.023516223,0.00044800772,0.0014565539,0.000034071545,0.0011817188,0.018750276],"genre_scores_gemma":[0.9446271,0.00036348825,0.05444837,0.00031666795,0.000033609052,0.000077909186,0.0000036606232,0.0000105224635,0.00011867613],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98777944,0.00027811833,0.0014141821,0.0029247464,0.004558022,0.0030454968],"domain_scores_gemma":[0.9975616,0.0003306611,0.0006354019,0.00074714713,0.00022476554,0.0005004701],"candidate_categories":["metaepi_narrow","bibliometrics","sts","open_science"],"consensus_categories":["sts"],"category_scores_codex":[0.016177088,0.0007290065,0.0009436893,0.0022790967,0.0040976745,0.00095081714,0.006291449,0.0007280158,0.00008511867],"category_scores_gemma":[0.002981386,0.00054459565,0.00014208033,0.02949048,0.080616064,0.004874593,0.00043706808,0.0011718328,0.0000039894144],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017482383,0.0010950937,0.06473111,0.000025199552,0.0000029027171,0.00007516631,0.0026483925,0.4581655,0.009324068,0.39515364,0.00006632102,0.06853778],"study_design_scores_gemma":[0.0012643615,0.0064645857,0.28596604,0.00071422296,0.000023842009,0.000038191192,0.011748731,0.41061658,0.009072802,0.27126163,0.00060893846,0.0022200604],"about_ca_topic_score_codex":0.00037230077,"about_ca_topic_score_gemma":0.00057496753,"teacher_disagreement_score":0.22123493,"about_ca_system_score_codex":0.0006153333,"about_ca_system_score_gemma":0.0018816809,"threshold_uncertainty_score":0.99970055},"labels":[],"label_agreement":null},{"id":"W2061468650","doi":"10.1109/vtcfall.2012.6399123","title":"Robust Traffic Assignment in Transportation Networks Using Network Criticality","year":2012,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Network traffic control; Robustness (evolution); Computer science; Traffic generation model; Criticality; Traffic congestion; Leverage (statistics); Intelligent transportation system; Computer network; Centrality; Traffic congestion reconstruction with Kerner's three-phase theory; Traffic optimization; Floating car data; Transport engineering; Engineering; Mathematics","score_opus":0.06522842129926638,"score_gpt":0.3115328616542299,"score_spread":0.24630444035496352,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2061468650","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.29706442,0.00017892726,0.6964079,0.0003108684,0.0005602701,0.00021516095,0.000002795888,0.00013228862,0.005127379],"genre_scores_gemma":[0.99148494,0.000027885424,0.007850433,0.00014968487,0.0003109964,0.00000599962,0.000046657366,0.0000067298006,0.00011668314],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99890786,0.00012511852,0.00023553865,0.00011392162,0.00021143482,0.00040612076],"domain_scores_gemma":[0.99967337,0.00008407534,0.000037601298,0.000046063004,0.000032852044,0.00012606701],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008638405,0.00006947183,0.00009653529,0.000033237742,0.00022544534,0.000028912378,0.000048525108,0.00010578602,0.00033408112],"category_scores_gemma":[0.00001714616,0.00007247306,0.000031972435,0.00034563686,0.000060488575,0.00034777506,6.455361e-7,0.00009369249,0.0000026925475],"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":[0.000006436754,0.0000493116,0.12015705,0.0000020580596,0.0000023729438,7.172652e-7,0.0028972637,0.86823237,6.964082e-7,0.007878977,0.00015706188,0.0006157157],"study_design_scores_gemma":[0.0006385112,0.000020396848,0.6124752,0.000079617144,0.000062870364,3.3855707e-7,0.007170261,0.37436822,0.000005321146,0.00017937495,0.0045409948,0.00045888522],"about_ca_topic_score_codex":0.0006795859,"about_ca_topic_score_gemma":0.0034175252,"teacher_disagreement_score":0.6944205,"about_ca_system_score_codex":0.000079097284,"about_ca_system_score_gemma":0.000043361673,"threshold_uncertainty_score":0.36579537},"labels":[],"label_agreement":null},{"id":"W2061622144","doi":"10.1139/l00-096","title":"Methodology for off-line assessment of advanced traffic signal control systems","year":2001,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Syracuse University; Oak Ridge National Laboratory; U.S. Department of Transportation","keywords":"Computer science; Key (lock); Control (management); SIGNAL (programming language); Signal timing; Reduction (mathematics); Traffic simulation; Traffic congestion; Service (business); Real-time computing; Simulation; Transport engineering; Microsimulation; Traffic signal; Engineering; Computer security","score_opus":0.03148963161133233,"score_gpt":0.3006707416175512,"score_spread":0.26918111000621886,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2061622144","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.060354862,0.0013555359,0.9362867,0.00030223525,0.00089369493,0.00017781903,0.000026602564,0.000012840956,0.00058971153],"genre_scores_gemma":[0.99271685,0.000050350078,0.0069652074,0.0000145652,0.00016882563,0.000003627284,0.0000037407053,0.000008344363,0.00006846449],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99928236,0.000055458167,0.00030882485,0.000050573253,0.000116436786,0.00018633399],"domain_scores_gemma":[0.9990373,0.0002800357,0.0001712446,0.000033970286,0.0002444592,0.00023294755],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00092332094,0.0000560726,0.00020323666,0.00020629723,0.00007290803,0.000016976697,0.00009623206,0.00005637611,0.000048462625],"category_scores_gemma":[0.00017256643,0.00005877343,0.0000625184,0.00014462289,0.000025529434,0.000107476044,2.8589142e-7,0.00008394441,9.669625e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008697556,0.000003170787,0.00073776307,0.000016408161,0.00002716144,0.000010710801,0.00085447996,0.99510986,0.00014690151,0.0018535289,0.00007046204,0.0011608592],"study_design_scores_gemma":[0.004570243,0.0006474801,0.017719354,0.00058101927,0.00024327879,0.00006597049,0.0051783845,0.81525165,0.000054835873,0.0000894492,0.15512326,0.0004750746],"about_ca_topic_score_codex":0.00056232675,"about_ca_topic_score_gemma":0.063250944,"teacher_disagreement_score":0.932362,"about_ca_system_score_codex":0.000089178335,"about_ca_system_score_gemma":0.0007384309,"threshold_uncertainty_score":0.9538423},"labels":[],"label_agreement":null},{"id":"W2061912490","doi":"10.1002/atr.5670340108","title":"Transport planning for urban areas: A retrospective look and future prospects","year":2000,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":35,"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":"Transportation planning; Sustainability; Urban planning; Environmental design and planning; Environmental planning; Business; Accountability; Land-use planning; Process management; Management science; Transport engineering; Political science; Engineering; Land use; Geography; Civil engineering","score_opus":0.0073257402549623575,"score_gpt":0.26776768522001854,"score_spread":0.2604419449650562,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2061912490","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9894,0.00082629896,0.006773368,0.0008357451,0.00039990354,0.00047267825,0.000053791475,0.000060063194,0.0011781134],"genre_scores_gemma":[0.98606116,0.0008009516,0.011805139,0.00007153364,0.00068505283,0.000012812277,0.000077900186,0.000018815015,0.0004666453],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9988185,0.00001961095,0.00045382208,0.0001679858,0.00034269175,0.00019735657],"domain_scores_gemma":[0.9991507,0.00004586774,0.00030201534,0.00005385049,0.00031786275,0.00012969882],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003097228,0.00012236774,0.00023545623,0.00010916435,0.00029647065,0.000024635225,0.000079765436,0.000103671766,0.00008918342],"category_scores_gemma":[0.000012312679,0.00011976722,0.000101445235,0.00023742742,0.00007703193,0.00075761916,1.1261748e-7,0.000153292,5.206581e-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":[0.0043136263,0.00029703355,0.42975444,0.0001847514,0.00020579135,0.00012875831,0.34978712,0.13404788,0.00037710767,0.027007213,0.00045598927,0.053440314],"study_design_scores_gemma":[0.0019481455,0.00030607317,0.9459578,0.00014287395,0.0001233746,0.0000043815635,0.009220684,0.000026636164,0.00008017765,0.0028714845,0.039116725,0.00020168097],"about_ca_topic_score_codex":0.00001555377,"about_ca_topic_score_gemma":0.00016441366,"teacher_disagreement_score":0.51620334,"about_ca_system_score_codex":0.000056260873,"about_ca_system_score_gemma":0.000103959996,"threshold_uncertainty_score":0.48839653},"labels":[],"label_agreement":null},{"id":"W2062410964","doi":"10.1002/atr.139","title":"Bus rapid transit operation analysis using the Downs–Thomson paradox","year":2010,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","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":"Reliability (semiconductor); Transit (satellite); Arrival time; Transit time; Static timing analysis; Computer science; Transport engineering; Variance (accounting); Simulation; Public transport; Automotive engineering; Engineering; Embedded system; Physics; Economics","score_opus":0.014405569680018515,"score_gpt":0.3021529860018742,"score_spread":0.2877474163218557,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2062410964","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8572831,0.00010660653,0.14069945,0.0008479355,0.00070296874,0.00015674099,0.00001689419,0.000025880294,0.00016043651],"genre_scores_gemma":[0.9777879,0.00021164521,0.021561842,0.00007499614,0.00023779362,0.0000024748565,0.00007433238,0.000010126851,0.000038905055],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99846923,0.000101766236,0.0005869718,0.00013105688,0.00053828827,0.00017267939],"domain_scores_gemma":[0.99878055,0.00010137304,0.00048536324,0.000111353125,0.00042329848,0.000098087134],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00083127443,0.00010962236,0.00022176468,0.00024066109,0.00051128195,0.00007399855,0.00017412976,0.00010690868,0.00016666923],"category_scores_gemma":[0.000042029176,0.000085278014,0.00022170966,0.00088566623,0.00010912161,0.0008837462,2.8670507e-7,0.0003238686,0.000001229229],"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":[0.00012347162,0.00004809629,0.0074338247,0.0000058637356,0.0001398447,0.000012611471,0.033617623,0.9394118,0.011085917,0.0023212489,0.000011639018,0.005788017],"study_design_scores_gemma":[0.002895148,0.00024582466,0.93406713,0.00007586092,0.003812562,0.000016233713,0.029584218,0.007885664,0.003357959,0.0013916038,0.016019436,0.0006483667],"about_ca_topic_score_codex":0.000133312,"about_ca_topic_score_gemma":0.0036959425,"teacher_disagreement_score":0.9315262,"about_ca_system_score_codex":0.000030677187,"about_ca_system_score_gemma":0.00017086639,"threshold_uncertainty_score":0.3932419},"labels":[],"label_agreement":null},{"id":"W2063042933","doi":"10.1016/j.cor.2006.02.002","title":"Parking buses in a depot using block patterns: A Benders decomposition approach for minimizing type mismatches","year":2006,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":16,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Depot; Benders' decomposition; Block (permutation group theory); Computer science; Decomposition; Mathematical optimization; Mathematics; Combinatorics","score_opus":0.14713577790120155,"score_gpt":0.42671062050840447,"score_spread":0.2795748426072029,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2063042933","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5726036,0.00008150052,0.42609167,0.00027717825,0.00009798829,0.00048707574,0.0000063995303,0.00004716362,0.0003074585],"genre_scores_gemma":[0.8615934,0.000021163638,0.13789666,0.000029365678,0.00015674607,0.000046041827,0.00018879154,0.00001203305,0.000055786997],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99851704,0.0002695374,0.00025138602,0.00026406217,0.0003452364,0.00035275694],"domain_scores_gemma":[0.99926263,0.00023305175,0.000023297225,0.00009864607,0.00032916275,0.000053207372],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008489346,0.00008189519,0.000112472706,0.00034540799,0.0010637719,0.00033463648,0.00016332227,0.000084316125,0.0000050590947],"category_scores_gemma":[0.000056699573,0.00009230165,0.000034630968,0.00067354616,0.000091436326,0.0002745134,0.000013385606,0.000140239,0.0000011786849],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019001163,0.000098572105,0.016159514,0.00002426104,0.000007228429,0.000002388937,0.0059926095,0.97537535,0.0004655859,0.0011975846,0.00017803682,0.00047987662],"study_design_scores_gemma":[0.0004925888,0.00003773827,0.007237988,0.00011022561,0.000008705648,0.0000016355649,0.0064891065,0.98470795,0.00026732255,0.000098072414,0.0003773005,0.00017133534],"about_ca_topic_score_codex":0.006846848,"about_ca_topic_score_gemma":0.0055920924,"teacher_disagreement_score":0.28898984,"about_ca_system_score_codex":0.000157628,"about_ca_system_score_gemma":0.0002403588,"threshold_uncertainty_score":0.99976665},"labels":[],"label_agreement":null},{"id":"W2063249628","doi":"10.1287/opre.1030.0091","title":"A Strategic Flow Model of Traffic Assignment in Static Capacitated Networks","year":2004,"lang":"en","type":"article","venue":"Operations Research","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":69,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science; Representation (politics); Flow network; Field (mathematics); Traffic flow (computer networking); Work (physics); Arc (geometry); Flow (mathematics); Operations research; Mathematical optimization; Theoretical computer science; Management science; Computer network; Mathematics","score_opus":0.1596230623152449,"score_gpt":0.40318978250698306,"score_spread":0.24356672019173817,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2063249628","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.86528146,0.000043692013,0.12829822,0.0012404976,0.00003927704,0.00047477122,0.000021303875,0.000032110685,0.0045686527],"genre_scores_gemma":[0.9921034,0.000097750366,0.0071771718,0.000012338301,0.000014719586,0.000048501654,0.000067444176,0.000006012613,0.00047268113],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986935,0.00022730784,0.0002158282,0.00013396404,0.00047740884,0.00025196004],"domain_scores_gemma":[0.99951744,0.00006388213,0.00001208444,0.00007766755,0.00025946944,0.000069440284],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011503025,0.00004741267,0.000081565995,0.00020719787,0.00035793608,0.00007412575,0.00010273207,0.000066272856,0.000071480485],"category_scores_gemma":[0.000066196255,0.00004785679,0.000017086171,0.0008163366,0.00017664333,0.00017521973,0.0000033118706,0.00019657472,0.000007463357],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008016575,0.00009638581,0.000046577272,0.000003849046,0.0000032718722,0.0000017160152,0.01520282,0.9719063,0.000081037724,0.012409289,0.0000204884,0.000220261],"study_design_scores_gemma":[0.00035089173,0.000037201844,0.00011206078,0.000030669697,0.0000018734395,6.356287e-8,0.00766218,0.9912199,0.000023405515,0.0005027891,0.000008293861,0.000050715455],"about_ca_topic_score_codex":0.0018562143,"about_ca_topic_score_gemma":0.023513071,"teacher_disagreement_score":0.1268219,"about_ca_system_score_codex":0.0001539456,"about_ca_system_score_gemma":0.00078406284,"threshold_uncertainty_score":0.99430525},"labels":[],"label_agreement":null},{"id":"W2063505935","doi":"10.1061/(asce)is.1943-555x.0000126","title":"Simulating Freight Traffic between Atlantic Canada and Québec to Support Pavement Management on New Brunswick’s Regional Highways","year":2012,"lang":"en","type":"article","venue":"Journal of Infrastructure Systems","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Concordia University","funders":"","keywords":"Transport engineering; Truck; Traffic management; Estimation; Environmental science; Land use; Asset management; TRIPS architecture; Distribution (mathematics); Environmental resource management; Engineering; Civil engineering; Business","score_opus":0.016462156860653016,"score_gpt":0.2537276003200261,"score_spread":0.2372654434593731,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2063505935","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9942879,0.00013196247,0.0022111675,0.00094667985,0.0011042425,0.00022888147,0.0000047889816,0.000024055333,0.0010602918],"genre_scores_gemma":[0.9971499,0.000010829552,0.0009470924,0.00017304606,0.0011842769,5.5148496e-7,0.000007335626,0.000009587864,0.0005173936],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9984351,0.0000815032,0.0004675988,0.0000949615,0.00067150476,0.00024937204],"domain_scores_gemma":[0.99899006,0.00010519256,0.00035499572,0.000068778936,0.00009230438,0.0003886927],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047197827,0.000114041715,0.00021691759,0.00011925863,0.00021625984,0.000060666272,0.00012592689,0.0000606404,0.00004425654],"category_scores_gemma":[0.000025681124,0.000092782735,0.000036739864,0.00014906032,0.000019897429,0.0001571729,0.000005670213,0.00013293566,0.0000014611392],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000032016636,0.00001411258,0.6425888,0.000069024456,0.00015307876,0.000031915653,0.016256059,0.2572679,0.0000048582406,0.0042175604,0.07634807,0.0030166053],"study_design_scores_gemma":[0.00071494014,0.0001703907,0.76268804,0.00030391134,0.000110217574,0.000022216935,0.0058645625,0.00016529432,0.0000059445897,0.000018071205,0.22967784,0.00025854955],"about_ca_topic_score_codex":0.529892,"about_ca_topic_score_gemma":0.54046977,"teacher_disagreement_score":0.2571026,"about_ca_system_score_codex":0.00031743877,"about_ca_system_score_gemma":0.0011767135,"threshold_uncertainty_score":0.47323847},"labels":[],"label_agreement":null},{"id":"W2063974640","doi":"10.1002/atr.5670350203","title":"Modelling the provision of transport with planned or market‐determined public transport services","year":2001,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","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":"Context (archaeology); Public transport; Relevance (law); Task (project management); Government (linguistics); Control (management); Transport engineering; Computer science; Operations research; Engineering; Political science; Systems engineering; Geography; Artificial intelligence","score_opus":0.01845233270170075,"score_gpt":0.2680120526625061,"score_spread":0.24955971996080537,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2063974640","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.91977435,0.0001297751,0.07781422,0.0008816297,0.00020664185,0.00039421086,0.00003004645,0.000046502184,0.0007226116],"genre_scores_gemma":[0.98817563,0.000928709,0.010251941,0.00005186347,0.00010150011,0.0000086622285,0.00009022532,0.000025446685,0.0003660251],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99757206,0.00008317144,0.0009637785,0.00019162729,0.00090236,0.00028702203],"domain_scores_gemma":[0.9979149,0.00014557046,0.000961253,0.00014440955,0.0006878658,0.00014597243],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007801615,0.00018937548,0.00035416492,0.00021553037,0.00031978983,0.000022378146,0.00031461957,0.000116168856,0.00012394959],"category_scores_gemma":[0.000009254167,0.00012059589,0.0001469969,0.000733585,0.00014961076,0.0013479847,3.7184031e-7,0.0002217841,4.6008228e-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":[0.0052418793,0.00016915235,0.048111502,0.000119087286,0.00007736264,0.00012032578,0.037399307,0.905283,0.0001324814,0.0004272136,0.000008348465,0.002910394],"study_design_scores_gemma":[0.01617281,0.0047031874,0.7581232,0.0029811074,0.0016810746,0.00013101206,0.12515692,0.020290524,0.0010099871,0.0013425013,0.066693254,0.0017144048],"about_ca_topic_score_codex":0.00023936652,"about_ca_topic_score_gemma":0.004753889,"teacher_disagreement_score":0.8849924,"about_ca_system_score_codex":0.00003653983,"about_ca_system_score_gemma":0.0003669087,"threshold_uncertainty_score":0.49177578},"labels":[],"label_agreement":null},{"id":"W2064202446","doi":"10.2495/ut110151","title":"Performance analysis of urban road intersections and its environmental implication: a case study of the Lagos metropolitan area","year":2011,"lang":"en","type":"article","venue":"WIT transactions on the built environment","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Transport Canada","funders":"","keywords":"Metropolitan area; Evening; Intersection (aeronautics); Transport engineering; Level of service; Traffic flow (computer networking); Traffic volume; Morning; Service (business); Urban area; Computer science; Geography; Engineering; Business; Computer security","score_opus":0.03199368060354899,"score_gpt":0.2452032636833734,"score_spread":0.2132095830798244,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2064202446","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9963343,0.000028007638,0.0025741945,0.000096373034,0.000036337973,0.00038994185,0.000053316908,0.000011328309,0.00047618613],"genre_scores_gemma":[0.99951184,0.00010512622,0.0000483278,0.000012936758,0.0000047278927,0.00005985906,0.0000028060301,0.000005869491,0.00024851173],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.9991355,0.00013097578,0.00023445787,0.00015934183,0.00023966467,0.000100043064],"domain_scores_gemma":[0.9995014,0.00004512517,0.0001347899,0.00026840164,0.000007896553,0.000042354593],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020058142,0.000086679946,0.00012242275,0.000116895295,0.00054404075,0.0000058064543,0.00013905493,0.000037713788,0.0004908729],"category_scores_gemma":[0.0000027782942,0.000060193106,0.00008343804,0.00032392776,0.00020517867,0.00008360868,0.0000032850287,0.00009331367,0.0000021796393],"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":[0.00018390933,0.0053896164,0.37672526,0.00002273016,0.003195819,0.000010807264,0.51528203,0.090237826,0.0016943013,0.001299276,0.000022273982,0.0059361667],"study_design_scores_gemma":[0.0005391574,0.0004703454,0.69804037,0.0000165726,0.0025585215,0.000013173574,0.28828028,0.006933174,0.0027541923,0.000015401727,0.0001670803,0.0002117142],"about_ca_topic_score_codex":0.0029283613,"about_ca_topic_score_gemma":0.0016761707,"teacher_disagreement_score":0.32131514,"about_ca_system_score_codex":0.00009726102,"about_ca_system_score_gemma":0.0000106712805,"threshold_uncertainty_score":0.53747135},"labels":[],"label_agreement":null},{"id":"W2064223206","doi":"10.3917/riges.272.0038","title":"Le secteur du transport interurbain et la recherche opérationnelle : une synergie méconnue à exploiter","year":2002,"lang":"fr","type":"article","venue":"Gestion","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Humanities; Political science; Philosophy","score_opus":0.12901631842840258,"score_gpt":0.3141600722903623,"score_spread":0.18514375386195972,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2064223206","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3760198,0.0026264554,0.23576833,0.25139713,0.0031178722,0.0006296143,0.000105131,0.000657625,0.12967803],"genre_scores_gemma":[0.94634616,0.0019072976,0.0057048295,0.00044921637,0.0002755208,0.000029290408,0.00020859612,0.000028309589,0.045050766],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9974667,0.00137638,0.0003426923,0.00030657338,0.0002516088,0.00025606347],"domain_scores_gemma":[0.99892193,0.00056341285,0.00013038702,0.00013441108,0.00013623155,0.000113644935],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00165017,0.00016915363,0.0001625744,0.00008865142,0.00031355405,0.00007398442,0.0001331142,0.0005275385,0.0007612016],"category_scores_gemma":[0.00026416985,0.00020174086,0.00009086649,0.0006768899,0.00019200804,0.00073882693,0.0000041386356,0.0004598423,0.00018936777],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000068653455,0.0008201527,0.012613534,0.00013272553,0.000086304135,0.00008582885,0.26999873,0.1578064,0.00042318818,0.47092783,0.01459643,0.07244021],"study_design_scores_gemma":[0.002444303,0.00028040394,0.0736777,0.0010242303,0.00026718713,0.000037417292,0.015381273,0.06347764,0.00030722667,0.011293929,0.8305449,0.001263794],"about_ca_topic_score_codex":0.002201545,"about_ca_topic_score_gemma":0.00339734,"teacher_disagreement_score":0.8159485,"about_ca_system_score_codex":0.00009303678,"about_ca_system_score_gemma":0.00011759737,"threshold_uncertainty_score":0.83346236},"labels":[],"label_agreement":null},{"id":"W2064722902","doi":"10.1111/j.1936-4490.2005.tb00356.x","title":"Transportation Policy and Management: Introduction to the Special Issue","year":2009,"lang":"en","type":"article","venue":"Canadian Journal of Administrative Sciences / Revue Canadienne des Sciences de l Administration","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Library science; Citation; Management; Computer science","score_opus":0.04523602037847067,"score_gpt":0.3262464537549736,"score_spread":0.2810104333765029,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2064722902","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.81623566,0.00018303753,0.004340993,0.12682506,0.0014840969,0.0006876444,0.00007225632,0.000026119531,0.050145112],"genre_scores_gemma":[0.98604286,0.000096166616,0.0077049076,0.0007417901,0.0046437825,0.000004136789,0.00000807879,0.0000043344817,0.0007539635],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.998148,0.0001849394,0.0004480848,0.00031715375,0.00028593937,0.0006158425],"domain_scores_gemma":[0.99826604,0.00008651215,0.00029267292,0.000095303316,0.00030816998,0.0009512775],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0026570258,0.00014627933,0.00016243562,0.0006302828,0.0032490466,0.0005743699,0.0005186277,0.00007450889,0.000118668795],"category_scores_gemma":[0.0005510455,0.00012757046,0.00005526611,0.0021223137,0.0024123676,0.0009089742,0.0000015810704,0.0001472398,0.0000042710158],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000076158365,0.00004224939,0.006224768,0.000031943586,0.000024952993,0.0001942507,0.20736061,0.010315677,0.00007081801,0.7315703,0.008410556,0.035677705],"study_design_scores_gemma":[0.0005468902,0.0062608905,0.2460407,0.00028574734,0.00012895324,0.0003988893,0.11826054,0.0004350074,0.00032553982,0.041663047,0.5848965,0.0007572684],"about_ca_topic_score_codex":0.007651319,"about_ca_topic_score_gemma":0.664076,"teacher_disagreement_score":0.68990725,"about_ca_system_score_codex":0.00048561167,"about_ca_system_score_gemma":0.004234976,"threshold_uncertainty_score":0.9989568},"labels":[],"label_agreement":null},{"id":"W2065142586","doi":"10.3328/tl.2009.01.04.257-269","title":"Investigating multiple activity participation and time-use decisions by using a multivariate Kuhn-Tucker demand system model","year":2009,"lang":"en","type":"article","venue":"Transportation Letters","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Multivariate statistics; Time allocation; Preference; Time budget; Econometric model; Econometrics; Computer science; Operations research; Statistics; Economics; Mathematics; Ecology; Machine learning","score_opus":0.04824996826471227,"score_gpt":0.3044641209704722,"score_spread":0.2562141527057599,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2065142586","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.74121296,0.000009685923,0.2575545,0.0007094759,0.00003530876,0.00023862669,0.000051438274,0.00016028815,0.000027711505],"genre_scores_gemma":[0.9791474,0.000008128175,0.020080088,0.0005095167,0.000026454494,0.000011045308,0.00016653675,0.000014092882,0.00003677096],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99864084,0.00013484074,0.00032942405,0.00029188013,0.00034135435,0.00026164902],"domain_scores_gemma":[0.9992291,0.00021832649,0.00019780674,0.00009729756,0.000084972235,0.00017250623],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034653393,0.00014735838,0.00017007004,0.000090804424,0.00064049667,0.00011481436,0.00006421028,0.00010458556,0.0000060073658],"category_scores_gemma":[0.000111147565,0.0001613691,0.000043730437,0.00025639255,0.000078106146,0.00094759604,7.524251e-7,0.00010890959,0.0000034542513],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000054977518,0.000069148584,0.04677737,0.00001777883,0.000023275454,0.000004370193,0.027371818,0.7784799,0.14424933,0.00094602833,0.00030664983,0.0016993871],"study_design_scores_gemma":[0.00077986205,0.00001628963,0.16375157,0.00011911964,0.000095737334,2.3585243e-7,0.00040717787,0.8336245,0.0008304925,0.000045074445,0.000064763444,0.0002652145],"about_ca_topic_score_codex":0.0013434318,"about_ca_topic_score_gemma":0.0003877778,"teacher_disagreement_score":0.2379344,"about_ca_system_score_codex":0.00006986791,"about_ca_system_score_gemma":0.00005406033,"threshold_uncertainty_score":0.65804404},"labels":[],"label_agreement":null},{"id":"W2065956978","doi":"10.1002/atr.5670390102","title":"Special issue: Behavior in networks","year":2005,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","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":"Chatterjee; Field (mathematics); Computer science; Operations research; Travel behavior; Cover (algebra); Data science; Management science; Transport engineering; Engineering; Artificial intelligence","score_opus":0.010291182086670167,"score_gpt":0.29486844279790403,"score_spread":0.28457726071123385,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2065956978","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98006475,0.00024719196,0.010003954,0.0016190154,0.0025596407,0.00029826185,0.0000114676695,0.000040338233,0.005155405],"genre_scores_gemma":[0.98200583,0.00049698504,0.011430747,0.00008327406,0.0056194626,0.0000039704964,0.000034220106,0.000009388839,0.00031609755],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9989465,0.00003923922,0.00048530183,0.00007798678,0.00030129214,0.00014969619],"domain_scores_gemma":[0.99939555,0.000040632945,0.00028720926,0.00003793453,0.00016435201,0.00007435094],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003241578,0.00006637153,0.0001356665,0.0001541488,0.00008876718,0.00001790235,0.00008838032,0.00007550047,0.00029122634],"category_scores_gemma":[0.000021753855,0.00006982561,0.000065252796,0.0002972639,0.000039813567,0.0007514318,1.9417753e-7,0.00016932795,0.000004085933],"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":[0.00014249799,0.00009993384,0.037988458,0.0000028737056,0.000004556184,0.00003075309,0.016557638,0.8809337,0.000032210286,0.00080042006,0.00064914266,0.0627578],"study_design_scores_gemma":[0.0010817244,0.000070907154,0.8017205,0.000046390112,0.000037248537,0.0000012932248,0.003630368,0.00009055153,0.00003397925,0.0000601849,0.19311011,0.00011673998],"about_ca_topic_score_codex":0.000027561748,"about_ca_topic_score_gemma":0.0038925614,"teacher_disagreement_score":0.88084316,"about_ca_system_score_codex":0.00007315016,"about_ca_system_score_gemma":0.00008231893,"threshold_uncertainty_score":0.31887242},"labels":[],"label_agreement":null},{"id":"W2066062846","doi":"10.1016/j.seps.2011.02.001","title":"Planning rapid transit networks","year":2011,"lang":"en","type":"article","venue":"Socio-Economic Planning Sciences","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":66,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"Junta de Andalucía; Ministerio de Ciencia e Innovación","keywords":"Heuristics; Transit (satellite); Network planning and design; Process (computing); Computer science; Quality (philosophy); Management science; Transportation planning; Transport engineering; Operations research; Transit system; Urban transit; Process management; Engineering management; Engineering; Public transport","score_opus":0.07419927816167061,"score_gpt":0.30717072063515355,"score_spread":0.23297144247348295,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2066062846","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6206175,0.0013194733,0.03388858,0.0008326764,0.0023931284,0.0003450101,0.000020822263,0.00070098985,0.33988178],"genre_scores_gemma":[0.9950681,0.000063986656,0.0037426467,0.0002709467,0.00028838505,0.00001215491,0.000017958258,0.0000127375915,0.0005231286],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9983234,0.00013404626,0.0003293082,0.00042138065,0.00022306723,0.00056880777],"domain_scores_gemma":[0.99932086,0.00018447937,0.00018700103,0.000111695445,0.00002956309,0.00016642375],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0015859659,0.00016137566,0.0002063976,0.00015675869,0.0019021513,0.00015591267,0.0004965852,0.00016586145,0.0006099657],"category_scores_gemma":[0.000034252673,0.00016754415,0.00009087297,0.0002430013,0.0009146302,0.00069627765,0.0000099182025,0.00018388515,0.00006400548],"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":[0.00004318868,0.000037441492,0.38776642,0.0000073391943,0.000048115628,0.000023730605,0.2179127,0.35626626,0.000008893443,0.030823411,0.003937436,0.0031250725],"study_design_scores_gemma":[0.0026047071,0.0006945124,0.41258675,0.0006005029,0.00023557828,0.000020764952,0.40356398,0.08805175,0.00015984848,0.014408442,0.07360775,0.0034653991],"about_ca_topic_score_codex":0.0009376816,"about_ca_topic_score_gemma":0.00005882069,"teacher_disagreement_score":0.37445053,"about_ca_system_score_codex":0.00009292019,"about_ca_system_score_gemma":0.00031538616,"threshold_uncertainty_score":0.9993972},"labels":[],"label_agreement":null},{"id":"W2066876689","doi":"10.2495/safe-v4-n1-19-37","title":"Integrated travel demand models for evacuations: a bridge between social science and engineering","year":2014,"lang":"en","type":"article","venue":"International Journal of Safety and Security Engineering","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Bridge (graph theory); Transport engineering; Engineering; Poison control; Human factors and ergonomics; Forensic engineering; Civil engineering; Computer science; Medical emergency; Medicine","score_opus":0.018032114784375712,"score_gpt":0.2741898204036285,"score_spread":0.2561577056192528,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2066876689","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.124942526,0.000104911094,0.8730705,0.0011536053,0.00032481033,0.000091288144,0.000037060334,0.000023712157,0.00025162395],"genre_scores_gemma":[0.9955826,0.0001356415,0.0038834498,0.000027564738,0.00034815207,0.0000015257963,0.000009582761,0.000005916444,0.0000055703417],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999128,0.000014159571,0.00026214716,0.00008911834,0.00038530512,0.00012131206],"domain_scores_gemma":[0.99895626,0.00019665595,0.00010042136,0.000019803263,0.00064089434,0.00008593591],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013709548,0.0000679393,0.00012701577,0.00021601524,0.00019919917,0.00010684237,0.00014503916,0.000051305124,0.000001885639],"category_scores_gemma":[0.00048535824,0.000070203176,0.000031350017,0.00014227504,0.000074037576,0.0005586206,0.000008399205,0.00011566192,7.803483e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027021466,0.00006744923,0.0038154111,0.000113383234,0.0003086403,0.0000062889176,0.08840078,0.24426055,0.0016038012,0.626097,0.000117039075,0.034939416],"study_design_scores_gemma":[0.0017523535,0.00008161354,0.09429194,0.0002356467,0.00007543144,0.000016719652,0.0010463814,0.88319355,0.00018981066,0.003964938,0.014842897,0.0003087075],"about_ca_topic_score_codex":0.000026434913,"about_ca_topic_score_gemma":0.0000066494863,"teacher_disagreement_score":0.8706401,"about_ca_system_score_codex":0.00007167421,"about_ca_system_score_gemma":0.00009298365,"threshold_uncertainty_score":0.28628024},"labels":[],"label_agreement":null},{"id":"W2066958483","doi":"10.1080/18128600802591384","title":"Modelling activity generation: a utility-based model for activity-agenda formation","year":2009,"lang":"en","type":"article","venue":"Transportmetrica","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":49,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto; University of Alberta","funders":"","keywords":"Baseline (sea); Econometric model; Econometrics; Budget constraint; Scope (computer science); Specification; Computer science; Constraint (computer-aided design); Function (biology); Sensitivity (control systems); Operations research; Economics; Mathematics; Microeconomics; Engineering","score_opus":0.13512875885472617,"score_gpt":0.3324931305714056,"score_spread":0.19736437171667945,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2066958483","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08747601,0.000032594933,0.9089947,0.0011556925,0.00008120373,0.00052379566,0.00008674096,0.00022370432,0.0014255914],"genre_scores_gemma":[0.9623959,0.00003697856,0.03663171,0.0002212574,0.00010474413,0.000037354217,0.00022648904,0.000011415699,0.00033416756],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99869204,0.000058429396,0.00023620657,0.00028352448,0.000412876,0.00031695553],"domain_scores_gemma":[0.99930817,0.000091165915,0.00013487267,0.00015437926,0.00019791164,0.00011352526],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005332059,0.0001450075,0.00018760611,0.00029019345,0.0007039398,0.0000668399,0.00013010514,0.00015530855,0.000029352344],"category_scores_gemma":[0.000030496341,0.00016029448,0.00014367001,0.0008720961,0.000042858454,0.00073033717,5.1796826e-7,0.00011662609,0.0000024760288],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012375308,0.00015789006,0.00010782347,0.000015924405,0.0000058558494,8.769567e-7,0.003405673,0.9786562,0.00022554105,0.0013628596,0.00024889348,0.015688747],"study_design_scores_gemma":[0.00053937075,0.000055170447,0.0005329589,0.0000074630925,0.00004516451,7.782373e-8,0.00004443531,0.99507487,0.00085983117,0.0003029305,0.002354435,0.00018329118],"about_ca_topic_score_codex":0.00016033831,"about_ca_topic_score_gemma":0.00035600984,"teacher_disagreement_score":0.8749199,"about_ca_system_score_codex":0.00008004406,"about_ca_system_score_gemma":0.00024221063,"threshold_uncertainty_score":0.6536619},"labels":[],"label_agreement":null},{"id":"W2067858420","doi":"10.3141/1702-02","title":"Transportation Market Reforms for Sustainability","year":2000,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Transport Canada","funders":"","keywords":"Equity (law); Sustainability; Business; Revenue; Sustainable transport; Economic efficiency; Traffic congestion; Environmental economics; Economics; Social equality; Productivity; Public economics; Finance; Transport engineering; Microeconomics; Economic growth; Engineering; Market economy","score_opus":0.06433095923379845,"score_gpt":0.4102354405784238,"score_spread":0.34590448134462537,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2067858420","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9758093,0.00020842007,0.0040606945,0.011806614,0.00074092136,0.0032337727,0.0003440389,0.00011293552,0.003683311],"genre_scores_gemma":[0.98115045,0.0017207166,0.0035523255,0.00009739054,0.00045096342,0.00026156468,0.0001341516,0.00007927809,0.012553146],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.98989356,0.0017683499,0.001942892,0.0005954864,0.0044017322,0.0013979569],"domain_scores_gemma":[0.9884923,0.0017047664,0.0005357006,0.00052494364,0.0081434,0.00059890817],"candidate_categories":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.010577881,0.00032046478,0.0005549027,0.0008960684,0.0024465804,0.00024338784,0.0013255216,0.00040770252,0.002501443],"category_scores_gemma":[0.00046974292,0.00025849193,0.00066941156,0.003007252,0.0013583655,0.001352504,0.0000013255316,0.0016951014,0.000017957584],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.020887291,0.0018617776,0.54472005,0.0016017358,0.00062713417,0.00019284265,0.10650353,0.026893957,0.00043806143,0.08380809,0.07871758,0.1337479],"study_design_scores_gemma":[0.0022824705,0.00063414394,0.6877329,0.00025677064,0.00010167618,1.7559302e-7,0.012442472,0.00017182784,0.00017244939,0.02108517,0.27478155,0.0003383779],"about_ca_topic_score_codex":0.023621367,"about_ca_topic_score_gemma":0.15266138,"teacher_disagreement_score":0.19606398,"about_ca_system_score_codex":0.00069049915,"about_ca_system_score_gemma":0.0021257654,"threshold_uncertainty_score":0.9999867},"labels":[],"label_agreement":null},{"id":"W2067907347","doi":"10.3141/2112-17","title":"Quantifying Impacts of Transit Reliability on User Costs","year":2009,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Reliability (semiconductor); Transit (satellite); Transport engineering; Computer science; Service (business); Mode (computer interface); Level of service; Public transport; Automatic vehicle location; Operations research; Reliability engineering; Engineering; Business; Telecommunications","score_opus":0.12425344235619672,"score_gpt":0.4410224095093565,"score_spread":0.3167689671531598,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2067907347","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9865148,0.00014594213,0.0019658206,0.008694194,0.0004913772,0.0010210623,0.00007201677,0.00004711805,0.0010476782],"genre_scores_gemma":[0.99611497,0.0012297196,0.0019057736,0.00010448956,0.00016918147,0.000014253561,0.000024888255,0.00003218897,0.00040452482],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.98945695,0.0022184588,0.0017035214,0.00042498886,0.0052492195,0.0009468678],"domain_scores_gemma":[0.9916547,0.0016338931,0.00067986135,0.00048849377,0.0050347825,0.0005082763],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009304419,0.00024902227,0.0005367311,0.0011197561,0.0011315492,0.00012804632,0.0010884083,0.00030388022,0.00027819246],"category_scores_gemma":[0.00067875395,0.0001980764,0.00048625362,0.0030823348,0.0009186173,0.00087941106,0.0000015503517,0.0019526748,0.0000137618645],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.007124079,0.0016493347,0.81040865,0.00036409026,0.00020036248,0.00014136056,0.046087455,0.033904612,0.0063307625,0.06413279,0.010086727,0.019569786],"study_design_scores_gemma":[0.0014414459,0.0010233532,0.9760073,0.0006771716,0.00005325242,1.4269187e-7,0.005164792,0.000057532867,0.0020862026,0.0017267278,0.011557057,0.00020502572],"about_ca_topic_score_codex":0.017172001,"about_ca_topic_score_gemma":0.074490294,"teacher_disagreement_score":0.16559865,"about_ca_system_score_codex":0.00038939915,"about_ca_system_score_gemma":0.0012010752,"threshold_uncertainty_score":0.98937273},"labels":[],"label_agreement":null},{"id":"W2068258696","doi":"10.1002/atr.132","title":"Designing robust rapid transit networks with alternative routes","year":2010,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":73,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Robustness (evolution); Computer science; Network planning and design; Flow network; Mathematical optimization; Transport engineering; Computer network; Engineering; Mathematics; Chemistry","score_opus":0.012579758588776632,"score_gpt":0.2565086730938245,"score_spread":0.2439289145050479,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2068258696","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3731251,0.00008479012,0.625193,0.0003571033,0.00062765233,0.00014154054,0.0000051113357,0.000038951024,0.0004267417],"genre_scores_gemma":[0.88858455,0.00020019805,0.11078901,0.000050255043,0.0002795489,0.0000031799595,0.000031350184,0.000015452597,0.000046439643],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99879754,0.000052979853,0.0004089635,0.00012488777,0.0004239448,0.0001917024],"domain_scores_gemma":[0.99872386,0.00012306038,0.00047420658,0.00006098552,0.0004889617,0.00012891919],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046211647,0.00011360183,0.00018512337,0.0001284477,0.00025737507,0.000044107343,0.00013548757,0.00008980482,0.00007449086],"category_scores_gemma":[0.000028309125,0.00009830285,0.00007636697,0.00030978085,0.0001084135,0.00095282897,1.9649453e-7,0.00037417092,7.1526864e-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":[0.00026668716,0.00003846931,0.006048761,0.0000055389123,0.000034656856,0.000031814725,0.019328047,0.96623486,0.0011548104,0.001280086,0.000015602123,0.005560656],"study_design_scores_gemma":[0.013895459,0.002218429,0.8785188,0.0009945137,0.0010566664,0.000053919845,0.061376315,0.0068931184,0.010778,0.0021870038,0.020176962,0.0018508147],"about_ca_topic_score_codex":0.000058293244,"about_ca_topic_score_gemma":0.002196941,"teacher_disagreement_score":0.95934176,"about_ca_system_score_codex":0.000022283511,"about_ca_system_score_gemma":0.00013947512,"threshold_uncertainty_score":0.4008674},"labels":[],"label_agreement":null},{"id":"W2068445215","doi":"10.1002/atr.5670380305","title":"Sequential optimization approach for the multi‐class user equilibrium problem in a continuous transportation system","year":2004,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Mathematical optimization; Computer science; Class (philosophy); Logit; Process (computing); Externality; Operations research; Optimization problem; Space (punctuation); Mathematics; Economics; Artificial intelligence","score_opus":0.017611891117061646,"score_gpt":0.27695566991686,"score_spread":0.25934377879979836,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2068445215","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08287896,0.00018708721,0.9146983,0.0004823922,0.00045688322,0.0010904651,0.000052764713,0.00006345443,0.0000896596],"genre_scores_gemma":[0.72077024,0.00008847224,0.27859646,0.000030717558,0.0001278087,0.000059965394,0.00025768438,0.000023447361,0.000045220277],"study_design_codex":"simulation_or_modeling","study_design_gemma":"qualitative","domain_scores_codex":[0.997959,0.000076467746,0.0009708242,0.00021037464,0.00050206913,0.00028123337],"domain_scores_gemma":[0.9983554,0.00011193053,0.0007698857,0.00009408442,0.0005780979,0.00009059959],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00085368945,0.00016496003,0.00029096057,0.00020583787,0.0002482977,0.00006355497,0.00020410508,0.00015145018,0.0000055531864],"category_scores_gemma":[0.000036758327,0.00014014204,0.00017556253,0.0005182001,0.0000941553,0.0010522784,3.7036872e-7,0.00020454508,3.1765924e-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":[0.0003829866,0.00012625515,0.0017855539,0.0000951327,0.000033193028,0.000008818172,0.020826517,0.9697599,0.00033461477,0.006192946,0.0000063423568,0.00044778542],"study_design_scores_gemma":[0.089923106,0.0023665302,0.28092417,0.0035704318,0.0030102835,0.00004596272,0.35961348,0.23924847,0.0044341516,0.0016900233,0.011661928,0.0035114628],"about_ca_topic_score_codex":0.0002631135,"about_ca_topic_score_gemma":0.0014292536,"teacher_disagreement_score":0.73051137,"about_ca_system_score_codex":0.00022875726,"about_ca_system_score_gemma":0.00032312176,"threshold_uncertainty_score":0.57148266},"labels":[],"label_agreement":null},{"id":"W2068456711","doi":"10.1145/1807406.1807459","title":"Information paradoxes in traffic networks","year":2010,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Aggregate (composite); Computer science; Routing (electronic design automation); A priori and a posteriori; Operations research; Adverse weather; Transport engineering; Computer network; Engineering","score_opus":0.006920311883958238,"score_gpt":0.25368298359542596,"score_spread":0.2467626717114677,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2068456711","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9369293,0.0000044045764,0.01231838,0.0007951786,0.0004003139,0.00009591633,6.6489827e-7,0.00012983134,0.049326],"genre_scores_gemma":[0.998489,0.000012952465,0.0010971413,0.00012106602,0.00003760961,0.000003318562,0.000022783219,0.0000010015469,0.00021508794],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9997086,0.000013205505,0.0000918655,0.000024917674,0.00007910675,0.00008232469],"domain_scores_gemma":[0.9998739,0.000027779255,0.000021080124,0.00002909424,0.000021263402,0.000026881562],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021222721,0.000022625043,0.000027868793,0.000046585898,0.00014849036,0.00002867907,0.00004246514,0.000057404555,0.00018984122],"category_scores_gemma":[0.000030945277,0.000021658505,0.000008741947,0.0001660954,0.000028275197,0.00042454115,0.0000010071046,0.0000784271,0.000016076701],"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":[0.000012228668,0.000029529023,0.072143994,0.000002703455,0.000002026872,6.5090705e-7,0.072442085,0.6721576,0.00000396692,0.12922926,0.0013986158,0.05257735],"study_design_scores_gemma":[0.00090941257,0.000024089215,0.5046417,0.00001589366,0.000007217821,5.371162e-7,0.01653016,0.20781335,0.000013830231,0.0006409421,0.26903763,0.00036522615],"about_ca_topic_score_codex":0.00040576182,"about_ca_topic_score_gemma":0.011925147,"teacher_disagreement_score":0.46434423,"about_ca_system_score_codex":0.0000043111945,"about_ca_system_score_gemma":0.000028073235,"threshold_uncertainty_score":0.66545117},"labels":[],"label_agreement":null},{"id":"W2068958864","doi":"10.1002/atr.5670420304","title":"Comparative analysis of three user equilibrium models under stochastic demand","year":2008,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":99,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Science Foundation","keywords":"Travel time; Reliability (semiconductor); Computer science; Process (computing); Operations research; Transport engineering; Engineering","score_opus":0.04633813620225089,"score_gpt":0.31682931384923446,"score_spread":0.27049117764698355,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2068958864","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.61253875,0.00012594525,0.38694146,0.000064280954,0.00009378811,0.00007019561,0.000015601343,0.000010123572,0.00013981533],"genre_scores_gemma":[0.9918082,0.00008432845,0.007932473,0.00002061733,0.000036317506,0.0000016989848,0.00005898352,0.0000055632318,0.000051830415],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99859315,0.000049285354,0.0006051895,0.00010438126,0.00051620114,0.00013179284],"domain_scores_gemma":[0.99846876,0.00013236926,0.00065266667,0.00006683383,0.0005915817,0.00008780505],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002449635,0.00009056258,0.0003815694,0.0003554581,0.00013194405,0.000007730439,0.00010666637,0.000063718464,0.00003964709],"category_scores_gemma":[0.000012927093,0.00008660002,0.00018583381,0.00090659736,0.00014081651,0.00086323114,4.2501011e-7,0.0001063426,4.942618e-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":[0.00016244827,0.000055917553,0.0072777695,0.000004985594,0.00024237094,0.000004103301,0.029596323,0.9588479,0.00028151475,0.0034731682,0.000011689559,0.00004184045],"study_design_scores_gemma":[0.0011107388,0.0001358265,0.9766218,0.0000582951,0.0013216549,0.000001357622,0.006842652,0.010634176,0.00020599198,0.0028397895,0.00005216302,0.00017553158],"about_ca_topic_score_codex":0.0000783216,"about_ca_topic_score_gemma":0.0018988975,"teacher_disagreement_score":0.9693441,"about_ca_system_score_codex":0.000035662353,"about_ca_system_score_gemma":0.00016635052,"threshold_uncertainty_score":0.35314465},"labels":[],"label_agreement":null},{"id":"W2069602842","doi":"10.1061/(asce)up.1943-5444.0000073","title":"Estimating Household Trip Rates for Cross-Classification Cells with No Data: Alternative Methods and Their Performance in Prediction of Travel","year":2011,"lang":"en","type":"article","venue":"Journal of Urban Planning and Development","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"University of Toronto","keywords":"Replicate; Aggregate (composite); Data set; Cross-validation; Computer science; Econometrics; Aggregate data; Set (abstract data type); Travel behavior; Statistics; Operations research; Transport engineering; Machine learning; Mathematics; Engineering; Artificial intelligence","score_opus":0.1511353582016067,"score_gpt":0.36521161644793176,"score_spread":0.21407625824632506,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2069602842","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.672697,0.00015257476,0.32663217,0.000009154467,0.0001490808,0.000107639185,0.000022934852,0.0000063440125,0.00022308808],"genre_scores_gemma":[0.73822856,0.00003181384,0.26164344,0.0000057908005,0.000036244644,0.0000025994277,0.000014366469,0.000004012058,0.000033199587],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9992913,0.000047656526,0.00035068343,0.00010325749,0.00011038642,0.00009672472],"domain_scores_gemma":[0.99924237,0.00013405608,0.00038946464,0.000041345316,0.000150272,0.00004247977],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015712696,0.000065336375,0.00014069285,0.00010850705,0.00015717524,0.000029616805,0.000080575206,0.000038753114,0.00000123006],"category_scores_gemma":[0.000060438866,0.000048513368,0.0000072481375,0.00007771864,0.000059640217,0.00036579583,0.0000052806513,0.00007642079,3.773856e-8],"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":[0.00038781238,0.00006627992,0.8247423,0.000086238026,0.000051639043,0.0000016006412,0.16208953,0.0053707776,0.00031837984,0.00006108155,0.000103905746,0.0067204335],"study_design_scores_gemma":[0.0011086905,0.00018807812,0.93894696,0.00040631925,0.000015373917,0.0000039776146,0.0067425165,0.0485976,0.0031154095,0.00002700317,0.0007548312,0.000093210445],"about_ca_topic_score_codex":0.000018586348,"about_ca_topic_score_gemma":0.0000059153576,"teacher_disagreement_score":0.15534702,"about_ca_system_score_codex":0.000022013459,"about_ca_system_score_gemma":0.0001443151,"threshold_uncertainty_score":0.19783178},"labels":[],"label_agreement":null},{"id":"W2069814636","doi":"10.5038/2375-0901.17.4.4","title":"A Transit Technology Selection Model","year":2014,"lang":"en","type":"article","venue":"Journal of Public Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Transit (satellite); Selection (genetic algorithm); Computer science; Range (aeronautics); Operations research; Sensitivity (control systems); Emerging technologies; Model selection; Transport engineering; Engineering; Public transport","score_opus":0.02126889635970298,"score_gpt":0.27736244838271695,"score_spread":0.256093552023014,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2069814636","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23479292,0.000030454366,0.75901496,0.0047177766,0.00016039443,0.00006184991,0.0000031934217,0.00007492514,0.0011435171],"genre_scores_gemma":[0.98422724,0.000055862907,0.015310737,0.00007834779,0.00012361283,0.0000025307068,0.000017255308,0.000009366542,0.00017505104],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99883467,0.00008331694,0.000422558,0.00009254771,0.00038934368,0.00017754416],"domain_scores_gemma":[0.9988927,0.000036627418,0.00032244594,0.000045066106,0.00059555704,0.00010755542],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010596138,0.00007218176,0.00014458726,0.0004488229,0.00022339108,0.00004543277,0.0001325149,0.00014819465,0.000044547654],"category_scores_gemma":[0.00009953405,0.00007204701,0.000079799254,0.0006790537,0.00007534391,0.0006801004,2.0068667e-7,0.00017501667,0.000002427641],"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":[0.00009185934,0.00024137013,0.081918344,0.00003489212,0.00008368897,0.0000053952504,0.025905492,0.38045412,0.0022057435,0.45340538,0.0008107836,0.054842923],"study_design_scores_gemma":[0.010573488,0.0016190066,0.37375298,0.00036875848,0.0009059263,0.00004591858,0.021226121,0.20318332,0.002790291,0.1076207,0.2760999,0.0018136215],"about_ca_topic_score_codex":0.000037737605,"about_ca_topic_score_gemma":0.001339614,"teacher_disagreement_score":0.7494343,"about_ca_system_score_codex":0.000048244052,"about_ca_system_score_gemma":0.0002747687,"threshold_uncertainty_score":0.2937992},"labels":[],"label_agreement":null},{"id":"W2070115034","doi":"10.1002/atr.5670370302","title":"The car pooling problem: Heuristic algorithms based on savings functions","year":2003,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":44,"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":"Pooling; Heuristic; Matching (statistics); Computer science; Order (exchange); Mathematical optimization; Scheme (mathematics); Macro; Algorithm; Operations research; Engineering; Mathematics; Artificial intelligence","score_opus":0.010912759007769597,"score_gpt":0.2680193243155324,"score_spread":0.25710656530776277,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2070115034","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.36129436,0.0007533514,0.618926,0.0042794603,0.0043326817,0.00091828505,0.00005281979,0.00019230838,0.00925077],"genre_scores_gemma":[0.98219633,0.00015590561,0.017075835,0.00009926341,0.000100182915,0.0000069009943,0.000023581022,0.000013408062,0.00032857363],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9986497,0.00010942421,0.00045530664,0.00010654642,0.00049252104,0.00018650078],"domain_scores_gemma":[0.9987191,0.00029311972,0.0004090349,0.00007411607,0.0004083373,0.000096306736],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000734688,0.00009222987,0.00012028372,0.000103849176,0.00076327607,0.000051683233,0.00009665034,0.00005778371,0.000024903204],"category_scores_gemma":[0.0001701477,0.000072808165,0.00009964412,0.000346975,0.00007476547,0.00030876943,1.2359257e-7,0.00020387235,0.000003210494],"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":[0.00010824159,0.000057914476,0.003141572,0.000009274797,0.000016147857,0.000011546429,0.005414546,0.97997355,0.000121571684,0.006605884,0.00012166554,0.0044181035],"study_design_scores_gemma":[0.009031793,0.0018110895,0.37753987,0.0010586053,0.000817768,0.000014621294,0.058760464,0.0040318384,0.001123236,0.013816883,0.53065044,0.0013434038],"about_ca_topic_score_codex":0.0000310011,"about_ca_topic_score_gemma":0.0003514983,"teacher_disagreement_score":0.9759417,"about_ca_system_score_codex":0.00006978861,"about_ca_system_score_gemma":0.0002559997,"threshold_uncertainty_score":0.58705795},"labels":[],"label_agreement":null},{"id":"W2070445465","doi":"10.1007/s11067-005-2626-1","title":"Dynamic Submodel Integration Using an Offer-Accept Discrete Event Simulation","year":2005,"lang":"en","type":"article","venue":"Networks and Spatial Economics","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Social Sciences and Humanities Research Council of Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Partition (number theory); Space (punctuation); Discrete choice; Database transaction; Operations research; Econometrics; Agent-based model; Simple (philosophy); Economics; Microeconomics; Simulation; Mathematics; Artificial intelligence","score_opus":0.019968469643811552,"score_gpt":0.2951143621593834,"score_spread":0.27514589251557187,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2070445465","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.42528054,0.00002745383,0.5741298,0.0001313883,0.00012625442,0.00008498828,0.000003847061,0.000027415585,0.00018827617],"genre_scores_gemma":[0.99615246,0.00017731878,0.0030775098,0.00007832241,0.00029059412,0.0000025650966,0.00015012146,0.000010322697,0.0000607981],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994011,0.000046870704,0.00020568377,0.00016378646,0.000046810128,0.00013576451],"domain_scores_gemma":[0.99968743,0.000030682546,0.00010107019,0.00006604829,0.000035306955,0.000079467325],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020799601,0.00007850564,0.000097157266,0.000037311213,0.00032834188,0.000110377885,0.000049908085,0.00010564223,0.00003524308],"category_scores_gemma":[0.000007693072,0.00008543371,0.000026340122,0.000043840897,0.00004705418,0.0004935161,0.000004165717,0.00006996647,0.0000010527921],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017074855,0.000011681331,0.0008936802,6.759547e-7,0.000003167547,6.376067e-8,0.0016222717,0.9332478,0.0000017478578,0.0015430236,0.0000011694497,0.062657624],"study_design_scores_gemma":[0.00015634314,0.000019656432,0.005960361,0.000007197106,0.000012659357,1.1432177e-7,0.00028308659,0.9926726,0.0000012454888,0.00019956772,0.0005801412,0.00010703348],"about_ca_topic_score_codex":0.0019669351,"about_ca_topic_score_gemma":0.028072475,"teacher_disagreement_score":0.5710523,"about_ca_system_score_codex":0.0000925021,"about_ca_system_score_gemma":0.000043745295,"threshold_uncertainty_score":0.98966265},"labels":[],"label_agreement":null},{"id":"W2070583489","doi":"10.1016/j.cor.2013.06.013","title":"Rapid transit network design for optimal cost and origin–destination demand capture","year":2013,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":50,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Group for Research in Decision Analysis; HEC Montréal","funders":"","keywords":"Computer science; Mathematical optimization; Graph; Vertex (graph theory); Transit (satellite); Network planning and design; Transit time; Integer (computer science); Travel time; Integer programming; Transit system; Graph theory; Operations research; Algorithm; Public transport; Theoretical computer science; Mathematics; Transport engineering; Combinatorics","score_opus":0.10275649401453127,"score_gpt":0.3781683368488936,"score_spread":0.2754118428343624,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2070583489","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016285876,0.00023575113,0.9782224,0.002846232,0.0001614043,0.001773287,0.0000069411926,0.00006934955,0.0003987462],"genre_scores_gemma":[0.7059882,0.00020668622,0.2917795,0.00013645864,0.00033424815,0.0004310979,0.00015033814,0.000015300235,0.00095815514],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985251,0.00038847214,0.0001677861,0.00023591393,0.00031985692,0.00036288332],"domain_scores_gemma":[0.9984949,0.0005332519,0.000014682058,0.00009264784,0.0007145205,0.00015000175],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0014630589,0.00008009359,0.00009980256,0.00012404432,0.0018065702,0.00052793784,0.00014584634,0.00009605653,0.000089341585],"category_scores_gemma":[0.0001156309,0.000079658894,0.000023933875,0.0004378754,0.0002066507,0.0004618162,0.0000075933863,0.00015610097,0.000016521037],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014367735,0.000023226661,0.00020278712,0.000008356962,0.000010840993,7.8572594e-7,0.00911413,0.9582456,0.00003298395,0.009654637,0.011277485,0.011414819],"study_design_scores_gemma":[0.0005936447,0.00011193795,0.0027488947,0.000040803552,0.000011538327,0.0000013367047,0.0020706714,0.9735286,0.000031888852,0.00025581886,0.020439994,0.0001648696],"about_ca_topic_score_codex":0.0008719811,"about_ca_topic_score_gemma":0.0005290279,"teacher_disagreement_score":0.68970233,"about_ca_system_score_codex":0.000056035216,"about_ca_system_score_gemma":0.000238696,"threshold_uncertainty_score":0.99949294},"labels":[],"label_agreement":null},{"id":"W2070596728","doi":"10.1287/trsc.2014.0560","title":"A Numerical Study of the Logit Network Pricing Problem","year":2015,"lang":"en","type":"article","venue":"Transportation Science","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Polytechnique Montréal","funders":"","keywords":"Mathematical optimization; Logit; Flow network; Local optimum; Computer science; Mixed logit; Operations research; Logistic regression; Mathematics; Machine learning","score_opus":0.051917756121479815,"score_gpt":0.3266476400342023,"score_spread":0.2747298839127225,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2070596728","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97896576,0.000014668471,0.0132353995,0.00041437897,0.00032991992,0.0005228145,0.0000021465455,0.00007811744,0.0064367796],"genre_scores_gemma":[0.9977683,0.0000013076572,0.0019804416,0.000053011572,0.000034467856,0.000011039386,0.0000023315656,0.0000037541233,0.0001453338],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99827224,0.00008115627,0.0002700507,0.00019266715,0.00097358297,0.00021032667],"domain_scores_gemma":[0.99919415,0.000043203996,0.00016560547,0.00013433937,0.00035467773,0.00010800427],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013760895,0.00005865774,0.00009320098,0.000050854105,0.0005032053,0.000037731203,0.00035906516,0.000027742948,0.00001335904],"category_scores_gemma":[0.00007766906,0.000043996002,0.00002490164,0.002123429,0.00034872396,0.0003626192,0.0000017890192,0.000071934344,0.0000030489598],"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":[0.000013770117,0.00010143733,0.43250173,0.0000029508308,0.000002691499,8.128142e-7,0.112466395,0.4475133,0.000020920168,0.0068278243,0.00009369123,0.00045446138],"study_design_scores_gemma":[0.000592118,0.00012489902,0.97161204,0.00002978976,0.000028501167,1.7423413e-7,0.023675622,0.0014930201,0.000082787825,0.001055066,0.0011733378,0.00013266865],"about_ca_topic_score_codex":0.0017431789,"about_ca_topic_score_gemma":0.0014577945,"teacher_disagreement_score":0.53911024,"about_ca_system_score_codex":0.000039399067,"about_ca_system_score_gemma":0.0005376509,"threshold_uncertainty_score":0.3870299},"labels":[],"label_agreement":null},{"id":"W2071119281","doi":"10.5038/2375-0901.11.4.1","title":"Impacts of Express Bus Service on Passenger Demand","year":2008,"lang":"en","type":"article","venue":"Journal of Public Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Metropolitan area; TRIPS architecture; Transport engineering; Transit (satellite); Destinations; Service (business); Public transport; Computer science; Business; Operations research; Geography; Engineering; Marketing; Tourism","score_opus":0.03993625427327071,"score_gpt":0.28844351905285,"score_spread":0.24850726477957927,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2071119281","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98906547,0.000096152,0.008016894,0.0018304067,0.00024832893,0.00008090232,0.000016697362,0.000021490863,0.000623687],"genre_scores_gemma":[0.99779654,0.0002933492,0.0015524173,0.0001290901,0.00012839556,0.00000129473,0.000034581808,0.000008886997,0.00005543345],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9984928,0.00010852722,0.00052664435,0.00008202825,0.0006290244,0.00016102468],"domain_scores_gemma":[0.9982642,0.000116201896,0.00058594835,0.0000682215,0.00080980797,0.00015562409],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00067493285,0.00008054272,0.00018140228,0.00022618705,0.00017948268,0.000019719231,0.00013891484,0.000095519674,0.00008908029],"category_scores_gemma":[0.00008780065,0.000073296105,0.00008716256,0.0004618607,0.00006413123,0.0007052196,3.257038e-7,0.00013216559,0.0000024359408],"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":[0.00052183226,0.0009047359,0.7206781,0.00017605897,0.00022602019,0.00016571739,0.20080744,0.045831963,0.00560279,0.018198842,0.0033956054,0.00349092],"study_design_scores_gemma":[0.0011175367,0.00016095437,0.9862844,0.00010223935,0.00005052133,0.0000058497644,0.0032307983,0.00004563186,0.0010307728,0.00017178508,0.0076687033,0.0001308145],"about_ca_topic_score_codex":0.00034182673,"about_ca_topic_score_gemma":0.0014236233,"teacher_disagreement_score":0.2656063,"about_ca_system_score_codex":0.000032992706,"about_ca_system_score_gemma":0.00033644348,"threshold_uncertainty_score":0.29889286},"labels":[],"label_agreement":null},{"id":"W2072726536","doi":"10.1007/s10589-013-9585-0","title":"Mixed-logit network pricing","year":2013,"lang":"en","type":"article","venue":"Computational Optimization and Applications","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":20,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Group for Research in Decision Analysis","funders":"","keywords":"Sensitivity (control systems); Mixed logit; Discrete choice; Mathematical optimization; Revenue; Population; Logit; Point (geometry); Smoothing; Contrast (vision); Mathematics; Flow network; Econometrics; Computer science; Logistic regression; Operations research; Statistics; Economics; Artificial intelligence","score_opus":0.018514707159831854,"score_gpt":0.2650374377517137,"score_spread":0.24652273059188182,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2072726536","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00094303343,0.000089368004,0.9819851,0.0021877298,0.00006370587,0.0005301066,0.00000495802,0.00019784385,0.013998178],"genre_scores_gemma":[0.6652833,0.00013705801,0.33153817,0.00081700424,0.00031379308,0.0003688698,0.00046552246,0.000016095128,0.0010601878],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99917215,0.00005627803,0.00021225457,0.00019454712,0.0001952051,0.00016954329],"domain_scores_gemma":[0.99925417,0.00016928102,0.00009799433,0.000070577575,0.0002877596,0.00012023605],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014338546,0.00008025068,0.00008284555,0.00004949944,0.0009025883,0.00016686441,0.0000856263,0.000064390464,0.00036851186],"category_scores_gemma":[0.000026997535,0.0000880474,0.000022925917,0.00045752001,0.00012528278,0.00030814417,0.000009431505,0.00006159267,0.00006764516],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[6.015654e-7,0.000015713089,0.0016310076,0.0000026548614,0.000004702709,5.281577e-8,0.0003395179,0.82642347,5.615969e-7,0.16451101,0.0017616868,0.005309041],"study_design_scores_gemma":[0.00033775176,0.000013083987,0.02079122,0.00001784671,0.000022095477,0.0000015161136,0.0009990988,0.90313363,0.0000016684693,0.02705194,0.047359016,0.0002711111],"about_ca_topic_score_codex":0.00010015047,"about_ca_topic_score_gemma":0.000019248471,"teacher_disagreement_score":0.66434026,"about_ca_system_score_codex":0.000026011276,"about_ca_system_score_gemma":0.000063224725,"threshold_uncertainty_score":0.6942071},"labels":[],"label_agreement":null},{"id":"W2073559189","doi":"10.5539/jsd.v6n2p94","title":"A Review of Traffic Congestion in Dar es Salaam City from the Physical Planning Perspective","year":2013,"lang":"en","type":"review","venue":"Journal of Sustainable Development","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":74,"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":"Dar es salaam; Traffic congestion; Transport engineering; Business; Public transport; Plan (archaeology); Urban planning; Population; Environmental planning; Strategic planning; Geography; Civil engineering; Marketing; Engineering; Tanzania","score_opus":0.04498291171930547,"score_gpt":0.3559198243470777,"score_spread":0.31093691262777223,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2073559189","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015485801,0.9970792,0.000067599314,0.00027499604,0.00010106807,0.0006548266,0.0000039403567,0.000008451629,0.00026136302],"genre_scores_gemma":[0.0011384487,0.9976448,0.00079666387,0.00004589328,0.00014528951,0.000026459602,0.000024890982,0.000011994155,0.00016557622],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99785286,0.00040225766,0.00086674636,0.00014617985,0.00049703993,0.000234902],"domain_scores_gemma":[0.9971997,0.0004838812,0.0012727147,0.000082180086,0.00089140027,0.00007012944],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015780283,0.0001793033,0.0009564209,0.0001740803,0.00018621034,0.00004635205,0.0003112793,0.00011837445,0.0000405955],"category_scores_gemma":[0.0006374977,0.00011901684,0.00019006609,0.0005949696,0.00010450781,0.00024825823,0.00001459568,0.00043184368,0.0000023110767],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004276092,0.0004872238,0.00034199623,0.041288607,0.0006514279,0.00029900242,0.23493378,0.004399544,6.5380846e-8,0.0053573535,0.008351516,0.7038467],"study_design_scores_gemma":[0.00016472829,0.000027756227,0.0011510846,0.08617611,0.0002616305,0.0000031789982,0.0430439,0.0000046430955,1.9019203e-7,0.00009977317,0.86889076,0.0001762368],"about_ca_topic_score_codex":0.00023177153,"about_ca_topic_score_gemma":0.000025277273,"teacher_disagreement_score":0.86053926,"about_ca_system_score_codex":0.0008487432,"about_ca_system_score_gemma":0.0031917721,"threshold_uncertainty_score":0.5662067},"labels":[],"label_agreement":null},{"id":"W2073673725","doi":"10.3844/ajeassp.2011.363.371","title":"Application of Multiple Imputations to Freight Transportation Survey Data: A Case Study of Commodity Flow Survey","year":2011,"lang":"en","type":"article","venue":"American Journal of Engineering and Applied Sciences","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"King Mongkut's University of Technology North Bangkok","keywords":"Survey data collection; Commodity; Data quality; Scope (computer science); Transport engineering; Quarter (Canadian coin); Survey methodology; Quality (philosophy); Business; Computer science; Statistics; Engineering; Marketing; Mathematics; Geography; Finance","score_opus":0.06487977152179507,"score_gpt":0.3027271510525511,"score_spread":0.23784737953075602,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2073673725","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.88046896,0.000009961642,0.119187936,0.0000062323616,0.000044666976,0.00012563041,0.00012998666,0.000008219705,0.000018377606],"genre_scores_gemma":[0.9815516,0.000007491974,0.018386772,0.0000039427587,0.000013532958,0.0000030069318,0.000029145287,0.0000039478323,5.8102523e-7],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9990085,0.000093208146,0.00038888413,0.00013629007,0.00027137526,0.000101767],"domain_scores_gemma":[0.9988611,0.0003888545,0.00035730127,0.00009953808,0.00020103618,0.00009219858],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021779563,0.000066337554,0.00021001963,0.00016124804,0.00014835055,0.000014008557,0.00022845832,0.000017546572,0.0000019415927],"category_scores_gemma":[0.00013272373,0.000060537328,0.000014320944,0.00088262314,0.00020163928,0.00017012194,0.0000050950207,0.00006337829,1.11632446e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000070354414,0.0002641597,0.56422275,0.000010516783,0.0000371507,0.000005815807,0.07190143,0.35516733,0.00013836533,0.00016402466,0.000016721759,0.008001369],"study_design_scores_gemma":[0.00032365395,0.00044280977,0.94601625,0.000014621149,0.00003560716,0.0000057145744,0.034020282,0.018897517,0.00009917299,0.000009947837,0.000018998293,0.00011540516],"about_ca_topic_score_codex":0.015870713,"about_ca_topic_score_gemma":0.015076125,"teacher_disagreement_score":0.38179353,"about_ca_system_score_codex":0.000007385971,"about_ca_system_score_gemma":0.00007630781,"threshold_uncertainty_score":0.99068266},"labels":[],"label_agreement":null},{"id":"W2073827670","doi":"10.1016/j.cor.2013.05.010","title":"Logit network pricing","year":2013,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Mathematical optimization; Connection (principal bundle); Flow network; Computer science; Class (philosophy); Logit; Product (mathematics); Order (exchange); Flow (mathematics); Line (geometry); Mathematical economics; Mathematics; Economics; Artificial intelligence; Machine learning","score_opus":0.08979264177209387,"score_gpt":0.4007145944085903,"score_spread":0.31092195263649647,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2073827670","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19304843,0.000267991,0.7128562,0.019764775,0.0011215329,0.001715722,0.0000035881649,0.00046655364,0.07075525],"genre_scores_gemma":[0.96257913,0.000060263726,0.03259966,0.00020689411,0.00043490445,0.000062220955,0.00003845434,0.0000078536505,0.0040105996],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99858797,0.00032490035,0.00014076283,0.00016608053,0.00041928937,0.00036102714],"domain_scores_gemma":[0.9990162,0.00020758071,0.0000086233595,0.00012862239,0.0005240662,0.000114880015],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0009119696,0.000048287493,0.0000643362,0.00011482266,0.001777995,0.0004843865,0.00022069241,0.000056816974,0.000461117],"category_scores_gemma":[0.000109213106,0.00004773205,0.000021339714,0.00075125217,0.000168883,0.00037894904,0.000020197002,0.00018762998,0.00044751065],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000018571624,0.00003265541,0.0042633857,0.000003539351,0.000009475418,0.0000021683686,0.010549424,0.85747206,0.000033009066,0.051477734,0.061994407,0.014160295],"study_design_scores_gemma":[0.0006566958,0.00015234921,0.08253562,0.00014564004,0.000010038527,0.0000016737395,0.008498654,0.73972744,0.000059267863,0.0031078714,0.16457951,0.00052521675],"about_ca_topic_score_codex":0.003818569,"about_ca_topic_score_gemma":0.0012371895,"teacher_disagreement_score":0.7695307,"about_ca_system_score_codex":0.00006404149,"about_ca_system_score_gemma":0.00020188249,"threshold_uncertainty_score":0.99952155},"labels":[],"label_agreement":null},{"id":"W2073988241","doi":"10.1002/atr.118","title":"Reform Beijing to a public transit oriented city – from the view of transportation equity","year":2010,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Beijing; Public transport; Equity (law); Transport engineering; Transit (satellite); Business; Environmental planning; Regional science; Political science; China; Engineering; Environmental science; Geography","score_opus":0.024237102701849967,"score_gpt":0.31772915845627914,"score_spread":0.29349205575442916,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2073988241","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94004065,0.00008568757,0.05385706,0.0043118065,0.00089083123,0.00030603353,0.00016803692,0.000036624846,0.00030325787],"genre_scores_gemma":[0.9836171,0.00016318489,0.015591924,0.0001706768,0.00018496062,0.0000071800227,0.00022890989,0.000015037308,0.000020986896],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.99774265,0.000072383926,0.0009490531,0.00017457314,0.00082221726,0.0002391127],"domain_scores_gemma":[0.99788696,0.00016280847,0.00071698695,0.00015121019,0.0008860286,0.00019598102],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010033665,0.00014139526,0.00029517114,0.00013626463,0.00030787874,0.000036873356,0.00031004203,0.00012925144,0.00013652063],"category_scores_gemma":[0.000096633805,0.00011290231,0.00017595153,0.00075283885,0.00016363226,0.0010054156,7.0239446e-7,0.00037806536,0.0000014238664],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0023062413,0.00087575085,0.16395272,0.0001733226,0.00040657696,0.000055542117,0.39891273,0.10065076,0.06471232,0.10329808,0.00024029528,0.16441566],"study_design_scores_gemma":[0.0016094499,0.00020815158,0.94143575,0.00022054678,0.00022332932,9.698216e-7,0.019640256,0.00002306853,0.002605622,0.001290774,0.032468412,0.00027365857],"about_ca_topic_score_codex":0.0010157551,"about_ca_topic_score_gemma":0.050424792,"teacher_disagreement_score":0.77748305,"about_ca_system_score_codex":0.000068947236,"about_ca_system_score_gemma":0.00029190342,"threshold_uncertainty_score":0.9669025},"labels":[],"label_agreement":null},{"id":"W2074045472","doi":"10.3141/2217-04","title":"Predicting the Mean and Variance of Transit Segment and Route Travel Times","year":2011,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Transit (satellite); Standard deviation; Travel time; Transport engineering; Schedule; Variance (accounting); Destinations; Computer science; Mode (computer interface); Traffic count; Automatic vehicle location; Statistics; Public transport; Geography; Engineering; Traffic volume; Mathematics; Global Positioning System; Telecommunications; Business","score_opus":0.09953058778587841,"score_gpt":0.36443481845202597,"score_spread":0.26490423066614754,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2074045472","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9902133,0.00046955043,0.004268267,0.0028635736,0.0002488797,0.0008503533,0.000070835405,0.000020807707,0.0009944733],"genre_scores_gemma":[0.99381423,0.0026790462,0.0027608287,0.00003265785,0.000092009635,0.0000264159,0.000008177757,0.000025863865,0.00056079926],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9942643,0.0014057091,0.0010463367,0.0002998636,0.0024250813,0.0005586986],"domain_scores_gemma":[0.9961483,0.0009762601,0.00045235583,0.00024310272,0.0018923627,0.00028764244],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0068889097,0.00017144604,0.00033407763,0.00042469183,0.0011826288,0.00008536145,0.0006072905,0.00017380688,0.00018090243],"category_scores_gemma":[0.00014820656,0.00011853323,0.00015701776,0.0011964892,0.0016586443,0.00061172445,0.00000347334,0.0011746717,0.0000014122454],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013460271,0.00023510757,0.69979453,0.0002751142,0.00023678335,0.00003940125,0.2572317,0.0012644381,0.00093868456,0.030308483,0.0005526096,0.007777081],"study_design_scores_gemma":[0.0009201421,0.00031330256,0.9635699,0.00029123403,0.0000922628,4.9346255e-7,0.029663106,0.0002188972,0.0006653433,0.0027183166,0.0014191641,0.00012783131],"about_ca_topic_score_codex":0.028394079,"about_ca_topic_score_gemma":0.07509811,"teacher_disagreement_score":0.26377535,"about_ca_system_score_codex":0.00006747639,"about_ca_system_score_gemma":0.0005297164,"threshold_uncertainty_score":0.9780759},"labels":[],"label_agreement":null},{"id":"W2074792188","doi":"10.3141/2419-03","title":"Rail Transit","year":2014,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Conseil Régional, Île-de-France","keywords":"Transport engineering; Transit (satellite); Public transport; Engineering","score_opus":0.09373091503472632,"score_gpt":0.40715311676591603,"score_spread":0.31342220173118973,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2074792188","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94796854,0.00021139848,0.027484912,0.016026914,0.0015352021,0.0012081304,0.000053205415,0.000114710565,0.0053969906],"genre_scores_gemma":[0.99282116,0.00086370256,0.002949129,0.00013009846,0.0005232324,0.00004106431,0.000025382553,0.00005446131,0.002591745],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.98841316,0.0033168688,0.0014192794,0.0004247752,0.005324681,0.0011012196],"domain_scores_gemma":[0.9918996,0.0017667962,0.00048658755,0.0004479737,0.004810487,0.0005885279],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.011419824,0.00024011976,0.0004531809,0.0010566715,0.002027785,0.00023948924,0.0013761363,0.00030616985,0.0006506322],"category_scores_gemma":[0.0005657801,0.00019489412,0.00045436155,0.003035215,0.001323486,0.0009232858,0.000002708743,0.0021264614,0.000052755062],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0025354067,0.00081059185,0.559939,0.00039992723,0.0003363169,0.00011975796,0.08303149,0.024836365,0.0020030881,0.24958305,0.040847477,0.035557497],"study_design_scores_gemma":[0.0016395748,0.000386784,0.69275254,0.0002993678,0.00006572639,2.1174466e-7,0.0068195495,0.00017620885,0.0003659303,0.009131864,0.28809702,0.0002652203],"about_ca_topic_score_codex":0.020001614,"about_ca_topic_score_gemma":0.13607556,"teacher_disagreement_score":0.24724954,"about_ca_system_score_codex":0.0002375086,"about_ca_system_score_gemma":0.00102591,"threshold_uncertainty_score":0.99927145},"labels":[],"label_agreement":null},{"id":"W2075117937","doi":"10.1002/atr.105","title":"Error correction of arrival time prediction in real time bus information system","year":2010,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Arrival time; Computer science; Real-time computing; Reduction (mathematics); Data mining; Transport engineering; Engineering; Mathematics","score_opus":0.005627117112963238,"score_gpt":0.24745657681914848,"score_spread":0.24182945970618525,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2075117937","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99285465,0.000005051943,0.0027188375,0.000061137725,0.001651797,0.00019782448,0.00003952996,0.000057498954,0.0024136812],"genre_scores_gemma":[0.9959128,0.000027630676,0.0035838317,0.00000526441,0.00012944805,0.0000035726246,0.00018770597,0.0000075195426,0.00014221636],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99841255,0.00006167615,0.00085577706,0.00007519291,0.00046683603,0.0001279739],"domain_scores_gemma":[0.9984498,0.000069605856,0.00086753396,0.00006355591,0.00047713466,0.00007236135],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00070453977,0.000086258224,0.0002022766,0.00033649273,0.00010043158,0.000018053604,0.00008969879,0.00015278936,0.000070569484],"category_scores_gemma":[0.00007772444,0.00008943821,0.00006830243,0.0004048831,0.00005603846,0.0017973458,3.7591082e-7,0.0002529172,0.000012288278],"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":[0.0010227675,0.00012379454,0.03177295,0.00010584634,0.000036267473,0.000010970289,0.058448244,0.8629512,0.032097936,0.0014743794,0.0002473124,0.01170833],"study_design_scores_gemma":[0.0025002686,0.0003158082,0.97166604,0.00035804728,0.00010827693,0.000008397797,0.011467642,0.008584309,0.0018823699,0.00008514314,0.0028076582,0.00021602136],"about_ca_topic_score_codex":0.00019183224,"about_ca_topic_score_gemma":0.0004675365,"teacher_disagreement_score":0.9398931,"about_ca_system_score_codex":0.000082141865,"about_ca_system_score_gemma":0.0001797242,"threshold_uncertainty_score":0.36471844},"labels":[],"label_agreement":null},{"id":"W2075644291","doi":"10.3141/1926-12","title":"Multiagent Learning-Based Approach to Transit Assignment Problem: A Prototype","year":2005,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Transit (satellite); Artificial intelligence; Transport engineering; Engineering; Public transport","score_opus":0.10063351859814194,"score_gpt":0.3969500110758202,"score_spread":0.2963164924776782,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2075644291","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7380286,0.00027882354,0.21090086,0.034067936,0.0006864379,0.009228287,0.00009576872,0.00025848273,0.006454817],"genre_scores_gemma":[0.9707116,0.00022713967,0.025930975,0.00016858571,0.0003502588,0.0003905728,0.00004487294,0.000067645895,0.0021083474],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9883727,0.002573805,0.0014971733,0.0005638047,0.005778711,0.0012138005],"domain_scores_gemma":[0.9935221,0.00071780896,0.00045422735,0.00037034202,0.0041156886,0.0008198363],"candidate_categories":["sts","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.008613737,0.00029609876,0.00045883466,0.0012504256,0.0018670876,0.00025923437,0.0012176846,0.00028542074,0.00038698295],"category_scores_gemma":[0.00025941094,0.0002425649,0.00041190616,0.0032445923,0.00064832746,0.0007256717,0.0000033372994,0.0023536643,0.00005968722],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0027667256,0.001286286,0.099716544,0.00026568555,0.00015167121,0.000034357967,0.05111535,0.81768185,0.0008385758,0.0064430824,0.006873586,0.012826308],"study_design_scores_gemma":[0.0040462255,0.0017472941,0.4641476,0.00069629785,0.0001357436,3.3424783e-7,0.013516336,0.0035560476,0.0014022596,0.0007078314,0.50936943,0.0006746134],"about_ca_topic_score_codex":0.010605771,"about_ca_topic_score_gemma":0.054761935,"teacher_disagreement_score":0.8141258,"about_ca_system_score_codex":0.0005667456,"about_ca_system_score_gemma":0.0016517087,"threshold_uncertainty_score":0.99994797},"labels":[],"label_agreement":null},{"id":"W2076566877","doi":"10.1061/40730(144)35","title":"Transit System Performance and Industrial Economics: Learning from Hong Kong Maxicabs and Philippine Jeepneys","year":2004,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Regional science; Geography","score_opus":0.024229514050324603,"score_gpt":0.21606959918457314,"score_spread":0.19184008513424852,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2076566877","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99059314,0.000059252892,0.0024946537,0.00050788256,0.000114792296,0.00009492168,0.0000041640756,0.000107737884,0.0060234717],"genre_scores_gemma":[0.9984661,0.0002529727,0.0006906176,0.00002565644,0.00015804547,0.0000023428502,0.000024163526,0.0000055398573,0.00037459153],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99948955,0.00002876734,0.00013857702,0.00015666199,0.000066311026,0.00012016404],"domain_scores_gemma":[0.9997909,0.000033600438,0.000045562832,0.00003452727,0.000017870258,0.000077528726],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019463089,0.0000643876,0.000101664926,0.000037655285,0.00037344085,0.000078465535,0.000038793194,0.000094100775,0.000053580112],"category_scores_gemma":[0.000011108548,0.000064997104,0.0000126419445,0.000064651715,0.00007482451,0.00027056973,0.0000028068775,0.00010206785,0.000004396055],"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":[0.00018347039,0.000043195527,0.5034815,0.00007243148,0.00009433977,0.000010333698,0.07871062,0.30487263,0.000116520685,0.031575568,0.00004060608,0.08079877],"study_design_scores_gemma":[0.019624049,0.0007478297,0.6966791,0.001227231,0.00038074606,0.000015733967,0.16737254,0.06788032,0.0012471655,0.00082667597,0.04157148,0.0024271174],"about_ca_topic_score_codex":0.0019148221,"about_ca_topic_score_gemma":0.0008594325,"teacher_disagreement_score":0.23699231,"about_ca_system_score_codex":0.000034642126,"about_ca_system_score_gemma":0.000050672534,"threshold_uncertainty_score":0.2894651},"labels":[],"label_agreement":null},{"id":"W2076829927","doi":"10.1007/s001680050124","title":"Private toll roads: Competition under various ownership regimes","year":2000,"lang":"en","type":"article","venue":"The Annals of Regional Science","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":170,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Allocative efficiency; Toll; Duopoly; Competition (biology); Road pricing; Toll road; Business; Economics; Microeconomics; Queueing theory; Industrial organization; Traffic congestion; Public economics; Transport engineering; Engineering; Cournot competition; Computer science","score_opus":0.09446718866757134,"score_gpt":0.3486764771085675,"score_spread":0.2542092884409961,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2076829927","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8346882,0.0002567671,0.0015958463,0.07200162,0.00014014296,0.00023837575,0.000006615197,0.00011422026,0.090958215],"genre_scores_gemma":[0.9938538,0.00045167663,0.0004292343,0.001463988,0.00006644873,0.0000029671926,0.0000061051555,0.0000032011092,0.0037225636],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99863946,0.0000952076,0.0001481146,0.00016765355,0.00070701115,0.00024252934],"domain_scores_gemma":[0.99937665,0.000112142494,0.000098866425,0.00015410925,0.00017453574,0.000083676954],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013136638,0.000061021285,0.000079336096,0.00006274542,0.000751729,0.000058870475,0.00046902272,0.000035179077,0.00028916192],"category_scores_gemma":[0.000048951773,0.00004298278,0.00004394289,0.00069588237,0.0015164176,0.00036576678,0.000006257121,0.00006492812,0.000034718574],"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":[0.00006324816,0.00006088591,0.0010973222,0.000004462389,0.0000084832955,0.0000014045854,0.011009183,0.031704184,0.00018058228,0.9471381,0.002713084,0.0060191015],"study_design_scores_gemma":[0.00065803516,0.0002238617,0.31626484,0.0002857167,0.000039184928,0.000011644572,0.007494356,0.00235666,0.0021169914,0.16709265,0.5027687,0.0006873861],"about_ca_topic_score_codex":0.00068277935,"about_ca_topic_score_gemma":0.0001413764,"teacher_disagreement_score":0.7800454,"about_ca_system_score_codex":0.000014909778,"about_ca_system_score_gemma":0.00021896027,"threshold_uncertainty_score":0.5781768},"labels":[],"label_agreement":null},{"id":"W2077764711","doi":"10.1016/j.tra.2005.08.005","title":"Modeling activity scheduling time horizon: Duration of time between planning and execution of pre-planned activities","year":2005,"lang":"en","type":"article","venue":"Transportation Research Part A Policy and Practice","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":56,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Wilfrid Laurier University","funders":"","keywords":"Interdependence; Computer science; Time horizon; Scheduling (production processes); Duration (music); TRIPS architecture; Operations research; Situational ethics; Operations management; Business; Engineering; Psychology","score_opus":0.09532411371827337,"score_gpt":0.4329473652296373,"score_spread":0.3376232515113639,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2077764711","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98490155,0.00015129641,0.009621763,0.002650681,0.000013001884,0.0003204499,0.00010785687,0.000047546993,0.0021858488],"genre_scores_gemma":[0.9956944,0.00040019042,0.0032218625,0.00001499563,0.00022129717,0.000014086234,0.00014574475,0.0000106311845,0.00027676666],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979608,0.0005915546,0.0003715574,0.00021461875,0.00060238136,0.00025908754],"domain_scores_gemma":[0.9975985,0.0016485075,0.00022512976,0.0000874037,0.00032313121,0.000117272924],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002727552,0.00010058748,0.00021197055,0.00030203813,0.00047514267,0.00005245412,0.00006887095,0.00014430365,0.000028847822],"category_scores_gemma":[0.00081713067,0.00011165894,0.000028471639,0.00044937056,0.0002501055,0.00166422,0.0000032083738,0.00023773967,0.0000026766247],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0077124536,0.0005875453,0.02718504,0.0007563928,0.0004204311,0.000010028386,0.3170116,0.54433095,0.029602008,0.03086852,0.000266158,0.041248888],"study_design_scores_gemma":[0.008089466,0.004088347,0.21122159,0.0026147987,0.0011741846,0.000012068136,0.065274164,0.62036943,0.037005473,0.0048912135,0.042783283,0.0024759916],"about_ca_topic_score_codex":0.003265927,"about_ca_topic_score_gemma":0.0003251305,"teacher_disagreement_score":0.25173742,"about_ca_system_score_codex":0.000035782487,"about_ca_system_score_gemma":0.0002814875,"threshold_uncertainty_score":0.49371266},"labels":[],"label_agreement":null},{"id":"W2078067655","doi":"10.1016/j.ecotra.2014.01.004","title":"Cost recovery from congestion tolls with long-run uncertainty","year":2014,"lang":"en","type":"article","venue":"Economics of Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":17,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Toll; Marginal cost; Economics; Investment (military); Microeconomics; Revenue; Constant (computer programming); Variable cost; Economies of scale; Finance; Computer science","score_opus":0.01677056426139053,"score_gpt":0.24375524418349673,"score_spread":0.2269846799221062,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2078067655","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95381993,0.000017736713,0.042772293,0.00034513848,0.00023306347,0.000228637,0.00019956147,0.000059835154,0.0023238158],"genre_scores_gemma":[0.9939874,0.00015737112,0.0038747005,0.00007632729,0.000079595404,0.000013190861,0.0016597724,0.000012226525,0.00013942982],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9992161,0.000049224844,0.0003020807,0.00019917397,0.0001017238,0.00013169885],"domain_scores_gemma":[0.9993032,0.00015932381,0.00024830087,0.00010952848,0.00010952215,0.00007012044],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028645858,0.000091396556,0.00016624475,0.000065141896,0.00012930781,0.000030013547,0.00009513663,0.00009833297,0.00012333912],"category_scores_gemma":[0.000018349148,0.00009893441,0.000045380588,0.00009354406,0.00010170166,0.00037679097,3.7593986e-7,0.000060086495,0.0000090544745],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031114992,0.00005472961,0.13599809,0.000015708249,0.00005854362,7.9733115e-7,0.009579336,0.82155174,0.000023518447,0.0148071945,0.000094121984,0.017505102],"study_design_scores_gemma":[0.0015347335,0.00015837126,0.97929925,0.000095109244,0.00013694077,1.2541229e-7,0.0018457517,0.0036817803,0.0004915354,0.0022999407,0.010092958,0.00036351485],"about_ca_topic_score_codex":0.0043109767,"about_ca_topic_score_gemma":0.048270907,"teacher_disagreement_score":0.8433012,"about_ca_system_score_codex":0.000049576105,"about_ca_system_score_gemma":0.000105778934,"threshold_uncertainty_score":0.96909565},"labels":[],"label_agreement":null},{"id":"W2079455620","doi":"10.3141/1756-04","title":"Evaluating Transportation Choice","year":2001,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Transport Canada","funders":"Federal Highway Administration","keywords":"Transportation planning; Variety (cybernetics); Transport engineering; Quality (philosophy); Mode choice; Land use; Business; Risk analysis (engineering); Computer science; Environmental economics; Economics; Public transport; Engineering; Civil engineering","score_opus":0.22549696840842243,"score_gpt":0.49275002753736546,"score_spread":0.26725305912894304,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2079455620","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9841673,0.00032172652,0.003929936,0.007479798,0.0012154417,0.0013431032,0.000073892654,0.000107451844,0.0013613947],"genre_scores_gemma":[0.9892806,0.002187476,0.004243496,0.000120381046,0.0006934234,0.00009421459,0.000085116335,0.00008025598,0.0032150617],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.98577476,0.002935871,0.0020565921,0.00060144026,0.0072271894,0.0014041328],"domain_scores_gemma":[0.98837686,0.002408317,0.0008559376,0.0005291615,0.0071654236,0.0006643327],"candidate_categories":["metaepi_narrow","sts","research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.011078444,0.00033577438,0.0005448962,0.0013491387,0.0026519212,0.00030337126,0.0015090245,0.0003916398,0.0010537035],"category_scores_gemma":[0.0008608179,0.00028699444,0.00055526523,0.00487845,0.001201974,0.0015922624,0.0000026971722,0.0025231175,0.00005254517],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017082312,0.00052490697,0.88559234,0.00018259433,0.00021549406,0.00018860782,0.038134232,0.023479296,0.0018735057,0.017475674,0.00886173,0.021763373],"study_design_scores_gemma":[0.001817768,0.00047011313,0.8666617,0.00038625355,0.00010544874,3.763297e-7,0.008684187,0.00021256914,0.00022540572,0.0028891745,0.11823827,0.0003087304],"about_ca_topic_score_codex":0.047342673,"about_ca_topic_score_gemma":0.28432104,"teacher_disagreement_score":0.23697837,"about_ca_system_score_codex":0.000444167,"about_ca_system_score_gemma":0.0016875847,"threshold_uncertainty_score":0.9999582},"labels":[],"label_agreement":null},{"id":"W2079550418","doi":"10.1002/atr.112","title":"Comparison of updating techniques in transferability analysis of work trip mode choice models in developing countries","year":2010,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Japan International Cooperation Agency","keywords":"Transferability; Predictability; Computer science; Estimator; Bayesian probability; Transfer (computing); Econometrics; Process (computing); Mode (computer interface); Scale (ratio); Work (physics); Statistics; Data mining; Machine learning; Engineering; Artificial intelligence; Mathematics; Logit","score_opus":0.02571435624255969,"score_gpt":0.3619285320186212,"score_spread":0.3362141757760615,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2079550418","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.928686,0.00004812697,0.070724756,0.00020764797,0.000080291575,0.0001432678,0.000015681142,0.000011260071,0.00008292764],"genre_scores_gemma":[0.94286096,0.00017968354,0.056884006,0.000011354374,0.000013656752,0.000003852947,0.000039205173,0.000005593114,0.0000016740704],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9979665,0.00007863618,0.0013057352,0.00011524758,0.0004033043,0.00013057126],"domain_scores_gemma":[0.9985162,0.00025544086,0.0006920797,0.00006838109,0.00043097846,0.000036910562],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00093543786,0.00008707563,0.0005068371,0.0006462257,0.000051084633,0.000008688832,0.00012843829,0.00011489619,0.000012106551],"category_scores_gemma":[0.00007988697,0.00009079161,0.00011036161,0.0017214612,0.00009295371,0.0007396195,3.694493e-7,0.00024888667,2.2923308e-8],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013582257,0.00006432608,0.4312827,0.00002794879,0.00002738688,6.8923174e-7,0.031526517,0.53185827,0.0006852458,0.0028511197,3.7337344e-7,0.0015395938],"study_design_scores_gemma":[0.00058120926,0.00004256209,0.9854813,0.00021801174,0.00017062125,4.991298e-8,0.0047876867,0.0024893144,0.0050702174,0.0009346218,0.000106155385,0.00011822884],"about_ca_topic_score_codex":0.0006197758,"about_ca_topic_score_gemma":0.04038114,"teacher_disagreement_score":0.5541986,"about_ca_system_score_codex":0.00005524929,"about_ca_system_score_gemma":0.00020218598,"threshold_uncertainty_score":0.9771294},"labels":[],"label_agreement":null},{"id":"W2079746138","doi":"10.1002/atr.5670380102","title":"Special issue: Importance of public transport","year":2004,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Public transport; Metropolitan area; Transport engineering; Incentive; Business; Traffic congestion; Order (exchange); Service (business); Revenue; Environmental economics; Flexibility (engineering); Reliability (semiconductor); Bus rapid transit; Computer science; Engineering; Finance; Economics; Marketing","score_opus":0.015654826483063693,"score_gpt":0.2881869001705594,"score_spread":0.2725320736874957,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2079746138","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9723218,0.00018111583,0.018741671,0.0018330743,0.0016261223,0.00021061202,0.000033826782,0.000040126473,0.0050116507],"genre_scores_gemma":[0.980991,0.00048561645,0.016473034,0.000058785372,0.0017825437,0.0000020909595,0.00006248563,0.000014765898,0.00012964866],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9980469,0.000038930404,0.0008976607,0.00012669402,0.00067751086,0.00021231832],"domain_scores_gemma":[0.9982159,0.000040913324,0.0008677497,0.000086761356,0.0006369343,0.00015170648],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060326245,0.000112824695,0.00027603956,0.00020661383,0.00014424894,0.000012150826,0.00018003931,0.00010075858,0.00028656208],"category_scores_gemma":[0.0000627907,0.000114699666,0.00016853909,0.0005308116,0.00014638877,0.0010644635,2.6951562e-7,0.00016643718,0.0000027964898],"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":[0.000568236,0.0006129903,0.24491757,0.00012489571,0.00014348765,0.00016834759,0.11991546,0.55047363,0.001357605,0.057993155,0.0005445608,0.023180088],"study_design_scores_gemma":[0.0035934038,0.00031758528,0.87198377,0.00019241447,0.00013946263,0.0000050404296,0.01491904,0.0000017194144,0.0010216455,0.0047933445,0.1027331,0.00029949174],"about_ca_topic_score_codex":0.000057716563,"about_ca_topic_score_gemma":0.0023846412,"teacher_disagreement_score":0.6270662,"about_ca_system_score_codex":0.00008472069,"about_ca_system_score_gemma":0.00047528924,"threshold_uncertainty_score":0.46773168},"labels":[],"label_agreement":null},{"id":"W2080143721","doi":"10.1002/atr.5670420309","title":"Robust control of traffic networks under uncertain conditions","year":2008,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Robustness (evolution); Statistic; Computer science; Standard deviation; Percentile; Reliability (semiconductor); Mathematical optimization; Operations research; Engineering; Mathematics; Statistics","score_opus":0.023412901349607836,"score_gpt":0.2777734448401882,"score_spread":0.25436054349058035,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2080143721","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7391909,0.00025395208,0.25943694,0.00039668867,0.00037968886,0.00013803128,0.000026996528,0.000026476919,0.00015037603],"genre_scores_gemma":[0.9942276,0.00057749235,0.0048520244,0.00007300996,0.000114063805,0.0000026774753,0.000071453585,0.00000976972,0.0000719398],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9987236,0.00007255252,0.0005953794,0.000086015876,0.00037453516,0.00014788914],"domain_scores_gemma":[0.9985976,0.00015324194,0.00060313195,0.000053997726,0.0004970007,0.00009502265],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002756698,0.00008356212,0.00022083052,0.0001365744,0.00024664967,0.0000065749723,0.000095500225,0.00008630951,0.000071070885],"category_scores_gemma":[0.00003063146,0.000083144245,0.00012616838,0.00033756922,0.00017125648,0.00048582372,1.450529e-7,0.00014654241,6.806063e-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":[0.0001237081,0.00006769873,0.0033314251,0.0000065866616,0.00003211893,0.000017613283,0.0073127965,0.98649836,0.00009876316,0.0020556706,0.000073089526,0.0003821843],"study_design_scores_gemma":[0.007393258,0.0005078889,0.95778596,0.00029190673,0.00036931015,0.000022696267,0.020327214,0.009122553,0.00013004606,0.0007499007,0.002852737,0.00044650488],"about_ca_topic_score_codex":0.000027810698,"about_ca_topic_score_gemma":0.00035774236,"teacher_disagreement_score":0.9773758,"about_ca_system_score_codex":0.000040240197,"about_ca_system_score_gemma":0.0002129942,"threshold_uncertainty_score":0.3390524},"labels":[],"label_agreement":null},{"id":"W2080863479","doi":"10.1016/j.tra.2008.11.006","title":"Modeling activity conflict resolution strategies using scheduling process data","year":2008,"lang":"en","type":"article","venue":"Transportation Research Part A Policy and Practice","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":22,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Wilfrid Laurier University","funders":"Illinois Department of Transportation","keywords":"Computer science; Conflict resolution; Scheduling (production processes); Two-level scheduling; Conflict resolution strategy; Dynamic priority scheduling; Fair-share scheduling; Operations research; Industrial engineering; Schedule; Mathematical optimization; Engineering; Mathematics","score_opus":0.4976692053826439,"score_gpt":0.5448965810255731,"score_spread":0.047227375642929226,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2080863479","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.89005923,0.00035296657,0.09654114,0.0054268064,0.00009845729,0.0005472708,0.00013676837,0.00018448154,0.0066528795],"genre_scores_gemma":[0.9915163,0.00222495,0.005418276,0.000087215965,0.00034325075,0.000015104844,0.00025385246,0.000014479443,0.00012656981],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972735,0.0007579161,0.00026259225,0.00038731445,0.0008794826,0.0004392114],"domain_scores_gemma":[0.9979445,0.00086350023,0.000104669496,0.00023410689,0.0006660417,0.00018721182],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0033614638,0.00010906277,0.00013885667,0.0002472861,0.0021986738,0.00016638548,0.00022192107,0.0001371482,0.00002629172],"category_scores_gemma":[0.0016978394,0.00012128298,0.000019754181,0.0008665698,0.0003954455,0.004044087,0.0000055408514,0.00040938577,0.0000051189863],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00074722455,0.00016702517,0.0016772992,0.000108218024,0.000053888627,0.00004533719,0.09778805,0.8531854,0.0002165272,0.045175552,0.00012055138,0.0007149321],"study_design_scores_gemma":[0.0010254155,0.000094349816,0.0041146665,0.0001621564,0.00009385395,0.000015285277,0.0625589,0.8712348,0.00004887975,0.0013509468,0.058826152,0.000474648],"about_ca_topic_score_codex":0.03557544,"about_ca_topic_score_gemma":0.0041995533,"teacher_disagreement_score":0.10145707,"about_ca_system_score_codex":0.00005290636,"about_ca_system_score_gemma":0.0018517,"threshold_uncertainty_score":0.9991003},"labels":[],"label_agreement":null},{"id":"W2081356886","doi":"10.1007/s12205-013-0318-9","title":"Enhancing algorithmic base for discrete choice modelling","year":2013,"lang":"en","type":"article","venue":"KSCE Journal of Civil Engineering","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University; Government of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Discrete choice; Computer science; Multinomial logistic regression; Convergence (economics); Mode (computer interface); Process (computing); Software; Estimation; Mathematical optimization; Logit; Code (set theory); Mixed logit; Logistic regression; Machine learning; Mathematics; Engineering","score_opus":0.012854473192329388,"score_gpt":0.24730761762079767,"score_spread":0.2344531444284683,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2081356886","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025830498,0.00017364821,0.9728533,0.00030161254,0.00036516614,0.00010805087,0.0000018901671,0.00003262737,0.00033318106],"genre_scores_gemma":[0.93963456,0.000056648154,0.059587028,0.000021672906,0.0004888979,0.0000061573664,0.0000021800652,0.000014276889,0.00018856795],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99926597,0.000013500158,0.00027538236,0.00006248604,0.00020220032,0.00018045634],"domain_scores_gemma":[0.9993127,0.00019342503,0.00013645276,0.000041576164,0.0002105214,0.00010529785],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004304287,0.00006660538,0.00011888076,0.00010303834,0.00011918181,0.000066587614,0.000102084865,0.00004614948,0.000064205735],"category_scores_gemma":[0.00016473091,0.000065558736,0.0000729626,0.00011984671,0.000010766844,0.0005027257,0.000001966145,0.00011210776,0.000002421792],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000021918606,0.000005075467,0.00013956534,0.00002033889,0.000012115932,9.441566e-7,0.0022095502,0.9959774,0.0009126667,0.00032726082,0.0001425995,0.00025029012],"study_design_scores_gemma":[0.0005095036,0.000047643367,0.00067778234,0.00028761895,0.00003728815,0.0000035385233,0.0009415574,0.9854873,0.00089568004,0.0002420358,0.010669621,0.00020043821],"about_ca_topic_score_codex":0.00012528591,"about_ca_topic_score_gemma":0.00018106197,"teacher_disagreement_score":0.91380405,"about_ca_system_score_codex":0.00004241275,"about_ca_system_score_gemma":0.000059831713,"threshold_uncertainty_score":0.26734078},"labels":[],"label_agreement":null},{"id":"W2082681272","doi":"10.1109/tits.2012.2189437","title":"Sensitivity Analysis of an Evolutionary-Based Time-Dependent Origin/Destination Estimation Framework","year":2012,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto; University of Calgary","funders":"","keywords":"A priori and a posteriori; Sensitivity (control systems); Closeness; Matrix (chemical analysis); Mathematical optimization; Evolutionary algorithm; Computer science; Matching (statistics); Algorithm; Flow network; Mathematics; Statistics; Engineering","score_opus":0.02919603784156137,"score_gpt":0.31506768503899646,"score_spread":0.2858716471974351,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2082681272","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16154888,0.000034404577,0.83620054,0.000054329426,0.00088329334,0.0004836628,0.0003813116,0.00025428215,0.00015929801],"genre_scores_gemma":[0.98807895,0.000021984642,0.010722178,0.00003409887,0.00007770494,0.0000815704,0.0007163798,0.000027089778,0.00024005708],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968988,0.00055422797,0.00080719375,0.0003390434,0.0010307697,0.00036999755],"domain_scores_gemma":[0.9979941,0.0005549022,0.00039999923,0.00028280687,0.00050792453,0.00026021386],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0014534099,0.0002300615,0.00038614674,0.0009429965,0.0005532522,0.00005660481,0.00011867903,0.00028331776,0.00034201788],"category_scores_gemma":[0.000028866969,0.00025847455,0.00024273794,0.0018707379,0.00013399159,0.00083601446,8.022854e-8,0.00021862568,0.000060611623],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000096628755,0.0005416014,0.004916821,0.000036165256,0.00024215833,0.0000016623885,0.008364661,0.9804671,0.0001784239,0.0029591916,0.000008248913,0.0021873356],"study_design_scores_gemma":[0.00043999893,0.00017713159,0.03778825,0.00021548389,0.002361056,0.000001180586,0.0040813233,0.9469479,0.0066838274,0.00007130954,0.000594328,0.0006382196],"about_ca_topic_score_codex":0.0028114822,"about_ca_topic_score_gemma":0.0009637491,"teacher_disagreement_score":0.82653004,"about_ca_system_score_codex":0.0002432747,"about_ca_system_score_gemma":0.00017645795,"threshold_uncertainty_score":0.99998677},"labels":[],"label_agreement":null},{"id":"W2082880140","doi":"10.1198/016214506000000320","title":"Optimizing the Expected Overlap of Survey Samples via the Northwest Corner Rule","year":2006,"lang":"en","type":"article","venue":"Journal of the American Statistical Association","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Statistics Canada","funders":"","keywords":"Selection (genetic algorithm); Sampling (signal processing); Variance (accounting); Computer science; Mathematical optimization; Hypergeometric distribution; Statistics; Simple random sample; Mathematics; Focus (optics); Algorithm; Population; Artificial intelligence","score_opus":0.017352205864050636,"score_gpt":0.27735369714539715,"score_spread":0.26000149128134653,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2082880140","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.86025625,0.00007556767,0.13289508,0.00490458,0.00054283644,0.00020453203,0.00035768774,0.000020679647,0.00074274774],"genre_scores_gemma":[0.9959405,0.000028726576,0.0034893572,0.00015374285,0.00017212851,0.0000015776435,0.000027742199,0.00000707929,0.0001791351],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9975562,0.0009878969,0.00046383476,0.000069123555,0.0007529826,0.00016997084],"domain_scores_gemma":[0.99455976,0.003052864,0.0017314445,0.000096905664,0.00052551844,0.000033488333],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018361276,0.00007150464,0.00020230148,0.000031142685,0.00046707512,0.00006317292,0.00027995266,0.000032016007,0.000028436549],"category_scores_gemma":[0.0016658694,0.000037171452,0.00008799776,0.0005104077,0.00031172502,0.00009976251,0.000009321026,0.00019511988,0.0000016099232],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007016947,0.00007370656,0.97227925,0.0000019879155,0.00006864543,9.701737e-7,0.0031686202,0.014334583,0.00009419972,0.0032435015,0.0051465733,0.0015177798],"study_design_scores_gemma":[0.00013981375,0.00003309615,0.99586797,0.000009470335,0.00006439568,7.1037425e-7,0.0015226698,0.00037328203,0.000020883677,0.0009935509,0.00092268764,0.000051485848],"about_ca_topic_score_codex":0.024276001,"about_ca_topic_score_gemma":0.010899983,"teacher_disagreement_score":0.13568424,"about_ca_system_score_codex":0.00017802302,"about_ca_system_score_gemma":0.00015591884,"threshold_uncertainty_score":0.9822214},"labels":[],"label_agreement":null},{"id":"W2082956897","doi":"10.1007/s11518-012-5200-7","title":"Modeling the congestion cost and vehicle emission within multimodal traffic network under the condition of equilibrium","year":2012,"lang":"en","type":"article","venue":"Journal of Systems Science and Systems Engineering","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Traffic congestion; Computer science; Transport engineering; Engineering","score_opus":0.024813188255303115,"score_gpt":0.2682090660969187,"score_spread":0.24339587784161557,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2082956897","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97974336,0.0021984347,0.016362049,0.00014477372,0.0013059956,0.00019939574,0.0000012323692,0.00001213752,0.00003260077],"genre_scores_gemma":[0.99948305,0.000049198985,0.0000706239,0.0000058238197,0.0003623017,0.0000033122838,4.4132773e-7,0.0000044442604,0.00002077795],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99875325,0.0000903426,0.00038056783,0.000065678694,0.0005186551,0.00019149206],"domain_scores_gemma":[0.9991452,0.0001458526,0.00025217823,0.000057380534,0.0002886572,0.00011070156],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0052643823,0.000063148116,0.0001366318,0.000068356174,0.00037923642,0.00014464224,0.000118493284,0.000049150254,3.430103e-7],"category_scores_gemma":[0.000114899296,0.000037069436,0.000018354638,0.00029732333,0.00015903058,0.0006599974,0.0000065251393,0.00011593968,1.3334683e-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":[0.0000041361673,0.000003931869,0.0017252114,0.000031813637,0.000005970892,2.3380812e-7,0.006492528,0.98649687,0.0023208205,0.0028577514,0.000015867083,0.000044898636],"study_design_scores_gemma":[0.00013405962,0.000018407061,0.0039223395,0.0003457845,0.00001861604,0.000027550872,0.014543524,0.98082554,0.000018970974,0.00000205999,0.000091634494,0.000051479947],"about_ca_topic_score_codex":0.00016172894,"about_ca_topic_score_gemma":0.000005614554,"teacher_disagreement_score":0.019739695,"about_ca_system_score_codex":0.000050768453,"about_ca_system_score_gemma":0.0001045779,"threshold_uncertainty_score":0.29168183},"labels":[],"label_agreement":null},{"id":"W2083125140","doi":"10.1002/atr.5670410305","title":"An algorithm for finding reasonable paths in transit networks","year":2007,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":27,"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":"Transit (satellite); Computer science; Transit system; Algorithm; Network planning and design; Transit time; Transportation planning; Rail transit; Urban transit; Transport engineering; Test (biology); Operations research; Simulation; Public transport; Engineering; Computer network","score_opus":0.012678804748060645,"score_gpt":0.3084149865033584,"score_spread":0.29573618175529776,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2083125140","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21779306,0.00013463791,0.781333,0.000083010185,0.0003669838,0.00017581717,0.000009314654,0.000019372193,0.00008478093],"genre_scores_gemma":[0.8679244,0.00018861154,0.13149963,0.000042011146,0.00020928554,0.000003352781,0.00008612078,0.000012246614,0.000034353718],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99878657,0.000033708417,0.0005510506,0.00011267313,0.00027060602,0.00024536453],"domain_scores_gemma":[0.9991184,0.00013775118,0.0003148679,0.000045931705,0.00026758396,0.000115466726],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014081566,0.00008092957,0.00016567315,0.00019099403,0.00016272214,0.000022478709,0.00009802781,0.00010494168,0.000012616455],"category_scores_gemma":[0.000023243594,0.00008617318,0.00007826512,0.00036264907,0.00003197067,0.00089979137,9.141557e-8,0.0001433293,1.6851872e-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":[0.000243976,0.000093118,0.008568954,0.000009303124,0.000009359444,0.00003238142,0.020691726,0.8134938,0.0002254122,0.0012527633,0.00001143569,0.15536778],"study_design_scores_gemma":[0.005853995,0.00073074846,0.9265189,0.00035995612,0.00012885705,0.0000045829997,0.03797949,0.015044403,0.00064521015,0.0023392667,0.009880315,0.00051424187],"about_ca_topic_score_codex":0.000064502005,"about_ca_topic_score_gemma":0.0029051902,"teacher_disagreement_score":0.91795,"about_ca_system_score_codex":0.000069362024,"about_ca_system_score_gemma":0.000094376825,"threshold_uncertainty_score":0.35140404},"labels":[],"label_agreement":null},{"id":"W2083343531","doi":"10.1068/b33070","title":"An Exploration of Issues Related to the Study of Generated Traffic and other Impacts Arising from Highway Improvements","year":2008,"lang":"en","type":"article","venue":"Environment and Planning B Planning and Design","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"McMaster University","funders":"","keywords":"Metropolitan area; Transport engineering; Computer science; Traffic congestion; Environmental science; Traffic speed; Geography; Engineering","score_opus":0.07063257218741055,"score_gpt":0.2994103601970216,"score_spread":0.22877778800961107,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2083343531","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9915633,0.00082671316,0.0071070273,0.0000627731,0.00004385473,0.00031913072,0.000012330534,0.000030196763,0.00003466797],"genre_scores_gemma":[0.9974872,0.000106954256,0.002237843,0.000033443604,0.00003090999,0.0000070590736,0.00002968142,0.000009434442,0.00005747018],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.998995,0.0002214337,0.00024095247,0.00020661531,0.00021069887,0.0001252734],"domain_scores_gemma":[0.99955297,0.000129008,0.0001359426,0.00008760904,0.000012315703,0.00008215239],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004704233,0.000106949774,0.0001568995,0.00007294973,0.00045454627,0.000035805493,0.000054331438,0.00006825746,0.000008708521],"category_scores_gemma":[0.000020757767,0.00008626842,0.0000084901185,0.00009105843,0.00009256382,0.00026546288,0.0000062093736,0.00007045239,5.05513e-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":[0.0001235182,0.00010015617,0.26454592,0.0000042599195,0.000045221506,0.000006746028,0.36900926,0.3630615,0.0019113526,0.000018130862,0.00012797728,0.0010459522],"study_design_scores_gemma":[0.0028752473,0.002475815,0.8098884,0.0002906742,0.00019241459,0.0000027635667,0.14637777,0.03300486,0.0022293911,0.00016230743,0.0018688336,0.00063149875],"about_ca_topic_score_codex":0.00074248237,"about_ca_topic_score_gemma":0.000012676293,"teacher_disagreement_score":0.5453425,"about_ca_system_score_codex":0.0000074598483,"about_ca_system_score_gemma":0.000015929198,"threshold_uncertainty_score":0.3517924},"labels":[],"label_agreement":null},{"id":"W2083848256","doi":"10.3141/1777-07","title":"Computational Issues in Increasing Spatial Precision of Traffic Assignments","year":2001,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Federal Highway Administration","keywords":"Computation; Centroid; Computer science; Geography; Transport engineering; Simulation; Operations research; Algorithm; Mathematics; Engineering; Artificial intelligence","score_opus":0.08368082778305527,"score_gpt":0.41918544826156945,"score_spread":0.33550462047851415,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2083848256","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9912614,0.00020977,0.0038654096,0.0029971087,0.00040872884,0.0008174329,0.00003765168,0.000025119058,0.00037733727],"genre_scores_gemma":[0.99367636,0.001521352,0.0040826197,0.00002122065,0.00016698171,0.000024795076,0.00004194581,0.00003286787,0.00043183664],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9882707,0.0038409785,0.0017429638,0.00035151685,0.0050439206,0.0007499073],"domain_scores_gemma":[0.9931542,0.0023651964,0.0006255753,0.0002500321,0.0032957383,0.00030921685],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012492505,0.00019509217,0.00045779612,0.0014835998,0.00072165753,0.00011083317,0.00086527417,0.00025491937,0.00042381673],"category_scores_gemma":[0.0007195747,0.00016825908,0.00026313274,0.0030789326,0.00084108306,0.00084132067,0.000003846737,0.0014030754,0.0000092985265],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0030630573,0.0005321691,0.7163362,0.00009205773,0.000068100686,0.00009575076,0.026183581,0.23571894,0.00031957944,0.0020126218,0.0009964019,0.014581591],"study_design_scores_gemma":[0.0017107812,0.00030468672,0.97939426,0.00058076874,0.000027046386,4.032484e-7,0.0056546633,0.001453834,0.00011663748,0.0026803592,0.007912872,0.00016369438],"about_ca_topic_score_codex":0.048866678,"about_ca_topic_score_gemma":0.1558043,"teacher_disagreement_score":0.2630581,"about_ca_system_score_codex":0.00030669963,"about_ca_system_score_gemma":0.0010502472,"threshold_uncertainty_score":0.957467},"labels":[],"label_agreement":null},{"id":"W2084517699","doi":"10.1002/atr.5670380306","title":"Modeling schedule recovery processes in transit operations for bus arrival time prediction","year":2004,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":79,"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":"Schedule; Computer science; Arrival time; Markov chain; Real-time computing; Transit (satellite); Process (computing); Stopping time; Public transport; Operations research; Simulation; Transport engineering; Engineering","score_opus":0.014347396486417127,"score_gpt":0.27886406151278803,"score_spread":0.2645166650263709,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2084517699","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.60560673,0.000119402226,0.39323243,0.00048687123,0.00021004159,0.00023802494,0.00004075816,0.000026754296,0.000038967726],"genre_scores_gemma":[0.9426688,0.0003425661,0.05658023,0.00003373149,0.00014648351,0.000017664363,0.00016154887,0.000013336024,0.000035624544],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988017,0.00002449687,0.00059016404,0.00012704519,0.00029700983,0.00015956354],"domain_scores_gemma":[0.999125,0.000044387732,0.00013580536,0.00004201954,0.0005822657,0.00007051575],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003932896,0.000089476336,0.00016787324,0.00020853206,0.00021470389,0.00003492844,0.00008274928,0.00009006418,0.0000121996845],"category_scores_gemma":[0.00012387394,0.00009497612,0.00007702804,0.00040089007,0.000028943174,0.0014623636,1.6624848e-7,0.000123335,0.0000011285517],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023966789,0.00008205638,0.00031107763,0.000035263136,0.00001195832,0.000003382084,0.018020358,0.9789807,0.0011162669,0.00057136395,0.0000017603464,0.0006261201],"study_design_scores_gemma":[0.11590378,0.010284812,0.2235427,0.0128192045,0.002901585,0.00007447656,0.20563632,0.28541592,0.029696861,0.09828417,0.00939434,0.0060458127],"about_ca_topic_score_codex":0.00007653616,"about_ca_topic_score_gemma":0.0032992926,"teacher_disagreement_score":0.69356483,"about_ca_system_score_codex":0.00011313379,"about_ca_system_score_gemma":0.00056708616,"threshold_uncertainty_score":0.38730142},"labels":[],"label_agreement":null},{"id":"W2084750728","doi":"10.1007/s10479-014-1663-x","title":"A short-turning policy for the management of demand disruptions in rapid transit systems","year":2014,"lang":"en","type":"article","venue":"Annals of Operations Research","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":89,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Computer science; Occupancy; Offset (computer science); Operations research; Transit (satellite); Theory of computation; Service (business); Demand management; Service level; Service quality; On demand; Order (exchange); Transport engineering; Business; Public transport; Economics; Engineering; Marketing","score_opus":0.219226179489936,"score_gpt":0.49442628401764077,"score_spread":0.27520010452770477,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2084750728","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4299203,0.0021410834,0.49242046,0.03739635,0.00024447692,0.005834023,0.00017216562,0.00006148942,0.031809665],"genre_scores_gemma":[0.9971639,0.0010483557,0.00082925137,0.000015289646,0.000054206863,0.00022529777,0.000023592609,0.0000046331525,0.0006354903],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99884874,0.00026802107,0.00024186067,0.00009785339,0.00035258415,0.00019095109],"domain_scores_gemma":[0.9990374,0.0003339403,0.00001594471,0.00012200443,0.00045370695,0.000037038244],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031596322,0.000037605434,0.00008876079,0.00023610539,0.0005280259,0.00005737192,0.00016783034,0.000037728525,0.000011645347],"category_scores_gemma":[0.00014090705,0.000030004992,0.000037347087,0.00067676045,0.00016522627,0.0001284915,0.0000054794355,0.00007506933,0.0000011000777],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002276105,0.0000694002,0.00095302897,0.000096983575,0.000039037586,2.5427283e-7,0.011824868,0.6141611,0.00009234833,0.36452332,0.00027379597,0.007943112],"study_design_scores_gemma":[0.0017998195,0.0005549037,0.21447322,0.0011475509,0.00008821553,9.155279e-7,0.10698089,0.5625297,0.0012056921,0.0016871211,0.109047525,0.00048444746],"about_ca_topic_score_codex":0.0023129042,"about_ca_topic_score_gemma":0.001897414,"teacher_disagreement_score":0.5672436,"about_ca_system_score_codex":0.000011085826,"about_ca_system_score_gemma":0.00009233962,"threshold_uncertainty_score":0.40612018},"labels":[],"label_agreement":null},{"id":"W2084954346","doi":"10.1002/atr.103","title":"Access mode choice behaviors of water transportation: a case of Bangkok","year":2010,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Multinomial logistic regression; Mode choice; Mode (computer interface); Transport engineering; Service (business); Level of service; Travel behavior; Order (exchange); Mixed logit; Computer science; Logistic regression; Econometrics; Public transport; Statistics; Business; Mathematics; Engineering; Marketing","score_opus":0.015040550451041471,"score_gpt":0.3391146008971032,"score_spread":0.32407405044606175,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2084954346","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99360794,0.000031486754,0.005173785,0.00018263646,0.0006035048,0.00017401189,0.0000910926,0.000018457833,0.00011707009],"genre_scores_gemma":[0.99154353,0.0000717358,0.008081981,0.000016331795,0.00009881867,0.000004606563,0.00012262979,0.000015804097,0.000044578584],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99819523,0.00004258117,0.0009927808,0.00012941126,0.0004614069,0.00017859227],"domain_scores_gemma":[0.9980027,0.00009642867,0.0007900851,0.00010912996,0.00088068336,0.000120984085],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038707198,0.00012165643,0.00030259156,0.00024343461,0.00013235283,0.000016560673,0.00020289145,0.00013676863,0.00017422962],"category_scores_gemma":[0.00003598087,0.00010554657,0.00017230114,0.00032421784,0.0001563965,0.001274087,4.1293058e-7,0.00025592968,4.6631249e-7],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007493684,0.0006598966,0.17822258,0.0002878211,0.00012764773,0.0005137501,0.17542455,0.49383166,0.13575616,0.0053284545,0.00005725909,0.009040858],"study_design_scores_gemma":[0.0047995998,0.00049225264,0.8458978,0.00031380937,0.000855559,0.00005488691,0.015305896,0.00010708761,0.12580308,0.0016326918,0.0041432246,0.0005941288],"about_ca_topic_score_codex":0.0010429707,"about_ca_topic_score_gemma":0.018124608,"teacher_disagreement_score":0.6676752,"about_ca_system_score_codex":0.000016486665,"about_ca_system_score_gemma":0.00015317496,"threshold_uncertainty_score":0.99979204},"labels":[],"label_agreement":null},{"id":"W2085557357","doi":"10.1002/atr.5670400208","title":"Implementation of cordon pricing on urban network with practical approach","year":2006,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Congestion pricing; Genetic algorithm; Mathematical optimization; Variable (mathematics); Flow network; Variable neighborhood search; Traffic congestion; Road pricing; Operations research; Transport engineering; Metaheuristic; Mathematics; Engineering; Algorithm","score_opus":0.0130080746450927,"score_gpt":0.31012916822599573,"score_spread":0.29712109358090305,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2085557357","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8684512,0.00004394559,0.1295669,0.00026603058,0.00016635668,0.00021145822,0.0000063961775,0.00002108621,0.0012666492],"genre_scores_gemma":[0.9334816,0.00003711407,0.06612353,0.00002667543,0.00021053308,0.000002891811,0.00007519777,0.000010092374,0.00003237632],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99864924,0.000062664,0.0005191863,0.00009901147,0.0005186194,0.00015126615],"domain_scores_gemma":[0.99865276,0.00009616104,0.0008213416,0.00004914118,0.00033260818,0.000047981353],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038948658,0.00008288113,0.00016957207,0.000098779325,0.00013722463,0.000015267657,0.000052784806,0.000049594564,0.000017793576],"category_scores_gemma":[0.000010618501,0.00007184503,0.00005394695,0.00035467502,0.000054889635,0.0005113454,2.0101419e-7,0.00012753243,3.0554514e-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":[0.0004705834,0.00012425559,0.097906016,0.00002304751,0.00002421137,0.000009671384,0.009471787,0.86844134,0.00018612215,0.021348584,0.00024701387,0.0017473757],"study_design_scores_gemma":[0.0019523054,0.0006624687,0.9757177,0.00013315014,0.00014331572,0.000003245934,0.016779317,0.00007927432,0.0006793947,0.0005727994,0.0031157536,0.00016122674],"about_ca_topic_score_codex":0.00026110647,"about_ca_topic_score_gemma":0.0011203004,"teacher_disagreement_score":0.87781173,"about_ca_system_score_codex":0.00004688444,"about_ca_system_score_gemma":0.00017638775,"threshold_uncertainty_score":0.29297554},"labels":[],"label_agreement":null},{"id":"W2085697310","doi":"10.1016/j.cor.2015.04.007","title":"Path based algorithms for metro network design","year":2015,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":36,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Path (computing); A priori and a posteriori; Network planning and design; Set (abstract data type); Integer (computer science); Mathematical optimization; Algorithm; Integer programming; Linear programming; Population; Mathematics","score_opus":0.29989119814690535,"score_gpt":0.4523902647933835,"score_spread":0.15249906664647817,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2085697310","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005725153,0.00009293448,0.99518466,0.0023342061,0.00038206985,0.0006761679,0.000009522406,0.00009021693,0.0006576848],"genre_scores_gemma":[0.20818335,0.000012462862,0.78962404,0.00021060935,0.0006024817,0.00017762401,0.00017289596,0.000014264966,0.0010022591],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981029,0.0006110672,0.00015601642,0.00020531184,0.00055152,0.00037321],"domain_scores_gemma":[0.99806726,0.00055760925,0.000012471734,0.00013700077,0.0010151258,0.00021052863],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037648156,0.00006232578,0.00009040333,0.00014470855,0.0012473747,0.00030734928,0.00023880559,0.00006866557,0.000023718041],"category_scores_gemma":[0.00034980997,0.00006280357,0.00003372384,0.00076728786,0.00013602835,0.00020620224,0.000009836826,0.00012438033,0.000026087762],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019552803,0.00003312582,0.0002564502,0.000002741023,0.0000066711477,0.0000014918011,0.0028470969,0.94064903,0.0000024074416,0.006847192,0.04631199,0.0030222249],"study_design_scores_gemma":[0.00050280953,0.00013565195,0.0002066467,0.000019041445,0.0000049263326,8.798119e-8,0.0011292299,0.9637398,0.000023083185,0.0004340996,0.03370864,0.00009596054],"about_ca_topic_score_codex":0.0005151562,"about_ca_topic_score_gemma":0.0005068909,"teacher_disagreement_score":0.20761083,"about_ca_system_score_codex":0.000121034536,"about_ca_system_score_gemma":0.000980352,"threshold_uncertainty_score":0.95939237},"labels":[],"label_agreement":null},{"id":"W2086163830","doi":"10.1016/j.trc.2011.02.010","title":"Traffic congestion pricing methodologies and technologies","year":2011,"lang":"en","type":"article","venue":"Transportation Research Part C Emerging Technologies","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":455,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Toll; Congestion pricing; Scope (computer science); Road pricing; Computer science; Traffic congestion; Singapore Area Licensing Scheme; Electronic toll collection; Transport engineering; Ranging; Telecommunications; Floating car data; Engineering","score_opus":0.23667589290769828,"score_gpt":0.42391434349762924,"score_spread":0.18723845058993097,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2086163830","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9481037,0.0031862755,0.013030452,0.0094255535,0.00042505466,0.0014033319,0.000040021572,0.017252773,0.0071328343],"genre_scores_gemma":[0.95096606,0.0060638688,0.04246809,0.000007863225,0.0000149597245,0.00016055736,0.000035717374,0.000019715037,0.0002631596],"study_design_codex":"design_other","study_design_gemma":"qualitative","domain_scores_codex":[0.9976338,0.00027743558,0.0003913528,0.00047047567,0.0006025132,0.00062440004],"domain_scores_gemma":[0.9987375,0.00047580604,0.00012694877,0.00023651589,0.00037335683,0.000049838574],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024060796,0.00017427695,0.00022976416,0.0007797231,0.0011095755,0.000068060435,0.00037823373,0.00038499714,0.0000622135],"category_scores_gemma":[0.001236746,0.00017129815,0.000047202942,0.0013483828,0.0017002354,0.00046134889,0.000014837493,0.0005463898,0.000011381227],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022573996,0.00017246106,0.09416226,0.00021452384,0.00012907218,0.00009022666,0.09220603,0.0019722038,0.00067968824,0.38421297,0.0022451908,0.4236896],"study_design_scores_gemma":[0.0011271755,0.00047964646,0.10408699,0.00048394696,0.00011680248,0.0000024921474,0.71160686,0.0004055653,0.009715066,0.06626392,0.10451901,0.0011924935],"about_ca_topic_score_codex":0.000649052,"about_ca_topic_score_gemma":0.0020033475,"teacher_disagreement_score":0.61940086,"about_ca_system_score_codex":0.000052516938,"about_ca_system_score_gemma":0.00010316621,"threshold_uncertainty_score":0.85340697},"labels":[],"label_agreement":null},{"id":"W2086181527","doi":"10.3141/1876-11","title":"Calibration and Application of a Simulation-Based Dynamic Traffic Assignment Model","year":2004,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":52,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Université de Montréal; Inro Consultants (Canada)","funders":"","keywords":"Calibration; Traffic simulation; Traffic flow (computer networking); Simulation; Set (abstract data type); Computer science; Subnetwork; Matrix (chemical analysis); Representation (politics); Traffic generation model; Algorithm; Simulation modeling; Basis (linear algebra); Data set; Trip distribution; Intersection (aeronautics); Mathematics; Real-time computing; Statistics; Engineering; Transport engineering; Artificial intelligence","score_opus":0.06003143686512434,"score_gpt":0.3995292099768136,"score_spread":0.33949777311168927,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2086181527","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.61352247,0.00009378584,0.3826261,0.0027047545,0.00011097331,0.0008240711,0.000047937832,0.000028980221,0.000040962506],"genre_scores_gemma":[0.9924068,0.0003169814,0.00692292,0.000039546452,0.000054273274,0.000057470657,0.000047663347,0.000034318957,0.000120016346],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9938134,0.0007811332,0.0012519659,0.00034398833,0.0032779374,0.0005315919],"domain_scores_gemma":[0.99501884,0.0010723198,0.00062129303,0.0002567518,0.0027245863,0.00030620903],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00393131,0.00018527245,0.00033907976,0.00085500197,0.0009413381,0.000097032455,0.00049954,0.00023165686,0.00003894118],"category_scores_gemma":[0.00024028496,0.00015995666,0.00021125033,0.0018115164,0.00095757836,0.0007022512,0.0000022296986,0.00089417805,0.0000019250544],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00044764826,0.0002097484,0.016269907,0.00010927582,0.00003591177,0.000005201962,0.009966932,0.9630438,0.0008311395,0.0052963155,0.000039771523,0.0037443487],"study_design_scores_gemma":[0.004295358,0.00065125583,0.32969102,0.0005873159,0.00014158529,1.1514157e-7,0.0062452364,0.64511997,0.0009155636,0.010147056,0.0017994177,0.0004061113],"about_ca_topic_score_codex":0.006833187,"about_ca_topic_score_gemma":0.061512303,"teacher_disagreement_score":0.37888435,"about_ca_system_score_codex":0.00033103934,"about_ca_system_score_gemma":0.0015457936,"threshold_uncertainty_score":0.9997804},"labels":[],"label_agreement":null},{"id":"W2086261161","doi":"10.1002/atr.5670380302","title":"Multimodal traveling and its impact on urban transit network design","year":2004,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":28,"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":"Hierarchy; Transport engineering; Transit (satellite); Multimodal transport; Computer science; TRIPS architecture; Service (business); Modal; Network planning and design; Urban transit; Mode choice; Public transport; Telecommunications; Engineering; Business","score_opus":0.016051211819005904,"score_gpt":0.2984599514342658,"score_spread":0.28240873961525986,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2086261161","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.80609787,0.00035253848,0.19268836,0.00032665223,0.00026275992,0.00018592914,0.000008730224,0.000029298872,0.000047879894],"genre_scores_gemma":[0.9703978,0.00043248007,0.028830262,0.000067339,0.00022671837,0.0000019402385,0.000016721237,0.000013511979,0.000013230779],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99884176,0.000060823982,0.0004138827,0.00011968588,0.00035743875,0.00020641074],"domain_scores_gemma":[0.9992058,0.00010319958,0.00029213744,0.00003739462,0.00021046944,0.00015099214],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004768986,0.0001200451,0.00019253959,0.00008366456,0.00025199403,0.000028800838,0.00007248265,0.000086648244,0.000016862516],"category_scores_gemma":[0.000028426379,0.00010633989,0.00009938802,0.00024861176,0.00003533875,0.00057054416,1.3575892e-7,0.00018160281,0.0000012787951],"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":[0.00045305712,0.000041442057,0.00082237495,0.0000068933387,0.000024375242,0.000020943877,0.032921206,0.9618028,0.00040734006,0.0014111588,0.000009584827,0.00207884],"study_design_scores_gemma":[0.01171185,0.00249398,0.9545257,0.0010836956,0.00041193343,0.000016550859,0.016561255,0.0022398927,0.002758433,0.006272652,0.001046501,0.0008775699],"about_ca_topic_score_codex":0.000042374242,"about_ca_topic_score_gemma":0.00014628985,"teacher_disagreement_score":0.9595629,"about_ca_system_score_codex":0.00007342383,"about_ca_system_score_gemma":0.00018180031,"threshold_uncertainty_score":0.4336415},"labels":[],"label_agreement":null},{"id":"W2086469603","doi":"10.3141/2003-01","title":"Random Utility Location, Production, and Exchange Choice; Additive Logit Model; and Spatial Choice Microsimulations","year":2007,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Logit; Commodity; Mixed logit; Discrete choice; Production (economics); Economics; Econometrics; Consumption (sociology); Microeconomics; Computer science; Operations research; Logistic regression; Mathematics","score_opus":0.09785761344366797,"score_gpt":0.40675782042021486,"score_spread":0.30890020697654685,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2086469603","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9467269,0.000526073,0.044678554,0.0055306912,0.0005080229,0.0015335643,0.00010440494,0.000058331567,0.00033343903],"genre_scores_gemma":[0.9941489,0.0018656446,0.0022585879,0.000062015635,0.0004260156,0.00004248941,0.000052361847,0.000036750906,0.0011072031],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99350685,0.001123564,0.0012690615,0.0005309891,0.0027313957,0.00083813525],"domain_scores_gemma":[0.99002665,0.0023451545,0.0005077317,0.00030133035,0.006300062,0.000519097],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.009433867,0.00023928747,0.00038520692,0.00090261956,0.002232046,0.00019979992,0.00046976254,0.0002741159,0.000154412],"category_scores_gemma":[0.0017775425,0.00020741452,0.00014669161,0.0021072398,0.0016935408,0.0010562398,0.000005823205,0.0013960135,0.0000039763477],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0019335017,0.00034227278,0.90699035,0.0002913503,0.00014827351,0.00002180746,0.042694267,0.017311873,0.0006169031,0.002536771,0.0049178386,0.022194775],"study_design_scores_gemma":[0.001594019,0.00014987665,0.974519,0.00025254648,0.00007276498,3.386385e-7,0.004606049,0.0013084947,0.00023977003,0.0015514411,0.015494485,0.00021121101],"about_ca_topic_score_codex":0.04160968,"about_ca_topic_score_gemma":0.51981455,"teacher_disagreement_score":0.47820485,"about_ca_system_score_codex":0.0001892005,"about_ca_system_score_gemma":0.0008197783,"threshold_uncertainty_score":0.9990669},"labels":[],"label_agreement":null},{"id":"W2086681505","doi":"10.5539/jmr.v2n1p28","title":"Applications of Maximal Network Flow Problems in Transportation and Assignment Problems","year":2010,"lang":"en","type":"article","venue":"Journal of Mathematics Research","topic":"Transportation Planning and Optimization","field":"Social Sciences","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":"Mathematics; Flow network; Flow (mathematics); Mathematical optimization; Transportation theory; Dual (grammatical number); Assignment problem; Process (computing); Tree (set theory); Algorithm; Combinatorics; Computer science","score_opus":0.05344592199758834,"score_gpt":0.36831338321933715,"score_spread":0.3148674612217488,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2086681505","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.73250026,0.0003628228,0.25818634,0.0019472461,0.00020521268,0.0016551089,0.00001903302,0.000022269445,0.0051016835],"genre_scores_gemma":[0.9463678,0.00025327786,0.053113624,0.0000030953909,0.00009160201,0.000024278923,0.000004581677,0.000007452,0.00013430069],"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9984408,0.0000876694,0.00049547857,0.000069234506,0.0007274565,0.00017936474],"domain_scores_gemma":[0.9988987,0.00028282386,0.00024445067,0.000070493916,0.0004204691,0.00008307278],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0043128678,0.00004628057,0.0001445096,0.0001985956,0.00013594213,0.000036899288,0.00014319894,0.0000812241,0.000050700895],"category_scores_gemma":[0.00009845158,0.00004052743,0.000028903614,0.0004371126,0.00016264057,0.00014939925,0.0000028468564,0.00038502124,0.0000010586477],"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":[0.000078448815,0.0024037946,0.3042953,0.0017903583,0.000119601515,0.00001752549,0.26690307,0.25202253,0.0069579785,0.13145329,0.0015562741,0.0324018],"study_design_scores_gemma":[0.0065902635,0.0014583456,0.27092227,0.003398788,0.00020275415,0.000039372677,0.06535258,0.080511406,0.0011672096,0.49897212,0.070321545,0.0010633647],"about_ca_topic_score_codex":0.00008628538,"about_ca_topic_score_gemma":0.0017231787,"teacher_disagreement_score":0.3675188,"about_ca_system_score_codex":0.000022989589,"about_ca_system_score_gemma":0.00017882476,"threshold_uncertainty_score":0.16727471},"labels":[],"label_agreement":null},{"id":"W2088906176","doi":"10.1214/13-aoas626","title":"Travel time estimation for ambulances using Bayesian data augmentation","year":2013,"lang":"en","type":"article","venue":"The Annals of Applied Statistics","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":109,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Global Positioning System; Bayesian probability; Computer science; Estimation; Path (computing); Data mining; Statistics; Mathematics; Artificial intelligence; Engineering","score_opus":0.14421433261847197,"score_gpt":0.3997035024737231,"score_spread":0.25548916985525116,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2088906176","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0055915504,0.000013698191,0.99077,0.00061158434,0.000048694365,0.0005370291,0.00066443457,0.000024064977,0.0017389827],"genre_scores_gemma":[0.6595069,0.000043041404,0.33837464,0.00021842314,0.00007354158,0.00002477561,0.0015763034,0.00001144703,0.00017088179],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99929726,0.000028521426,0.00021648941,0.00011536606,0.00020769061,0.00013464439],"domain_scores_gemma":[0.99918735,0.0002686128,0.00019163993,0.00016224568,0.00015578217,0.000034388097],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047188162,0.00005922241,0.00009775727,0.00003156291,0.0002988578,0.000054644406,0.00022038064,0.000034699922,0.00009942732],"category_scores_gemma":[0.00007312853,0.00005035216,0.000011124159,0.00011113088,0.00011965517,0.00017657378,0.0000067886135,0.000030519295,0.000012548504],"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":[0.00024027226,0.00016246657,0.00020564787,0.00018925795,0.00014694367,4.2302372e-7,0.042808946,0.26439172,0.0035710821,0.48754805,0.064049125,0.13668609],"study_design_scores_gemma":[0.0002808029,0.000029163322,0.004817868,0.00002046753,0.00005928511,9.2086225e-8,0.003049316,0.9244984,0.0006221131,0.06597679,0.00050564145,0.00014007793],"about_ca_topic_score_codex":0.0005746029,"about_ca_topic_score_gemma":0.00006144228,"teacher_disagreement_score":0.66010666,"about_ca_system_score_codex":0.000006523734,"about_ca_system_score_gemma":0.000071360875,"threshold_uncertainty_score":0.22986028},"labels":[],"label_agreement":null},{"id":"W2089427756","doi":"10.5539/jsd.v2n1p117","title":"Current Situation of Energy Consumption in Guangzhou Railway Station and Analysis on the Potential of Energy Conservation","year":2009,"lang":"en","type":"article","venue":"Journal of Sustainable Development","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Consumption (sociology); Energy consumption; Electricity; Energy conservation; Environmental economics; Environmental science; Air conditioning; Energy (signal processing); Business; Engineering; Economics; Electrical engineering","score_opus":0.014361824746726575,"score_gpt":0.2665773537191192,"score_spread":0.25221552897239263,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2089427756","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.944756,0.00028338775,0.053766236,0.0009651816,0.00005033563,0.00006874817,0.0000014325774,0.000003723743,0.00010495584],"genre_scores_gemma":[0.9982385,0.000733093,0.0008408862,0.000060499147,0.000015353135,0.00000205389,0.000032333908,0.0000021051533,0.000075154465],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99869156,0.00015587296,0.0005466289,0.000073046016,0.00041851736,0.00011436708],"domain_scores_gemma":[0.9985897,0.000100192105,0.0006490319,0.000041188454,0.00058627484,0.000033596552],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001066685,0.0000614027,0.00016414764,0.0005245054,0.00012486932,0.000022217971,0.00006443668,0.000044285156,0.000023029552],"category_scores_gemma":[0.00011065062,0.000050179602,0.000040162235,0.00062637613,0.000049675622,0.00022687294,0.0000030250617,0.00005536565,7.103489e-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":[0.0010119161,0.00082835264,0.24386571,0.00014022425,0.0004407632,0.00004152055,0.072416194,0.21869327,0.0010329573,0.3613082,0.0007710039,0.0994499],"study_design_scores_gemma":[0.0005239633,0.00009212319,0.9818458,0.000075147735,0.00009964349,5.24979e-7,0.010588351,0.001500843,0.0010902003,0.002068412,0.00203164,0.00008335617],"about_ca_topic_score_codex":0.00023902833,"about_ca_topic_score_gemma":0.00025977485,"teacher_disagreement_score":0.73798007,"about_ca_system_score_codex":0.00013807796,"about_ca_system_score_gemma":0.0003287761,"threshold_uncertainty_score":0.20462647},"labels":[],"label_agreement":null},{"id":"W2089509243","doi":"10.1016/j.orl.2007.03.005","title":"New formulations and valid inequalities for a bilevel pricing problem","year":2007,"lang":"en","type":"article","venue":"Operations Research Letters","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":49,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; Fédération Wallonie-Bruxelles","keywords":"Bilevel optimization; Toll; Commodity; Mathematical optimization; Computer science; Revenue; Inequality; Mathematical economics; Flow network; Operations research; Revenue management; Mathematics; Optimization problem; Economics","score_opus":0.135590153453481,"score_gpt":0.43403059732770183,"score_spread":0.29844044387422086,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2089509243","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14052278,0.00003294153,0.8419759,0.013874303,0.000057312667,0.00080168055,0.000014136961,0.0000649325,0.0026560077],"genre_scores_gemma":[0.7881468,0.000027435219,0.20710367,0.00041712204,0.00025708377,0.000070783004,0.00008593831,0.000012583999,0.0038785764],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9989197,0.00008807537,0.00018418864,0.00014701764,0.0003442883,0.00031673288],"domain_scores_gemma":[0.9991706,0.0004205922,0.000013505571,0.000071766284,0.00019998233,0.00012357974],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.002206722,0.00005061088,0.00006068186,0.00025495558,0.0015752397,0.00024031989,0.00007944608,0.00004486449,0.00003757251],"category_scores_gemma":[0.00026989885,0.000050570263,0.000019791642,0.0004198059,0.000082005514,0.00040166048,0.000006869038,0.000101147314,0.00000346464],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000060476523,0.00005820463,0.010398581,0.00006125384,0.000040989893,0.000003319136,0.16141202,0.10166191,0.009245742,0.6733068,0.018121468,0.025629206],"study_design_scores_gemma":[0.0071683275,0.00068211264,0.09479328,0.0005697243,0.00011864414,0.0000074378204,0.09589564,0.065791406,0.0059622303,0.020997932,0.70582783,0.0021854197],"about_ca_topic_score_codex":0.0038901404,"about_ca_topic_score_gemma":0.0090127885,"teacher_disagreement_score":0.68770635,"about_ca_system_score_codex":0.000066589804,"about_ca_system_score_gemma":0.00022807081,"threshold_uncertainty_score":0.99972457},"labels":[],"label_agreement":null},{"id":"W2089646109","doi":"10.1139/l01-025","title":"User costs at the work zone","year":2001,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Work zone; Work (physics); Computer science; Transport engineering; Process (computing); Control (management); Dilemma; Key (lock); Operations research; Engineering; Computer security","score_opus":0.008937897531197425,"score_gpt":0.21023142202177558,"score_spread":0.20129352449057816,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2089646109","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.82441896,0.00468933,0.09527607,0.0127506675,0.004120105,0.00023836142,0.00001289487,0.000075083415,0.0584185],"genre_scores_gemma":[0.99722004,0.000058586582,0.00013646108,0.00008145872,0.00019618477,5.0483686e-7,0.0000013979738,0.0000065465133,0.0022988396],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995387,0.000015897916,0.00012042892,0.000033029963,0.00012398454,0.00016793246],"domain_scores_gemma":[0.99952,0.000052279138,0.000049925806,0.000043363074,0.00007399942,0.00026042352],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029188246,0.000040152783,0.000056290046,0.00008657619,0.00021594681,0.00004466966,0.000111667614,0.000034204935,0.00051021314],"category_scores_gemma":[0.000088326524,0.000033645072,0.000032770975,0.0002701924,0.000031447857,0.00010644298,8.8700625e-7,0.000099398734,0.000007998825],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009307987,0.0000032329597,0.108155265,0.0000032775604,0.000032269443,0.000188335,0.010703696,0.854666,0.000015875637,0.0026080075,0.02163843,0.0019763312],"study_design_scores_gemma":[0.00014991092,0.000009168388,0.06230091,0.000081583894,0.000014709957,0.00002021329,0.0005820793,0.00022130214,0.0000063661223,0.000009181527,0.9365164,0.00008818323],"about_ca_topic_score_codex":0.0033701677,"about_ca_topic_score_gemma":0.6598304,"teacher_disagreement_score":0.91487795,"about_ca_system_score_codex":0.00018580916,"about_ca_system_score_gemma":0.00026614347,"threshold_uncertainty_score":0.5586476},"labels":[],"label_agreement":null},{"id":"W2089896716","doi":"10.1109/itst.2011.6060044","title":"Dynamic traffic information sharing for public development and application of ITS","year":2011,"lang":"en","type":"article","venue":"","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":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Traffic congestion; Advanced Traffic Management System; Transport engineering; Computer science; Floating car data; Intelligent transportation system; Service (business); Information sharing; Business; Engineering; World Wide Web; Marketing","score_opus":0.033567340923945005,"score_gpt":0.26692783715340973,"score_spread":0.23336049622946473,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2089896716","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5002768,0.000019985335,0.48988637,0.00013029647,0.000038038772,0.00038348153,0.000003326056,0.00008825309,0.009173421],"genre_scores_gemma":[0.97156054,0.000010589187,0.028191779,0.000016789149,0.0000020701887,0.000030971532,0.000055269626,0.0000014606971,0.00013052084],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99968994,0.0000037883158,0.00013067677,0.00004678003,0.000067507084,0.00006132355],"domain_scores_gemma":[0.99978364,0.000011216206,0.00006134121,0.000024967298,0.000093039365,0.000025816691],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020729467,0.000024800402,0.000033618548,0.000055446257,0.0001232228,0.000015208646,0.000043871096,0.00003000167,0.000014361983],"category_scores_gemma":[0.000020400012,0.00002473987,0.0000068268996,0.0000856816,0.000017133527,0.00042359976,0.0000018706658,0.0000109626335,0.0000024995977],"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":[0.000028575345,0.000058153557,0.013642923,0.00014030132,0.000020139949,2.8229874e-8,0.2620319,0.0018070478,0.000036182104,0.33654985,0.00004149583,0.3856434],"study_design_scores_gemma":[0.001858435,0.00010528966,0.44718468,0.0000635691,0.000047801594,5.446748e-7,0.031978954,0.40332162,0.0018118215,0.0014023907,0.1115753,0.00064958894],"about_ca_topic_score_codex":0.000035601923,"about_ca_topic_score_gemma":0.00056221546,"teacher_disagreement_score":0.4712837,"about_ca_system_score_codex":0.000012949847,"about_ca_system_score_gemma":0.000041449533,"threshold_uncertainty_score":0.100886256},"labels":[],"label_agreement":null},{"id":"W2089938056","doi":"10.1287/trsc.35.3.238.10152","title":"A Modeling Framework for Passenger Assignment on a Transport Network with Timetables","year":2001,"lang":"en","type":"article","venue":"Transportation Science","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":121,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Computer Research Institute of Montréal","funders":"","keywords":"Computer science; FIFO (computing and electronics); Mathematical optimization; Flow network; Graph; Travel time; Graph theory; Operations research; Transport engineering; Engineering; Theoretical computer science; Mathematics","score_opus":0.03983165338560238,"score_gpt":0.32306177551059817,"score_spread":0.28323012212499576,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2089938056","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1697389,0.000025173786,0.8263348,0.0008040697,0.00017645922,0.00050347054,0.000016482254,0.00017000633,0.0022306473],"genre_scores_gemma":[0.94538784,0.00006753948,0.05357649,0.0003148917,0.00013863962,0.000101590085,0.00006597215,0.000017596407,0.00032946118],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976872,0.0000293697,0.00031384744,0.00046326118,0.0009658864,0.00054041354],"domain_scores_gemma":[0.9990787,0.00015116937,0.00010027195,0.0001719613,0.00029820664,0.00019971518],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011576597,0.00015468025,0.00017378465,0.00013983157,0.0012024504,0.000083703824,0.00026239658,0.00009089235,0.000112401925],"category_scores_gemma":[0.000037952483,0.00013705895,0.00006201145,0.001376298,0.00030503457,0.0005868076,4.321834e-7,0.00012216813,0.0000078883],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018193561,0.00005282446,0.014309189,0.0000062709632,0.000008810323,0.000005078407,0.0076786326,0.85834837,0.000022689048,0.11852475,0.000018277688,0.0008431534],"study_design_scores_gemma":[0.0074152085,0.0020747865,0.36843076,0.0019340453,0.0008680411,0.0000059538424,0.030128019,0.43494034,0.00071979564,0.07963564,0.06949161,0.0043557826],"about_ca_topic_score_codex":0.00029604556,"about_ca_topic_score_gemma":0.0029591322,"teacher_disagreement_score":0.7756489,"about_ca_system_score_codex":0.00006567742,"about_ca_system_score_gemma":0.00041248652,"threshold_uncertainty_score":0.9248398},"labels":[],"label_agreement":null},{"id":"W2090914983","doi":"10.1016/j.disopt.2011.01.001","title":"Valid inequalities and branch-and-cut for the clique pricing problem","year":2011,"lang":"en","type":"article","venue":"Discrete Optimization","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Université de Montréal; HEC Montréal","funders":"","keywords":"Branch and cut; Mathematics; Polyhedron; Clique; Integer programming; Mathematical optimization; Inequality; Linear programming; Embedding; Flow network; Cutting-plane method; Combinatorics; Computer science","score_opus":0.04587999302022705,"score_gpt":0.29869370758595015,"score_spread":0.2528137145657231,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2090914983","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007962426,0.00017897954,0.98210806,0.0009808035,0.00010259355,0.0006223628,0.00001696101,0.00010229908,0.0079254955],"genre_scores_gemma":[0.9434117,0.0006932203,0.054708797,0.00017303217,0.00007039579,0.00007669467,0.000071949806,0.000014845919,0.0007793625],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993992,0.00006743337,0.00015760766,0.00013790614,0.00010466879,0.00013319685],"domain_scores_gemma":[0.9995687,0.00014974276,0.00008816181,0.00006017322,0.00009189454,0.00004133571],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045555894,0.00007024152,0.000076873795,0.00003753749,0.0005686823,0.00007696164,0.000057222514,0.00006081179,0.000030263294],"category_scores_gemma":[0.00008671486,0.00005437969,0.000020062422,0.000112235815,0.000121619094,0.00028229842,0.000005967252,0.000041250827,3.8731022e-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":[0.0001453422,0.00003158209,0.026885599,0.00012739122,0.00006301232,5.406197e-7,0.2981934,0.29608694,0.000016431957,0.37082663,0.0002567832,0.007366382],"study_design_scores_gemma":[0.0045346688,0.000736515,0.06022049,0.0005292789,0.0007396126,0.0000064224305,0.09854063,0.7790857,0.0009026443,0.034325827,0.018349463,0.0020287482],"about_ca_topic_score_codex":0.0006212598,"about_ca_topic_score_gemma":0.00029026065,"teacher_disagreement_score":0.9354493,"about_ca_system_score_codex":0.0000103052835,"about_ca_system_score_gemma":0.000035357785,"threshold_uncertainty_score":0.43739024},"labels":[],"label_agreement":null},{"id":"W2091508949","doi":"10.3141/1986-08","title":"Algorithm for Optimal Bid Packaging for Competitive Contracting in Public Transit","year":2006,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Procurement; Transit (satellite); Public transport; Service (business); Subsidy; Business; Sustainability; Perfect competition; Industrial organization; Transport engineering; Computer science; Marketing; Economics; Microeconomics; Engineering","score_opus":0.09728309913727053,"score_gpt":0.4063050876001653,"score_spread":0.3090219884628948,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2091508949","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.63922167,0.00027171368,0.33991855,0.014115889,0.0009562332,0.0042014173,0.0005002637,0.000077413046,0.0007368203],"genre_scores_gemma":[0.955289,0.00028663353,0.04263539,0.00004499559,0.0004999255,0.00028887557,0.00015934497,0.000060329487,0.0007354996],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9931919,0.0010918318,0.0015249045,0.00043878268,0.0025153162,0.0012372778],"domain_scores_gemma":[0.99056906,0.0032415283,0.0005298085,0.00020871234,0.0051687276,0.00028219156],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.00915928,0.00022907292,0.00047826927,0.0012664401,0.0015426989,0.00028515677,0.00076187757,0.00025695376,0.00008931157],"category_scores_gemma":[0.00035464618,0.00020562085,0.00044297165,0.0021121132,0.00070207025,0.0010786164,0.000001663167,0.0012338317,0.0000027763788],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0047653676,0.0019603393,0.5243061,0.0007590434,0.00042485233,0.00017401311,0.050599754,0.06269069,0.0027821285,0.2292897,0.008738447,0.11350956],"study_design_scores_gemma":[0.008166511,0.0007479238,0.8054227,0.000685228,0.000108449305,4.4445514e-7,0.034882262,0.0046592294,0.00094231596,0.011105638,0.13272412,0.00055517105],"about_ca_topic_score_codex":0.01664252,"about_ca_topic_score_gemma":0.19287539,"teacher_disagreement_score":0.3160673,"about_ca_system_score_codex":0.00036844373,"about_ca_system_score_gemma":0.0012628714,"threshold_uncertainty_score":0.9997572},"labels":[],"label_agreement":null},{"id":"W2091904506","doi":"10.1002/atr.108","title":"The methodology of multiple criteria decision making/aiding in public transportation","year":2010,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":82,"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":"ELECTRE; Multiple-criteria decision analysis; Public transport; Operations research; Computer science; Heuristic; Decision analysis; Management science; Transport engineering; Engineering; Mathematics; Artificial intelligence","score_opus":0.05343108239410409,"score_gpt":0.37879782857855665,"score_spread":0.32536674618445255,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2091904506","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92950195,0.00009753659,0.0683414,0.0005390218,0.0012455285,0.00015965506,0.000014390345,0.000016212642,0.00008428501],"genre_scores_gemma":[0.9054152,0.00026998005,0.09415786,0.000024942401,0.00008266259,0.0000047726676,0.000024186327,0.000011383865,0.000009025034],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9980196,0.00016920432,0.0010046054,0.00012966861,0.00046183454,0.0002151249],"domain_scores_gemma":[0.99705637,0.0013782821,0.00083018246,0.00009891689,0.0005667291,0.000069510534],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021854148,0.00010165074,0.00024061272,0.00026766738,0.000274213,0.00003405849,0.00022597489,0.0001385768,0.000040574414],"category_scores_gemma":[0.0007336958,0.00008467126,0.00011693167,0.000528887,0.0001595834,0.0008158554,4.0968865e-7,0.00033004023,4.9072673e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0026267294,0.00033606938,0.38122305,0.000085886495,0.000080538746,0.000058528763,0.1226888,0.14940985,0.0627162,0.0712012,0.00010855132,0.20946462],"study_design_scores_gemma":[0.001399913,0.00010212622,0.9725264,0.00013969187,0.000041979005,0.0000018401274,0.009694039,0.00017218954,0.0010568819,0.008809844,0.0059139696,0.00014113566],"about_ca_topic_score_codex":0.00007585475,"about_ca_topic_score_gemma":0.02522597,"teacher_disagreement_score":0.59130335,"about_ca_system_score_codex":0.000030993135,"about_ca_system_score_gemma":0.00018743178,"threshold_uncertainty_score":0.9925611},"labels":[],"label_agreement":null},{"id":"W2092018023","doi":"10.4028/www.scientific.net/amr.446-449.326","title":"A few Issues about Relieving Traffic Congestion of Cities by Improving Management Based on Foreign Mode","year":2012,"lang":"en","type":"article","venue":"Advanced materials research","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Traffic congestion; China; Zoning; Transport engineering; Scheduling (production processes); Mode (computer interface); Business; Computer science; Environmental economics; Operations management; Civil engineering; Engineering; Economics; Geography","score_opus":0.040891390598781295,"score_gpt":0.3914087299329988,"score_spread":0.3505173393342175,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2092018023","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9858755,0.00041249665,0.002457127,0.00031186896,0.00026627607,0.00064333156,0.00007343594,0.00012086251,0.009839096],"genre_scores_gemma":[0.99241745,0.00044661,0.0057010693,0.000022133234,0.00007664676,0.000075954726,0.00009955901,0.000015491692,0.0011450637],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99826795,0.0003048715,0.00023203567,0.00016349313,0.0006176821,0.00041394224],"domain_scores_gemma":[0.9993649,0.00017958092,0.00008586258,0.00013503838,0.00015540772,0.00007922258],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016668909,0.00008125045,0.00014143849,0.00016182817,0.00034587504,0.000066926965,0.00013761323,0.000068262176,0.00014829454],"category_scores_gemma":[0.0001490304,0.00008087748,0.000020440733,0.00022421588,0.000150736,0.0003359501,0.000010028554,0.00007227128,0.000011620065],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002129391,0.0007949439,0.0034948601,0.0020517781,0.0000783145,0.000011030273,0.03294564,0.37309435,0.29281184,0.19803469,0.0046633626,0.08988981],"study_design_scores_gemma":[0.0075970963,0.0017159294,0.032148913,0.004319491,0.00016203902,0.000001091796,0.07018457,0.011389296,0.7913672,0.0052008056,0.07355725,0.0023563323],"about_ca_topic_score_codex":0.00046211501,"about_ca_topic_score_gemma":0.00003327112,"teacher_disagreement_score":0.49855536,"about_ca_system_score_codex":0.00007539983,"about_ca_system_score_gemma":0.00004057512,"threshold_uncertainty_score":0.32980877},"labels":[],"label_agreement":null},{"id":"W2092313169","doi":"10.1061/40730(144)14","title":"Stated Response Examination of Factors Influencing Commercial Movement Route Choice Behavior","year":2004,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Credence; Toll; Sensitivity (control systems); Work (physics); Toll road; Data collection; Value of time; Selection (genetic algorithm); Value (mathematics); Computer science; Transport engineering; Operations research; Statistics; Econometrics; Travel time; Mathematics; Engineering; Medicine","score_opus":0.03157025598324915,"score_gpt":0.31676193308216616,"score_spread":0.285191677098917,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2092313169","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9957529,0.0000025038787,0.0026223646,0.00013239053,0.000085028754,0.00016610399,0.000016127322,0.00007795928,0.0011446684],"genre_scores_gemma":[0.9980819,0.0000041224307,0.0014285627,0.00007472427,0.000013568503,0.0000065344275,0.000042213353,0.000004759992,0.00034361554],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99918973,0.00011726179,0.00020306277,0.00009230989,0.00028292646,0.00011471062],"domain_scores_gemma":[0.9995586,0.000103408325,0.0000982573,0.00005829858,0.00013202614,0.000049384424],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003932965,0.000055066103,0.00007264732,0.00008731959,0.0001868522,0.000020990632,0.00007082573,0.000054250744,0.000088963505],"category_scores_gemma":[0.0001305237,0.00005442271,0.00002326774,0.0002495124,0.00005607966,0.00023413845,0.0000029552064,0.000044347027,0.0000024533376],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009766347,0.00019578083,0.8322979,0.0000072117728,0.00001346206,0.0000034417637,0.1334792,0.023202056,0.002186345,0.0043234453,0.000024166127,0.0041693193],"study_design_scores_gemma":[0.00035414897,0.000036472997,0.9912799,0.000011135865,0.000013982888,1.5956418e-8,0.006001268,0.000013237387,0.00196839,0.0000255002,0.00023104422,0.000064898006],"about_ca_topic_score_codex":0.011697706,"about_ca_topic_score_gemma":0.0066165705,"teacher_disagreement_score":0.15898198,"about_ca_system_score_codex":0.0001278779,"about_ca_system_score_gemma":0.00013930375,"threshold_uncertainty_score":0.9948835},"labels":[],"label_agreement":null},{"id":"W2093649999","doi":"10.1007/s11116-013-9510-5","title":"Learning-based framework for transit assignment modeling under information provision","year":2013,"lang":"en","type":"article","venue":"Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":23,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Transit (satellite); Computer science; Operations research; Process (computing); Service (business); Microsimulation; Focus (optics); Markov process; Public transport; Transport engineering; Engineering; Economics; Mathematics","score_opus":0.020306690363796356,"score_gpt":0.2835261948582314,"score_spread":0.26321950449443504,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2093649999","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08024686,0.000009322882,0.9165984,0.0015851654,0.00013378216,0.0008790558,0.000014214232,0.0002325252,0.00030072383],"genre_scores_gemma":[0.9688214,0.000013823069,0.029392801,0.0002844189,0.000049001897,0.00021224066,0.0011327028,0.0000131049155,0.00008050706],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99886537,0.000048687994,0.00034554108,0.00014658218,0.00036806284,0.00022575415],"domain_scores_gemma":[0.9993319,0.000107255815,0.00012231541,0.000068971916,0.00027995987,0.000089600166],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029285287,0.000109157976,0.00010842616,0.000106841275,0.00048405546,0.0001129944,0.00007861295,0.00016937387,0.00020082691],"category_scores_gemma":[0.000041362826,0.00011487049,0.00007399135,0.00020738279,0.000033499055,0.0010788386,2.0174537e-7,0.00012837653,0.00003524115],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003536011,0.000025581476,0.0011058364,0.000032624335,0.0000067242563,7.535826e-8,0.013446684,0.96116793,0.00002503858,0.019974373,0.00003996773,0.0041398266],"study_design_scores_gemma":[0.0014996715,0.00022689425,0.020056503,0.0001573987,0.00009790698,4.4207134e-8,0.015395121,0.9355369,0.00022645289,0.021912416,0.0044074245,0.00048326273],"about_ca_topic_score_codex":0.00083679735,"about_ca_topic_score_gemma":0.00031989254,"teacher_disagreement_score":0.88857454,"about_ca_system_score_codex":0.000057943555,"about_ca_system_score_gemma":0.00013076929,"threshold_uncertainty_score":0.46842828},"labels":[],"label_agreement":null},{"id":"W2093794525","doi":"10.3166/jds.18.185-201","title":"A Hybrid Approach for Evaluating Environmental Impacts for Urban Transportation Mode Sharing","year":2009,"lang":"en","type":"article","venue":"Journal of Decision System","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"European Commission","keywords":"Measure (data warehouse); Computer science; Analytic hierarchy process; Variable (mathematics); Performance indicator; Sustainable transport; Operations research; Transport engineering; Environmental economics; Sustainability; Mathematics; Business; Data mining; Engineering","score_opus":0.04501613370304321,"score_gpt":0.35345843474148336,"score_spread":0.30844230103844017,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2093794525","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4144294,0.000105370775,0.5847683,0.000034373836,0.00015026555,0.00033733016,0.00004681058,0.0000150358,0.00011314782],"genre_scores_gemma":[0.9093599,0.000011010894,0.09027198,0.000020068324,0.00021773591,0.000007955411,0.00004778026,0.000008742296,0.000054882203],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984284,0.00003938631,0.00065758335,0.0001374336,0.0005669228,0.00017025345],"domain_scores_gemma":[0.99888504,0.00020434857,0.0005715941,0.00007037277,0.0001497226,0.00011893448],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017835267,0.000084424086,0.0002130184,0.00013021113,0.00031902007,0.000078398785,0.00015905485,0.000058963866,0.0000041414046],"category_scores_gemma":[0.00012724745,0.00007409637,0.00017875477,0.00007573307,0.0000130279595,0.0003730433,6.657184e-7,0.00006337453,6.9320043e-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":[0.004192581,0.00053467485,0.029721407,0.00032823248,0.00019111212,0.000029572948,0.07379856,0.7711453,0.0054776864,0.010677049,0.0038474747,0.100056395],"study_design_scores_gemma":[0.016927812,0.0037624226,0.10294549,0.0025033224,0.00088240847,0.00011037222,0.05559796,0.80340135,0.0018105573,0.0049825157,0.005866895,0.001208893],"about_ca_topic_score_codex":0.0000072997896,"about_ca_topic_score_gemma":0.0000039486026,"teacher_disagreement_score":0.49493045,"about_ca_system_score_codex":0.00015973698,"about_ca_system_score_gemma":0.00008280725,"threshold_uncertainty_score":0.30215624},"labels":[],"label_agreement":null},{"id":"W2094382890","doi":"10.1115/jrc2012-74169","title":"High Speed Rail and Commuting Patterns in Yangtze River Delta: An Initial Observation","year":2012,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Coquitlam College; University of British Columbia","funders":"","keywords":"Yangtze river; Delta; Transport engineering; Travel time; Order (exchange); Business; China; Environmental science; Computer science; Geography; Engineering; Finance","score_opus":0.06060335501621356,"score_gpt":0.32436899931347757,"score_spread":0.263765644297264,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2094382890","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99571776,0.00001055423,0.002380782,0.00029284813,0.0001341728,0.0000709263,0.000005748884,0.00005492662,0.0013322803],"genre_scores_gemma":[0.99605936,0.000013908572,0.0033059523,0.00024811842,0.00012944723,0.0000013109179,0.000113138405,0.0000041027065,0.00012467046],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.999451,0.00009214044,0.00011715383,0.00007035129,0.00011772667,0.00015164002],"domain_scores_gemma":[0.9997686,0.00005579323,0.000038148137,0.00004136265,0.000032825534,0.000063256346],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039945592,0.000043491378,0.00005742345,0.000041609113,0.00016393131,0.000033552682,0.000042793123,0.000054882745,0.000114724025],"category_scores_gemma":[0.000034225348,0.000044001423,0.0000075788334,0.00011403141,0.00004320475,0.00069461245,0.0000037540383,0.00005364333,0.0000036681467],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000053505096,0.000026892676,0.9585934,0.0000024773647,0.0000014425266,5.076169e-7,0.031510502,0.0003674803,0.000014801329,0.0071464432,0.000024810091,0.0023058448],"study_design_scores_gemma":[0.00025596013,0.000008975649,0.99419874,0.000009327946,0.000004096671,1.1682985e-7,0.004377452,0.0005831606,0.00003352228,0.000108682354,0.00035116676,0.000068795925],"about_ca_topic_score_codex":0.016805952,"about_ca_topic_score_gemma":0.008934106,"teacher_disagreement_score":0.035605296,"about_ca_system_score_codex":0.000018863135,"about_ca_system_score_gemma":0.000016313443,"threshold_uncertainty_score":0.9897412},"labels":[],"label_agreement":null},{"id":"W2096339969","doi":"10.1002/atr.1206","title":"Users' views on current and future real‐time bus information systems","year":2012,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":49,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Public transport; Binary logit model; Real-time data; Transport engineering; Ordered logit; Travel time; Transit (satellite); Computer science; Service (business); Arrival time; Statistics; Engineering; Business; World Wide Web; Marketing; Mathematics","score_opus":0.013312077636348098,"score_gpt":0.29023270137857454,"score_spread":0.2769206237422264,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2096339969","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9861924,0.0019685568,0.004807917,0.0006221835,0.0039068833,0.00041302864,0.0000408054,0.00006960212,0.0019786078],"genre_scores_gemma":[0.9910044,0.0057548666,0.002140118,0.00003992699,0.0008804918,0.000005111487,0.00012214373,0.0000084525345,0.000044466848],"study_design_codex":"qualitative","study_design_gemma":"not_applicable","domain_scores_codex":[0.99882346,0.0000659124,0.0004902398,0.000053219977,0.00040468504,0.00016249572],"domain_scores_gemma":[0.9989517,0.00005194584,0.00054726226,0.000048700596,0.0002536431,0.00014680183],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005148503,0.00009072014,0.0001594508,0.00015356473,0.00017512965,0.000040665636,0.00005728471,0.00007079453,0.000015321724],"category_scores_gemma":[0.000018668525,0.000080530095,0.000049850976,0.00019560626,0.000030799907,0.002336057,2.7420322e-7,0.00013946637,0.000008950481],"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":[0.0011409236,0.00041404567,0.06347939,0.000386203,0.00009131545,0.000007746362,0.37967324,0.25983998,0.0006492936,0.07516855,0.0021881245,0.21696118],"study_design_scores_gemma":[0.0012477261,0.00017191861,0.48134872,0.0002360397,0.00010335302,0.0000030686267,0.016534328,0.0000536322,0.000046341363,0.00007670875,0.49994573,0.00023241641],"about_ca_topic_score_codex":0.000027332644,"about_ca_topic_score_gemma":0.0000256386,"teacher_disagreement_score":0.4977576,"about_ca_system_score_codex":0.000053563537,"about_ca_system_score_gemma":0.00005868799,"threshold_uncertainty_score":0.3283922},"labels":[],"label_agreement":null},{"id":"W2096699167","doi":"10.1007/978-1-4020-8327-3_6","title":"Coastal Transport and Dispersion Modelling","year":2008,"lang":"en","type":"book-chapter","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Environment and Climate Change Canada","funders":"","keywords":"Environmental science; Overexploitation; Land reclamation; Population; Pollution; Habitat; Sewage; Coastal erosion; Marine habitats; Environmental protection; Water resource management; Geography; Oceanography; Environmental engineering; Fishery; Ecology; Shore","score_opus":0.03032342132048422,"score_gpt":0.24165350834489113,"score_spread":0.21133008702440692,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2096699167","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0002547284,0.00022518968,0.04013583,0.00015865894,0.000094104864,0.00011113349,0.000033049342,0.000113135175,0.95887417],"genre_scores_gemma":[0.04311246,0.00891279,0.003074198,0.00006299134,0.000111870766,0.0000010859715,0.00028297503,0.000022144752,0.9444195],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99932176,0.0000051347433,0.00013916995,0.0001794869,0.0002498137,0.000104660714],"domain_scores_gemma":[0.99974245,0.000021712354,0.000051529885,0.000051106832,0.00004932301,0.0000838853],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000074808646,0.000106875115,0.00012460856,0.000057405123,0.0003230033,0.000012268729,0.000046103876,0.00021093129,0.0003991318],"category_scores_gemma":[0.0000011596591,0.00010709421,0.000047919028,0.000014549804,0.0001501717,0.0000947777,0.0000015635725,0.00011118304,0.000014102654],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000075696385,0.000029687526,0.0023136926,0.000062541956,0.00007730744,0.00010614856,0.052945,0.092977904,0.0000011815881,0.83871,0.0060342555,0.006666601],"study_design_scores_gemma":[0.00023312808,0.000023777733,0.00018360917,0.00011325048,0.00006197723,0.0000023424195,0.0004861281,0.0035676665,9.804321e-7,0.0013969934,0.9935273,0.00040288168],"about_ca_topic_score_codex":0.0006711492,"about_ca_topic_score_gemma":0.0012824888,"teacher_disagreement_score":0.98749304,"about_ca_system_score_codex":0.000011800879,"about_ca_system_score_gemma":0.00007601407,"threshold_uncertainty_score":0.43702134},"labels":[],"label_agreement":null},{"id":"W2096819890","doi":"10.1016/j.jtrangeo.2005.10.003","title":"Network Robustness Index: A new method for identifying critical links and evaluating the performance of transportation networks","year":2005,"lang":"en","type":"article","venue":"Journal of Transport Geography","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":528,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Robustness (evolution); Computer science; Network planning and design; Network topology; Flow network; Complex network; Network structure; Transport engineering; Operations research; Distributed computing; Topology (electrical circuits); Mathematical optimization; Engineering; Computer network; Mathematics","score_opus":0.04034196305000869,"score_gpt":0.3705880883371341,"score_spread":0.3302461252871254,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2096819890","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.37131524,0.0010245861,0.62603575,0.0011544466,0.0002530412,0.00017364511,0.0000054256693,0.0000125674405,0.000025279165],"genre_scores_gemma":[0.8827374,0.00039637293,0.11606134,0.00012233814,0.0006354786,0.0000051546963,0.000013561027,0.000011495502,0.000016849612],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99827,0.00010486127,0.00075376115,0.00013036994,0.00048009938,0.00026091668],"domain_scores_gemma":[0.99844605,0.00046596167,0.0004468328,0.00007308213,0.00044142103,0.00012665534],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028208839,0.00011359778,0.00026475525,0.00013971636,0.0004490154,0.000035600235,0.00017827099,0.0001838621,0.00002108492],"category_scores_gemma":[0.00003730913,0.00009187492,0.00021311465,0.00048543522,0.00015287095,0.00048493373,8.078732e-7,0.00034880874,4.160755e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023925249,0.000025307114,0.16767168,0.000044398195,0.00004779455,8.949257e-7,0.0042175124,0.81452185,0.000011544456,0.0006694288,0.000064605316,0.012485749],"study_design_scores_gemma":[0.0017439693,0.000524307,0.7435787,0.0005548814,0.000851099,0.0000074986747,0.0017548457,0.24789931,0.000036581045,0.00063152896,0.0021288977,0.00028837746],"about_ca_topic_score_codex":0.00010119669,"about_ca_topic_score_gemma":0.00048957794,"teacher_disagreement_score":0.575907,"about_ca_system_score_codex":0.000010467518,"about_ca_system_score_gemma":0.00016129638,"threshold_uncertainty_score":0.37465507},"labels":[],"label_agreement":null},{"id":"W2097186897","doi":"10.5383/juspn.04.01.004","title":"A Multimodal Transport Network Model for Advanced Traveler Information System","year":2012,"lang":"en","type":"article","venue":"Journal of Ubiquitous Systems and Pervasive Networks","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Technische Universiteit Eindhoven","keywords":"Multimodal transport; Computer science; Transport engineering; Engineering","score_opus":0.01756176611234764,"score_gpt":0.26212820871469816,"score_spread":0.24456644260235053,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2097186897","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08246007,0.0021473723,0.91138023,0.00009574357,0.002462995,0.00061194063,0.00002485454,0.000043216813,0.00077357603],"genre_scores_gemma":[0.9940139,0.00028158998,0.0034011942,0.00006247305,0.0020407552,0.000023646548,0.000023035442,0.000012469013,0.00014095704],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99847674,0.000085903965,0.00067145407,0.000072774244,0.00030322516,0.00038987724],"domain_scores_gemma":[0.9984351,0.00012934518,0.00062721205,0.00006233695,0.0005115889,0.00023445106],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001412452,0.00012850907,0.0003186273,0.000074496566,0.0004442908,0.000067656976,0.00010202506,0.00019404692,0.000002304513],"category_scores_gemma":[0.000026517397,0.000111327405,0.000119778575,0.0001384762,0.000044263044,0.0011199582,0.000002266047,0.00015659997,5.5514397e-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":[0.00015529564,0.000014353517,0.007281205,0.000103851,0.00004080919,9.934402e-7,0.020548668,0.96507436,0.0000018934912,0.0048000515,0.0007262873,0.0012522585],"study_design_scores_gemma":[0.0017837604,0.00015928892,0.009340804,0.0006270452,0.00018487919,0.0000469664,0.021111535,0.95166963,0.0000012612634,0.00002014751,0.014750607,0.00030405878],"about_ca_topic_score_codex":0.00010489262,"about_ca_topic_score_gemma":0.00011132349,"teacher_disagreement_score":0.9115538,"about_ca_system_score_codex":0.000079853824,"about_ca_system_score_gemma":0.00012696399,"threshold_uncertainty_score":0.45397997},"labels":[],"label_agreement":null},{"id":"W2097894336","doi":"10.1080/15568310802259882","title":"Introduction to the Special Issue on Road Pricing and Infrastructure Financing","year":2009,"lang":"en","type":"article","venue":"International Journal of Sustainable Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Revenue; Nexus (standard); Road pricing; Finance; Investment (military); Business; Transportation infrastructure; Fuel tax; Alternative fuel vehicle; Innovative financing; Public transport; Transport engineering; Economics; Alternative fuels; Traffic congestion; Engineering; Politics; Political science","score_opus":0.004035444178113798,"score_gpt":0.25776669396589164,"score_spread":0.25373124978777784,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2097894336","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8552511,0.00007130728,0.016919585,0.120763026,0.0026184465,0.0004286565,0.000009638305,0.000040598265,0.0038976544],"genre_scores_gemma":[0.9848954,0.00010248626,0.0009940704,0.00074074976,0.012255186,0.0000016255882,0.00001971231,0.0000055537835,0.0009851839],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99878275,0.00004583318,0.00033393427,0.000117264906,0.0005652094,0.0001550215],"domain_scores_gemma":[0.9987852,0.000037190282,0.00023790322,0.000049865266,0.00082056713,0.00006930078],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00057375117,0.000081100894,0.0001000755,0.0002462723,0.00027275435,0.00011827084,0.0001823302,0.000054185188,0.00012234264],"category_scores_gemma":[0.00017766222,0.000065905544,0.00004234144,0.00026400923,0.00003356862,0.00051454996,0.0000010368697,0.0001669728,0.0000032710404],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001317985,0.00011209797,0.004649376,0.00001523596,0.00007621431,0.0002100966,0.13453588,0.45396075,0.00012728646,0.13716175,0.039856587,0.22797675],"study_design_scores_gemma":[0.00050494686,0.00029414022,0.40149555,0.00005057645,0.000032924767,0.0000073749484,0.022896906,0.00007326586,0.00012394316,0.0023272396,0.5720715,0.0001216478],"about_ca_topic_score_codex":0.00015057465,"about_ca_topic_score_gemma":0.00013625617,"teacher_disagreement_score":0.5322149,"about_ca_system_score_codex":0.0001546431,"about_ca_system_score_gemma":0.00013885404,"threshold_uncertainty_score":0.26875502},"labels":[],"label_agreement":null},{"id":"W2098596873","doi":"10.1139/cjce-2013-0595","title":"Modeling significant factors affecting commuters’ perspectives and propensity to use the new proposed metro service in Doha","year":2014,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":37,"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; Transport engineering; Public transport; Traffic congestion; Sample (material); Service (business); Government (linguistics); Business; Engineering; Marketing","score_opus":0.02574705871777161,"score_gpt":0.22577344297568439,"score_spread":0.20002638425791278,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2098596873","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93581367,0.000045664787,0.06271167,0.0011313491,0.000105857245,0.000104261235,9.03879e-7,0.000009246565,0.00007735877],"genre_scores_gemma":[0.9989856,0.000004434794,0.0008809028,0.000052488576,0.000057569054,2.280554e-7,5.0905606e-7,0.000007662725,0.000010625789],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9994507,0.000055763347,0.00014161969,0.00006697672,0.00011245851,0.0001725264],"domain_scores_gemma":[0.99936396,0.000113720474,0.00004251173,0.00004924407,0.00011151055,0.00031904964],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005774306,0.00006390775,0.00011299135,0.00018683824,0.00017583658,0.0001335253,0.000110562636,0.000031286527,0.0000063757634],"category_scores_gemma":[0.00044777928,0.000051238098,0.000019572033,0.0002850945,0.000013358727,0.00025064047,0.0000024783174,0.00016628597,2.1132497e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000029529574,0.0000015386332,0.02920776,0.0000059656036,0.000007395316,0.0000036044532,0.09755325,0.8727337,0.00007846255,0.00031612103,0.000015505577,0.00007374225],"study_design_scores_gemma":[0.00086930086,0.0001382144,0.4800845,0.00071199384,0.000068124755,0.000007996448,0.109145194,0.4061614,0.00016402989,0.00010560336,0.0019704162,0.00057319814],"about_ca_topic_score_codex":0.06690482,"about_ca_topic_score_gemma":0.8107637,"teacher_disagreement_score":0.7438589,"about_ca_system_score_codex":0.00011262765,"about_ca_system_score_gemma":0.0003042036,"threshold_uncertainty_score":0.93930876},"labels":[],"label_agreement":null},{"id":"W2098796811","doi":"10.3141/1760-05","title":"Bus Rapid Transit Institutional Issues: The Route from Research to Experience","year":2001,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Bus rapid transit; Politics; Liability; Public transport; Business; Public relations; Marketing; Opposition (politics); Finance; Transport engineering; Political science; Engineering","score_opus":0.16468227055565912,"score_gpt":0.45231173954224047,"score_spread":0.28762946898658137,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2098796811","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9472342,0.00066474144,0.00652124,0.040846087,0.00114796,0.001811664,0.00019137138,0.0000717247,0.0015110051],"genre_scores_gemma":[0.9887411,0.0042289444,0.002858188,0.0002466169,0.00089649676,0.0002025051,0.00007800616,0.000055673663,0.0026924529],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.98227,0.00438992,0.0016377198,0.0006897188,0.009422331,0.001590329],"domain_scores_gemma":[0.98788565,0.0027017416,0.00033831588,0.0007242629,0.007535133,0.0008148704],"candidate_categories":["sts","research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.013695045,0.00030631738,0.00047602726,0.0013516034,0.0050447695,0.0005239212,0.0026636422,0.00033125794,0.0014708026],"category_scores_gemma":[0.00088654744,0.0002200808,0.00040324067,0.006792203,0.0025876623,0.0013036211,0.000010395381,0.0032855119,0.00012545707],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0063606575,0.0010167098,0.42047736,0.00012186274,0.00041933765,0.00071464654,0.37890756,0.05330708,0.0027872615,0.046488963,0.044715595,0.04468297],"study_design_scores_gemma":[0.0008864802,0.0002841546,0.5275436,0.00030784553,0.000031914802,5.2422786e-7,0.02875133,0.00007523983,0.00031681376,0.0024300443,0.43915573,0.00021634146],"about_ca_topic_score_codex":0.11871787,"about_ca_topic_score_gemma":0.2491713,"teacher_disagreement_score":0.3944401,"about_ca_system_score_codex":0.0005604063,"about_ca_system_score_gemma":0.0021905447,"threshold_uncertainty_score":0.999442},"labels":[],"label_agreement":null},{"id":"W2098967912","doi":"10.1139/cjce-2013-0313","title":"Preliminary economic appraisal of personal rapid transit (PRT) and urban gondola feeder systems serving a university campus and its surrounding major attractions","year":2014,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; U.S. Environmental Protection Agency","keywords":"Transport engineering; VisSim; Capital cost; Microsimulation; Reliability (semiconductor); Transit (satellite); Externality; Engineering; Public transport; Environmental economics; Economics","score_opus":0.009042439899408276,"score_gpt":0.20193204571995887,"score_spread":0.1928896058205506,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2098967912","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98581374,0.0017700904,0.010439133,0.00022423778,0.00065385806,0.00006995629,0.00002293199,0.000012905941,0.0009931428],"genre_scores_gemma":[0.99964863,0.000056760487,0.00009271613,0.000003317685,0.00011193017,1.7080522e-7,0.0000020279942,0.0000069776,0.000077460376],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9995406,0.000039006205,0.00014560367,0.0000680033,0.0000676826,0.00013906193],"domain_scores_gemma":[0.99939895,0.00014155405,0.00010005995,0.00002357404,0.00005736762,0.0002784941],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003610845,0.00006098301,0.00013012055,0.0002101549,0.00019793805,0.00004067793,0.0000578215,0.00005623968,0.000020894606],"category_scores_gemma":[0.00005358005,0.00007438092,0.000029366352,0.0000748506,0.000031666143,0.0003254652,0.0000010337906,0.000107792155,2.3001361e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000073498515,0.00001542472,0.09647311,0.00052221515,0.00024747587,0.00007812137,0.079831555,0.8048718,0.0007083062,0.015660295,0.00074578036,0.0007724334],"study_design_scores_gemma":[0.0029514206,0.000399077,0.49037778,0.0015258353,0.00047852693,0.00036084128,0.041503612,0.41174397,0.000054303742,0.000017283413,0.04969034,0.0008970205],"about_ca_topic_score_codex":0.0040091607,"about_ca_topic_score_gemma":0.044582814,"teacher_disagreement_score":0.39390466,"about_ca_system_score_codex":0.00009105676,"about_ca_system_score_gemma":0.00028058872,"threshold_uncertainty_score":0.97285104},"labels":[],"label_agreement":null},{"id":"W2100997713","doi":"10.71781/10768","title":"Parallel metaheuristics for stochastic capacitated multicommodity network design","year":2008,"lang":"en","type":"dissertation","venue":"Open MIND","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Centre interuniversitaire de recherche sur les reseaux d'entreprise, la logistique et le transport","keywords":"Tabu search; Metaheuristic; Mathematical optimization; Computer science; Maintainability; Software; Mathematics","score_opus":0.11286194909515042,"score_gpt":0.36115093133623244,"score_spread":0.24828898224108203,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2100997713","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00694888,0.00034845233,0.96530133,0.0001306163,0.0025323967,0.0050060106,0.0003867575,0.000027552896,0.019317998],"genre_scores_gemma":[0.1007058,0.00019413195,0.8200139,0.000046754612,0.00064267416,0.00042197714,0.015719611,0.00008815378,0.06216701],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9984837,0.00018844093,0.00036342334,0.00034277205,0.0002869745,0.00033468078],"domain_scores_gemma":[0.9985216,0.00054259604,0.00033876044,0.00014837281,0.00032741574,0.00012121129],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00066016533,0.00020020678,0.00034102055,0.000059421436,0.0009962624,0.00020176066,0.00044117172,0.000332368,0.00036624935],"category_scores_gemma":[0.00039509576,0.00021768085,0.0000836293,0.00021456764,0.00007519598,0.00017474887,0.000004607892,0.00017893885,0.00006355796],"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":[0.00057351944,0.00010392673,0.000035752244,0.000020441785,0.00013166918,0.000008438228,0.043514464,0.92987746,0.0000038812955,0.00042344647,0.014723443,0.010583531],"study_design_scores_gemma":[0.012986999,0.001010175,0.008748256,0.0020447678,0.004805817,0.0000068298014,0.06529365,0.26439014,0.00026175464,0.009210353,0.62386686,0.00737442],"about_ca_topic_score_codex":0.00054540776,"about_ca_topic_score_gemma":0.0050075753,"teacher_disagreement_score":0.66548735,"about_ca_system_score_codex":0.00005631985,"about_ca_system_score_gemma":0.00060791,"threshold_uncertainty_score":0.8876767},"labels":[],"label_agreement":null},{"id":"W2101205940","doi":"10.1002/atr.5670420105","title":"Multimodal, multiclass stochastic dynamic traffic assignment for evaluating information provision strategies","year":2008,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Travel time; Computer science; Traffic congestion; Modal; Operations research; Transport engineering; Engineering","score_opus":0.021287160872357998,"score_gpt":0.33353258363010907,"score_spread":0.3122454227577511,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2101205940","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5579067,0.000042488944,0.44114748,0.00014210053,0.00030083393,0.00039211882,0.000016822121,0.000033299108,0.00001819398],"genre_scores_gemma":[0.9449824,0.00008395399,0.05463787,0.000024519819,0.000050684248,0.000023006658,0.00015988176,0.000010724043,0.000026918877],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99824286,0.0000587765,0.0007576427,0.00010355365,0.0006375114,0.00019963668],"domain_scores_gemma":[0.9981136,0.00019164656,0.0007764245,0.000051360224,0.0007716257,0.000095366195],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00058187323,0.00012010785,0.00019370198,0.00019151153,0.0004794659,0.000046648445,0.00009858434,0.000088859524,0.000012591015],"category_scores_gemma":[0.00015716502,0.000116454874,0.000114350725,0.00021056352,0.00007863195,0.002654702,4.09253e-7,0.00013254139,0.0000018196594],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002991511,0.000055620258,0.00009305745,0.000030120502,0.000016133208,0.000002127168,0.047958504,0.9312895,0.00059570174,0.00022689217,0.000013078224,0.019420091],"study_design_scores_gemma":[0.019904317,0.0035659883,0.34683648,0.0009022345,0.0005769854,0.000022529579,0.16533358,0.4561612,0.00040314285,0.0015302761,0.0034736036,0.0012896603],"about_ca_topic_score_codex":0.00002242766,"about_ca_topic_score_gemma":0.00020484047,"teacher_disagreement_score":0.47512832,"about_ca_system_score_codex":0.00013415606,"about_ca_system_score_gemma":0.0004741746,"threshold_uncertainty_score":0.4748892},"labels":[],"label_agreement":null},{"id":"W2102256777","doi":"10.1016/s0739-8859(04)08014-x","title":"TECHNOLOGY CONSIDERATIONS FOR THE IMPLEMENTATION OF A STATEWIDE ROAD USER FEE SYSTEM","year":2004,"lang":"en","type":"article","venue":"Research in Transportation Economics","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Oregon Department of Transportation; McGill University","keywords":"Toll; Road pricing; Transport engineering; Toll road; User fee; State (computer science); Computer science; Data collection; Risk analysis (engineering); Business; Engineering; Traffic congestion","score_opus":0.08116379517197826,"score_gpt":0.40245337409991305,"score_spread":0.3212895789279348,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2102256777","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.972266,0.000088995774,0.014168662,0.011001532,0.00016431866,0.0015654973,0.00027427517,0.00006762707,0.00040306232],"genre_scores_gemma":[0.99539983,0.00026805614,0.0038866685,0.000020076308,0.000028498522,0.00025729873,0.00009926806,0.000010544579,0.000029783596],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","domain_scores_codex":[0.998883,0.00006847159,0.0004581162,0.00017180672,0.00015425752,0.00026434078],"domain_scores_gemma":[0.9989798,0.00041906495,0.00012454149,0.000119392964,0.00031506002,0.00004212978],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015045246,0.00006165296,0.000119973476,0.00035555166,0.0004497001,0.00004197668,0.00012128626,0.00008956657,0.000028543212],"category_scores_gemma":[0.00005238453,0.000060682487,0.000040065217,0.00042797334,0.00022841101,0.00024979253,0.0000011373971,0.000120174955,0.0000032878108],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029869598,0.000030116946,0.018483518,0.000045466764,0.000022157914,0.0000014136261,0.014956703,0.1553739,0.000035287125,0.80900687,0.00003247475,0.0019822195],"study_design_scores_gemma":[0.008374761,0.00031547065,0.37727457,0.00024693477,0.00008927126,0.0000019764327,0.5053526,0.0023885032,0.0054541416,0.084037706,0.015927868,0.0005361782],"about_ca_topic_score_codex":0.0064765373,"about_ca_topic_score_gemma":0.08821096,"teacher_disagreement_score":0.72496915,"about_ca_system_score_codex":0.00023812121,"about_ca_system_score_gemma":0.00074059214,"threshold_uncertainty_score":0.97906303},"labels":[],"label_agreement":null},{"id":"W2105200492","doi":"10.1287/trsc.1040.0109","title":"Route Choice on Transit Networks with Online Information at Stops","year":2005,"lang":"en","type":"article","venue":"Transportation Science","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":96,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"Ministero dell’Istruzione, dell’Università e della Ricerca; Istituto Nazionale di Alta Matematica \"Francesco Severi\"","keywords":"Headway; Transit (satellite); Line (geometry); Set (abstract data type); Computer science; Queueing theory; Operations research; Transport engineering; Travel time; Public transport; Engineering; Computer network; Mathematics","score_opus":0.014884257003874827,"score_gpt":0.2855968884167197,"score_spread":0.2707126314128449,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2105200492","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.87884235,0.000018849358,0.108926825,0.0030568105,0.00020530164,0.00040152614,0.000101175,0.0003245582,0.008122598],"genre_scores_gemma":[0.9933638,0.000039557286,0.0045302324,0.00096049864,0.00010365079,0.000011574729,0.00039975872,0.0000074564546,0.0005834603],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9981167,0.000029145283,0.0003129105,0.00024505163,0.000952725,0.00034347462],"domain_scores_gemma":[0.99914414,0.00007390188,0.00015345738,0.000146028,0.00029493327,0.0001875719],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005092368,0.000126214,0.000112403264,0.00019886842,0.0009945175,0.00010811435,0.00025727882,0.00007182233,0.00015246168],"category_scores_gemma":[0.000027914835,0.000114117545,0.000034025557,0.0012000874,0.00042633418,0.0023464365,8.2874595e-7,0.00012879373,0.000042442567],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000089113455,0.000053657877,0.02402428,0.0000045339034,0.0000041304224,0.000001182098,0.020030325,0.9367772,0.00003061658,0.006490108,0.00010535029,0.012389554],"study_design_scores_gemma":[0.000987828,0.00011245214,0.88964206,0.00005227906,0.000029643112,6.0122903e-7,0.0015226033,0.015989726,0.0002006541,0.00001248847,0.091118366,0.0003313212],"about_ca_topic_score_codex":0.0005195041,"about_ca_topic_score_gemma":0.020962106,"teacher_disagreement_score":0.9207874,"about_ca_system_score_codex":0.00016990876,"about_ca_system_score_gemma":0.00026297453,"threshold_uncertainty_score":0.99690276},"labels":[],"label_agreement":null},{"id":"W2105858673","doi":"10.1002/atr.5670390305","title":"Short-term travel time forecasts for transport information system in hong kong","year":2005,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":25,"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":"Travel time; Term (time); Interval (graph theory); Path (computing); Traffic flow (computer networking); Scale (ratio); Flow network; Computer science; Simulation; Transport engineering; Statistics; Operations research; Geography; Mathematics; Engineering; Mathematical optimization; Cartography; Physics","score_opus":0.011898338019811975,"score_gpt":0.2684234098624672,"score_spread":0.2565250718426552,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2105858673","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.89928454,0.000055430697,0.09881483,0.00028167779,0.00034536692,0.0005204044,0.000063308406,0.000048276117,0.00058616337],"genre_scores_gemma":[0.98302794,0.000058526733,0.016368521,0.00002984092,0.0001924709,0.0000157831,0.00023411696,0.00001357503,0.000059258444],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99816936,0.000032874585,0.0010130316,0.00010692977,0.00043669515,0.00024111854],"domain_scores_gemma":[0.9989208,0.00007237955,0.0004388839,0.000061682,0.00039682208,0.00010941503],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006915388,0.00013008258,0.00027128635,0.0003321249,0.00015788339,0.000027770293,0.00013141066,0.00012614255,0.000015265172],"category_scores_gemma":[0.000022942428,0.00013523681,0.00014751541,0.0003178026,0.000039943472,0.0027095985,2.1556076e-7,0.00014419397,0.0000035442154],"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":[0.00089062203,0.00010698574,0.044217538,0.00020261595,0.000042154054,0.000017042705,0.089167215,0.7803949,0.0006663116,0.0038688788,0.000052918625,0.08037278],"study_design_scores_gemma":[0.0027260128,0.00014814714,0.97994417,0.0004091431,0.000102218306,0.000005150722,0.010896043,0.0019507647,0.00042341388,0.00007074421,0.003045912,0.0002782952],"about_ca_topic_score_codex":0.000022055967,"about_ca_topic_score_gemma":0.0008101376,"teacher_disagreement_score":0.93572664,"about_ca_system_score_codex":0.00020568143,"about_ca_system_score_gemma":0.00015834175,"threshold_uncertainty_score":0.55147976},"labels":[],"label_agreement":null},{"id":"W2106017125","doi":"","title":"Congestion Relief: Assessing the Case for Road Tolls in Canada","year":2007,"lang":"en","type":"article","venue":"C.D. Howe Institute Commentary","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Road pricing; Singapore Area Licensing Scheme; Transport engineering; Congestion pricing; Traffic congestion; Business; Regional science; Economics; Geography; Engineering","score_opus":0.031190517850434682,"score_gpt":0.32789771106761534,"score_spread":0.29670719321718064,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2106017125","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95840055,0.00015446359,0.010440468,0.025536502,0.0018208914,0.00050086225,0.000031296633,0.000041612813,0.0030733836],"genre_scores_gemma":[0.99392176,0.000032597236,0.0016308244,0.0040784436,0.00014397364,0.00001348935,0.00011957129,0.000005404818,0.000053935306],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992451,0.00005136478,0.00021246,0.0001200544,0.0001414702,0.00022955328],"domain_scores_gemma":[0.9995073,0.00021144254,0.00007351474,0.00009140597,0.000051518564,0.00006480177],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00081697217,0.00006892462,0.00007959204,0.0000467607,0.00070469483,0.00005649749,0.000112892514,0.00004050408,0.000013505458],"category_scores_gemma":[0.000071708026,0.000061389845,0.000020639654,0.00020385747,0.00008683209,0.00036952656,0.000005573318,0.00010612438,5.7525e-7],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017772964,0.0002590459,0.40925884,0.00008248221,0.00008735231,0.0043044332,0.03626564,0.071960144,0.00006375902,0.017675387,0.1455543,0.31431088],"study_design_scores_gemma":[0.0018599224,0.000040511586,0.20348214,0.0001854918,0.00006244269,0.000060074737,0.058853623,0.0023038215,0.00015760677,0.00036284982,0.73214847,0.00048307623],"about_ca_topic_score_codex":0.93346566,"about_ca_topic_score_gemma":0.99552435,"teacher_disagreement_score":0.5865941,"about_ca_system_score_codex":0.0005275358,"about_ca_system_score_gemma":0.0005578094,"threshold_uncertainty_score":0.5420014},"labels":[],"label_agreement":null},{"id":"W2106285233","doi":"10.3141/2067-10","title":"Simulation Model for Assessing the Impact of Climate Change on Transportation and the Economy in Canada","year":2008,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Waterloo; McMaster University","funders":"Government of Canada","keywords":"Climate change; Economic impact analysis; Truck; Extreme weather; Economy; Environmental science; Economics; Engineering","score_opus":0.17624607252012908,"score_gpt":0.44454336718350496,"score_spread":0.2682972946633759,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2106285233","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9894512,0.0001466384,0.003924528,0.0039656335,0.00016122496,0.0020043233,0.00015588987,0.000010689481,0.0001798642],"genre_scores_gemma":[0.99729747,0.0017715776,0.00045201177,0.00006932873,0.00011621516,0.00016104063,0.000039365314,0.000031644355,0.000061351355],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9944907,0.0012748406,0.0013346409,0.00029619926,0.0019105113,0.0006931033],"domain_scores_gemma":[0.9922298,0.004394415,0.00069628307,0.00026901966,0.00222427,0.0001862013],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0057711974,0.0002073151,0.00044121913,0.0004999617,0.0015168388,0.00008755515,0.0006199338,0.00013556771,0.000031630407],"category_scores_gemma":[0.0002927717,0.00012384048,0.00032394112,0.0013550302,0.0010301168,0.00086410437,0.0000013402392,0.0010417385,5.4693555e-7],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016013326,0.00007833561,0.34794,0.0000796649,0.000055070057,0.000010379066,0.030028721,0.61187303,0.000016124219,0.00622628,0.00020930718,0.001881738],"study_design_scores_gemma":[0.002538516,0.0001421176,0.89516926,0.00022405667,0.000039717455,9.881587e-8,0.00607991,0.093048446,0.000022219903,0.002280461,0.00032300563,0.00013216464],"about_ca_topic_score_codex":0.7136354,"about_ca_topic_score_gemma":0.9304634,"teacher_disagreement_score":0.5472293,"about_ca_system_score_codex":0.0005170948,"about_ca_system_score_gemma":0.0028563072,"threshold_uncertainty_score":0.99978304},"labels":[],"label_agreement":null},{"id":"W2106584098","doi":"","title":"Modeling Household Weekend Activity Durations in Calgary","year":2005,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Travel behavior; Personal income; Sample (material); Travel survey; Scale (ratio); Econometrics; Computer science; Transport engineering; Geography; Economics; Engineering","score_opus":0.044787270313746665,"score_gpt":0.29664079074151334,"score_spread":0.25185352042776665,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2106584098","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8093197,0.00001620073,0.12698978,0.002820122,0.000058915557,0.00009326068,0.0000026397217,0.00016155439,0.060537864],"genre_scores_gemma":[0.9934198,0.000029670804,0.0048743347,0.00010634463,0.000058316728,0.0000049800688,0.0000067015535,0.0000035127823,0.0014963186],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995654,0.00003409455,0.00008646977,0.00008718628,0.00011799489,0.00010883169],"domain_scores_gemma":[0.9998655,0.000022275142,0.00001272287,0.000044049353,0.000017864993,0.000037630172],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017103522,0.000033439825,0.00004226153,0.00005636518,0.0001465191,0.000026284273,0.000043350545,0.000045925233,0.00009526384],"category_scores_gemma":[0.000020040976,0.000035025572,0.000014645247,0.0001668076,0.000018572373,0.00033662363,0.0000015629726,0.000060172788,0.000010287406],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004746902,0.000044165183,0.005551653,9.2957043e-7,0.0000013589448,8.1406637e-7,0.005336322,0.9724222,0.00004138891,0.012634926,0.00012297592,0.0038385184],"study_design_scores_gemma":[0.0002828365,0.000006067572,0.022545073,0.000010285436,0.000005041283,1.7146787e-7,0.0014413067,0.9711329,0.00007795248,0.00022217658,0.004128745,0.00014749262],"about_ca_topic_score_codex":0.004640397,"about_ca_topic_score_gemma":0.05104723,"teacher_disagreement_score":0.18410017,"about_ca_system_score_codex":0.000040165058,"about_ca_system_score_gemma":0.00006439304,"threshold_uncertainty_score":0.96626866},"labels":[],"label_agreement":null},{"id":"W2106944463","doi":"10.18757/ejtir.2003.3.1.4236","title":"Effects of Anticipatory Control with Multiple User Classes","year":2003,"lang":"en","type":"article","venue":"European journal of transport and infrastructure research","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ministry of Transportation of Ontario","funders":"","keywords":"Control (management); Computer science; Genetic algorithm; Focus (optics); Mathematical optimization; Operations research; Traffic generation model; Optimal control; Game theory; Computer network; Engineering; Artificial intelligence; Mathematics; Machine learning","score_opus":0.018332275039688897,"score_gpt":0.30091105062770407,"score_spread":0.2825787755880152,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2106944463","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98447496,0.00047565586,0.010239814,0.00008601524,0.0000960974,0.00014669556,0.000004391612,0.000008349275,0.004468005],"genre_scores_gemma":[0.9971688,0.00032182896,0.0023238156,0.0000199337,0.00006558582,4.1348176e-7,0.000001415384,0.000013292162,0.00008493521],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9980395,0.00077749137,0.00029347904,0.00009371821,0.0005831283,0.00021268807],"domain_scores_gemma":[0.9988599,0.00028437574,0.00015268335,0.000057240337,0.00048884173,0.00015697788],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020744316,0.000076159515,0.00018406395,0.00018222073,0.000209455,0.000022112437,0.0001296534,0.00003358957,0.000043192438],"category_scores_gemma":[0.0001784402,0.000055302688,0.000042915017,0.00026274094,0.0003791066,0.00020083913,0.0000011471658,0.0003280545,5.939491e-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":[0.00040678913,0.00005932847,0.9789578,0.00013973757,0.000093472445,0.0002868856,0.011657162,0.0025807365,0.0017559581,0.002300687,0.00036442705,0.0013970607],"study_design_scores_gemma":[0.0021021385,0.0003793825,0.9648413,0.00016153627,0.00004762327,0.0000094753095,0.0015653025,0.000008483301,0.00094179745,0.000056029705,0.029801205,0.00008569271],"about_ca_topic_score_codex":0.00001903765,"about_ca_topic_score_gemma":0.000032806747,"teacher_disagreement_score":0.029436778,"about_ca_system_score_codex":0.0000125372735,"about_ca_system_score_gemma":0.00021948172,"threshold_uncertainty_score":0.22551782},"labels":[],"label_agreement":null},{"id":"W2107127880","doi":"10.1007/s11116-015-9659-1","title":"Capturing, measuring and responding to changes that influence travel behavior","year":2015,"lang":"en","type":"article","venue":"Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Destinations; Travel behavior; Focus (optics); Work (physics); Journey to work; Behaviour change; Service (business); Transport engineering; Data science; Regional science; Computer science; Operations research; Sociology; Marketing; Psychology; Engineering; Political science; Public transport; Tourism; Business","score_opus":0.07422810352594321,"score_gpt":0.31122444536482163,"score_spread":0.2369963418388784,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2107127880","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99464417,0.000056164787,0.0031337605,0.0006503138,0.00012132147,0.0002651436,0.00001656389,0.00011223468,0.0010003279],"genre_scores_gemma":[0.9972331,0.000027114984,0.0019253471,0.00011390531,0.000036611247,0.000043003798,0.000044069486,0.0000110771025,0.00056579954],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9990926,0.00004269335,0.0001274927,0.00018914382,0.00036600232,0.00018206879],"domain_scores_gemma":[0.9995209,0.000026952539,0.00005576833,0.000062986575,0.00011414096,0.00021924797],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046232494,0.00008419212,0.000093938695,0.0002494479,0.0002038131,0.00005389136,0.00007090988,0.00006923145,0.000011826402],"category_scores_gemma":[0.000041825017,0.00009390418,0.000014802595,0.0003663201,0.000047030295,0.0003034995,7.2705274e-7,0.000062351806,0.000006647671],"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":[0.00012058168,0.0000372995,0.6345875,0.000026123336,0.0000098939245,0.000025822183,0.3414435,0.006566151,0.0015560588,0.0108542405,0.000066821856,0.004705946],"study_design_scores_gemma":[0.0003325244,0.000037540663,0.9830118,0.000051737836,0.00003956063,4.34487e-7,0.012341954,0.000020192601,0.0011669702,0.00009291941,0.0027348408,0.00016956654],"about_ca_topic_score_codex":0.0018809636,"about_ca_topic_score_gemma":0.012307641,"teacher_disagreement_score":0.34842423,"about_ca_system_score_codex":0.000042135565,"about_ca_system_score_gemma":0.000066841734,"threshold_uncertainty_score":0.68679523},"labels":[],"label_agreement":null},{"id":"W2107832004","doi":"10.1353/urb.2008.a249793","title":"Prospects for Urban Road Pricing in Canada","year":2008,"lang":"en","type":"article","venue":"Brookings-Wharton papers on urban affairs","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Geography; Road pricing; Transport engineering; Engineering; Traffic congestion","score_opus":0.013271088141448091,"score_gpt":0.22589198960661466,"score_spread":0.21262090146516657,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2107832004","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7210913,0.000305039,0.00010827161,0.0016797148,0.0010623825,0.0016144525,0.000044426757,0.00032515073,0.2737693],"genre_scores_gemma":[0.9943544,0.000048559115,0.00036995977,0.00037776778,0.0001571311,0.00006834697,0.000044659748,0.000027783011,0.0045513576],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.998219,0.00005851428,0.00031565528,0.00038284686,0.00051714526,0.0005068429],"domain_scores_gemma":[0.99934155,0.00013153673,0.00014292488,0.00014990145,0.00007093265,0.00016313519],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024062685,0.00017767977,0.00021902667,0.00013423011,0.00056184706,0.000035970657,0.00019696075,0.00009503684,0.00008401393],"category_scores_gemma":[0.00015242689,0.00019111331,0.000068084075,0.00041245547,0.000091165144,0.00017183846,0.000004906263,0.00015773106,0.000007440428],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003953983,0.00025672742,0.74868375,0.00010166019,0.000068418725,0.00019545978,0.113517284,0.018234229,0.0007056398,0.023304239,0.08896504,0.0055721737],"study_design_scores_gemma":[0.00406984,0.00038979985,0.6079647,0.0004733913,0.000050607636,0.0000063667403,0.037832156,0.0011864585,0.0015364918,0.00012989125,0.34482706,0.0015332609],"about_ca_topic_score_codex":0.40268186,"about_ca_topic_score_gemma":0.8320025,"teacher_disagreement_score":0.42932066,"about_ca_system_score_codex":0.000778377,"about_ca_system_score_gemma":0.0012810488,"threshold_uncertainty_score":0.7793375},"labels":[],"label_agreement":null},{"id":"W2108335025","doi":"10.1007/978-1-4757-6871-8_2","title":"Real Time Simulation of Traffic Demand-Supply Interactions within DynaMIT","year":2002,"lang":"en","type":"book-chapter","venue":"Applied optimization","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":149,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Traffic flow (computer networking); Computer science; Network traffic simulation; Flow network; State (computer science); Supply and demand; Transport engineering; Traffic simulation; Simulation; Operations research; Engineering; Network traffic control; Computer network; Economics; Microsimulation; Mathematical optimization; Microeconomics","score_opus":0.014675569033121023,"score_gpt":0.25545986933772,"score_spread":0.240784300304599,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2108335025","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00023589512,0.00002819731,0.1108518,0.00008928685,0.0003123242,0.0008348741,0.000096948854,0.00039285698,0.8871578],"genre_scores_gemma":[0.39934865,0.00093212747,0.04416247,0.00006278342,0.00046911184,0.000052674044,0.0053662476,0.00019673248,0.5494092],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981856,0.000039419097,0.0006866592,0.00037622426,0.00051920913,0.00019293779],"domain_scores_gemma":[0.99843144,0.00020503963,0.0007710897,0.00021627106,0.00027704958,0.0000991057],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002821696,0.00026850222,0.00035490445,0.0003166477,0.00035411044,0.00005924403,0.00016524353,0.00044560013,0.002947832],"category_scores_gemma":[0.000024520441,0.00031804512,0.00011127574,0.00016091687,0.00016801701,0.00023260775,0.000008853503,0.0002321896,0.00012553265],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003605441,0.000030308394,0.000004930319,0.000019379559,0.00004603105,9.73326e-7,0.0040571187,0.9569466,0.000008240079,0.03753362,0.0002721994,0.0010445364],"study_design_scores_gemma":[0.0003749614,0.000029723906,0.0000203914,0.000127949,0.0002015325,3.951561e-7,0.00015323683,0.9929191,0.0000056342215,0.00046668938,0.00531866,0.0003817599],"about_ca_topic_score_codex":0.00004156201,"about_ca_topic_score_gemma":0.000087694476,"teacher_disagreement_score":0.39911276,"about_ca_system_score_codex":0.00015956757,"about_ca_system_score_gemma":0.000120369776,"threshold_uncertainty_score":0.99992716},"labels":[],"label_agreement":null},{"id":"W2108693756","doi":"10.1287/trsc.1030.0061","title":"Economies of Scale in Empty Freight Car Distribution in Scheduled Railways","year":2004,"lang":"en","type":"article","venue":"Transportation Science","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":82,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Computer Research Institute of Montréal; Université de Montréal; Université du Québec à Montréal","funders":"","keywords":"Economies of scale; Tabu search; Flow network; Constraint (computer-aided design); Distribution (mathematics); Scale (ratio); Heuristic; Computer science; Transport engineering; Operations research; Mathematical optimization; Engineering; Economics; Mathematics; Microeconomics; Geography","score_opus":0.015025830092528251,"score_gpt":0.28379457351207643,"score_spread":0.2687687434195482,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2108693756","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9913255,0.000022929762,0.006771038,0.00037116735,0.00011043835,0.00018123034,0.000049006067,0.000032870328,0.0011358078],"genre_scores_gemma":[0.9980746,0.00004539914,0.0017052776,0.0000205819,0.000010846778,0.000011767001,0.000105019266,0.0000034597117,0.000023071838],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9987292,0.000028524317,0.00040210914,0.00024549456,0.00035671517,0.00023794542],"domain_scores_gemma":[0.99955535,0.00003130036,0.0001207182,0.00009291104,0.00012799616,0.000071699185],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009504377,0.00007260459,0.0001389052,0.00023287954,0.0001712484,0.00002695677,0.00019815828,0.00006387467,0.000041889485],"category_scores_gemma":[0.00005512607,0.000080240796,0.0000304176,0.0015045996,0.00057197636,0.0008259957,9.276348e-7,0.00008223732,0.000005600254],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000034201756,0.00010629033,0.7027652,0.000016268981,0.000001346949,0.0000032772193,0.047572084,0.17487326,0.0015177769,0.072674826,0.0000031139043,0.00043233536],"study_design_scores_gemma":[0.0007030621,0.000015311634,0.9894082,0.000053242446,0.0000037107452,7.137579e-8,0.003970272,0.00019286748,0.003543752,0.0019072713,0.00009888102,0.00010338723],"about_ca_topic_score_codex":0.0037934422,"about_ca_topic_score_gemma":0.043953303,"teacher_disagreement_score":0.28664294,"about_ca_system_score_codex":0.00017303058,"about_ca_system_score_gemma":0.00054985343,"threshold_uncertainty_score":0.9734921},"labels":[],"label_agreement":null},{"id":"W2108953490","doi":"10.3968/j.css.1923669720130903.3127","title":"The Challenges of Mobility Within Owerri City, Nigeria","year":2013,"lang":"en","type":"article","venue":"Canadian social science","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":"Pedestrian; Government (linguistics); Business; Systematic sampling; Stratified sampling; Transport engineering; Traffic congestion; Transit (satellite); Politics; Environmental planning; Public transport; Geography; Political science; Engineering","score_opus":0.025658864294266788,"score_gpt":0.2814752385627403,"score_spread":0.25581637426847353,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2108953490","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.43718642,0.00021959525,0.000066315944,0.010970715,0.00083396956,0.0004252437,0.000016107422,0.000054871933,0.55022675],"genre_scores_gemma":[0.999368,0.00003497134,0.00008527511,0.000098791985,0.00006722084,0.000011521063,7.3736373e-7,0.0000023378989,0.00033113954],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9989797,0.0000624431,0.00013641932,0.00014590794,0.0003585572,0.0003170056],"domain_scores_gemma":[0.99923307,0.00006289416,0.00007573361,0.000091870585,0.0002848195,0.00025161754],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0013105919,0.000045235472,0.00006357035,0.000052261104,0.002126167,0.000110889,0.0004039593,0.000053602704,0.00010791503],"category_scores_gemma":[0.0002787882,0.000037439666,0.000024339279,0.00051578245,0.0018346634,0.00028801363,0.0000058177293,0.00006242741,0.000014835529],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000025667182,0.0000184471,0.07422227,0.0000063699335,0.000006052029,9.3191835e-7,0.14058815,0.000044854467,0.00011519106,0.7689036,0.0018166581,0.01427488],"study_design_scores_gemma":[0.0001850772,0.00003754495,0.7487955,0.000022586213,0.000008279052,2.6622496e-7,0.19326386,0.00017307988,0.00019039611,0.019905446,0.037054617,0.0003633614],"about_ca_topic_score_codex":0.12882988,"about_ca_topic_score_gemma":0.5198549,"teacher_disagreement_score":0.74899817,"about_ca_system_score_codex":0.00016385842,"about_ca_system_score_gemma":0.0012396196,"threshold_uncertainty_score":0.9991729},"labels":[],"label_agreement":null},{"id":"W2109148429","doi":"10.1002/atr.5670350202","title":"Special issue ‐ public transportation (I) — Recent methodological advances","year":2001,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"China; Library science; Citation; Wright; Operations research; Political science; Engineering; History; Computer science; Law; Art history","score_opus":0.06867021079785622,"score_gpt":0.36556433820208006,"score_spread":0.29689412740422383,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2109148429","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.74511385,0.005903656,0.18271773,0.027929777,0.021161951,0.0013220103,0.00007932943,0.00040364848,0.015368073],"genre_scores_gemma":[0.67960787,0.17000388,0.13051908,0.0008875574,0.01708285,0.0000289374,0.000679047,0.00007412685,0.0011166645],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9974164,0.0002559867,0.0009639075,0.00021962613,0.00081709796,0.000327002],"domain_scores_gemma":[0.99770194,0.00025495223,0.0008212843,0.00009368459,0.0008950799,0.00023307365],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0011914691,0.00017089602,0.00034815253,0.00025381637,0.00033558905,0.00010133874,0.00021798364,0.00016281128,0.0012753897],"category_scores_gemma":[0.00021522226,0.00016243382,0.0001730125,0.00080889993,0.00014155278,0.0038160454,3.3681948e-7,0.00027669518,0.000010307563],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013312502,0.0003745481,0.021333998,0.000042319916,0.00008098034,0.00021955895,0.048493937,0.14064617,0.00038558693,0.0105554825,0.0017993897,0.77473676],"study_design_scores_gemma":[0.0010326519,0.00018140407,0.10707637,0.00007183158,0.00007120496,0.00000416094,0.016847195,0.0000051089055,0.00010264546,0.0018714707,0.87253803,0.00019793729],"about_ca_topic_score_codex":0.000017900633,"about_ca_topic_score_gemma":0.0016971346,"teacher_disagreement_score":0.8707386,"about_ca_system_score_codex":0.00009817452,"about_ca_system_score_gemma":0.0002224672,"threshold_uncertainty_score":0.9996376},"labels":[],"label_agreement":null},{"id":"W2109834329","doi":"10.1023/b:nets.0000027770.27906.82","title":"Congestion Pricing with Heterogeneous Travelers: A General-Equilibrium Welfare Analysis","year":2004,"lang":"en","type":"article","venue":"Networks and Spatial Economics","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":78,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Equity (law); Economics; Congestion pricing; Road pricing; General equilibrium theory; Budget constraint; Welfare; Microeconomics; Public transport; Constraint (computer-aided design); Traffic congestion; Public economics; Econometrics; Transport engineering; Engineering","score_opus":0.00825998363721217,"score_gpt":0.21754445327154803,"score_spread":0.20928446963433586,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2109834329","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8442482,0.00006687194,0.15388367,0.0008168879,0.000094840376,0.00011025918,0.000006191046,0.00004826856,0.00072482554],"genre_scores_gemma":[0.9982125,0.00025144315,0.0011003321,0.000102932114,0.00018658675,0.000005076212,0.00008687082,0.0000084456415,0.000045771147],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994239,0.000023403052,0.0001507958,0.00018593077,0.000048004233,0.0001679614],"domain_scores_gemma":[0.9997088,0.000014324529,0.00008868925,0.00006493061,0.00003415415,0.000089107925],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011683819,0.00008185971,0.00014769555,0.000064812775,0.00025534054,0.00010301926,0.000046490797,0.000088707486,0.00002474235],"category_scores_gemma":[0.0000035088904,0.000080858736,0.000039271243,0.00015843543,0.000071992414,0.000119840595,0.0000031960715,0.000059026315,5.676284e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026451959,0.000008317815,0.0116443215,0.0000013869968,0.00007154494,0.0000020656094,0.001301237,0.97997844,3.8789085e-7,0.0027325575,0.0000011515486,0.0042321086],"study_design_scores_gemma":[0.0012179321,0.00016480588,0.10712248,0.000021749649,0.0004868489,0.0000033419199,0.0007005539,0.88678205,0.000015608328,0.00019505146,0.002855417,0.00043414184],"about_ca_topic_score_codex":0.006956726,"about_ca_topic_score_gemma":0.034444574,"teacher_disagreement_score":0.15396436,"about_ca_system_score_codex":0.000052271098,"about_ca_system_score_gemma":0.00004656625,"threshold_uncertainty_score":0.999656},"labels":[],"label_agreement":null},{"id":"W2110180527","doi":"10.3141/2089-05","title":"Integrating Equity Objectives in a Road Network Design Model","year":2008,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":78,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Equity (law); Transport engineering; Gini coefficient; Maximization; Public economics; Network planning and design; Economics; Computer science; Business; Econometrics; Environmental economics; Engineering; Microeconomics; Inequality; Mathematics; Political science; Telecommunications; Economic inequality","score_opus":0.21117662384656957,"score_gpt":0.4418380755809317,"score_spread":0.23066145173436212,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2110180527","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9121089,0.00032678162,0.0810264,0.002746853,0.00052631635,0.0014583997,0.000027696678,0.00006572257,0.0017129453],"genre_scores_gemma":[0.97222704,0.0019905139,0.024270574,0.00005645641,0.000255892,0.0000775346,0.000014485886,0.000044337616,0.001063182],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.98912466,0.0032093981,0.0015545056,0.00043734803,0.0044115884,0.0012624926],"domain_scores_gemma":[0.99393153,0.0014668759,0.0005173278,0.0003206421,0.0033557545,0.00040788378],"candidate_categories":["sts","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.012130135,0.00024311246,0.0004803044,0.0010273556,0.0020691804,0.00013130986,0.0011785523,0.00028884114,0.00014891292],"category_scores_gemma":[0.0006498292,0.00020166606,0.00031365326,0.003932202,0.0012378659,0.0011467172,0.000006467008,0.0025343958,0.000011019253],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014683838,0.0003039466,0.30421698,0.000068790185,0.00007258186,0.00018593835,0.06933102,0.602091,0.00030707213,0.012242746,0.0047862143,0.004925305],"study_design_scores_gemma":[0.0017746771,0.00037262458,0.9591889,0.00060973177,0.0000335065,6.433602e-7,0.01153801,0.010232913,0.00016318141,0.013351803,0.0024078873,0.0003261719],"about_ca_topic_score_codex":0.029874783,"about_ca_topic_score_gemma":0.1823013,"teacher_disagreement_score":0.65497184,"about_ca_system_score_codex":0.0004977011,"about_ca_system_score_gemma":0.00277486,"threshold_uncertainty_score":0.9997668},"labels":[],"label_agreement":null},{"id":"W2110922380","doi":"10.1002/atr.1260","title":"Vehicle trajectory reconstruction using automatic vehicle identification and traffic count data","year":2014,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":78,"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":"Trajectory; Computer science; Map matching; Consistency (knowledge bases); VisSim; Traffic flow (computer networking); Matrix (chemical analysis); Simulation; Algorithm; Data mining; Real-time computing; Engineering; Global Positioning System; Artificial intelligence; Transport engineering; Microsimulation","score_opus":0.024718942427285544,"score_gpt":0.29643299305074,"score_spread":0.27171405062345444,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2110922380","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97259164,0.00014170421,0.026324712,0.0002021936,0.00053229445,0.00011709594,0.000015338053,0.000043081498,0.00003193051],"genre_scores_gemma":[0.9843685,0.00019467389,0.015169025,0.000025394202,0.00014607492,0.0000010929716,0.000067524554,0.000011259306,0.00001645284],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99864787,0.00011342408,0.0005830303,0.00016466774,0.00036192007,0.00012911251],"domain_scores_gemma":[0.99882764,0.00010064963,0.0006029716,0.00013153479,0.00024904998,0.00008818475],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00095309794,0.00008443099,0.00016259283,0.00013924297,0.00028402125,0.000056802084,0.00014339648,0.0000739733,0.000018194676],"category_scores_gemma":[0.00009550215,0.00008923759,0.00003516464,0.00023366048,0.00009900782,0.0015524488,8.2763364e-7,0.00012287727,0.000001054335],"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":[0.00012190976,0.0000993773,0.013118915,0.000103799204,0.000050425704,0.0000053003523,0.024865728,0.6812685,0.01436877,0.0009227589,0.000040510015,0.265034],"study_design_scores_gemma":[0.0024260085,0.00016658672,0.81461877,0.00036037687,0.0003525085,0.000018076102,0.013343317,0.1643317,0.0005422069,0.00074969284,0.0027089214,0.00038183155],"about_ca_topic_score_codex":0.000042794,"about_ca_topic_score_gemma":0.0004426338,"teacher_disagreement_score":0.80149984,"about_ca_system_score_codex":0.00005712747,"about_ca_system_score_gemma":0.00011911233,"threshold_uncertainty_score":0.36390033},"labels":[],"label_agreement":null},{"id":"W2110922472","doi":"10.1002/atr.177","title":"Distributed computing approaches for large‐scale probit‐based stochastic user equilibrium problems","year":2011,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":68,"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":"Monte Carlo method; Computer science; Probit; Stochastic simulation; Mathematical optimization; Workload; Path (computing); Mathematics; Machine learning; Statistics","score_opus":0.04326761981257687,"score_gpt":0.2764259703563284,"score_spread":0.2331583505437515,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2110922472","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26493943,0.00004706166,0.7340377,0.000120424884,0.00026520752,0.0004073023,0.00007829241,0.000046751888,0.0000578094],"genre_scores_gemma":[0.9273233,0.000003900585,0.07217306,0.0000267896,0.00009827666,0.000012492932,0.0003167952,0.00001754883,0.000027872475],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99864155,0.000048703576,0.0005803479,0.00015088498,0.000313925,0.00026459555],"domain_scores_gemma":[0.99868405,0.000095145595,0.0006156286,0.00006584875,0.00042577524,0.00011353053],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00057573355,0.00011738372,0.00021126625,0.00011340217,0.00021858874,0.000025327261,0.00013542446,0.000089239955,0.000020043357],"category_scores_gemma":[0.00004974991,0.000113965325,0.00013154528,0.0002789225,0.000053102856,0.00057065405,5.626389e-7,0.00012855062,6.6899395e-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":[0.00029579384,0.00021780255,0.0047083697,0.0000781798,0.000025225983,0.0000026687092,0.048400257,0.94294244,0.00016394752,0.0023515485,0.000028917171,0.0007848564],"study_design_scores_gemma":[0.02544772,0.0026777831,0.7514875,0.0020784843,0.0013993604,0.00000862048,0.10844293,0.07373448,0.0035654118,0.0137462765,0.015114277,0.0022971337],"about_ca_topic_score_codex":0.000014476749,"about_ca_topic_score_gemma":0.00030629797,"teacher_disagreement_score":0.869208,"about_ca_system_score_codex":0.000045812492,"about_ca_system_score_gemma":0.00017195774,"threshold_uncertainty_score":0.4647371},"labels":[],"label_agreement":null},{"id":"W2110948062","doi":"10.1061/40952(317)16","title":"A Land Use Transport Modeling Framework and Its Design and Development for the Province of Alberta","year":2008,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Calgary; University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada; University of Calgary","keywords":"Commodity; Context (archaeology); Novelty; Government (linguistics); Production (economics); China; Business; Land use; Transport engineering; Regional science; Economy; Geography; Economics; Civil engineering; Engineering; Finance","score_opus":0.0657248704267473,"score_gpt":0.278769677470913,"score_spread":0.21304480704416573,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2110948062","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25604734,0.00009359757,0.74331415,0.00019829399,0.000013095891,0.00026026,0.0000010309315,0.0000108998365,0.00006132742],"genre_scores_gemma":[0.90662885,0.00021514548,0.09240523,0.00004124504,0.000007698885,0.000012235709,0.0000030788651,0.0000030973235,0.0006834446],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99964684,0.00001391259,0.0001012721,0.00007947407,0.00008518552,0.000073292584],"domain_scores_gemma":[0.9994861,0.00038631508,0.000024757477,0.000025433414,0.000048723537,0.000028662258],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001794843,0.00003717369,0.000054308242,0.000013848264,0.00032592224,0.000009099343,0.00003201318,0.000040770617,0.0000055085998],"category_scores_gemma":[0.000053311494,0.00002607861,0.000007011023,0.00004150527,0.000038536196,0.00010959701,9.805217e-7,0.000024928448,1.4053957e-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":[0.00020888129,0.0000615391,0.21054801,0.000071151684,0.00006241763,0.0000030099893,0.18692206,0.5777813,0.000013237172,0.02177182,0.00010339315,0.0024531537],"study_design_scores_gemma":[0.001468942,0.00011586161,0.10078195,0.00020379493,0.00010215709,0.0000041775747,0.0047228457,0.87937856,0.0005891012,0.00085064827,0.011208864,0.00057307805],"about_ca_topic_score_codex":0.0006759656,"about_ca_topic_score_gemma":0.0018704217,"teacher_disagreement_score":0.65090895,"about_ca_system_score_codex":0.0000035965168,"about_ca_system_score_gemma":0.00012619824,"threshold_uncertainty_score":0.25067633},"labels":[],"label_agreement":null},{"id":"W2111418994","doi":"10.1002/atr.214","title":"A probability model and sampling algorithm for the inter‐day stochastic traffic assignment problem","year":2012,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Joint probability distribution; Interval (graph theory); Time horizon; Conditional probability distribution; Conditional probability; Probability distribution; Computer science; Gibbs sampling; Stochastic modelling; Marginal distribution; Mathematical optimization; Stochastic process; Traffic flow (computer networking); Flow network; Random variable; Algorithm; Mathematics; Statistics; Artificial intelligence","score_opus":0.03829806290140944,"score_gpt":0.3079644708153795,"score_spread":0.26966640791397,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2111418994","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08648638,0.00030384836,0.912105,0.00039855982,0.00023480346,0.00042634254,0.000018097257,0.000016913791,0.000010034226],"genre_scores_gemma":[0.8280826,0.00006689404,0.17163792,0.000026658132,0.00011540206,0.000025184674,0.000013910338,0.0000078550565,0.000023550496],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990557,0.000037620277,0.0003866446,0.00008489062,0.00025136996,0.00018381806],"domain_scores_gemma":[0.9990667,0.0002664446,0.0003149836,0.000039145845,0.00021627876,0.00009642905],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010924011,0.000080702244,0.00012796973,0.000049236703,0.0002822882,0.000026669768,0.00006780494,0.000048962793,0.0000044054004],"category_scores_gemma":[0.000046956928,0.000060329374,0.000068211004,0.000104960396,0.000071654016,0.00060710177,4.510536e-7,0.00011114042,1.4377326e-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":[0.00004423762,0.000053431555,0.00023217521,0.000014383395,0.000018632321,1.1774256e-7,0.031995796,0.86578226,0.000044395852,0.0007765958,0.0000072589846,0.10103071],"study_design_scores_gemma":[0.010782308,0.0012767216,0.24164672,0.0010932299,0.00198654,0.000014650262,0.07830369,0.6299721,0.00019043365,0.02400991,0.009143398,0.0015802848],"about_ca_topic_score_codex":0.0000066493553,"about_ca_topic_score_gemma":0.0001161717,"teacher_disagreement_score":0.7415962,"about_ca_system_score_codex":0.000056937755,"about_ca_system_score_gemma":0.00009034806,"threshold_uncertainty_score":0.24601604},"labels":[],"label_agreement":null},{"id":"W2111731757","doi":"10.1287/ijoc.1090.0335","title":"A Lagrangean Heuristic for Hub-and-Spoke System Design with Capacity Selection and Congestion","year":2009,"lang":"en","type":"article","venue":"INFORMS journal on computing","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":108,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Mathematical optimization; Subgradient method; Heuristic; Computer science; Queue; Branch and bound; Transshipment (information security); Nonlinear system; Upper and lower bounds; Flow network; Nonlinear programming; Integer (computer science); Piecewise linear function; Network congestion; Cutting-plane method; Integer programming; Mathematics","score_opus":0.03055224279145047,"score_gpt":0.27175312602159096,"score_spread":0.2412008832301405,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2111731757","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.44397777,0.000020502526,0.5549315,0.00023243397,0.00009636467,0.0001857406,0.0000010496207,0.000074941934,0.00047967437],"genre_scores_gemma":[0.98526275,0.00001239409,0.014460803,0.00009334909,0.00014552359,8.16019e-7,0.0000020476296,0.0000036351112,0.000018658631],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999393,0.00004170832,0.00017661774,0.000076946715,0.0001652798,0.00014647224],"domain_scores_gemma":[0.9994001,0.00016038242,0.0001766592,0.00001929656,0.0001556952,0.00008787946],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00081891945,0.00006824125,0.00009704735,0.000083132814,0.00088709744,0.00018140137,0.00003293942,0.00005030614,7.351959e-7],"category_scores_gemma":[0.00007188929,0.00005284489,0.000015405363,0.00011656425,0.000029673552,0.0002211507,8.414177e-7,0.00012641455,4.4091445e-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":[0.00088694785,0.00006659742,0.022793317,0.00013179952,0.0000738168,0.00001672779,0.050803345,0.697893,0.000055095818,0.06940759,0.00022489739,0.15764682],"study_design_scores_gemma":[0.006013921,0.0045923605,0.31906104,0.0023874147,0.00023542231,0.000698236,0.014768997,0.6449179,0.00016266164,0.0023015628,0.003866575,0.0009938789],"about_ca_topic_score_codex":0.000036990838,"about_ca_topic_score_gemma":0.000057607507,"teacher_disagreement_score":0.541285,"about_ca_system_score_codex":0.000060612216,"about_ca_system_score_gemma":0.000051352235,"threshold_uncertainty_score":0.6822926},"labels":[],"label_agreement":null},{"id":"W2111837930","doi":"10.1287/trsc.34.4.402.12322","title":"On the Existence of Solutions to the Dynamic User Equilibrium Problem","year":2000,"lang":"en","type":"article","venue":"Transportation Science","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":97,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Variational inequality; FIFO (computing and electronics); Path (computing); Mathematical optimization; Flow network; Computer science; Flow (mathematics); Mathematical economics; Mathematics; Applied mathematics; Geometry","score_opus":0.029736884155051848,"score_gpt":0.3035717216101579,"score_spread":0.27383483745510606,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2111837930","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9550395,0.000020444875,0.008194485,0.015909338,0.0001507307,0.00064050785,0.000052332496,0.000091239046,0.019901415],"genre_scores_gemma":[0.99579924,0.000019569585,0.0013135263,0.0005137892,0.000012974734,0.000032673168,0.000010420339,0.000004751571,0.0022930638],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9984139,0.000063298634,0.00024731935,0.00022289102,0.0007756781,0.0002769085],"domain_scores_gemma":[0.9992482,0.00017775572,0.000074833784,0.00022025079,0.00019622229,0.00008271756],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013448679,0.0000722142,0.00007087863,0.00007483958,0.0009807644,0.000064251646,0.0005616496,0.000029046072,0.00054117164],"category_scores_gemma":[0.00006352465,0.00004585813,0.000040767925,0.0016406741,0.0007544695,0.0003315608,0.000001317215,0.00008269637,0.00007515631],"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":[0.000063327265,0.00009371269,0.0021611224,0.000009577541,0.000007200699,0.0000012958888,0.099541076,0.3373237,0.0014312452,0.5519986,0.0009463374,0.0064227744],"study_design_scores_gemma":[0.00072450103,0.00034052835,0.8752729,0.0002910999,0.00009623436,0.0000010706959,0.019847516,0.010195416,0.0015268497,0.015115853,0.07579744,0.00079053035],"about_ca_topic_score_codex":0.00051628967,"about_ca_topic_score_gemma":0.0028429157,"teacher_disagreement_score":0.87311184,"about_ca_system_score_codex":0.000033077118,"about_ca_system_score_gemma":0.0002787877,"threshold_uncertainty_score":0.7543346},"labels":[],"label_agreement":null},{"id":"W2112283300","doi":"10.1023/a:1023995710308","title":"A New Demand-Supply Decomposition Method for a Class of Economic Equilibrium Models","year":2003,"lang":"en","type":"article","venue":"Computational Economics","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University; University of Waterloo","funders":"","keywords":"Decomposition; Mathematical optimization; Supply and demand; Linear programming; Commodity; Scale (ratio); Mathematical economics; Mathematics; Computer science; Economics; Microeconomics; Chemistry; Physics","score_opus":0.028063922950088437,"score_gpt":0.32599480183165497,"score_spread":0.29793087888156655,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2112283300","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024538044,0.000047398582,0.96764654,0.0005852406,0.00024132298,0.00024311722,0.0000655002,0.00003256188,0.006600247],"genre_scores_gemma":[0.5166777,0.00001859882,0.4827235,0.0001014472,0.000065188615,0.000011379062,0.00017098985,0.000010166555,0.00022104284],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99927604,0.000063836436,0.0003071074,0.00017362801,0.000046679794,0.0001327104],"domain_scores_gemma":[0.9992508,0.00036027114,0.00017614874,0.00005565412,0.00007145276,0.00008562772],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003879528,0.000074043965,0.00014846821,0.0000786769,0.000117625175,0.000039597522,0.000087283726,0.000070569,0.000054271077],"category_scores_gemma":[0.000019758078,0.00009592292,0.00008089692,0.000044906647,0.000033080403,0.00030536542,0.0000029438638,0.000031004456,0.000006779298],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013632815,0.0000068635536,0.0001750228,0.000003700308,0.000011824879,2.8675544e-8,0.000591615,0.5786191,0.000002213728,0.41948938,0.00038999759,0.0006965957],"study_design_scores_gemma":[0.0006652839,0.000024431285,0.00029421368,0.0000070999763,0.000018329609,8.574227e-7,0.00015722848,0.7197692,0.000072951996,0.2724808,0.0064037405,0.000105872125],"about_ca_topic_score_codex":0.00012696076,"about_ca_topic_score_gemma":0.00017314564,"teacher_disagreement_score":0.49213964,"about_ca_system_score_codex":0.00010742294,"about_ca_system_score_gemma":0.0006431765,"threshold_uncertainty_score":0.39116234},"labels":[],"label_agreement":null},{"id":"W2112368500","doi":"10.3141/1994-12","title":"Development of a Fleet Allocator Model for Calgary, Canada","year":2007,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Calgary; BP (Canada)","funders":"","keywords":"Allocator; Fleet management; Transport engineering; Process (computing); Computer science; Engineering; Operations research; Automotive engineering; Simulation; Operating system","score_opus":0.1146243959090826,"score_gpt":0.4067556330898566,"score_spread":0.29213123718077405,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2112368500","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8666337,0.000101766185,0.129562,0.0015952471,0.00048490395,0.0011554861,0.000079391124,0.000021059486,0.0003664221],"genre_scores_gemma":[0.95240283,0.00015206335,0.045934465,0.000048961625,0.0001236811,0.000045787605,0.000046451965,0.000037974332,0.0012077711],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99221665,0.00042068606,0.0017769426,0.00030027516,0.00434667,0.0009387759],"domain_scores_gemma":[0.99180996,0.0012603586,0.00060941465,0.0002545809,0.005598529,0.00046715434],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010194411,0.00018378168,0.00038297733,0.00069128536,0.0012580567,0.00005350785,0.0009168785,0.0001955854,0.000078891375],"category_scores_gemma":[0.00032289376,0.0001553027,0.00023633041,0.0016249276,0.00054314977,0.00039023586,0.0000026300006,0.00090503396,0.000001373743],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.010815138,0.0013654324,0.4405469,0.0015999761,0.0009042778,0.00015321912,0.26161972,0.08841763,0.0099383695,0.090032615,0.03577141,0.0588353],"study_design_scores_gemma":[0.0031974507,0.00031349453,0.8232049,0.000603024,0.000107987995,1.606719e-7,0.02753153,0.004407155,0.005469886,0.0023232198,0.13234055,0.0005006856],"about_ca_topic_score_codex":0.34341115,"about_ca_topic_score_gemma":0.9853249,"teacher_disagreement_score":0.6419138,"about_ca_system_score_codex":0.00052885857,"about_ca_system_score_gemma":0.00808658,"threshold_uncertainty_score":0.99753666},"labels":[],"label_agreement":null},{"id":"W2113067066","doi":"10.5198/jtlu.v6i3.350","title":"A multidimensional decisions modeling framework for built space supply","year":2013,"lang":"en","type":"article","venue":"Journal of Transport and Land Use","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Toronto Metropolitan University; University of Toronto; Polytechnique Montréal","funders":"University of Toronto; Imperial College London; Harvard University","keywords":"Profit maximization; Zoning; Space (punctuation); Computer science; Profit (economics); Operations research; Supply and demand; Maximization; Work (physics); Econometric model; Econometrics; Economics; Civil engineering; Microeconomics; Engineering","score_opus":0.04469594390319629,"score_gpt":0.3114893631383488,"score_spread":0.2667934192351525,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2113067066","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.74036086,0.00012344733,0.25802505,0.0012047634,0.00014129779,0.000101349906,0.000009563864,0.000008655248,0.000024999077],"genre_scores_gemma":[0.87922895,0.00044235564,0.11996218,0.000085561805,0.00012211467,0.0000027446388,0.000010677784,0.000006094391,0.00013933923],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9993134,0.000016537982,0.0002550059,0.000072839444,0.00021275338,0.0001294204],"domain_scores_gemma":[0.9991389,0.00030627623,0.00010032397,0.00003918949,0.00027148268,0.00014386112],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027113897,0.00006155136,0.00013482275,0.00007224491,0.00023356896,0.000040673043,0.000053466843,0.000088172164,0.00007699072],"category_scores_gemma":[0.00011483725,0.000047187074,0.000077477365,0.00007558101,0.00003072493,0.00040741396,8.2632357e-7,0.00011307546,0.0000010912705],"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":[0.0003232894,0.0001464528,0.84731984,0.000017811752,0.000083315375,0.000023393484,0.019338766,0.11155485,0.00008316249,0.017651847,0.0010510292,0.0024062179],"study_design_scores_gemma":[0.005079331,0.00044397736,0.8122555,0.0012091657,0.00047953444,0.00003307186,0.0071324375,0.07295222,0.000059358303,0.037657022,0.061906606,0.00079176645],"about_ca_topic_score_codex":0.00051631575,"about_ca_topic_score_gemma":0.00035108833,"teacher_disagreement_score":0.13886806,"about_ca_system_score_codex":0.0000075446505,"about_ca_system_score_gemma":0.00008158556,"threshold_uncertainty_score":0.1924233},"labels":[],"label_agreement":null},{"id":"W2113660803","doi":"10.1504/ijlsm.2012.044052","title":"An efficient hybrid heuristic method for prioritising large transportation projects with interdependent activities","year":2011,"lang":"en","type":"article","venue":"International Journal of Logistics Systems and Management","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Interdependence; Computer science; Heuristic; Travelling salesman problem; Ant colony optimization algorithms; Mathematical optimization; Genetic algorithm; Operations research; Process (computing); Heuristics; Path (computing); Artificial intelligence; Machine learning; Algorithm; Mathematics","score_opus":0.037344212313183654,"score_gpt":0.33139658952717577,"score_spread":0.29405237721399213,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2113660803","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014336434,0.00006382865,0.98248,0.000046973284,0.0010256427,0.00028915677,0.000048345057,0.00001799707,0.0016916072],"genre_scores_gemma":[0.94061923,0.00008160951,0.058962062,0.000022838689,0.00017833858,0.000010606108,0.000025177957,0.000008259822,0.00009184913],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","domain_scores_codex":[0.99898803,0.00008714384,0.00030725196,0.00011120343,0.00038961563,0.000116736635],"domain_scores_gemma":[0.9991837,0.00004352,0.00033075127,0.000043924916,0.00033751279,0.000060579485],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00094837364,0.00007366018,0.00011776264,0.00015489379,0.00012736073,0.00011375099,0.0001441847,0.000020830614,0.000005695586],"category_scores_gemma":[0.000017560667,0.00006300315,0.000031513675,0.000036671565,0.000038811242,0.00014877465,0.000003128943,0.000048261532,1.8468394e-7],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017856163,0.0013830892,0.018934164,0.0006793975,0.0011040384,0.0009589269,0.10719898,0.24242426,0.00007486379,0.6038764,0.0006095047,0.020970719],"study_design_scores_gemma":[0.024723014,0.0064740493,0.20408659,0.0070845466,0.0032399306,0.0005213447,0.42107475,0.23078409,0.00067120435,0.0067968145,0.09155237,0.0029913061],"about_ca_topic_score_codex":0.0002599315,"about_ca_topic_score_gemma":0.00024699458,"teacher_disagreement_score":0.9262828,"about_ca_system_score_codex":0.00006077063,"about_ca_system_score_gemma":0.000037796086,"threshold_uncertainty_score":0.25691938},"labels":[],"label_agreement":null},{"id":"W2113687967","doi":"10.3141/1831-13","title":"Prototype Model of Household Activity-Travel Scheduling","year":2003,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":384,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Operationalization; Scheduling (production processes); Microsimulation; Travel behavior; Operations research; Computer science; Transport engineering; Operations management; Engineering","score_opus":0.17269711471382423,"score_gpt":0.40702732309758743,"score_spread":0.2343302083837632,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2113687967","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9701773,0.00011894497,0.024716724,0.0014071943,0.00038630966,0.0013298534,0.000075653035,0.000035051704,0.001752968],"genre_scores_gemma":[0.98749346,0.00084212265,0.010519926,0.0000237951,0.000090673704,0.000064253676,0.000008592436,0.000051361643,0.0009058397],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9909952,0.0020219556,0.0012762893,0.00037015913,0.0044550635,0.00088129484],"domain_scores_gemma":[0.9934488,0.0008898503,0.00068489125,0.00039237578,0.0041758707,0.00040822144],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008561936,0.00022108936,0.0004695714,0.001003925,0.001154542,0.000108114924,0.0008940856,0.00029131665,0.00017701254],"category_scores_gemma":[0.0005705889,0.00018607166,0.00038261907,0.0026177994,0.0010541026,0.0008851556,0.0000022881065,0.0018800749,0.000005848872],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0031605528,0.0011389818,0.42305994,0.0006310565,0.000305075,0.000056779652,0.041585643,0.34690902,0.019327844,0.15880722,0.0014549283,0.003562978],"study_design_scores_gemma":[0.0041424283,0.0010412483,0.9245423,0.0011281391,0.00019277791,4.747403e-7,0.019394446,0.0032157986,0.01832509,0.017394004,0.0099208,0.0007024631],"about_ca_topic_score_codex":0.0086190775,"about_ca_topic_score_gemma":0.026730023,"teacher_disagreement_score":0.5014824,"about_ca_system_score_codex":0.0002102894,"about_ca_system_score_gemma":0.002322335,"threshold_uncertainty_score":0.9979826},"labels":[],"label_agreement":null},{"id":"W2113715734","doi":"10.3141/1710-25","title":"Delay Variability at Signalized Intersections","year":2000,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":77,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Intersection (aeronautics); Signal timing; Randomness; Percentile; Variance (accounting); Computer science; Variation (astronomy); Level of service; Simulation; Statistics; Transport engineering; Engineering; Traffic signal; Mathematics; Real-time computing","score_opus":0.07951272062905604,"score_gpt":0.4016037484291487,"score_spread":0.3220910278000927,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2113715734","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98560745,0.00009351279,0.0022516665,0.0047524087,0.0006634563,0.000971704,0.00008417148,0.00007765654,0.005498001],"genre_scores_gemma":[0.98648536,0.0012894338,0.0012610542,0.000080197584,0.00028746342,0.00006816626,0.000042198266,0.00004467355,0.010441461],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9890521,0.0037728657,0.0014811362,0.00046263306,0.0042326795,0.0009985775],"domain_scores_gemma":[0.9930592,0.0018026354,0.00037980865,0.00045438422,0.003741471,0.000562512],"candidate_categories":["sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.009868324,0.00023376114,0.0004128434,0.00074594014,0.0026773978,0.00017957766,0.0011088101,0.00029884384,0.0091371015],"category_scores_gemma":[0.00036640046,0.00019252187,0.00047948508,0.0029892598,0.001404321,0.0008919144,0.0000035508328,0.001856707,0.00011565083],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.007693522,0.0010375208,0.8060541,0.00022493782,0.0004213729,0.000218687,0.078442946,0.03027541,0.0017594609,0.011801969,0.0375511,0.024518948],"study_design_scores_gemma":[0.0016058136,0.00037646323,0.7593557,0.00022894792,0.00008084869,6.186243e-7,0.0042493124,0.00014654065,0.00027372612,0.0042246864,0.22919413,0.0002631552],"about_ca_topic_score_codex":0.04673504,"about_ca_topic_score_gemma":0.28738716,"teacher_disagreement_score":0.24065213,"about_ca_system_score_codex":0.0006093039,"about_ca_system_score_gemma":0.001023955,"threshold_uncertainty_score":0.998621},"labels":[],"label_agreement":null},{"id":"W2113765225","doi":"10.1080/03081060.2015.1059121","title":"Incorporating uncertainty and risk in transportation investment decision-making","year":2015,"lang":"en","type":"article","venue":"Transportation Planning and Technology","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"University of Toledo; Michigan Department of Transportation; U.S. Department of Transportation","keywords":"Investment (military); Transport engineering; Poison control; Business; Risk analysis (engineering); Engineering; Economics; Actuarial science; Operations research; Environmental health","score_opus":0.019913206257983967,"score_gpt":0.2971534544220996,"score_spread":0.27724024816411563,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2113765225","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9868384,0.00060309406,0.010958663,0.0004216955,0.00009941971,0.00022305876,0.000036241894,0.00026046106,0.0005589753],"genre_scores_gemma":[0.98680395,0.00014600126,0.012775928,0.000065499305,0.000019482157,0.000027741718,0.00012979931,0.000012433568,0.000019136633],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9987259,0.00006646384,0.00040999637,0.00031992982,0.00024699836,0.00023074973],"domain_scores_gemma":[0.99929696,0.0001726896,0.00020915447,0.00008628033,0.0001216629,0.00011326263],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006789703,0.00014002767,0.00019945996,0.00048486673,0.00028965747,0.000039701143,0.00008001496,0.0002420007,0.0000053557064],"category_scores_gemma":[0.00017542657,0.00015149795,0.000015702046,0.00077127333,0.00025181673,0.00027455864,0.0000012578898,0.00024141707,0.0000013459139],"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":[0.00006549847,0.000023427365,0.90171665,0.000011404259,0.0000109663815,0.000033763128,0.025338046,0.03033334,0.000007342328,0.031944755,0.00003986036,0.010474928],"study_design_scores_gemma":[0.0024201167,0.0001857918,0.8544792,0.0005175434,0.000098343655,0.0000032787925,0.048869945,0.005530517,0.000027614358,0.08410404,0.0032292972,0.00053428864],"about_ca_topic_score_codex":0.0012506262,"about_ca_topic_score_gemma":0.011202879,"teacher_disagreement_score":0.052159287,"about_ca_system_score_codex":0.000046664572,"about_ca_system_score_gemma":0.00013703937,"threshold_uncertainty_score":0.625147},"labels":[],"label_agreement":null},{"id":"W2114003337","doi":"10.1023/b:nets.0000039784.00352.66","title":"A Strategic Model for Dynamic Traffic Assignment","year":2004,"lang":"en","type":"article","venue":"Networks and Spatial Economics","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":42,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Node (physics); Set (abstract data type); Computer science; Traffic flow (computer networking); Flow network; Mathematical optimization; Preference; Order (exchange); Star (game theory); Assignment problem; Operations research; Mathematics; Engineering; Computer network; Economics","score_opus":0.02120370495492416,"score_gpt":0.25447211145542226,"score_spread":0.2332684065004981,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2114003337","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2678979,0.000052618812,0.7305232,0.00048303363,0.00014820743,0.00017790841,0.000013739648,0.000033186934,0.00067019486],"genre_scores_gemma":[0.9971147,0.0004895055,0.0020083813,0.00010022397,0.00009262814,0.000016242235,0.00005535798,0.000007202271,0.0001157186],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995747,0.000007253412,0.0001230475,0.00012563981,0.000025668449,0.00014367799],"domain_scores_gemma":[0.99981695,0.000021773294,0.00005028899,0.00003219655,0.000015556421,0.000063221574],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001337338,0.000056328612,0.000080327496,0.000017451579,0.00024244246,0.00006373831,0.00003765191,0.00008009529,0.0000054404504],"category_scores_gemma":[0.0000021972467,0.00006187125,0.000027544775,0.000021331196,0.000044154305,0.00007042951,0.0000017006544,0.00004010154,5.450776e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001454893,0.000011520194,0.000040055656,0.0000016655725,0.0000046135833,1.0776384e-7,0.0014173021,0.9698094,1.4589813e-7,0.020816676,0.0000051015018,0.007878863],"study_design_scores_gemma":[0.00041818336,0.000026483604,0.00017414067,0.000005076916,0.000009794615,9.1098514e-8,0.00025601083,0.9952103,1.3829161e-7,0.00356464,0.000256355,0.00007878583],"about_ca_topic_score_codex":0.00040466906,"about_ca_topic_score_gemma":0.019610448,"teacher_disagreement_score":0.7292168,"about_ca_system_score_codex":0.0000593696,"about_ca_system_score_gemma":0.000109394,"threshold_uncertainty_score":0.9982791},"labels":[],"label_agreement":null},{"id":"W2114466178","doi":"10.1002/atr.5670370301","title":"Service effectiveness of prt vs collective ‐ corridor transport","year":2003,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Monorail; Transport engineering; Travel time; Service (business); Level of service; Simulation; Computer science; Environmental science; Engineering; Business; Civil engineering","score_opus":0.010174939495513758,"score_gpt":0.27712242265426074,"score_spread":0.266947483158747,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2114466178","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9697063,0.00011844132,0.027559899,0.00007970128,0.00061393867,0.0003159114,0.000028615399,0.000024473264,0.0015527195],"genre_scores_gemma":[0.99248403,0.00013817915,0.0071718073,0.0000320235,0.00003404509,0.0000066995162,0.000039317143,0.000015061405,0.000078858284],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99833006,0.00024003624,0.000619146,0.00013208813,0.0005084559,0.00017019411],"domain_scores_gemma":[0.9978068,0.00026581582,0.00062397873,0.0000709995,0.0011257824,0.00010661089],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001014879,0.00011868584,0.0003246751,0.0001793537,0.00016440249,0.000007796977,0.00011419791,0.00010068963,0.000059817936],"category_scores_gemma":[0.00009244826,0.000119688375,0.00012959658,0.00082049606,0.00007250601,0.00059649255,1.5543849e-7,0.00015409656,0.0000011789277],"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":[0.0032757416,0.00050933386,0.1253422,0.00041004107,0.00020218718,0.00004925265,0.07063791,0.7781733,0.006635806,0.01388427,0.00003260758,0.00084731914],"study_design_scores_gemma":[0.003987001,0.0005898065,0.9623378,0.00056362327,0.00031287517,0.0000055443684,0.012927948,0.000017407105,0.012340325,0.0028049704,0.0038174496,0.0002952375],"about_ca_topic_score_codex":0.00011374338,"about_ca_topic_score_gemma":0.001042554,"teacher_disagreement_score":0.8369956,"about_ca_system_score_codex":0.000096586184,"about_ca_system_score_gemma":0.00065540423,"threshold_uncertainty_score":0.48807502},"labels":[],"label_agreement":null},{"id":"W2114874444","doi":"10.3141/2117-01","title":"Bilevel Programming for Evaluating Revenue Strategy of Railway Passenger Transport under Multimodal Market Competition","year":2009,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Revenue; Bilevel optimization; Revenue management; Competition (biology); Operations research; Agency (philosophy); Service (business); Total revenue; Transport engineering; Microeconomics; Business; Economics; Computer science; Marketing; Optimization problem; Engineering; Finance","score_opus":0.15547624150127126,"score_gpt":0.4555348684204919,"score_spread":0.30005862691922064,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2114874444","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9607021,0.00020425218,0.029942118,0.005113045,0.0005150405,0.0026944638,0.00032369513,0.000054229873,0.00045104083],"genre_scores_gemma":[0.98093003,0.00046297576,0.016821003,0.00004816733,0.00025464132,0.0000935918,0.0001031534,0.00004549021,0.0012409347],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9907275,0.0017355096,0.0019397096,0.00046201202,0.0041358806,0.0009994268],"domain_scores_gemma":[0.9903042,0.0017100421,0.0009100454,0.00034822145,0.006351054,0.00037649355],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011473298,0.0002725226,0.0005901956,0.00094180787,0.0012862924,0.000116175164,0.0008790848,0.00033004995,0.00031615983],"category_scores_gemma":[0.0004050281,0.00023518551,0.0005383049,0.002193265,0.00079522264,0.00085905136,0.0000015910047,0.0013441471,0.0000029411733],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.016168192,0.0036864087,0.31850168,0.001942647,0.000925591,0.0001450816,0.08460484,0.15337934,0.022640554,0.096656725,0.013164753,0.2881842],"study_design_scores_gemma":[0.0023801748,0.0012863992,0.96520305,0.00074685604,0.00013811915,3.152327e-7,0.009317486,0.00069037953,0.0007761082,0.009200765,0.009951557,0.00030878294],"about_ca_topic_score_codex":0.0071231863,"about_ca_topic_score_gemma":0.043266214,"teacher_disagreement_score":0.6467014,"about_ca_system_score_codex":0.0002454482,"about_ca_system_score_gemma":0.0015619468,"threshold_uncertainty_score":0.9994885},"labels":[],"label_agreement":null},{"id":"W2115025677","doi":"10.1109/itsc.2008.4732616","title":"System Optimization Model for Traffic Network with ATIS","year":2008,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Computer science; Sensitivity (control systems); Extension (predicate logic); Programming paradigm; Mathematical optimization; Engineering; Mathematics","score_opus":0.026147267780631332,"score_gpt":0.24877060568093506,"score_spread":0.22262333790030372,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2115025677","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005767595,0.00001509415,0.98523796,0.00015224179,0.00006126774,0.00026653326,0.00000455623,0.0002783038,0.008216435],"genre_scores_gemma":[0.73490083,0.000020279904,0.26090223,0.00004673546,0.00008174567,0.000029283987,0.00005511579,0.00000815018,0.0039555896],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994603,0.000020449792,0.00010724634,0.00011141934,0.00014980373,0.0001508282],"domain_scores_gemma":[0.9997021,0.00003910841,0.000047956928,0.000048910908,0.00011145187,0.000050486236],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001373919,0.00005118116,0.00007199797,0.000022529613,0.0006359109,0.000018730962,0.00005097506,0.00005040326,0.000017692779],"category_scores_gemma":[0.000007323456,0.00004373786,0.000020670252,0.00018155835,0.000047431226,0.00014362186,6.6634493e-7,0.000021208398,0.0000020645034],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016192129,0.000006560261,0.00079751975,0.0000059726176,0.000004663077,5.787267e-7,0.004470586,0.97965986,3.8370363e-8,0.013414242,0.001538714,0.0000851014],"study_design_scores_gemma":[0.00023890147,0.000015363457,0.00014358544,0.000014678423,0.00001094599,7.8397795e-7,0.00079168956,0.9983192,3.3307947e-7,0.000006125606,0.0003860691,0.0000723284],"about_ca_topic_score_codex":0.00006823084,"about_ca_topic_score_gemma":0.0005067517,"teacher_disagreement_score":0.72913325,"about_ca_system_score_codex":0.000030685347,"about_ca_system_score_gemma":0.00011606112,"threshold_uncertainty_score":0.48909765},"labels":[],"label_agreement":null},{"id":"W2117804213","doi":"10.1002/atr.116","title":"Effects of regulation changes in seoul bus system: private bus operation under non‐competitive fixed price contract","year":2010,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","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":"Business; Automotive engineering; Transport engineering; Finance; Industrial organization; Engineering","score_opus":0.005300159369087325,"score_gpt":0.2551071290746376,"score_spread":0.2498069697055503,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2117804213","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97647333,0.000051032912,0.021615762,0.00027162212,0.000786245,0.0004734171,0.00002561756,0.000028366365,0.00027461437],"genre_scores_gemma":[0.9906911,0.00010495369,0.008837577,0.000025945194,0.00015267922,0.000012817803,0.00012450073,0.000013676746,0.00003672825],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99852896,0.00009620292,0.0006137751,0.00014108294,0.0004513133,0.00016865894],"domain_scores_gemma":[0.99823207,0.00028512807,0.00083829084,0.000068403235,0.00049703836,0.00007904502],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00052269606,0.00012015346,0.00027826367,0.00025655003,0.00012225728,0.000024995772,0.000106505584,0.00014852127,0.000059912305],"category_scores_gemma":[0.00010106297,0.000118709584,0.000064729684,0.0003823357,0.00006790785,0.0007587685,5.73486e-7,0.000264214,0.0000013092402],"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":[0.00087191875,0.00033051858,0.0139726335,0.00052855915,0.000080853744,0.00005830075,0.051557425,0.5402708,0.34033257,0.04771711,0.000020913893,0.0042584343],"study_design_scores_gemma":[0.0019086603,0.00014322404,0.9752458,0.00047225328,0.0000707041,0.0000022818047,0.0033643215,0.00039998515,0.017628131,0.00018047259,0.0004365291,0.00014763481],"about_ca_topic_score_codex":0.00013651907,"about_ca_topic_score_gemma":0.005036963,"teacher_disagreement_score":0.9612732,"about_ca_system_score_codex":0.000081128484,"about_ca_system_score_gemma":0.00015234655,"threshold_uncertainty_score":0.48408362},"labels":[],"label_agreement":null},{"id":"W2118351333","doi":"","title":"Institutional Aspects of Bus Rapid Transit – A Macroscopic Examination","year":2000,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Transit (satellite); Brainstorming; Agency (philosophy); Public transport; Business; Administration (probate law); Politics; Process (computing); Liability; Public administration; Public relations; Finance; Marketing; Transport engineering; Political science; Engineering; Computer science; Sociology","score_opus":0.03377398736966296,"score_gpt":0.32971811901144543,"score_spread":0.29594413164178246,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2118351333","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5325199,0.00014917091,0.00006389484,0.00037838772,0.00035310173,0.00059630664,0.0001298505,0.000049047805,0.46576035],"genre_scores_gemma":[0.9896743,0.008258882,0.00066965073,0.000022101642,0.00012353474,0.000059654834,0.00030014437,0.000015342179,0.0008763579],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9979917,0.00036896832,0.00045849118,0.00042965022,0.00039017282,0.00036104125],"domain_scores_gemma":[0.99914104,0.00022877472,0.00013454887,0.00024764158,0.00014495938,0.000103064776],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017044357,0.00013452316,0.0002692407,0.00044837696,0.00028891704,0.00007198239,0.00033475648,0.0003732482,0.0004162597],"category_scores_gemma":[0.00016972523,0.00016653634,0.000085267806,0.00021036586,0.00055402715,0.00013743585,0.00002677183,0.00061204954,0.0000053140725],"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":[0.00011608839,0.00027743657,0.0047084354,0.0002418875,0.000067482564,0.000028431517,0.020029528,0.4623607,0.000061614395,0.02174207,0.000023404165,0.4903429],"study_design_scores_gemma":[0.0065432973,0.00056267617,0.71802425,0.0034065964,0.00011923625,0.0000071489203,0.016165832,0.041811977,0.0015903729,0.032096818,0.17683916,0.0028326435],"about_ca_topic_score_codex":0.0006521944,"about_ca_topic_score_gemma":0.0025631262,"teacher_disagreement_score":0.7133158,"about_ca_system_score_codex":0.00046558783,"about_ca_system_score_gemma":0.0015353048,"threshold_uncertainty_score":0.6791155},"labels":[],"label_agreement":null},{"id":"W2118662782","doi":"10.1002/atr.100","title":"Current issues of public transportation","year":2010,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","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":true,"ca_institutions":"","funders":"","keywords":"Public transport; Metropolitan area; Context (archaeology); Transport engineering; Sustainability; Transportation planning; Business; Public policy; Regional science; Economic growth; Engineering; Economics; Geography","score_opus":0.019976685086751444,"score_gpt":0.3266196328883479,"score_spread":0.30664294780159645,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2118662782","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9830713,0.00047236824,0.013678586,0.000810351,0.0015838877,0.00014354373,0.000035999492,0.000033949353,0.00016998337],"genre_scores_gemma":[0.984591,0.0010278401,0.01398515,0.000012406131,0.00021542677,0.0000029472099,0.00011625206,0.000012332406,0.000036624973],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99826413,0.000056337532,0.00078323414,0.00011268338,0.00060984155,0.00017379882],"domain_scores_gemma":[0.9979453,0.00007597326,0.00082817615,0.00008419409,0.000928906,0.00013742171],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00068939634,0.00010260145,0.00022647894,0.00023922882,0.00013709247,0.000021962827,0.00016686821,0.00009546721,0.00012535474],"category_scores_gemma":[0.00009744708,0.000101519276,0.00014065985,0.00042127538,0.00013354664,0.0010558901,1.7042855e-7,0.00029331926,0.0000015497388],"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":[0.00052265957,0.0009827062,0.32661918,0.00028652363,0.00014672481,0.0000335827,0.1736239,0.08306936,0.042583797,0.15980971,0.0004878518,0.211834],"study_design_scores_gemma":[0.0012596552,0.00014398366,0.90943784,0.000114177004,0.000107136875,9.548198e-7,0.005943752,0.000016023674,0.0025991949,0.0024926993,0.07768117,0.00020339621],"about_ca_topic_score_codex":0.00006546858,"about_ca_topic_score_gemma":0.0033856905,"teacher_disagreement_score":0.5828187,"about_ca_system_score_codex":0.000018303823,"about_ca_system_score_gemma":0.00024856094,"threshold_uncertainty_score":0.41398358},"labels":[],"label_agreement":null},{"id":"W2119535440","doi":"10.3141/2438-05","title":"Development and Evaluation of Adaptive Transit Signal Priority Control with Updated Transit Delay Model","year":2014,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Alberta","funders":"","keywords":"VisSim; Computer science; Context (archaeology); Signal timing; MATLAB; Transit (satellite); Reliability (semiconductor); SIGNAL (programming language); Real-time computing; Mathematical optimization; Simulation; Public transport; Engineering; Transport engineering; Traffic signal; Microsimulation; Mathematics","score_opus":0.10304888636555837,"score_gpt":0.3837179696716353,"score_spread":0.2806690833060769,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2119535440","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.89463866,0.000104316976,0.10206181,0.0013401993,0.000096091055,0.0012575752,0.00005296213,0.0000279983,0.0004204063],"genre_scores_gemma":[0.9847987,0.00015401692,0.014669185,0.00003281965,0.000071881674,0.000059464546,0.000035763656,0.000037192945,0.00014091915],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9885508,0.002646328,0.0013509232,0.0004173993,0.006332199,0.000702378],"domain_scores_gemma":[0.98997027,0.00073891424,0.0005828049,0.0002504882,0.008047261,0.00041025228],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.015005885,0.00024816816,0.000512538,0.00077453745,0.001102631,0.00009471444,0.0005846131,0.0002438383,0.00014399011],"category_scores_gemma":[0.00010764901,0.00019212397,0.00017692373,0.001610014,0.0010695641,0.0007746324,0.0000015754824,0.0012173348,0.0000024116157],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.009078014,0.0007441257,0.13689436,0.00030465372,0.00069242954,0.00003300295,0.119563386,0.64996594,0.0027521036,0.026891043,0.0006188478,0.052462112],"study_design_scores_gemma":[0.011100388,0.0012733192,0.8851291,0.0008171388,0.00066927174,8.3329485e-7,0.01503578,0.07142745,0.0029250628,0.0040874192,0.0069239903,0.0006102379],"about_ca_topic_score_codex":0.004521069,"about_ca_topic_score_gemma":0.10040621,"teacher_disagreement_score":0.74823475,"about_ca_system_score_codex":0.00023967958,"about_ca_system_score_gemma":0.002476313,"threshold_uncertainty_score":0.916009},"labels":[],"label_agreement":null},{"id":"W2119610721","doi":"10.1002/atr.1266","title":"A stochastic multimodal reliable network design problem under adverse weather conditions","year":2014,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Hong Kong Polytechnic University","keywords":"Adverse weather; Computer science; Network planning and design; Environmental science; Risk analysis (engineering); Meteorology; Medicine; Geography; Computer network","score_opus":0.013816453938189347,"score_gpt":0.2749020858264243,"score_spread":0.2610856318882349,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2119610721","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.046835225,0.000053626067,0.9514197,0.00043506362,0.00046385746,0.00026562656,0.0000074507975,0.000055697055,0.0004637286],"genre_scores_gemma":[0.8686531,0.0000428763,0.1306458,0.00011001198,0.0002341784,0.000009250016,0.0000450147,0.000016493224,0.00024326233],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99865466,0.0001086879,0.00049550587,0.00012477339,0.0003874052,0.00022897964],"domain_scores_gemma":[0.9987495,0.00019219503,0.0004812186,0.00006877079,0.00037926427,0.0001290789],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00068466895,0.00010796239,0.00018142877,0.000101286,0.00034412052,0.000020194178,0.00010560253,0.00009021244,0.000102152924],"category_scores_gemma":[0.000044254706,0.00010603685,0.00009499849,0.00029636882,0.0000796693,0.0006484482,3.6910012e-7,0.00016749495,0.000007602863],"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":[0.00012459225,0.000047010293,0.00075512583,0.0000068926397,0.00002246118,0.000002875944,0.005996303,0.98290807,0.00012402642,0.0093563665,0.00021213637,0.00044411686],"study_design_scores_gemma":[0.01847913,0.0022551701,0.71051913,0.0021382836,0.001495727,0.000021390208,0.036598045,0.031351805,0.00030203877,0.16009767,0.034597218,0.002144375],"about_ca_topic_score_codex":0.00004658069,"about_ca_topic_score_gemma":0.00030133862,"teacher_disagreement_score":0.95155627,"about_ca_system_score_codex":0.00005732773,"about_ca_system_score_gemma":0.00016859682,"threshold_uncertainty_score":0.43240574},"labels":[],"label_agreement":null},{"id":"W2119912392","doi":"10.1787/5kmmncbvh5xs-en","title":"Dedicated Lanes, Tolls and ITS Technology","year":2009,"lang":"en","type":"paratext","venue":"OECD/ITF Joint Transport Research Centre discussion papers","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Truck; Toll; Transport engineering; Differential (mechanical device); Hazard; Computer science; Business; Engineering; Automotive engineering","score_opus":0.03599301356398845,"score_gpt":0.3445267745074082,"score_spread":0.3085337609434198,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2119912392","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014390035,0.009097019,0.00013181075,0.11648099,0.002317927,0.0041291458,0.0010028103,0.0009912006,0.8514591],"genre_scores_gemma":[0.36089116,0.026947036,0.0002926704,0.00022866095,0.0004482275,0.000068270914,0.005492528,0.00010838239,0.60552305],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99412256,0.00056593237,0.00074165745,0.0010821515,0.002067288,0.0014203952],"domain_scores_gemma":[0.99810416,0.000109669054,0.00022262611,0.0003832949,0.00044992802,0.0007303184],"candidate_categories":["metaepi_narrow","sts","research_integrity","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0016540134,0.00048011623,0.0007078424,0.001150124,0.0014895614,0.00013226265,0.00056131015,0.0013581666,0.015420274],"category_scores_gemma":[0.00013457151,0.00033472507,0.0001886449,0.001207188,0.0006946666,0.00025436407,0.000023133927,0.001683224,0.0021682293],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00081394287,0.0014869365,0.008859525,0.0014922433,0.0005594104,0.0012462292,0.049393978,0.00164713,0.004686511,0.016519247,0.78184193,0.13145289],"study_design_scores_gemma":[0.0007400679,0.0000910685,0.0034307824,0.0007153695,0.0000669659,0.0000029876692,0.007421694,0.000023813716,0.00026889867,0.00013582983,0.98651594,0.00058656983],"about_ca_topic_score_codex":0.0004984144,"about_ca_topic_score_gemma":0.0016722693,"teacher_disagreement_score":0.3465011,"about_ca_system_score_codex":0.00022768382,"about_ca_system_score_gemma":0.0011637586,"threshold_uncertainty_score":0.9999383},"labels":[],"label_agreement":null},{"id":"W2119971885","doi":"10.1002/atr.169","title":"Productivity changes in consideration of accident costs in Seoul's bus industry","year":2011,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","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":"Productivity; Transport engineering; Business; Natural resource economics; Agricultural economics; Environmental science; Engineering; Economics; Economic growth","score_opus":0.03149924864080685,"score_gpt":0.3014350284497531,"score_spread":0.2699357798089463,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2119971885","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9982481,0.00007489316,0.00054424885,0.00039462515,0.00024405985,0.00017564878,0.000003503407,0.000008124485,0.00030677518],"genre_scores_gemma":[0.9958537,0.00016521047,0.00387561,0.000023573779,0.00004077401,0.000004408138,0.000012060115,0.000005873601,0.000018774754],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99890596,0.00009258333,0.0004984365,0.00010026294,0.00028334206,0.000119438446],"domain_scores_gemma":[0.9990702,0.00004628848,0.0005122696,0.00004933849,0.00027401038,0.000047893387],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063892454,0.00006904759,0.00018245455,0.00029050367,0.00003936705,0.00000658677,0.000065776374,0.00012983577,0.00004570921],"category_scores_gemma":[0.0001392351,0.00007259971,0.00003055426,0.00041590325,0.000055347748,0.00066816184,3.8573825e-7,0.00024991075,2.5206478e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003133077,0.00020664446,0.8700674,0.000020115986,0.000008711858,0.000036693327,0.07072374,0.05074169,0.0010282418,0.0016494661,0.000007383853,0.0051966025],"study_design_scores_gemma":[0.0007899621,0.00008457717,0.9872007,0.00016072608,0.000013141449,9.888211e-7,0.0070814583,0.000008386076,0.003917762,0.00060311303,0.00006848401,0.00007069987],"about_ca_topic_score_codex":0.000628933,"about_ca_topic_score_gemma":0.056866996,"teacher_disagreement_score":0.1171333,"about_ca_system_score_codex":0.00008843364,"about_ca_system_score_gemma":0.00020272275,"threshold_uncertainty_score":0.9603427},"labels":[],"label_agreement":null},{"id":"W2120224572","doi":"10.1007/s11116-010-9285-x","title":"The validity of using activity type to structure tour-based scheduling models","year":2010,"lang":"en","type":"article","venue":"Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":22,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Wilfrid Laurier University","funders":"University of Toronto; Social Sciences and Humanities Research Council of Canada; Wilfrid Laurier University","keywords":"Duration (music); Operations research; Scheduling (production processes); Hierarchy; Set (abstract data type); Computer science; Schedule; Travel behavior; Operations management; Mathematics; Engineering; Transport engineering; Economics","score_opus":0.058804027458139646,"score_gpt":0.3299638936076152,"score_spread":0.2711598661494755,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2120224572","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9139159,0.0000031416394,0.08516285,0.00021173418,0.00034873738,0.00015843715,0.00003182548,0.000041710893,0.00012568854],"genre_scores_gemma":[0.9841608,0.0000029572238,0.015682796,0.000027159254,0.000054338852,0.000001784653,0.000041201325,0.000007561531,0.000021406244],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99928534,0.0000452324,0.00015085464,0.00011641013,0.0002713722,0.00013077083],"domain_scores_gemma":[0.99941766,0.000095726944,0.00009983975,0.000101307494,0.00022447733,0.000060959144],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031179006,0.00006316845,0.00007791815,0.000042019037,0.00041123544,0.00002715546,0.000101183,0.00008802341,0.00004153081],"category_scores_gemma":[0.00004681912,0.00005469202,0.00003334688,0.00031661143,0.00006792532,0.0001970406,3.5856925e-7,0.00012630298,7.283133e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008043462,0.0000208812,0.020992544,0.000011999017,0.000006253376,5.209458e-7,0.006909757,0.9316196,0.030099928,0.0089361435,0.000008993453,0.0013129592],"study_design_scores_gemma":[0.0012740152,0.000147444,0.6708381,0.00011130864,0.00024455966,2.6922945e-7,0.0033980464,0.24918011,0.062054675,0.009251459,0.002722956,0.00077703194],"about_ca_topic_score_codex":0.0010160098,"about_ca_topic_score_gemma":0.018849285,"teacher_disagreement_score":0.68243945,"about_ca_system_score_codex":0.000013721115,"about_ca_system_score_gemma":0.00020064437,"threshold_uncertainty_score":0.99905413},"labels":[],"label_agreement":null},{"id":"W2121151285","doi":"10.1016/j.tra.2004.04.005","title":"Integrated scheduling of daily work activities and morning–evening commutes with bottleneck congestion","year":2004,"lang":"en","type":"article","venue":"Transportation Research Part A Policy and Practice","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":117,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China; University of Alberta","keywords":"Bottleneck; Computer science; Rush hour; Schedule; Scheduling (production processes); Work (physics); Queueing theory; Traffic congestion; Duration (music); Operations research; Minification; Transport engineering; Operations management; Computer network; Engineering","score_opus":0.09370824430663553,"score_gpt":0.41411964360033693,"score_spread":0.3204113992937014,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2121151285","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98421085,0.00030067816,0.004810857,0.007523677,0.000025007534,0.00031447038,0.000032472868,0.00007205849,0.00270995],"genre_scores_gemma":[0.98586637,0.0010001939,0.012699978,0.00007565598,0.0000561935,0.000025119583,0.00008602337,0.000010989585,0.00017945895],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.99846333,0.00042178185,0.00021584236,0.00018225073,0.00046808066,0.0002486868],"domain_scores_gemma":[0.99763805,0.0016424906,0.00014657271,0.00007639424,0.00037505158,0.00012144032],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001781084,0.00009131754,0.0001403505,0.00021966026,0.0006273793,0.00009887867,0.00006256345,0.00008854713,0.000013896587],"category_scores_gemma":[0.000979575,0.00008609919,0.000014117548,0.0007988882,0.0005696304,0.00091502967,0.0000019301497,0.00031271306,0.0000010432191],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0044353255,0.00039382197,0.103884235,0.00033452432,0.00025267954,0.000050994207,0.41185448,0.09972607,0.00089964824,0.3656395,0.00010882936,0.012419889],"study_design_scores_gemma":[0.007409862,0.001547291,0.5482352,0.0030812533,0.00038459915,0.000018860763,0.2924858,0.00044710623,0.002187785,0.0054305987,0.13760886,0.0011628114],"about_ca_topic_score_codex":0.00965471,"about_ca_topic_score_gemma":0.0034298317,"teacher_disagreement_score":0.44435093,"about_ca_system_score_codex":0.000036005433,"about_ca_system_score_gemma":0.00049757626,"threshold_uncertainty_score":0.9969401},"labels":[],"label_agreement":null},{"id":"W2122326140","doi":"10.1108/s0573-8555(2005)0000266007","title":"Land-use, Transportation and Urban Development","year":2005,"lang":"en","type":"book-chapter","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Subject matter; Traffic congestion; Land use; Economic geography; Quarter (Canadian coin); Location theory; Space (punctuation); Regional science; Urban planning; Environmental planning; Geography; Economics; Transport engineering; Computer science; Economic growth; Civil engineering; Engineering","score_opus":0.025433465577308176,"score_gpt":0.2478931821880862,"score_spread":0.222459716610778,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2122326140","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007773957,0.00024198288,0.0023670078,0.00035368014,0.00013838646,0.00024504305,0.000033309803,0.00022484029,0.99561834],"genre_scores_gemma":[0.012479589,0.000561426,0.007480365,0.00017028279,0.00015187095,0.000004368735,0.0006242149,0.000020640642,0.9785072],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99924767,0.000006956666,0.00020731632,0.00018347839,0.00024269511,0.0001119093],"domain_scores_gemma":[0.9996759,0.000038614504,0.00008447691,0.000049344577,0.00006919664,0.00008242401],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011427975,0.00012024504,0.0001211795,0.00007717242,0.00023103453,0.00005849308,0.00004612004,0.00021918044,0.00077035703],"category_scores_gemma":[0.000004650731,0.000121703044,0.000025767953,0.000018733432,0.0000633028,0.00016773773,7.518196e-7,0.00009343967,0.000034990564],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002410418,0.00001641885,0.024061445,0.000033218937,0.00006979841,0.000010885494,0.034525566,0.00037962303,6.393817e-7,0.90673405,0.008453729,0.025690539],"study_design_scores_gemma":[0.00011044484,0.0000052094283,0.013218326,0.00004516455,0.00002427438,1.0112483e-7,0.00007318663,0.0000053225413,0.0000018392606,0.00027028398,0.9860632,0.0001826566],"about_ca_topic_score_codex":0.00018779833,"about_ca_topic_score_gemma":0.011479164,"teacher_disagreement_score":0.97760946,"about_ca_system_score_codex":0.000035033914,"about_ca_system_score_gemma":0.00014712184,"threshold_uncertainty_score":0.8434869},"labels":[],"label_agreement":null},{"id":"W2122463624","doi":"10.1139/l03-014","title":"Reinforcement learning: Introduction to theory and potential for transport applications","year":2003,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":115,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Reinforcement learning; Computer science; Intelligent transportation system; Field (mathematics); Key (lock); Domain (mathematical analysis); Control (management); Artificial intelligence; Transport engineering; Engineering; Computer security","score_opus":0.005085514470838052,"score_gpt":0.20959630456167502,"score_spread":0.20451079009083697,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2122463624","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004599885,0.000101136204,0.993564,0.00052169745,0.00023253757,0.0001363937,0.0000015001049,0.0000094154775,0.0008334011],"genre_scores_gemma":[0.9976068,0.000015891179,0.0016463685,0.000023556486,0.00027266415,0.000008075763,0.0000045719567,0.0000062819227,0.000415766],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996079,0.000016651496,0.00013327891,0.000050631053,0.000069503505,0.00012202185],"domain_scores_gemma":[0.9995467,0.000024738003,0.000040895833,0.000025637644,0.00010482503,0.00025720443],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006197093,0.000038380662,0.00005813494,0.00015154561,0.00017855674,0.000021086005,0.000036995614,0.000028859176,0.00006407574],"category_scores_gemma":[0.0001478558,0.00004360697,0.000024255996,0.000115990835,0.000015338892,0.00009059265,1.7738641e-7,0.00006387913,4.3136762e-7],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000481282,0.0000010536046,0.00032821714,0.0000069414223,0.000008937012,0.0000011084092,0.0026741147,0.93460363,0.000053869295,0.061840974,0.00020154889,0.00027481135],"study_design_scores_gemma":[0.0003355756,0.0000774608,0.0025731549,0.000028199414,0.000052939602,0.000011852988,0.0017452708,0.0010299477,0.00008799573,0.0006578324,0.99324304,0.00015675706],"about_ca_topic_score_codex":0.0001602296,"about_ca_topic_score_gemma":0.013184721,"teacher_disagreement_score":0.99304146,"about_ca_system_score_codex":0.000048359565,"about_ca_system_score_gemma":0.00024506223,"threshold_uncertainty_score":0.73573834},"labels":[],"label_agreement":null},{"id":"W2123184120","doi":"10.1139/l06-151","title":"Locating changeable message signs for advanced traffic information and management systems","year":2007,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Queueing theory; Key (lock); Sensitivity (control systems); Quality (philosophy); Operations research; Distributed computing; Transport engineering; Engineering; Computer network; Computer security","score_opus":0.009438515714550793,"score_gpt":0.21704954179105626,"score_spread":0.20761102607650547,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2123184120","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04804628,0.0012976595,0.94409597,0.00021898784,0.0010179818,0.00036922802,0.000010350033,0.000033602195,0.0049099214],"genre_scores_gemma":[0.9977153,0.000044902397,0.002075877,0.000016946735,0.00007423992,0.000002818947,0.000004870215,0.0000047388776,0.000060310824],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99950165,0.000004703003,0.00019773844,0.000029479465,0.00009130121,0.0001751038],"domain_scores_gemma":[0.9995465,0.000039609557,0.00009279348,0.000022630185,0.00009644359,0.00020201455],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006883624,0.00004232972,0.00006778891,0.00023945858,0.00014458482,0.00006555769,0.00005009159,0.00003172144,0.0000051129664],"category_scores_gemma":[0.000051741205,0.000047304275,0.000016241785,0.00013664273,0.000011187353,0.00039872064,5.291432e-7,0.00004430385,2.7579893e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000003743663,9.1338376e-7,0.00017372795,0.00011133669,0.000011352836,0.000005979544,0.006975139,0.98422295,0.000009219387,0.003915071,0.00013920922,0.0044313455],"study_design_scores_gemma":[0.0033768127,0.00030622684,0.011649683,0.0018519736,0.00018325094,0.00004007671,0.067939825,0.16158131,0.00009403451,0.000068435766,0.75196636,0.0009419855],"about_ca_topic_score_codex":0.00041060525,"about_ca_topic_score_gemma":0.06568178,"teacher_disagreement_score":0.949669,"about_ca_system_score_codex":0.000084475505,"about_ca_system_score_gemma":0.00009880377,"threshold_uncertainty_score":0.9513671},"labels":[],"label_agreement":null},{"id":"W2123318342","doi":"","title":"Expressway Single-lane Work Zone Capacities with Commercial Vehicle Impacts","year":2010,"lang":"en","type":"article","venue":"Transportation Research Board 89th Annual MeetingTransportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Work (physics); Transport engineering; Christian ministry; Data collection; Work zone; Computer science; Queueing theory; Meaning (existential); Operations research; Variation (astronomy); Environmental science; Statistics; Engineering; Mathematics","score_opus":0.06323680628515813,"score_gpt":0.3671385719834437,"score_spread":0.30390176569828553,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2123318342","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98002106,0.000113651986,0.0020417308,0.004590068,0.0004823138,0.0019931407,0.0005096514,0.0006656625,0.009582717],"genre_scores_gemma":[0.9863992,0.00021174486,0.0074276114,0.00014201841,0.0006882898,0.00043938338,0.0011006555,0.00015003202,0.0034410818],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9863579,0.0017193013,0.001270968,0.0012702519,0.006666665,0.0027148821],"domain_scores_gemma":[0.9894623,0.002116467,0.00033457484,0.00074182556,0.00587663,0.0014681936],"candidate_categories":["metaepi_narrow","sts","research_integrity","insufficient_payload"],"consensus_categories":["sts"],"category_scores_codex":[0.008160709,0.00055449107,0.00064917206,0.0015611172,0.0037323486,0.0006048358,0.0010707058,0.0007043823,0.0013741145],"category_scores_gemma":[0.0007426495,0.00055003416,0.00021661712,0.0044285553,0.00413987,0.0018078398,0.000009799745,0.0036425623,0.00019789595],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0069171516,0.0017612057,0.60411966,0.00059769937,0.0003040264,0.0006432751,0.29135486,0.005160186,0.013587373,0.04524775,0.020084696,0.010222118],"study_design_scores_gemma":[0.0035289838,0.0011366926,0.77920187,0.00057583395,0.000090984024,8.940756e-7,0.074325055,0.00005487199,0.0036868919,0.0006942637,0.13546044,0.0012432174],"about_ca_topic_score_codex":0.03912468,"about_ca_topic_score_gemma":0.26856932,"teacher_disagreement_score":0.22944464,"about_ca_system_score_codex":0.00022385374,"about_ca_system_score_gemma":0.0013875628,"threshold_uncertainty_score":0.9996951},"labels":[],"label_agreement":null},{"id":"W2123946067","doi":"10.3141/1883-10","title":"Relationships Between Occupancy and Density Reflecting Average Vehicle Lengths","year":2004,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; McMaster University","keywords":"Linearity; Occupancy; Diagram; Range (aeronautics); Linear relationship; Statistics; Nonlinear system; Truck; Mathematics; Econometrics; Statistical physics; Engineering; Physics; Thermodynamics","score_opus":0.19045996935779522,"score_gpt":0.438301933188507,"score_spread":0.2478419638307118,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2123946067","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9863195,0.00017504269,0.0055315415,0.0062482012,0.0003381836,0.0007151585,0.000036646758,0.00006234675,0.0005733719],"genre_scores_gemma":[0.994367,0.0009147571,0.0038585828,0.00003328774,0.00031524655,0.000021033567,0.00002547306,0.000039440696,0.00042515228],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9920255,0.0021586686,0.0012122936,0.00041373805,0.0033394326,0.0008503952],"domain_scores_gemma":[0.9938804,0.0017240698,0.00048114164,0.00030529976,0.0030578875,0.0005512355],"candidate_categories":["sts","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.009288347,0.00020962355,0.00038295262,0.0008513869,0.003260886,0.0002308966,0.0006552389,0.00032241992,0.00008186185],"category_scores_gemma":[0.00084579457,0.0001822412,0.0002240615,0.002458236,0.00096217677,0.0011125684,0.0000048869724,0.0029484918,0.000017488297],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003444388,0.00010424237,0.9385882,0.000084026695,0.000080624945,0.00005603955,0.03586818,0.003629229,0.00038445785,0.017652076,0.00039042122,0.0028180764],"study_design_scores_gemma":[0.001389353,0.00020048665,0.96970797,0.0003166799,0.0000554665,2.9442555e-7,0.010836135,0.000019539275,0.0004600478,0.011785932,0.0050376654,0.00019041204],"about_ca_topic_score_codex":0.023079474,"about_ca_topic_score_gemma":0.106649846,"teacher_disagreement_score":0.08357037,"about_ca_system_score_codex":0.00036794366,"about_ca_system_score_gemma":0.0011429816,"threshold_uncertainty_score":0.99935174},"labels":[],"label_agreement":null},{"id":"W2124997479","doi":"10.1002/atr.5670340204","title":"Optimal feeder bus routes on irregular street networks","year":2000,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":70,"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":"Headway; Mathematical optimization; Computer science; Intersection (aeronautics); Price elasticity of demand; Computation; Travel time; Routing (electronic design automation); Algorithm; Simulation; Transport engineering; Engineering; Mathematics; Computer network","score_opus":0.00862064257529433,"score_gpt":0.2634441833847492,"score_spread":0.25482354080945485,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2124997479","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98142266,0.00022538568,0.015689775,0.00058615906,0.0004088972,0.00013711244,0.00001147462,0.000052867366,0.0014656815],"genre_scores_gemma":[0.98861825,0.0006963792,0.009681436,0.00013096996,0.00028749328,0.0000024919225,0.00007247951,0.000014957197,0.00049552653],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99866426,0.00005927456,0.00047198648,0.00012769349,0.0004717567,0.00020503944],"domain_scores_gemma":[0.9992184,0.000077923236,0.00028613632,0.00007173481,0.00022057541,0.00012523934],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029551974,0.000113994894,0.00018039453,0.00010312814,0.00024495856,0.00003733819,0.00012636386,0.00010016872,0.00036414995],"category_scores_gemma":[0.000017769597,0.00010983117,0.000113836075,0.00028268443,0.00006642395,0.00062154426,1.6855743e-7,0.00019250691,0.000005433371],"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":[0.00027743634,0.00006488621,0.0030685219,0.0000036564704,0.000021239071,0.000024262994,0.005894415,0.96983826,0.000020549498,0.0011788752,0.00016537355,0.019442536],"study_design_scores_gemma":[0.0046012434,0.001011862,0.9037122,0.00047128636,0.00027111825,0.0000051345996,0.014585994,0.002667026,0.00034939332,0.00071955717,0.070887,0.00071818806],"about_ca_topic_score_codex":0.00004993634,"about_ca_topic_score_gemma":0.00037943124,"teacher_disagreement_score":0.9671712,"about_ca_system_score_codex":0.0000447768,"about_ca_system_score_gemma":0.00008403975,"threshold_uncertainty_score":0.4478785},"labels":[],"label_agreement":null},{"id":"W2125692600","doi":"","title":"The Ethical Challenges and Professional Responses of Travel Demand Forecasters","year":2003,"lang":"en","type":"article","venue":"eScholarship (California Digital Library)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"University of California Transportation Center; National Science Foundation","keywords":"Excuse; Suspect; Feeling; Field (mathematics); Work (physics); Public relations; Economics; Political science; Marketing; Business; Psychology; Social psychology; Law; Engineering","score_opus":0.032093743697676015,"score_gpt":0.2711148405279633,"score_spread":0.2390210968302873,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2125692600","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8889584,0.004431631,0.00045042863,0.016195163,0.00033968835,0.00048683287,0.00054347294,0.00016631321,0.08842804],"genre_scores_gemma":[0.99694794,0.00064941216,0.000469936,0.00017983683,0.000026012453,0.000008296221,0.0000335695,0.000013856113,0.0016711655],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99871975,0.00036341848,0.00023389354,0.00016355088,0.00031474893,0.00020464558],"domain_scores_gemma":[0.99875456,0.0008928208,0.00009041859,0.00009145752,0.000038078604,0.00013267946],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007392951,0.00009295013,0.000110199544,0.000049175287,0.0004274698,0.00022807,0.00013417944,0.000184126,0.000035033012],"category_scores_gemma":[0.00086422084,0.0000667035,0.00004423251,0.0001312681,0.00034692822,0.0007715337,0.000011359555,0.00028277372,0.000014124828],"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":[0.00066953077,0.00016113714,0.19770387,0.000090725785,0.000060576833,0.000012668697,0.008050563,0.000028386175,0.000050165505,0.7676806,0.0012922442,0.024199534],"study_design_scores_gemma":[0.0013231063,0.00018535735,0.24456628,0.00030363363,0.000036842295,0.000007848333,0.013560847,0.000059155285,0.0013294861,0.08659279,0.6514659,0.0005687539],"about_ca_topic_score_codex":0.0000011903145,"about_ca_topic_score_gemma":0.000012462087,"teacher_disagreement_score":0.6810878,"about_ca_system_score_codex":0.0000059177273,"about_ca_system_score_gemma":0.00016493473,"threshold_uncertainty_score":0.32877952},"labels":[],"label_agreement":null},{"id":"W2125730754","doi":"10.18757/ejtir.2009.9.2.3291","title":"Preliminary Monetary Values for the Reliability of Travel Times in Freight Transport","year":2009,"lang":"en","type":"article","venue":"European journal of transport and infrastructure research","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":50,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ministry of Transportation of Ontario","funders":"Vrije Universiteit Amsterdam","keywords":"Reliability (semiconductor); Ministry of Transport; Valuation (finance); Travel time; Christian ministry; Transport engineering; Operations research; Computer science; Business; Engineering; Accounting","score_opus":0.027762658873088625,"score_gpt":0.31686258282122326,"score_spread":0.28909992394813466,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2125730754","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9773337,0.0015461044,0.009848382,0.003203274,0.0001599964,0.0005239881,0.000039189435,0.000011738184,0.007333631],"genre_scores_gemma":[0.9951131,0.0012020074,0.003321336,0.0000333287,0.00015480365,9.104292e-7,0.0000136345425,0.000010076511,0.0001508534],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9979816,0.00038487854,0.0006225053,0.00014811404,0.0006050992,0.000257782],"domain_scores_gemma":[0.99889624,0.00030601752,0.0001635776,0.00011956867,0.00040368427,0.00011091402],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005205519,0.00010036064,0.0002347653,0.0002117409,0.00027761323,0.000014387195,0.0003338512,0.000056344605,0.00006555363],"category_scores_gemma":[0.00009128167,0.000068342975,0.00011413459,0.00035332906,0.00044445062,0.00025610306,0.0000018670827,0.00045542335,2.2914038e-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":[0.0064603826,0.00048653534,0.5896537,0.00034811924,0.0001538377,0.00032814484,0.19339918,0.10120487,0.0011855268,0.005760793,0.0038810968,0.09713781],"study_design_scores_gemma":[0.00066223997,0.00057140854,0.9902305,0.00009831245,0.00003433877,0.0000043512537,0.0026214963,0.00018890656,0.00009845919,0.0021727083,0.0032429076,0.00007435504],"about_ca_topic_score_codex":0.00006313994,"about_ca_topic_score_gemma":0.000031304382,"teacher_disagreement_score":0.40057683,"about_ca_system_score_codex":0.000019898898,"about_ca_system_score_gemma":0.0001813884,"threshold_uncertainty_score":0.27869457},"labels":[],"label_agreement":null},{"id":"W2126850506","doi":"10.1002/atr.5670400105","title":"Equity based land‐use and transportation problem","year":2006,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":30,"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":"Equity (law); Travel time; Trip distribution; Traffic network; Computer science; Flow network; Land use; Operations research; Transport engineering; Economics; Mathematical optimization; Engineering; Mathematics; Civil engineering","score_opus":0.01686333773431046,"score_gpt":0.28998788867864617,"score_spread":0.2731245509443357,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2126850506","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9503519,0.00012640863,0.04785705,0.00069409254,0.00022623212,0.00021852006,0.000050164952,0.000058025194,0.00041758225],"genre_scores_gemma":[0.9638947,0.00012668388,0.035472102,0.000060838265,0.00010806111,0.0000039229794,0.00021360014,0.000012447376,0.0001075938],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99861324,0.0000568502,0.0005610696,0.00013301917,0.0004585781,0.0001772243],"domain_scores_gemma":[0.9988535,0.000117360156,0.0004677257,0.00005269068,0.0004136647,0.00009507012],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004623351,0.00010524779,0.00017665114,0.00015376751,0.00021097441,0.000066836925,0.00007432955,0.00008955369,0.000038383318],"category_scores_gemma":[0.000026537604,0.000104480285,0.000076257,0.00026752762,0.0000819137,0.0012388617,1.973846e-7,0.00013021572,0.0000010818045],"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":[0.00042809846,0.00016652563,0.27027547,0.00008052154,0.00002236954,0.000046599802,0.008402097,0.70022416,0.0013110638,0.012341589,0.00020864049,0.0064928397],"study_design_scores_gemma":[0.0015381709,0.00008452619,0.9862268,0.000106932246,0.00009830751,8.2176336e-7,0.0007807475,0.000088339344,0.00029113336,0.0024737036,0.0081490185,0.00016149403],"about_ca_topic_score_codex":0.00057041313,"about_ca_topic_score_gemma":0.007044762,"teacher_disagreement_score":0.7159513,"about_ca_system_score_codex":0.000045112243,"about_ca_system_score_gemma":0.00015996791,"threshold_uncertainty_score":0.42605826},"labels":[],"label_agreement":null},{"id":"W2127473652","doi":"10.5198/jtlu.v6i1.325","title":"Microsimulation framework for urban price-taker markets","year":2013,"lang":"en","type":"article","venue":"Journal of Transport and Land Use","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Clearing; Generality; Market clearing; Microsimulation; Operationalization; Computer science; Context (archaeology); Bipartite graph; Exploit; Graph; Economics; Microeconomics; Transport engineering; Theoretical computer science; Engineering","score_opus":0.020170966494118963,"score_gpt":0.2786030773668319,"score_spread":0.2584321108727129,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2127473652","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95475054,0.00017390614,0.043837376,0.00068774266,0.00020574364,0.00016424916,0.000008689154,0.00001200389,0.0001597476],"genre_scores_gemma":[0.9855827,0.00023885576,0.013481929,0.0001144061,0.00016070511,0.0000023825903,0.000013254591,0.0000061240303,0.00039964722],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9994438,0.00001884591,0.00023036546,0.000058843012,0.00013825916,0.00010985247],"domain_scores_gemma":[0.9993828,0.00015337685,0.00015350463,0.000031848762,0.00018543121,0.00009307127],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002671242,0.000053128224,0.00010972253,0.000055042034,0.00015847146,0.00006032808,0.00004471116,0.00008672818,0.0001080704],"category_scores_gemma":[0.00004183057,0.000042418556,0.000057332047,0.00007243863,0.000031841042,0.0004623982,3.597728e-7,0.000079782956,8.236442e-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":[0.00011139524,0.000034337143,0.9878487,0.000017425626,0.000023409764,0.000003074544,0.008093948,0.00040546077,0.000028849468,0.0013995616,0.0010690953,0.0009647604],"study_design_scores_gemma":[0.0005015944,0.000047963746,0.9557316,0.00006479434,0.00004691206,0.0000014870814,0.0003299471,0.00015283871,0.000012899189,0.0012087987,0.041821655,0.000079544414],"about_ca_topic_score_codex":0.000088849214,"about_ca_topic_score_gemma":0.000063198284,"teacher_disagreement_score":0.04075256,"about_ca_system_score_codex":0.000008436706,"about_ca_system_score_gemma":0.000042545227,"threshold_uncertainty_score":0.17297786},"labels":[],"label_agreement":null},{"id":"W2127941462","doi":"10.5278/ojs.td.v1i1.5507","title":"The Usability of Passenger Delay Models in Socio-Economic Analysis","year":2020,"lang":"en","type":"article","venue":"Aalborg University Library","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Transport Canada","funders":"","keywords":"Computer science; Passenger transport; Operations research; Transport engineering; Engineering","score_opus":0.016851847471488976,"score_gpt":0.21522476240182273,"score_spread":0.19837291493033377,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2127941462","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9647707,0.000036930865,0.0020060628,0.012054368,0.000031582105,0.00008715237,0.00004187136,0.00006241679,0.020908955],"genre_scores_gemma":[0.99897355,0.00014988815,0.00039835207,0.00008343771,0.000014514905,9.487978e-8,0.000026239044,0.0000022660663,0.00035164185],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9994947,0.0001351344,0.000097202625,0.000111056004,0.00007442116,0.00008749743],"domain_scores_gemma":[0.99965054,0.00015907086,0.000056896744,0.000067280285,0.000011412942,0.000054790104],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010930894,0.000036247082,0.00008943416,0.00004971091,0.00019264467,0.000016444741,0.00018427115,0.000047634036,0.00009935361],"category_scores_gemma":[0.000009273885,0.000035643483,0.00006772287,0.00051682146,0.00013690305,0.00071269466,0.000013573883,0.00005773509,0.000003929343],"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":[0.00006429447,0.000019169256,0.6833665,0.0000043201876,0.00008984819,0.000004972393,0.024128549,0.22955818,0.0000022644135,0.061514527,0.00095187535,0.00029553595],"study_design_scores_gemma":[0.0010327373,0.000054560252,0.68185353,0.0000115463645,0.00039508656,4.6361396e-8,0.095238954,0.16085744,0.00007027879,0.005501382,0.05455406,0.00043036084],"about_ca_topic_score_codex":0.0011465165,"about_ca_topic_score_gemma":0.0014467656,"teacher_disagreement_score":0.071110405,"about_ca_system_score_codex":0.000025643953,"about_ca_system_score_gemma":0.00015318794,"threshold_uncertainty_score":0.17331977},"labels":[],"label_agreement":null},{"id":"W2128178859","doi":"10.1287/trsc.1090.0265","title":"A Study of Demand Stochasticity in Service Network Design","year":2009,"lang":"en","type":"article","venue":"Transportation Science","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":199,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Consolidation (business); Flexibility (engineering); A priori and a posteriori; Computer science; Service (business); Operations research; Mathematical optimization; Demand patterns; Engineering; Economics; Demand management; Business; Mathematics; Marketing","score_opus":0.05179541063175576,"score_gpt":0.33514159422305084,"score_spread":0.2833461835912951,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2128178859","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8031417,0.000010760484,0.195667,0.00019773493,0.00006448223,0.0003992957,0.000001783005,0.00004171166,0.00047555167],"genre_scores_gemma":[0.994297,0.0000054235848,0.0055301557,0.00011902378,0.00001742048,0.0000069367466,0.0000041751023,0.0000023784148,0.000017468848],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9985497,0.00008300924,0.0003153571,0.00021951065,0.0005990903,0.00023333562],"domain_scores_gemma":[0.9993821,0.000082138955,0.0001203037,0.000092523944,0.00024642455,0.00007651907],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001609157,0.00006627137,0.00012197592,0.00015092111,0.00029520752,0.000027194852,0.00022870097,0.000034679335,0.000019579684],"category_scores_gemma":[0.00004536607,0.00006946458,0.000013178951,0.0025919867,0.00012334352,0.00045967425,5.042502e-7,0.00006386236,0.0000019313775],"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":[0.00003644548,0.00015726889,0.039187733,0.0000019938598,0.0000010343565,0.0000034089755,0.08418991,0.8732043,0.00011096131,0.0027224922,0.000006225799,0.00037824965],"study_design_scores_gemma":[0.0005537747,0.0001546344,0.98501515,0.000027040345,0.0000124652715,6.980427e-8,0.0097691165,0.0037348163,0.000065149135,0.0005556326,0.000014591668,0.00009755629],"about_ca_topic_score_codex":0.0010249342,"about_ca_topic_score_gemma":0.010299218,"teacher_disagreement_score":0.9458274,"about_ca_system_score_codex":0.00002773038,"about_ca_system_score_gemma":0.0002653966,"threshold_uncertainty_score":0.5747205},"labels":[],"label_agreement":null},{"id":"W2128672185","doi":"10.1016/j.trb.2015.03.015","title":"A nested recursive logit model for route choice analysis","year":2015,"lang":"en","type":"article","venue":"Transportation Research Part B Methodological","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":144,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Nested logit; Logit; Logistic regression; Econometrics; Computer science; Economics; Mathematics; Statistics","score_opus":0.7775442788050025,"score_gpt":0.5854176334124309,"score_spread":0.19212664539257163,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2128672185","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0876488,0.00006391893,0.90612936,0.002883553,0.00013066843,0.00087085186,0.00019258865,0.00021613561,0.0018641443],"genre_scores_gemma":[0.7239002,0.00010555377,0.26870495,0.00023836536,0.00023905259,0.0004804766,0.0016799245,0.000020945821,0.0046305573],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99537027,0.0020331738,0.00044915912,0.00048662655,0.001065927,0.0005948177],"domain_scores_gemma":[0.99419343,0.0035139078,0.00012481249,0.00019100633,0.0015266456,0.00045017555],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010865175,0.00013454487,0.00035968988,0.0003821509,0.00057640986,0.00008276066,0.00029500743,0.0002804305,0.00011925392],"category_scores_gemma":[0.004249564,0.00012238993,0.00020717854,0.0020915503,0.00037564835,0.00028648868,0.0000026382017,0.00030453986,0.000014517131],"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":[0.00069590413,0.00020015641,0.1040889,0.00002471369,0.0002769531,0.000013225379,0.03842778,0.7445314,0.00002910073,0.1039947,0.006227495,0.0014896704],"study_design_scores_gemma":[0.0046145306,0.0008523971,0.41903245,0.00006380971,0.0015703293,3.6012023e-7,0.027702607,0.30868152,0.00018653434,0.083954334,0.1520752,0.0012659099],"about_ca_topic_score_codex":0.0014916649,"about_ca_topic_score_gemma":0.010638825,"teacher_disagreement_score":0.6374244,"about_ca_system_score_codex":0.00010447984,"about_ca_system_score_gemma":0.00047516465,"threshold_uncertainty_score":0.5936714},"labels":[],"label_agreement":null},{"id":"W2128947491","doi":"10.3141/2430-17","title":"Encapsulating and Visualizing Disaggregated Origin–Destination Desire Lines to Identify Demand Corridors","year":2014,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Context (archaeology); Identification (biology); Raw data; Travel behavior; Computer science; Cluster analysis; Supply and demand; Process (computing); Transportation planning; Demand patterns; Survey data collection; Transport engineering; Demand management; Operations research; Geography; Economics; Engineering; Mathematics","score_opus":0.12402863127339238,"score_gpt":0.4621635053362832,"score_spread":0.33813487406289083,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2128947491","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9764444,0.000114451366,0.018221326,0.0033704785,0.00058241043,0.00088061707,0.000021372789,0.000065870256,0.0002990353],"genre_scores_gemma":[0.99361163,0.0004942039,0.0048584957,0.00008112912,0.00033720632,0.00004171434,0.00003008596,0.000046588484,0.00049893116],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99167526,0.00227525,0.001284022,0.00045118303,0.0034787294,0.0008355518],"domain_scores_gemma":[0.9927635,0.0017928492,0.00051843416,0.00026931154,0.0040472886,0.00060858787],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.009805135,0.00022614884,0.00039581236,0.0011147433,0.0020122353,0.00037135862,0.0006942075,0.00022015737,0.00009656501],"category_scores_gemma":[0.0014574636,0.00019741298,0.0001801832,0.0028412766,0.00067059894,0.0009856821,0.0000055872256,0.0011653486,0.000014107967],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00092424074,0.00021937501,0.8674678,0.0003082504,0.000116127354,0.00004735457,0.042795975,0.023781868,0.0043171826,0.02817947,0.0018916072,0.029950747],"study_design_scores_gemma":[0.0010085525,0.00032336204,0.97476006,0.0006663847,0.000058357964,3.8644546e-7,0.0056849443,0.0012354798,0.00047849253,0.0035617244,0.011976528,0.0002457255],"about_ca_topic_score_codex":0.011498021,"about_ca_topic_score_gemma":0.04246822,"teacher_disagreement_score":0.10729226,"about_ca_system_score_codex":0.00019777013,"about_ca_system_score_gemma":0.0005095496,"threshold_uncertainty_score":0.999287},"labels":[],"label_agreement":null},{"id":"W2129667532","doi":"10.1016/j.trc.2008.10.005","title":"Service network design with asset management: Formulations and comparative analyses","year":2008,"lang":"en","type":"article","venue":"Transportation Research Part C Emerging Technologies","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":134,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal; Natural Sciences and Engineering Research Council; Université de Montréal","funders":"","keywords":"Asset management; IT asset management; Computer science; Asset (computer security); Service (business); Network planning and design; Level of service; Operations research; Risk analysis (engineering); Engineering; Business; Transport engineering; Computer security; Marketing","score_opus":0.35487587980568414,"score_gpt":0.45150067487479323,"score_spread":0.09662479506910909,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2129667532","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6128025,0.0010004494,0.36163506,0.009218864,0.00011754349,0.0024276848,0.000062467814,0.0038299588,0.008905467],"genre_scores_gemma":[0.93734485,0.0015741371,0.060401954,0.000024975734,0.000020136118,0.00014746093,0.00014154449,0.000009745942,0.00033518652],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99821395,0.00016494653,0.00024330188,0.0002975589,0.000640563,0.00043969555],"domain_scores_gemma":[0.9989956,0.00027699998,0.00008334507,0.00017190605,0.00041150567,0.00006066837],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0006537317,0.00012435227,0.00016985384,0.00033213382,0.0019522635,0.000050708106,0.0002072044,0.000106135165,0.00003344453],"category_scores_gemma":[0.000025562855,0.00011325509,0.000021921182,0.0021513836,0.0005385923,0.0004158897,0.0000052923733,0.00024121098,0.000008636912],"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":[0.0002546852,0.00012796638,0.12221456,0.000101636164,0.000298525,0.00011407227,0.038846087,0.64951557,0.000038671536,0.17413472,0.0116133215,0.0027401773],"study_design_scores_gemma":[0.0030225632,0.0005484959,0.5749974,0.0007318471,0.00032166083,0.00000548423,0.22865254,0.016985772,0.0010911108,0.021450464,0.15061864,0.0015739806],"about_ca_topic_score_codex":0.0006935652,"about_ca_topic_score_gemma":0.0054433453,"teacher_disagreement_score":0.6325298,"about_ca_system_score_codex":0.000029755742,"about_ca_system_score_gemma":0.00008264614,"threshold_uncertainty_score":0.99934703},"labels":[],"label_agreement":null},{"id":"W2129686650","doi":"10.1002/atr.218","title":"Risk‐based stochastic equilibrium assignment model in augmented urban railway network","year":2012,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":14,"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":"Flow network; Stochastic modelling; Computer science; Flow (mathematics); State (computer science); Traffic network; Mathematical optimization; Event (particle physics); Travel time; Traffic flow (computer networking); Operations research; Transport engineering; Engineering; Mathematics; Algorithm; Computer network; Statistics","score_opus":0.015720865076836163,"score_gpt":0.28104578742721426,"score_spread":0.2653249223503781,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2129686650","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5550285,0.00031660555,0.4434423,0.00020471164,0.0005988905,0.00020645153,0.000015841768,0.000030378249,0.00015632258],"genre_scores_gemma":[0.9792482,0.000043739434,0.020262433,0.000063804575,0.00024494098,0.0000073686006,0.00005586006,0.000014921246,0.000058705795],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9982979,0.000116944364,0.0006196267,0.000103064194,0.00051686104,0.00034558223],"domain_scores_gemma":[0.99884146,0.00012533902,0.0006269733,0.00006745695,0.00016300853,0.00017573411],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00096346484,0.000115576164,0.00020364037,0.00015377003,0.00013081604,0.000017901453,0.00010450186,0.000086542284,0.000034159795],"category_scores_gemma":[0.00005979021,0.000116131014,0.000092451075,0.00038325076,0.000048948616,0.0009099416,4.6343544e-7,0.00022423615,0.0000018116258],"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":[0.00021702817,0.00010910304,0.04785966,0.0000070829656,0.000012242273,0.000002708869,0.011146751,0.939206,0.00014636233,0.00060038286,0.00009895079,0.00059370865],"study_design_scores_gemma":[0.0066233124,0.00030282623,0.9031535,0.0005040367,0.0003260993,0.0000010968532,0.00702,0.07773873,0.00024205369,0.0018801634,0.0015670302,0.0006411415],"about_ca_topic_score_codex":0.000035859095,"about_ca_topic_score_gemma":0.00028300114,"teacher_disagreement_score":0.8614673,"about_ca_system_score_codex":0.00013887328,"about_ca_system_score_gemma":0.00018500422,"threshold_uncertainty_score":0.47356853},"labels":[],"label_agreement":null},{"id":"W2129849731","doi":"10.1287/trsc.35.3.304.10148","title":"Modeling Bus Stops in Transit Networks: A Survey and New Formulations","year":2001,"lang":"en","type":"article","venue":"Transportation Science","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":52,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Transit (satellite); Computer science; Transport engineering; Public transport; Mathematical optimization; Line (geometry); Order (exchange); Rail transit; Operations research; Engineering; Mathematics; Economics","score_opus":0.07238598915321463,"score_gpt":0.33272747396402164,"score_spread":0.260341484810807,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2129849731","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.59507895,0.00006281614,0.403781,0.000283101,0.000075583135,0.00013696679,0.000005694569,0.000055816832,0.00052007096],"genre_scores_gemma":[0.9965292,0.00027746477,0.002811002,0.00007913005,0.000028646327,0.0000051279703,0.0000636626,0.0000062840263,0.00019948736],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99864435,0.000048666985,0.0002953881,0.00028614563,0.000423601,0.00030182875],"domain_scores_gemma":[0.99944866,0.000076266144,0.000045979712,0.000085394335,0.00015626456,0.00018742158],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012885922,0.00008513691,0.0001086642,0.00023273824,0.0005064189,0.00009653329,0.00015305608,0.00006196032,0.000044747358],"category_scores_gemma":[0.000062146195,0.00009398394,0.000019757721,0.0019060571,0.00019931923,0.0010383826,7.175165e-7,0.00009313397,0.000002154762],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021501477,0.000013167815,0.19850303,0.0000013860929,9.646944e-7,0.0000025319164,0.015865825,0.77837086,0.000017296969,0.0049785622,0.000004741612,0.0022201387],"study_design_scores_gemma":[0.00035374152,0.000009758946,0.74044,0.000016905306,0.000005398419,3.3317377e-7,0.00094945007,0.25740933,0.0000024477647,0.0005045973,0.00018405892,0.0001239696],"about_ca_topic_score_codex":0.01513835,"about_ca_topic_score_gemma":0.15200046,"teacher_disagreement_score":0.54193693,"about_ca_system_score_codex":0.000041509087,"about_ca_system_score_gemma":0.00037141665,"threshold_uncertainty_score":0.9914199},"labels":[],"label_agreement":null},{"id":"W2129992531","doi":"10.1109/itsc.2006.1707435","title":"MILATRAS: a microsimulation platform for testing transit-ITS policies and technologies","year":2006,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Microsimulation; Transit (satellite); Software deployment; Transport engineering; Computer science; Rail transit; Public transport; Operations research; Engineering","score_opus":0.037574662108030156,"score_gpt":0.2959658878743582,"score_spread":0.258391225766328,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2129992531","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8881978,0.00041962843,0.088852026,0.0028297885,0.00008530109,0.0006223321,0.000033225268,0.001321303,0.017638626],"genre_scores_gemma":[0.9788015,0.000016370055,0.020494347,0.000029438943,0.000032929707,0.000010163865,0.00002192033,0.000004535697,0.00058878824],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99958235,0.0000042593533,0.00011182893,0.000094202274,0.00007255683,0.00013479039],"domain_scores_gemma":[0.99969774,0.00015211853,0.000034053723,0.000027301689,0.00007643889,0.000012326828],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011855576,0.00004948175,0.000056556368,0.00006189622,0.00038187794,0.000057436286,0.00003822758,0.0000774719,0.000004446872],"category_scores_gemma":[0.00007440851,0.000046747784,0.000014540657,0.00017760934,0.000066665074,0.00018822584,0.0000012973662,0.00002539217,8.0708907e-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":[0.00010333744,0.00014887076,0.32180357,0.00018528255,0.00003251613,0.000002271512,0.03683871,0.12585422,0.024459142,0.41607916,0.004036847,0.07045607],"study_design_scores_gemma":[0.0050156773,0.0004375109,0.5860995,0.000289213,0.00022548526,0.00000593464,0.080326125,0.11825645,0.0335851,0.07424834,0.099534616,0.0019760653],"about_ca_topic_score_codex":0.0011070551,"about_ca_topic_score_gemma":0.0019532675,"teacher_disagreement_score":0.34183082,"about_ca_system_score_codex":0.000011844322,"about_ca_system_score_gemma":0.000023883347,"threshold_uncertainty_score":0.29371348},"labels":[],"label_agreement":null},{"id":"W2130497792","doi":"10.1109/cdc.2001.914675","title":"Distributed stochastic approximation for adaptive frequency allocation in subway networks","year":2002,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Bishop's University","funders":"","keywords":"Variable (mathematics); Computer science; Control variable; Control (management); Random variable; Transit (satellite); TRIPS architecture; Mathematical optimization; Operations research; Real-time computing; Public transport; Transport engineering; Engineering; Statistics; Mathematics; Artificial intelligence","score_opus":0.03211245953312121,"score_gpt":0.26529771840291416,"score_spread":0.23318525886979297,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2130497792","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0034329372,0.00004702647,0.99210286,0.0005947675,0.00009891921,0.00042233555,0.000013457804,0.00009057412,0.003197116],"genre_scores_gemma":[0.9918437,0.000013610877,0.0073068156,0.000042846692,0.000057956342,0.00007825184,0.00029228698,0.000005157558,0.00035939476],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993804,0.000044711967,0.0001660854,0.000128045,0.00012065338,0.00016007232],"domain_scores_gemma":[0.999633,0.000116229196,0.00006050725,0.00004844869,0.00010229631,0.000039514285],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024447253,0.000054573644,0.000067390945,0.000056211295,0.00017328582,0.000029173498,0.000059205773,0.00007931741,0.00011729573],"category_scores_gemma":[0.00010235241,0.000057022255,0.000021394166,0.00031881346,0.00003822568,0.00023037753,0.000001011856,0.000047156114,0.000006235726],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018710063,0.00008297816,0.002288643,0.0000049876835,0.0000064485057,3.550206e-7,0.008364315,0.80980223,0.0000065583704,0.1749164,0.00087116106,0.0036372032],"study_design_scores_gemma":[0.00041338874,0.000032283293,0.0073032933,0.00001824507,0.000009420437,7.537507e-8,0.001861809,0.98789036,0.000003913294,0.002171767,0.00017501216,0.000120448785],"about_ca_topic_score_codex":0.0004931943,"about_ca_topic_score_gemma":0.0025512043,"teacher_disagreement_score":0.9884108,"about_ca_system_score_codex":0.00007272876,"about_ca_system_score_gemma":0.000018843248,"threshold_uncertainty_score":0.23253001},"labels":[],"label_agreement":null},{"id":"W2130571608","doi":"10.5555/1161734.1161998","title":"Optimization of traffic signal light timing using simulation","year":2004,"lang":"en","type":"article","venue":"Winter Simulation Conference","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Traffic signal; Computer science; Signal timing; SIGNAL (programming language); Traffic flow (computer networking); Traffic simulation; Traffic congestion reconstruction with Kerner's three-phase theory; Traffic congestion; Real-time computing; Simulation; Network traffic simulation; Traffic optimization; Floating car data; Transport engineering; Engineering; Network traffic control; Microsimulation; Computer network","score_opus":0.06767652236324061,"score_gpt":0.3377629388672429,"score_spread":0.2700864165040023,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2130571608","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16743998,0.000011227244,0.8307393,0.00017155001,0.00014307161,0.00018133766,0.000004947944,0.000097039476,0.0012115287],"genre_scores_gemma":[0.9827403,0.0000037692112,0.016989814,0.000037585396,0.00008516823,0.000002010909,0.000046982408,0.00001199211,0.00008241168],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99883556,0.000082625054,0.00037363145,0.00020215861,0.000338875,0.00016717409],"domain_scores_gemma":[0.9990079,0.000118026896,0.00023708322,0.000100628284,0.000469865,0.000066496366],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002394629,0.000107882115,0.00014196378,0.000159333,0.0002560025,0.0000679,0.00011303222,0.00011493088,0.0003848296],"category_scores_gemma":[0.00008807815,0.000120967125,0.000054901877,0.00033082152,0.00007772785,0.00055803795,0.000005937249,0.00007396144,0.000006453216],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026592987,0.00003430376,0.00043514764,0.000008803449,0.000008160081,6.3012567e-7,0.012409899,0.98468417,0.00010034791,0.0011749893,0.0000010147712,0.0011159126],"study_design_scores_gemma":[0.00044442047,0.000026510768,0.0003890094,0.00010659148,0.000024577943,1.3592033e-7,0.0007771301,0.99758995,0.00017645878,0.00012759824,0.00020410042,0.0001334973],"about_ca_topic_score_codex":0.000087021006,"about_ca_topic_score_gemma":0.00008857537,"teacher_disagreement_score":0.8153003,"about_ca_system_score_codex":0.000088722525,"about_ca_system_score_gemma":0.00021092208,"threshold_uncertainty_score":0.49328962},"labels":[],"label_agreement":null},{"id":"W2131563918","doi":"10.1061/9780784413036.162","title":"A Constraint Optimization Algorithm for Automated Estimation of Turning Movement Counts at Signalized Intersections","year":2013,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Intersection (aeronautics); Computer science; Constraint (computer-aided design); Real-time computing; Radar; Algorithm; SIGNAL (programming language); Ambiguity; Engineering; Transport engineering","score_opus":0.014285890102002433,"score_gpt":0.2888875228020924,"score_spread":0.27460163270008997,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2131563918","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0033788143,0.0000049923533,0.98623127,0.000121725185,0.00013122281,0.0003466135,0.000014559613,0.0002196523,0.00955117],"genre_scores_gemma":[0.6451132,0.000009207258,0.35170248,0.00010015794,0.000022222363,0.000079121426,0.00026078947,0.0000074305362,0.0027053363],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995799,0.000021972504,0.00014981169,0.00006785771,0.00010682455,0.00007360632],"domain_scores_gemma":[0.99964416,0.000054019754,0.00008113435,0.000029534713,0.00016313275,0.00002803131],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00011926798,0.000037859205,0.000056876655,0.000049598628,0.00014595815,0.000022066273,0.000029080149,0.000039920087,0.001915502],"category_scores_gemma":[0.00003658025,0.00003726128,0.000024231827,0.00010063761,0.000047755697,0.00011932528,0.0000019014403,0.000015538386,0.000016269645],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000043278383,0.000025256975,0.00015874013,0.000008061027,0.000018549279,6.2485434e-8,0.0030982106,0.97881347,0.000117235344,0.0016440729,0.003905294,0.012206738],"study_design_scores_gemma":[0.00021335692,0.000015395315,0.0003166541,0.0000137835805,0.0000100472835,8.057962e-8,0.00089444127,0.9978666,0.00018231032,0.00012202535,0.00032069924,0.000044609547],"about_ca_topic_score_codex":0.00066596403,"about_ca_topic_score_gemma":0.000076737466,"teacher_disagreement_score":0.6417344,"about_ca_system_score_codex":0.00005079152,"about_ca_system_score_gemma":0.000039796105,"threshold_uncertainty_score":0.99899685},"labels":[],"label_agreement":null},{"id":"W2131852559","doi":"10.1139/l2012-080","title":"Adaptive route choice model for public transit systems: an application of Markov decision processes","year":2012,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Ministère des Transports; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Public transport; Computer science; Transit (satellite); Routing (electronic design automation); Traffic congestion; Real-time computing; Intelligent transportation system; Markov chain; Bus rapid transit; Transport engineering; Markov process; Computer network; Engineering","score_opus":0.026938481804010515,"score_gpt":0.2507366641699224,"score_spread":0.2237981823659119,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2131852559","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015490044,0.0007502007,0.9830502,0.0000671428,0.00019477136,0.00014266896,0.00003345222,0.000011127777,0.00026035268],"genre_scores_gemma":[0.9933812,0.000017057106,0.0063672396,0.000007475772,0.00017681511,0.000008591162,0.000009624619,0.000011266636,0.000020738205],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99932027,0.00001344602,0.00024777668,0.000053509255,0.00015596405,0.00020901485],"domain_scores_gemma":[0.99884063,0.000102904,0.00014413171,0.00005003062,0.00045076132,0.00041152257],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005176735,0.00005977678,0.000120086836,0.00021134125,0.00010761087,0.00003709546,0.00012848893,0.00006323871,0.000004544113],"category_scores_gemma":[0.00022962835,0.00006296707,0.000033150198,0.00023828434,0.000018927429,0.00076444517,4.357038e-7,0.000061703286,1.6587312e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000072630437,0.000008296119,0.0042854636,0.000054900887,0.000013418244,2.5663203e-7,0.00665865,0.9841181,0.00002107452,0.003557002,0.000067127454,0.0012084191],"study_design_scores_gemma":[0.00041079085,0.000057309728,0.0073626563,0.00021941279,0.00006077291,0.0000045478255,0.0019576002,0.9773796,0.000021467298,0.00009354854,0.012237675,0.00019458267],"about_ca_topic_score_codex":0.001982217,"about_ca_topic_score_gemma":0.20090717,"teacher_disagreement_score":0.97789115,"about_ca_system_score_codex":0.00010157111,"about_ca_system_score_gemma":0.00084498106,"threshold_uncertainty_score":0.8136742},"labels":[],"label_agreement":null},{"id":"W2132055012","doi":"10.1002/atr.5670380103","title":"Validation of a schedule‐based capacity restraint transit assignment model for a large‐scale network","year":2004,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Schedule; Transit (satellite); Computer science; Scale (ratio); Path (computing); Set (abstract data type); Flow network; Simulation; Function (biology); Mathematical optimization; Transport engineering; Public transport; Engineering; Mathematics","score_opus":0.02736574916431853,"score_gpt":0.29554095186041196,"score_spread":0.2681752026960934,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2132055012","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3929903,0.000023770077,0.6061372,0.00040554517,0.0001323181,0.00021563192,0.00005097242,0.000014663817,0.00002958938],"genre_scores_gemma":[0.78599596,0.000029890052,0.21373057,0.00004553227,0.000078944744,0.000010299473,0.00008799729,0.000010679663,0.000010118987],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99844843,0.000044395278,0.0006811029,0.00012720496,0.00048204276,0.0002168282],"domain_scores_gemma":[0.9985788,0.00006825404,0.0006532085,0.000069356254,0.00052814477,0.00010227683],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00078561297,0.00010460173,0.00023325351,0.00010437554,0.0002028537,0.000014922649,0.0000999687,0.000103440456,0.000009765616],"category_scores_gemma":[0.000038252118,0.00010747269,0.00017827771,0.0002608302,0.000054128654,0.0004932291,2.0046451e-7,0.00012133143,2.0205097e-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":[0.00032967614,0.00014873763,0.00075944915,0.000042152275,0.000019900095,0.0000013812723,0.02473562,0.96508425,0.0019293273,0.0067307106,0.0000073966235,0.00021138783],"study_design_scores_gemma":[0.079692096,0.005110719,0.25609377,0.005173375,0.0030873234,0.000008330032,0.066974245,0.22470385,0.12764186,0.22383726,0.0044928454,0.0031843157],"about_ca_topic_score_codex":0.000020759348,"about_ca_topic_score_gemma":0.00068186707,"teacher_disagreement_score":0.7403804,"about_ca_system_score_codex":0.00010382724,"about_ca_system_score_gemma":0.0003916847,"threshold_uncertainty_score":0.4382609},"labels":[],"label_agreement":null},{"id":"W2132209385","doi":"10.1109/itsc.2001.948666","title":"A numerical analytical model for the continuous dynamic network equilibrium problem with limited capacity and spill back","year":2002,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science; Mathematical optimization; Traffic network; Algorithm; Mathematics","score_opus":0.03487055875666646,"score_gpt":0.25956172146664486,"score_spread":0.2246911627099784,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2132209385","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04786117,0.00010335872,0.9273378,0.0068813693,0.000061441926,0.00078267214,0.000012907438,0.00015107922,0.016808178],"genre_scores_gemma":[0.95964134,0.000025753192,0.034328025,0.00026808883,0.00003859211,0.000016894204,0.000008206491,0.00000790214,0.0056652194],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993226,0.000030324452,0.00012006587,0.00015294581,0.00014474281,0.00022934412],"domain_scores_gemma":[0.9995543,0.00018223717,0.000041031573,0.0000685091,0.00007747697,0.00007649322],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018719061,0.00007093402,0.00010473262,0.000013750393,0.00024818763,0.00007010583,0.00007139278,0.00005653043,0.000088439156],"category_scores_gemma":[0.00002333565,0.000044495322,0.000026277443,0.00020221926,0.00017291041,0.0000876602,0.0000026678526,0.00006415471,0.0000039267156],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000082977196,0.000057176847,0.021228548,0.000015841832,0.000049728926,9.69442e-7,0.008153799,0.9385273,0.0000018103001,0.024671935,0.00609142,0.001118488],"study_design_scores_gemma":[0.00026080201,0.000047150308,0.0013098503,0.000008814757,0.000032824777,6.2199564e-7,0.0002428971,0.9964161,2.3242971e-7,0.0003984154,0.0012006457,0.00008167644],"about_ca_topic_score_codex":0.00015463616,"about_ca_topic_score_gemma":0.0007163571,"teacher_disagreement_score":0.9117802,"about_ca_system_score_codex":0.000014938211,"about_ca_system_score_gemma":0.000019061405,"threshold_uncertainty_score":0.19088836},"labels":[],"label_agreement":null},{"id":"W2132916971","doi":"10.1016/j.ijpe.2007.06.005","title":"Response to a factual comment on the erratum to “Incorporating transportation cost in the lot size and pricing decisions with downward sloping demand” by Prof. S.K. Goyal","year":2007,"lang":"en","type":"article","venue":"International Journal of Production Economics","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Operations research; Economics; Computer science; Mathematics","score_opus":0.024420100885157364,"score_gpt":0.29823571118338194,"score_spread":0.27381561029822454,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2132916971","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8439176,0.0000069836383,0.02289348,0.13221733,0.0004614291,0.0003658041,0.0000106147745,0.000006701512,0.00012003289],"genre_scores_gemma":[0.9940065,0.00002296253,0.002342216,0.0033333672,0.00018495948,0.000009290274,0.000006709376,0.0000067435053,0.00008722001],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.998892,0.00013170704,0.0004324927,0.0001383645,0.00028673722,0.00011869325],"domain_scores_gemma":[0.9986659,0.000645603,0.00030328124,0.000062551524,0.00025402626,0.00006862876],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037060268,0.00007812549,0.000093061826,0.00015892065,0.0002337419,0.00012489838,0.00020332665,0.000033016753,0.000007606516],"category_scores_gemma":[0.00075706205,0.000053141903,0.00002426655,0.00017083618,0.00004691846,0.00025471955,0.0000032949176,0.00016842851,0.0000017504584],"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":[0.010470136,0.00040524433,0.060140792,0.000006197782,0.00014783241,0.000033911976,0.21777958,0.65214336,0.0016683082,0.021381393,0.01868679,0.017136432],"study_design_scores_gemma":[0.0027418872,0.0015568399,0.6558696,0.0008326843,0.00007231252,0.00008073488,0.11668705,0.0005765248,0.0032423306,0.002181592,0.21549267,0.00066578505],"about_ca_topic_score_codex":0.00009423063,"about_ca_topic_score_gemma":0.0015943041,"teacher_disagreement_score":0.65156686,"about_ca_system_score_codex":0.00019729105,"about_ca_system_score_gemma":0.00012122791,"threshold_uncertainty_score":0.2167064},"labels":[],"label_agreement":null},{"id":"W2133618009","doi":"10.3386/w15376","title":"The Fundamental Law of Road Congestion: Evidence from US cities","year":2009,"lang":"en","type":"report","venue":"National Bureau of Economic Research","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":185,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Economic and Social Research Council","keywords":"Law; Political science","score_opus":0.3948230985881109,"score_gpt":0.5349212278916091,"score_spread":0.14009812930349824,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2133618009","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02439097,0.0050376668,0.000009701027,0.0030420923,0.0010124352,0.00060091243,0.00039905787,0.00003009757,0.96547705],"genre_scores_gemma":[0.9803218,0.008452225,0.00018772593,0.000015660036,0.00072396727,0.00002774642,0.0005237578,0.0000111858,0.009735896],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9965079,0.00032681,0.00060019345,0.0002613448,0.002071698,0.0002320556],"domain_scores_gemma":[0.99477726,0.0025043697,0.00041214138,0.00016297618,0.0020754745,0.00006776339],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005411698,0.00010532194,0.00025415106,0.00017858477,0.00059380336,0.00011073927,0.0004692014,0.00026144568,0.0003950413],"category_scores_gemma":[0.0012220881,0.00009537003,0.00010860211,0.00014472523,0.0011293164,0.00025334477,0.000019756983,0.0003461514,0.0000238436],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022230252,0.000095707226,0.013848164,0.000076999226,0.0002662732,0.0000035030766,0.0039834552,0.0101072425,0.000034229386,0.9096224,0.055323184,0.0064165955],"study_design_scores_gemma":[0.0010541622,0.0004969442,0.15215248,0.0023080169,0.00011503895,0.0000042052534,0.006540501,0.0007921378,0.00038539455,0.4484116,0.3869346,0.0008048944],"about_ca_topic_score_codex":0.07038409,"about_ca_topic_score_gemma":0.019347139,"teacher_disagreement_score":0.9559309,"about_ca_system_score_codex":0.0008747417,"about_ca_system_score_gemma":0.0046644867,"threshold_uncertainty_score":0.9985472},"labels":[],"label_agreement":null},{"id":"W2133808551","doi":"10.1287/trsc.2016.0672","title":"On the Existence and Uniqueness of Equilibrium in the Bottleneck Model with Atomic Users","year":2016,"lang":"en","type":"article","venue":"Transportation Science","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Social Sciences and Humanities Research Council of Canada; Northwestern University","keywords":"Bottleneck; Uniqueness; Nash equilibrium; Queueing theory; Queue; Strategy; Computer science; Mathematical optimization; Traffic congestion; Operations research; Mathematical economics; Game theory; Economics; Transport engineering; Engineering; Mathematics; Computer network","score_opus":0.03381374117409109,"score_gpt":0.2930704907206937,"score_spread":0.2592567495466026,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2133808551","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9866689,0.0000068459026,0.008799889,0.0030883707,0.00001939291,0.00017763783,0.000011168564,0.000016773985,0.0012110122],"genre_scores_gemma":[0.9990087,0.000030032157,0.0006687816,0.00018571934,0.000003425711,0.000013037653,0.0000015421308,0.0000030940696,0.00008564395],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99894375,0.000056525718,0.00015454383,0.00017495456,0.0005185385,0.00015166933],"domain_scores_gemma":[0.9993544,0.0002920719,0.000085408064,0.00013101695,0.00010287588,0.000034228156],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011984585,0.00005802461,0.00006397268,0.00007343413,0.0002084054,0.000029804212,0.00029928295,0.000024111994,0.000009596523],"category_scores_gemma":[0.00004874091,0.000026705198,0.000012241196,0.00067203096,0.0011232942,0.00040420442,7.7651345e-7,0.000042666004,8.652159e-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":[0.00009519496,0.000035578338,0.060530193,0.000009766954,0.0000023014263,0.0000023594537,0.06521125,0.022463763,0.0031509253,0.847829,0.00001724362,0.00065242185],"study_design_scores_gemma":[0.0010076844,0.00014535556,0.9475983,0.00032682763,0.00002595556,7.816599e-7,0.0152184,0.0072321324,0.004702999,0.023294054,0.00013930003,0.0003082477],"about_ca_topic_score_codex":0.00025172535,"about_ca_topic_score_gemma":0.0015789774,"teacher_disagreement_score":0.8870681,"about_ca_system_score_codex":0.000018693232,"about_ca_system_score_gemma":0.00025663117,"threshold_uncertainty_score":0.41388243},"labels":[],"label_agreement":null},{"id":"W2134022000","doi":"10.1002/atr.195","title":"Dynamic marginal cost, access control, and pollution charge: a comparison of bottleneck and whole link models","year":2012,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":40,"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":"Bottleneck; Queue; Queueing theory; Computer science; Dynamic pricing; Externality; Mathematical optimization; Marginal cost; Inflow; Operations research; Computer network; Engineering; Economics; Mathematics; Microeconomics","score_opus":0.023335812934511545,"score_gpt":0.3348682252146101,"score_spread":0.3115324122800986,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2134022000","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92270756,0.002026529,0.07365937,0.0010025102,0.00024860766,0.00020341361,0.000050782597,0.000012450127,0.000088799316],"genre_scores_gemma":[0.99402773,0.00076558994,0.0050380896,0.000037618815,0.00006225516,0.000003101911,0.00003194742,0.00000810775,0.000025576637],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9989237,0.000055165645,0.00047631,0.00008190369,0.00029893126,0.00016395006],"domain_scores_gemma":[0.9989765,0.000048646376,0.0005734791,0.000038832135,0.00023097954,0.00013151615],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041560395,0.00008624609,0.00024168508,0.00013215259,0.00014347628,0.00002518701,0.00006619992,0.00007845905,0.000008646634],"category_scores_gemma":[0.000021269347,0.00008452257,0.000042560932,0.00016000638,0.00009814586,0.0014885272,6.612894e-7,0.00012655478,2.4829416e-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":[0.0012374499,0.00028707844,0.3041573,0.00017666837,0.00010632243,0.000004212051,0.07231375,0.56318796,0.008170336,0.010509818,0.000058181628,0.039790966],"study_design_scores_gemma":[0.0024588902,0.00015003892,0.9843833,0.00015714794,0.0001736137,0.000002661371,0.0037541096,0.005942112,0.00018409405,0.0009279089,0.0017049435,0.00016120095],"about_ca_topic_score_codex":0.000043521883,"about_ca_topic_score_gemma":0.00020098536,"teacher_disagreement_score":0.68022597,"about_ca_system_score_codex":0.000031342108,"about_ca_system_score_gemma":0.00006001205,"threshold_uncertainty_score":0.34467307},"labels":[],"label_agreement":null},{"id":"W2134115685","doi":"10.1139/l10-115","title":"Estimation of expected travel time using the method of moment","year":2011,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Science Foundation","keywords":"Moment (physics); Monte Carlo method; Piecewise; Range (aeronautics); Computer science; Trajectory; Second moment of area; Expected value; Variance (accounting); Travel time; Random variable; Time point; Mathematical optimization; Statistics; Mathematics; Engineering","score_opus":0.026595972973510153,"score_gpt":0.2537242298162055,"score_spread":0.22712825684269536,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2134115685","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06738266,0.00012274993,0.9306071,0.000048413975,0.00016520372,0.000051302777,0.000005551812,0.0000040749173,0.0016129612],"genre_scores_gemma":[0.97217786,0.000003312981,0.027762027,0.0000034409966,0.000023053253,2.463936e-7,7.998778e-7,0.000004561845,0.000024721234],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995176,0.000029157363,0.00022027,0.000026818865,0.000117416406,0.00008872568],"domain_scores_gemma":[0.99955183,0.000037491827,0.00015924504,0.00003776934,0.00011250304,0.000101144076],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004161263,0.000036187153,0.00009479037,0.00015296851,0.000052041425,0.0000060172024,0.00008864433,0.000029369188,0.00014246731],"category_scores_gemma":[0.0000748922,0.000031524996,0.00003613549,0.00018708972,0.000028294537,0.000097671356,5.5442337e-7,0.00005354799,1.6898154e-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":[0.0000031701195,0.000004016125,0.0005407419,0.0000110235815,0.00002571853,0.0000034417706,0.04061241,0.95535475,0.0008469518,0.0018350586,0.000033273423,0.00072944426],"study_design_scores_gemma":[0.0010051013,0.00019296313,0.08267005,0.00090917526,0.00032640056,0.000045016033,0.01405599,0.88425756,0.014146564,0.0006978667,0.0012384658,0.00045485553],"about_ca_topic_score_codex":0.0050606797,"about_ca_topic_score_gemma":0.012867741,"teacher_disagreement_score":0.90479517,"about_ca_system_score_codex":0.00004624118,"about_ca_system_score_gemma":0.00034799796,"threshold_uncertainty_score":0.76502675},"labels":[],"label_agreement":null},{"id":"W2134343714","doi":"10.3141/2254-15","title":"Joint Model of Participation in Nonwork Activities and Time-of-Day Choice Set Formation for Workers","year":2011,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Flexibility (engineering); Set (abstract data type); Schedule; Relation (database); Travel survey; Discrete choice; Operations research; Randomness; Component (thermodynamics); Choice set; Travel behavior; Computer science; Econometrics; Simulation; Engineering; Transport engineering; Mathematics; Statistics; Data mining","score_opus":0.2141277441873472,"score_gpt":0.4185946964870836,"score_spread":0.20446695229973638,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2134343714","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.980114,0.00007841796,0.017719727,0.0005238608,0.00012955995,0.0010328328,0.00009450119,0.000013475423,0.00029365593],"genre_scores_gemma":[0.99279416,0.000436036,0.006302173,0.000009883808,0.00004434625,0.000077495766,0.000029311206,0.0000232204,0.0002833775],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9952113,0.0009581367,0.0013594496,0.00021717492,0.0017495872,0.0005043292],"domain_scores_gemma":[0.99567676,0.0012185111,0.00071622786,0.00018538683,0.0020212692,0.00018187346],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006632772,0.0001429991,0.0003893484,0.00094706734,0.00039136168,0.000035296547,0.00037156206,0.00018260899,0.000077209996],"category_scores_gemma":[0.0003927303,0.00012389346,0.00018854653,0.0013240273,0.00070399407,0.0010657476,0.0000024929027,0.0006050291,9.470895e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002614481,0.00047285456,0.7264105,0.0006723854,0.00011690446,0.0000054157795,0.20595478,0.042965677,0.004753407,0.009630454,0.0009959615,0.005407158],"study_design_scores_gemma":[0.0016933163,0.00046244514,0.9622709,0.00069840887,0.00007102616,5.0575935e-8,0.016290313,0.0071511194,0.0054396465,0.0053528757,0.00039008816,0.00017978987],"about_ca_topic_score_codex":0.009198702,"about_ca_topic_score_gemma":0.046461884,"teacher_disagreement_score":0.23586039,"about_ca_system_score_codex":0.000117634096,"about_ca_system_score_gemma":0.000586851,"threshold_uncertainty_score":0.99739915},"labels":[],"label_agreement":null},{"id":"W2134781427","doi":"10.1002/atr.161","title":"Adaptive road border crossing system management: the role of priority programs","year":2011,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"Carleton University; Cochrane","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Level crossing; Transport engineering; Queue; Microsimulation; Jump; Border crossing; Truck; Computer science; Bridge (graph theory); Operations research; Engineering; Geography","score_opus":0.01601834381961749,"score_gpt":0.2799525586164711,"score_spread":0.26393421479685364,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2134781427","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9608517,0.00063485565,0.028005999,0.00005737552,0.0005187983,0.00050333637,0.000010357032,0.00006026798,0.009357258],"genre_scores_gemma":[0.9785048,0.000077446246,0.02129417,0.000005975321,0.00004713817,0.000004719116,0.000008899024,0.000007810693,0.00004907283],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.9988355,0.00006487992,0.0004828385,0.00008523911,0.00039733964,0.00013420364],"domain_scores_gemma":[0.9988076,0.000020399837,0.0006635501,0.00007012362,0.00038678618,0.000051546234],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047340218,0.00007600282,0.0001449863,0.00007497354,0.0003045427,0.00002653497,0.00014633042,0.000050672174,0.00001141163],"category_scores_gemma":[0.000007171963,0.00005897306,0.00009134916,0.0003033087,0.00015126116,0.00052308396,5.9699937e-7,0.000111023415,6.9359015e-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":[0.001041812,0.00033643746,0.0672562,0.00015162685,0.00023708616,0.00004569215,0.42726192,0.040545806,0.00022547669,0.08913201,0.0000049029327,0.37376103],"study_design_scores_gemma":[0.0010059968,0.00021273592,0.8086158,0.0004340599,0.00023473005,0.000002250012,0.18342656,0.00013771522,0.00056870544,0.0016054373,0.0035971187,0.00015890817],"about_ca_topic_score_codex":0.00025685632,"about_ca_topic_score_gemma":0.0004424726,"teacher_disagreement_score":0.7413596,"about_ca_system_score_codex":0.000038982325,"about_ca_system_score_gemma":0.00008628595,"threshold_uncertainty_score":0.24048516},"labels":[],"label_agreement":null},{"id":"W2134971607","doi":"10.1002/atr.5670400204","title":"Preliminary analysis on dynamic route choice behavior: Using probe‐vehicle data","year":2006,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":39,"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":"Node (physics); Computer science; Position (finance); Process (computing); Traffic simulation; Travel behavior; Operations research; Transport engineering; Simulation; Engineering; Microsimulation; Economics","score_opus":0.024060435235986558,"score_gpt":0.3329595925241289,"score_spread":0.3088991572881423,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2134971607","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.954881,0.00013543028,0.04413527,0.00016614396,0.00024717307,0.00017856536,0.00010453292,0.00003767248,0.00011421483],"genre_scores_gemma":[0.97644913,0.00005773713,0.022449588,0.00002574284,0.00011600776,0.000002522601,0.0007714121,0.000013253168,0.00011461491],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99841046,0.00006731789,0.0005883506,0.00020203523,0.0005535949,0.0001782428],"domain_scores_gemma":[0.9987485,0.00008600545,0.00063205534,0.0001887482,0.0002723073,0.00007240667],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037685857,0.00011007931,0.00021918475,0.0003169105,0.00025791116,0.00004065117,0.00025512473,0.000081503174,0.00002799932],"category_scores_gemma":[0.000027941192,0.00011133577,0.0001156726,0.00082908326,0.00005417384,0.0011341166,0.0000011513614,0.00016284558,0.000001107007],"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":[0.00015040292,0.0001800689,0.13428484,0.000008968038,0.00006730475,0.00002974736,0.0019868664,0.85997146,0.00085870794,0.00032503097,0.00001638876,0.0021202255],"study_design_scores_gemma":[0.0006122018,0.00011320935,0.99201757,0.00005064466,0.001092907,8.5503643e-7,0.00080215855,0.0039521935,0.000053234955,0.00013241518,0.0010319067,0.00014071206],"about_ca_topic_score_codex":0.0007155395,"about_ca_topic_score_gemma":0.005726829,"teacher_disagreement_score":0.8577327,"about_ca_system_score_codex":0.00010581752,"about_ca_system_score_gemma":0.00014031584,"threshold_uncertainty_score":0.4540141},"labels":[],"label_agreement":null},{"id":"W2135647177","doi":"","title":"Book review: Urban transport development. A complex issue (Gunilla Jönson and Emin Tengström (eds.))","year":2007,"lang":"en","type":"article","venue":"Research Repository (Delft University of Technology)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Transport engineering; Regional science; Economic geography; Engineering; Sociology; Geography","score_opus":0.02332995442720339,"score_gpt":0.3057974676536798,"score_spread":0.28246751322647645,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2135647177","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6525498,0.18515943,0.0064861006,0.020183662,0.00028538247,0.0029838234,0.000029752466,0.0016258017,0.13069625],"genre_scores_gemma":[0.7909572,0.06015464,0.022308294,0.00043102712,0.00013372893,0.0000032835114,0.000097958575,0.000039762243,0.1258741],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9984605,0.00009572079,0.0002236686,0.0002807556,0.00055932894,0.0003800753],"domain_scores_gemma":[0.99903435,0.00009431194,0.00010688772,0.00019303689,0.00042699833,0.00014441075],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014177267,0.000092941766,0.00020658763,0.0005143059,0.0010930429,0.000009521005,0.0003677411,0.00024805768,0.000106405445],"category_scores_gemma":[0.0000906888,0.000110630346,0.000039405626,0.0008485291,0.001051406,0.00017853446,0.000028958402,0.00033152185,0.000011921999],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005492354,0.0006603447,0.1490902,0.002098801,0.0003490232,0.0017196098,0.043891873,0.00007094861,0.013743122,0.007528489,0.71468407,0.065614305],"study_design_scores_gemma":[0.00024767497,0.000044397926,0.015028412,0.00031910633,0.0000184588,0.0000047804833,0.0052104783,0.0000145110025,0.0012279754,0.000009294434,0.9777582,0.00011665431],"about_ca_topic_score_codex":0.00056974083,"about_ca_topic_score_gemma":0.00021571292,"teacher_disagreement_score":0.26307422,"about_ca_system_score_codex":0.00011473456,"about_ca_system_score_gemma":0.00027048786,"threshold_uncertainty_score":0.84069127},"labels":[],"label_agreement":null},{"id":"W2136483635","doi":"10.1002/atr.1306","title":"A new logit‐artificial neural network ensemble for mode choice modeling: a case study for border transport","year":2015,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":36,"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":"Multinomial logistic regression; Mode choice; Artificial neural network; Mode (computer interface); Computer science; Metropolitan area; Logit; Logistic regression; Discrete choice; Artificial intelligence; Ensemble learning; Econometrics; Machine learning; Operations research; Public transport; Transport engineering; Engineering; Mathematics; Geography","score_opus":0.07244231297133959,"score_gpt":0.3850144158304051,"score_spread":0.3125721028590655,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2136483635","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4981619,0.00008246157,0.5000331,0.00029612178,0.0006306683,0.00072025607,0.000022157268,0.000034654215,0.000018692816],"genre_scores_gemma":[0.92464805,0.000013584555,0.07410273,0.000059441358,0.0008989486,0.00003909641,0.00009110045,0.000028978975,0.00011809418],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980416,0.000053859243,0.00089629,0.00021857291,0.00045456705,0.000335074],"domain_scores_gemma":[0.9979157,0.00017532853,0.0004789984,0.0000905976,0.0010345889,0.0003047961],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00079246686,0.0001675828,0.00033186804,0.00011863551,0.00034933368,0.00004018527,0.00013541299,0.00011143545,0.000006330675],"category_scores_gemma":[0.00010736221,0.00016835619,0.00019501334,0.0003273014,0.000025400366,0.00083791337,3.7860633e-7,0.00014570961,2.639271e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010704168,0.00014951477,0.0036440247,0.000014939212,0.000043588367,0.000084146406,0.04221786,0.9459166,0.000023437891,0.0005019849,0.00017222864,0.006161284],"study_design_scores_gemma":[0.047550794,0.010554322,0.015827384,0.00039795757,0.0039842054,0.0002049263,0.36246964,0.48285863,0.00017196147,0.040540226,0.032664083,0.0027758724],"about_ca_topic_score_codex":0.001117094,"about_ca_topic_score_gemma":0.024521962,"teacher_disagreement_score":0.46305794,"about_ca_system_score_codex":0.00006735127,"about_ca_system_score_gemma":0.0005987088,"threshold_uncertainty_score":0.99327797},"labels":[],"label_agreement":null},{"id":"W2137065129","doi":"10.1111/j.1538-4632.2000.tb00431.x","title":"Using Decision Tree Induction Systems for Modeling Space‐Time Behavior","year":2000,"lang":"en","type":"article","venue":"Geographical Analysis","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":53,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ministry of Transportation of Ontario","funders":"","keywords":"CHAID; Decision tree; Computer science; Decision rule; Decision tree learning; Incremental decision tree; Decision support system; Machine learning; Heuristic; Decision tree model; Artificial intelligence; Operations research; Data mining; Mathematics","score_opus":0.03724005568645729,"score_gpt":0.31549392770268225,"score_spread":0.278253872016225,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2137065129","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7554429,0.00010694855,0.24364974,0.00009821622,0.00006528961,0.00017734591,0.000016445236,0.00008570968,0.00035742667],"genre_scores_gemma":[0.99098593,0.00020255963,0.008188929,0.000012918863,0.00011874421,0.000028620287,0.00012320794,0.00000781405,0.00033125372],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988272,0.000073926385,0.0002655485,0.00026068892,0.00035406134,0.00021853925],"domain_scores_gemma":[0.9994926,0.00006764361,0.000059790083,0.000118006654,0.00015309143,0.00010890517],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048676576,0.00008699374,0.00020123948,0.00044966664,0.0006322532,0.000113481205,0.000094932344,0.00015711969,0.0002987766],"category_scores_gemma":[0.00003125799,0.00008431165,0.00028768464,0.0022503182,0.000058551504,0.00018600385,0.0000019744896,0.000070181326,0.000008157964],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028988596,0.000041224925,0.018174917,0.0000022925399,0.000104363586,8.9279126e-7,0.00042162937,0.973591,0.000014025727,0.00038585646,0.000013993926,0.007220779],"study_design_scores_gemma":[0.00018368398,0.000014825444,0.0065283766,0.000012450444,0.0017430027,3.4305327e-7,0.00036185383,0.9900577,0.0000010852104,0.00025033765,0.0007039467,0.00014239845],"about_ca_topic_score_codex":0.0044901655,"about_ca_topic_score_gemma":0.0009200144,"teacher_disagreement_score":0.23554306,"about_ca_system_score_codex":0.000025197987,"about_ca_system_score_gemma":0.000026474068,"threshold_uncertainty_score":0.6787817},"labels":[],"label_agreement":null},{"id":"W2137432187","doi":"10.18757/ejtir.2014.14.2.3021","title":"Simplified model of local transit services","year":2014,"lang":"en","type":"article","venue":"European journal of transport and infrastructure research","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Surdna Foundation; California Department of Transportation; University of California, Davis; U.S. Department of Transportation","keywords":"Transit (satellite); Transport engineering; Public transport; Service (business); Computer science; Engineering; Business","score_opus":0.03065342550418278,"score_gpt":0.31300546852955147,"score_spread":0.2823520430253687,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2137432187","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7089725,0.00014674524,0.27096984,0.00050214387,0.0000904116,0.000081571874,0.000012838364,0.0000143983125,0.019209506],"genre_scores_gemma":[0.9962542,0.00026423513,0.0032085972,0.000038675105,0.00012747603,1.4169566e-7,0.0000072479856,0.000012748223,0.0000866791],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99817276,0.00041494283,0.00042103976,0.00010676935,0.0006717405,0.00021273181],"domain_scores_gemma":[0.9990369,0.00007267699,0.00014537408,0.00007948954,0.00048351497,0.0001820083],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032062873,0.00007792259,0.00018729636,0.00020132143,0.00022975983,0.000024761262,0.00026185196,0.00004643289,0.000054718606],"category_scores_gemma":[0.000015428654,0.000064524625,0.00006495308,0.00023572396,0.00038298423,0.00022723453,0.0000033410001,0.00035616945,9.663746e-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":[0.00095634046,0.00010723949,0.055344734,0.0005172744,0.00013982356,0.00007164789,0.116472684,0.6870513,0.004561229,0.024160484,0.0006585539,0.109958686],"study_design_scores_gemma":[0.0052686613,0.0015537835,0.82871854,0.0009089407,0.00021675057,0.00003285276,0.03127708,0.037082985,0.0017134766,0.009752358,0.08273414,0.000740444],"about_ca_topic_score_codex":0.000042037555,"about_ca_topic_score_gemma":0.00006766078,"teacher_disagreement_score":0.7733738,"about_ca_system_score_codex":0.0000116910705,"about_ca_system_score_gemma":0.000120154815,"threshold_uncertainty_score":0.26312378},"labels":[],"label_agreement":null},{"id":"W2138523972","doi":"10.3141/2275-03","title":"Modeling Dwell Time for Streetcars in Melbourne, Australia, and Toronto, Canada","year":2012,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"St. Clair College","funders":"Monash University","keywords":"Dwell time; Transport engineering; Payment; Ticket; Engineering; Computer science; Operations research; Business; Computer security; Finance; Psychology","score_opus":0.11164894535381739,"score_gpt":0.40873236769945837,"score_spread":0.297083422345641,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2138523972","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99397457,0.00043585198,0.0017936424,0.0020570545,0.00047942123,0.00095911516,0.00009100796,0.000015189976,0.00019416113],"genre_scores_gemma":[0.99536747,0.0006945577,0.0022910007,0.000036513673,0.0002602243,0.00005364226,0.000034119344,0.00003173625,0.0012307358],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.994005,0.0009119258,0.0010441658,0.0002596404,0.0027097217,0.001069527],"domain_scores_gemma":[0.9962451,0.0008982995,0.00023517072,0.00019024989,0.0019179032,0.00051330775],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006712951,0.00017832466,0.00034899075,0.00036318708,0.00080392795,0.00010779855,0.0005673688,0.00018959501,0.00029431583],"category_scores_gemma":[0.00021923387,0.00015492314,0.0001490358,0.00089192606,0.0003249085,0.0011854249,0.000002527914,0.00089183677,0.0000028221134],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014751651,0.00034961532,0.8761876,0.00028443942,0.00014584082,0.000029774683,0.040620837,0.0521342,0.00054331106,0.01323137,0.0102239875,0.004773818],"study_design_scores_gemma":[0.002164646,0.00023366426,0.9418431,0.0003756544,0.00006947481,2.3614875e-7,0.020808227,0.00427349,0.00015279854,0.0012442435,0.028477805,0.00035664754],"about_ca_topic_score_codex":0.9095186,"about_ca_topic_score_gemma":0.9931827,"teacher_disagreement_score":0.08366408,"about_ca_system_score_codex":0.0006231058,"about_ca_system_score_gemma":0.0015171514,"threshold_uncertainty_score":0.6317582},"labels":[],"label_agreement":null},{"id":"W2138952887","doi":"10.1061/41127(382)185","title":"Super-Network Based Equilibrium Model and Algorithm for Multi-Mode Urban Transport System","year":2010,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"National Natural Science Foundation of China","keywords":"Mode (computer interface); Variational inequality; Computer science; Algorithm; Basis (linear algebra); Mathematical optimization; Mathematics; Geometry","score_opus":0.025090273345196576,"score_gpt":0.29114804549130685,"score_spread":0.26605777214611026,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2138952887","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.044570062,0.000017349374,0.95164686,0.00021519673,0.00032762776,0.0003828637,0.00008213055,0.00029310648,0.002464805],"genre_scores_gemma":[0.6519372,0.0000016932179,0.3461678,0.00007460952,0.00013841242,0.000029552493,0.00009555125,0.000011486873,0.0015436971],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991986,0.000019510497,0.0001725299,0.00020291263,0.00015941207,0.00024701707],"domain_scores_gemma":[0.9995821,0.00006789576,0.00003615973,0.0000871061,0.00009856853,0.00012812992],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000369843,0.00008897844,0.000121224075,0.00003264432,0.00029318483,0.000036003104,0.00008740313,0.00013480562,0.000020417094],"category_scores_gemma":[0.000011149521,0.00008494243,0.000047385085,0.00010284515,0.00008360183,0.0001683358,0.0000012465796,0.00008027235,0.0000014890132],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000108862,0.00024059537,0.07986,0.00022914127,0.00006173375,0.000007771648,0.027997296,0.7451971,0.0010728047,0.13046166,0.0052489885,0.009514],"study_design_scores_gemma":[0.0005235188,0.00001176178,0.0013509523,0.000012728442,0.000023161765,1.6862232e-7,0.0006060664,0.99556184,0.000035409354,0.000031549287,0.001716488,0.00012634078],"about_ca_topic_score_codex":0.00055013044,"about_ca_topic_score_gemma":0.0032469565,"teacher_disagreement_score":0.6073671,"about_ca_system_score_codex":0.000012384014,"about_ca_system_score_gemma":0.00013312869,"threshold_uncertainty_score":0.34638518},"labels":[],"label_agreement":null},{"id":"W2139950021","doi":"10.1016/j.jue.2012.02.001","title":"Step tolling with bottleneck queuing congestion","year":2012,"lang":"en","type":"article","venue":"Journal of Urban Economics","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":101,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"European Research Council; Social Sciences and Humanities Research Council of Canada","keywords":"Toll; Bottleneck; Queueing theory; Interval (graph theory); Computer science; Congestion pricing; Limit (mathematics); Limiting; Queue; Road pricing; Scheme (mathematics); Mathematical optimization; Traffic congestion; Economics; Transport engineering; Mathematics; Engineering; Computer network","score_opus":0.01857008569657123,"score_gpt":0.24787972982835682,"score_spread":0.2293096441317856,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2139950021","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9874863,0.00027886007,0.0076632896,0.00050664466,0.00052527635,0.00003807282,0.0000017671981,0.000011948421,0.0034878172],"genre_scores_gemma":[0.9898519,0.00019291099,0.0091165,0.00006198946,0.0005255348,3.2314674e-7,0.000001970441,0.0000062450895,0.00024265172],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.99952793,0.000030460647,0.00021213121,0.000036041256,0.00006516069,0.00012829364],"domain_scores_gemma":[0.9994137,0.000058210895,0.00031202124,0.00003232824,0.000082608356,0.000101078884],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005538116,0.000042740838,0.000096531454,0.00005985808,0.000119156604,0.000042912263,0.000060452447,0.000042943815,0.000028874822],"category_scores_gemma":[0.00003230028,0.00003899387,0.000029696797,0.00004807377,0.000034276363,0.0006362646,0.0000011447777,0.00008277318,0.0000041478447],"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":[0.00009954154,0.000055051543,0.87432384,0.000007367939,0.00005671579,0.0000026554033,0.018953163,0.07748798,0.000010860924,0.024001237,0.001301665,0.003699916],"study_design_scores_gemma":[0.0032244879,0.0004724194,0.4431821,0.00037915702,0.0003322868,0.00007769603,0.031161757,0.0049632387,0.0003249509,0.0004323623,0.51461226,0.0008372887],"about_ca_topic_score_codex":0.000041811338,"about_ca_topic_score_gemma":0.0001254176,"teacher_disagreement_score":0.5133106,"about_ca_system_score_codex":0.000081841674,"about_ca_system_score_gemma":0.00010428193,"threshold_uncertainty_score":0.15901239},"labels":[],"label_agreement":null},{"id":"W2140048151","doi":"10.1139/cjce-2014-0054","title":"Bus networks as graphs: new connectivity indicators with operational characteristics","year":2014,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Bus network; Public transport; Transport engineering; Graph; Network analysis; Operations research; Engineering; Theoretical computer science; System bus","score_opus":0.005032703724296516,"score_gpt":0.1942867198074674,"score_spread":0.18925401608317088,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2140048151","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3613939,0.00015565714,0.6309597,0.0010770484,0.0010934942,0.000098885386,0.0000072115886,0.000037853722,0.00517626],"genre_scores_gemma":[0.99889934,0.000013756835,0.0005694006,0.000097416254,0.00032055937,5.032568e-7,0.0000065722484,0.000009147441,0.000083313294],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99945104,0.00002385053,0.00015025033,0.000055525474,0.00014831756,0.000171038],"domain_scores_gemma":[0.9991564,0.000063524356,0.00012379595,0.000038970873,0.000095958,0.0005213449],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000311219,0.00006277517,0.00010546212,0.00022728954,0.00018456065,0.00007946587,0.00009464699,0.000056209185,0.00013395115],"category_scores_gemma":[0.00024748585,0.00006154248,0.000026059077,0.00026671233,0.00003479443,0.00018292095,6.0449645e-7,0.0001515289,0.0000012720836],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010455588,0.0000052481805,0.120068654,0.0000065236213,0.000046204193,0.00003629077,0.006328343,0.83058375,0.0000054056995,0.039954916,0.0009374014,0.002016832],"study_design_scores_gemma":[0.0012251644,0.00029179637,0.77623504,0.00042574428,0.00010530839,0.00007332673,0.0009614702,0.03606461,0.00002297386,0.00046150436,0.18348229,0.00065078435],"about_ca_topic_score_codex":0.003477349,"about_ca_topic_score_gemma":0.13892478,"teacher_disagreement_score":0.7945191,"about_ca_system_score_codex":0.000059476246,"about_ca_system_score_gemma":0.0010239866,"threshold_uncertainty_score":0.8767876},"labels":[],"label_agreement":null},{"id":"W2140531852","doi":"10.1002/atr.5670390202","title":"Improved modeling of park-and-ride transfer time: Capturing the within-day dynamics","year":2010,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"University of Toronto","keywords":"Transfer (computing); Queueing theory; Attractiveness; Transfer station; Arrival time; Travel time; Simulation; Transport engineering; Value of time; Computer science; Operations research; Engineering","score_opus":0.006941164337162834,"score_gpt":0.24577883754626842,"score_spread":0.2388376732091056,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2140531852","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.90823084,0.00005491199,0.090595655,0.00046462024,0.00038813148,0.00012441086,0.000014306118,0.000016099595,0.00011099507],"genre_scores_gemma":[0.99207175,0.00011620259,0.007623231,0.000025304382,0.00008127016,0.0000019506633,0.000020686684,0.0000114328195,0.000048154197],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99888337,0.000044955326,0.0005585042,0.00009232539,0.00029751848,0.00012330718],"domain_scores_gemma":[0.9991741,0.000105574814,0.00025916405,0.00006753273,0.00032224675,0.000071374416],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000821043,0.000087189124,0.0001691095,0.00007914101,0.00016945215,0.00002128087,0.000119217664,0.00008338358,0.000015825271],"category_scores_gemma":[0.000058930767,0.000067488356,0.000080797625,0.00016113475,0.00010402142,0.00047970997,3.919453e-7,0.00030121213,2.3648957e-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":[0.00013603832,0.000031053176,0.0021175032,0.000021998998,0.00003349547,0.000003325031,0.06571892,0.91061014,0.0132835535,0.0051374245,0.0000013670622,0.0029052077],"study_design_scores_gemma":[0.007436688,0.0005964971,0.14490476,0.0006651848,0.0012112751,0.000022818509,0.073126234,0.74965,0.010579014,0.009411289,0.0011707675,0.0012254758],"about_ca_topic_score_codex":0.00012768939,"about_ca_topic_score_gemma":0.0035323487,"teacher_disagreement_score":0.16096012,"about_ca_system_score_codex":0.000017336042,"about_ca_system_score_gemma":0.00011859783,"threshold_uncertainty_score":0.27520952},"labels":[],"label_agreement":null},{"id":"W2140917754","doi":"10.5267/j.ijiec.2011.04.004","title":"Pricing for freight carriers in a competitive environment: A game theory approach","year":2011,"lang":"en","type":"article","venue":"International Journal of Industrial Engineering Computations","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Profit (economics); Game theory; Industrial organization; Competition (biology); Microeconomics; Business; Economics","score_opus":0.03866667599235438,"score_gpt":0.25701314171378237,"score_spread":0.218346465721428,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2140917754","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026348086,0.000041321575,0.9680463,0.00015558512,0.001131651,0.00021735688,0.00002437261,0.0000301099,0.0040052338],"genre_scores_gemma":[0.97111684,0.000023501563,0.028395208,0.000020849679,0.00037095384,0.000008483434,0.00002144554,0.000009502803,0.00003324072],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991379,0.000054152,0.0003456665,0.00007889054,0.00026843246,0.000114967166],"domain_scores_gemma":[0.99925226,0.00032182757,0.00019276518,0.000028811915,0.00013984628,0.00006447658],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005928641,0.00006827202,0.00011370023,0.00032331262,0.000051224928,0.000032097374,0.00019828847,0.00007481021,0.000032504544],"category_scores_gemma":[0.00035897337,0.000072688184,0.00006907681,0.00013187871,0.0000421139,0.00021380953,0.000005238413,0.0001743261,8.6193e-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":[0.000113310285,0.00010058578,0.0021217598,0.0000026957987,0.000116563955,0.000012194221,0.030531602,0.834663,0.0000132701225,0.128969,0.000038788014,0.003317288],"study_design_scores_gemma":[0.03769203,0.0013482162,0.06505364,0.002497069,0.0006871002,0.00016751038,0.09575553,0.602798,0.0013500123,0.038863145,0.1508299,0.0029578668],"about_ca_topic_score_codex":0.000067504865,"about_ca_topic_score_gemma":0.0000076073925,"teacher_disagreement_score":0.9447687,"about_ca_system_score_codex":0.00015021313,"about_ca_system_score_gemma":0.00014485164,"threshold_uncertainty_score":0.2964138},"labels":[],"label_agreement":null},{"id":"W2141331411","doi":"10.1061/(asce)te.1943-5436.0000493","title":"AHP-Based Approach for Location Planning of Pedestrian Zones: Application in Montréal, Canada","year":2012,"lang":"en","type":"article","venue":"Journal of Transportation Engineering","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":40,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Concordia University","funders":"","keywords":"Analytic hierarchy process; Pedestrian; Downtown; Transport engineering; Computer science; Operations research; Site selection; Environmental planning; Geography; Engineering; Political science; Archaeology","score_opus":0.013160996905735288,"score_gpt":0.24215132969901934,"score_spread":0.22899033279328404,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2141331411","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1750471,0.0004360792,0.8240783,0.000081951264,0.00013987384,0.0001645547,0.000011604139,0.000010624447,0.000029907622],"genre_scores_gemma":[0.96913505,0.000015641148,0.030611476,0.000012083289,0.00009624891,0.000014292055,0.0001011251,0.000009838372,0.000004232819],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99901867,0.000016652948,0.00047907198,0.000056516936,0.0002674897,0.00016162444],"domain_scores_gemma":[0.99924374,0.00010026543,0.0003195258,0.00004219395,0.0002116198,0.000082651706],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000578395,0.00006822858,0.00015036005,0.00017007846,0.00004871291,0.000006882139,0.00007229637,0.000058087822,0.0000017308962],"category_scores_gemma":[0.000051896608,0.000074773714,0.0000409653,0.00032995347,0.000010760266,0.00029084468,1.6161059e-7,0.0000795922,3.5713562e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003448389,0.000041462394,0.06584055,0.00009687315,0.000008454733,4.1271792e-7,0.0035721955,0.9294575,0.00011177753,0.00052249985,0.00003327199,0.00028055042],"study_design_scores_gemma":[0.0027846976,0.00009238929,0.7464038,0.0003600176,0.00015786632,0.0000014095164,0.0102767125,0.22864234,0.001223931,0.00002875067,0.009605053,0.0004230087],"about_ca_topic_score_codex":0.045070063,"about_ca_topic_score_gemma":0.052562524,"teacher_disagreement_score":0.79408795,"about_ca_system_score_codex":0.00011191422,"about_ca_system_score_gemma":0.0003597413,"threshold_uncertainty_score":0.96472573},"labels":[],"label_agreement":null},{"id":"W2141333550","doi":"10.1109/cec.2007.4424772","title":"A Genetic Algorithm with cycle representation and contraction digraph model for Guideway Network design of Personal Rapid Transit","year":2007,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Digraph; Representation (politics); Genetic algorithm; Computer science; Contraction (grammar); Locality; Algorithm; Set (abstract data type); Directed graph; Theoretical computer science; Mathematical optimization; Mathematics; Discrete mathematics; Machine learning","score_opus":0.02975678994113964,"score_gpt":0.29178854472187926,"score_spread":0.26203175478073965,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2141333550","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016402101,0.00008398392,0.9824942,0.00011452408,0.00003542021,0.00036274345,0.0000057239213,0.00003727691,0.0004640234],"genre_scores_gemma":[0.60026556,0.000066001114,0.39936352,0.000039046277,0.000048935497,0.000010029307,0.00001289113,0.0000052233218,0.00018879659],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99934965,0.00003236891,0.00016683897,0.0001335009,0.00017182165,0.00014581002],"domain_scores_gemma":[0.9995347,0.00016761804,0.0000781781,0.00003449375,0.0001324219,0.000052562762],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049156195,0.000053710766,0.00008525399,0.000044120807,0.00020485761,0.000018949968,0.00002733094,0.000053983847,0.000014581678],"category_scores_gemma":[0.000012683182,0.000048565274,0.000024691486,0.00016591685,0.000078892284,0.00014462433,4.6622796e-7,0.0000265861,1.2554297e-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":[0.00023780111,0.00002711077,0.002761364,0.0000067322667,0.000024777404,9.832174e-7,0.012883832,0.9469339,0.00008695092,0.00081377925,0.00019107587,0.036031652],"study_design_scores_gemma":[0.0006599611,0.00007683743,0.015289139,0.000012378343,0.000047056743,0.0000011289128,0.0023052513,0.98069346,0.00009320233,0.000659652,0.000075897486,0.00008604187],"about_ca_topic_score_codex":0.0003690299,"about_ca_topic_score_gemma":0.00058076135,"teacher_disagreement_score":0.5838635,"about_ca_system_score_codex":0.000009448707,"about_ca_system_score_gemma":0.000052078154,"threshold_uncertainty_score":0.19804344},"labels":[],"label_agreement":null},{"id":"W2141538651","doi":"10.1016/j.trb.2011.05.026","title":"Bounded rationality and irreversible network change","year":2011,"lang":"en","type":"article","venue":"Transportation Research Part B Methodological","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":114,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"National Science Foundation","keywords":"Bounded rationality; Phenomenon; Bounded function; Separable space; Computer science; Mathematical optimization; Network model; Mathematical economics; Flow network; Mathematics; Physics; Artificial intelligence","score_opus":0.8196598081722887,"score_gpt":0.5330015503462245,"score_spread":0.28665825782606413,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2141538651","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.83112425,0.00070054445,0.10626023,0.005421969,0.000958616,0.0024937969,0.0001049594,0.0007251419,0.052210514],"genre_scores_gemma":[0.8685482,0.0008574277,0.12836677,0.0003347014,0.0004409167,0.00020242434,0.00022025313,0.000012779188,0.0010165193],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9960799,0.0022444047,0.00027605394,0.00032226613,0.0006272805,0.00045005674],"domain_scores_gemma":[0.99821925,0.0010717046,0.000065704626,0.000108836066,0.0003134836,0.0002210325],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.008308317,0.00009206835,0.00017063308,0.0000907691,0.00094895443,0.00004519128,0.00013922315,0.00018064142,0.0015679711],"category_scores_gemma":[0.00041466637,0.00008605608,0.000046801597,0.0005921414,0.0006060832,0.0003457022,0.0000033423173,0.000281918,0.000024071811],"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":[0.00029684443,0.00013137747,0.29537582,0.000031883836,0.000031768977,0.000030440808,0.054737885,0.000116505056,0.000019933354,0.64282,0.0027452935,0.0036622072],"study_design_scores_gemma":[0.0002919509,0.00008690388,0.9154236,0.000020667621,0.000016353206,2.2715383e-7,0.0026903437,0.000045211837,0.000035509936,0.034711387,0.046535537,0.00014236273],"about_ca_topic_score_codex":0.0023463327,"about_ca_topic_score_gemma":0.0038342539,"teacher_disagreement_score":0.6200477,"about_ca_system_score_codex":0.000027109003,"about_ca_system_score_gemma":0.00010497746,"threshold_uncertainty_score":0.9993447},"labels":[],"label_agreement":null},{"id":"W2143138030","doi":"10.1002/atr.1331","title":"Welfare maximization for bus transit systems with timed transfers and financial constraints","year":2015,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Universidad del Atlántico; Morgan State University; Pennsylvania State University","keywords":"Price elasticity of demand; Subsidy; Bounded function; Maximization; Mathematical optimization; Welfare; Service (business); Computer science; Transfer (computing); Elasticity (physics); Economic surplus; Genetic algorithm; Public transport; Service level; Operations research; Economics; Microeconomics; Business; Transport engineering; Engineering; Mathematics; Economy; Marketing","score_opus":0.014903095274666534,"score_gpt":0.2578230168629149,"score_spread":0.24291992158824838,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2143138030","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.38244566,0.00025352588,0.61435795,0.0012950049,0.00058106595,0.00065879733,0.00010423544,0.000048211892,0.0002555479],"genre_scores_gemma":[0.98582953,0.000109047665,0.013766012,0.000030930973,0.00009939574,0.000012104591,0.00010639687,0.0000143479665,0.00003224528],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99885917,0.00004740216,0.00042576203,0.00012598083,0.00038110168,0.00016056717],"domain_scores_gemma":[0.9987942,0.000051031086,0.00019828322,0.00003996981,0.00073405524,0.0001824382],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043019862,0.00010889055,0.00021905088,0.00012729126,0.00019932058,0.00004016484,0.00006430395,0.00009608087,0.0000064686783],"category_scores_gemma":[0.000052777985,0.00009878448,0.00004958194,0.00022062736,0.00014320071,0.00064335123,1.1241495e-7,0.000093785944,2.0087961e-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":[0.0021087984,0.00008045421,0.0034104253,0.00011188445,0.000050338116,0.000029871258,0.059053373,0.9031709,0.00015353323,0.017407829,0.00007269627,0.014349901],"study_design_scores_gemma":[0.07589721,0.010250767,0.4906448,0.0024147062,0.0027287055,0.00015648647,0.29824305,0.00427261,0.0013617313,0.0045216004,0.10641333,0.0030950196],"about_ca_topic_score_codex":0.000041249245,"about_ca_topic_score_gemma":0.00040533885,"teacher_disagreement_score":0.8988983,"about_ca_system_score_codex":0.00004594666,"about_ca_system_score_gemma":0.0003085033,"threshold_uncertainty_score":0.40283144},"labels":[],"label_agreement":null},{"id":"W2143248238","doi":"10.3141/1776-05","title":"Examination of Effect of Driver Population at Freeway Reconstruction Zones","year":2001,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"McMaster University","funders":"","keywords":"Population; Transport engineering; Statistics; Environmental science; Engineering; Mathematics; Environmental health; Medicine","score_opus":0.05721870621678372,"score_gpt":0.38124948123335506,"score_spread":0.32403077501657135,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2143248238","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9954166,0.00014753942,0.0013995983,0.00068686163,0.000650548,0.00092606625,0.000049946426,0.000028289174,0.00069458626],"genre_scores_gemma":[0.99611205,0.0014851668,0.0011875053,0.0000058494347,0.00015664683,0.000028994991,0.00006300822,0.00003368119,0.00092710566],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99024427,0.0031432165,0.0016994615,0.00032970865,0.0040001343,0.0005831982],"domain_scores_gemma":[0.99239916,0.0017100434,0.0011657738,0.0003316506,0.0041468963,0.00024650048],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008634674,0.0002095239,0.0005423627,0.0013224332,0.0008656844,0.000047129153,0.00065857696,0.0002971772,0.0005136755],"category_scores_gemma":[0.0004332996,0.00016999333,0.00038679934,0.002838476,0.0010149593,0.00095449336,0.000004294619,0.0008795683,0.000006877363],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018019303,0.00015170847,0.95130485,0.0002579296,0.000105864834,0.000018679051,0.011486719,0.0046172477,0.002791949,0.0029975432,0.00041656214,0.02404901],"study_design_scores_gemma":[0.0016973646,0.0006997469,0.988152,0.00046810502,0.0000896442,7.0266594e-7,0.0025673655,0.00015777592,0.0032888723,0.00076186575,0.0019677593,0.00014875273],"about_ca_topic_score_codex":0.023166819,"about_ca_topic_score_gemma":0.084297515,"teacher_disagreement_score":0.06113069,"about_ca_system_score_codex":0.00034572624,"about_ca_system_score_gemma":0.00033945913,"threshold_uncertainty_score":0.983338},"labels":[],"label_agreement":null},{"id":"W2143794766","doi":"10.1002/atr.1203","title":"Development of a transfer‐cost‐based logit assignment model for the Beijing rail transit network using automated fare collection data","year":2012,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"National Key Research and Development Program of China; State Key Laboratory of Rail Traffic Control and Safety; National Natural Science Foundation of China","keywords":"Beijing; Transfer (computing); Transfer station; Computer science; Revenue; Flow network; Mixed logit; Logit; Public transport; Travel behavior; Operations research; Transit (satellite); Transport engineering; Function (biology); Logistic regression; Mathematical optimization; Engineering; Mathematics; Economics; Machine learning","score_opus":0.10021671493472314,"score_gpt":0.3514044512221114,"score_spread":0.2511877362873882,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2143794766","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12107722,0.00021878425,0.8775718,0.00016169088,0.00036463203,0.0004907558,0.000061474726,0.00004370974,0.000009904933],"genre_scores_gemma":[0.7802627,0.000053166914,0.21932118,0.000031943106,0.00009317874,0.0000120559525,0.00020540538,0.000013925548,0.000006454636],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998243,0.00006079449,0.0008030171,0.00012673074,0.0004905451,0.00027592698],"domain_scores_gemma":[0.99884266,0.00018815446,0.00041953425,0.00011627249,0.00033982526,0.00009355222],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013351318,0.0001228273,0.00022589223,0.00009552895,0.00059073354,0.000021472633,0.00021557571,0.00009264965,0.000009856654],"category_scores_gemma":[0.000032654127,0.00010327376,0.00009400698,0.00038887007,0.000053223153,0.0008582656,6.75874e-7,0.00011731298,8.871871e-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":[0.0003275709,0.00006750314,0.001338146,0.000038664235,0.000061514904,4.0698407e-7,0.03407618,0.95988464,0.0009365477,0.00024728858,0.00002697939,0.002994527],"study_design_scores_gemma":[0.0029037497,0.00007748071,0.028304512,0.0003407947,0.00059736334,0.0000011489497,0.01300557,0.9497543,0.0011518428,0.000075643075,0.0034861406,0.00030148568],"about_ca_topic_score_codex":0.000021715938,"about_ca_topic_score_gemma":0.0010698123,"teacher_disagreement_score":0.65918547,"about_ca_system_score_codex":0.0001199883,"about_ca_system_score_gemma":0.00055801304,"threshold_uncertainty_score":0.45435044},"labels":[],"label_agreement":null},{"id":"W2144165524","doi":"10.3141/2274-05","title":"Analyzing Passenger Incidence Behavior in Heterogeneous Transit Services Using Smartcard Data and Schedule-Based Assignment","year":2012,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":37,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vancouver Community College; University of British Columbia","funders":"","keywords":"Incidence (geometry); Reliability (semiconductor); Schedule; Headway; Service (business); Computer science; Transit (satellite); Public transport; Transport engineering; Operations research; Simulation; Engineering; Mathematics; Business","score_opus":0.16180598825114673,"score_gpt":0.43448519014310844,"score_spread":0.2726792018919617,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2144165524","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98997027,0.00074379914,0.006540675,0.0013317732,0.00037880108,0.00087734027,0.00009768021,0.000026539474,0.000033100772],"genre_scores_gemma":[0.9930605,0.00072203577,0.005789187,0.000042049833,0.00019585747,0.000038878923,0.00006623646,0.00004050289,0.00004473291],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9919923,0.0020669105,0.0012442732,0.00044162327,0.0031832422,0.0010716297],"domain_scores_gemma":[0.9962097,0.0008805646,0.00043228877,0.00050178415,0.0014320028,0.0005436526],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009425426,0.0002331128,0.0004102106,0.0011013543,0.0010244411,0.0002121979,0.0011358388,0.00024675947,0.00014759756],"category_scores_gemma":[0.00012075318,0.0002025458,0.00016146166,0.0020751928,0.0006599649,0.0017917403,0.000008214782,0.0014176953,0.000003976906],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004136228,0.00027220225,0.9710385,0.00014689374,0.00004811076,0.0000623846,0.009398438,0.015008172,0.0012133038,0.00032569,0.000046684243,0.0020259563],"study_design_scores_gemma":[0.0011775234,0.00014727253,0.9856983,0.0004613152,0.0001262474,6.061331e-7,0.0064075473,0.002670309,0.00041754122,0.000120903525,0.002528037,0.00024440888],"about_ca_topic_score_codex":0.027881771,"about_ca_topic_score_gemma":0.17668562,"teacher_disagreement_score":0.14880385,"about_ca_system_score_codex":0.00030906903,"about_ca_system_score_gemma":0.0008150357,"threshold_uncertainty_score":0.9785916},"labels":[],"label_agreement":null},{"id":"W2144885871","doi":"10.1007/0-387-24109-4_1","title":"Application of a Simulation-Based Dynamic Traffic Assignment Model","year":2006,"lang":"en","type":"book-chapter","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":20,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Inro Consultants (Canada)","funders":"","keywords":"Computer science; Queueing theory; Computation; Mathematical optimization; Path (computing); Set (abstract data type); Iterative method; Line (geometry); Travel time; Flow network; Traffic flow (computer networking); Simulation; Traffic simulation; Traffic generation model; Algorithm; Real-time computing; Engineering; Transport engineering; Mathematics; Computer network","score_opus":0.015659338857314273,"score_gpt":0.2774884098922508,"score_spread":0.2618290710349365,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2144885871","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000030401307,0.000021628754,0.7163328,0.00008533541,0.000023573322,0.0002631608,0.000040791456,0.000092339076,0.28311],"genre_scores_gemma":[0.76861155,0.0000075251,0.0032941904,0.00003555064,0.000020084313,0.00001042152,0.0006571697,0.000018342342,0.22734518],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989774,0.000011367472,0.0003081368,0.00019702209,0.000407854,0.00009821698],"domain_scores_gemma":[0.9993259,0.00012830924,0.00025708927,0.000108887696,0.0001402043,0.00003966245],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014679573,0.00012160079,0.00015816408,0.00011678054,0.00012066402,0.000014010503,0.00008616372,0.00024689003,0.00013529547],"category_scores_gemma":[0.0000073161636,0.00013049631,0.00008252706,0.000039166636,0.00008800224,0.000046938712,0.0000013713039,0.00007498568,0.00001292176],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000060149164,0.000017141283,0.00001110922,0.000012860134,0.0000063482426,1.3843035e-7,0.00026110973,0.9623227,0.0000018976075,0.034308363,0.00011576052,0.0029365707],"study_design_scores_gemma":[0.00013174558,0.000009091615,0.000030970004,0.000025873755,0.000039174076,4.354769e-9,0.000015157758,0.98950666,0.0000012140049,0.0013486051,0.008756135,0.00013536502],"about_ca_topic_score_codex":0.00015889226,"about_ca_topic_score_gemma":0.00086281693,"teacher_disagreement_score":0.76858115,"about_ca_system_score_codex":0.000105365536,"about_ca_system_score_gemma":0.00022928251,"threshold_uncertainty_score":0.5321485},"labels":[],"label_agreement":null},{"id":"W2145000410","doi":"10.1007/978-90-481-8572-6_8","title":"Multiscale Modeling of Virtual Urban Environments and Associated Populations","year":2010,"lang":"en","type":"book-chapter","venue":"˜The œGeojournal library","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"TRIPS architecture; Computer science; Modal; VisSim; Taxis; Population; Matching (statistics); Scale (ratio); Service (business); Bipartite graph; Transport engineering; Operations research; Geography; Theoretical computer science; Microsimulation; Cartography; Engineering; Mathematics","score_opus":0.024524965334329348,"score_gpt":0.23606219772602305,"score_spread":0.2115372323916937,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2145000410","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04417155,0.0021809447,0.016005715,0.0028365988,0.0021776294,0.0011125114,0.0011777816,0.00032387106,0.9300134],"genre_scores_gemma":[0.6764557,0.00038491152,0.0011130802,0.000112305235,0.00028577395,0.0000021976653,0.00035781297,0.000046591336,0.32124165],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99896556,0.00005178654,0.0003435962,0.00014544708,0.0003534547,0.00014017896],"domain_scores_gemma":[0.99937844,0.0000718811,0.0003258816,0.000104067105,0.000016730111,0.000103025166],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019691739,0.00013401084,0.00017914813,0.00009028539,0.0005641467,0.000064820124,0.00017271405,0.00032769685,0.00038955323],"category_scores_gemma":[0.000011053204,0.00011524621,0.00008276622,0.00003374016,0.00018510481,0.00038005522,0.000014943416,0.0005146035,0.000007693359],"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":[0.00030136507,0.00042875824,0.13209304,0.0000604254,0.0009647148,0.00008208469,0.07532505,0.1659425,0.0003095848,0.59939677,0.010671609,0.01442409],"study_design_scores_gemma":[0.0035167546,0.0003837008,0.06361476,0.0016570727,0.00131741,0.000020211557,0.004180368,0.138203,0.000035887224,0.11587303,0.6683637,0.00283408],"about_ca_topic_score_codex":0.00007035717,"about_ca_topic_score_gemma":0.00022919563,"teacher_disagreement_score":0.65769213,"about_ca_system_score_codex":0.000013746113,"about_ca_system_score_gemma":0.00007828983,"threshold_uncertainty_score":0.4699604},"labels":[],"label_agreement":null},{"id":"W2145197556","doi":"10.3141/2395-13","title":"Quantifying Measurement Error in Arterial Travel Times Measured by Bluetooth Detectors","year":2013,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Bluetooth; Detector; Traffic flow (computer networking); Real-time computing; Computer science; Intelligent transportation system; Reliability (semiconductor); Simulation; Standard deviation; Observational error; Statistics; Engineering; Telecommunications; Transport engineering; Mathematics; Physics; Wireless; Computer network","score_opus":0.17937047653201826,"score_gpt":0.39807849318686295,"score_spread":0.21870801665484468,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2145197556","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9892035,0.000277542,0.0020403222,0.0054225274,0.0008091466,0.0016635257,0.00004897683,0.000045458477,0.0004889785],"genre_scores_gemma":[0.9972999,0.00050443615,0.0011957899,0.000048492355,0.0001896986,0.00013977768,0.000026559757,0.00005378794,0.0005415308],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9871735,0.002637867,0.0017081741,0.00046952808,0.0068274443,0.0011834694],"domain_scores_gemma":[0.99276793,0.00069848786,0.0005305702,0.00033976874,0.005153813,0.0005094472],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.010113379,0.000279055,0.00050774706,0.0011627622,0.0011227439,0.0003244087,0.0010824644,0.00030865145,0.0010497319],"category_scores_gemma":[0.0005473329,0.00023152608,0.00032084115,0.0026870253,0.0008639359,0.0012836342,0.0000029981477,0.0018351587,0.000046687495],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0024244185,0.0011903961,0.8010757,0.00037079092,0.0003263709,0.00010155816,0.07038592,0.0068930737,0.056279473,0.005298011,0.04516537,0.010488951],"study_design_scores_gemma":[0.0020397108,0.0002524166,0.9709802,0.00043108288,0.000037854334,1.3267113e-7,0.014495666,0.00016852275,0.0017907431,0.0012030281,0.008312711,0.00028791372],"about_ca_topic_score_codex":0.06432039,"about_ca_topic_score_gemma":0.3101883,"teacher_disagreement_score":0.24586791,"about_ca_system_score_codex":0.00057680655,"about_ca_system_score_gemma":0.0013247977,"threshold_uncertainty_score":0.99986345},"labels":[],"label_agreement":null},{"id":"W2145518506","doi":"10.3141/2014-11","title":"Creating Synthetic Household Populations","year":2007,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":79,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ministry of Transportation of Ontario","funders":"","keywords":"Albatross; Synthetic data; Preprocessor; Sample (material); Econometrics; Basis (linear algebra); Computer science; Relation (database); Survey data collection; Data mining; Statistics; Mathematics; Algorithm; Ecology; Artificial intelligence; Biology","score_opus":0.17624070000249717,"score_gpt":0.4339967392296098,"score_spread":0.25775603922711265,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2145518506","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9743943,0.00023524792,0.015940908,0.0036541265,0.001148935,0.00095860387,0.000058634952,0.00009630465,0.003512902],"genre_scores_gemma":[0.99051696,0.00042563732,0.006623006,0.00006074148,0.00044332506,0.000023641916,0.000027021995,0.000057802205,0.0018218444],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.990123,0.0014929598,0.0016981266,0.0003979838,0.00506571,0.0012222672],"domain_scores_gemma":[0.99248534,0.0019147353,0.00062324916,0.00041982302,0.003968154,0.0005886926],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.014596876,0.00022599002,0.00037877253,0.0013958876,0.0027608613,0.00022863955,0.0010756177,0.00029398792,0.00034387177],"category_scores_gemma":[0.000723305,0.00018932247,0.00038887045,0.003609826,0.0010932017,0.00092892925,0.0000037436992,0.0020272722,0.000020503743],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010721199,0.00044002064,0.8384171,0.00013733335,0.00014185753,0.00018034429,0.040661033,0.013522291,0.0010630715,0.08862057,0.0060185604,0.009725676],"study_design_scores_gemma":[0.0008501836,0.0001943382,0.94246644,0.00034799578,0.000071854935,3.5207057e-7,0.013116375,0.00005194735,0.00038718173,0.0040770625,0.038215946,0.00022034184],"about_ca_topic_score_codex":0.03390733,"about_ca_topic_score_gemma":0.23582548,"teacher_disagreement_score":0.20191814,"about_ca_system_score_codex":0.00039089343,"about_ca_system_score_gemma":0.0009628237,"threshold_uncertainty_score":0.9985374},"labels":[],"label_agreement":null},{"id":"W2145546752","doi":"10.3141/1917-22","title":"Trip-Chaining Trends in the United States: Understanding Travel Behavior for Policy Making","year":2005,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":109,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Golder Associates (Canada)","funders":"","keywords":"Chaining; TRIPS architecture; Work (physics); Travel behavior; Trip generation; Transport engineering; Business; Travel survey; Demographic economics; Geography; Advertising; Marketing; Economics; Engineering; Psychology","score_opus":0.25968687838892746,"score_gpt":0.48215996064012895,"score_spread":0.2224730822512015,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2145546752","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9213702,0.0001284314,0.03189885,0.043173753,0.00042319443,0.0019108686,0.00017031064,0.00005142931,0.0008729374],"genre_scores_gemma":[0.9950732,0.00062629394,0.0025110955,0.00025189927,0.0004717631,0.00016963214,0.00014658777,0.000047006313,0.00070250814],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.9916564,0.0019373553,0.0014110404,0.0003597111,0.0035173455,0.0011181002],"domain_scores_gemma":[0.9953352,0.00182505,0.00049654546,0.00030128277,0.0018050817,0.00023684262],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.011111901,0.00022881376,0.00036685404,0.0033187044,0.0018395545,0.0002958562,0.0011931703,0.00023479818,0.00014454081],"category_scores_gemma":[0.0003154612,0.0001696661,0.00036654825,0.006487563,0.00080303,0.00077427394,0.0000022983688,0.0016888411,0.0000033367621],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0035304578,0.0011007158,0.26651642,0.00022687578,0.00020374838,0.0001484314,0.36743265,0.11920924,0.00027304102,0.19179426,0.010980849,0.038583323],"study_design_scores_gemma":[0.003322157,0.000499066,0.8165396,0.0004365327,0.00008766416,6.153981e-7,0.12299764,0.0016998292,0.00005596415,0.005880468,0.048151758,0.00032870946],"about_ca_topic_score_codex":0.027755154,"about_ca_topic_score_gemma":0.21769999,"teacher_disagreement_score":0.5500232,"about_ca_system_score_codex":0.00088011555,"about_ca_system_score_gemma":0.000940726,"threshold_uncertainty_score":0.9994599},"labels":[],"label_agreement":null},{"id":"W2145604114","doi":"10.1139/l06-127","title":"Genetically-optimized origin-destination estimation (GOODE) model: application to regional commodity movements in Ontario","year":2007,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Estimation; Commodity; Matrix (chemical analysis); Operations research; Mathematics; Economics","score_opus":0.019395964549170044,"score_gpt":0.25524816416688506,"score_spread":0.23585219961771503,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2145604114","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1415039,0.000012814392,0.8564804,0.00033854664,0.00013282226,0.0001299448,0.0000019799825,0.000013142473,0.0013864591],"genre_scores_gemma":[0.9533337,0.0000027514325,0.046398472,0.00009419825,0.00004653921,0.0000032230482,0.00001707654,0.000008859839,0.0000951858],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99905825,0.0000150339365,0.00034182263,0.00008680706,0.00025775234,0.00024034729],"domain_scores_gemma":[0.9992065,0.00005118635,0.000104358165,0.000059209757,0.00018687875,0.0003919081],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00093914283,0.00007322162,0.00010846922,0.000404317,0.00011363996,0.00003948347,0.00012414297,0.000069431466,0.000032269596],"category_scores_gemma":[0.000120334684,0.00008929408,0.000029223378,0.0002821419,0.000018615956,0.00020691908,0.0000014221085,0.00016671956,0.0000018980779],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010413821,0.0000071710774,0.0076450026,0.0000029875605,0.000004022075,0.0000056869694,0.004995698,0.98374254,0.00005266713,0.0027644108,0.00008283876,0.0006865756],"study_design_scores_gemma":[0.000811176,0.00004032449,0.1674858,0.00014550479,0.000014273665,0.000004707799,0.00031393915,0.8214012,0.000045602195,0.0011036054,0.008376304,0.0002575804],"about_ca_topic_score_codex":0.063720986,"about_ca_topic_score_gemma":0.9603834,"teacher_disagreement_score":0.8966625,"about_ca_system_score_codex":0.0008348437,"about_ca_system_score_gemma":0.0010020302,"threshold_uncertainty_score":0.94251376},"labels":[],"label_agreement":null},{"id":"W2147299164","doi":"10.3141/2185-10","title":"Modeling Travelers' Responses to Incident Information Provided by Variable Message Signs in Calgary, Canada","year":2010,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":37,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Alberta; University of Calgary","funders":"Centre for Transportation Engineering and Planning","keywords":"Destinations; Discrete choice; Transport engineering; Variable (mathematics); Latent variable; Travel behavior; Econometric model; Computer science; Engineering; Geography; Tourism","score_opus":0.042778027570188025,"score_gpt":0.35490179751531503,"score_spread":0.312123769945127,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2147299164","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9769583,0.000036760164,0.013749005,0.006383093,0.0007637498,0.0014224038,0.00014368678,0.00003229031,0.0005107273],"genre_scores_gemma":[0.9944842,0.00018004891,0.0040156897,0.00019164408,0.00010758174,0.00010604389,0.00007670317,0.000032203727,0.00080589665],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9907875,0.0014281664,0.001667018,0.0003361112,0.0047534043,0.0010277837],"domain_scores_gemma":[0.9939717,0.0010205598,0.00034212778,0.00034333344,0.0036874178,0.00063486304],"candidate_categories":["research_integrity"],"consensus_categories":[],"category_scores_codex":[0.00830115,0.00023059908,0.00038635763,0.0011907005,0.0010900563,0.0002693442,0.0011210401,0.000272617,0.0003069569],"category_scores_gemma":[0.0010974983,0.0002037317,0.00013953244,0.003202224,0.0002698884,0.0016121574,0.00000487563,0.002562566,0.0000086991795],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0067395563,0.0006545203,0.6205591,0.0004173931,0.00018519889,0.0001923042,0.09644534,0.17388332,0.022073543,0.028692272,0.041402645,0.008754811],"study_design_scores_gemma":[0.0042986604,0.00083170325,0.7962566,0.00071932946,0.00007949152,6.571565e-7,0.03345937,0.007488311,0.0016133984,0.0026869592,0.1516526,0.00091289374],"about_ca_topic_score_codex":0.884876,"about_ca_topic_score_gemma":0.985417,"teacher_disagreement_score":0.17569752,"about_ca_system_score_codex":0.000568297,"about_ca_system_score_gemma":0.0058386005,"threshold_uncertainty_score":0.99979734},"labels":[],"label_agreement":null},{"id":"W2147630773","doi":"10.3141/2133-12","title":"Sensitivity Testing with the Oregon Statewide Integrated Model","year":2009,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Microsimulation; Metropolitan area; Transport engineering; Land use; Population; Transportation planning; Sensitivity (control systems); Vehicle miles of travel; Work (physics); Engineering; Operations research; Geography; Civil engineering","score_opus":0.11477128831007942,"score_gpt":0.39318752627670045,"score_spread":0.278416237966621,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2147630773","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9495032,0.000073446085,0.027205419,0.020999897,0.00017835031,0.001015781,0.00005802305,0.000078094236,0.000887825],"genre_scores_gemma":[0.9907146,0.00027180152,0.007409638,0.00019196441,0.00015744621,0.000023389799,0.000028363982,0.000035044468,0.001167755],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99080706,0.0024343878,0.0009532088,0.00038270355,0.0044368263,0.0009858218],"domain_scores_gemma":[0.98988885,0.0020430968,0.00053026894,0.00038580233,0.006772349,0.00037962676],"candidate_categories":["sts","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.009801803,0.00024816676,0.00036680073,0.00062411575,0.0023821238,0.0002931694,0.000841062,0.0001912294,0.00006739027],"category_scores_gemma":[0.0005135705,0.00014965232,0.00021144471,0.003935738,0.0011688949,0.00094298524,0.0000021427995,0.0023927302,0.0000084077255],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.004516885,0.00069583143,0.2821937,0.00012701341,0.00025935972,0.00043872074,0.07521489,0.54536146,0.0045271693,0.031816088,0.019755306,0.035093598],"study_design_scores_gemma":[0.0015374398,0.0008038058,0.95532465,0.00049606967,0.00010374702,9.573266e-7,0.016491178,0.006350807,0.00051877456,0.0040240893,0.014009709,0.00033878896],"about_ca_topic_score_codex":0.020091187,"about_ca_topic_score_gemma":0.17889304,"teacher_disagreement_score":0.6731309,"about_ca_system_score_codex":0.00027736934,"about_ca_system_score_gemma":0.0019841609,"threshold_uncertainty_score":0.9999088},"labels":[],"label_agreement":null},{"id":"W2148110619","doi":"10.3141/1981-07","title":"Advanced Activity-Based Models in Context of Planning Decisions","year":2006,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":45,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"TRIPS architecture; Context (archaeology); Metropolitan area; Transportation planning; Consistency (knowledge bases); Microsimulation; Operations research; Computer science; Travel behavior; Atlanta; Transport engineering; Geography; Engineering; Artificial intelligence","score_opus":0.12940937687238843,"score_gpt":0.4238619702336571,"score_spread":0.2944525933612687,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2148110619","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.980739,0.00022849717,0.014583404,0.0023718763,0.00030460447,0.0008238133,0.000070606366,0.00002755389,0.00085066963],"genre_scores_gemma":[0.99520653,0.0002890581,0.0039575873,0.000027648093,0.000087538356,0.000044612003,0.000030342671,0.00003502003,0.00032168123],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9919278,0.0016089865,0.0014455558,0.00034088793,0.0038906173,0.00078616163],"domain_scores_gemma":[0.99298954,0.0024954993,0.0006395921,0.00031151096,0.0033294368,0.00023444109],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0057123285,0.00019445027,0.0004700892,0.0015835539,0.00073282566,0.000082611085,0.0008508156,0.00023836442,0.00010130892],"category_scores_gemma":[0.0003031446,0.00016737165,0.00031264624,0.0032299787,0.0008562668,0.0010063968,0.0000024341227,0.0014989051,0.0000034233992],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0026324354,0.00062234944,0.37072545,0.00010961951,0.00004365574,0.00009755084,0.016636081,0.5742739,0.002279831,0.022375358,0.0012490479,0.008954734],"study_design_scores_gemma":[0.002922246,0.00032253424,0.9645985,0.0009747524,0.000032444706,8.289405e-8,0.01107229,0.0021844606,0.0018952956,0.009786665,0.0059799193,0.0002308331],"about_ca_topic_score_codex":0.049331963,"about_ca_topic_score_gemma":0.23578554,"teacher_disagreement_score":0.593873,"about_ca_system_score_codex":0.00027287228,"about_ca_system_score_gemma":0.0015071675,"threshold_uncertainty_score":0.9569986},"labels":[],"label_agreement":null},{"id":"W2148508529","doi":"10.3141/2263-08","title":"Day-to-Day Dynamic Model in Discrete–Continuum Transportation Networks","year":2011,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"National Science Foundation","keywords":"Disequilibrium; Flow network; Mathematical optimization; Computer science; Network model; Inertia; Minification; Discrete system; Mathematics; Algorithm; Physics; Classical mechanics; Artificial intelligence","score_opus":0.09171977974889209,"score_gpt":0.39251301554942236,"score_spread":0.3007932358005303,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2148508529","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9053772,0.0001857965,0.08721569,0.0029562707,0.0009488031,0.0019255169,0.00013454334,0.00009019982,0.0011659471],"genre_scores_gemma":[0.9899641,0.0012987981,0.0062023164,0.00010660079,0.00015934023,0.00014526973,0.00009217712,0.0000896807,0.0019417105],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9889687,0.0024564802,0.0022076624,0.0006950565,0.0041011274,0.0015710257],"domain_scores_gemma":[0.9937469,0.0011112678,0.0006210301,0.00053320185,0.0031940455,0.0007935704],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.011268427,0.00038229898,0.00064007705,0.0019391926,0.0011626658,0.00017147796,0.0016104017,0.00044330285,0.0004016173],"category_scores_gemma":[0.0003973337,0.00033554254,0.00045736146,0.0046100123,0.00087293657,0.0013981636,0.000004069614,0.0025084051,0.000025243029],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002573845,0.0007674302,0.58254564,0.00018460974,0.00017994642,0.0002786981,0.1677572,0.21625412,0.00069899746,0.016545765,0.004702511,0.0075112674],"study_design_scores_gemma":[0.0015061228,0.00033940488,0.9747229,0.0005635562,0.00007108145,1.2181764e-7,0.009057885,0.0050908923,0.00014235488,0.0043555107,0.003748182,0.00040198883],"about_ca_topic_score_codex":0.029601017,"about_ca_topic_score_gemma":0.63424593,"teacher_disagreement_score":0.60464495,"about_ca_system_score_codex":0.0004819986,"about_ca_system_score_gemma":0.0013935185,"threshold_uncertainty_score":0.99990964},"labels":[],"label_agreement":null},{"id":"W2148796839","doi":"","title":"Information provision and driver compliance to advanced traveller information system application: case study on the interaction between variable message sign and other sources of traffic updates in Calgary, Canada","year":2011,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Variable (mathematics); Compliance (psychology); Computer science; Sign (mathematics); Computer security; Transport engineering; Information system; Telecommunications; Operations research; Engineering; Psychology; Mathematics; Social psychology","score_opus":0.014623637626740918,"score_gpt":0.2131995473828847,"score_spread":0.1985759097561438,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2148796839","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9837023,0.0000143616035,0.015392448,0.000057476016,0.00006860223,0.00029092786,0.000010501381,0.000005458705,0.00045791405],"genre_scores_gemma":[0.9997369,0.000001026244,0.00021203919,0.000025927062,0.000011581947,0.0000056920076,0.0000018586614,0.000002953473,0.0000020320933],"study_design_codex":"simulation_or_modeling","study_design_gemma":"qualitative","domain_scores_codex":[0.9994607,0.00002826463,0.00027535978,0.00003341214,0.00011442965,0.000087811175],"domain_scores_gemma":[0.9995518,0.00005618328,0.00014520418,0.00004084586,0.00008852671,0.00011744503],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034070775,0.000055442983,0.000094313225,0.00017056754,0.00010324736,0.000032774984,0.00005674734,0.000026757067,0.000007149668],"category_scores_gemma":[0.000037881324,0.000047641242,0.000006758883,0.00018064331,0.000013976951,0.00047934425,0.0000014766408,0.00008294121,2.2091949e-7],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000034639397,0.000010358909,0.060969114,0.00011878804,0.000037005888,0.000024920153,0.2541493,0.6746782,0.0000063223442,0.0014563539,0.0000795019,0.008435499],"study_design_scores_gemma":[0.0020438689,0.0005050602,0.35948086,0.0015202357,0.00010218198,0.00015112256,0.5322296,0.086599715,0.00012077164,0.000013754571,0.016643902,0.0005889089],"about_ca_topic_score_codex":0.24545927,"about_ca_topic_score_gemma":0.75913316,"teacher_disagreement_score":0.5880785,"about_ca_system_score_codex":0.00010628084,"about_ca_system_score_gemma":0.00022576463,"threshold_uncertainty_score":0.7595653},"labels":[],"label_agreement":null},{"id":"W2149247234","doi":"10.3141/2135-12","title":"Implementation of Scheduling Conflict Resolution Model in Activity-Scheduling System","year":2009,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"National Science Foundation","keywords":"Computer science; Scheduling (production processes); Conflict resolution; Fair-share scheduling; Dynamic priority scheduling; Rate-monotonic scheduling; Two-level scheduling; Fixed-priority pre-emptive scheduling; Round-robin scheduling; Lottery scheduling; Earliest deadline first scheduling; Real-time computing; Distributed computing; Operations research; Mathematical optimization; Engineering; Schedule; Mathematics; Operating system","score_opus":0.12433675280102781,"score_gpt":0.4405163748408466,"score_spread":0.3161796220398188,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2149247234","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97532415,0.00015890252,0.019592961,0.0031120915,0.00027419705,0.0010119033,0.000043558935,0.000042471147,0.000439777],"genre_scores_gemma":[0.9911281,0.0007000293,0.0078041214,0.000026208072,0.000135832,0.000028512612,0.00002107708,0.000027725246,0.00012840243],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9916266,0.001677326,0.0016602908,0.00038041995,0.0037925479,0.0008627798],"domain_scores_gemma":[0.9945978,0.00061446585,0.00082811015,0.00031290375,0.0033538414,0.00029290316],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008778676,0.00021272623,0.0004867623,0.001535916,0.0008447817,0.000108653076,0.0007721189,0.00026886392,0.00006239001],"category_scores_gemma":[0.00017438414,0.00019304393,0.00029792186,0.0029601946,0.0004707119,0.0011201809,0.0000026056728,0.0015827382,0.0000037519753],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018772808,0.00040073387,0.23176552,0.00034220537,0.000088831526,0.000056109628,0.04738012,0.64090157,0.008482794,0.056474414,0.0003239708,0.011906425],"study_design_scores_gemma":[0.0025051506,0.00046874315,0.9355451,0.00090470817,0.00006262821,2.482549e-7,0.037103258,0.018329436,0.0025212662,0.0014492854,0.0008457558,0.00026440393],"about_ca_topic_score_codex":0.029299969,"about_ca_topic_score_gemma":0.09901679,"teacher_disagreement_score":0.7037796,"about_ca_system_score_codex":0.00055808376,"about_ca_system_score_gemma":0.001583215,"threshold_uncertainty_score":0.97716403},"labels":[],"label_agreement":null},{"id":"W2149266540","doi":"10.1002/atr.194","title":"Travel demand estimation risk for high‐speed railway transport considering travel price competition","year":2012,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","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":"Estimation; Competition (biology); Travel time; Demand curve; Economics; Econometrics; Modal; Variance (accounting); Transport engineering; Microeconomics; Engineering","score_opus":0.016122240737469314,"score_gpt":0.2829999348793003,"score_spread":0.266877694141831,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2149266540","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.48006347,0.00015032764,0.51828414,0.00021424593,0.0006693559,0.00034843438,0.00007500011,0.00003262672,0.00016239431],"genre_scores_gemma":[0.9205433,0.00036713594,0.07835789,0.0000492491,0.0002990826,0.0000106863945,0.00028452824,0.00002556039,0.00006257431],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9979627,0.00009758689,0.0009039413,0.00015658385,0.00052663276,0.0003525484],"domain_scores_gemma":[0.9978943,0.00028877443,0.0010119153,0.0000793607,0.0005016173,0.00022406245],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012657532,0.00017656691,0.00034124666,0.00022065344,0.00044512525,0.0000270762,0.00010919139,0.00014794836,0.000063322914],"category_scores_gemma":[0.00012182877,0.00018305899,0.0001718004,0.00029918595,0.000094448216,0.0016789483,2.8722096e-7,0.00020980964,0.000002290193],"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":[0.0010103914,0.0003799693,0.04905745,0.00018009369,0.00014843947,0.000008949807,0.091723435,0.80285186,0.0057233665,0.041819196,0.000062556945,0.007034278],"study_design_scores_gemma":[0.0033530113,0.00019145817,0.97839504,0.00016930707,0.0003720638,0.0000052415658,0.010139856,0.00049824634,0.0033114825,0.0023801504,0.0008625606,0.00032155766],"about_ca_topic_score_codex":0.000088463494,"about_ca_topic_score_gemma":0.00024178234,"teacher_disagreement_score":0.9293376,"about_ca_system_score_codex":0.00009517867,"about_ca_system_score_gemma":0.00015765718,"threshold_uncertainty_score":0.74649286},"labels":[],"label_agreement":null},{"id":"W2150132365","doi":"10.1016/j.sbspro.2011.08.006","title":"Decomposition Methods for Network Design","year":2011,"lang":"en","type":"article","venue":"Procedia - Social and Behavioral Sciences","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Decomposition; Computer science; Biology; Ecology","score_opus":0.27253039398346557,"score_gpt":0.48073604418936766,"score_spread":0.2082056502059021,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2150132365","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13584171,0.00033916932,0.8550695,0.00092752924,0.00071640214,0.0010167524,0.000010708005,0.00029165624,0.0057865786],"genre_scores_gemma":[0.51404583,0.00003205765,0.48543173,0.000067612935,0.00023559906,0.00006205562,0.000005941984,0.0000039038246,0.000115245726],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99921095,0.00010652305,0.00012147187,0.00017977694,0.00013151184,0.00024977612],"domain_scores_gemma":[0.99968356,0.00008132352,0.00007172142,0.000016256614,0.000079038,0.00006809114],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0012622598,0.000064681044,0.000088098444,0.00003162698,0.0019053362,0.00007742606,0.00010785946,0.00007794158,0.000042784864],"category_scores_gemma":[0.000025630594,0.00005803488,0.000033271197,0.00030007653,0.00042653404,0.00030074187,0.00000530755,0.000038584367,0.0000011354339],"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":[0.00020395168,0.00039809517,0.138056,0.00003797657,0.000016968632,0.0000018650666,0.26891315,0.0003291999,0.00054734974,0.17145635,0.003286977,0.41675213],"study_design_scores_gemma":[0.0028530757,0.0033295732,0.4244277,0.00016507019,0.0009356003,0.0000066394423,0.068711214,0.0065988614,0.004066366,0.42888016,0.056944646,0.0030811217],"about_ca_topic_score_codex":0.0005171223,"about_ca_topic_score_gemma":0.00022513098,"teacher_disagreement_score":0.413671,"about_ca_system_score_codex":0.000013255019,"about_ca_system_score_gemma":0.0001021234,"threshold_uncertainty_score":0.99939406},"labels":[],"label_agreement":null},{"id":"W2150328770","doi":"10.1002/atr.5670400302","title":"Train boarding and alighting rates at high passenger loads","year":2006,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":71,"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":"Train; Passenger train; Transport engineering; Stock (firearms); Acceleration; Engineering; Line (geometry); Simulation; Automotive engineering; Geography; Mathematics; Physics; Cartography","score_opus":0.00777549736921138,"score_gpt":0.2657302233291689,"score_spread":0.2579547259599575,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2150328770","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9928565,0.000284816,0.005254601,0.00079497584,0.00028870715,0.00007658001,0.000008309031,0.000028998116,0.0004064924],"genre_scores_gemma":[0.9873442,0.00020391682,0.011881057,0.000035332712,0.00021829836,0.0000016294841,0.000049724113,0.000010464522,0.00025538963],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9989383,0.000049675542,0.00044211932,0.000104946645,0.00031201387,0.0001529412],"domain_scores_gemma":[0.99920326,0.000108433174,0.00039390987,0.00003370881,0.00019414222,0.00006655898],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038441975,0.00008523257,0.00015878877,0.00010840988,0.00029885402,0.00003386108,0.000050541435,0.0000660967,0.000034710894],"category_scores_gemma":[0.00002491077,0.00008217871,0.00005753977,0.00018554907,0.00006151997,0.0005943482,3.9265984e-7,0.000093378214,9.180006e-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":[0.0005663416,0.00015569857,0.23833308,0.00009590413,0.00009013938,0.00015977246,0.056607187,0.6270567,0.030252725,0.028613733,0.0007534699,0.017315203],"study_design_scores_gemma":[0.0013016668,0.00006981794,0.97946495,0.00010512859,0.00008243047,0.000003980963,0.0044115023,0.000037162667,0.0026126169,0.0024618907,0.009263583,0.00018528075],"about_ca_topic_score_codex":0.00016811503,"about_ca_topic_score_gemma":0.0014100039,"teacher_disagreement_score":0.74113184,"about_ca_system_score_codex":0.000054078664,"about_ca_system_score_gemma":0.000047176396,"threshold_uncertainty_score":0.33511505},"labels":[],"label_agreement":null},{"id":"W2150600488","doi":"10.1007/s10732-009-9112-8","title":"A metaheuristic for stochastic service network design","year":2009,"lang":"en","type":"article","venue":"Journal of Heuristics","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":57,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Université de Montréal","keywords":"Metaheuristic; Computer science; Service (business); Set (abstract data type); Network planning and design; Operations research; Mathematical optimization; Artificial intelligence; Engineering; Computer network; Mathematics; Business","score_opus":0.044919849815607496,"score_gpt":0.31950411863800854,"score_spread":0.27458426882240106,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2150600488","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00069201225,0.00026700122,0.9963555,0.0017455554,0.00050513144,0.00014370291,0.000005152666,0.00002024623,0.00026569908],"genre_scores_gemma":[0.7765418,0.0000640674,0.2211178,0.0008810297,0.0011493126,0.0000015835764,0.000005874217,0.000010105963,0.0002283942],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99901587,0.000093379414,0.0003570565,0.00006342414,0.00027663982,0.00019362969],"domain_scores_gemma":[0.99832803,0.00053258386,0.00034329479,0.0000531292,0.00063047913,0.000112504116],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010097151,0.00006916889,0.00017193421,0.00005832388,0.00026684365,0.00005120038,0.00014852567,0.000059064325,0.000016507329],"category_scores_gemma":[0.0007170217,0.00006393419,0.00006360974,0.0002450367,0.000024159503,0.0001028316,0.0000011733638,0.000106060266,0.0000029920616],"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":[0.00016158824,0.000055321587,0.000063552085,0.000008705492,0.000026308791,0.000012387,0.0025698622,0.97018117,0.000008208225,0.010283068,0.014990602,0.0016392043],"study_design_scores_gemma":[0.009481957,0.0052866437,0.026916912,0.0012053482,0.0036607538,0.00014781559,0.006524781,0.3070046,0.00006858192,0.39514595,0.24244235,0.002114323],"about_ca_topic_score_codex":0.000007951778,"about_ca_topic_score_gemma":0.000017137587,"teacher_disagreement_score":0.7758498,"about_ca_system_score_codex":0.000032774355,"about_ca_system_score_gemma":0.0002361452,"threshold_uncertainty_score":0.26071605},"labels":[],"label_agreement":null},{"id":"W2150670932","doi":"10.1139/cjce-2013-0331","title":"Optimizing route choice in multimodal transportation networks","year":2014,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"Concordia University","funders":"Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Computer science; Routing (electronic design automation); Transport engineering; Public transport; Traffic congestion; Flow network; Mode (computer interface); Markov chain; Process (computing); Multimodal transport; Markov process; Operations research; Transportation planning; Service (business); Engineering; Computer network; Mathematical optimization","score_opus":0.0075890519314590656,"score_gpt":0.21408837053902727,"score_spread":0.2064993186075682,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2150670932","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16586752,0.0005223277,0.8266295,0.000728132,0.0015654027,0.00012720248,0.0000076092056,0.000044532775,0.0045077605],"genre_scores_gemma":[0.997247,0.000015198418,0.002404985,0.000046415116,0.00023367202,9.339778e-7,0.000007797903,0.00001040943,0.00003360647],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993098,0.000027467171,0.00025399195,0.00006083327,0.00011816813,0.00022973625],"domain_scores_gemma":[0.9994312,0.00008084463,0.000086312975,0.000037843074,0.000079338875,0.0002844385],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004588177,0.000063741434,0.00011465666,0.0002611359,0.0000898912,0.000044300843,0.00010619294,0.000072791016,0.000053714375],"category_scores_gemma":[0.00014140461,0.00007478198,0.00004084661,0.00025718933,0.00001948859,0.00027490626,2.2630383e-7,0.00017948783,5.7183587e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000015494982,0.000001683727,0.0279197,0.000003899572,0.0000041945204,0.000010588333,0.004032555,0.9666343,0.0000050211834,0.0008308361,0.000057581557,0.0004981059],"study_design_scores_gemma":[0.0010926598,0.00004610595,0.45354325,0.00033610855,0.000034394492,0.0000035008038,0.0007471033,0.47270375,0.000010957693,0.000050284678,0.071076594,0.000355298],"about_ca_topic_score_codex":0.015677448,"about_ca_topic_score_gemma":0.8591441,"teacher_disagreement_score":0.84346664,"about_ca_system_score_codex":0.00012926594,"about_ca_system_score_gemma":0.0003120648,"threshold_uncertainty_score":0.9908772},"labels":[],"label_agreement":null},{"id":"W2150859951","doi":"","title":"Cost-benefit rules for transport projects when labor supply is endogenous and taxes are distortionary","year":2007,"lang":"en","type":"article","venue":"PROCEEDINGS OF THE EUROPEAN TRANSPORT CONFERENCE (ETC) 2007 HELD 17-19 OCTOBER 2007, LEIDEN, THE NETHERLANDS","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Transport Canada","funders":"","keywords":"Stylized fact; Economics; Distortion (music); Investment (military); Microeconomics; General equilibrium theory; Marginal cost; Macroeconomics; Computer science","score_opus":0.05404704527109703,"score_gpt":0.2729696202565408,"score_spread":0.2189225749854438,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2150859951","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.928275,0.0015994029,0.0029276058,0.0040616053,0.0007254271,0.0030267532,0.0013127851,0.00031296723,0.05775848],"genre_scores_gemma":[0.9852182,0.00084227807,0.0010583444,0.0009473123,0.00056154723,0.000053240867,0.00011868324,0.000083248255,0.011117116],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9969486,0.000044250246,0.00084112864,0.00061334687,0.00079432764,0.00075833336],"domain_scores_gemma":[0.9978108,0.00019749037,0.0006515531,0.00024969337,0.000822032,0.0002684352],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0023905614,0.0004272538,0.00045616497,0.00015585279,0.00090514694,0.00009898377,0.00094598,0.00020393611,0.00039398772],"category_scores_gemma":[0.00007902812,0.0003117003,0.00024370366,0.0003750945,0.00065004546,0.0004585428,0.000017917064,0.00038587418,0.000009182621],"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":[0.0005280639,0.0001700755,0.9301577,0.00023965909,0.00012872703,0.000015120458,0.054269005,0.00012744343,0.0004962126,0.00359581,0.007850811,0.002421353],"study_design_scores_gemma":[0.001502952,0.00013604175,0.8848345,0.00042684813,0.00035006652,0.000017664224,0.008330795,0.00003848753,0.0011974201,0.0013334823,0.10118236,0.0006493531],"about_ca_topic_score_codex":0.001725641,"about_ca_topic_score_gemma":0.0031377384,"teacher_disagreement_score":0.093331546,"about_ca_system_score_codex":0.00006888983,"about_ca_system_score_gemma":0.00017480247,"threshold_uncertainty_score":0.9999335},"labels":[],"label_agreement":null},{"id":"W2151069056","doi":"10.1002/atr.146","title":"On seat capacity in traffic assignment to a transit network","year":2010,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":25,"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":"Transit (satellite); Transport engineering; Probabilistic logic; Service (business); Variable (mathematics); Operations research; Computer science; Set (abstract data type); Level of service; Flow network; Transit system; Simulation; Mathematical optimization; Engineering; Public transport; Mathematics; Economics","score_opus":0.01609193256671307,"score_gpt":0.2725808457173303,"score_spread":0.2564889131506173,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2151069056","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.983403,0.000011392127,0.014441982,0.00085891306,0.000822797,0.00019826123,0.000007282726,0.000022323433,0.00023403534],"genre_scores_gemma":[0.98677707,0.000030243504,0.012776609,0.00018205508,0.00017427126,0.000005550601,0.000012720479,0.000010156607,0.00003132724],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99869,0.000056243654,0.0004591565,0.00012453561,0.0004567527,0.00021329275],"domain_scores_gemma":[0.9993259,0.00010884862,0.00020043582,0.000054935954,0.00014850534,0.00016138272],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006111797,0.00009529711,0.00017816391,0.00016357253,0.0002006319,0.000017503897,0.00010193476,0.00009268388,0.000063019026],"category_scores_gemma":[0.00004777968,0.00009411958,0.000077114564,0.000440965,0.000037018377,0.00033617107,2.2866348e-7,0.0003379882,0.0000029349992],"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":[0.00024422066,0.000083035324,0.0027149871,0.000005329784,0.000006893348,0.000017082828,0.03506836,0.9524709,0.0008504332,0.0025367697,0.00004911356,0.005952875],"study_design_scores_gemma":[0.004057228,0.00086218235,0.96175784,0.0004191695,0.00009132975,0.0000036387853,0.009619615,0.0003989553,0.00065798895,0.0026925062,0.018882103,0.0005574249],"about_ca_topic_score_codex":0.0000467585,"about_ca_topic_score_gemma":0.0128967855,"teacher_disagreement_score":0.95904285,"about_ca_system_score_codex":0.00005689493,"about_ca_system_score_gemma":0.00010476389,"threshold_uncertainty_score":0.7196709},"labels":[],"label_agreement":null},{"id":"W2151091162","doi":"10.1007/978-3-662-04197-0_17","title":"Applications of Network Flow Models","year":2000,"lang":"en","type":"book-chapter","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University; University of New Brunswick","funders":"","keywords":"Computer science; Section (typography); Flow network; Flow (mathematics); Mathematics; Mechanics; Mathematical optimization; Physics; Operating system","score_opus":0.020859038893846518,"score_gpt":0.2557514890070254,"score_spread":0.23489245011317886,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2151091162","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[2.0369094e-7,0.00017504714,0.12413375,0.000079181285,0.00003736286,0.00018231936,0.000028968989,0.0000740463,0.87528914],"genre_scores_gemma":[0.0017648052,0.0013847442,0.014809167,0.00008324762,0.00028826433,0.000015284486,0.00027762054,0.000016982556,0.9813599],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99938077,0.0000074622026,0.0001889339,0.00012443642,0.00020398317,0.000094390576],"domain_scores_gemma":[0.99963856,0.000036722748,0.00009198171,0.000109848486,0.00007742999,0.00004547064],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00010323436,0.000075332115,0.00012531893,0.000042092564,0.00014998607,0.000011026181,0.0001055045,0.00019578637,0.0031264035],"category_scores_gemma":[9.127818e-7,0.000079316924,0.00005966914,0.0000416312,0.00008943164,0.00005936222,0.0000017325692,0.00007424353,0.000052449333],"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":[0.0000022710672,0.0000029094156,0.0000038432127,0.0000032153953,0.000009234068,1.4250216e-7,0.00060362916,0.1910866,9.728401e-9,0.7923298,0.0031853495,0.012772992],"study_design_scores_gemma":[0.00003670041,0.0000039947604,0.0000034863654,0.000029295321,0.000026912921,4.0591036e-8,0.000028789995,0.002218108,1.2632134e-7,0.23441485,0.7631417,0.00009600532],"about_ca_topic_score_codex":0.0001835112,"about_ca_topic_score_gemma":0.0006179232,"teacher_disagreement_score":0.75995636,"about_ca_system_score_codex":0.00001341643,"about_ca_system_score_gemma":0.00010838768,"threshold_uncertainty_score":0.99778485},"labels":[],"label_agreement":null},{"id":"W2151899978","doi":"10.1007/s11116-011-9358-5","title":"Large-scale application of MILATRAS: case study of the Toronto transit network","year":2011,"lang":"en","type":"article","venue":"Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":24,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto; University of British Columbia","funders":"University of Toronto; University of British Columbia; University of Cambridge","keywords":"Transit (satellite); Computer science; Trip distribution; Microsimulation; Transport engineering; Operationalization; Operations research; Mode choice; Simulation; Public transport; Engineering","score_opus":0.02036935754986377,"score_gpt":0.27266966341228105,"score_spread":0.25230030586241725,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2151899978","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9668695,0.000059291342,0.030889416,0.000013684954,0.00011717305,0.00058628456,0.000027233596,0.000039619146,0.0013978258],"genre_scores_gemma":[0.9987769,0.000019758043,0.0010621094,0.000012055392,0.000030894214,0.000026235462,0.000030443593,0.000008453879,0.000033142118],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.999006,0.00008828391,0.0003720554,0.00013955265,0.00026212932,0.0001320278],"domain_scores_gemma":[0.9994324,0.000025441717,0.000211144,0.00015784928,0.00013600342,0.000037201997],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034214443,0.00007232975,0.00012775931,0.000017012328,0.00021590633,0.0000031622985,0.00011917295,0.00007067801,0.00010750818],"category_scores_gemma":[0.000003015162,0.00006318525,0.000061353974,0.00027475753,0.00005945136,0.00018723578,5.0083696e-7,0.00004793734,6.063102e-7],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005468997,0.00043584176,0.36235675,0.000023633838,0.000024338964,0.00000590991,0.6242726,0.010010845,0.000049252172,0.0019078993,0.000022313629,0.0008359809],"study_design_scores_gemma":[0.00087228266,0.00013121073,0.67277765,0.000024722101,0.00022094033,0.0000013367766,0.32447714,0.00060420064,0.00022906878,0.00020211982,0.00032488487,0.0001344599],"about_ca_topic_score_codex":0.041017566,"about_ca_topic_score_gemma":0.5010591,"teacher_disagreement_score":0.46004155,"about_ca_system_score_codex":0.000017219072,"about_ca_system_score_gemma":0.00004234031,"threshold_uncertainty_score":0.9653684},"labels":[],"label_agreement":null},{"id":"W2152785281","doi":"10.1002/atr.5670350303","title":"Enhanced bus operations: The Curitiba model and its applicability to China","year":2001,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","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":"Transport engineering; Ticket; Public transport; Curitiba; Taxis; Context (archaeology); Flexibility (engineering); Telematics; Toll; Smart card; Payment; Computer science; Business; Engineering; Telecommunications; Computer network; Computer security; Finance","score_opus":0.012892712510127252,"score_gpt":0.300986058673466,"score_spread":0.28809334616333876,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2152785281","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8386072,0.00021875872,0.15785629,0.0026341584,0.00011505057,0.0002739402,0.0000101018895,0.000021361608,0.00026312296],"genre_scores_gemma":[0.99096805,0.0008724937,0.007731602,0.00017329215,0.00008971978,0.000014604249,0.000013636138,0.0000074337772,0.00012919314],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9989947,0.000045325756,0.0003884264,0.00012037132,0.00031643303,0.0001347153],"domain_scores_gemma":[0.99931157,0.000050230774,0.00013010563,0.00006519503,0.00032922497,0.00011369792],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043437514,0.0000797721,0.00012854039,0.00006688079,0.00036901387,0.00003827604,0.00011226315,0.000049031718,0.000019542204],"category_scores_gemma":[0.00007719711,0.00006368116,0.000046325564,0.00030985998,0.000043807624,0.0006084067,6.448283e-7,0.0001289021,0.0000015641664],"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":[0.000097231656,0.000034932258,0.00040051996,0.0000058634537,0.000006968785,0.0000020573016,0.033794504,0.95350105,0.0020039103,0.006037426,0.000011034592,0.004104512],"study_design_scores_gemma":[0.004065668,0.00057324755,0.91369414,0.00033350676,0.00029304228,0.000013085911,0.023964385,0.02611915,0.0034073817,0.012453055,0.014206766,0.00087658374],"about_ca_topic_score_codex":0.000042115476,"about_ca_topic_score_gemma":0.0015059069,"teacher_disagreement_score":0.9273819,"about_ca_system_score_codex":0.000032569325,"about_ca_system_score_gemma":0.00010230766,"threshold_uncertainty_score":0.28381935},"labels":[],"label_agreement":null},{"id":"W2152791982","doi":"10.1139/l03-098","title":"Unit of analysis in conventional trip generation modelling: an investigation","year":2004,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"","funders":"University of Toronto","keywords":"Trip generation; Unit (ring theory); TRIPS architecture; Travel behavior; Demand forecasting; Trip distribution; Transport engineering; Survey data collection; Estimation; Sampling (signal processing); Time-use survey; Econometrics; Computer science; Statistics; Operations research; Engineering; Work (physics); Economics; Mathematics","score_opus":0.03552326278766574,"score_gpt":0.2394964057027914,"score_spread":0.20397314291512567,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2152791982","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6990368,0.00008628411,0.30044252,0.00014231917,0.00011630173,0.000027925376,0.00000461249,0.000004062223,0.0001392019],"genre_scores_gemma":[0.9969976,0.000012207349,0.0028566872,0.0000105381,0.00007308235,4.6491886e-7,0.000031781685,0.0000038007695,0.000013832771],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99942756,0.000024781622,0.00026564454,0.000045578112,0.00014156027,0.00009486148],"domain_scores_gemma":[0.99947953,0.000012178041,0.000111339265,0.00003219516,0.00016341823,0.00020136092],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043278528,0.00003905075,0.000099383484,0.00069753884,0.000053065116,0.000021939066,0.00006427391,0.000046997433,0.000047119353],"category_scores_gemma":[0.00003994675,0.00004670096,0.00004439195,0.0006973883,0.000026220754,0.00029404368,2.749674e-7,0.00007420942,1.9528696e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000011017426,0.000003134333,0.01889592,0.0000028305083,0.000020195683,0.00000430333,0.004478485,0.9690714,0.00006716237,0.0074345865,0.0000035648793,0.00001731377],"study_design_scores_gemma":[0.0011076436,0.00007530106,0.09191431,0.00014455791,0.00021166635,0.0000032559842,0.0016086355,0.90208423,0.00036926212,0.0015885216,0.00065267,0.00023995241],"about_ca_topic_score_codex":0.012039694,"about_ca_topic_score_gemma":0.6106064,"teacher_disagreement_score":0.59856665,"about_ca_system_score_codex":0.00009918046,"about_ca_system_score_gemma":0.0008305505,"threshold_uncertainty_score":0.9945392},"labels":[],"label_agreement":null},{"id":"W2152939240","doi":"10.1287/opre.1060.0274","title":"A New Decomposition Method for Multiregional Economic Equilibrium Models","year":2006,"lang":"en","type":"article","venue":"Operations Research","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Toronto Metropolitan University; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Decomposition; Mathematical optimization; Convergence (economics); Heuristic; Linear programming; Mathematical economics; Decomposition method (queueing theory); Computer science; Applied mathematics; Mathematics; Economics","score_opus":0.11767786919244236,"score_gpt":0.48055121186255945,"score_spread":0.36287334267011706,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2152939240","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008817,0.000041098552,0.9705002,0.0048913048,0.000102450846,0.00051923445,0.000042073003,0.000060836534,0.015025802],"genre_scores_gemma":[0.4162608,0.000016601103,0.5585887,0.000040629573,0.00065917405,0.00015554109,0.00059469586,0.000015235999,0.023668626],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989744,0.00019398861,0.00015834402,0.00018642744,0.00024538708,0.00024145722],"domain_scores_gemma":[0.99934316,0.0002406574,0.000011103969,0.00008597375,0.00023846207,0.00008063378],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001020171,0.000048301044,0.00006441541,0.00015207817,0.0008697709,0.00022576748,0.00012102422,0.000069667774,0.00017959485],"category_scores_gemma":[0.000032976706,0.00005301214,0.000039508282,0.00017282313,0.00005551364,0.00045547946,0.000005892847,0.00007930481,0.000039921797],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014150379,0.000016914655,0.000054772714,0.0000016559349,0.0000030591225,1.9836078e-7,0.0009914443,0.70799917,0.00037177477,0.27909768,0.010347941,0.0011012573],"study_design_scores_gemma":[0.00042084893,0.000029141733,0.0003616897,0.00000831218,0.000004973977,4.293282e-7,0.00046733653,0.967012,0.0003295285,0.010503711,0.02076734,0.000094693794],"about_ca_topic_score_codex":0.023069339,"about_ca_topic_score_gemma":0.020504428,"teacher_disagreement_score":0.41191152,"about_ca_system_score_codex":0.00013704914,"about_ca_system_score_gemma":0.0006997845,"threshold_uncertainty_score":0.9973688},"labels":[],"label_agreement":null},{"id":"W2153514362","doi":"10.3141/1780-07","title":"Comparisons from Sacramento Model Test Bed","year":2001,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":46,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; University of California, Davis","keywords":"Futures contract; Transport engineering; Computer science; Occupancy; Aggregate (composite); Simulation modeling; Scenario analysis; Land use; Government (linguistics); Econometrics; Operations research; Engineering; Civil engineering; Business; Economics","score_opus":0.15020316784034074,"score_gpt":0.4261692612595003,"score_spread":0.27596609341915956,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2153514362","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9684503,0.00025520832,0.01721189,0.010686921,0.0006002973,0.0009785783,0.00021145843,0.000086870896,0.001518449],"genre_scores_gemma":[0.98710036,0.0023384166,0.007368872,0.00011506568,0.00034307654,0.00005352216,0.00008676221,0.000054446522,0.0025394906],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9903803,0.0013763496,0.0015009304,0.00046702093,0.0051805116,0.0010948812],"domain_scores_gemma":[0.99188995,0.0022317914,0.0005660654,0.0004641793,0.0042097326,0.00063828134],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.005060346,0.00025929618,0.00047794054,0.00088188937,0.0022121053,0.00030255833,0.0013910574,0.00030438878,0.0007449595],"category_scores_gemma":[0.00051331264,0.00021869491,0.00039270456,0.002849071,0.0010706718,0.0010227642,0.00000455819,0.0021193994,0.000049606857],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00095386937,0.0005814933,0.90168875,0.00004001422,0.00013046393,0.00010848955,0.026202034,0.033601314,0.0011892662,0.0067880675,0.02583805,0.0028781705],"study_design_scores_gemma":[0.002153568,0.00033242957,0.8878231,0.00038677544,0.00011243884,2.2028516e-7,0.015778849,0.0034956136,0.00029793306,0.0074706567,0.08178034,0.00036806313],"about_ca_topic_score_codex":0.07523133,"about_ca_topic_score_gemma":0.34369713,"teacher_disagreement_score":0.2684658,"about_ca_system_score_codex":0.00037104712,"about_ca_system_score_gemma":0.0014162317,"threshold_uncertainty_score":0.99908686},"labels":[],"label_agreement":null},{"id":"W2153783194","doi":"10.3141/1864-17","title":"Results of 2003 Transportation Management Association Survey: Analysis of Evolving Characteristics of Transportation Management Associations","year":2004,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"General partnership; Business; Organizational structure; Diversification (marketing strategy); Government (linguistics); Service (business); Marketing; Finance; Management; Economics","score_opus":0.07033552735388754,"score_gpt":0.38249019628671577,"score_spread":0.3121546689328282,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2153783194","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9782245,0.00014101458,0.012881911,0.00092551723,0.000676921,0.0017579981,0.004627494,0.000042385225,0.0007222484],"genre_scores_gemma":[0.98873657,0.0027826468,0.005718296,0.000016666405,0.000066635825,0.00005425649,0.001910536,0.000052757972,0.0006616545],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9854312,0.0019411368,0.004361064,0.0005766944,0.0067724246,0.0009175033],"domain_scores_gemma":[0.9808292,0.0014324902,0.0039038556,0.00055807433,0.012955725,0.00032066624],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.013183875,0.00035349122,0.0011938217,0.0031733415,0.000707133,0.00008088892,0.001107432,0.00043072304,0.00016912738],"category_scores_gemma":[0.0006102922,0.0003409927,0.00077871437,0.012882931,0.00065491773,0.0009602961,0.0000031020945,0.0011098675,0.0000034459006],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0022969202,0.0012336798,0.8887371,0.0007477441,0.0037517676,0.00004437797,0.03389869,0.041147,0.00043151673,0.02365341,0.001464442,0.0025933564],"study_design_scores_gemma":[0.002937915,0.00031676472,0.9849935,0.0005682864,0.0016258792,2.2077018e-8,0.0064926436,0.00012026136,0.00076246326,0.00068209507,0.0012346214,0.00026551416],"about_ca_topic_score_codex":0.047013637,"about_ca_topic_score_gemma":0.19551608,"teacher_disagreement_score":0.14850244,"about_ca_system_score_codex":0.0008066306,"about_ca_system_score_gemma":0.00096712366,"threshold_uncertainty_score":0.9999042},"labels":[],"label_agreement":null},{"id":"W2153790331","doi":"10.3141/2412-10","title":"Modeling Riders’ Behavioral Responses to Real-Time Information at Light Rail Transit Stations","year":2014,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Travel behavior; Transport engineering; Multinomial logistic regression; Context (archaeology); Transit (satellite); Mode choice; Public transport; Real-time data; TRIPS architecture; Computer science; Geography; Engineering","score_opus":0.09137719248379554,"score_gpt":0.4057995445348275,"score_spread":0.314422352051032,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2153790331","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95499295,0.000024278228,0.02648958,0.014943182,0.0005357313,0.0013336496,0.00014819451,0.000111935115,0.0014204864],"genre_scores_gemma":[0.9900512,0.0005115952,0.005642623,0.00013089256,0.0002374793,0.00009966458,0.00012606886,0.0000562752,0.0031442125],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.98871416,0.0027519872,0.0018707968,0.00041866474,0.005101731,0.0011426914],"domain_scores_gemma":[0.9913813,0.0011382129,0.00044316743,0.00048499665,0.005737102,0.0008152139],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.008998106,0.00029982932,0.0004952603,0.0018390563,0.0026861792,0.0003513523,0.0011259336,0.00030981316,0.0005826522],"category_scores_gemma":[0.00055512384,0.0002641665,0.00039339715,0.0030676748,0.00048332784,0.0020993522,0.0000067226015,0.0013200083,0.00017662645],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.009324825,0.0008973519,0.20098221,0.00037501,0.00031321982,0.00008412906,0.2820962,0.41258648,0.010259965,0.036574315,0.035132147,0.011374148],"study_design_scores_gemma":[0.0055159302,0.0022116615,0.7290136,0.001052397,0.00033430205,0.0000012671627,0.03302788,0.009344459,0.001420306,0.0063310773,0.21047907,0.0012679988],"about_ca_topic_score_codex":0.03224197,"about_ca_topic_score_gemma":0.123432934,"teacher_disagreement_score":0.52803147,"about_ca_system_score_codex":0.00059427344,"about_ca_system_score_gemma":0.0011041601,"threshold_uncertainty_score":0.99998105},"labels":[],"label_agreement":null},{"id":"W2153804838","doi":"10.3141/1985-10","title":"Modeling Skeletal Components of Workers' Daily Activity Schedules","year":2006,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Multilevel model; Hazard; Econometrics; Parametric statistics; Covariate; Parametric model; Computer science; Scheduling (production processes); Random effects model; Work (physics); Statistics; Engineering; Mathematics; Operations management","score_opus":0.09835888289049473,"score_gpt":0.3923386715221131,"score_spread":0.2939797886316184,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2153804838","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9816497,0.00018359713,0.014198,0.0021068756,0.00046990332,0.00068280805,0.00006646839,0.0000397288,0.00060293346],"genre_scores_gemma":[0.9944874,0.0004090112,0.004118429,0.000010294081,0.0002540145,0.000024687895,0.00004754882,0.000040220883,0.00060837273],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99093235,0.0016996413,0.0013940246,0.00036109568,0.004761409,0.00085148093],"domain_scores_gemma":[0.9936504,0.00080624415,0.0005594412,0.00034200243,0.0043501146,0.00029182018],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0058559696,0.00022051288,0.00046814504,0.0010088211,0.0012332005,0.00012845032,0.0009896881,0.00026467998,0.00018592391],"category_scores_gemma":[0.00017911808,0.00018698916,0.00042904812,0.0024249696,0.0010588972,0.0009647926,0.000004133572,0.0016016144,0.00000981939],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017973776,0.00079853943,0.73411995,0.00027363183,0.00017262348,0.00005797461,0.008365827,0.22005616,0.005197196,0.023997068,0.0015211344,0.0036425248],"study_design_scores_gemma":[0.0015060055,0.00021321385,0.9802519,0.00046989653,0.00006585856,1.2387083e-7,0.005429063,0.003071469,0.00072551623,0.004385893,0.0036536865,0.0002273944],"about_ca_topic_score_codex":0.11813175,"about_ca_topic_score_gemma":0.14814785,"teacher_disagreement_score":0.24613194,"about_ca_system_score_codex":0.00022482905,"about_ca_system_score_gemma":0.00094381545,"threshold_uncertainty_score":0.9484906},"labels":[],"label_agreement":null},{"id":"W2154889564","doi":"10.3141/2498-01","title":"Heuristic Approach to Capacitated Traffic Assignment Problem for Large-Scale Transport Networks","year":2015,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Göteborgs Universitet; Chalmers Tekniska Högskola","keywords":"Mathematical optimization; Heuristic; Benchmark (surveying); Computer science; Convergence (economics); Reliability (semiconductor); Flow network; Scale (ratio); Traffic flow (computer networking); Sensitivity (control systems); Operations research; Mathematics; Engineering; Economics","score_opus":0.12030280244723394,"score_gpt":0.38813565186123344,"score_spread":0.2678328494139995,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2154889564","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.61384857,0.00024369612,0.3721025,0.005333511,0.0012030298,0.0049046464,0.0003101361,0.0001423804,0.0019115527],"genre_scores_gemma":[0.9738418,0.00027233246,0.022718864,0.00011774951,0.00043826562,0.00040710176,0.0001881967,0.000089782145,0.0019259475],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9893033,0.001707304,0.0017683947,0.0006400547,0.0049756197,0.0016053184],"domain_scores_gemma":[0.98998743,0.0009262401,0.0005104736,0.0003838215,0.006858652,0.0013333837],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.013426286,0.00033891926,0.0006194288,0.0010823086,0.0015828304,0.00020021158,0.0012256519,0.00036618646,0.00007121649],"category_scores_gemma":[0.00031215622,0.00028383662,0.00046713423,0.0035499989,0.00056131446,0.00073684956,0.0000034337374,0.0016691795,0.000013560645],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0044454616,0.0015721847,0.08856274,0.00033824396,0.00027808233,0.000049645667,0.11749803,0.72922415,0.00011619256,0.012595601,0.041664936,0.003654754],"study_design_scores_gemma":[0.012046363,0.0031470761,0.53375846,0.0010878429,0.000468741,0.0000013739065,0.11731126,0.016527815,0.0001851258,0.004637551,0.30936924,0.0014591238],"about_ca_topic_score_codex":0.005367453,"about_ca_topic_score_gemma":0.06279232,"teacher_disagreement_score":0.7126963,"about_ca_system_score_codex":0.00055897905,"about_ca_system_score_gemma":0.0018340069,"threshold_uncertainty_score":0.9999614},"labels":[],"label_agreement":null},{"id":"W2155187918","doi":"10.3141/2054-04","title":"Studying Differences of Household Weekday and Weekend Activities","year":2008,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Calgary; University of New Brunswick","funders":"","keywords":"Weekend effect; Weibull distribution; Duration (music); Names of the days of the week; Statistics; Demography; Computer science; Medicine; Mathematics; Emergency medicine; Sociology","score_opus":0.19282392499132794,"score_gpt":0.38861926599753294,"score_spread":0.195795341006205,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2155187918","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99564654,0.00035435637,0.0008463316,0.0016316194,0.00035734967,0.00058568025,0.000065832624,0.00003199305,0.00048032068],"genre_scores_gemma":[0.99267876,0.004766901,0.0013530044,0.000018165556,0.00014406198,0.00002546634,0.000008232102,0.000030450965,0.0009749744],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9926197,0.0015097893,0.0011301293,0.00030983085,0.0037742965,0.0006562458],"domain_scores_gemma":[0.9953845,0.0015757834,0.00055186474,0.00025255218,0.0019110583,0.00032424417],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.004205408,0.00018842929,0.0004623307,0.0009304523,0.0016843411,0.00008211546,0.00069970713,0.0001890877,0.00012959546],"category_scores_gemma":[0.00023149578,0.00014963705,0.0002237196,0.0017385995,0.0021688114,0.0008645726,0.000004302472,0.001278064,0.0000017707296],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00058830826,0.00023264244,0.93839157,0.00013805612,0.00011045019,0.00005822812,0.050122354,0.0009529528,0.0008551868,0.0051601483,0.0013769504,0.002013166],"study_design_scores_gemma":[0.00091828546,0.00034343358,0.9724215,0.0002648413,0.00004101872,4.911139e-7,0.020127367,0.000026486226,0.0005013914,0.00093512435,0.0042699934,0.00015003867],"about_ca_topic_score_codex":0.021078238,"about_ca_topic_score_gemma":0.048274003,"teacher_disagreement_score":0.03402997,"about_ca_system_score_codex":0.000106199885,"about_ca_system_score_gemma":0.0008916319,"threshold_uncertainty_score":0.9996153},"labels":[],"label_agreement":null},{"id":"W2155488640","doi":"10.1002/atr.162","title":"A fuel tankering model applied to a domestic airline network","year":2011,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":21,"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":"Schedule; Operations research; Linear programming; Volume (thermodynamics); Fuel efficiency; Environmental science; Computer science; Engineering; Transport engineering; Automotive engineering; Algorithm","score_opus":0.0218348659817147,"score_gpt":0.2816061672915521,"score_spread":0.2597713013098374,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2155488640","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.34278953,0.000117620606,0.6518921,0.00022478453,0.00051680155,0.000283072,0.000008427485,0.00007464322,0.0040930253],"genre_scores_gemma":[0.8281009,0.00009717581,0.17143755,0.00012633918,0.00013693052,0.0000061163164,0.000012281703,0.00001232529,0.00007033802],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99882424,0.000019666002,0.00049106404,0.00011443425,0.0003408238,0.00020979796],"domain_scores_gemma":[0.99915355,0.00003600867,0.0003216357,0.00006431055,0.00025254235,0.00017193345],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003755499,0.000095078634,0.00018201153,0.00011983787,0.00014504301,0.000014131846,0.00012523438,0.00006134188,0.000037511243],"category_scores_gemma":[0.000029218116,0.000095985066,0.00006929439,0.00040524677,0.000033177537,0.0003713633,5.5744397e-7,0.00012705594,0.000003929963],"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":[0.0002884711,0.00003771534,0.0010966815,0.000011973285,0.000014214873,0.000014571911,0.052079935,0.9353147,0.00021737732,0.008796079,0.000038917377,0.0020893717],"study_design_scores_gemma":[0.007914432,0.0010666933,0.8594598,0.0009426265,0.0007574787,0.000014856208,0.03388254,0.007067891,0.000571474,0.07356866,0.013051968,0.0017015443],"about_ca_topic_score_codex":0.000025135841,"about_ca_topic_score_gemma":0.0005347213,"teacher_disagreement_score":0.9282468,"about_ca_system_score_codex":0.000042457363,"about_ca_system_score_gemma":0.00013733811,"threshold_uncertainty_score":0.39141575},"labels":[],"label_agreement":null},{"id":"W2157686634","doi":"10.1002/atr.5670350204","title":"A bi‐level programming approach — Optimal transit fare under line capacity constraints","year":2001,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Transit (satellite); Heuristic; Flow network; Public transport; Computer science; Line (geometry); Sensitivity (control systems); Network planning and design; Mathematical optimization; Transport engineering; Programming paradigm; Integer programming; Linear programming; Transit system; Operations research; Engineering; Computer network; Mathematics; Algorithm","score_opus":0.04792166520528408,"score_gpt":0.3016636375152281,"score_spread":0.25374197230994405,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2157686634","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.48564634,0.00007579226,0.5129536,0.00042011452,0.00022153127,0.00019323737,0.000027452,0.000048462367,0.00041346942],"genre_scores_gemma":[0.86320156,0.000174067,0.13619404,0.00006423221,0.00014958213,0.0000055451596,0.00010077739,0.000015264372,0.00009490153],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9982428,0.00007443019,0.0006463147,0.00017489151,0.00058135396,0.0002802321],"domain_scores_gemma":[0.9987064,0.000059180253,0.0004584764,0.0000712789,0.0005254065,0.0001792439],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051186647,0.00014884234,0.00025550617,0.00017834328,0.00029846473,0.000049136943,0.0001449325,0.00013493837,0.00007270056],"category_scores_gemma":[0.00003572655,0.00014638991,0.00015446873,0.0004963267,0.00021946103,0.00083522644,3.5369348e-7,0.00027483376,0.0000015576412],"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":[0.00028575823,0.0002375636,0.004868563,0.00003317855,0.000062765066,0.00004223574,0.036273252,0.921994,0.00048947125,0.00448457,0.000013198932,0.03121547],"study_design_scores_gemma":[0.013534095,0.0012623315,0.7545008,0.00078482117,0.0009175326,0.00016573211,0.18204923,0.0017961406,0.0010634249,0.0027032676,0.039417733,0.0018048838],"about_ca_topic_score_codex":0.00006971497,"about_ca_topic_score_gemma":0.000519402,"teacher_disagreement_score":0.92019784,"about_ca_system_score_codex":0.00007150057,"about_ca_system_score_gemma":0.00019677752,"threshold_uncertainty_score":0.5969607},"labels":[],"label_agreement":null},{"id":"W2158309285","doi":"10.1016/j.trb.2014.06.007","title":"Pre-trip information and route-choice decisions with stochastic travel conditions: Experiment","year":2014,"lang":"en","type":"article","venue":"Transportation Research Part B Methodological","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":94,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia; University of Alberta","funders":"","keywords":"Travel time; Stochastic game; Test (biology); Travel behavior; Operations research; Complete information; Contrast (vision); Computer science; Econometrics; Economics; Microeconomics; Transport engineering; Mathematics; Engineering; Ecology","score_opus":0.3016774599698332,"score_gpt":0.49178813155020545,"score_spread":0.19011067158037226,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2158309285","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39322084,0.00004078603,0.6035478,0.00087847596,0.00007940849,0.0006534184,0.000058866422,0.00012510187,0.0013952664],"genre_scores_gemma":[0.9643449,0.00010255025,0.034346484,0.00014319937,0.00008622467,0.00024523633,0.0004948475,0.000008868738,0.00022769687],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9962971,0.001597956,0.00042675444,0.0002823417,0.0009933634,0.00040247323],"domain_scores_gemma":[0.9936061,0.0053806715,0.00011156534,0.00014691975,0.00046997608,0.00028474873],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004835243,0.00013019747,0.00022057531,0.00023587485,0.00097306207,0.0001319717,0.00015599286,0.00017278576,0.0002812911],"category_scores_gemma":[0.0024296741,0.00010527669,0.000039508082,0.0005613028,0.0005501644,0.0006543683,0.0000029370372,0.0003163228,0.000019912572],"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":[0.0017892156,0.0005990535,0.08080726,0.00010550305,0.00014826463,0.000015844127,0.14265294,0.123456664,0.0006745242,0.6124021,0.0031228506,0.03422581],"study_design_scores_gemma":[0.0018025541,0.0006193078,0.9400975,0.00014505793,0.000055351997,0.0000013127294,0.01585097,0.0026893304,0.00019690151,0.0045921295,0.033560112,0.00038947185],"about_ca_topic_score_codex":0.0009873208,"about_ca_topic_score_gemma":0.0015321712,"teacher_disagreement_score":0.85929024,"about_ca_system_score_codex":0.00004458326,"about_ca_system_score_gemma":0.00015594272,"threshold_uncertainty_score":0.7484105},"labels":[],"label_agreement":null},{"id":"W2158313237","doi":"10.1016/j.ecotra.2012.07.001","title":"Road pricing and investment","year":2012,"lang":"en","type":"article","venue":"Economics of Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":48,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Toll; Singapore Area Licensing Scheme; Road pricing; Congestion pricing; Traffic congestion; Toll road; Investment (military); Marginal cost; Business; Economics; Transport engineering; Microeconomics; Engineering","score_opus":0.01783797694041126,"score_gpt":0.2537334758896698,"score_spread":0.23589549894925857,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2158313237","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.994058,0.00012340983,0.00097408536,0.00017037163,0.00013397417,0.000085637555,0.00001337752,0.000021468506,0.00441966],"genre_scores_gemma":[0.99575967,0.00019087286,0.003776669,0.00007028217,0.000043749755,0.0000036357817,0.00007357082,0.0000044222866,0.00007710725],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9996202,0.0000137902325,0.00016481767,0.000060972496,0.00004049641,0.000099710815],"domain_scores_gemma":[0.9997694,0.000020045609,0.00009157081,0.000035078643,0.000021205273,0.00006268309],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023683408,0.00003894325,0.0000672395,0.000043598553,0.00008294621,0.0000077617415,0.000027604274,0.00003832481,0.000025503266],"category_scores_gemma":[0.000005858513,0.000045918416,0.000017480437,0.000043037442,0.000049297596,0.00035276433,2.8159738e-7,0.000023304887,0.0000018731415],"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":[0.000020453,0.000057787216,0.5406675,0.00002836976,0.000023562035,1.4373255e-7,0.08173784,0.0065240134,0.00007893967,0.36250043,0.00006339778,0.008297585],"study_design_scores_gemma":[0.0002247655,0.000012605236,0.9883252,0.000011641199,0.000025459349,6.694535e-8,0.0021951464,0.00016342287,0.00034125894,0.0005773403,0.008032081,0.00009100501],"about_ca_topic_score_codex":0.0005422072,"about_ca_topic_score_gemma":0.0007300546,"teacher_disagreement_score":0.44765773,"about_ca_system_score_codex":0.000017419932,"about_ca_system_score_gemma":0.0000282749,"threshold_uncertainty_score":0.18724985},"labels":[],"label_agreement":null},{"id":"W2159609023","doi":"10.3141/1886-08","title":"Traveler Information Provision for Incident Management: Implications for Vehicle Emissions","year":2004,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"University of Texas at Austin; California Air Resources Board","keywords":"Software deployment; Transport engineering; Work (physics); Greenhouse gas; Blueprint; Traffic congestion; Intelligent transportation system; Traffic simulation; Environmental science; Engineering; Microsimulation","score_opus":0.09104844652829744,"score_gpt":0.4239607621236667,"score_spread":0.33291231559536927,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2159609023","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7137436,0.00012485497,0.24435176,0.03237132,0.0009300771,0.0070302123,0.00051709334,0.00010863851,0.00082244107],"genre_scores_gemma":[0.97628254,0.0007810076,0.020975063,0.00012076339,0.00022236792,0.0006375196,0.00020327712,0.000040917563,0.00073655526],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99502057,0.00040058632,0.0013570336,0.00030460584,0.002087018,0.0008302141],"domain_scores_gemma":[0.99238026,0.00089983374,0.0005633226,0.00032827503,0.0054329834,0.00039529696],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.004963522,0.00019265915,0.00029574032,0.0009778099,0.00251132,0.00024984824,0.00090625876,0.00022784641,0.000052705615],"category_scores_gemma":[0.0003695085,0.00016150506,0.00039879672,0.0018761397,0.00047080012,0.0015776975,0.0000034996185,0.00074190856,0.000009129552],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.005073751,0.0011564455,0.13025491,0.0013734596,0.00046899053,0.000014005753,0.08174861,0.06518719,0.0022035406,0.6134395,0.025901606,0.07317801],"study_design_scores_gemma":[0.0042034136,0.00049697154,0.79893744,0.00041842018,0.00011165983,1.5785427e-7,0.010868954,0.00009425446,0.00049678516,0.040828496,0.14328611,0.00025736092],"about_ca_topic_score_codex":0.0043385737,"about_ca_topic_score_gemma":0.021982504,"teacher_disagreement_score":0.6686825,"about_ca_system_score_codex":0.0004372235,"about_ca_system_score_gemma":0.001101942,"threshold_uncertainty_score":0.9987873},"labels":[],"label_agreement":null},{"id":"W2159789307","doi":"10.3141/1854-11","title":"Empirical Investigation of Household Vehicle Type Choice Decisions","year":2003,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":102,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Social Sciences and Humanities Research Council of Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Discrete choice; Macro; Automotive industry; Greenhouse gas; Environmental economics; Process (computing); Work (physics); Mode choice; Simulation modeling; Nested logit; Travel behavior; Transport engineering; Computer science; Economics; Engineering; Econometrics; Microeconomics; Public transport","score_opus":0.23363473893569145,"score_gpt":0.4423780492795777,"score_spread":0.20874331034388624,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2159789307","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9936585,0.00021459415,0.0013818025,0.0025906703,0.0006765586,0.0006626257,0.00004287966,0.00003679292,0.00073559367],"genre_scores_gemma":[0.994539,0.0010471086,0.0034449608,0.00006826983,0.00013384895,0.000019927571,0.000021621005,0.00004086783,0.00068438525],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9901523,0.002954529,0.0014760285,0.00034482055,0.004335334,0.00073701434],"domain_scores_gemma":[0.99010247,0.0031084835,0.0006039016,0.0003959753,0.0053004925,0.00048865256],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0077806404,0.0001903222,0.00039736409,0.0009712524,0.001144603,0.00010733377,0.0008501926,0.00028145956,0.0003012989],"category_scores_gemma":[0.0019458918,0.00015645448,0.0002953795,0.004397236,0.00120362,0.0007698152,0.0000026028836,0.0015207786,0.000012582656],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047335308,0.00016430639,0.9623178,0.000057032128,0.00007198898,0.000019157558,0.013478986,0.0058719115,0.0016822558,0.008822216,0.0062916856,0.0007492719],"study_design_scores_gemma":[0.0010227934,0.00039130388,0.9458198,0.00028430074,0.000052566225,1.5799839e-7,0.004961122,0.000031037365,0.0016141034,0.0044478513,0.0412129,0.00016206512],"about_ca_topic_score_codex":0.010752292,"about_ca_topic_score_gemma":0.053864192,"teacher_disagreement_score":0.043111898,"about_ca_system_score_codex":0.00020887914,"about_ca_system_score_gemma":0.0019878326,"threshold_uncertainty_score":0.9958352},"labels":[],"label_agreement":null},{"id":"W2160529455","doi":"10.1002/atr.1196","title":"Comparison of static vehicle flow assignment methods and microsimulations for a personal rapid transit network","year":2012,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Benchmark (surveying); Public transport; Computer science; Microsimulation; Flow network; Transport engineering; Service (business); Transit (satellite); Traffic flow (computer networking); Operations research; Linear programming; Work (physics); Simulation; Mathematical optimization; Engineering; Computer network; Algorithm","score_opus":0.039809644029340355,"score_gpt":0.4015217852383029,"score_spread":0.36171214120896256,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2160529455","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.41498795,0.00089872984,0.5833884,0.00019941064,0.0002779721,0.00019628953,0.000027001372,0.000008909107,0.000015365771],"genre_scores_gemma":[0.73409003,0.000099082026,0.2656247,0.00002338669,0.00010312756,0.0000046645146,0.000037935824,0.000007396169,0.000009701337],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99880505,0.00011263471,0.0005704587,0.00007419216,0.00024793646,0.00018975046],"domain_scores_gemma":[0.99874204,0.00037785224,0.00048309835,0.00002917366,0.00025270117,0.00011512256],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009117757,0.00008097504,0.00024856447,0.00007219511,0.00021176324,0.000012695556,0.000044013836,0.000060903276,0.000025995],"category_scores_gemma":[0.00003968157,0.0000803943,0.000093625385,0.00017815144,0.00007089091,0.00051804277,2.6531313e-7,0.00008946027,1.16667586e-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":[0.00035181435,0.00016766027,0.050268423,0.00007151874,0.000063735795,3.292633e-7,0.17950228,0.7175065,0.004133364,0.0007220812,0.000067691326,0.047144573],"study_design_scores_gemma":[0.005521551,0.00086693926,0.8677121,0.0003634369,0.0008763648,0.0000018103191,0.0658269,0.03638848,0.0044053206,0.0011900911,0.016372211,0.00047481805],"about_ca_topic_score_codex":0.000009893905,"about_ca_topic_score_gemma":0.00007590966,"teacher_disagreement_score":0.81744367,"about_ca_system_score_codex":0.0000313562,"about_ca_system_score_gemma":0.000072461466,"threshold_uncertainty_score":0.32783845},"labels":[],"label_agreement":null},{"id":"W2160592557","doi":"10.3141/2430-09","title":"Multidimensional Indicator Analysis for Transport Policy Evaluation","year":2014,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; European Commission","keywords":"Microsimulation; Econometrics; Estimator; Variance (accounting); Sustainability; Investment (military); Aggregate (composite); Land use; Point (geometry); Computer science; Economics; Process (computing); Sustainable transport; Environmental economics; Transport engineering; Statistics; Mathematics; Engineering","score_opus":0.11765297462793368,"score_gpt":0.4554470901522883,"score_spread":0.3377941155243546,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2160592557","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92743033,0.0001239967,0.05993934,0.00868434,0.00066555414,0.002226869,0.00016062832,0.000062030595,0.00070692337],"genre_scores_gemma":[0.9906901,0.00034827134,0.006907359,0.00011223374,0.0006173166,0.00020992006,0.00020504803,0.000051012645,0.0008587022],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9868407,0.0030681915,0.0016703676,0.000552616,0.006787235,0.0010808614],"domain_scores_gemma":[0.98777086,0.0024498021,0.0007700405,0.0004670321,0.007947664,0.0005945765],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.020908168,0.0002689814,0.0005965281,0.0030281465,0.0021163346,0.000117342606,0.001023996,0.00034760308,0.00055816065],"category_scores_gemma":[0.0011534955,0.00022453938,0.0008645997,0.0060669594,0.0010105554,0.0007671014,0.0000026242428,0.0012910521,0.0000199873],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015092125,0.00044879547,0.8293626,0.00014230475,0.0008642708,0.000008878228,0.021694602,0.06782376,0.0004864397,0.064482935,0.0030659018,0.010110311],"study_design_scores_gemma":[0.0021312463,0.000315553,0.92095935,0.000110608664,0.0005539355,6.589729e-8,0.0027752148,0.0020979934,0.00018499045,0.0043051615,0.066322714,0.000243161],"about_ca_topic_score_codex":0.023967063,"about_ca_topic_score_gemma":0.12804256,"teacher_disagreement_score":0.10407551,"about_ca_system_score_codex":0.0005005949,"about_ca_system_score_gemma":0.002752927,"threshold_uncertainty_score":0.99918276},"labels":[],"label_agreement":null},{"id":"W2161647435","doi":"10.1002/atr.199","title":"Signal setting with demand assignment: global optimization with day‐to‐day dynamic stability constraints","year":2012,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":37,"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":"Mathematical optimization; Stability (learning theory); Computer science; Jacobian matrix and determinant; Representation (politics); Simple (philosophy); Optimization problem; Smoothing; Uniqueness; Process (computing); SIGNAL (programming language); Mathematics; Applied mathematics","score_opus":0.00785599595187935,"score_gpt":0.267821449787773,"score_spread":0.25996545383589365,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2161647435","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.36333862,0.000051698138,0.6356197,0.00026060865,0.00014288125,0.00023431277,0.000036265996,0.000031227937,0.0002846823],"genre_scores_gemma":[0.8999161,0.00003442903,0.09980231,0.00005932872,0.00007533504,0.0000059083936,0.00008350152,0.000012919906,0.000010173909],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9981555,0.00015961828,0.00049930444,0.00015896284,0.0007027318,0.00032389318],"domain_scores_gemma":[0.9984876,0.00013587404,0.000556616,0.00007337186,0.0004586563,0.0002878994],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00109387,0.00015336418,0.00021503436,0.000086705666,0.00028524068,0.00004416031,0.000103697406,0.00007708414,0.00012210908],"category_scores_gemma":[0.00005249313,0.00012676937,0.00004978337,0.00048212695,0.00015434758,0.0013578851,6.290583e-7,0.00014236459,0.0000013914045],"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":[0.0002940284,0.00009168173,0.11414062,0.000017983091,0.000035265795,0.000008648218,0.018010663,0.86346954,0.00014988349,0.0003703841,0.000004916299,0.0034063675],"study_design_scores_gemma":[0.003567076,0.0007929183,0.9642638,0.0005651027,0.00038473774,0.000016461267,0.027384851,0.0012058795,0.0005776818,0.00014469653,0.000510372,0.00058643566],"about_ca_topic_score_codex":0.000015663521,"about_ca_topic_score_gemma":0.00066764903,"teacher_disagreement_score":0.8622637,"about_ca_system_score_codex":0.00021238458,"about_ca_system_score_gemma":0.0003180955,"threshold_uncertainty_score":0.5169505},"labels":[],"label_agreement":null},{"id":"W2161816172","doi":"","title":"Light rail in Australia - performance and prospects","year":2013,"lang":"en","type":"article","venue":"Griffith Research Online (Griffith University, Queensland, Australia)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Light rail; Transport engineering; Productivity; Public transport; Light rail transit; Redevelopment; Investment (military); Quarter (Canadian coin); Service (business); Business; Pace; Engineering; Geography; Economic growth; Economics; Civil engineering; Political science; Marketing","score_opus":0.09688834687978495,"score_gpt":0.3641267937188653,"score_spread":0.2672384468390804,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2161816172","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99067706,0.00002791981,0.000018510578,0.004851754,0.00014055321,0.0008544383,0.000052248604,0.00011525524,0.003262254],"genre_scores_gemma":[0.9425217,0.000506083,0.0012285786,0.0000266137,0.00018536398,0.0000066346342,0.00011517072,0.00001794521,0.055391926],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99664223,0.0004610356,0.00030066978,0.0005720277,0.0009920334,0.0010320186],"domain_scores_gemma":[0.99845153,0.00016843517,0.00010349887,0.00025775674,0.0005501622,0.0004686309],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010510045,0.00023248294,0.0002956906,0.0007308319,0.0007699311,0.00020004263,0.00047461022,0.00032731172,0.0006100985],"category_scores_gemma":[0.00012264185,0.00023918775,0.000053994485,0.001569796,0.0004800567,0.0010037589,0.000071404334,0.0009141784,0.0002065275],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017388305,0.00035007956,0.96044886,0.00012854683,0.000038387865,0.00014885863,0.009615837,0.00087815383,0.00019519671,0.0065693157,0.020378271,0.001074619],"study_design_scores_gemma":[0.0014992914,0.0001900536,0.9520446,0.0001759148,0.000015896418,0.0000039317183,0.0063865534,0.00028766226,0.00005027766,0.00021628848,0.038763054,0.00036648137],"about_ca_topic_score_codex":0.029382663,"about_ca_topic_score_gemma":0.035641115,"teacher_disagreement_score":0.05212967,"about_ca_system_score_codex":0.00023732029,"about_ca_system_score_gemma":0.00028121268,"threshold_uncertainty_score":0.98195595},"labels":[],"label_agreement":null},{"id":"W2161843229","doi":"10.1002/atr.1256","title":"Optimization of pedestrian phase patterns at signalized intersections: a multi‐objective approach","year":2013,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":37,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Pedestrian; Intersection (aeronautics); Heuristic; Genetic algorithm; Sensitivity (control systems); Computer science; Signal timing; Phase (matter); Simulation; Mathematical optimization; Transport engineering; Engineering; Control (management); Artificial intelligence; Machine learning; Mathematics; Electronic engineering","score_opus":0.02046771538295306,"score_gpt":0.3060145829796856,"score_spread":0.28554686759673253,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2161843229","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.54917514,0.000059918388,0.44999698,0.00006590689,0.00020821899,0.0002978688,0.000023324059,0.000022344739,0.00015029508],"genre_scores_gemma":[0.95115256,0.00026377986,0.048202366,0.000020492615,0.00006703247,0.00001990986,0.0001620119,0.0000146846405,0.00009714087],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9984546,0.00011491532,0.0007350415,0.00013806958,0.00038978367,0.00016763793],"domain_scores_gemma":[0.9979917,0.00008306414,0.00092485116,0.000070529124,0.00080985646,0.00012000323],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030692937,0.00011595894,0.00024669332,0.0002444679,0.00018983113,0.000024496116,0.00011199426,0.00009393737,0.0002655757],"category_scores_gemma":[0.00006988216,0.000113011316,0.0001427702,0.00035477022,0.000079202095,0.0010520535,8.3637354e-7,0.00013198332,0.0000017177321],"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":[0.00042154395,0.00038173707,0.013284607,0.000045279223,0.000058826376,0.000003097077,0.04027518,0.94154984,0.0017187528,0.00006244109,0.000025577492,0.002173119],"study_design_scores_gemma":[0.038083307,0.00227854,0.7257461,0.00096389226,0.0009092095,0.000019872954,0.18296011,0.038407885,0.007270343,0.0007812041,0.0013032213,0.0012763478],"about_ca_topic_score_codex":0.00079119497,"about_ca_topic_score_gemma":0.0012010672,"teacher_disagreement_score":0.903142,"about_ca_system_score_codex":0.00011402928,"about_ca_system_score_gemma":0.00012540155,"threshold_uncertainty_score":0.46084678},"labels":[],"label_agreement":null},{"id":"W2162487117","doi":"10.1007/s11116-008-9166-8","title":"Modelling daily activity program generation considering within-day and day-to-day dynamics in activity-travel behaviour","year":2008,"lang":"en","type":"article","venue":"Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":89,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Canadian Natural Resources; University of Alberta","funders":"Social Sciences and Humanities Research Council of Canada; Transport Canada; McLean Foundation","keywords":"Duration (music); Scheduling (production processes); Econometric model; Set (abstract data type); Econometrics; Computer science; Operations research; Simulation; Mathematics; Engineering; Operations management","score_opus":0.04801424598588602,"score_gpt":0.2936223921541173,"score_spread":0.24560814616823126,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2162487117","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7444237,0.0000104549945,0.25437263,0.00021504806,0.00014494732,0.00049216626,0.00002976239,0.00012334099,0.00018797469],"genre_scores_gemma":[0.98939985,0.000096379,0.00990751,0.00003086253,0.000060886014,0.00007103671,0.0002763684,0.000022689488,0.00013444384],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9985384,0.00016141571,0.00029689257,0.00036955322,0.00036146448,0.0002723217],"domain_scores_gemma":[0.9994243,0.000094295516,0.00013745813,0.0001008583,0.000103950486,0.00013915174],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00071177044,0.00016375,0.0001957882,0.0001869143,0.0004944022,0.000059864415,0.000067937784,0.00016360005,0.000010008841],"category_scores_gemma":[0.000031090523,0.00019597147,0.000037628102,0.00041867947,0.00010666773,0.00068538805,0.000001171201,0.00019742925,0.00000238525],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000046430618,0.0002104537,0.19599663,0.000023833718,0.000012158129,0.00003191478,0.08967702,0.70457673,0.001085386,0.0025494527,0.000010174669,0.005779809],"study_design_scores_gemma":[0.0005506552,0.00006403311,0.5151751,0.00005184858,0.000039703966,9.915718e-7,0.0019302979,0.4807506,0.0009956887,0.00007350831,0.000035886467,0.0003316858],"about_ca_topic_score_codex":0.0051746834,"about_ca_topic_score_gemma":0.057911742,"teacher_disagreement_score":0.3191785,"about_ca_system_score_codex":0.00014379293,"about_ca_system_score_gemma":0.00019550115,"threshold_uncertainty_score":0.9592789},"labels":[],"label_agreement":null},{"id":"W2162564886","doi":"10.1287/inte.1030.0055","title":"The Canadian Pacific Railway Transforms Operations by Using Models to Develop Its Operating Plans","year":2004,"lang":"en","type":"article","venue":"INFORMS Journal on Applied Analytics","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":47,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Canadian Pacific Railway (Canada)","funders":"","keywords":"Tonnage; Train; Productivity; Service (business); Operations research; Block (permutation group theory); Engineering; Suite; Fuel efficiency; Transport engineering; Rail freight transport; Heuristic; Operations management; Computer science; Business; Economics; Automotive engineering; Marketing","score_opus":0.039494680634362164,"score_gpt":0.2849870721228541,"score_spread":0.24549239148849195,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2162564886","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26424804,0.000073186995,0.5283243,0.017051341,0.0007569519,0.0014283643,0.00026913168,0.00017814936,0.18767054],"genre_scores_gemma":[0.99384874,0.00007641411,0.004550332,0.000706947,0.00011410253,0.00000654646,0.000037265876,0.000014630029,0.00064501644],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99847114,0.000013580667,0.00045149797,0.00011328415,0.00050617685,0.00044429756],"domain_scores_gemma":[0.99896467,0.000032406337,0.00007489842,0.000084916486,0.0003522904,0.0004907965],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0007609387,0.00013611808,0.0001259471,0.00017618101,0.0046398416,0.0008220084,0.0002390711,0.00009772984,0.000018700159],"category_scores_gemma":[0.00005578512,0.00009536989,0.000039545368,0.0007576379,0.00006226078,0.00043673345,0.0000029650632,0.00033841262,0.000030067426],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006948948,0.00000729201,0.00002084681,0.0000010250934,0.000018445373,0.0000026253974,0.01421918,0.88326484,0.00006897207,0.101212084,0.00018194187,0.0009957864],"study_design_scores_gemma":[0.006247318,0.0004049877,0.00045440672,0.000668607,0.000284705,0.00007002702,0.18409663,0.3554506,0.004035921,0.0122663835,0.43241325,0.0036071506],"about_ca_topic_score_codex":0.008159806,"about_ca_topic_score_gemma":0.29292494,"teacher_disagreement_score":0.7296007,"about_ca_system_score_codex":0.0005928781,"about_ca_system_score_gemma":0.0022567527,"threshold_uncertainty_score":0.9984449},"labels":[],"label_agreement":null},{"id":"W2162579447","doi":"10.1061/41177(415)210","title":"Design LRT Signal Priority Strategy to Improve Arterial Traffic Mobility","year":2011,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Preemption; VisSim; Transit (satellite); Intersection (aeronautics); SIGNAL (programming language); Computer science; Bus priority; Transport engineering; Public transport; Real-time computing; Engineering","score_opus":0.05107902660125704,"score_gpt":0.288481222290477,"score_spread":0.23740219568921997,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2162579447","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.74687684,0.000003789184,0.23018356,0.0000828271,0.00040027616,0.0005765719,0.000008850303,0.00028144533,0.021585856],"genre_scores_gemma":[0.98414856,0.000001743441,0.014966172,0.00005878441,0.00009824959,0.00001623882,0.0000046868795,0.0000045478223,0.00070103677],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.9991522,0.000116797244,0.000160076,0.00019667148,0.00017866887,0.00019560395],"domain_scores_gemma":[0.99962074,0.0000389633,0.000034537676,0.00008326744,0.000083551466,0.00013892038],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005431071,0.00006902522,0.000083646824,0.00003133506,0.00020826932,0.000044793665,0.00011127172,0.00007945953,0.0015027769],"category_scores_gemma":[0.000022273825,0.00006531575,0.000027751956,0.00015167125,0.00006117749,0.00017850653,0.000002505517,0.000052997973,0.00005789363],"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":[0.002758599,0.002694189,0.010664786,0.0000635062,0.00013353337,0.000042903986,0.5111972,0.17703995,0.013010911,0.061227977,0.0065672863,0.21459915],"study_design_scores_gemma":[0.008299679,0.0075643132,0.7970082,0.00009966773,0.00036821063,0.0000032470587,0.08230948,0.037260596,0.031389475,0.01448505,0.01623496,0.004977096],"about_ca_topic_score_codex":0.0011259269,"about_ca_topic_score_gemma":0.0010370191,"teacher_disagreement_score":0.78634346,"about_ca_system_score_codex":0.00002895664,"about_ca_system_score_gemma":0.00019373892,"threshold_uncertainty_score":0.99941},"labels":[],"label_agreement":null},{"id":"W2163638079","doi":"10.3141/2276-14","title":"Assessment of Potential Improvements to Metro–Bus Transfers in Bangkok, Thailand","year":2012,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Transport engineering; Transfer (computing); Public transport; Transit (satellite); Engineering; Metropolitan area; Computer science; Geography","score_opus":0.07948370446229842,"score_gpt":0.43048864562433686,"score_spread":0.35100494116203845,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2163638079","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98762685,0.000110739675,0.0068784975,0.0025076477,0.00078785856,0.0012377242,0.000093502706,0.000019867766,0.0007372901],"genre_scores_gemma":[0.9945387,0.0004261111,0.0042529562,0.000050317845,0.00020146744,0.00006522012,0.000028250213,0.000037444774,0.00039949908],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99057955,0.0017262802,0.0015546707,0.00030847488,0.004678389,0.0011526258],"domain_scores_gemma":[0.9956351,0.0006383454,0.00038430665,0.00030446507,0.0024213358,0.00061645283],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01014173,0.00021005739,0.0004741285,0.0016819439,0.00058637204,0.00008485492,0.0008997255,0.00021794443,0.00037763355],"category_scores_gemma":[0.00019352516,0.00017589913,0.0003193587,0.0034027228,0.00049149286,0.0010044503,0.0000037262384,0.0013953596,0.000007393593],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005843575,0.00047573852,0.9614543,0.00012059224,0.00008745473,0.000021896705,0.01709852,0.009000136,0.0028455628,0.0042018197,0.0007534135,0.0033562074],"study_design_scores_gemma":[0.0014612703,0.00042806406,0.98264056,0.00023237226,0.00004709661,7.355136e-8,0.010406656,0.000070130605,0.0006765096,0.00036804515,0.0035025028,0.0001667279],"about_ca_topic_score_codex":0.027365735,"about_ca_topic_score_gemma":0.10177011,"teacher_disagreement_score":0.074404374,"about_ca_system_score_codex":0.00037888685,"about_ca_system_score_gemma":0.0010095355,"threshold_uncertainty_score":0.97911114},"labels":[],"label_agreement":null},{"id":"W2163826616","doi":"10.1287/trsc.1030.0045","title":"Existence, Uniqueness, and Trip Cost Function Properties of User Equilibrium in the Bottleneck Model with Multiple User Classes","year":2004,"lang":"en","type":"article","venue":"Transportation Science","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":235,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Bottleneck; Uniqueness; Schedule; Travel time; Function (biology); Unit (ring theory); Computer science; Mathematical optimization; Unit of time; Total cost; Retard; Average cost; Operations research; Mathematics; Transport engineering; Economics; Engineering; Microeconomics","score_opus":0.04486344256649982,"score_gpt":0.279911263394904,"score_spread":0.2350478208284042,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2163826616","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9723072,0.000042428215,0.026091982,0.000541849,0.00004154843,0.0004057174,0.000010462842,0.00004015632,0.000518647],"genre_scores_gemma":[0.9962356,0.000045225708,0.003439462,0.00011656102,0.000008938375,0.00003819591,0.000012605524,0.000005837011,0.00009756936],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9986544,0.000040471423,0.00025055514,0.00024049656,0.00061193446,0.00020212732],"domain_scores_gemma":[0.9994355,0.000041024283,0.000115397044,0.0001146682,0.00024037388,0.000053063424],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00077071856,0.00008934803,0.00010905834,0.00014048583,0.00028420016,0.0000730204,0.000204702,0.000048309925,0.0000035333574],"category_scores_gemma":[0.000041651663,0.000061858766,0.000017740143,0.001087671,0.00092323124,0.0011544012,0.0000011187831,0.000078102064,6.510496e-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":[0.00029438964,0.00013473303,0.23398371,0.00005647484,0.000005151544,0.0000033156255,0.07243483,0.58743846,0.0073008053,0.09806564,0.000007707754,0.00027476708],"study_design_scores_gemma":[0.0016564069,0.00012843264,0.9659437,0.00021504171,0.000042769214,8.7165057e-7,0.019865032,0.005312808,0.004644889,0.001454402,0.00045739004,0.00027826763],"about_ca_topic_score_codex":0.0014613019,"about_ca_topic_score_gemma":0.011189344,"teacher_disagreement_score":0.73196,"about_ca_system_score_codex":0.000034333232,"about_ca_system_score_gemma":0.00059529993,"threshold_uncertainty_score":0.6243917},"labels":[],"label_agreement":null},{"id":"W2164159470","doi":"10.3141/1791-02","title":"Design and Implementation of Bus–Holding Control Strategies with Real-Time Information","year":2002,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":134,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Headway; Real-time Control System; Control (management); Point (geometry); Real-time data; Set (abstract data type); Computer science; Time point; Operations research; Simulation; Real-time computing; Transport engineering; Engineering; Mathematics","score_opus":0.06752210414353563,"score_gpt":0.3797237726882229,"score_spread":0.3122016685446872,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2164159470","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9628024,0.00010423671,0.032454994,0.002456149,0.0001334549,0.001429194,0.000053199932,0.000041144373,0.0005252172],"genre_scores_gemma":[0.99072725,0.0020056262,0.0069157383,0.000022505725,0.00006535065,0.000045599823,0.000020288762,0.000023431301,0.00017419942],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9934652,0.0014770126,0.0012067601,0.0002170365,0.0030392578,0.0005947686],"domain_scores_gemma":[0.99397385,0.0011137686,0.000728287,0.00020806564,0.0037158919,0.0002601195],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005047984,0.00018107408,0.00035703997,0.000902137,0.00092212803,0.00023610375,0.00046746404,0.00015601901,0.00053940475],"category_scores_gemma":[0.00008264755,0.00014034074,0.000105035266,0.0017157627,0.00081827585,0.0025531317,0.0000017653023,0.00074169727,0.000009140702],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0049124854,0.0005365863,0.4994828,0.0008716052,0.0007398965,0.00009763204,0.26176542,0.1258436,0.010024237,0.04433403,0.008289758,0.04310193],"study_design_scores_gemma":[0.004685536,0.0015060473,0.9271566,0.00046714503,0.00015876033,7.177106e-7,0.056073908,0.0016475634,0.0009984345,0.0015040328,0.005468046,0.00033315708],"about_ca_topic_score_codex":0.018165346,"about_ca_topic_score_gemma":0.017446486,"teacher_disagreement_score":0.42767385,"about_ca_system_score_codex":0.00013463988,"about_ca_system_score_gemma":0.00062676053,"threshold_uncertainty_score":0.9883728},"labels":[],"label_agreement":null},{"id":"W2164161362","doi":"","title":"Cost of Congestion in the Greater Toronto Area","year":2007,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Prosperity; Traffic congestion; Population; Welfare; Geography; Urban area; Business; Regional science; Transport engineering; Economics; Economic growth; Economy; Engineering; Demography","score_opus":0.05203660647117469,"score_gpt":0.33174434523419344,"score_spread":0.27970773876301874,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2164161362","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5419475,0.00003680045,0.025259975,0.0006700719,0.00012077962,0.0002514889,0.0000024271073,0.000037449645,0.43167353],"genre_scores_gemma":[0.99895406,0.000010761786,0.00035440407,0.0000946505,0.000013744482,0.0000014404261,0.000005934913,8.087369e-7,0.0005641707],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99970806,0.00002429203,0.00007374195,0.000033392003,0.000102151025,0.000058389098],"domain_scores_gemma":[0.9998574,0.00005552029,0.00001922427,0.000029229066,0.000027800821,0.000010790408],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005657563,0.000016730617,0.00002489949,0.00001065663,0.00004082659,0.000006612977,0.000041717478,0.0000248817,0.00020409265],"category_scores_gemma":[0.000014745535,0.000011306592,0.000007686549,0.00006284153,0.000031377058,0.000077953635,4.1974556e-7,0.000014383823,0.0000016584507],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000050965235,0.000061410385,0.6567195,0.000005260895,0.0000043235837,0.0000049133023,0.23751794,0.0025468583,0.000048388505,0.0921791,0.0010652978,0.009796042],"study_design_scores_gemma":[0.00012701136,0.000009956252,0.96401614,0.000008296972,0.0000028225202,1.0375992e-7,0.030987924,0.000054387256,0.00006699533,0.00008256434,0.004614537,0.000029259583],"about_ca_topic_score_codex":0.008011665,"about_ca_topic_score_gemma":0.10117292,"teacher_disagreement_score":0.4570066,"about_ca_system_score_codex":0.000018243592,"about_ca_system_score_gemma":0.000011648796,"threshold_uncertainty_score":0.99859405},"labels":[],"label_agreement":null},{"id":"W2164211305","doi":"10.3141/1777-03","title":"Development of Microsimulation Activity-Based Model for San Francisco: Destination and Mode Choice Models","year":2001,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":107,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Microsimulation; Multinomial logistic regression; Mode choice; Discrete choice; Nested logit; Mode (computer interface); Computer science; Econometrics; Mixed logit; Choice set; Trip distribution; Estimation; Travel survey; Logit; Operations research; Travel behavior; Logistic regression; Economics; Transport engineering; Mathematics; Engineering; Public transport; Machine learning","score_opus":0.17295274134467659,"score_gpt":0.43966836891705646,"score_spread":0.2667156275723799,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2164211305","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.67382276,0.000054212007,0.32414588,0.00085460563,0.00011002619,0.0008734289,0.000045455643,0.000021286036,0.000072376555],"genre_scores_gemma":[0.94562346,0.0002036563,0.05362162,0.000023722438,0.00007591539,0.00008430558,0.000046558634,0.000037100872,0.00028364224],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9946557,0.0006066514,0.0011467113,0.00034685223,0.0026250854,0.0006189611],"domain_scores_gemma":[0.9931541,0.0013460468,0.0006155109,0.00021946465,0.0043833093,0.00028154926],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.004565219,0.00019518627,0.00036073505,0.0008809549,0.0013617267,0.00010448405,0.00050094357,0.00022882922,0.00003245833],"category_scores_gemma":[0.00026272135,0.00017419008,0.00017683742,0.0014286062,0.00056409795,0.0010312535,0.0000024567767,0.0007260539,7.86125e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0025004242,0.00044664033,0.11867568,0.00037897177,0.00009492894,0.000005917009,0.041382585,0.7999862,0.009784467,0.0062457873,0.00034754782,0.020150892],"study_design_scores_gemma":[0.003497994,0.000311196,0.4754308,0.00057161046,0.00009377823,1.248479e-7,0.00296564,0.50279015,0.002665453,0.006547176,0.004774185,0.00035188638],"about_ca_topic_score_codex":0.005222447,"about_ca_topic_score_gemma":0.15178658,"teacher_disagreement_score":0.3567551,"about_ca_system_score_codex":0.00022326336,"about_ca_system_score_gemma":0.0015726815,"threshold_uncertainty_score":0.99993837},"labels":[],"label_agreement":null},{"id":"W2166201798","doi":"10.3141/1968-04","title":"Predicting Directional Design Hourly Volume from Statutory Holiday Traffic","year":2006,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Regina","funders":"Natural Sciences and Engineering Research Council of Canada; University of Regina","keywords":"Traffic volume; Recreation; Transport engineering; Volume (thermodynamics); Environmental science; Travel time; Statistics; Meteorology; Computer science; Geography; Econometrics; Mathematics; Engineering; Ecology","score_opus":0.07995021905932018,"score_gpt":0.3681293791735894,"score_spread":0.28817916011426925,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2166201798","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9733823,0.00041957415,0.019526688,0.0031929458,0.001231411,0.0010386659,0.00026157597,0.0001303613,0.00081649894],"genre_scores_gemma":[0.98531157,0.00056087854,0.009621818,0.000037923593,0.00093985733,0.00006903239,0.00013752622,0.00006838537,0.003253014],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.98782444,0.0032260919,0.0016289983,0.0005258732,0.005666319,0.0011282527],"domain_scores_gemma":[0.9921849,0.0021588313,0.00068615057,0.00035421475,0.0041728,0.00044313734],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.00765904,0.0002839908,0.00045192515,0.0010572802,0.002296041,0.00034083976,0.0010699738,0.00034143226,0.00076083967],"category_scores_gemma":[0.0003254291,0.00024760398,0.00041112726,0.0026203129,0.0011531349,0.0012129887,0.0000033261967,0.0021858455,0.000040211595],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015692249,0.00053243735,0.7361238,0.00006918998,0.00018858747,0.0001394986,0.018789204,0.20153493,0.0010863079,0.0035415904,0.031098355,0.0053269025],"study_design_scores_gemma":[0.0013429653,0.0002703779,0.9433597,0.00025907878,0.000078834826,2.112304e-7,0.0053960104,0.00081443804,0.00030577756,0.00298347,0.04492056,0.00026859908],"about_ca_topic_score_codex":0.10254669,"about_ca_topic_score_gemma":0.18665197,"teacher_disagreement_score":0.2072359,"about_ca_system_score_codex":0.00047195095,"about_ca_system_score_gemma":0.0019131135,"threshold_uncertainty_score":0.9999976},"labels":[],"label_agreement":null},{"id":"W2167724589","doi":"10.1061/41177(415)103","title":"Applying GIS-Based Traditional Travel Demand Model for Improved Network-Wide Traffic Estimation: New Brunswick Case Study","year":2011,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Computer science; Estimation; Transport engineering; Interpolation (computer graphics); TRIPS architecture; Geographic information system; Geography; Telecommunications; Cartography; Engineering","score_opus":0.09405221787150682,"score_gpt":0.2984225525535256,"score_spread":0.20437033468201882,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2167724589","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04970489,0.000015291069,0.9469128,0.00024056449,0.00013234663,0.0015912835,0.00001027478,0.00017448643,0.0012180412],"genre_scores_gemma":[0.84381706,0.0000013885204,0.15439543,0.000273401,0.00012247209,0.00014125553,0.000080987025,0.000014522276,0.001153483],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99893403,0.000051605748,0.0003056672,0.0002535165,0.00018557822,0.00026957504],"domain_scores_gemma":[0.99931574,0.00021386806,0.00010346187,0.000095295996,0.000095321666,0.0001762926],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005109195,0.0001259419,0.00015010899,0.00006003756,0.000738604,0.000060582814,0.00010298187,0.000089132685,0.000135537],"category_scores_gemma":[0.00004188399,0.00012755832,0.000072466806,0.00020010388,0.000054364762,0.00026421022,0.0000014409318,0.000072274655,0.0000026706311],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006335613,0.00020646938,0.0013846586,0.0000071977515,0.000020305288,0.00001512243,0.021160573,0.9679146,6.647487e-7,0.004551779,0.001230747,0.0034445697],"study_design_scores_gemma":[0.0013216949,0.00010057447,0.0016118403,0.0000084201765,0.000070729904,0.000003946455,0.0051622596,0.9906036,0.000003383029,0.00086906453,0.00006714531,0.0001773232],"about_ca_topic_score_codex":0.00279134,"about_ca_topic_score_gemma":0.024264215,"teacher_disagreement_score":0.7941122,"about_ca_system_score_codex":0.00003558164,"about_ca_system_score_gemma":0.00086964684,"threshold_uncertainty_score":0.9935404},"labels":[],"label_agreement":null},{"id":"W2167846462","doi":"","title":"Recent developments and current policy issues in road pricing in the US and Canada","year":2005,"lang":"en","type":"article","venue":"OpenstarTs (Univeristy of Trieste https://www.units.it/)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Road pricing; Congestion pricing; Revenue; Singapore Area Licensing Scheme; Business; Finance; Road transport; Transport economics; Transport engineering; Economics; Traffic congestion; Engineering","score_opus":0.03082524441575113,"score_gpt":0.2941329639241745,"score_spread":0.26330771950842335,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2167846462","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9760002,0.0010464224,0.000017937196,0.013142391,0.00012446681,0.00041187048,0.00002525368,0.000013772457,0.009217674],"genre_scores_gemma":[0.988273,0.0087437015,0.00056731567,0.00047118444,0.00005272854,0.0000069207854,0.000060623537,0.000010419537,0.0018141064],"study_design_codex":"qualitative","study_design_gemma":"not_applicable","domain_scores_codex":[0.99849766,0.00021041033,0.00034552562,0.00023809972,0.00040338727,0.00030492758],"domain_scores_gemma":[0.99932736,0.000091574424,0.00016833385,0.00012737932,0.00018261651,0.00010274752],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006449181,0.00014905764,0.00023958963,0.00020770139,0.0003832551,0.00008416783,0.00021473171,0.00006812268,0.00005523953],"category_scores_gemma":[0.0002054739,0.00014011854,0.000009966987,0.0010570195,0.00019643894,0.0004580186,0.000028367975,0.00018947636,0.0000022284355],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024872195,0.00022290615,0.25690153,0.00015404102,0.000048791622,0.00008193143,0.3794638,0.0039764056,0.000014832331,0.00968774,0.016107325,0.33309197],"study_design_scores_gemma":[0.0007840359,0.00002688426,0.28691086,0.00023686425,0.000011898907,0.0000016124064,0.038900636,0.00038887584,0.000008511638,0.000027433398,0.6724896,0.00021281462],"about_ca_topic_score_codex":0.5779655,"about_ca_topic_score_gemma":0.91809815,"teacher_disagreement_score":0.65638226,"about_ca_system_score_codex":0.00021809243,"about_ca_system_score_gemma":0.0015106241,"threshold_uncertainty_score":0.5713868},"labels":[],"label_agreement":null},{"id":"W2168035812","doi":"10.3141/2477-10","title":"Border Crossing Choice Behavior of Trucks along Trade Corridor between Toronto, Ontario, Canada, and Chicago, Illinois","year":2015,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada; FedDev Ontario; Transport Canada; Government of Ontario","keywords":"Truck; Transport engineering; Border crossing; Bridge (graph theory); Vehicle miles of travel; Traverse; Travel time; Business; Geography; Engineering; Immigration","score_opus":0.09386821655819215,"score_gpt":0.4018757340133563,"score_spread":0.30800751745516414,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2168035812","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9953348,0.00037561354,0.00053076656,0.0018404166,0.0005495935,0.00081273244,0.00015158016,0.000022215132,0.00038227512],"genre_scores_gemma":[0.99659854,0.00023724574,0.0016708149,0.0000360029,0.00023469461,0.000034167955,0.000059176353,0.000039768267,0.0010896083],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99146515,0.0011537297,0.001543552,0.00038091704,0.004631805,0.000824824],"domain_scores_gemma":[0.9941466,0.0011547977,0.0006600129,0.00030307434,0.0029320198,0.0008035053],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005109687,0.00023795984,0.0005386723,0.00036014346,0.0012221156,0.00020703301,0.0007757822,0.00026076654,0.0003782852],"category_scores_gemma":[0.00036631967,0.00020437829,0.00019891496,0.0011239109,0.0012781325,0.0009830265,0.0000042579386,0.0014732474,9.845841e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034261207,0.00010723433,0.95720094,0.000064835076,0.00008823032,0.00003763881,0.033311263,0.0009726089,0.000113749054,0.00046305102,0.0022984033,0.004999425],"study_design_scores_gemma":[0.0014290682,0.00026895132,0.95675516,0.00019432488,0.00012645856,2.9862247e-7,0.009149307,0.00001922535,0.00023202276,0.00020075346,0.0314297,0.00019472397],"about_ca_topic_score_codex":0.99242455,"about_ca_topic_score_gemma":0.99954695,"teacher_disagreement_score":0.029131297,"about_ca_system_score_codex":0.00097524124,"about_ca_system_score_gemma":0.008118089,"threshold_uncertainty_score":0.99750495},"labels":[],"label_agreement":null},{"id":"W2168512025","doi":"10.7202/600928ar","title":"Le navettage et la tarification du transport par automobile en milieu urbain","year":2009,"lang":"en","type":"article","venue":"L Actualité économique","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"TRIPS architecture; Metropolitan area; Public transport; Context (archaeology); Subsidy; Welfare economics; Business; Economics; Transport engineering; Geography; Engineering","score_opus":0.013142639098126943,"score_gpt":0.2670268124255511,"score_spread":0.25388417332742413,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2168512025","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.87653506,0.000044315686,0.013189837,0.011027889,0.00013755157,0.00041152956,0.00004318463,0.00047922286,0.098131396],"genre_scores_gemma":[0.99411786,0.000100533725,0.0023644953,0.0014342108,0.0001082516,0.000027820462,0.00023434922,0.000011759863,0.0016007181],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99887836,0.00032331768,0.00027691733,0.00024308632,0.000080006204,0.00019833629],"domain_scores_gemma":[0.9993831,0.00021575947,0.000101596226,0.00014885301,0.000046282974,0.00010442904],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010870965,0.00011269556,0.0001495166,0.00006318911,0.0002608513,0.00005655468,0.0001734828,0.00019527096,0.00009888086],"category_scores_gemma":[0.00007568429,0.00012698893,0.00007049221,0.00013124774,0.0000784915,0.00038571432,0.0000015472654,0.00014166183,0.000024791001],"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":[0.000037834187,0.00017757615,0.009405755,0.000008502357,0.000017065413,0.000008774299,0.07143543,0.007090225,0.00041334017,0.9058749,0.00057888887,0.00495171],"study_design_scores_gemma":[0.0008473865,0.00008345588,0.34847084,0.00003555515,0.00003142264,0.0000024416975,0.003713509,0.0005870489,0.0011818126,0.010545293,0.63400406,0.0004971732],"about_ca_topic_score_codex":0.002770812,"about_ca_topic_score_gemma":0.002254858,"teacher_disagreement_score":0.8953296,"about_ca_system_score_codex":0.000041356023,"about_ca_system_score_gemma":0.00036682436,"threshold_uncertainty_score":0.5178458},"labels":[],"label_agreement":null},{"id":"W2169191293","doi":"10.1108/9781615832460-007","title":"Traffic Congestion And Congestion Pricing","year":2001,"lang":"en","type":"book-chapter","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":175,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Koninklijke Nederlandse Akademie van Wetenschappen","keywords":"Singapore Area Licensing Scheme; Congestion pricing; Traffic congestion; Business; Computer science; Computer network; Transport engineering; Engineering","score_opus":0.025005889117894566,"score_gpt":0.26520548599820565,"score_spread":0.2401995968803111,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2169191293","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0008810136,0.00026989268,0.0010976205,0.0006792446,0.00023061935,0.00021550865,0.000006392473,0.0002506543,0.99636906],"genre_scores_gemma":[0.10689962,0.0025679376,0.00057775114,0.00011656052,0.00022355033,0.0000036040503,0.00018447719,0.000023563243,0.8894029],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99926984,0.000018834688,0.00016874955,0.00020358714,0.0002209,0.000118058175],"domain_scores_gemma":[0.9995745,0.00007812726,0.00011030264,0.000058295613,0.00009995526,0.00007885689],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021965972,0.00011964361,0.00013612508,0.00010070181,0.00026706664,0.00006181553,0.000043439977,0.0003038791,0.0006363452],"category_scores_gemma":[0.000027640941,0.00012561379,0.000028044775,0.000029972874,0.00013530253,0.0001190516,0.0000023387277,0.00013661929,0.000031948795],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017564706,0.000008740012,0.00031535092,0.000023162773,0.000026223275,0.000013491714,0.0046869176,0.011006763,0.0000012981236,0.9313841,0.0018373362,0.050679073],"study_design_scores_gemma":[0.000424823,0.00006499981,0.0038477483,0.00032815686,0.00015726982,0.000005390097,0.00072624563,0.00061865145,7.6623496e-7,0.0042581665,0.9890056,0.0005621447],"about_ca_topic_score_codex":0.0001552888,"about_ca_topic_score_gemma":0.0025005257,"teacher_disagreement_score":0.9871683,"about_ca_system_score_codex":0.00004578824,"about_ca_system_score_gemma":0.00007448749,"threshold_uncertainty_score":0.6967533},"labels":[],"label_agreement":null},{"id":"W2169672572","doi":"","title":"Past Presidents' Award for Merit in Transportation: Bus Rapid Transit Land Development Guidelines","year":2008,"lang":"en","type":"article","venue":"ITE journal","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Bus rapid transit; Public transport; Transit-oriented development; Transport engineering; Business; Sustainability; Government (linguistics); Transit (satellite); Land use; Process (computing); Environmental planning; Engineering; Computer science; Geography; Civil engineering","score_opus":0.0644913369858759,"score_gpt":0.32855600837943433,"score_spread":0.26406467139355844,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2169672572","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8933593,0.00090045185,0.095850356,0.0077167037,0.00090419524,0.00045059173,0.000041618292,0.000086419524,0.0006903336],"genre_scores_gemma":[0.9403012,0.0015055949,0.055105172,0.00037860314,0.0009102767,0.00003125586,0.00014284348,0.000022619732,0.0016024476],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99880034,0.000046055702,0.00048498187,0.00011612201,0.00034137518,0.00021112357],"domain_scores_gemma":[0.99941754,0.000049695263,0.00012033247,0.000037341357,0.00026949716,0.000105597814],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005279587,0.00008336599,0.0001329801,0.00011954162,0.0005549224,0.000043056807,0.00010872137,0.000070624206,0.00010860123],"category_scores_gemma":[0.00003944514,0.0000807922,0.000058775327,0.0001884016,0.000043957425,0.00027805896,4.2808418e-7,0.00010371907,0.0000038905146],"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":[0.00036002745,0.0001688924,0.69762415,0.00004644811,0.00008730363,0.00018510953,0.17415604,0.08130419,0.00009005229,0.00024046376,0.018105464,0.027631886],"study_design_scores_gemma":[0.0016546608,0.00003398204,0.65540403,0.00009955251,0.00002255886,0.000018160004,0.0017254296,0.00014597118,0.00009916863,0.00012220883,0.3404601,0.00021416055],"about_ca_topic_score_codex":0.00016545474,"about_ca_topic_score_gemma":0.0028955378,"teacher_disagreement_score":0.32235464,"about_ca_system_score_codex":0.00004419098,"about_ca_system_score_gemma":0.00030973193,"threshold_uncertainty_score":0.42680708},"labels":[],"label_agreement":null},{"id":"W2170106860","doi":"10.1109/tits.2006.888620","title":"An Examination of the Public Transport Information Requirements of Users","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":118,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Trinity College","funders":"","keywords":"Public transport; The Internet; Computer science; Passenger information; Preference; Information system; Focus (optics); Exploratory research; Transport engineering; World Wide Web; Engineering","score_opus":0.03995829115604335,"score_gpt":0.30249004713195426,"score_spread":0.26253175597591094,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2170106860","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3609165,0.000011105275,0.63637465,0.000046452,0.0011033614,0.0005514039,0.0001267271,0.00007114887,0.0007986478],"genre_scores_gemma":[0.9993167,0.00006566349,0.00026715072,0.000028959272,0.00002724986,0.000030083476,0.00012511354,0.000015109174,0.00012398993],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9970475,0.00019068649,0.0012394921,0.00018495016,0.0010787672,0.00025863748],"domain_scores_gemma":[0.9981696,0.00011472693,0.00059196353,0.0002842083,0.00070869114,0.00013082176],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017929928,0.00016166842,0.0002246352,0.00044123858,0.00035968496,0.000030071871,0.00030523638,0.0001934866,0.00007778755],"category_scores_gemma":[0.000009772131,0.00014731984,0.00016488571,0.0010250473,0.00021874628,0.0012971667,7.024453e-8,0.00015670314,0.000006185742],"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":[0.0004907155,0.0014653422,0.031568963,0.00060004566,0.0002985866,0.0000033256238,0.2061628,0.6475306,0.0034378509,0.052781362,0.0000396235,0.055620793],"study_design_scores_gemma":[0.0030039477,0.0009584363,0.63760465,0.0010807312,0.0006517084,0.0000035811468,0.1587716,0.005547288,0.17638543,0.00014552564,0.014465294,0.0013817889],"about_ca_topic_score_codex":0.0008486803,"about_ca_topic_score_gemma":0.0033996198,"teacher_disagreement_score":0.64198333,"about_ca_system_score_codex":0.00012427094,"about_ca_system_score_gemma":0.00018443384,"threshold_uncertainty_score":0.6007529},"labels":[],"label_agreement":null},{"id":"W2170354462","doi":"10.1023/b:heur.0000045323.83583.bd","title":"A Slope Scaling/Lagrangean Perturbation Heuristic with Long-Term Memory for Multicommodity Capacitated Fixed-Charge Network Design","year":2004,"lang":"en","type":"article","venue":"Journal of Heuristics","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":82,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Université du Québec à Montréal","funders":"","keywords":"Scaling; Mathematical optimization; Perturbation (astronomy); Heuristic; Fixed charge; Computer science; Term (time); Mathematics; Algorithm; Physics","score_opus":0.03750003750920303,"score_gpt":0.2883837595759827,"score_spread":0.25088372206677967,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2170354462","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.094736345,0.00023540475,0.9033377,0.00051405927,0.0005836638,0.0003587269,0.000023636892,0.000044139848,0.00016631251],"genre_scores_gemma":[0.904741,0.00012666374,0.09411993,0.000119557335,0.0006464298,0.000005346268,0.000039632152,0.000022776829,0.00017869062],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9984969,0.00015412313,0.0004753653,0.00012748131,0.00043451108,0.0003116264],"domain_scores_gemma":[0.9979239,0.00047734883,0.00055220973,0.00009075539,0.00078488275,0.00017090279],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001199869,0.00013546825,0.00024890256,0.00009309125,0.0005925024,0.00009460653,0.00016706338,0.00010665386,0.000024215606],"category_scores_gemma":[0.0006111501,0.00011387276,0.00008184692,0.00024133862,0.00013403792,0.00022158219,0.0000031303348,0.00021038229,0.0000037773632],"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":[0.0010361199,0.00033714078,0.012139331,0.0001057631,0.00013919006,0.00014147366,0.016333437,0.96227366,0.00005404904,0.003214226,0.002387948,0.0018376639],"study_design_scores_gemma":[0.090380825,0.015430029,0.6176283,0.01172467,0.010171893,0.0008830043,0.019155,0.13152762,0.004168507,0.07402234,0.015672833,0.009235027],"about_ca_topic_score_codex":0.000045334742,"about_ca_topic_score_gemma":0.00016803827,"teacher_disagreement_score":0.83074605,"about_ca_system_score_codex":0.00013212072,"about_ca_system_score_gemma":0.00035318892,"threshold_uncertainty_score":0.46435964},"labels":[],"label_agreement":null},{"id":"W2170491313","doi":"10.3141/2413-04","title":"Joint Model of Weekend Discretionary Activity Participation and Episode Duration","year":2014,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Duration (music); Copula (linguistics); Sample (material); Computer science; Econometrics; Travel behavior; Context (archaeology); Component (thermodynamics); Operations research; Engineering; Geography; Economics; Transport engineering","score_opus":0.13229188059333002,"score_gpt":0.4076947439261939,"score_spread":0.2754028633328639,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2170491313","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9533859,0.0000599783,0.039466143,0.0056162463,0.00023655522,0.0006376846,0.000052697975,0.000027826702,0.00051698746],"genre_scores_gemma":[0.9945965,0.0007272469,0.0038943994,0.000026140537,0.00013636271,0.00004231572,0.00002681776,0.000028846092,0.00052138785],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9927158,0.0019766756,0.001262665,0.0003271415,0.0031414612,0.0005762531],"domain_scores_gemma":[0.9946395,0.00093832455,0.00073609396,0.00028037254,0.0030687854,0.00033690262],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007295426,0.00017646652,0.00038658024,0.00072225643,0.0010539294,0.000096402146,0.00042805332,0.00021355315,0.000104220824],"category_scores_gemma":[0.00058296154,0.00014732424,0.00021499651,0.0012841613,0.0010889897,0.0010246825,0.000004097439,0.0010260425,0.0000038005692],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0027114009,0.0008757229,0.6575618,0.00063178426,0.00021827615,0.000013874267,0.048491843,0.12292015,0.020752877,0.11577389,0.0030838603,0.026964553],"study_design_scores_gemma":[0.0010358661,0.00035733683,0.96924764,0.0002692439,0.00006853737,8.8646686e-8,0.0020647526,0.013115873,0.0020392227,0.009210072,0.002408085,0.00018331278],"about_ca_topic_score_codex":0.01142112,"about_ca_topic_score_gemma":0.06581604,"teacher_disagreement_score":0.31168583,"about_ca_system_score_codex":0.00014034819,"about_ca_system_score_gemma":0.00052004756,"threshold_uncertainty_score":0.9951619},"labels":[],"label_agreement":null},{"id":"W2171169840","doi":"10.3141/2003-03","title":"Dynamic Choice Model of Urban Commercial Activity Patterns of Vehicles and People","year":2007,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"TRIPS architecture; Travel survey; Schedule; Consistency (knowledge bases); Transport engineering; Duration (music); Microsimulation; Mode choice; Process (computing); Travel behavior; Computer science; Work (physics); Train; Operations research; Estimation; Survey data collection; Discrete choice; Engineering; Public transport; Geography; Statistics; Mathematics","score_opus":0.07969423198928043,"score_gpt":0.40381744499075584,"score_spread":0.3241232130014754,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2171169840","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9819594,0.00009092668,0.015587799,0.0012956491,0.00018745747,0.0005666704,0.00015717055,0.000016519434,0.00013837911],"genre_scores_gemma":[0.9964307,0.0010292156,0.0020541663,0.000012908307,0.00005757935,0.000009309328,0.00001807763,0.00002800102,0.00036004122],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9940057,0.00078846235,0.0011820305,0.0002665978,0.0031305733,0.00062667613],"domain_scores_gemma":[0.9940527,0.0015476539,0.00070393755,0.0002545957,0.0031262168,0.0003149133],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0067055738,0.00016584873,0.00045133944,0.0008666637,0.00064347597,0.00004550716,0.000639506,0.00021945791,0.00006126623],"category_scores_gemma":[0.00021749562,0.00014331708,0.000239214,0.0014772,0.000795325,0.0006269982,0.0000042317397,0.0011305253,8.209851e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012167498,0.00027875806,0.9499426,0.0002410009,0.00010720691,0.000009439455,0.02837076,0.005527076,0.0053893793,0.003468846,0.00022398426,0.005224191],"study_design_scores_gemma":[0.001004016,0.00027061722,0.98986226,0.00023940449,0.0000706091,8.6560235e-8,0.004740956,0.0010095784,0.0016470936,0.00064849167,0.00038164068,0.00012521463],"about_ca_topic_score_codex":0.036227148,"about_ca_topic_score_gemma":0.35433325,"teacher_disagreement_score":0.3181061,"about_ca_system_score_codex":0.00013025886,"about_ca_system_score_gemma":0.00068612274,"threshold_uncertainty_score":0.9701907},"labels":[],"label_agreement":null},{"id":"W2172237755","doi":"10.1287/trsc.34.3.312.12298","title":"The Shortest Path Problem with Time Windows and Linear Waiting Costs","year":2000,"lang":"en","type":"article","venue":"Transportation Science","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":54,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Shortest path problem; Constrained Shortest Path First; K shortest path routing; Mathematical optimization; Path (computing); Longest path problem; Node (physics); Yen's algorithm; Computer science; Shortest Path Faster Algorithm; Integer programming; Fast path; Euclidean shortest path; Linear programming; Mathematics; Dijkstra's algorithm; Graph; Engineering; Theoretical computer science; Computer network","score_opus":0.010198130635898821,"score_gpt":0.2597355747385383,"score_spread":0.24953744410263948,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2172237755","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97637564,0.000050970968,0.0013665481,0.0012512798,0.000034770055,0.0003874331,0.000016628515,0.00016071925,0.020356039],"genre_scores_gemma":[0.99513286,0.00011898996,0.002962534,0.000088074434,0.00003098887,0.000014548655,0.000024304642,0.000006793123,0.0016209054],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99865264,0.00003357656,0.00019488923,0.00025183734,0.0005898997,0.00027718232],"domain_scores_gemma":[0.9994322,0.00011111277,0.0000673,0.00009281905,0.00017020563,0.00012634973],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.00092704006,0.00008140291,0.00007316843,0.000042952783,0.001872868,0.00016257247,0.00018462306,0.000032463737,0.00012923776],"category_scores_gemma":[0.000018728728,0.000057784375,0.000014126676,0.00074349897,0.000917353,0.00059733813,5.552263e-7,0.0000800946,0.000023785162],"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":[0.00039478275,0.00017576868,0.32075214,0.00002615678,0.000036069134,0.00004881515,0.17161576,0.137561,0.0016389,0.08650883,0.00035474784,0.28088704],"study_design_scores_gemma":[0.0015273466,0.00027549715,0.8844213,0.0002470575,0.00009131431,0.0000056909216,0.010636066,0.040374238,0.00046866012,0.00057919556,0.060522303,0.0008513372],"about_ca_topic_score_codex":0.00042720244,"about_ca_topic_score_gemma":0.0016521978,"teacher_disagreement_score":0.56366915,"about_ca_system_score_codex":0.000027718614,"about_ca_system_score_gemma":0.00025704963,"threshold_uncertainty_score":0.99942654},"labels":[],"label_agreement":null},{"id":"W2172452763","doi":"10.1139/l2012-048","title":"Are transit users loyal? Revelations from a hazard model based on smart card data","year":2012,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":50,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"University of Toronto; Polytechnique Montréal","funders":"","keywords":"Loyalty; Smart card; TRIPS architecture; Ticket; Public transport; Population; Hazard; Transit (satellite); Data collection; Business; Credit card; Postal service; Computer science; Advertising; Transport engineering; Computer security; Engineering; Marketing; Statistics; Mathematics","score_opus":0.039271434882846096,"score_gpt":0.24343412000941014,"score_spread":0.20416268512656405,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2172452763","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.070603214,0.0006061737,0.91918176,0.0028689129,0.0014564645,0.00014123895,0.0007033517,0.000055802746,0.004383105],"genre_scores_gemma":[0.99583066,0.000006338987,0.003689536,0.00014197503,0.00020686342,6.7883263e-7,0.000052180156,0.000013275904,0.00005850113],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992853,0.000024872526,0.00018539545,0.00007258014,0.00019598924,0.00023591095],"domain_scores_gemma":[0.9990726,0.000068020105,0.000103928476,0.0001402859,0.00007952382,0.0005356355],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037455675,0.000072539464,0.00011603711,0.00019532621,0.00015748895,0.000045058292,0.00021041011,0.00006665003,0.000091276226],"category_scores_gemma":[0.0001826088,0.00008030501,0.00004205829,0.00016687809,0.000021206732,0.00041202197,9.815135e-7,0.00016456189,0.0000031064021],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000031283835,0.0000036037823,0.026549948,0.0000033228148,0.000013642983,0.000007935632,0.0026628575,0.96866244,0.000004162266,0.00017021497,0.0018622277,0.000056515935],"study_design_scores_gemma":[0.00044098758,0.000014821708,0.10954275,0.0003217703,0.00010530168,0.0000015360458,0.0008519733,0.8489515,0.0000073293013,0.00003653641,0.03945649,0.0002689742],"about_ca_topic_score_codex":0.0035816121,"about_ca_topic_score_gemma":0.3659285,"teacher_disagreement_score":0.92522746,"about_ca_system_score_codex":0.000114748924,"about_ca_system_score_gemma":0.0005926529,"threshold_uncertainty_score":0.6456416},"labels":[],"label_agreement":null},{"id":"W2173890128","doi":"","title":"Meeting the Public’s Need for Transit Options: Characteristics of Socially Equitable Transit Networks","year":2015,"lang":"en","type":"article","venue":"ITE journal","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Equity (law); Metropolitan area; Transit (satellite); Business; Public transport; Social equality; Public economics; Transport engineering; Public administration; Political science; Economics; Engineering; Geography; Law","score_opus":0.057076395164721495,"score_gpt":0.309183047133691,"score_spread":0.2521066519689695,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2173890128","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.048315942,0.0010112202,0.91155314,0.030566487,0.0019075403,0.00050927885,0.00005940526,0.0000893181,0.005987655],"genre_scores_gemma":[0.9931492,0.00027535134,0.0050911414,0.00019296067,0.00081707444,0.000006647152,0.00002779518,0.000011496549,0.0004282908],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9988369,0.00014412666,0.00034552207,0.00007025599,0.0003222093,0.00028102275],"domain_scores_gemma":[0.9988407,0.00015160322,0.0002493857,0.00005167159,0.000572452,0.0001341986],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002617924,0.000065932174,0.00013670449,0.000054957254,0.00081322104,0.00019333539,0.00018395462,0.00008912986,0.000030260364],"category_scores_gemma":[0.0002177024,0.00005296774,0.000095797885,0.00022737448,0.000095844596,0.00032722225,0.0000022149416,0.00016226279,0.0000010036156],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00070045184,0.00055648515,0.024169099,0.000084991014,0.0005021953,0.00002113437,0.2834261,0.41575405,0.00058027275,0.16488914,0.03355248,0.07576357],"study_design_scores_gemma":[0.010466037,0.00077001075,0.07257798,0.00072611356,0.0011964138,0.0000743144,0.17429926,0.0907067,0.000085920256,0.02248941,0.62495506,0.0016527653],"about_ca_topic_score_codex":0.00005625496,"about_ca_topic_score_gemma":0.00044300666,"teacher_disagreement_score":0.9448333,"about_ca_system_score_codex":0.00004556043,"about_ca_system_score_gemma":0.00048747723,"threshold_uncertainty_score":0.62547207},"labels":[],"label_agreement":null},{"id":"W2175645702","doi":"10.3141/2254-06","title":"Design of Comprehensive Microsimulator of Household Vehicle Fleet Composition, Utilization, and Evolution","year":2011,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Southwest University; University of Texas at Austin; California Energy Commission","keywords":"Microsimulation; Context (archaeology); Sample (material); Commercial vehicle; Vehicle miles of travel; Transport engineering; Preference; Fleet management; Composition (language); Computer science; Operations research; Engineering; Automotive engineering; Economics; Geography","score_opus":0.22672831551933662,"score_gpt":0.3893433175583016,"score_spread":0.16261500203896498,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2175645702","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9647972,0.00048313616,0.033024497,0.00034979952,0.00023717899,0.00088670995,0.000092061215,0.000025267811,0.00010417374],"genre_scores_gemma":[0.9912155,0.0014194001,0.007142565,0.000016265576,0.000050756782,0.000016813416,0.00002028956,0.00003066492,0.000087734166],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9937343,0.0017456894,0.0013953989,0.0002772435,0.0023620347,0.0004853535],"domain_scores_gemma":[0.99201727,0.0009128767,0.000780028,0.00026575165,0.0057702544,0.00025382583],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033173873,0.00017092259,0.00041998038,0.00085986545,0.0006995948,0.000039061888,0.00057688885,0.0002094929,0.000114724804],"category_scores_gemma":[0.00009803455,0.00014934746,0.00017358146,0.001957946,0.0015424216,0.00065265514,0.0000039360807,0.00070675835,0.0000017101491],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.003943253,0.0008259918,0.8343044,0.0005622417,0.00027236607,0.000029105695,0.06898267,0.030363917,0.026912306,0.030585213,0.0015691338,0.0016493953],"study_design_scores_gemma":[0.0013669578,0.0004749016,0.9783019,0.00038251517,0.00007869828,2.1870953e-7,0.008743494,0.00038120826,0.0066956626,0.0026825941,0.0007431988,0.00014867986],"about_ca_topic_score_codex":0.013860696,"about_ca_topic_score_gemma":0.0060740914,"teacher_disagreement_score":0.14399746,"about_ca_system_score_codex":0.00012192441,"about_ca_system_score_gemma":0.00074547663,"threshold_uncertainty_score":0.9927061},"labels":[],"label_agreement":null},{"id":"W2178045684","doi":"10.1139/l11-097","title":"A fuzzy intervening opportunity model to predict home-based shopping trips","year":2012,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Transportation Planning and Optimization","field":"Social Sciences","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":"TRIPS architecture; Destinations; Fuzzy logic; Ranking (information retrieval); Computer science; Presumption; Operations research; Marketing; Econometrics; Mathematics; Business; Geography; Machine learning; Artificial intelligence; Tourism","score_opus":0.036260227278253544,"score_gpt":0.2500607048944296,"score_spread":0.21380047761617607,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2178045684","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23745829,0.0005491188,0.7497575,0.0013588417,0.0015827869,0.00015624706,0.000030560426,0.00007106269,0.009035543],"genre_scores_gemma":[0.9960346,0.0000037731354,0.0034775448,0.0001364978,0.00024513865,0.0000017978635,0.000003897218,0.000013322438,0.00008338035],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991378,0.000026538011,0.0002542735,0.00005168467,0.00018150137,0.0003482132],"domain_scores_gemma":[0.99826694,0.000043658354,0.00008550095,0.00005507082,0.0001240868,0.0014247628],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00084028847,0.000075856784,0.00012612145,0.00041387675,0.00015784506,0.000054195873,0.00014550674,0.00005796038,0.000077702694],"category_scores_gemma":[0.0003841351,0.00008580602,0.00006822696,0.0002662345,0.0000176885,0.0003568225,0.000001542409,0.0001640971,0.0000016259759],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000040899677,0.0000043319783,0.012505092,0.00001186369,0.000011459081,0.000010281177,0.0066169677,0.97876686,0.000028588414,0.00080487196,0.000863545,0.0003720437],"study_design_scores_gemma":[0.00383625,0.0004440553,0.098091014,0.0043159714,0.00040386582,0.00007146049,0.013093733,0.7167374,0.00022883306,0.00045597044,0.15979306,0.0025283762],"about_ca_topic_score_codex":0.0009553923,"about_ca_topic_score_gemma":0.0650887,"teacher_disagreement_score":0.75857633,"about_ca_system_score_codex":0.00019598506,"about_ca_system_score_gemma":0.0011232794,"threshold_uncertainty_score":0.951971},"labels":[],"label_agreement":null},{"id":"W2180203774","doi":"10.1080/15472450590916840","title":"The Impact of Advanced Traveler Information Services on Travel Time and Schedule Delay Costs","year":2005,"lang":"en","type":"article","venue":"Journal of Intelligent Transportation Systems","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Trinity College","funders":"","keywords":"Schedule; Travel time; Service (business); Operations research; Transport engineering; Computer science; Travel behavior; Business; Marketing; Engineering","score_opus":0.009918051892147539,"score_gpt":0.28613722680191783,"score_spread":0.27621917490977027,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2180203774","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9906045,0.00046791337,0.0072340956,0.00021290916,0.00021760154,0.0002784503,0.000050198123,0.000013886924,0.0009204054],"genre_scores_gemma":[0.9987831,0.00062221737,0.0003214824,0.000021054975,0.00008395982,0.000003022446,0.000038697377,0.0000063382845,0.000120087236],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9983547,0.00008723343,0.00087606284,0.00006135405,0.00048418355,0.00013651916],"domain_scores_gemma":[0.9982634,0.00016546312,0.000809575,0.00006389012,0.00059496646,0.000102694576],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008529931,0.00010219461,0.00019679616,0.00015039262,0.00020868624,0.0000829807,0.00013213287,0.000080384874,0.000025872734],"category_scores_gemma":[0.000023213084,0.00006909951,0.000114352406,0.00019830471,0.000064210595,0.0008288505,3.9044667e-7,0.0001182584,0.000009988271],"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":[0.00061243796,0.00010789843,0.007167435,0.000060798295,0.00018034766,0.0000023691398,0.062746786,0.8931425,0.0005854558,0.0055218716,0.00018923527,0.029682875],"study_design_scores_gemma":[0.0050562983,0.0027680101,0.75018555,0.0026204477,0.00038636342,0.000041174942,0.14819925,0.04286499,0.0072192564,0.00015357649,0.039435692,0.001069372],"about_ca_topic_score_codex":0.00037385983,"about_ca_topic_score_gemma":0.00032646835,"teacher_disagreement_score":0.8502775,"about_ca_system_score_codex":0.000100616766,"about_ca_system_score_gemma":0.00013302932,"threshold_uncertainty_score":0.28177962},"labels":[],"label_agreement":null},{"id":"W2181230069","doi":"10.1139/cjce-2015-0295","title":"Travel demand modeling to simulate traffic loads for pavement deterioration curves: dealing with aggregate data at urban and regional scales","year":2015,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"Concordia University","funders":"","keywords":"Aggregate (composite); Transport engineering; Civil engineering; Environmental science; Computer science; Engineering","score_opus":0.052663108340253124,"score_gpt":0.26309743329705676,"score_spread":0.21043432495680364,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2181230069","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4915204,0.0023970332,0.5043989,0.0011505542,0.00018921557,0.00023522784,0.000048242968,0.000015951178,0.00004449031],"genre_scores_gemma":[0.9958523,0.000062663516,0.0038347947,0.000076715885,0.00010330209,0.0000016696856,0.00003687887,0.000011722711,0.000019955747],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993533,0.00001203931,0.00019976239,0.00009947782,0.00016334152,0.00017208654],"domain_scores_gemma":[0.99912333,0.00003182543,0.00007422869,0.00006422381,0.0001734167,0.0005329747],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005554452,0.00007140482,0.00011085363,0.00013342139,0.00015356019,0.000057931167,0.00011079116,0.000033054363,0.0000034089112],"category_scores_gemma":[0.000078071156,0.00007000325,0.000015022178,0.00009212507,0.000018899402,0.00027089598,0.0000024595436,0.000050047296,1.964801e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013718588,0.0000011785398,0.0005402427,0.000022742155,0.000012695399,0.0000065920594,0.003778094,0.9951405,0.000016869793,0.00007603061,0.00025438928,0.00013696437],"study_design_scores_gemma":[0.0005786473,0.00009361772,0.00060371973,0.0007473665,0.0000487838,0.0000099630815,0.0008836725,0.99352294,0.0000092667515,0.000019567182,0.0033124357,0.0001699912],"about_ca_topic_score_codex":0.00056240516,"about_ca_topic_score_gemma":0.25054023,"teacher_disagreement_score":0.5043319,"about_ca_system_score_codex":0.0001039,"about_ca_system_score_gemma":0.00034590534,"threshold_uncertainty_score":0.76313543},"labels":[],"label_agreement":null},{"id":"W2181710250","doi":"","title":"Future paths for regional fare collection in Atlanta: a case study analysis of the planning and implementation of next generation fare collection systems for regional transit in North America","year":2012,"lang":"en","type":"dissertation","venue":"SMARTech Repository (Georgia Institute of Technology)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Atlanta; Transit (satellite); Data collection; Rapid transit; Transport engineering; Regional planning; Operations research; Regional science; Geography; Computer science; Public transport; Metropolitan area; Urban planning; Engineering; Sociology; Civil engineering; Archaeology","score_opus":0.027824165270982516,"score_gpt":0.3021530402286507,"score_spread":0.27432887495766817,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2181710250","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9942651,0.0008046216,0.001104187,0.00009683317,0.0008060155,0.002762132,0.000090707006,0.000039752904,0.0000306996],"genre_scores_gemma":[0.9967688,0.00011761294,0.0007604924,0.0000030330652,0.00009388219,0.00072219025,0.0013930448,0.00001609165,0.0001249011],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99808073,0.00010430927,0.0009173103,0.0003665649,0.00032984704,0.00020126064],"domain_scores_gemma":[0.9980886,0.000053356856,0.0011215636,0.00019180766,0.00051519385,0.000029490227],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034524538,0.0002056362,0.0005561326,0.0015127339,0.00053101225,0.000025160845,0.00015388729,0.00048946333,0.0000011101945],"category_scores_gemma":[0.000034844626,0.00020147969,0.0001516464,0.0029715884,0.00014924539,0.00028954053,0.000005548938,0.00020424658,1.3989843e-8],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00064046605,0.00047696967,0.8653778,0.0007702509,0.00088549603,0.000027664124,0.08630816,0.039161053,0.0026515585,0.00097744,0.00025622925,0.0024669315],"study_design_scores_gemma":[0.0028443292,0.0006607585,0.5157646,0.00041962194,0.0024504613,0.000034578738,0.46558705,0.008638158,0.0008653864,0.0000233288,0.0022018459,0.0005099045],"about_ca_topic_score_codex":0.012773954,"about_ca_topic_score_gemma":0.2245485,"teacher_disagreement_score":0.3792789,"about_ca_system_score_codex":0.00024421333,"about_ca_system_score_gemma":0.0005348335,"threshold_uncertainty_score":0.99380004},"labels":[],"label_agreement":null},{"id":"W2182566326","doi":"","title":"Towards optimal patrol strategies for fare inspection in transit systems","year":2012,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Stackelberg competition; Transit (satellite); Revenue; Computer science; Transit system; Operations research; Bilevel optimization; Transport engineering; Public transport; Engineering; Optimization problem; Business; Economics; Microeconomics; Algorithm","score_opus":0.029902683029038448,"score_gpt":0.3072279904654011,"score_spread":0.2773253074363627,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2182566326","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2718407,0.00032677714,0.6691027,0.00062067574,0.00104055,0.0007185213,0.000028759194,0.0004266148,0.055894695],"genre_scores_gemma":[0.99715847,0.00001783613,0.0022560044,0.000021324584,0.00016685974,0.00004689973,0.000031202962,0.00000516349,0.00029624655],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","domain_scores_codex":[0.99943084,0.000039299553,0.0001305582,0.00007692582,0.00012674778,0.00019560789],"domain_scores_gemma":[0.9998118,0.000024501956,0.00002843619,0.000033132394,0.000051690407,0.000050384326],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033169164,0.000049771283,0.00007349017,0.000060333085,0.00016940688,0.00006804438,0.000043309083,0.00007330015,0.000031828502],"category_scores_gemma":[0.000011025252,0.00004729766,0.000027266118,0.00014774658,0.000027018641,0.0005481728,5.507147e-7,0.00003855227,0.0000036873191],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000074148986,0.00012444798,0.045344066,0.00006496357,0.000016570042,7.157451e-7,0.122455604,0.24437752,0.00006329352,0.5842597,0.0008522308,0.0023667389],"study_design_scores_gemma":[0.0024310967,0.0002069409,0.22178692,0.00009476212,0.00006976997,0.000001757714,0.6667306,0.034772467,0.0001432855,0.00066004635,0.0723366,0.0007658103],"about_ca_topic_score_codex":0.0037249648,"about_ca_topic_score_gemma":0.0022968908,"teacher_disagreement_score":0.7253178,"about_ca_system_score_codex":0.00004314834,"about_ca_system_score_gemma":0.00009352384,"threshold_uncertainty_score":0.56310576},"labels":[],"label_agreement":null},{"id":"W2182939506","doi":"","title":"THE GREATER TORONTO AREA TRAVEL DEMAND MODELLING SYSTEM VERSION 2.0 VOLUME I: MODEL OVERVIEW","year":2001,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Volume (thermodynamics); Computer science; Environmental science; Regional science; Geography; Transport engineering; Operations research; Engineering","score_opus":0.048700246796231306,"score_gpt":0.2695993650656901,"score_spread":0.2208991182694588,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2182939506","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027842622,0.00071796065,0.83866936,0.00053157896,0.00016828066,0.0001864284,0.000005404861,0.00016742287,0.13171092],"genre_scores_gemma":[0.97567546,0.0012819826,0.0016240873,0.00005931674,0.000045581884,0.0000068065037,0.000012150355,0.000006909774,0.021287706],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992219,0.0000498564,0.00014984205,0.00013459606,0.00025420738,0.0001895782],"domain_scores_gemma":[0.99968445,0.000029364455,0.000042848875,0.000098937926,0.00007668155,0.000067723246],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037755969,0.00006895077,0.000078355006,0.0000105366435,0.0008453328,0.00008735199,0.00011790716,0.00006587906,0.00011330996],"category_scores_gemma":[0.0000042711354,0.000048274873,0.00004350273,0.000070000075,0.00003554442,0.00027409967,0.0000033186934,0.000034843208,0.00002541387],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024312345,0.000007818614,0.0026257667,0.000008994097,0.000009187799,0.00000200173,0.0075251264,0.9692718,0.0000025922252,0.018811088,0.001354749,0.00035656325],"study_design_scores_gemma":[0.00013242764,0.000006501219,0.0005277095,0.000031327414,0.000017421096,5.1858837e-7,0.0075079207,0.9858618,0.0000033786253,0.0000925169,0.00573776,0.000080729646],"about_ca_topic_score_codex":0.0031213656,"about_ca_topic_score_gemma":0.0037438786,"teacher_disagreement_score":0.9478328,"about_ca_system_score_codex":0.00011801722,"about_ca_system_score_gemma":0.00004497508,"threshold_uncertainty_score":0.6501702},"labels":[],"label_agreement":null},{"id":"W2184190629","doi":"10.1139/cjce-2012-0306","title":"The impact of dwell time variability on transit signal priority performance","year":2014,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Dwell time; Transit (satellite); Variable (mathematics); Bus priority; SIGNAL (programming language); Transport engineering; Computer science; Engineering; Mathematics; Public transport; Medicine","score_opus":0.005323936382654501,"score_gpt":0.20907410694593498,"score_spread":0.20375017056328049,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2184190629","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9612691,0.000061862396,0.02681166,0.00026037928,0.00030374745,0.00008446239,0.00001067977,0.000014005688,0.011184095],"genre_scores_gemma":[0.9997349,0.000010398461,0.0000941143,0.0000048932498,0.00010271816,2.3604676e-7,8.377259e-7,0.0000046105642,0.00004733794],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9994195,0.000050767518,0.00018740058,0.00004072362,0.00013942734,0.00016216535],"domain_scores_gemma":[0.9993346,0.00018040818,0.00008454909,0.00005673326,0.00012068893,0.00022303678],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012168471,0.000052212396,0.000097610704,0.000073179624,0.00017065096,0.000027181646,0.00013119949,0.00004089525,0.00011596831],"category_scores_gemma":[0.0001698772,0.000039955918,0.0000713364,0.00013542364,0.000047867783,0.00010684613,3.8861882e-7,0.00013455196,0.0000017801755],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009802625,0.000003953662,0.010843554,0.0000069857597,0.00001661725,0.0000011333608,0.0023847057,0.98446256,0.00006486907,0.0005904756,0.00015414998,0.0014612003],"study_design_scores_gemma":[0.0006536856,0.00053623744,0.74950284,0.0002779149,0.000050622388,0.000007679642,0.0001510331,0.23210672,0.00024123995,0.00025699113,0.01590669,0.00030833876],"about_ca_topic_score_codex":0.0018033624,"about_ca_topic_score_gemma":0.018468425,"teacher_disagreement_score":0.7523558,"about_ca_system_score_codex":0.00010761764,"about_ca_system_score_gemma":0.0005897194,"threshold_uncertainty_score":0.999442},"labels":[],"label_agreement":null},{"id":"W2187326486","doi":"","title":"The Etobicoke York Community Council enacts: 1. Municipal Code Chapter 950, Traffic and Parking, is amended as follows: A. By inserting in Schedule XXXV (Speed Limits on Public Highways) in § 950-1334, the","year":2014,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Schedule; Transport engineering; Code (set theory); Engineering; Operations research; Computer science; Programming language; Set (abstract data type)","score_opus":0.08851660085852754,"score_gpt":0.2930353366853473,"score_spread":0.2045187358268198,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2187326486","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95025206,0.00016015934,0.000032430624,0.006389123,0.00012313142,0.00028682948,0.0000073794754,0.00014275435,0.04260612],"genre_scores_gemma":[0.99649954,0.00021979498,0.00010302873,0.0019705608,0.000030160885,0.000019415,0.000020281885,0.00001613292,0.0011210584],"study_design_codex":"qualitative","study_design_gemma":"not_applicable","domain_scores_codex":[0.9974781,0.00082445785,0.000402864,0.00022939543,0.00063612475,0.00042903313],"domain_scores_gemma":[0.9980846,0.001204857,0.00017300945,0.0003138585,0.00011939685,0.00010429936],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.004972168,0.00017012056,0.000192702,0.00006092539,0.0014887062,0.00033761957,0.00046706613,0.00015895214,0.000040487597],"category_scores_gemma":[0.00086211733,0.00011554571,0.000042587188,0.00040247836,0.00030368892,0.0002496495,0.000020015277,0.0005637332,0.00001423242],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00042419872,0.00084994093,0.06344797,0.000033628683,0.00010373533,0.000004950204,0.7640966,0.01576684,0.00042525845,0.12755236,0.008338154,0.01895635],"study_design_scores_gemma":[0.0056147403,0.000583881,0.036094584,0.0003796095,0.000055964985,0.000001934612,0.21888883,0.09138511,0.00054336863,0.0015212029,0.64376974,0.0011610024],"about_ca_topic_score_codex":0.0118107665,"about_ca_topic_score_gemma":0.18829784,"teacher_disagreement_score":0.6354316,"about_ca_system_score_codex":0.00016591193,"about_ca_system_score_gemma":0.00016539723,"threshold_uncertainty_score":0.99981123},"labels":[],"label_agreement":null},{"id":"W2191916585","doi":"10.13034/jsst.v8i2.71","title":"Simulating Land Use: An Exploration of the Stability of a Two Zone City","year":2015,"lang":"en","type":"article","venue":"Journal of Student Science and Technology","topic":"Transportation Planning and Optimization","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":"Operations research; Grid; Control (management); Computer science; Population; Plan (archaeology); Stability (learning theory); Set (abstract data type); Agent-based model; Distribution (mathematics); Order (exchange); Simulation; Transport engineering; Engineering; Artificial intelligence; Geography; Mathematics; Business; Machine learning","score_opus":0.11253550709469663,"score_gpt":0.3891715205386829,"score_spread":0.2766360134439863,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2191916585","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99620825,0.000023111186,0.0027759834,0.00075966225,0.00009122323,0.000059960857,8.408102e-7,0.0000064789338,0.000074490985],"genre_scores_gemma":[0.998734,0.000014203522,0.0012256607,0.000008286173,0.000012430803,3.3560084e-7,9.994754e-8,8.5487807e-7,0.000004132335],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9990434,0.000042932545,0.00023550437,0.00006268902,0.0005416705,0.000073773546],"domain_scores_gemma":[0.9985546,0.000040007646,0.00034800114,0.00007291605,0.000944297,0.00004019028],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001958272,0.000028095796,0.0000945633,0.00013285063,0.00016160823,0.000022067712,0.00022394856,0.000032903783,0.0000018029282],"category_scores_gemma":[0.0004923989,0.000018939925,0.000011511377,0.0007761389,0.00078815885,0.0010009427,0.000019132423,0.000073754,3.4200962e-8],"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":[0.000006704076,0.000050872444,0.98337233,0.0000015572872,0.000002286465,3.325542e-7,0.0098154545,0.0018878762,0.00090498314,0.0031363904,0.0000019001237,0.0008192903],"study_design_scores_gemma":[0.0015322369,0.00066484394,0.90314114,0.000081860744,0.00004984293,0.000004374892,0.07504699,0.0017660762,0.008204957,0.009071308,0.0003096146,0.00012676827],"about_ca_topic_score_codex":0.00015036124,"about_ca_topic_score_gemma":0.00050095166,"teacher_disagreement_score":0.08023122,"about_ca_system_score_codex":0.00002429231,"about_ca_system_score_gemma":0.0002691636,"threshold_uncertainty_score":0.29040042},"labels":[],"label_agreement":null},{"id":"W2193573602","doi":"","title":"La mobilité urbaine dans le Grand Montréal : analyse de l'intermodalité à travers son corollaire, le pôle d'échanges intermodaux","year":2014,"lang":"fr","type":"article","venue":"Archipelago (Université du Québec à Montréal)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Humanities; Political science; Art","score_opus":0.0061433066748392,"score_gpt":0.18936812300760625,"score_spread":0.18322481633276705,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2193573602","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.903611,0.004052794,0.0687574,0.016404737,0.000312951,0.00031312482,0.00008505217,0.00021839408,0.0062445346],"genre_scores_gemma":[0.99169564,0.0010900893,0.0011485606,0.00025024218,0.00018012853,0.000011510124,0.000093601266,0.000059544716,0.005470694],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.996555,0.0011334431,0.00042801874,0.000675419,0.00044783246,0.00076032605],"domain_scores_gemma":[0.99800205,0.00058452436,0.0003079406,0.00043837604,0.00015004494,0.00051704136],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00095650874,0.00043785834,0.0005510742,0.00030193012,0.0012586486,0.000103350045,0.0006297238,0.0003428445,0.00014843399],"category_scores_gemma":[0.000116719435,0.0005197187,0.00040970024,0.00066341547,0.0011014559,0.0006382908,0.00007561471,0.00044164533,0.000049582584],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005324146,0.00052314333,0.018710079,0.00009965475,0.00033270233,0.00020519606,0.83016765,0.025592502,0.0003479093,0.04000446,0.0014230295,0.082061246],"study_design_scores_gemma":[0.0069206976,0.0007169785,0.2390968,0.0004116575,0.00094556063,0.00008784093,0.5142541,0.14461733,0.00023661395,0.0047255615,0.086418025,0.001568833],"about_ca_topic_score_codex":0.8308669,"about_ca_topic_score_gemma":0.90965044,"teacher_disagreement_score":0.3159136,"about_ca_system_score_codex":0.000536633,"about_ca_system_score_gemma":0.0010228945,"threshold_uncertainty_score":0.99972546},"labels":[],"label_agreement":null},{"id":"W219822362","doi":"10.1023/a:1021388515849","title":"Solvability of Implicit Complementarity Problems","year":2002,"lang":"en","type":"article","venue":"Annals of Operations Research","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":17,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Theory of computation; Complementarity (molecular biology); Mathematics; Mixed complementarity problem; Complementarity theory; Mathematical economics; Computer science; Applied mathematics; Algorithm; Nonlinear system","score_opus":0.48455297427459304,"score_gpt":0.5179340628099413,"score_spread":0.03338108853534821,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W219822362","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9502855,0.00019416036,0.0013936564,0.009628918,0.000043001335,0.0006453208,0.00011503313,0.00002999149,0.037664454],"genre_scores_gemma":[0.99764764,0.00026158232,0.0011769389,0.00003186814,0.000021041727,0.000018718058,0.000032986343,0.0000030407166,0.00080616743],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9985952,0.00028703007,0.00026783446,0.00011645178,0.00053517375,0.00019835595],"domain_scores_gemma":[0.9984428,0.00013445444,0.000024066343,0.00014783212,0.0011905337,0.000060259907],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0019281966,0.000035370194,0.00009507104,0.0001242091,0.00048496353,0.00003098208,0.00016340372,0.00003946953,0.0015827365],"category_scores_gemma":[0.00028638175,0.000035473244,0.000034563618,0.0005272634,0.00034342456,0.00019756834,0.000011169247,0.00010366977,0.000013597899],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000045091354,0.0023077936,0.23316275,0.00023230187,0.00011629999,0.0000015986734,0.15816015,0.16023248,0.005066271,0.38610807,0.04268395,0.011883247],"study_design_scores_gemma":[0.002467143,0.0017779386,0.59581757,0.00038773497,0.00004399107,0.0000011831362,0.047778692,0.10999132,0.029416231,0.013671171,0.19762664,0.0010203867],"about_ca_topic_score_codex":0.01144567,"about_ca_topic_score_gemma":0.01033315,"teacher_disagreement_score":0.37243688,"about_ca_system_score_codex":0.000011553457,"about_ca_system_score_gemma":0.00008114576,"threshold_uncertainty_score":0.9993299},"labels":[],"label_agreement":null},{"id":"W2201884470","doi":"10.15396/eres2006_171","title":"DOES THE OVERALL QUALITY IN THE SUPPLY OF AN URBAN BUS SERVICE AFFECT HOUSE PRICES? - A NORTH-AMERICAN CASE STUDY","year":2006,"lang":"en","type":"preprint","venue":"","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":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Downtown; Metropolitan area; Public transport; Service (business); Transport engineering; Population; Business; Geography; Agricultural economics; Engineering; Marketing; Sociology; Demography","score_opus":0.028456747804287866,"score_gpt":0.34044789044424817,"score_spread":0.3119911426399603,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2201884470","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99638367,0.000010466416,0.00017499777,0.0010291252,0.00013121212,0.0013236938,0.00008208001,0.00012442748,0.00074035575],"genre_scores_gemma":[0.9987429,0.0000118441285,0.0005094618,0.0003351401,0.00010451355,0.00008358807,0.0000915619,0.000015166726,0.000105851344],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99685365,0.0015104575,0.0004541593,0.0003643017,0.000598384,0.00021907601],"domain_scores_gemma":[0.9982536,0.00051705295,0.000500293,0.0005225141,0.00015945752,0.000047121568],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002010957,0.00018400459,0.0002950328,0.000077159486,0.000318493,0.0001569582,0.0006154866,0.00008356992,0.000018228111],"category_scores_gemma":[0.000056275014,0.000087885484,0.000071671086,0.00061302725,0.0001650892,0.00013682603,0.00003530437,0.00036823002,0.0000012159437],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000237769,0.00046442676,0.7731979,0.000032459993,0.000021911728,0.000083097926,0.19966292,0.02594478,2.0831392e-7,0.00015154362,0.000097175536,0.0003197964],"study_design_scores_gemma":[0.0002207682,0.00006213952,0.91677165,0.000011263125,0.000053751217,0.0000016722914,0.08220376,0.00033061308,8.862599e-7,0.000040548333,0.0001453862,0.00015758006],"about_ca_topic_score_codex":0.7320766,"about_ca_topic_score_gemma":0.9229277,"teacher_disagreement_score":0.19085109,"about_ca_system_score_codex":0.0000476939,"about_ca_system_score_gemma":0.00021755652,"threshold_uncertainty_score":0.3583866},"labels":[],"label_agreement":null},{"id":"W2206071134","doi":"10.1609/aaai.v29i1.9364","title":"Value-Directed Compression of Large-Scale Assignment Problems","year":2015,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Mathematical optimization; Linear programming; Population; Integer programming; Column generation; Analytics; Scale (ratio); Compression (physics); Algorithm; Data mining; Mathematics","score_opus":0.10165706352886701,"score_gpt":0.3362485228983332,"score_spread":0.23459145936946618,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2206071134","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8149559,0.0000949032,0.02055419,0.0061703627,0.0013620325,0.0016498934,0.00005546546,0.0004119112,0.15474534],"genre_scores_gemma":[0.99765104,0.000030288682,0.0016943027,0.000043974844,0.000041425734,0.000013132485,0.0000029259288,0.000007716109,0.00051520974],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99843174,0.000039620463,0.00042282042,0.00020797906,0.00067837577,0.000219485],"domain_scores_gemma":[0.99847895,0.000053297852,0.00038662602,0.00009203162,0.0008836055,0.000105480234],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00087383523,0.000107773514,0.00018268243,0.000071715614,0.00020353326,0.00004936436,0.00045828606,0.00009170516,0.000083046165],"category_scores_gemma":[0.00031956265,0.00008315827,0.00005982796,0.00045244864,0.0002781678,0.00017923655,0.000031884803,0.00013551298,0.0000149845155],"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":[0.00027176717,0.00072745024,0.010364845,0.00009135274,0.000028536184,2.2501376e-7,0.09828327,0.012283471,0.021889778,0.8447148,0.0014532655,0.009891217],"study_design_scores_gemma":[0.00029763643,0.0006051135,0.003726618,0.0017823396,0.00010246573,7.741738e-7,0.06807771,0.113125585,0.6449884,0.16124326,0.005361481,0.0006885877],"about_ca_topic_score_codex":0.0003281917,"about_ca_topic_score_gemma":0.000115797666,"teacher_disagreement_score":0.68347156,"about_ca_system_score_codex":0.000042669322,"about_ca_system_score_gemma":0.00016394396,"threshold_uncertainty_score":0.3391096},"labels":[],"label_agreement":null},{"id":"W2206600317","doi":"","title":"The OPUS Card: Yesterday, Today & Tomorrow","year":2014,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Opus; Public transport; Transit (satellite); Yesterday; Smart card; Engineering; Computer science; Transport engineering; Computer security","score_opus":0.010515667676179221,"score_gpt":0.27182158780519017,"score_spread":0.26130592012901094,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2206600317","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.047662,0.00007294564,0.036376625,0.012840992,0.0012363993,0.00018037013,0.0000022647505,0.00033853578,0.9012899],"genre_scores_gemma":[0.9729364,0.000027703798,0.00068219414,0.00027379976,0.00011870816,0.000003936852,0.000004634163,0.0000030381934,0.025949577],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99951845,0.000075976335,0.00007086933,0.00006613948,0.00015204387,0.0001165013],"domain_scores_gemma":[0.99968815,0.00013295884,0.000022580609,0.000074393,0.00004040946,0.00004151984],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044438196,0.00003029283,0.000033915934,0.000010355449,0.0005722136,0.00008006596,0.00010105011,0.00002914546,0.000059254762],"category_scores_gemma":[0.00007468117,0.000020101972,0.00002019567,0.00008207899,0.00006290662,0.00007143614,0.0000021609121,0.00003218291,0.00005309053],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014581244,0.000022354487,0.049313493,0.0000037055947,0.000014488803,0.0000013042883,0.027001869,0.004272802,0.000031998276,0.8138238,0.038945217,0.06655436],"study_design_scores_gemma":[0.000105316525,0.000013331026,0.017819138,0.000003764517,0.0000061860155,1.4494302e-7,0.0022644247,0.0005998078,0.000058826812,0.001361434,0.9776937,0.00007393105],"about_ca_topic_score_codex":0.00089655974,"about_ca_topic_score_gemma":0.00297662,"teacher_disagreement_score":0.9387485,"about_ca_system_score_codex":0.000009307005,"about_ca_system_score_gemma":0.000027842298,"threshold_uncertainty_score":0.4401062},"labels":[],"label_agreement":null},{"id":"W2206979893","doi":"10.1002/atr.1361","title":"Quantification of motorway capacity variation: influence of day type specific variation and capacity drop","year":2015,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Nederlandse Organisatie voor Toegepast Natuurwetenschappelijk Onderzoek; Nederlandse Organisatie voor Wetenschappelijk Onderzoek","keywords":"Weibull distribution; Variation (astronomy); Highway Capacity Manual; Estimation; Heat capacity; Environmental science; Statistics; Econometrics; Computer science; Mathematics; Transport engineering; Engineering","score_opus":0.03692639786886579,"score_gpt":0.2802020889076633,"score_spread":0.2432756910387975,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2206979893","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93315786,0.00010281784,0.065962,0.00012389096,0.00037642248,0.00015135494,0.000023833716,0.000013085666,0.00008871304],"genre_scores_gemma":[0.97709244,0.00031065152,0.02246291,0.000008224074,0.0000699975,0.0000014186328,0.000030703657,0.000007612691,0.000016016778],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9982932,0.00013879401,0.0007764721,0.00012546248,0.00056299835,0.00010303979],"domain_scores_gemma":[0.9966155,0.000119739554,0.0012321625,0.00009465775,0.0018314148,0.00010646684],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011818154,0.000090947025,0.00023406207,0.00017432538,0.000088086716,0.000015071456,0.0000998829,0.0001026494,0.000011342429],"category_scores_gemma":[0.00025403107,0.000093282935,0.000046640194,0.000492102,0.00013780079,0.0010208181,6.371127e-7,0.000119689175,7.2554064e-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":[0.00034389386,0.00014531646,0.03436113,0.00007270843,0.000041923,0.0000017467554,0.13689114,0.7547765,0.054529857,0.017680213,0.000021664428,0.0011339553],"study_design_scores_gemma":[0.0007705246,0.00017764742,0.9920163,0.00009175788,0.00006161588,9.781443e-7,0.0018117656,0.00020066916,0.0019957556,0.0021263394,0.00064520346,0.000101402125],"about_ca_topic_score_codex":0.0002973657,"about_ca_topic_score_gemma":0.00019882135,"teacher_disagreement_score":0.9576552,"about_ca_system_score_codex":0.00005955687,"about_ca_system_score_gemma":0.00019371697,"threshold_uncertainty_score":0.38039678},"labels":[],"label_agreement":null},{"id":"W2207888809","doi":"","title":"What is Generating Transit Ridership Increase in US and Canadian Cities","year":2015,"lang":"en","type":"article","venue":"European Transport Conference 2015Association for European Transport (AET)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Immigration; Restructuring; Population; Boom; Baby boom; Geography; Public transport; Economic growth; Demographic economics; Business; Political science; Economics; Demography; Finance; Engineering; Sociology","score_opus":0.050086960820595945,"score_gpt":0.2619391762237945,"score_spread":0.21185221540319854,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2207888809","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8890898,0.0006902757,0.007895941,0.0040624887,0.0010094984,0.001378745,0.0006973288,0.0004464842,0.0947294],"genre_scores_gemma":[0.99068695,0.0007146156,0.0012419372,0.0010491695,0.00028824987,0.000019034773,0.0015076288,0.00011053372,0.004381898],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9959957,0.00075660105,0.0009603854,0.0007386026,0.00067746796,0.0008712399],"domain_scores_gemma":[0.9977828,0.00009536031,0.00031576277,0.00025563626,0.0005948996,0.00095553155],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0044054748,0.00041381622,0.0004515907,0.0004226146,0.00067801593,0.0003185595,0.0004152025,0.00015595871,0.00017618912],"category_scores_gemma":[0.000095722746,0.00048772036,0.00016005807,0.0004759006,0.00027115238,0.0015776687,0.0000036183458,0.00034711199,0.000052720814],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022832489,0.0001430819,0.72089446,0.000121431935,0.00011615098,0.00026606215,0.25711784,0.0043997327,0.000065303146,0.008480821,0.0019663698,0.0062004435],"study_design_scores_gemma":[0.0043648053,0.00020105288,0.80421156,0.00042197533,0.00023818584,0.00000339748,0.025141582,0.0009694144,0.000040854226,0.00041128122,0.16263892,0.0013569759],"about_ca_topic_score_codex":0.037171267,"about_ca_topic_score_gemma":0.33532843,"teacher_disagreement_score":0.29815716,"about_ca_system_score_codex":0.00027167474,"about_ca_system_score_gemma":0.0010089925,"threshold_uncertainty_score":0.99975747},"labels":[],"label_agreement":null},{"id":"W2213679401","doi":"10.1007/0-387-21613-8_16","title":"Individual-Based Modeling","year":2002,"lang":"en","type":"book-chapter","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":17,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science","score_opus":0.06599562016199616,"score_gpt":0.27769055585132824,"score_spread":0.21169493568933206,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2213679401","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000043735836,0.00007913835,0.029557602,0.00034688352,0.00012828821,0.00009543644,0.000020901918,0.00020279152,0.96956456],"genre_scores_gemma":[0.011798818,0.000117684045,0.0037931618,0.0003538602,0.00014875847,0.0000024392396,0.00022974853,0.000022249957,0.98353326],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991768,0.000009605656,0.00015637645,0.00015813294,0.00037997423,0.00011912274],"domain_scores_gemma":[0.9996764,0.000032546493,0.000063528474,0.000083272535,0.00007878475,0.00006545826],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00015639965,0.00010222434,0.00010740371,0.00010082262,0.0002316092,0.000053696545,0.0001103734,0.00026295832,0.007061639],"category_scores_gemma":[0.000010504526,0.000107211934,0.00006281278,0.000022368791,0.000047122092,0.000058197707,0.0000019358995,0.00012895571,0.00018316068],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000029052876,0.000008735573,0.000046267414,0.000009848713,0.000023929086,0.000006154985,0.0028592453,0.18512319,4.3865782e-8,0.8019596,0.007622327,0.0023377954],"study_design_scores_gemma":[0.00026327325,0.000018110653,0.000009650956,0.00014140696,0.00010741747,1.07339645e-7,0.00017908007,0.07649307,7.550351e-7,0.010798312,0.9114796,0.00050919707],"about_ca_topic_score_codex":0.00017225498,"about_ca_topic_score_gemma":0.00031551553,"teacher_disagreement_score":0.9038573,"about_ca_system_score_codex":0.000026139714,"about_ca_system_score_gemma":0.0000961919,"threshold_uncertainty_score":0.99384606},"labels":[],"label_agreement":null},{"id":"W2219062175","doi":"10.1139/cjce-2014-0513","title":"Optimal congestion pricing toll design for revenue maximization: comprehensive numerical results and implications","year":2015,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Toll; Congestion pricing; Revenue; Mathematical optimization; Maximization; Value of time; Genetic algorithm; Homogeneous; Sensitivity (control systems); Computer science; Optimal design; Operations research; Traffic congestion; Transport engineering; Engineering; Economics; Mathematics; Travel time","score_opus":0.044835703772642475,"score_gpt":0.2653922229875016,"score_spread":0.2205565192148591,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2219062175","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0063575287,0.00040681023,0.9905721,0.0019098772,0.00029709662,0.00015490189,0.000038150276,0.000014264108,0.00024926057],"genre_scores_gemma":[0.96463764,0.00002317858,0.03506867,0.00003330814,0.00015492782,0.0000037286761,0.0000129216605,0.000008592585,0.000057037392],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994823,0.000028471868,0.00020223818,0.00006576868,0.00008195981,0.0001393144],"domain_scores_gemma":[0.99895895,0.00013373229,0.00010705166,0.0000394765,0.00037826388,0.00038254177],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035581822,0.000051687675,0.000096111486,0.00013064756,0.00014793732,0.00005363348,0.00006162244,0.000048980895,0.0000029543025],"category_scores_gemma":[0.0005372387,0.00005873281,0.000020204361,0.00015392347,0.00002727183,0.0001721523,9.4033334e-7,0.000069517686,3.3471372e-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":[0.000014633571,0.0000016534989,0.00045733416,0.000004886396,0.000008186628,0.0000036943238,0.0043219784,0.99139386,0.000009750343,0.0009376948,0.002552759,0.0002935863],"study_design_scores_gemma":[0.00573872,0.00082460395,0.09174873,0.00087116996,0.00031972208,0.0002509419,0.008739316,0.3556791,0.00013330633,0.0015217463,0.5329782,0.0011944752],"about_ca_topic_score_codex":0.00064856507,"about_ca_topic_score_gemma":0.008096057,"teacher_disagreement_score":0.9582801,"about_ca_system_score_codex":0.00010299613,"about_ca_system_score_gemma":0.0006365231,"threshold_uncertainty_score":0.45177898},"labels":[],"label_agreement":null},{"id":"W2222191281","doi":"","title":"Étude sur la tarification routière pour la région métropolitaine de Montréal","year":2013,"lang":"fr","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Political science; Humanities; Art","score_opus":0.02281637067988278,"score_gpt":0.30181088085502045,"score_spread":0.27899451017513766,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2222191281","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8327817,0.00054801407,0.00047524524,0.013525459,0.00041819026,0.0009791554,0.00009145231,0.000118745505,0.15106206],"genre_scores_gemma":[0.94051725,0.022650942,0.0048909793,0.000059138605,0.00035418663,0.0002138768,0.00029670296,0.00005950609,0.030957446],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9955959,0.0016716623,0.000705799,0.0007641982,0.0003866455,0.0008758153],"domain_scores_gemma":[0.99712306,0.0014671339,0.00028913087,0.00048362836,0.00031444133,0.00032258785],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0039119874,0.0002980486,0.0004034709,0.00031265817,0.0004279134,0.00027629788,0.00058894866,0.0008978356,0.00021708882],"category_scores_gemma":[0.000707225,0.00035556714,0.00016852342,0.00025605946,0.00066873786,0.00033283845,0.00009834907,0.00144922,0.000081879756],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013705865,0.00058792706,0.09634015,0.00017876674,0.00014869748,0.00005101686,0.019203858,0.4988332,0.00013115353,0.18273835,0.0010967476,0.20055306],"study_design_scores_gemma":[0.0014236855,0.00008821236,0.4534218,0.0004959734,0.00004912801,0.000014266575,0.010368266,0.17725149,0.000059633636,0.009366696,0.34652036,0.0009404889],"about_ca_topic_score_codex":0.028437844,"about_ca_topic_score_gemma":0.014324659,"teacher_disagreement_score":0.35708165,"about_ca_system_score_codex":0.0019183808,"about_ca_system_score_gemma":0.0012878515,"threshold_uncertainty_score":0.9998896},"labels":[],"label_agreement":null},{"id":"W2228204725","doi":"10.1007/s12469-015-0118-3","title":"Cost-of-crowding model for light rail train and platform length","year":2016,"lang":"en","type":"article","venue":"Public Transport","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Headway; Crowding; Flexibility (engineering); Light rail; Engineering; Simulation; Transport engineering; Light rail transit; Transit (satellite); Dimension (graph theory); Public transport; Statistics; Mathematics","score_opus":0.09005991354362858,"score_gpt":0.3105957124529716,"score_spread":0.220535798909343,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2228204725","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20894098,0.000083223546,0.77480114,0.008213323,0.0001722246,0.000608515,0.00021676921,0.00018585517,0.006777996],"genre_scores_gemma":[0.99373645,0.00010714624,0.004463152,0.000058777605,0.000045112025,0.00004580982,0.000040115843,0.0000123662085,0.0014910824],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99911267,0.000010127715,0.00023823512,0.00017966353,0.00020204546,0.0002572351],"domain_scores_gemma":[0.99950635,0.00009788973,0.00007827633,0.00007501208,0.00011352181,0.00012893061],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00057015923,0.00008381769,0.00013911664,0.000092904935,0.00021681121,0.000019392277,0.00010901956,0.00009770237,0.00004309526],"category_scores_gemma":[0.00005014711,0.000065575936,0.00005741142,0.00014057448,0.00011478809,0.000547125,0.0000010305754,0.000036083267,8.3384316e-7],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022849753,0.00020965136,0.16817254,0.00015876279,0.0001154663,0.000003576671,0.11926997,0.0021816397,0.001584481,0.53057235,0.0012910803,0.17621197],"study_design_scores_gemma":[0.015344858,0.00034318125,0.2395328,0.0005664964,0.00040011562,0.000004752577,0.01348439,0.045005318,0.002836113,0.019217385,0.66084045,0.002424156],"about_ca_topic_score_codex":0.000060702052,"about_ca_topic_score_gemma":0.0009735544,"teacher_disagreement_score":0.78479546,"about_ca_system_score_codex":0.00002367641,"about_ca_system_score_gemma":0.00018205527,"threshold_uncertainty_score":0.2674109},"labels":[],"label_agreement":null},{"id":"W2232678697","doi":"10.1287/opre.2017.1712","title":"Informational Braess’ Paradox: The Effect of Information on Traffic Congestion","year":2018,"lang":"en","type":"preprint","venue":"Operations Research","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Inefficiency; Computer science; Class (philosophy); Complement (music); Set (abstract data type); Theoretical computer science; Focus (optics); Embedding; Graph; Mathematical economics; Mathematical optimization; Mathematics; Artificial intelligence; Economics","score_opus":0.05161637199998291,"score_gpt":0.4102067467891671,"score_spread":0.35859037478918415,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2232678697","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9531253,0.000053622116,0.0024748077,0.005147515,0.00085524435,0.0026245331,0.00024555827,0.00012635764,0.035347078],"genre_scores_gemma":[0.9969191,0.00011933962,0.000360322,0.00004518453,0.0002244106,0.00026422465,0.0015016447,0.000007404244,0.0005583493],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9967003,0.0010658279,0.0004487714,0.00014675825,0.0013913979,0.00024694428],"domain_scores_gemma":[0.9976897,0.00072494504,0.00009501046,0.00030607136,0.0011129325,0.000071337585],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0045456123,0.00012424416,0.00015958074,0.00042062893,0.0015001037,0.00044039884,0.00042894456,0.0002868554,0.00026129626],"category_scores_gemma":[0.001305573,0.00009169894,0.00006948004,0.000555306,0.0005904814,0.00060316257,0.000045446413,0.0006897273,0.00023781066],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008349599,0.00001893077,0.0003686979,0.000062111605,0.000021865611,1.6283191e-7,0.016546587,0.9587052,0.0000017709914,0.0138929365,0.0045547266,0.005743503],"study_design_scores_gemma":[0.004088063,0.0035228522,0.08998141,0.0020762966,0.00021043686,0.0000037203586,0.014824871,0.6944434,0.0015901251,0.0013500372,0.18634671,0.0015620773],"about_ca_topic_score_codex":0.00080924,"about_ca_topic_score_gemma":0.00096153346,"teacher_disagreement_score":0.2642618,"about_ca_system_score_codex":0.0001363447,"about_ca_system_score_gemma":0.00076337246,"threshold_uncertainty_score":0.9997998},"labels":[],"label_agreement":null},{"id":"W2240141342","doi":"","title":"The foundation of a new Transportation Planning Model for the Manitoba Capital Region","year":2001,"lang":"en","type":"article","venue":"Mspace (University of Manitoba)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Transport engineering; Capital (architecture); Transportation planning; Government (linguistics); TRIPS architecture; Work (physics); Foundation (evidence); Operations research; County government; Order (exchange); Regional science; Computer science; Geography; Business; Engineering; Finance; Public administration; Political science","score_opus":0.04406055799069638,"score_gpt":0.2505147631298052,"score_spread":0.2064542051391088,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2240141342","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.46288294,0.000075958334,0.53233397,0.0036177612,0.000119717995,0.00031098523,0.0000065936892,0.00003652458,0.00061554083],"genre_scores_gemma":[0.99634755,0.0002902478,0.0020429117,0.000015463445,0.00004976584,6.1785414e-7,0.00003633622,0.0000056325234,0.0012114785],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9994236,0.00003128525,0.000090369336,0.00010311113,0.0002234368,0.00012818932],"domain_scores_gemma":[0.9993496,0.00017758115,0.00018888032,0.0000990749,0.0001485843,0.000036297402],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024078279,0.000057375546,0.00007867138,0.00004498232,0.0009291154,0.000020907586,0.00020198054,0.000058780486,0.0000018641244],"category_scores_gemma":[0.000023743913,0.00005233449,0.000069696,0.00017843068,0.00015695374,0.00026556363,0.000002525133,0.000053102383,0.0000014445048],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00089385203,0.00007870135,0.24000767,0.000048289367,0.00012753005,0.000008554005,0.1182626,0.5588354,0.000089155685,0.058793075,0.007844522,0.015010645],"study_design_scores_gemma":[0.0011331392,0.00008527332,0.48827255,0.00007029704,0.00019860613,7.772317e-7,0.3516099,0.12693913,0.000019094667,0.0027426982,0.02874102,0.00018753456],"about_ca_topic_score_codex":0.010189249,"about_ca_topic_score_gemma":0.59154564,"teacher_disagreement_score":0.5813564,"about_ca_system_score_codex":0.000042988842,"about_ca_system_score_gemma":0.000116529685,"threshold_uncertainty_score":0.99640197},"labels":[],"label_agreement":null},{"id":"W2240228391","doi":"","title":"PEAK-PERIOD SERVICE SUPPLY VERSUS OBSERVED PASSENGER UTILIZATION FOR RAPID BUS AND RAPID RAIL MODES: ISSUES AND IMPLICATIONS","year":2003,"lang":"en","type":"article","venue":"Transportation Research E-Circular","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Public transport; Service (business); Consumption (sociology); Revealed preference; Crowding; Business; Regression analysis; Transport engineering; Preference; Level of service; Supply and demand; Econometrics; Economics; Microeconomics; Marketing; Computer science; Engineering","score_opus":0.21021485063099754,"score_gpt":0.4044123746884447,"score_spread":0.19419752405744717,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2240228391","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92803705,0.007882986,0.04144547,0.013597394,0.00032799863,0.0048391256,0.00059017923,0.0004750819,0.0028046882],"genre_scores_gemma":[0.99066556,0.0036217433,0.0036661383,0.00009718058,0.000059507973,0.00047452055,0.0011442606,0.000036401794,0.00023467124],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99758625,0.00039237321,0.00038557296,0.00051820825,0.000620471,0.0004971363],"domain_scores_gemma":[0.9979119,0.00040665636,0.000094577896,0.00023489073,0.0010867612,0.00026524274],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015758832,0.00017822269,0.00021632768,0.0002306421,0.0012923819,0.0002069029,0.00015693893,0.0002033037,0.00023981964],"category_scores_gemma":[0.00030313522,0.00020012949,0.000060003942,0.00090231444,0.00027098408,0.00055689877,0.000002107358,0.00018843041,0.0000064021783],"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":[0.0015694788,0.0008163236,0.120198846,0.0019609563,0.0005673476,0.000030332296,0.18923296,0.012374515,0.014245493,0.6262401,0.0039394894,0.028824156],"study_design_scores_gemma":[0.008249237,0.000424639,0.59873146,0.00016925755,0.0002794138,0.0000020411028,0.04713734,0.004785751,0.0015452951,0.0061603934,0.33142355,0.0010916073],"about_ca_topic_score_codex":0.0012230874,"about_ca_topic_score_gemma":0.0030428835,"teacher_disagreement_score":0.6200797,"about_ca_system_score_codex":0.00005653641,"about_ca_system_score_gemma":0.00030362947,"threshold_uncertainty_score":0.9940088},"labels":[],"label_agreement":null},{"id":"W2240574427","doi":"","title":"Calgary's CTrain: Effective Capital Utilization","year":2007,"lang":"en","type":"article","venue":"Transportation research circular","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Light rail transit; Transport engineering; Public transport; Transit (satellite); Per capita; Futures studies; Business; Capital investment; Investment (military); Engineering; Finance; Computer science; Population","score_opus":0.06959974835905823,"score_gpt":0.39829220766833756,"score_spread":0.3286924593092793,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2240574427","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.76113516,0.00031165074,0.2175935,0.00048156254,0.0002797176,0.0013244535,0.00002653085,0.0003317706,0.01851564],"genre_scores_gemma":[0.99784684,0.0001101614,0.0008094843,0.00006607699,0.00013621294,0.00004948237,0.00046840555,0.000023166047,0.0004901494],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9969554,0.0003156035,0.0003353448,0.00034778405,0.0014086374,0.0006372479],"domain_scores_gemma":[0.99836826,0.00037861528,0.000067221205,0.00015819284,0.00075004785,0.00027765782],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004278929,0.000117275886,0.00013514444,0.00039258512,0.0009222462,0.000075969,0.00018628269,0.00018925717,0.0003760281],"category_scores_gemma":[0.000199442,0.0001334562,0.00008126451,0.0012433339,0.00037244696,0.0003954554,0.0000011699636,0.00032840643,0.00007738482],"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":[0.00031299994,0.00044186527,0.20206599,0.00017760733,0.00013071856,0.00039660433,0.21541399,0.004678914,0.0027505627,0.5192087,0.0010497515,0.05337229],"study_design_scores_gemma":[0.0007591065,0.00008773869,0.94203657,0.000045052024,0.000026066207,4.5488397e-7,0.012682027,0.0002109246,0.0012161811,0.002601572,0.04005941,0.00027489138],"about_ca_topic_score_codex":0.003957466,"about_ca_topic_score_gemma":0.010729978,"teacher_disagreement_score":0.73997056,"about_ca_system_score_codex":0.000167386,"about_ca_system_score_gemma":0.00023393944,"threshold_uncertainty_score":0.7093265},"labels":[],"label_agreement":null},{"id":"W2240584779","doi":"10.1007/11553762_26","title":"A General Multi-agent Modelling Framework for the Transit Assignment Problem – A Learning-Based Approach","year":2006,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Transit (satellite); Process (computing); Conceptual framework; Operations research; Public transport; Artificial intelligence; Transport engineering; Engineering","score_opus":0.03837403273412427,"score_gpt":0.27569354000828733,"score_spread":0.23731950727416307,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2240584779","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000009049935,0.00024000152,0.9967513,0.0007213212,0.000366558,0.00095227006,0.00000969164,0.00010096002,0.00084884785],"genre_scores_gemma":[0.100788444,0.000023937542,0.89734966,0.0003898689,0.00040598153,0.00006235475,0.000048605903,0.000028829318,0.00090229244],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99779636,0.00005014415,0.00031795705,0.0006577924,0.00074329524,0.00043446032],"domain_scores_gemma":[0.9987671,0.0005726739,0.00019843395,0.00022082835,0.00016233057,0.00007863524],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010668528,0.00025775185,0.00023671612,0.00018806278,0.0010429807,0.00028213827,0.0006069492,0.00032178118,0.0000105232275],"category_scores_gemma":[0.000030730247,0.00020315404,0.00013092648,0.00026341336,0.0005234656,0.00009955563,0.000014720756,0.0005727894,0.0000016668711],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009085907,0.00002751511,0.00004348002,0.000021455378,0.000006620705,0.0000015099037,0.003906496,0.974219,0.0000011056934,0.008113031,0.000008788579,0.0136418985],"study_design_scores_gemma":[0.00021352868,0.000043003944,0.000016650523,0.00013888135,0.000025361212,3.2681655e-7,0.000003059774,0.98452306,0.000014156568,0.010494274,0.0042638774,0.00026381825],"about_ca_topic_score_codex":0.00024266454,"about_ca_topic_score_gemma":0.00022221835,"teacher_disagreement_score":0.10077939,"about_ca_system_score_codex":0.00019572441,"about_ca_system_score_gemma":0.00048176362,"threshold_uncertainty_score":0.8284381},"labels":[],"label_agreement":null},{"id":"W2241036302","doi":"10.2495/ut030221","title":"TRAVEL DEMAND FORECASTING AND TDM MEASURES: THE EXAMPLE OF MONTREAL'S SOUTH SHORE, 2001-2021","year":2003,"lang":"en","type":"article","venue":"WIT transactions on the built environment","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Metropolitan area; Demand forecasting; Demand management; Shore; Transport engineering; Traffic congestion; Work (physics); Business; Regional science; Operations research; Geography; Economics; Engineering","score_opus":0.04973730286399573,"score_gpt":0.22986236251094402,"score_spread":0.1801250596469483,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2241036302","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14654018,0.00037223884,0.8426319,0.002211102,0.000119430006,0.00069081894,0.000059183432,0.000027953516,0.0073472033],"genre_scores_gemma":[0.997821,0.00040199165,0.00083729834,0.00005709634,0.000018256467,0.000034931305,0.0000033217339,0.000009838979,0.0008163011],"study_design_codex":"simulation_or_modeling","study_design_gemma":"qualitative","domain_scores_codex":[0.99893475,0.00021387883,0.00018161272,0.00016874004,0.00031932653,0.00018167187],"domain_scores_gemma":[0.999444,0.00022850046,0.00007527837,0.0001806522,0.00001231476,0.000059245453],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00076913135,0.000098127595,0.000100832745,0.000033359393,0.00068499084,0.000026102387,0.000103435304,0.000055434877,0.00037589096],"category_scores_gemma":[0.000019607603,0.00006413567,0.000052601263,0.00010897446,0.00025281386,0.000058052097,6.943325e-7,0.000116054114,0.0000052609535],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013942922,0.00055152655,0.007650474,0.000028311688,0.00031624004,0.0000050459303,0.26908335,0.6578185,0.00065905915,0.00986032,0.00037367686,0.05351407],"study_design_scores_gemma":[0.0073440187,0.0011817182,0.35164598,0.0005844395,0.0024361338,0.000036917034,0.42601195,0.028660934,0.019656643,0.014424467,0.14509964,0.002917151],"about_ca_topic_score_codex":0.001903394,"about_ca_topic_score_gemma":0.00092330243,"teacher_disagreement_score":0.8512808,"about_ca_system_score_codex":0.000030357227,"about_ca_system_score_gemma":0.000030567262,"threshold_uncertainty_score":0.5268465},"labels":[],"label_agreement":null},{"id":"W2241068904","doi":"","title":"IDENTIFYING PASSENGER CORRIDORS ON THE U.S. HIGHWAY SYSTEM USING ATS DATA","year":2001,"lang":"en","type":"article","venue":"Transportation research circular","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"TRIPS architecture; Transport engineering; Recreation; Truck; Travel behavior; Travel survey; Geography; Mode choice; Trip generation; Business; Public transport; Engineering","score_opus":0.3191408475093272,"score_gpt":0.4336144898544439,"score_spread":0.1144736423451167,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2241068904","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.959603,0.00021820744,0.030743254,0.0022401316,0.0005329173,0.00094060396,0.00013273598,0.00035415767,0.0052349605],"genre_scores_gemma":[0.9983336,0.00016083567,0.00038458616,0.00007445986,0.0001811618,0.000025144658,0.00049861055,0.000025555717,0.0003160952],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9962654,0.0006510696,0.00034665258,0.0004559522,0.001769834,0.0005110998],"domain_scores_gemma":[0.9982758,0.00042366888,0.000101552214,0.0006125297,0.00042551153,0.00016090134],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.003844451,0.00012231783,0.00014133254,0.00022507505,0.0017957715,0.00022305097,0.0007050462,0.000121218654,0.00027312856],"category_scores_gemma":[0.00014706004,0.00010459559,0.000056651796,0.0011969948,0.00026130557,0.0004951862,0.000005054572,0.00037729472,0.00010028217],"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":[0.00037164873,0.00050902716,0.23793848,0.00053052156,0.00040263694,0.0014037648,0.09625881,0.15051629,0.0039095986,0.49294856,0.0111786155,0.004032058],"study_design_scores_gemma":[0.002084575,0.00012628044,0.5641776,0.001378001,0.0002874504,0.000008179497,0.14088906,0.076291025,0.0005176827,0.001501717,0.21137619,0.0013622823],"about_ca_topic_score_codex":0.005125107,"about_ca_topic_score_gemma":0.0026858395,"teacher_disagreement_score":0.49144682,"about_ca_system_score_codex":0.00016379186,"about_ca_system_score_gemma":0.00029103717,"threshold_uncertainty_score":0.99950373},"labels":[],"label_agreement":null},{"id":"W2241799903","doi":"","title":"Economies of Scale in Operating Costs for Light Rail Transit and Streetcars","year":2013,"lang":"en","type":"article","venue":"Transportation research circular","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Transport engineering; Mile; Unit (ring theory); Economies of scale; Transit (satellite); Service (business); Modal shift; Light rail transit; Public transport; Scale (ratio); Business; Light rail; Vehicle miles of travel; Modal; Engineering; Geography; Marketing","score_opus":0.03856113733386288,"score_gpt":0.34377373636235,"score_spread":0.3052125990284871,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2241799903","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99258035,0.00014012969,0.003791539,0.001502609,0.000029778592,0.0008986957,0.000035638128,0.000024542845,0.0009967051],"genre_scores_gemma":[0.9973541,0.00013159201,0.0020856247,0.000020665315,0.00001997189,0.00015186981,0.00009626233,0.000010476413,0.00012945938],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9987722,0.00013199501,0.00031686778,0.00020667377,0.00030587538,0.00026640817],"domain_scores_gemma":[0.9992469,0.00017909464,0.000046554025,0.00007678876,0.00035479153,0.000095874464],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010732707,0.00006784171,0.00014941438,0.00020728465,0.0002420103,0.00005775757,0.00009701797,0.000089557885,0.000118833836],"category_scores_gemma":[0.000051882962,0.00007495037,0.000038157636,0.0003122101,0.00017552973,0.00043596502,6.2432264e-7,0.000108763124,0.000003298254],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000692746,0.00018836,0.7690306,0.00042905466,0.00005341993,0.000005206608,0.13813107,0.022544943,0.00960313,0.045468535,0.0005320483,0.0139443735],"study_design_scores_gemma":[0.0013644197,0.00008415351,0.96112615,0.00013380841,0.0000152597,7.448557e-8,0.028432198,0.0023705005,0.0020055424,0.0018473878,0.0024214557,0.00019905998],"about_ca_topic_score_codex":0.0076423,"about_ca_topic_score_gemma":0.018247271,"teacher_disagreement_score":0.19209555,"about_ca_system_score_codex":0.000044381195,"about_ca_system_score_gemma":0.00013454774,"threshold_uncertainty_score":0.99966717},"labels":[],"label_agreement":null},{"id":"W2241809640","doi":"","title":"EFFECT OF DARKNESS ON THE CAPACITY OF LONG-TERM FREEWAY RECONSTRUCTION ZONES","year":2000,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Darkness; Daylight; Work (physics); Transport engineering; Term (time); Environmental science; Geography; Engineering; Biology; Physics","score_opus":0.015176773477734815,"score_gpt":0.2679936667592755,"score_spread":0.25281689328154067,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2241809640","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.963287,0.0000046510136,0.00041124685,0.00015016085,0.00009612143,0.00010809205,0.000005133533,0.000023506402,0.035914067],"genre_scores_gemma":[0.9990834,0.000024970495,0.00012924605,0.000011788018,0.000022463468,0.0000029443424,0.00000430563,0.0000020562766,0.00071882515],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9994395,0.0001701506,0.000117952935,0.00006424157,0.00014826849,0.000059927163],"domain_scores_gemma":[0.9995851,0.00023111104,0.00005819838,0.000071425144,0.000037865655,0.000016336364],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004280136,0.0000385396,0.00007719228,0.0000233771,0.00011864515,0.000006865536,0.00006702697,0.000041499377,0.0014574296],"category_scores_gemma":[0.0000380786,0.00002447579,0.00003273094,0.0001352174,0.00017413909,0.00006989274,5.9259725e-7,0.000035955854,0.000006060488],"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":[0.0003171524,0.00010545172,0.62418014,0.000105031606,0.000051308045,0.000001212684,0.017276531,0.0070406673,0.00047762957,0.0310135,0.00025547537,0.3191759],"study_design_scores_gemma":[0.00093756383,0.00066686026,0.9249368,0.0003009095,0.000093962444,0.0000026652713,0.0012385204,0.0004180506,0.07025576,0.00058953854,0.00028581286,0.0002735798],"about_ca_topic_score_codex":0.0007807133,"about_ca_topic_score_gemma":0.0007033805,"teacher_disagreement_score":0.31890234,"about_ca_system_score_codex":0.00000725729,"about_ca_system_score_gemma":0.00001614887,"threshold_uncertainty_score":0.9994554},"labels":[],"label_agreement":null},{"id":"W2241924265","doi":"","title":"Sustainable Mobility of Small Tourist Places","year":2014,"lang":"en","type":"article","venue":"Proceedings of the International Conference on Road and Rail Infrastructure CETRA","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Transport Canada","funders":"","keywords":"Tourism; Environmental economics; Attractiveness; Business; Transport engineering; Sustainable development; Greenhouse gas; Population; Environmental planning; Consumption (sociology); Sustainable transport; Public transport; Sustainability; Geography; Engineering; Economics","score_opus":0.013038823460377186,"score_gpt":0.25421879994307583,"score_spread":0.24117997648269865,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2241924265","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.91856414,0.000007032866,0.000083552055,0.0015827101,0.00017634424,0.00011378725,0.000009950318,0.000018569595,0.07944392],"genre_scores_gemma":[0.99765426,0.000030982923,0.0005526432,0.000069478134,0.00006874863,0.0000033505999,0.000003232135,0.0000035528726,0.0016137649],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9992583,0.0000104496485,0.00018858795,0.00013688378,0.00029380474,0.00011196873],"domain_scores_gemma":[0.99902695,0.00003907479,0.00022128513,0.00004067929,0.0006324675,0.00003956149],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037285275,0.00007547561,0.00011307507,0.00006274672,0.00015325566,0.000061980754,0.00033070074,0.00007083353,0.0001070159],"category_scores_gemma":[0.000322613,0.000054741773,0.000037531743,0.00011247056,0.00026074215,0.00013728242,0.000024170557,0.00010939063,2.537244e-7],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012372296,0.000040526273,0.07638067,0.00008807208,0.00002907091,9.7215235e-8,0.0082534,0.000656158,0.0018230589,0.90912646,0.0007148107,0.0027639542],"study_design_scores_gemma":[0.0011558898,0.00028530823,0.8346841,0.00036996164,0.00005412349,0.000002426988,0.035319354,0.007188748,0.009324327,0.09764686,0.013584192,0.00038474533],"about_ca_topic_score_codex":0.0004474896,"about_ca_topic_score_gemma":0.00004685427,"teacher_disagreement_score":0.81147957,"about_ca_system_score_codex":0.000023436083,"about_ca_system_score_gemma":0.00006873554,"threshold_uncertainty_score":0.22323048},"labels":[],"label_agreement":null},{"id":"W2242138927","doi":"10.2495/ut020281","title":"IMPACT OF TELEWORK AND FLEXTIME ON REDUCING FUTURE URBAN TRAVEL DEMAND: THE CASE OF MONTREAL AND QUEBEC (CANADA), 1996-2016","year":2002,"lang":"en","type":"article","venue":"WIT transactions on the built environment","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Telecommuting; Travel behavior; Flexibility (engineering); Business; Demand management; Transport engineering; On demand; Regional science; Geography; Engineering; Economics","score_opus":0.009177454646178843,"score_gpt":0.21563804930824906,"score_spread":0.20646059466207023,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2242138927","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9900525,0.00052860775,0.0054341066,0.002847921,0.000059828828,0.00031384005,0.000082033424,0.0000114365475,0.0006696925],"genre_scores_gemma":[0.9983858,0.00049091334,0.00013230862,0.00003484903,0.000033448578,0.000009119097,0.000001831026,0.0000075030302,0.0009042193],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9992776,0.0001122172,0.00016420142,0.00013989421,0.000183109,0.00012297671],"domain_scores_gemma":[0.99950534,0.00017809415,0.00008628135,0.00016202433,0.000006723708,0.000061515326],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018002959,0.0000986758,0.00011220223,0.000031751082,0.00044192185,0.000013100481,0.00006970034,0.00005607105,0.00025727562],"category_scores_gemma":[0.000004248808,0.000059571372,0.00004710373,0.00007853782,0.00021349464,0.000048246915,7.802206e-7,0.00011960801,8.651564e-7],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005165282,0.0011032305,0.005911432,0.00006595449,0.0006920011,0.00009507969,0.15863937,0.5918226,0.0008958477,0.0032479041,0.0039294795,0.23308055],"study_design_scores_gemma":[0.0024234962,0.0009973112,0.93687946,0.00038881873,0.0006382599,0.000092444854,0.036887135,0.015101211,0.001573506,0.00039622895,0.0037461931,0.0008759484],"about_ca_topic_score_codex":0.41520035,"about_ca_topic_score_gemma":0.3633838,"teacher_disagreement_score":0.930968,"about_ca_system_score_codex":0.00007677801,"about_ca_system_score_gemma":0.0000411804,"threshold_uncertainty_score":0.64823276},"labels":[],"label_agreement":null},{"id":"W2242758376","doi":"","title":"Tackling Fare Evasion on Calgary Transit’s CTrain System","year":2013,"lang":"en","type":"article","venue":"Transportation research circular","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":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Transit (satellite); Public transport; Transport engineering; Enforcement; Payment; Business; Engineering; Finance; Political science","score_opus":0.05629050372824674,"score_gpt":0.34803732348728117,"score_spread":0.29174681975903444,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2242758376","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.91317827,0.00029084974,0.047684286,0.0045334827,0.0004706909,0.0028482738,0.00007795137,0.0009677852,0.02994841],"genre_scores_gemma":[0.99785644,0.00007556648,0.00069007464,0.00013415107,0.0001111197,0.00017864739,0.00040278747,0.000032025713,0.00051919004],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9962928,0.0005151347,0.00040613543,0.00043408517,0.0017309953,0.0006208412],"domain_scores_gemma":[0.9984711,0.00030287023,0.00006920189,0.00023529568,0.0006070332,0.00031453933],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016666128,0.00015490237,0.00019051007,0.00035663895,0.001136396,0.00015920657,0.00026611745,0.00021673745,0.000626348],"category_scores_gemma":[0.00008947165,0.00016058401,0.00011425659,0.0008623823,0.00020834076,0.00044260648,9.047259e-7,0.0004355113,0.00036219304],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031636652,0.000827223,0.060782943,0.001498326,0.00027378477,0.0005308755,0.20450087,0.12073796,0.010431983,0.55180556,0.009456617,0.038837492],"study_design_scores_gemma":[0.003454048,0.00046640786,0.8049706,0.0013121495,0.00012102426,0.0000021655064,0.10078912,0.0144116245,0.0017530412,0.0021662482,0.06908248,0.0014710806],"about_ca_topic_score_codex":0.007904753,"about_ca_topic_score_gemma":0.0013495598,"teacher_disagreement_score":0.74418765,"about_ca_system_score_codex":0.00019877103,"about_ca_system_score_gemma":0.00026021508,"threshold_uncertainty_score":0.9987017},"labels":[],"label_agreement":null},{"id":"W2243100029","doi":"","title":"IMPLEMENTING PASSENGER INFORMATION, ENTERTAINMENT, AND SECURITY SYSTEMS IN LIGHT RAIL TRANSIT","year":2003,"lang":"en","type":"article","venue":"Transportation Research E-Circular","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Entertainment; Public transport; Revenue; Passenger information; Business; Government (linguistics); Transit (satellite); Transport engineering; Computer security; Finance; Engineering; Computer science","score_opus":0.029743170871518527,"score_gpt":0.33383850373700474,"score_spread":0.30409533286548623,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2243100029","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9635957,0.0008880319,0.015417515,0.0018305066,0.00024280588,0.001770981,0.000089702204,0.0001704344,0.015994309],"genre_scores_gemma":[0.9989832,0.00034910487,0.00014899122,0.00004868915,0.000023482151,0.00009576338,0.0002292119,0.000011076612,0.00011045799],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9970526,0.000570065,0.0005427843,0.00024338865,0.0009557799,0.00063538336],"domain_scores_gemma":[0.99912274,0.00013116939,0.00009188971,0.0001314434,0.00034377826,0.0001790094],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037826449,0.00012302678,0.00016966532,0.00039675998,0.00065788545,0.00020577926,0.00011929081,0.00013042642,0.00018633575],"category_scores_gemma":[0.000108948356,0.0001365058,0.000045359928,0.0008023616,0.00011452761,0.0009934393,0.0000012900337,0.00028576044,0.000016107293],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000061887746,0.00018085096,0.39326268,0.00057459023,0.00007391848,0.000057451918,0.26967624,0.0055707693,0.00050444494,0.32860488,0.00078950723,0.0006428156],"study_design_scores_gemma":[0.0025558572,0.0000639421,0.18766485,0.00018176342,0.00003759902,0.0000013480856,0.091430694,0.00081532047,0.00033072237,0.0024360619,0.71398026,0.00050156616],"about_ca_topic_score_codex":0.0039882767,"about_ca_topic_score_gemma":0.0052084485,"teacher_disagreement_score":0.7131908,"about_ca_system_score_codex":0.00009487846,"about_ca_system_score_gemma":0.00019716125,"threshold_uncertainty_score":0.60291076},"labels":[],"label_agreement":null},{"id":"W2244315564","doi":"","title":"Light Rail Transit in Calgary: The First 25 Years","year":2007,"lang":"en","type":"article","venue":"Transportation Research E-Circular","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Light rail transit; Light rail; Track (disk drive); Transit (satellite); Transport engineering; Transit-oriented development; Security system; Market segmentation; Mode (computer interface); Public transport; Transit system; Business; Geography; Engineering; Computer science; Marketing; Computer security","score_opus":0.04323211927798713,"score_gpt":0.3501661843861259,"score_spread":0.30693406510813875,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2244315564","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.933404,0.0007162662,0.031088943,0.013386006,0.0003425842,0.0015685988,0.000024613046,0.00025600469,0.01921298],"genre_scores_gemma":[0.9986292,0.00020013363,0.00026673486,0.00019097218,0.0000894552,0.000038441165,0.00009360779,0.000020768939,0.00047065676],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.99708986,0.00021376742,0.00041406264,0.00031622697,0.0013171271,0.0006489377],"domain_scores_gemma":[0.9990025,0.00031126622,0.000055341443,0.00021407694,0.00025034868,0.00016645322],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004670382,0.00011262859,0.00014593331,0.0003570606,0.00069232646,0.00008307959,0.00035900902,0.00017776364,0.0003119763],"category_scores_gemma":[0.000079512014,0.00010471517,0.00008992084,0.0016758679,0.00024596843,0.00027331067,0.0000012820175,0.00048795677,0.000067175555],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038174744,0.00050584134,0.37553626,0.00019095161,0.00010057779,0.0010274551,0.41562408,0.014693123,0.0009292005,0.17880525,0.0036690398,0.008536485],"study_design_scores_gemma":[0.0006124946,0.000025097206,0.7827566,0.00004546349,0.0000122618,2.601973e-7,0.00623489,0.000103377875,0.00013466389,0.0007530618,0.20916912,0.00015266187],"about_ca_topic_score_codex":0.008333601,"about_ca_topic_score_gemma":0.084669404,"teacher_disagreement_score":0.4093892,"about_ca_system_score_codex":0.00011808999,"about_ca_system_score_gemma":0.00023481822,"threshold_uncertainty_score":0.99827},"labels":[],"label_agreement":null},{"id":"W2245381200","doi":"","title":"Factors affecting urban transit ridership","year":2000,"lang":"en","type":"article","venue":"Rosa P: A digital library for transportation research (United States Department of Transportation)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":49,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Public transport; Transit (satellite); Transport engineering; Urban transit; Business; Engineering","score_opus":0.05701436770222572,"score_gpt":0.3163949314762306,"score_spread":0.25938056377400487,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2245381200","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8571305,0.00011640972,0.003389515,0.0008912548,0.00009073738,0.0014947484,0.13586663,0.00046490293,0.000555278],"genre_scores_gemma":[0.6646821,0.00024106567,0.00071253296,0.000051421,0.000052257372,0.00010049704,0.33246472,0.00006361355,0.0016317854],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9962141,0.000182497,0.0009169187,0.0005735443,0.0012710274,0.00084194826],"domain_scores_gemma":[0.99767655,0.0010598947,0.00020916793,0.00022564405,0.00040191066,0.00042684117],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005317175,0.00034334528,0.0004066491,0.0006717329,0.0009319401,0.00034199157,0.00039268378,0.00022152133,0.0013846904],"category_scores_gemma":[0.000037576003,0.00035818887,0.0003541936,0.0023334413,0.00045457084,0.0032879354,8.1475537e-7,0.0003082344,0.000018485931],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0060598142,0.003864443,0.32971936,0.0023235797,0.001330363,0.00009662089,0.32009372,0.09182719,0.000026380507,0.09251344,0.13174678,0.020398293],"study_design_scores_gemma":[0.002154425,0.0007066954,0.06428818,0.00021887275,0.00013975943,1.1457364e-7,0.022867914,0.00056555524,0.0011032204,0.0024774515,0.90478104,0.00069675606],"about_ca_topic_score_codex":0.00008641667,"about_ca_topic_score_gemma":0.000682115,"teacher_disagreement_score":0.7730343,"about_ca_system_score_codex":0.00004925528,"about_ca_system_score_gemma":0.00027773183,"threshold_uncertainty_score":0.999887},"labels":[],"label_agreement":null},{"id":"W2245631386","doi":"","title":"THE TRANSPORT FOR PERSONS WITH DISABILITIES: AN ECONOMIC EVALUATION","year":2000,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Subsidy; Commission; Business; Government (linguistics); Local government; Public administration; Service (business); Public transport; Transit (satellite); Public economics; Finance; Economic growth; Economic policy; Economics; Political science; Market economy; Marketing; Law","score_opus":0.03459650519982874,"score_gpt":0.3249044935145661,"score_spread":0.2903079883147373,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2245631386","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8933523,0.000016402937,0.0033630775,0.0021389744,0.00007875348,0.0006608419,0.000031118936,0.00010583915,0.10025269],"genre_scores_gemma":[0.9882054,0.000019301844,0.0009523365,0.000045094406,0.000066879555,0.000083755884,0.00007788835,0.000004831449,0.010544536],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99955326,0.00003989555,0.000079192716,0.00009924315,0.000120653385,0.00010775621],"domain_scores_gemma":[0.9997234,0.000120281366,0.00001547232,0.00006746578,0.000035027962,0.000038402522],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005658758,0.00003663419,0.0000356987,0.000007836983,0.0006851773,0.000038069305,0.00006455747,0.000024527902,0.0015303035],"category_scores_gemma":[0.000008700223,0.000024154962,0.000020561265,0.000032876716,0.00011689966,0.00020978366,9.3009554e-8,0.00001719632,0.000008753429],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00049644115,0.00010856459,0.07379134,0.000010643555,0.00005417098,1.6474732e-7,0.29818368,0.3534507,0.0000021813498,0.13539985,0.0024206338,0.13608164],"study_design_scores_gemma":[0.0023046175,0.00048226063,0.07586023,0.00002032787,0.000212508,6.287072e-7,0.3056567,0.10018706,0.000024165183,0.004804265,0.50989896,0.0005482583],"about_ca_topic_score_codex":0.0010801232,"about_ca_topic_score_gemma":0.031349707,"teacher_disagreement_score":0.5074783,"about_ca_system_score_codex":0.00004346119,"about_ca_system_score_gemma":0.00017140506,"threshold_uncertainty_score":0.99938244},"labels":[],"label_agreement":null},{"id":"W2245708694","doi":"","title":"Global Navigation Satellite System Road Pricing in the Greater Toronto Area","year":2011,"lang":"en","type":"article","venue":"York University Digital Library (York University)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Satellite; Satellite system; Global Positioning System; Computer science; Telecommunications; Remote sensing; Transport engineering; Meteorology; Geography; GNSS applications; Engineering","score_opus":0.02004506995479417,"score_gpt":0.18191795118351728,"score_spread":0.16187288122872312,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2245708694","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27030355,0.000043479118,0.0006892059,0.000085661224,0.00010503938,0.00022482022,0.000130079,0.00032999733,0.72808814],"genre_scores_gemma":[0.9928443,0.00003272589,0.00033996248,0.000033543143,0.000030013929,6.9929385e-8,0.00019876698,0.0000064273872,0.0065141986],"study_design_codex":"observational","study_design_gemma":"qualitative","domain_scores_codex":[0.9989757,0.00015823859,0.000111015055,0.0002577666,0.00023712638,0.00026017937],"domain_scores_gemma":[0.999566,0.000037031077,0.00010008518,0.0001575827,0.00002840469,0.000110898305],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007266635,0.00013010367,0.0001240454,0.00010765539,0.0004724681,0.0001617484,0.0005416112,0.00012722462,0.000069407404],"category_scores_gemma":[0.000003980791,0.00013427481,0.00008255058,0.0010695329,0.00014702877,0.0036012414,0.000034847453,0.00008139493,0.000024829369],"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":[0.00036687602,0.00012355186,0.74441636,0.00003357916,0.000047434773,0.00064182444,0.04904453,0.0003850576,4.768922e-7,0.2004926,0.0009329281,0.0035148016],"study_design_scores_gemma":[0.0009940023,0.000090713635,0.35987306,0.0002590243,0.00008198738,0.0000071645777,0.42197472,0.00012774392,0.0000048166603,0.00015561572,0.21590917,0.00052199943],"about_ca_topic_score_codex":0.003184181,"about_ca_topic_score_gemma":0.0013168192,"teacher_disagreement_score":0.72254074,"about_ca_system_score_codex":0.00033898887,"about_ca_system_score_gemma":0.00013424263,"threshold_uncertainty_score":0.5475568},"labels":[],"label_agreement":null},{"id":"W2246653258","doi":"","title":"Evaluation of Multiple-Unit Streetcar Operation in Toronto, Canada","year":2007,"lang":"en","type":"article","venue":"Transportation research circular","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Headway; Transport engineering; Transit (satellite); Service (business); Unit (ring theory); Reliability (semiconductor); Public transport; Level of service; Traffic congestion; Traffic flow (computer networking); Computer science; Engineering; Business; Computer network","score_opus":0.10469348726804757,"score_gpt":0.42269831011957604,"score_spread":0.3180048228515285,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2246653258","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9863701,0.00042682953,0.0040531997,0.0001611281,0.00012416915,0.00072161155,0.00003616252,0.000026293157,0.0080805095],"genre_scores_gemma":[0.9991161,0.00007984325,0.0003201521,0.00001634866,0.000041171737,0.000027363072,0.00033987925,0.000010429061,0.000048671678],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99510866,0.00056250655,0.00047091942,0.00022321015,0.00326522,0.00036945753],"domain_scores_gemma":[0.9975999,0.0002759069,0.000082617276,0.00014052207,0.0017766213,0.00012442918],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011933221,0.00007872113,0.00012451048,0.00016926501,0.00025060785,0.000020397723,0.00015359792,0.00010600875,0.0005934643],"category_scores_gemma":[0.0004184763,0.00009325311,0.000030978095,0.0005966926,0.00011147109,0.00037340834,8.5394873e-7,0.00016000039,0.000001966986],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010636208,0.00020785893,0.78043854,0.00006813732,0.000047725367,0.000032712698,0.03502005,0.1357867,0.0018694709,0.027816443,0.0001547463,0.018451268],"study_design_scores_gemma":[0.00091885554,0.000021971273,0.9662412,0.00003879198,0.000025971283,4.506329e-8,0.025319286,0.0045305747,0.0007387113,0.00016400414,0.0018863914,0.00011423097],"about_ca_topic_score_codex":0.95587695,"about_ca_topic_score_gemma":0.999177,"teacher_disagreement_score":0.18580264,"about_ca_system_score_codex":0.0009556578,"about_ca_system_score_gemma":0.002506585,"threshold_uncertainty_score":0.6498018},"labels":[],"label_agreement":null},{"id":"W2247852206","doi":"10.22004/ag.econ.306075","title":"The State of the Canadian Intercity Highway System, 1986-1993","year":2020,"lang":"en","type":"article","venue":"AgEcon Search (University of Minnesota, USA)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Transport engineering; Highway system; State (computer science); State highway; Quality (philosophy); Computer science; Engineering","score_opus":0.02864862259105338,"score_gpt":0.23212113553657002,"score_spread":0.20347251294551663,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2247852206","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97546667,0.000030386154,0.0004339365,0.01179538,0.00016475665,0.00023559624,0.000116982854,0.000024099578,0.011732189],"genre_scores_gemma":[0.9973301,0.00004327345,0.000117162905,0.000057774098,0.000015775107,8.3483684e-8,0.0000070706797,0.0000032045134,0.002425534],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.999032,0.00023224922,0.000098941615,0.000118928205,0.00032207658,0.00019579632],"domain_scores_gemma":[0.999295,0.00012712948,0.000093380244,0.00012400914,0.00018533315,0.00017513351],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00052236003,0.000050386905,0.00010178052,0.000046269073,0.0009015385,0.000023637409,0.00048589247,0.00004469681,0.000094524374],"category_scores_gemma":[0.000051006504,0.00004300009,0.00006531896,0.00037428603,0.00045374534,0.00015343363,0.00002391789,0.00012569007,0.0000313038],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032027424,0.00007369253,0.18867303,0.0003942088,0.00029199835,0.00009841481,0.6838209,0.01268158,0.000586766,0.06171434,0.029551549,0.02179322],"study_design_scores_gemma":[0.0011719133,0.0001683813,0.60659,0.00019750804,0.000092480805,0.0000012306122,0.17373148,0.009601504,0.00068999286,0.00011691304,0.2072508,0.000387836],"about_ca_topic_score_codex":0.47388607,"about_ca_topic_score_gemma":0.8665797,"teacher_disagreement_score":0.51008946,"about_ca_system_score_codex":0.00009865949,"about_ca_system_score_gemma":0.0005068702,"threshold_uncertainty_score":0.6933996},"labels":[],"label_agreement":null},{"id":"W2248023200","doi":"10.2495/ut040411","title":"TRANSIT FOR SMALL URBAN AREAS","year":2004,"lang":"en","type":"article","venue":"WIT transactions on the built environment","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Transit (satellite); Revenue; Public transport; Environmental planning; Business; Population; Geography; Transport engineering; Engineering; Finance; Sociology","score_opus":0.02769814792933668,"score_gpt":0.23780944333932091,"score_spread":0.21011129540998424,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2248023200","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021006761,0.000039819763,0.96560055,0.010965866,0.00013344937,0.00056314614,0.000049198865,0.00008808008,0.0015531455],"genre_scores_gemma":[0.99296874,0.00013324652,0.0038629933,0.00038371363,0.000053553835,0.00016622095,0.000017672659,0.00001583041,0.0023980225],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99922866,0.00004229989,0.00014113609,0.0001809883,0.00020799121,0.00019895112],"domain_scores_gemma":[0.9996217,0.000089919355,0.000037245496,0.00016658817,0.000009721279,0.000074822274],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023765779,0.00009879846,0.00007870769,0.00003960171,0.0008044437,0.000030736544,0.00014699825,0.00006908551,0.00034603148],"category_scores_gemma":[0.000004880942,0.00008092275,0.00009995905,0.00008364952,0.00013857652,0.00006214216,2.7608576e-7,0.000098593635,0.00005884253],"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":[0.00010115481,0.00037040625,0.0001316471,0.000008440698,0.00006528919,0.0000016187773,0.021030158,0.9485407,0.00018314572,0.022538466,0.00016241272,0.0068665897],"study_design_scores_gemma":[0.007546617,0.0010616166,0.019379554,0.00024092781,0.00082596805,0.000005734131,0.02254938,0.0016759337,0.014948578,0.024047147,0.90589774,0.0018207782],"about_ca_topic_score_codex":0.00032919535,"about_ca_topic_score_gemma":0.0005969561,"teacher_disagreement_score":0.971962,"about_ca_system_score_codex":0.000117353185,"about_ca_system_score_gemma":0.000045240988,"threshold_uncertainty_score":0.6187212},"labels":[],"label_agreement":null},{"id":"W2248214702","doi":"","title":"CONDUCTING TELEPHONE ORIGIN-DESTINATION HOUSEHOLD SURVEYS WITH AN INTEGRATED INFORMATIONAL APPROACH","year":2000,"lang":"en","type":"article","venue":"Transportation research circular","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Transport engineering; Travel survey; TRIPS architecture; Geocoding; Computer science; Trip distribution; Presentation (obstetrics); Set (abstract data type); Pedestrian; Travel behavior; Graphics; Modal; Operations research; Data science; Geography; Engineering","score_opus":0.16582292975941815,"score_gpt":0.36203184689976065,"score_spread":0.1962089171403425,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2248214702","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94046515,0.000025094749,0.050324395,0.0001739397,0.00003997445,0.0006003656,0.00009107921,0.0003542266,0.0079257535],"genre_scores_gemma":[0.9867707,0.000059534505,0.008375183,0.00004056777,0.00007802175,0.00007580233,0.0038200011,0.00002761517,0.0007525739],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99632007,0.00088778965,0.00038919706,0.00034617222,0.0015826542,0.00047410198],"domain_scores_gemma":[0.9984655,0.00015272512,0.00008879428,0.00019331423,0.0008589299,0.00024073113],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004398584,0.00014796043,0.000159298,0.00031840274,0.0010555254,0.000201928,0.0002386408,0.00014715549,0.0008510719],"category_scores_gemma":[0.000068030786,0.00014291162,0.00003844417,0.001695623,0.0003647431,0.001537029,5.924516e-7,0.00041561652,0.000035520312],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000335539,0.0007690353,0.13099667,0.0001736119,0.00012412062,0.000059189013,0.09109931,0.7031211,0.0003005434,0.027988458,0.00044327806,0.044589132],"study_design_scores_gemma":[0.0033327683,0.00044915327,0.88756317,0.0001643495,0.000075057585,0.0000062664312,0.037621263,0.016613735,0.00029997702,0.0007043783,0.052095197,0.001074712],"about_ca_topic_score_codex":0.00722136,"about_ca_topic_score_gemma":0.0028025722,"teacher_disagreement_score":0.75656646,"about_ca_system_score_codex":0.00012413094,"about_ca_system_score_gemma":0.0006281827,"threshold_uncertainty_score":0.99938965},"labels":[],"label_agreement":null},{"id":"W2252407422","doi":"","title":"Gestion du marquage routier : analyse des acquis et perspectives pour le Québec","year":2009,"lang":"fr","type":"article","venue":"Espace ÉTS (ETS)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Humanities; Political science; Art","score_opus":0.013614716724948808,"score_gpt":0.2755481209555529,"score_spread":0.2619334042306041,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2252407422","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6532644,0.013157027,0.035070438,0.25836095,0.00056105026,0.0002951526,0.000037737453,0.00030322725,0.03895003],"genre_scores_gemma":[0.9261069,0.0010010042,0.0060728365,0.00048035834,0.00026704534,0.0000043439036,0.000028726598,0.000016545693,0.0660222],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.9983493,0.00040240894,0.00021868268,0.00036407128,0.00030368564,0.00036182313],"domain_scores_gemma":[0.99914974,0.00014467613,0.00015611517,0.00016086962,0.00021801711,0.00017057359],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005944037,0.00020185777,0.00021028344,0.0001350598,0.0006257833,0.00015226712,0.0001608791,0.00020343681,0.0006780266],"category_scores_gemma":[0.00027476923,0.00022081746,0.00013271587,0.000686391,0.00032368655,0.0007115471,0.0000064211386,0.00020962214,0.0001636508],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016714353,0.00084255985,0.065849766,0.00003248401,0.00013375074,0.00007079192,0.64695305,0.11043055,0.00033738508,0.12216922,0.020647833,0.03236545],"study_design_scores_gemma":[0.0009030284,0.00019207918,0.79907596,0.00022601557,0.00021010896,0.0000063065568,0.14530617,0.00751329,0.00014781837,0.0019526483,0.043857396,0.0006092078],"about_ca_topic_score_codex":0.057774745,"about_ca_topic_score_gemma":0.0871492,"teacher_disagreement_score":0.7332262,"about_ca_system_score_codex":0.00032046292,"about_ca_system_score_gemma":0.0007256461,"threshold_uncertainty_score":0.9484996},"labels":[],"label_agreement":null},{"id":"W2253692137","doi":"","title":"Application of Gradient Projection Algorithm for Solving the Combined Modal Split and Traffic Assignment Problem","year":2016,"lang":"en","type":"article","venue":"Transportation Research Board 95th Annual MeetingTransportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Modal; Algorithm; Mathematical optimization; Robustness (evolution); Function (biology); Mathematics; Assignment problem; Computer science","score_opus":0.04421606988179512,"score_gpt":0.36830886808909785,"score_spread":0.32409279820730275,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2253692137","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.718393,0.00020278775,0.26157025,0.009276678,0.00023865391,0.008490646,0.00089908676,0.00033816605,0.00059072085],"genre_scores_gemma":[0.9806753,0.0006342292,0.015074473,0.000031349027,0.00019141364,0.0020557875,0.00032870533,0.00006495478,0.000943831],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99256825,0.0009226622,0.001162869,0.00091384805,0.003184229,0.0012481121],"domain_scores_gemma":[0.992712,0.002298387,0.0003706657,0.00033496195,0.003866542,0.0004174364],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.009781932,0.0002975034,0.0004027038,0.00079232716,0.0021919224,0.00014304288,0.00048504752,0.000306222,0.000051997053],"category_scores_gemma":[0.00034516596,0.00022419114,0.0001772427,0.0016915783,0.001643287,0.0007788305,0.0000070849446,0.0005626344,0.000009506125],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0031076802,0.0013015055,0.07880043,0.0011263144,0.00037341574,0.000019368068,0.13813198,0.008094483,0.007986074,0.13466719,0.0039349445,0.6224566],"study_design_scores_gemma":[0.012878063,0.0044003064,0.72870815,0.001573518,0.00034518813,0.0000010004177,0.11692047,0.036792357,0.0053948355,0.014677018,0.076372,0.001937065],"about_ca_topic_score_codex":0.0075960467,"about_ca_topic_score_gemma":0.014882507,"teacher_disagreement_score":0.64990777,"about_ca_system_score_codex":0.00025788764,"about_ca_system_score_gemma":0.00067241077,"threshold_uncertainty_score":0.99910706},"labels":[],"label_agreement":null},{"id":"W2253827978","doi":"10.55016/ojs/sppp.v7i1.42466","title":"The Free Ride is Over: Why Cities, and Citizens, Must Start Paying For Much-Needed Infrastructure","year":2014,"lang":"en","type":"article","venue":"The School of Public Policy Publications","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Business; Finance; Transport engineering; Engineering","score_opus":0.025534243715731684,"score_gpt":0.2986275750846865,"score_spread":0.2730933313689548,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2253827978","genre_codex":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14326419,0.00033082074,0.03159062,0.7738777,0.0003662725,0.0014559746,0.0009680093,0.0004312216,0.047715213],"genre_scores_gemma":[0.9851037,0.00027434234,0.0020726256,0.006661735,0.0006965442,0.00019479905,0.00015671403,0.000026147627,0.0048133833],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9984906,0.00022256955,0.00035064193,0.00019832497,0.00033240992,0.00040545454],"domain_scores_gemma":[0.9975689,0.0008417599,0.00028561015,0.000567761,0.0005015328,0.00023445064],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0017062589,0.00012359965,0.0001413589,0.0002226347,0.0020959866,0.0005975328,0.0007574896,0.00010880234,0.0001290287],"category_scores_gemma":[0.005590372,0.00008627249,0.000074475895,0.00084307534,0.0005644188,0.000562157,0.000041983738,0.00017180266,0.0000030643776],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000053507683,0.000009735281,0.010748162,0.000008347731,0.000034956207,5.5348046e-9,0.00406245,0.000045845976,0.000019637964,0.67531514,0.30784732,0.0019030729],"study_design_scores_gemma":[0.00053628185,0.000021276652,0.12294122,0.000008876236,0.000033037737,6.250062e-7,0.006493754,0.00073672656,0.00001749733,0.0982348,0.7708285,0.0001474029],"about_ca_topic_score_codex":0.0053433096,"about_ca_topic_score_gemma":0.0027343775,"teacher_disagreement_score":0.84183955,"about_ca_system_score_codex":0.000078411926,"about_ca_system_score_gemma":0.0007465936,"threshold_uncertainty_score":0.99920315},"labels":[],"label_agreement":null},{"id":"W2254351130","doi":"10.1002/atr.1450","title":"A hybrid scheme for real‐time prediction of bus trajectories","year":2016,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","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":"Stockholms Läns Landsting; Queensland University of Technology","keywords":"Schedule; Heuristic; Computer science; Transferability; Service (business); Public transport; Scheme (mathematics); Service provider; Predictive modelling; Operations research; Level of service; Regression analysis; Control (management); Transport engineering; Engineering; Machine learning; Artificial intelligence","score_opus":0.012023219237710395,"score_gpt":0.27212993580787603,"score_spread":0.26010671657016565,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2254351130","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9100781,0.000058390953,0.08836929,0.00038978792,0.00046095226,0.00021363476,0.00021596614,0.000038194772,0.00017566232],"genre_scores_gemma":[0.97054726,0.00050400407,0.028452966,0.000007261033,0.00017660152,0.000006897002,0.000052727108,0.000011363263,0.00024089817],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99887383,0.000030519168,0.0005525555,0.00009140245,0.00032548953,0.00012619606],"domain_scores_gemma":[0.99845546,0.00017365439,0.0005929322,0.000051870782,0.00065939536,0.00006670909],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038879237,0.00007288747,0.00018082053,0.00011857559,0.000108100576,0.000007267169,0.0000800941,0.000051636976,0.00003709245],"category_scores_gemma":[0.00010980126,0.000057269688,0.0001135167,0.00013618878,0.0000785343,0.0007199543,1.852865e-7,0.000040990297,7.305429e-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":[0.005739955,0.0006233955,0.092893906,0.0003481309,0.00041114652,0.000028304681,0.07889435,0.08135033,0.62815154,0.034799762,0.0016759867,0.075083226],"study_design_scores_gemma":[0.010815786,0.001821769,0.8782159,0.0011890479,0.0004501482,0.000006219574,0.0065459735,0.00014149329,0.06132088,0.011505411,0.027470872,0.000516465],"about_ca_topic_score_codex":0.000025744373,"about_ca_topic_score_gemma":0.0000621959,"teacher_disagreement_score":0.785322,"about_ca_system_score_codex":0.000047752314,"about_ca_system_score_gemma":0.00016413657,"threshold_uncertainty_score":0.233539},"labels":[],"label_agreement":null},{"id":"W2255446455","doi":"","title":"Optroad: A Computer Program For Interurban Road Network Planning","year":2006,"lang":"en","type":"article","venue":"Proceedings of the Joint CIB W78, W102, ICCCBE, ICCC, and DMUCE International Conference on Computing and Decision Making in Civil and Building Engineering, Montreal, Canada, 14-16 June","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Interurban; Transport engineering; Computer science; Operations research; Robustness (evolution); Term (time); Engineering","score_opus":0.014202322560861715,"score_gpt":0.27055190598781503,"score_spread":0.2563495834269533,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2255446455","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9444958,0.0004993486,0.04858952,0.0010292501,0.0014874368,0.0005825305,0.000027227774,0.00015133455,0.0031375277],"genre_scores_gemma":[0.9812749,0.000060085964,0.017984778,0.00009695485,0.0004011848,0.000015935462,0.000008476544,0.000019310048,0.00013835705],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99819505,0.000012823456,0.000526881,0.00041993463,0.00047848787,0.00036681158],"domain_scores_gemma":[0.9989167,0.00027547136,0.00032035922,0.000069766604,0.00032515428,0.00009254111],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00065309333,0.00024237271,0.0003286346,0.00020674733,0.0003551534,0.00030714806,0.00024683605,0.000114315975,0.000003760728],"category_scores_gemma":[0.00017996159,0.0002101838,0.000059686296,0.00022641214,0.00009429554,0.00015985657,0.0000826491,0.0002428102,5.455429e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005827251,0.00027981252,0.07929888,0.00042392508,0.0001744101,0.000016874073,0.0047358423,0.49283895,0.00031351863,0.12467752,0.033804163,0.26285338],"study_design_scores_gemma":[0.0013670798,0.00016802382,0.14474522,0.003890557,0.0000410467,0.000016219781,0.0006312055,0.83584744,0.00006176238,0.006144223,0.006566426,0.0005208311],"about_ca_topic_score_codex":0.033420503,"about_ca_topic_score_gemma":0.06314788,"teacher_disagreement_score":0.34300846,"about_ca_system_score_codex":0.000118519165,"about_ca_system_score_gemma":0.00010407944,"threshold_uncertainty_score":0.973016},"labels":[],"label_agreement":null},{"id":"W2256753229","doi":"10.14288/1.0102633","title":"A Downtown Streetcar in Vancouver: Transit Trend or Transit Need","year":2008,"lang":"en","type":"article","venue":"cIRcle (University of British Columbia)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Downtown; Transit (satellite); Transport engineering; Public transport; Urban transit; Geography; Advertising; Business; Engineering; Archaeology","score_opus":0.013866848359755494,"score_gpt":0.1958857653120787,"score_spread":0.18201891695232322,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2256753229","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99001825,0.000047053076,0.001531324,0.00014257192,0.0001564267,0.00017671204,0.0001395289,0.00009458761,0.00769356],"genre_scores_gemma":[0.99558425,0.0003382222,0.00072543,0.00002944211,0.00002443516,3.6997614e-7,0.00003465566,0.000007262344,0.0032559242],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.999027,0.00007762697,0.00013151087,0.00024048764,0.00029473467,0.0002286426],"domain_scores_gemma":[0.99960953,0.000044171375,0.00006707581,0.00009585954,0.00007195594,0.000111425856],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015405944,0.000037051555,0.00020310647,0.000074563264,0.00042983363,0.000028569946,0.00019494374,0.00012832732,0.000536704],"category_scores_gemma":[0.000013274687,0.00012110467,0.00008473679,0.0006246364,0.00032020238,0.00041082036,0.0000039370134,0.000103849736,0.000003899613],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032037272,0.00078488985,0.2815556,0.00012490555,0.00010265811,0.0027601947,0.13381757,0.0079174,0.000080473714,0.000020326575,0.025259761,0.5472559],"study_design_scores_gemma":[0.0014684355,0.000048774036,0.9774546,0.000073263596,0.000023453142,0.000005822416,0.017817546,0.00017572418,2.3168899e-7,0.000027282693,0.0027478177,0.00015705764],"about_ca_topic_score_codex":0.43091062,"about_ca_topic_score_gemma":0.9910597,"teacher_disagreement_score":0.695899,"about_ca_system_score_codex":0.000062213876,"about_ca_system_score_gemma":0.00020722146,"threshold_uncertainty_score":0.5876533},"labels":[],"label_agreement":null},{"id":"W2260066774","doi":"","title":"Optimal fares and capacity decisions for crowded public transport systems","year":2015,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Train; Revenue; Social Welfare; Externality; Public transport; Social cost; Computer science; Crowding; Transport engineering; Microeconomics; Business; Economics; Engineering; Finance","score_opus":0.05573219933114683,"score_gpt":0.2794190678434666,"score_spread":0.22368686851231978,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2260066774","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3290553,0.0013162052,0.6323827,0.009301636,0.00062406744,0.0013042517,0.0006302266,0.00045522762,0.024930397],"genre_scores_gemma":[0.9315969,0.00055742275,0.06072406,0.00003353332,0.000047140296,0.00017582865,0.0013607173,0.00003461596,0.0054698284],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99567246,0.0023109724,0.00050585595,0.0006083088,0.0005354113,0.00036698685],"domain_scores_gemma":[0.993322,0.0015107582,0.00039044942,0.0006962855,0.0037195988,0.00036091157],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0078098765,0.00023986558,0.00034994257,0.00019884776,0.00093149557,0.00058751856,0.0006647279,0.00041119926,0.000029238938],"category_scores_gemma":[0.0026496279,0.0002635815,0.00013512168,0.00028877414,0.0004072136,0.00024680077,0.00008634837,0.0003464639,0.0000042691863],"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":[0.00007700355,0.00084040436,0.025385577,0.0004347955,0.000265866,0.0000066637012,0.24487007,0.020710835,0.00013909725,0.68501157,0.008076371,0.014181756],"study_design_scores_gemma":[0.0061914446,0.00000559043,0.048264567,0.008220341,0.0007665111,0.000017642797,0.023618285,0.18684834,0.0016716532,0.02356761,0.69661456,0.004213471],"about_ca_topic_score_codex":0.0054844883,"about_ca_topic_score_gemma":0.010239935,"teacher_disagreement_score":0.6885382,"about_ca_system_score_codex":0.000119374046,"about_ca_system_score_gemma":0.00077324134,"threshold_uncertainty_score":0.99998164},"labels":[],"label_agreement":null},{"id":"W2261053775","doi":"10.3141/2539-09","title":"Trade-Offs Between Bus and Private Vehicle Delays at Signalized Intersections: Case Study of a Multiobjective Model","year":2016,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"VisSim; Bus priority; Sorting; Multi-objective optimization; Mathematical optimization; Computer science; Pareto principle; Minification; Set (abstract data type); Genetic algorithm; Engineering; Transport engineering; Simulation; Operations research; Microsimulation; Public transport; Mathematics","score_opus":0.11654285804584406,"score_gpt":0.40755707225886023,"score_spread":0.29101421421301615,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2261053775","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9898624,0.00007717323,0.006682922,0.001571606,0.00016109692,0.0014199358,0.00011977431,0.000036460366,0.00006866734],"genre_scores_gemma":[0.9979773,0.00049415324,0.0008748919,0.000012891926,0.00009692628,0.000071732364,0.000007327493,0.000040100134,0.00042466883],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9926209,0.0020340718,0.0014356108,0.00043239028,0.0028159646,0.00066108257],"domain_scores_gemma":[0.99457103,0.0019172471,0.0006348438,0.00030681465,0.0021561543,0.00041390865],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0047826758,0.00022096102,0.0004822449,0.00096011895,0.0014437456,0.00007308349,0.00054132193,0.0002106715,0.00011027817],"category_scores_gemma":[0.00028064894,0.00015519078,0.0002286052,0.001659235,0.0010356976,0.00084473845,0.0000073096867,0.00089495134,0.000003149155],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0022878116,0.00058396865,0.85418,0.00010404424,0.00031485315,0.00039492603,0.12434929,0.0061970386,0.003543764,0.0007705975,0.0003880476,0.006885657],"study_design_scores_gemma":[0.0056579076,0.0013518283,0.93020266,0.00042788818,0.00019294121,0.0000037275972,0.057783443,0.0005786653,0.0009771595,0.0016937205,0.0008491083,0.0002809205],"about_ca_topic_score_codex":0.03880762,"about_ca_topic_score_gemma":0.24730496,"teacher_disagreement_score":0.20849733,"about_ca_system_score_codex":0.0003441098,"about_ca_system_score_gemma":0.00051505974,"threshold_uncertainty_score":0.99985623},"labels":[],"label_agreement":null},{"id":"W2261144659","doi":"10.1016/j.trc.2016.01.002","title":"Classifying the purpose of stopped truck events: An application of entropy to GPS data","year":2016,"lang":"en","type":"article","venue":"Transportation Research Part C Emerging Technologies","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":100,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada; FedDev Ontario; Transport Canada","keywords":"Truck; Global Positioning System; Entropy (arrow of time); Categorization; Dwell time; Transport engineering; Computer science; Engineering; Automotive engineering; Telecommunications; Artificial intelligence","score_opus":0.14266758752732794,"score_gpt":0.4334741243610239,"score_spread":0.29080653683369595,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2261144659","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.72427106,0.00016817251,0.25229776,0.020167584,0.00014370482,0.0015408212,0.00040147544,0.0007731594,0.0002362594],"genre_scores_gemma":[0.9943027,0.00056396343,0.004723254,0.00000984139,0.000025058114,0.00011379258,0.00015784783,0.000012291937,0.000091252],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99779946,0.0001835602,0.0004669523,0.00035011442,0.00087043527,0.00032948653],"domain_scores_gemma":[0.998139,0.00035259718,0.00019307819,0.0007784053,0.00048062642,0.000056268065],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020825511,0.0000935067,0.0001631379,0.00032441516,0.0003975218,0.000014222945,0.0011509573,0.00012394846,0.00004257733],"category_scores_gemma":[0.0005962739,0.00006389797,0.00003375916,0.0011575499,0.0005684323,0.00047651533,0.000018331973,0.00015098431,0.0000070398014],"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":[0.00034270732,0.0003455792,0.25762996,0.00013809103,0.00008993349,0.0000030379551,0.02396771,0.0032574774,0.035102073,0.38582528,0.002049104,0.29124904],"study_design_scores_gemma":[0.0029315911,0.0009133129,0.34761697,0.0010824664,0.0001785628,4.3363283e-7,0.16419661,0.002545036,0.054900978,0.052740946,0.37178385,0.0011092399],"about_ca_topic_score_codex":0.001053127,"about_ca_topic_score_gemma":0.0041070404,"teacher_disagreement_score":0.36973476,"about_ca_system_score_codex":0.000039359424,"about_ca_system_score_gemma":0.00014557081,"threshold_uncertainty_score":0.30574563},"labels":[],"label_agreement":null},{"id":"W2263447138","doi":"","title":"Bus transit service reliability: Understanding the impacts of overlapping bus service on headway delays and determinants of bus bunching","year":2016,"lang":"en","type":"article","venue":"eScholarship@McGill (McGill)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Headway; Service (business); Public transport; Transport engineering; Schedule; TRIPS architecture; Reliability (semiconductor); Computer science; Level of service; Train; Engineering; Business; Geography","score_opus":0.035308807123699236,"score_gpt":0.2677133480042742,"score_spread":0.23240454088057497,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2263447138","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99413335,0.000064907574,0.000092846516,0.00061319646,0.0002085893,0.00037489468,0.00027817156,0.00008979797,0.00414425],"genre_scores_gemma":[0.99877703,0.0001303779,0.0005354193,0.00043551714,0.000020592854,0.000008079545,0.0000070321726,0.00003349142,0.0000524858],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9973636,0.0004247843,0.00063489313,0.0004444032,0.00067444396,0.0004578477],"domain_scores_gemma":[0.9976125,0.0010386186,0.00042479314,0.0003555959,0.00036855214,0.00019996031],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019524065,0.00025129254,0.0003816079,0.00015450007,0.0012271074,0.00004258237,0.0003481212,0.00023109383,0.00003561475],"category_scores_gemma":[0.0005648636,0.00018311811,0.0000941401,0.00081202644,0.00016494076,0.00081010774,0.000028218297,0.00026575834,0.0000070519736],"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":[0.0028519116,0.0010733654,0.08560026,0.002837595,0.0005985223,0.000091620546,0.018884739,0.02798776,0.2055082,0.51570106,0.000011216525,0.13885376],"study_design_scores_gemma":[0.023788702,0.0029009844,0.40276164,0.023166155,0.0021244946,0.000117862925,0.0825329,0.0057966677,0.1952541,0.23248519,0.021722903,0.007348421],"about_ca_topic_score_codex":0.0045048716,"about_ca_topic_score_gemma":0.0139027145,"teacher_disagreement_score":0.31716138,"about_ca_system_score_codex":0.0003277815,"about_ca_system_score_gemma":0.00007811289,"threshold_uncertainty_score":0.94380414},"labels":[],"label_agreement":null},{"id":"W2265690809","doi":"","title":"Multi-winner social choice with incomplete preferences","year":2013,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Regret; Heuristics; Preference; Preference elicitation; Computer science; Perspective (graphical); Greedy algorithm; Product (mathematics); Social choice theory; Mathematical optimization; Operations research; Management science; Artificial intelligence; Mathematical economics; Machine learning; Economics; Microeconomics; Mathematics; Algorithm","score_opus":0.044438788465081806,"score_gpt":0.2985124762995516,"score_spread":0.25407368783446976,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2265690809","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8097673,0.000015013144,0.024819912,0.004718534,0.00016806228,0.00047300867,0.000006858536,0.00048129345,0.15955],"genre_scores_gemma":[0.9783617,0.0000031274553,0.014482301,0.00021938048,0.00009526905,0.000021306936,0.00001708035,0.0000043183145,0.0067954836],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.999449,0.000043679516,0.000081171565,0.00010546106,0.00018052415,0.00014014675],"domain_scores_gemma":[0.9997245,0.000045454908,0.000037528658,0.000033905202,0.00011035086,0.000048272348],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00007927149,0.00005028828,0.00005834173,0.000027422648,0.00042204614,0.000084546715,0.00008742832,0.00004478348,0.001383742],"category_scores_gemma":[0.000018058025,0.00003688498,0.000013636314,0.00015239671,0.00010166536,0.00029555638,0.000002322635,0.000045787743,0.00011441698],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019989438,0.0002006311,0.8425062,0.000020165638,0.000064649605,0.0000016562353,0.091370024,0.0019491081,0.00022752285,0.025711443,0.019979099,0.017949525],"study_design_scores_gemma":[0.0003917097,0.000024486342,0.96468943,0.0000074551376,0.000009574873,7.414011e-8,0.005764233,0.0007849394,0.000015623185,0.00018552321,0.027966011,0.0001609654],"about_ca_topic_score_codex":0.011120322,"about_ca_topic_score_gemma":0.011599506,"teacher_disagreement_score":0.16859442,"about_ca_system_score_codex":0.000011266055,"about_ca_system_score_gemma":0.000051228697,"threshold_uncertainty_score":0.9995291},"labels":[],"label_agreement":null},{"id":"W2265998275","doi":"10.4271/2007-01-0442","title":"Guidelines for Designing and Evaluating In-Vehicle Navigation Devices: A Preliminary Study Of Their Usage And Value","year":2007,"lang":"en","type":"article","venue":"SAE technical papers on CD-ROM/SAE technical paper series","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Computer science; Value (mathematics); Human–computer interaction; Machine learning","score_opus":0.06237865067089508,"score_gpt":0.3696751988272391,"score_spread":0.307296548156344,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2265998275","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9952091,0.00033184173,0.000040904877,0.0008716671,0.000053461248,0.0014594401,0.000012515732,0.0003344874,0.0016865546],"genre_scores_gemma":[0.9803827,0.00008460684,0.018931195,0.00032048326,0.000057665464,0.00012588181,0.000022778215,0.00003092519,0.00004380324],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99734724,0.00021114967,0.00092984695,0.00055784435,0.0005476098,0.00040632638],"domain_scores_gemma":[0.99769264,0.0013778475,0.00026953025,0.00024505332,0.00027049088,0.00014442942],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037683367,0.00025788957,0.00041438497,0.00016087267,0.00050063926,0.000058481168,0.00024299277,0.0003263462,0.000011699666],"category_scores_gemma":[0.0014318477,0.00022852414,0.000071773946,0.0005519709,0.0004544839,0.0004113595,0.000050539842,0.00029570822,5.301917e-7],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001250987,0.00070033217,0.038533453,0.00015965237,0.00004255574,0.000015702513,0.009306917,0.0013673895,0.9128399,0.006828109,0.0001059177,0.028849069],"study_design_scores_gemma":[0.00094768533,0.0019391046,0.98364836,0.00035241764,0.000057383913,0.000003640089,0.011161039,0.0000049579576,0.000060918734,0.0010862205,0.00049451157,0.000243784],"about_ca_topic_score_codex":0.00037613744,"about_ca_topic_score_gemma":0.12287995,"teacher_disagreement_score":0.9451149,"about_ca_system_score_codex":0.0000767313,"about_ca_system_score_gemma":0.000081237755,"threshold_uncertainty_score":0.9318944},"labels":[],"label_agreement":null},{"id":"W2266970999","doi":"10.4230/oasics.atmos.2015.95","title":"A Mixed Integer Linear Program for the Rapid Transit Network Design Problem with Static Modal Competition (Short Paper)","year":2015,"lang":"en","type":"article","venue":"DROPS (Schloss Dagstuhl – Leibniz Center for Informatics)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"Comisión Nacional de Investigación Científica y Tecnológica; Instituto de Sistemas Complejos de Ingeniería","keywords":"Modal; Competition (biology); Integer (computer science); Integer programming; Computer science; Mathematical optimization; Linear programming; Flow network; Network planning and design; Algorithm; Mathematics; Telecommunications","score_opus":0.04996539748351885,"score_gpt":0.29056564212230135,"score_spread":0.2406002446387825,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2266970999","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004824878,0.000046372268,0.98647314,0.001120402,0.00041193297,0.0052078683,0.00020729925,0.00031786313,0.0013902494],"genre_scores_gemma":[0.62856317,0.00007227346,0.36615872,0.0008375339,0.00041482717,0.0017545375,0.0019056257,0.00006747248,0.00022583986],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977865,0.00010550705,0.00070463476,0.00018813883,0.0005718294,0.00064335053],"domain_scores_gemma":[0.99824995,0.00035420424,0.00024349784,0.00023051837,0.000698911,0.00022293365],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016299767,0.00026454366,0.00030235233,0.00007781325,0.00082495064,0.00029390331,0.00035421483,0.00017092185,0.000024805133],"category_scores_gemma":[0.0000752485,0.00018590741,0.00013169057,0.0003726366,0.00026239583,0.00095935614,0.000011287233,0.00023619027,0.000011703915],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0020580871,0.0005876603,0.0022883746,0.00047227673,0.00045342997,0.0000030571593,0.1561409,0.76518923,0.0000027095534,0.021655299,0.011330802,0.0398182],"study_design_scores_gemma":[0.0058419,0.002010075,0.0005255433,0.0004032947,0.00038289427,0.000010003631,0.048837855,0.6235292,0.000043208125,0.0013217004,0.3162651,0.0008292039],"about_ca_topic_score_codex":0.00007609118,"about_ca_topic_score_gemma":0.00045986313,"teacher_disagreement_score":0.6237383,"about_ca_system_score_codex":0.00009812106,"about_ca_system_score_gemma":0.00031103427,"threshold_uncertainty_score":0.75810844},"labels":[],"label_agreement":null},{"id":"W2267992617","doi":"10.7307/ptt.v27i6.1734","title":"SOLVING TRAFFIC CONGESTION FROM THE DEMAND SIDE","year":2015,"lang":"en","type":"article","venue":"PROMET - Traffic&Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Transport Canada","funders":"","keywords":"Demand management; Traffic congestion; Singapore Area Licensing Scheme; Demand side; Congestion pricing; Public transport; Transport engineering; Road pricing; On demand; Operations research; Computer science; Business; Economics; Engineering; Environmental economics","score_opus":0.036318086075713414,"score_gpt":0.282689701285381,"score_spread":0.2463716152096676,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2267992617","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9893705,0.00038043957,0.004860575,0.0026542717,0.00062919204,0.00060255727,0.00014167445,0.00051484874,0.00084590947],"genre_scores_gemma":[0.994478,0.000120955854,0.0030048937,0.00016595249,0.0002890208,0.000056408455,0.0015708721,0.00002730066,0.00028659898],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99790466,0.00023043278,0.00045088073,0.0003653711,0.00070611323,0.0003425659],"domain_scores_gemma":[0.99880123,0.00030775016,0.00021749984,0.00017262314,0.00028602415,0.00021484772],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010385438,0.00018939513,0.00020230091,0.00009178167,0.00055829313,0.00016001749,0.00024366933,0.00018671449,0.00009166704],"category_scores_gemma":[0.0001812151,0.00016458688,0.00008701626,0.00059174054,0.00018320899,0.00061550963,9.976026e-7,0.00021464539,0.000048945745],"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":[0.00015033784,0.00016547098,0.015984409,0.000020199328,0.00007538486,0.000017998305,0.15593883,0.80359685,0.00008634264,0.003711407,0.0033269017,0.016925838],"study_design_scores_gemma":[0.0040763086,0.00029317665,0.85667425,0.00030062956,0.000705995,0.0000015216265,0.046883237,0.017658133,0.00022175272,0.0015121016,0.07033044,0.0013424344],"about_ca_topic_score_codex":0.0009993903,"about_ca_topic_score_gemma":0.01501073,"teacher_disagreement_score":0.84068984,"about_ca_system_score_codex":0.00008297842,"about_ca_system_score_gemma":0.00029669463,"threshold_uncertainty_score":0.83763397},"labels":[],"label_agreement":null},{"id":"W2268064098","doi":"","title":"Transportation Economic Issues in the Proposed Annexation of Wendover, Utah","year":2007,"lang":"en","type":"article","venue":"Journal of rural and community development","topic":"Transportation Planning and Optimization","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":"Annexation; Revenue; Debt; Capital (architecture); Finance; Legislature; Business; Economy; Economics; Geography; Political science; Archaeology; Law","score_opus":0.021043322655720963,"score_gpt":0.2929307768931552,"score_spread":0.27188745423743427,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2268064098","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99812436,0.00011037526,0.00057076424,0.0006139652,0.00006758027,0.00006451587,0.0000010341812,0.0000029327455,0.00044447696],"genre_scores_gemma":[0.9981592,0.00022954987,0.001507674,0.00003250375,0.000019600177,3.3981038e-7,0.0000110704805,0.0000014416881,0.000038602804],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.9992411,0.00018294528,0.0003388405,0.000015631556,0.0001547848,0.00006667683],"domain_scores_gemma":[0.99950254,0.00013701407,0.0002362274,0.00003216243,0.00006947393,0.00002255426],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002382933,0.00003703281,0.000083726576,0.00007572155,0.00024491214,0.000015924761,0.00011165587,0.000029653027,0.000010143875],"category_scores_gemma":[0.000021057846,0.00002677659,0.000017673605,0.000083297324,0.000049576378,0.00018374456,0.0000012378233,0.00016221502,3.2605064e-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":[0.00020145497,0.00021794197,0.16137165,0.000036464044,0.000028328806,0.0000041469093,0.76571494,0.0017150943,0.00015087161,0.0037368855,0.00012786496,0.06669435],"study_design_scores_gemma":[0.0003067497,0.00005674335,0.8805076,0.00005896262,0.0000065397517,0.0000014227217,0.11430988,0.000008239413,0.0003822377,0.0004017713,0.0039170813,0.000042810396],"about_ca_topic_score_codex":0.0010923696,"about_ca_topic_score_gemma":0.008482215,"teacher_disagreement_score":0.71913594,"about_ca_system_score_codex":0.000041851756,"about_ca_system_score_gemma":0.00010127343,"threshold_uncertainty_score":0.4733275},"labels":[],"label_agreement":null},{"id":"W2271589751","doi":"10.3138/cpp.37.4.541","title":"The Feasibility of Implementing a Congestion Charge on the Halifax Peninsula: Filling the “Missing Link” of Implementation","year":2011,"lang":"en","type":"article","venue":"Canadian Public Policy","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Congestion pricing; Dilemma; Context (archaeology); Spell; Externality; Balance (ability); Peninsula; Traffic congestion; Operations research; Business; Computer science; Public economics; Economics; Operations management; Transport engineering; Engineering; Microeconomics; Sociology; Geography","score_opus":0.12062519302817123,"score_gpt":0.3587283410635288,"score_spread":0.23810314803535756,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2271589751","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7513411,0.00017154888,0.00056875666,0.1832805,0.0003369115,0.0014869603,0.0005334651,0.00006758755,0.062213153],"genre_scores_gemma":[0.9991381,0.000025474645,0.000058289042,0.00045904663,0.00012194031,0.000015249402,0.000044141674,0.0000065075765,0.00013122782],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99875915,0.00025670984,0.00029916444,0.00011033397,0.00021387631,0.00036074707],"domain_scores_gemma":[0.9989277,0.00025514394,0.0002680067,0.00020098839,0.00021649929,0.00013162568],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0020944315,0.000068210116,0.000076297125,0.00011316921,0.0015604515,0.0000798816,0.00028389477,0.0000447963,0.00024384042],"category_scores_gemma":[0.0007232576,0.00004181285,0.00004911374,0.0004858834,0.00024701553,0.00013953442,0.0000063800117,0.00009608801,0.000002503522],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000076110277,0.000010472772,0.07067623,0.000007840593,0.00003190764,3.3047502e-7,0.037546836,0.000015687356,0.000044670072,0.8624862,0.0011157369,0.028056512],"study_design_scores_gemma":[0.0010358349,0.00016875562,0.8096063,0.00010899204,0.00010922424,0.0000016409044,0.059717197,0.001789203,0.0015494954,0.017468197,0.10798134,0.00046378945],"about_ca_topic_score_codex":0.46184632,"about_ca_topic_score_gemma":0.49399096,"teacher_disagreement_score":0.84501797,"about_ca_system_score_codex":0.00015196204,"about_ca_system_score_gemma":0.0018721322,"threshold_uncertainty_score":0.9997394},"labels":[],"label_agreement":null},{"id":"W2274908539","doi":"10.5951/mathteacher.105.5.0330","title":"Media Clips: Hoarding Subway Tokens: Does the Transit Commission's Math Add Up?; Voting Fractions","year":2011,"lang":"en","type":"article","venue":"Mathematics Teacher Learning and Teaching PK-12","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"CLIPS; Commission; Hoarding (animal behavior); Voting; Decimal; Mathematics education; Computer science; Advertising; Psychology; Political science; Mathematics; Arithmetic; Law; Business; Artificial intelligence; Politics","score_opus":0.048161717012509535,"score_gpt":0.2956779709017962,"score_spread":0.24751625388928666,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2274908539","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.59249324,0.00029922952,0.1784977,0.0019393303,0.0012022584,0.00063882995,0.000012533449,0.0016194077,0.22329748],"genre_scores_gemma":[0.9763504,0.00004274168,0.01552878,0.000040148057,0.00020510591,0.000015945807,0.000019267623,0.000038406015,0.007759188],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.9979863,0.0005920798,0.00038101643,0.00026878377,0.00040960195,0.0003621906],"domain_scores_gemma":[0.9985552,0.00082676933,0.0002617888,0.00016207586,0.000048757047,0.00014541818],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0030582666,0.00020432957,0.0002565863,0.00010138182,0.0029414564,0.00022049561,0.00022175412,0.00016855441,0.00026259074],"category_scores_gemma":[0.0011003779,0.00013822269,0.000099702906,0.000087622546,0.00018967007,0.0002940933,0.000020296166,0.0011186238,0.00001642526],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015299665,0.00019743851,0.048721053,0.000074248244,0.00008450642,0.000005616972,0.86189485,0.00054683443,0.000042281285,0.07324046,0.00047116206,0.014706245],"study_design_scores_gemma":[0.0016682267,0.00013888151,0.037253328,0.0013200742,0.00076456484,0.000033472676,0.65220755,0.05055113,0.00010177089,0.005733356,0.24863428,0.0015933509],"about_ca_topic_score_codex":0.00086051424,"about_ca_topic_score_gemma":0.00065380434,"teacher_disagreement_score":0.3838572,"about_ca_system_score_codex":0.0000379705,"about_ca_system_score_gemma":0.000046883924,"threshold_uncertainty_score":0.9983566},"labels":[],"label_agreement":null},{"id":"W2278114748","doi":"10.3141/2540-07","title":"Planning Urban Ring Rail Transit Lines: Case Study of Shanghai, China","year":2016,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"U.S. Department of Transportation","keywords":"Transit (satellite); Beijing; Ring road; Ring (chemistry); Shanghai china; Line (geometry); China; Transport engineering; Urban rail transit; Rail transit; Rapid transit; Public transport; Engineering; Geography; Mathematics; Regional science","score_opus":0.11773976085502509,"score_gpt":0.419474711305807,"score_spread":0.3017349504507819,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2278114748","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9895906,0.0001772432,0.005801596,0.0021707094,0.00054032,0.0012921396,0.00006474812,0.00005570573,0.00030695775],"genre_scores_gemma":[0.9973796,0.00037778544,0.0008208693,0.000013730022,0.00030469755,0.00005449187,0.0000068698255,0.000056564615,0.0009853966],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.989751,0.002396301,0.0019070278,0.00048612387,0.004500693,0.0009588709],"domain_scores_gemma":[0.99292594,0.0016346712,0.0007727295,0.0004612982,0.0037198216,0.00048551685],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0075790384,0.0002772944,0.0005739177,0.001373516,0.0015221516,0.00012050442,0.0010639458,0.0002500423,0.00033505182],"category_scores_gemma":[0.00037544136,0.00019015069,0.00036370152,0.0028310562,0.0009018416,0.0011031536,0.0000044789335,0.0013180407,0.0000069980324],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016515263,0.0010121524,0.7772494,0.00018964974,0.0002851223,0.0019023888,0.19412023,0.010195616,0.0016732384,0.0024867225,0.0018816361,0.0073523056],"study_design_scores_gemma":[0.005158029,0.0015049888,0.8654225,0.0011401193,0.00016257942,0.0000053886215,0.1185505,0.00009832793,0.0006398294,0.0008828382,0.0060556107,0.000379278],"about_ca_topic_score_codex":0.047056906,"about_ca_topic_score_gemma":0.14952111,"teacher_disagreement_score":0.102464214,"about_ca_system_score_codex":0.00023290179,"about_ca_system_score_gemma":0.00090742373,"threshold_uncertainty_score":0.99977773},"labels":[],"label_agreement":null},{"id":"W2280735818","doi":"10.14288/1.0099062","title":"A multiple criteria decision process : the case study of rapid transit in Greater Vancouver","year":2009,"lang":"en","type":"article","venue":"cIRcle (University of British Columbia)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Process (computing); Transit (satellite); Computer science; Decision process; Transport engineering; Business; Process management; Public transport; Engineering","score_opus":0.014292662876078534,"score_gpt":0.23550383768977218,"score_spread":0.22121117481369365,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2280735818","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99855334,0.000015833792,0.0006111659,0.00004122696,0.00006599353,0.00030513547,0.000021186814,0.000021819558,0.00036429815],"genre_scores_gemma":[0.9996938,0.000013888624,0.00018641635,0.00001619111,0.000007698952,3.3335886e-7,0.0000026710022,0.00000277384,0.00007619744],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9993275,0.00009247626,0.000115433875,0.00016201197,0.00019022204,0.00011235232],"domain_scores_gemma":[0.999592,0.00005628953,0.000072374234,0.000095082905,0.00014584813,0.000038409806],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027231386,0.000023678016,0.00012932382,0.00004071316,0.00028211196,0.000033021202,0.00015391462,0.000053297896,0.00007333298],"category_scores_gemma":[0.000024874029,0.0000598168,0.000035802164,0.0003670402,0.00010373173,0.00026236637,0.000003803457,0.000059969057,5.15698e-7],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000096105156,0.0007892968,0.2031406,0.000025322468,0.000018767925,0.0019528952,0.17871432,0.0023285348,0.000015945921,5.975606e-7,0.0009110268,0.6120066],"study_design_scores_gemma":[0.0011755619,0.00011255506,0.8575556,0.000052377654,0.000018705603,0.000010154388,0.14052483,0.0004071116,2.0240059e-7,0.00005527856,0.000026836113,0.000060779734],"about_ca_topic_score_codex":0.25519827,"about_ca_topic_score_gemma":0.9510565,"teacher_disagreement_score":0.6958582,"about_ca_system_score_codex":0.000020001353,"about_ca_system_score_gemma":0.00004855482,"threshold_uncertainty_score":0.74976146},"labels":[],"label_agreement":null},{"id":"W2281674806","doi":"10.5353/th_b4818559","title":"Design and development criteria of a public transit information system","year":2010,"lang":"en","type":"dissertation","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Transit (satellite); Public transport; Development (topology); Computer science; Transport engineering; Engineering; Mathematics","score_opus":0.030703132211544925,"score_gpt":0.28139663621641137,"score_spread":0.25069350400486645,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2281674806","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13556413,0.00010622688,0.78024644,0.00018971716,0.0016129698,0.0013483178,0.000019503655,0.00043839615,0.08047428],"genre_scores_gemma":[0.9410205,0.000030247758,0.055802196,0.000015707512,0.000024731971,0.00003709638,0.0014975589,0.000009110781,0.0015628632],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.9991048,0.0000743134,0.00034383475,0.00008716564,0.00027511347,0.00011476327],"domain_scores_gemma":[0.9993428,0.000038060338,0.00020014448,0.000051159932,0.00030185888,0.000066003],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006324992,0.00009202456,0.00014409285,0.00018411674,0.00023238803,0.00010309494,0.00007891585,0.00028692145,0.00011707631],"category_scores_gemma":[0.000032646338,0.00009114185,0.000016186616,0.00012861908,0.00002864219,0.00046583824,7.6689844e-7,0.000095370575,0.000007376193],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016717591,0.000053535903,0.0010250079,0.002147287,0.00011973223,0.0000016698488,0.85182,0.0005548344,0.00035602055,0.042185884,0.00074578554,0.10082305],"study_design_scores_gemma":[0.0031496447,0.00018974564,0.11603738,0.0017768353,0.0003491524,0.00000450884,0.6541361,0.006257832,0.010312938,0.00015797849,0.20509833,0.002529547],"about_ca_topic_score_codex":0.00022066687,"about_ca_topic_score_gemma":0.0013790749,"teacher_disagreement_score":0.80545634,"about_ca_system_score_codex":0.00002632445,"about_ca_system_score_gemma":0.0005162479,"threshold_uncertainty_score":0.37166566},"labels":[],"label_agreement":null},{"id":"W2282218278","doi":"","title":"Evaluation of Factors Affecting Bus On-Time Performance in Edmonton, Canada","year":2016,"lang":"en","type":"article","venue":"Transportation Research Board 95th Annual MeetingTransportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Reliability (semiconductor); Arrival time; Multinomial logistic regression; Adverse weather; Time of day; Standard deviation; Variance (accounting); Statistics; Transport engineering; Environmental science; Computer science; Meteorology; Engineering; Geography; Mathematics; Business","score_opus":0.07843729628160546,"score_gpt":0.391488513666436,"score_spread":0.31305121738483055,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2282218278","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98964983,0.00007698555,0.00011472793,0.0014473216,0.00021810799,0.0019003805,0.00036788534,0.00011997735,0.0061047906],"genre_scores_gemma":[0.9977272,0.00030690833,0.0003124093,0.00002459274,0.00012428031,0.00019157928,0.00025781247,0.00006338631,0.0009918225],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9808781,0.0036242527,0.0013771729,0.00096456124,0.011492862,0.0016630496],"domain_scores_gemma":[0.98721474,0.0032356314,0.00034995016,0.0004514767,0.008201795,0.00054638204],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.023840422,0.0003565186,0.000524847,0.0015782566,0.0011184583,0.000074002506,0.00066176965,0.00034069785,0.0007529604],"category_scores_gemma":[0.0026295795,0.00031279118,0.00013558271,0.0032298968,0.00084397994,0.0010002851,0.0000053633557,0.0009154349,0.000040539093],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013040764,0.0005478501,0.87022865,0.00029557286,0.00013231453,0.000055345845,0.066447705,0.033982355,0.003037757,0.0041658897,0.002543868,0.017258633],"study_design_scores_gemma":[0.002159184,0.00034782608,0.96957886,0.0007492888,0.00004534516,4.9730705e-8,0.01970323,0.0004122367,0.0033624833,0.00025958466,0.0029862877,0.0003955959],"about_ca_topic_score_codex":0.5700081,"about_ca_topic_score_gemma":0.9119501,"teacher_disagreement_score":0.34194195,"about_ca_system_score_codex":0.001326355,"about_ca_system_score_gemma":0.0055266153,"threshold_uncertainty_score":0.9999324},"labels":[],"label_agreement":null},{"id":"W2287676811","doi":"10.1016/j.trb.2016.01.017","title":"Long-term planning for ring-radial urban rail transit networks","year":2016,"lang":"en","type":"article","venue":"Transportation Research Part B Methodological","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":67,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Transit (satellite); Urban rail transit; Ring (chemistry); Line (geometry); Network planning and design; Computer science; Transport engineering; Transit system; Rail transit; Public transport; Operations research; Engineering; Telecommunications; Mathematics","score_opus":0.4472855086529312,"score_gpt":0.5049233095014272,"score_spread":0.05763780084849596,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2287676811","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14922303,0.00017046127,0.84594065,0.0021581643,0.0003796994,0.00085329456,0.00007299625,0.00026428865,0.0009374067],"genre_scores_gemma":[0.97700834,0.0003016526,0.019110527,0.00011519747,0.0008684224,0.00032083146,0.00027118486,0.000030820604,0.0019730316],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9954265,0.0017967415,0.00052043685,0.0005519464,0.0008203598,0.0008840406],"domain_scores_gemma":[0.9935181,0.0054044835,0.000109438944,0.0001778528,0.0004344939,0.0003556001],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007905269,0.0001772072,0.00032042636,0.00020163707,0.0010181528,0.00008243025,0.00032554808,0.00036130435,0.0006445211],"category_scores_gemma":[0.0009471121,0.00013279454,0.00017644023,0.00052270194,0.0006076893,0.0003263187,0.0000022514685,0.0003191109,0.000017033855],"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":[0.0032723628,0.0003522939,0.7234095,0.00012572798,0.00017167354,0.00014636366,0.027719252,0.02698479,0.0013508579,0.1705537,0.0096576745,0.03625586],"study_design_scores_gemma":[0.002876777,0.00043918344,0.94463235,0.00025311677,0.000071159615,6.8896645e-7,0.00100755,0.00042116563,0.00042230886,0.00304169,0.04630129,0.0005327463],"about_ca_topic_score_codex":0.00010117275,"about_ca_topic_score_gemma":0.00072603114,"teacher_disagreement_score":0.8277853,"about_ca_system_score_codex":0.00007404285,"about_ca_system_score_gemma":0.00017778271,"threshold_uncertainty_score":0.7830911},"labels":[],"label_agreement":null},{"id":"W2288650108","doi":"","title":"Constrained optimization for bottleneck peak hour pricing","year":2015,"lang":"en","type":"article","venue":"Scholarship at UWindsor (University of Windsor)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Bottleneck; Economics; Computer science; Mathematical optimization; Econometrics; Mathematics; Operations management","score_opus":0.04504143540532478,"score_gpt":0.2706012799242771,"score_spread":0.22555984451895233,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2288650108","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.87703216,0.000098798846,0.098733015,0.0024201425,0.00044280285,0.0007187594,0.0001154461,0.00027616124,0.020162733],"genre_scores_gemma":[0.9582189,0.000022868842,0.038287412,0.00006598489,0.00007956957,7.353383e-7,0.00016966744,0.000015479149,0.003139429],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99870926,0.00015199777,0.00016227397,0.00026378568,0.00042685712,0.0002858453],"domain_scores_gemma":[0.99873996,0.00012311475,0.0002226246,0.00015100963,0.000511168,0.0002521074],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001102517,0.00011632254,0.0001955639,0.00018599848,0.0007604893,0.00004110999,0.00029159806,0.00019989279,0.00021911123],"category_scores_gemma":[0.00035862904,0.00015577656,0.00010575869,0.00041545604,0.00024291484,0.00077614514,0.000021934973,0.00012645258,0.00001827842],"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":[0.0015929872,0.00038695772,0.32622984,0.00016976999,0.00024093871,0.000034443365,0.14747024,0.49150646,0.00097362156,0.02224377,0.004508686,0.0046422975],"study_design_scores_gemma":[0.036016542,0.0017592427,0.46188045,0.0009818247,0.0014508127,0.000021553993,0.2788949,0.068198085,0.0018012545,0.007685051,0.13703038,0.0042798985],"about_ca_topic_score_codex":0.00029194047,"about_ca_topic_score_gemma":0.0007112863,"teacher_disagreement_score":0.42330837,"about_ca_system_score_codex":0.00015137742,"about_ca_system_score_gemma":0.00036215055,"threshold_uncertainty_score":0.63523835},"labels":[],"label_agreement":null},{"id":"W2289806813","doi":"10.14288/1.0097831","title":"An evaluation framework for citizen participation in urban transportation planning","year":2010,"lang":"en","type":"article","venue":"cIRcle (University of British Columbia)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Citizen science; Public participation; Urban planning; Community participation; Environmental planning; Transportation planning; Business; Public relations; Political science; Geography; Transport engineering; Sociology; Engineering; Socioeconomics; Civil engineering","score_opus":0.02182614975959857,"score_gpt":0.2833110708923134,"score_spread":0.2614849211327148,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2289806813","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96468157,0.00001229025,0.03417783,0.00007622717,0.00017806405,0.0003906565,0.000079852325,0.00005616141,0.00034734147],"genre_scores_gemma":[0.9911185,0.000007331625,0.008429105,0.000016713904,0.00005068806,0.0000043563055,0.00032407374,0.0000067697792,0.000042443175],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99918085,0.00007530681,0.00012177242,0.00020034956,0.00026692986,0.00015477352],"domain_scores_gemma":[0.99937737,0.000088265835,0.00011522751,0.00008895949,0.0002552444,0.00007490601],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00070232473,0.000024140561,0.000101333855,0.00005210296,0.0003145694,0.00006476713,0.000120164725,0.000155585,0.00010405467],"category_scores_gemma":[0.00010549232,0.00008843478,0.00003834203,0.00025772027,0.00010992245,0.0005040575,9.490064e-7,0.00010860225,0.0000011526004],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000047091617,0.00021338281,0.712483,0.00003686745,0.000013394266,0.000008826618,0.04680126,0.009623754,0.0002858228,0.0007350839,0.00034229443,0.22940923],"study_design_scores_gemma":[0.00058216933,0.00003596524,0.98501974,0.000063881824,0.00003325358,1.243951e-7,0.0061035007,0.004862633,9.3911524e-7,0.0030248475,0.0001749427,0.00009799616],"about_ca_topic_score_codex":0.051656738,"about_ca_topic_score_gemma":0.7761619,"teacher_disagreement_score":0.7245051,"about_ca_system_score_codex":0.000033576343,"about_ca_system_score_gemma":0.0001295742,"threshold_uncertainty_score":0.9546584},"labels":[],"label_agreement":null},{"id":"W2289974458","doi":"","title":"Truck Freight Generators & Attractors in the Province of Ontario","year":2014,"lang":"en","type":"article","venue":"MacSphere (McMaster University)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Truck; Transport engineering; Business; Engineering; Automotive engineering","score_opus":0.013574806828744096,"score_gpt":0.20342065997161815,"score_spread":0.18984585314287405,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2289974458","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.33111525,0.0000065778463,0.0019476243,0.00018362948,0.00009243523,0.00012914033,0.0000027423569,0.000023815744,0.6664988],"genre_scores_gemma":[0.87949234,0.0000032439339,0.0008352233,0.00007116515,0.000029789382,2.1899079e-7,0.0000062575014,0.000003775845,0.11955796],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.99933046,0.0001587966,0.00009454919,0.00011648574,0.00017458468,0.0001251396],"domain_scores_gemma":[0.9997269,0.000055514698,0.00007029426,0.00008239702,0.000031397965,0.00003349608],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00020201692,0.00005876104,0.00008063595,0.000056417477,0.00013732704,0.00002041273,0.00022635942,0.000060146824,0.007191011],"category_scores_gemma":[0.000010298174,0.000051566964,0.00003305705,0.00031225695,0.000084678584,0.00018086271,0.0000056326894,0.00009138133,0.000005637064],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000046108707,0.00008338082,0.839143,0.000014130576,0.000013836227,0.000023772905,0.054677207,0.005184395,0.000016450946,0.058367398,0.00047496535,0.04195536],"study_design_scores_gemma":[0.0003734038,0.00003396984,0.121873505,0.000017886628,0.000017627844,1.5818821e-7,0.004767116,0.00006785725,0.000048027356,0.000101538666,0.87259513,0.000103775616],"about_ca_topic_score_codex":0.025433417,"about_ca_topic_score_gemma":0.47743407,"teacher_disagreement_score":0.87212014,"about_ca_system_score_codex":0.000112502734,"about_ca_system_score_gemma":0.0002260321,"threshold_uncertainty_score":0.99371654},"labels":[],"label_agreement":null},{"id":"W2289979422","doi":"10.3141/2533-10","title":"Evaluation of Bus Reliability Measures and Development of a New Composite Indicator","year":2015,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Punctuality; Reliability (semiconductor); Headway; Automatic vehicle location; Transport engineering; Computer science; Index (typography); Service quality; Service (business); Quality of service; Level of service; Quality (philosophy); Performance indicator; Reliability engineering; Engineering; Telecommunications; Business; Global Positioning System","score_opus":0.2644148468767897,"score_gpt":0.4501361411432913,"score_spread":0.18572129426650164,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2289979422","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99273807,0.0005145189,0.0040121004,0.0012486352,0.00025923902,0.00089717784,0.000022703953,0.000015394085,0.0002921791],"genre_scores_gemma":[0.9876269,0.0003292911,0.011769722,0.000008262201,0.000075959215,0.000021295033,0.000018811133,0.00002000195,0.00012976657],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9863198,0.0026025306,0.0015812033,0.00028380554,0.008760115,0.0004525835],"domain_scores_gemma":[0.98665553,0.00062324444,0.0007950412,0.00026365457,0.011134261,0.00052825524],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.027090762,0.00015300207,0.0004050891,0.00080954295,0.0004924383,0.000051465973,0.0005996469,0.00018825974,0.00010122432],"category_scores_gemma":[0.0009714793,0.0001252368,0.0001392474,0.0015104454,0.0009285602,0.0005421264,0.0000040293344,0.0007835679,0.0000031092145],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016526582,0.00042779255,0.7676686,0.0001938512,0.00022694154,0.000006617346,0.15413328,0.014404546,0.0014262946,0.001951503,0.002132506,0.055775393],"study_design_scores_gemma":[0.0024133306,0.00026814596,0.9657121,0.00030966714,0.00013350106,1.18249424e-7,0.015378807,0.00014716657,0.0027848368,0.0032085008,0.009501402,0.00014245273],"about_ca_topic_score_codex":0.016638374,"about_ca_topic_score_gemma":0.06390875,"teacher_disagreement_score":0.19804345,"about_ca_system_score_codex":0.00031546154,"about_ca_system_score_gemma":0.0068388823,"threshold_uncertainty_score":0.9987914},"labels":[],"label_agreement":null},{"id":"W229123256","doi":"10.1007/978-3-662-04853-5_6","title":"Deterministic Time Table Transit Assignment","year":2002,"lang":"en","type":"book-chapter","venue":"Advances in spatial science","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":16,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Inro Consultants (Canada)","funders":"","keywords":"Path (computing); Node (physics); Transit (satellite); Computer science; Travel time; Mathematical optimization; Table (database); Transit time; Operations research; Mathematics; Transport engineering; Engineering; Computer network; Public transport; Data mining","score_opus":0.013006629718580985,"score_gpt":0.2757580087913769,"score_spread":0.26275137907279594,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W229123256","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000029185467,0.00062110124,0.010826989,0.00012885318,0.0005745702,0.00029563965,0.000032268254,0.000082324965,0.98740906],"genre_scores_gemma":[0.31764546,0.005519719,0.003419333,0.00021243552,0.00042157818,0.000030637108,0.000053704607,0.000047159305,0.67265],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9977772,0.0000228457,0.0003245271,0.00049268606,0.0009780142,0.0004047023],"domain_scores_gemma":[0.9992848,0.00010281499,0.00018861132,0.00017516674,0.000100530066,0.00014806417],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005579553,0.00018480295,0.00023113059,0.00020281963,0.00052211474,0.00009470799,0.00047660613,0.00015122993,0.0019734947],"category_scores_gemma":[0.000077383185,0.00019548822,0.000041025974,0.00023632396,0.0011298062,0.0006895672,0.000012267723,0.00019501212,0.00019628477],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008513711,0.00017539224,0.0015264179,0.00012561544,0.000013337389,0.00024882244,0.026890129,0.12053375,0.00015382985,0.37669897,0.00066669204,0.4728819],"study_design_scores_gemma":[0.00041076553,0.00014106654,0.00046338094,0.0004782733,0.000040795825,0.0000017742508,0.00013688508,0.0047470373,0.000038222053,0.014214422,0.97847044,0.0008569044],"about_ca_topic_score_codex":0.00034169664,"about_ca_topic_score_gemma":0.0022402783,"teacher_disagreement_score":0.97780377,"about_ca_system_score_codex":0.00024506723,"about_ca_system_score_gemma":0.0003268563,"threshold_uncertainty_score":0.99893886},"labels":[],"label_agreement":null},{"id":"W2293452991","doi":"10.14288/1.0075652","title":"Electric cars and the city of Vancouver","year":2012,"lang":"en","type":"article","venue":"cIRcle (University of British Columbia)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Geography","score_opus":0.00787957824916558,"score_gpt":0.18717442785227306,"score_spread":0.1792948496031075,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2293452991","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.992708,0.00023469377,0.0005665035,0.000049285984,0.000091317284,0.000085497755,0.00001388037,0.00001617333,0.006234615],"genre_scores_gemma":[0.9988805,0.00018522824,0.00020303034,0.00001396205,0.000015048208,9.056638e-8,0.000002239682,0.0000017670089,0.0006981521],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9995859,0.0000629839,0.00004781196,0.00006186192,0.00013808758,0.000103328566],"domain_scores_gemma":[0.99969524,0.0000609491,0.000071045426,0.000048176822,0.00008003532,0.000044548706],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032403023,0.000011437869,0.00008472183,0.0000148943,0.0002497727,0.000013458772,0.00008115058,0.00004081333,0.00004710296],"category_scores_gemma":[0.000029024422,0.000032199634,0.00003159153,0.00020351018,0.0003135904,0.00020413828,0.0000066971247,0.00004003549,6.2461294e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023985473,0.000075500364,0.81614083,0.00002408138,0.00004069673,0.0000030103981,0.018965006,0.000034946162,0.000018671128,0.0002053827,0.0043560904,0.1601118],"study_design_scores_gemma":[0.00046540127,0.0000062904087,0.9940468,0.000012089564,0.000029064187,5.1956476e-7,0.0044796783,0.000029187044,2.7762238e-7,0.00012290783,0.0007737547,0.000034020537],"about_ca_topic_score_codex":0.24886127,"about_ca_topic_score_gemma":0.53293985,"teacher_disagreement_score":0.28407857,"about_ca_system_score_codex":0.000013495839,"about_ca_system_score_gemma":0.000038700626,"threshold_uncertainty_score":0.75614065},"labels":[],"label_agreement":null},{"id":"W2293698321","doi":"10.1609/socs.v1i1.18154","title":"High-Quality Policies for the Canadian Traveler's Problem","year":2010,"lang":"en","type":"article","venue":"Proceedings of the International Symposium on Combinatorial Search","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":48,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Quality (philosophy); Path (computing); Adverse weather; Property (philosophy); Operations research; Distribution (mathematics); Mathematical optimization; Mathematics; Geography","score_opus":0.024311054884565798,"score_gpt":0.3153885064059963,"score_spread":0.2910774515214305,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2293698321","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.82271546,0.0000039990596,0.000019201998,0.10555209,0.008973865,0.0011312338,0.00010816075,0.000057524972,0.06143847],"genre_scores_gemma":[0.99767905,0.000008298839,0.00024444377,0.00019575562,0.0007955468,0.000052431304,0.000007787885,0.000010236712,0.0010064717],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9985341,0.000014352572,0.00022493099,0.0001542439,0.0008357897,0.00023658613],"domain_scores_gemma":[0.9984515,0.00024356334,0.00012836681,0.00006784942,0.001010936,0.000097809374],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015081718,0.00008103622,0.00009147561,0.00006414405,0.0010292953,0.00022235172,0.00093966327,0.000110497094,0.000044814995],"category_scores_gemma":[0.0003401247,0.000055506018,0.000083985185,0.0002122408,0.00025306334,0.00013808775,0.000020860278,0.00027436277,0.0000032171292],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000063264364,0.000039667626,0.007485502,0.000007895846,0.000022277354,2.5261379e-8,0.0034138039,0.00018872223,0.004299349,0.98287094,0.0014167732,0.00019175628],"study_design_scores_gemma":[0.0046985955,0.0004047378,0.194602,0.00018193759,0.00012388288,0.0000023284633,0.005946116,0.0009734694,0.094388776,0.21226197,0.4855138,0.00090238295],"about_ca_topic_score_codex":0.24712677,"about_ca_topic_score_gemma":0.1039426,"teacher_disagreement_score":0.77060896,"about_ca_system_score_codex":0.0001489864,"about_ca_system_score_gemma":0.00034371967,"threshold_uncertainty_score":0.9124081},"labels":[],"label_agreement":null},{"id":"W2295438822","doi":"10.14288/1.0063036","title":"Transit operations, city transportation plans and overall transportation network safety","year":2011,"lang":"en","type":"article","venue":"cIRcle (University of British Columbia)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Transport engineering; Flow network; Transit (satellite); Public transport; Business; Transportation planning; Computer science; Engineering","score_opus":0.014310496921192774,"score_gpt":0.18940727589296086,"score_spread":0.1750967789717681,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2295438822","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9695444,0.00006870325,0.027104741,0.00009522829,0.0001095047,0.0002631388,0.00072316796,0.0001102799,0.0019808004],"genre_scores_gemma":[0.99428004,0.00060475623,0.004104357,0.00004324369,0.000029664723,6.8577776e-7,0.00050623046,0.000008632097,0.00042240322],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99905694,0.00007295134,0.00018232562,0.00026086436,0.00022906324,0.0001978482],"domain_scores_gemma":[0.9995383,0.000026638683,0.00007833141,0.000084473555,0.00015088893,0.000121381105],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026184812,0.00004402637,0.00017085853,0.000030357814,0.000702667,0.000047799957,0.000121034296,0.00013887894,0.0002717604],"category_scores_gemma":[0.0000060440943,0.0001456404,0.00006594567,0.00022647882,0.00024293811,0.00061145914,9.889851e-7,0.00009467686,0.0000021686428],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022871641,0.00029967676,0.73227525,0.00011931106,0.00013408695,0.00013774839,0.11008444,0.012051425,0.000053248856,0.001437546,0.0013628893,0.14181569],"study_design_scores_gemma":[0.00068146625,0.000038833572,0.993528,0.000059573948,0.00007310916,9.4221934e-7,0.004236884,0.00015572402,4.348148e-7,0.0003167165,0.0007506757,0.00015763294],"about_ca_topic_score_codex":0.30497688,"about_ca_topic_score_gemma":0.9262226,"teacher_disagreement_score":0.62124574,"about_ca_system_score_codex":0.000027484071,"about_ca_system_score_gemma":0.00009664204,"threshold_uncertainty_score":0.69965136},"labels":[],"label_agreement":null},{"id":"W2295601855","doi":"","title":"Missing the Connection? A case study approach to understanding effective public transit transfers in dispersed lower density cities","year":2015,"lang":"en","type":"article","venue":"Transport Research Forum","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":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Public transport; Transit (satellite); Transport engineering; Transfer (computing); Service (business); Rail transit; Rapid transit; Compensation (psychology); Agency (philosophy); Investment (military); Business; Computer science; Telecommunications; Engineering; Marketing","score_opus":0.2232643316096052,"score_gpt":0.39970700505857343,"score_spread":0.17644267344896822,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2295601855","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.89626956,0.000023598966,0.083703,0.008304475,0.00016168728,0.002584989,0.000013457875,0.00012891661,0.008810336],"genre_scores_gemma":[0.9992798,0.000005733999,0.00014931343,0.00007201839,0.000045350665,0.00016917138,0.000020288615,0.000020626589,0.00023767892],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.9964668,0.00086556346,0.00029649053,0.0004200986,0.001151351,0.0007996865],"domain_scores_gemma":[0.99876094,0.00035999474,0.000024785655,0.00020005512,0.0002436612,0.00041057737],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0056689954,0.00015738302,0.00022932726,0.0004435439,0.0015177828,0.00017851756,0.0002465825,0.000113584756,0.0000261631],"category_scores_gemma":[0.00011745359,0.000130298,0.000081338134,0.0018013481,0.0004407476,0.00045426888,0.0000042922074,0.0005158501,0.000004130568],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00066119956,0.0009514049,0.22969659,0.000030878567,0.0000879071,0.0011134264,0.7295452,0.009608474,0.000012770074,0.027015878,0.0002791936,0.0009970893],"study_design_scores_gemma":[0.0016826967,0.00046228917,0.014039338,0.000034560697,0.000030392848,0.000024664905,0.98023707,0.0008266774,0.00001139611,0.0016182275,0.000786489,0.00024617836],"about_ca_topic_score_codex":0.025042271,"about_ca_topic_score_gemma":0.1534026,"teacher_disagreement_score":0.2506919,"about_ca_system_score_codex":0.00057259796,"about_ca_system_score_gemma":0.00049202883,"threshold_uncertainty_score":0.9997821},"labels":[],"label_agreement":null},{"id":"W2296239200","doi":"","title":"Application of RP/SP Data to the Joint Estimation of Mode Choice Models: Lessons Learned from an Empirical Investigation into Cross-Regional Commuting Trips in the Greater Toronto and Hamilton Area","year":2016,"lang":"en","type":"article","venue":"Transportation Research Board 95th Annual MeetingTransportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Mode choice; Multinomial logistic regression; TRIPS architecture; Revealed preference; Econometrics; Nested logit; Choice set; Estimation; Mode (computer interface); Logit; Discrete choice; Economics; Regional science; Computer science; Operations research; Geography; Transport engineering; Statistics; Mathematics; Public transport; Engineering","score_opus":0.3288023387579384,"score_gpt":0.4909816055855734,"score_spread":0.162179266827635,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2296239200","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9479766,0.00014567228,0.030051049,0.01889546,0.000047102843,0.0017978468,0.0008904533,0.00007377163,0.0001220405],"genre_scores_gemma":[0.9911473,0.00044714514,0.005772224,0.00016732673,0.00011087984,0.00035831978,0.0018939689,0.000040575644,0.00006226618],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.99038076,0.00277597,0.0014292293,0.0010330569,0.003592259,0.0007887064],"domain_scores_gemma":[0.99313706,0.002793238,0.00040219375,0.0010363354,0.0022605734,0.00037058193],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012076198,0.00027990085,0.00042839738,0.00052745757,0.0011941952,0.00017240657,0.00137079,0.00030985157,0.000042851683],"category_scores_gemma":[0.0009647458,0.00019656989,0.00008631374,0.0016131882,0.0015686386,0.0023474663,0.000020364194,0.00064828916,0.0000053644844],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014261244,0.0003965573,0.37777218,0.00022405898,0.00007966728,0.000007326877,0.48491967,0.08444096,0.004196409,0.011834589,0.0009281689,0.033774268],"study_design_scores_gemma":[0.0013167374,0.0002659514,0.90415084,0.0003627676,0.0000419573,1.08611204e-7,0.03857441,0.0458085,0.0005558404,0.006962016,0.0016755803,0.0002853108],"about_ca_topic_score_codex":0.24502426,"about_ca_topic_score_gemma":0.44767314,"teacher_disagreement_score":0.52637863,"about_ca_system_score_codex":0.00022005127,"about_ca_system_score_gemma":0.0006016037,"threshold_uncertainty_score":0.91849047},"labels":[],"label_agreement":null},{"id":"W2301368578","doi":"","title":"Loyalty in Transit: An Analysis of Bus and Rail Users in Two Canadian Cities","year":2016,"lang":"en","type":"article","venue":"Transportation Research Board 95th Annual MeetingTransportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Loyalty; Attractiveness; Customer satisfaction; Transit (satellite); Marketing; Business; Service quality; Public transport; Advertising; Perception; Quality (philosophy); Order (exchange); Loyalty business model; Structural equation modeling; Service (business); Transport engineering; Psychology; Computer science; Engineering","score_opus":0.058506488347119384,"score_gpt":0.40740365715963156,"score_spread":0.3488971688125122,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2301368578","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9932079,0.00012708752,0.0007118231,0.002675117,0.00007749207,0.0011426209,0.00086384546,0.00008490459,0.0011092237],"genre_scores_gemma":[0.99705744,0.00074932777,0.0010633651,0.000049483348,0.000051195286,0.00018733005,0.00045210632,0.000048175163,0.0003415754],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9910195,0.0018615883,0.0013778766,0.0010106339,0.0029863792,0.0017440412],"domain_scores_gemma":[0.994652,0.0014862178,0.00018175333,0.00039650995,0.0022403554,0.001043168],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.010348979,0.00030897342,0.00069180346,0.0070602866,0.0007001776,0.00011985506,0.0005587785,0.00032001498,0.0004637804],"category_scores_gemma":[0.0004957529,0.00029945013,0.0001608952,0.007371918,0.0016478869,0.0014262638,0.0000033283866,0.0008201759,0.000009375329],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006490148,0.00014502295,0.90613574,0.00008264253,0.00009222452,0.00010370185,0.06414683,0.01240941,0.0002805761,0.013135785,0.000053022773,0.0027660483],"study_design_scores_gemma":[0.0018875555,0.00018402605,0.96089864,0.00024799205,0.00007400926,5.192874e-8,0.033380825,0.00075050624,0.00012737587,0.0008141613,0.0012989676,0.00033590366],"about_ca_topic_score_codex":0.82343674,"about_ca_topic_score_gemma":0.99574405,"teacher_disagreement_score":0.17230736,"about_ca_system_score_codex":0.00047529396,"about_ca_system_score_gemma":0.001785647,"threshold_uncertainty_score":0.99994576},"labels":[],"label_agreement":null},{"id":"W2301587583","doi":"10.3141/2541-10","title":"Reproducing Longitudinal In-Vehicle Traveler Experience and the Impact of a Service Reduction with Public Transit Smart Card Data","year":2016,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Transport Canada","funders":"","keywords":"Public transport; Transport engineering; Smart card; Service (business); Level of service; Crowding; Database transaction; Computer science; Engineering; Computer security; Marketing; Business","score_opus":0.17816260011627888,"score_gpt":0.42626792406051867,"score_spread":0.2481053239442398,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2301587583","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9800722,0.00017985834,0.0023305279,0.016162908,0.00013613829,0.0009055771,0.00007290884,0.000018144226,0.00012172819],"genre_scores_gemma":[0.99747956,0.0013239738,0.00080993265,0.000017100576,0.000107739674,0.00004273692,0.000017850378,0.00002827381,0.00017285708],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99282956,0.0019328,0.001153338,0.000558815,0.0028180806,0.0007074186],"domain_scores_gemma":[0.9940996,0.0010778971,0.00050374964,0.0007202573,0.0033265352,0.0002719719],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009841565,0.00019541473,0.00043408302,0.00068786694,0.00078200974,0.00013249762,0.0012033201,0.00014471404,0.00010572357],"category_scores_gemma":[0.00036609577,0.00010180337,0.00016342719,0.003087554,0.0019477141,0.001697802,0.0000065572935,0.0009251393,0.0000018893123],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0060799457,0.00023418597,0.90760183,0.000115962765,0.00019947969,0.000037764465,0.06558978,0.0032094575,0.0021375136,0.003302076,0.0004465617,0.011045457],"study_design_scores_gemma":[0.0033764795,0.00027165122,0.9832016,0.00048127183,0.000043320568,0.0000011572498,0.010678688,0.00015791853,0.00024409122,0.0006054792,0.00079367583,0.00014462667],"about_ca_topic_score_codex":0.08272122,"about_ca_topic_score_gemma":0.16873056,"teacher_disagreement_score":0.086009346,"about_ca_system_score_codex":0.00020279709,"about_ca_system_score_gemma":0.0013351686,"threshold_uncertainty_score":0.92338705},"labels":[],"label_agreement":null},{"id":"W2301908748","doi":"10.5038/2375-0901.19.1.1","title":"Increasing the Capacities of Cable Cars for Use in Public Transport","year":2016,"lang":"en","type":"article","venue":"Journal of Public Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":39,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Transport Canada","funders":"","keywords":"Public transport; Transport engineering; Passenger transport; Mode of transport; Relation (database); Engineering; Computer science","score_opus":0.06765708683879414,"score_gpt":0.2855008715906466,"score_spread":0.21784378475185245,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2301908748","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92831737,0.00006972355,0.06720391,0.0036954219,0.00024246394,0.00020529919,0.000087477456,0.000015739375,0.0001625674],"genre_scores_gemma":[0.9960245,0.0001727248,0.003497543,0.000040267674,0.000081246246,0.000009277704,0.000030605734,0.0000115281755,0.00013234396],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99826705,0.00018285637,0.0007446378,0.00009915313,0.00046300175,0.00024326878],"domain_scores_gemma":[0.9979062,0.00060748484,0.00056202855,0.00008137535,0.0007477341,0.00009514248],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026254286,0.00008819922,0.00021150842,0.00031182953,0.0001716073,0.000043885862,0.00019478422,0.00009727793,0.000046269026],"category_scores_gemma":[0.00034456002,0.000057169986,0.00013682927,0.0004307846,0.00025534845,0.0018008628,3.1986247e-7,0.00009290193,2.2841904e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015781236,0.00011241125,0.9141396,0.000036055717,0.000059392565,0.0000062828512,0.033164546,0.0016719975,0.0008054738,0.042920616,0.00017153987,0.0067542447],"study_design_scores_gemma":[0.00203291,0.00011591354,0.95791984,0.00019591964,0.00008234848,0.000004003999,0.015519594,0.00005578143,0.00024715127,0.0014792492,0.022184456,0.00016286541],"about_ca_topic_score_codex":0.0021369976,"about_ca_topic_score_gemma":0.023106817,"teacher_disagreement_score":0.06770707,"about_ca_system_score_codex":0.00008148529,"about_ca_system_score_gemma":0.00059150293,"threshold_uncertainty_score":0.9947189},"labels":[],"label_agreement":null},{"id":"W2303006289","doi":"10.1002/atr.1357","title":"Flexible feeder transit route design to enhance service accessibility in urban area","year":2015,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Shandong University","keywords":"Transit (satellite); Flexibility (engineering); Computer science; Routing (electronic design automation); Service (business); Heuristic; Travelling salesman problem; Mode (computer interface); Plan (archaeology); Transport engineering; Mathematical optimization; Operations research; Simulation; Public transport; Engineering; Computer network; Algorithm","score_opus":0.04485903019588245,"score_gpt":0.3449584609509849,"score_spread":0.3000994307551025,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2303006289","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.59270406,0.00012106194,0.40424296,0.0018230539,0.000393854,0.000307049,0.000010153422,0.000042437532,0.0003553654],"genre_scores_gemma":[0.96143806,0.000045722576,0.03795546,0.00030603533,0.000082455765,0.0000084343565,0.000025639722,0.00001216318,0.00012602255],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99825877,0.0001253367,0.00065352774,0.00018364341,0.0005550533,0.0002236467],"domain_scores_gemma":[0.99852186,0.0000830831,0.00031907888,0.00009238659,0.0007247067,0.00025888215],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011208603,0.00011859732,0.00023641583,0.00020680975,0.0000988761,0.000043202694,0.000199712,0.00009126416,0.000026842796],"category_scores_gemma":[0.00009174159,0.00011961662,0.000057479294,0.0007742623,0.000027271199,0.0012830718,5.5184273e-7,0.00017605712,0.000004503527],"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":[0.0007624839,0.00010012211,0.022331143,0.000014605776,0.000010567605,0.00001918834,0.10696514,0.86525273,0.00064947293,0.00014249471,0.00014561022,0.0036064247],"study_design_scores_gemma":[0.002966493,0.0004404608,0.9495289,0.00042759022,0.00009924599,0.0000021850271,0.026386166,0.00039264205,0.008892127,0.0025488823,0.0077802297,0.0005351253],"about_ca_topic_score_codex":0.0002567363,"about_ca_topic_score_gemma":0.0050902134,"teacher_disagreement_score":0.9271977,"about_ca_system_score_codex":0.00015990179,"about_ca_system_score_gemma":0.00045057133,"threshold_uncertainty_score":0.48778242},"labels":[],"label_agreement":null},{"id":"W2303161261","doi":"10.14288/1.0098439","title":"Coordination of transportation and land use planning : a case study of Greater Vancouver","year":2010,"lang":"en","type":"article","venue":"cIRcle (University of British Columbia)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Land use; Land-use planning; Transportation planning; Environmental planning; Regional planning; Business; Urban planning; Transport engineering; Geography; Engineering; Civil engineering","score_opus":0.014257597794956887,"score_gpt":0.21522681895508494,"score_spread":0.20096922116012805,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2303161261","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99925625,0.0000043666487,0.0003055386,0.0000066575244,0.00007836641,0.0001672444,0.00006254921,0.000017116126,0.00010190053],"genre_scores_gemma":[0.99940634,0.00000436006,0.00040634515,0.0000017788376,0.0000059102067,2.0971595e-7,0.000012281563,0.0000033573153,0.00015941674],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9995225,0.000036344958,0.000103565595,0.00012024357,0.0001520108,0.00006532799],"domain_scores_gemma":[0.9995149,0.000043218333,0.00014027108,0.000057668418,0.00020433901,0.000039619845],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015682024,0.000019507333,0.00011642259,0.00004392293,0.0001696743,0.000020161538,0.00004811889,0.0000621658,0.000022334372],"category_scores_gemma":[0.000015351683,0.000059088885,0.000021611091,0.00016096573,0.00014510889,0.0003600985,0.000002445384,0.00005626999,7.2331886e-8],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011429368,0.000099246114,0.95718825,0.000024683506,0.000016065755,0.00018193737,0.031716052,0.00023376543,0.00005668398,0.000001734646,0.00011311296,0.010357034],"study_design_scores_gemma":[0.00081396726,0.00006625624,0.9544626,0.000032682565,0.00004829103,0.0000063578655,0.044342358,0.00012927041,7.234835e-7,0.000010490207,0.000033290064,0.00005371389],"about_ca_topic_score_codex":0.49139073,"about_ca_topic_score_gemma":0.94834644,"teacher_disagreement_score":0.45695567,"about_ca_system_score_codex":0.0000057742486,"about_ca_system_score_gemma":0.000032179247,"threshold_uncertainty_score":0.51199615},"labels":[],"label_agreement":null},{"id":"W2310745800","doi":"","title":"Quebec-Windsor Corridor High Speed Rail Market Forecast Profiles in Context: Level-of-Service Response Curvature Sensitivity and Attitude to Risk or to Distance in Forty Logit Core Model Applications of the Law of Demand","year":2010,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Logit; Context (archaeology); Section (typography); Windsor; Operations research; Econometrics; Economics; Engineering; History; Business; Advertising; Archaeology","score_opus":0.043318810163599904,"score_gpt":0.30461594506541373,"score_spread":0.26129713490181383,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2310745800","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97455686,0.000005270195,0.022199828,0.0012378001,0.00003315637,0.00093181524,0.00071797,0.000011214814,0.0003060942],"genre_scores_gemma":[0.99090075,0.000003420775,0.0077701155,0.00023607178,0.0000082875695,0.000028727429,0.000013430566,0.0000060358157,0.0010331664],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.999041,0.0001485701,0.0002984032,0.00018645677,0.00019598438,0.00012955461],"domain_scores_gemma":[0.9988757,0.0004851038,0.00017502168,0.00017691278,0.00022726768,0.00006001066],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010962257,0.00008603608,0.00020353273,0.00005628927,0.00009524217,0.00001191886,0.00013066684,0.0001094975,0.000022150032],"category_scores_gemma":[0.00030127517,0.00006182543,0.00002308905,0.00059618335,0.00016137978,0.00011316871,0.00001902093,0.00013249334,3.146636e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.005120784,0.00026243035,0.85396236,0.00013766874,0.000019764284,0.0000018815738,0.0366646,0.05589049,0.005511609,0.040032595,0.00059081044,0.0018049892],"study_design_scores_gemma":[0.000505948,0.000024150128,0.9828167,0.00008451998,0.000014246424,2.4671627e-7,0.002466121,0.011950403,0.0014022747,0.00030919784,0.00031765146,0.000108524866],"about_ca_topic_score_codex":0.057616398,"about_ca_topic_score_gemma":0.94853526,"teacher_disagreement_score":0.89091885,"about_ca_system_score_codex":0.000021673739,"about_ca_system_score_gemma":0.00018774503,"threshold_uncertainty_score":0.948659},"labels":[],"label_agreement":null},{"id":"W2322821347","doi":"10.1061/9780784479797.019","title":"Efficiency Improvement of Automated Transit Systems Compared to Conventional Train Operation","year":2016,"lang":"en","type":"article","venue":"","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":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Headway; Benchmark (surveying); Reliability (semiconductor); Transit (satellite); Operational efficiency; Computer science; Transit system; Service (business); Transport engineering; Public transport; Reliability engineering; Simulation; Engineering; Power (physics)","score_opus":0.019565072653716088,"score_gpt":0.29450368422052653,"score_spread":0.27493861156681043,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2322821347","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.41475517,0.000014412204,0.572946,0.0020681515,0.00043046233,0.0006619861,0.00006603868,0.00043869397,0.0086190915],"genre_scores_gemma":[0.9976998,0.0000030472972,0.00072096323,0.000040678868,0.000022116636,0.0000152346865,0.000022789704,0.000003151733,0.0014722127],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99924266,0.00004290528,0.00023303427,0.000109422195,0.00026331662,0.0001086575],"domain_scores_gemma":[0.9996709,0.000041222527,0.000047368267,0.000047618756,0.00013000125,0.00006286096],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034348154,0.00004532448,0.00008442785,0.000058465394,0.00012076561,0.00002112348,0.00006975422,0.000034529676,0.00025820994],"category_scores_gemma":[0.000019656552,0.000032745986,0.000025607382,0.00014482843,0.00004422707,0.00010421932,0.000001114609,0.000011139015,0.000020396668],"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":[0.00021846205,0.0006793468,0.010695348,0.00011280985,0.00010318083,0.000003195395,0.051413387,0.28080356,0.36094296,0.27508798,0.007892624,0.012047161],"study_design_scores_gemma":[0.022604074,0.002382912,0.30020323,0.0017640074,0.00028915232,0.0000035049516,0.07373882,0.4053132,0.10376828,0.0005294734,0.08634081,0.0030625137],"about_ca_topic_score_codex":0.0007117088,"about_ca_topic_score_gemma":0.00037490248,"teacher_disagreement_score":0.58294463,"about_ca_system_score_codex":0.000035430763,"about_ca_system_score_gemma":0.00009321822,"threshold_uncertainty_score":0.2827218},"labels":[],"label_agreement":null},{"id":"W2325017782","doi":"10.3141/2520-02","title":"Evaluation of the Passing Behavior of Motorized Vehicles When Overtaking Bicycles on Urban Arterial Roadways","year":2015,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":37,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina; University of Waterloo","funders":"","keywords":"Overtaking; Separation (statistics); Transport engineering; Automotive engineering; Environmental science; Computer science; Simulation; Engineering","score_opus":0.2419910058240375,"score_gpt":0.4405982203132173,"score_spread":0.19860721448917984,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2325017782","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9945476,0.00013290958,0.0005146816,0.0018484746,0.0010691633,0.0013560699,0.00007748077,0.000020193038,0.00043344309],"genre_scores_gemma":[0.998171,0.000122043966,0.0010763421,0.000017652814,0.0002725522,0.00006462029,0.00001836022,0.000035230132,0.00022221713],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.98333037,0.004175828,0.0015438262,0.0003113659,0.010060219,0.0005783891],"domain_scores_gemma":[0.987666,0.0008594422,0.0011287923,0.00043353817,0.0096139265,0.00029827663],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.016841093,0.00020099986,0.00043691127,0.000739367,0.00080929155,0.00009931643,0.0010589776,0.00023681138,0.00016812634],"category_scores_gemma":[0.001136951,0.00014360144,0.00038718877,0.0017620898,0.0010529377,0.0006459627,0.0000049681707,0.0010770758,0.0000032187004],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0030262184,0.001138799,0.7783301,0.00018963043,0.0003130932,0.000017547483,0.12500769,0.031488605,0.025598828,0.010524293,0.0071213036,0.017243909],"study_design_scores_gemma":[0.0031438754,0.00042708762,0.96819305,0.0006666753,0.00029169873,1.387621e-7,0.013710941,0.0004267508,0.0043325583,0.0039061632,0.0047241887,0.0001768551],"about_ca_topic_score_codex":0.016473882,"about_ca_topic_score_gemma":0.0284845,"teacher_disagreement_score":0.18986298,"about_ca_system_score_codex":0.00042421772,"about_ca_system_score_gemma":0.0027366532,"threshold_uncertainty_score":0.9900755},"labels":[],"label_agreement":null},{"id":"W2327724678","doi":"","title":"Development of an Updated \"Traffic Signal Operations Policies and Strategies\" Document for the City of Toronto","year":2016,"lang":"en","type":"article","venue":"Transportation Research Board 95th Annual MeetingTransportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Metropolitan area; Traffic congestion; Process (computing); Plan (archaeology); Control (management); Transport engineering; Business; Computer science; Engineering; Geography","score_opus":0.06648901271197175,"score_gpt":0.4128637307720808,"score_spread":0.346374718060109,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2327724678","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9810253,0.0002008205,0.013922277,0.0016206593,0.000085832886,0.0020464975,0.00056247757,0.00010018109,0.00043598222],"genre_scores_gemma":[0.9803154,0.00048659663,0.017694313,0.000019374242,0.00009395715,0.00041444637,0.00042258846,0.000037008616,0.00051633664],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.9936544,0.0008135359,0.001267204,0.0005996227,0.0027235704,0.0009417255],"domain_scores_gemma":[0.99301916,0.001431332,0.00022377718,0.000334097,0.0045799874,0.0004116508],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.007979306,0.00024474837,0.00037635845,0.0003613608,0.0019342974,0.00014999016,0.000582224,0.00023527702,0.00050960405],"category_scores_gemma":[0.00021130173,0.00018121023,0.000114175215,0.00080827257,0.0016527012,0.0015994804,0.0000066051307,0.00031605604,0.000002759091],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.004117291,0.0011402066,0.032095198,0.0006729418,0.00055943377,0.000013395044,0.58269984,0.028287662,0.028910305,0.254155,0.0013154219,0.0660333],"study_design_scores_gemma":[0.0038530366,0.0011699589,0.65779257,0.00043576403,0.0001255298,1.8765829e-7,0.28481942,0.00091166556,0.010284589,0.0013191714,0.038569022,0.00071908155],"about_ca_topic_score_codex":0.040168528,"about_ca_topic_score_gemma":0.40159506,"teacher_disagreement_score":0.6256974,"about_ca_system_score_codex":0.00018614637,"about_ca_system_score_gemma":0.0019431099,"threshold_uncertainty_score":0.99936503},"labels":[],"label_agreement":null},{"id":"W2329320394","doi":"10.1061/40996(330)514","title":"Disaggregate Model on Drivers' Route Choice Behavior under Traffic State Information","year":2009,"lang":"en","type":"article","venue":"Logistics","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ministry of Education and Child Care","funders":"National Natural Science Foundation of China; National Science Foundation","keywords":"Computer science; Logit; Rationality; Homogeneous; Mixed logit; Nested logit; State (computer science); Logistic regression; Transport engineering; Travel behavior; Econometrics; Operations research; Engineering; Economics; Machine learning; Mathematics","score_opus":0.03199691297703694,"score_gpt":0.30641771544485974,"score_spread":0.2744208024678228,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2329320394","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.34915394,0.000013213861,0.6210607,0.0020608401,0.0004922553,0.0004597958,0.00020011379,0.0005446588,0.026014457],"genre_scores_gemma":[0.9958896,0.000048639413,0.002195803,0.00064951717,0.000033497265,0.0000034477778,0.00017686533,0.0000033401543,0.0009993089],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99931127,0.000025077557,0.00015796049,0.00008492684,0.0002518799,0.00016888516],"domain_scores_gemma":[0.99963695,0.000046314417,0.00008672713,0.000081821,0.00007746157,0.00007072768],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010626227,0.0000733762,0.00006873238,0.000053985303,0.0002619257,0.00008163983,0.00009508336,0.000067854446,0.000014194687],"category_scores_gemma":[0.0000668836,0.0000739646,0.000025655063,0.00013785741,0.00006860187,0.00033914807,0.0000015948468,0.000089725305,0.000038558042],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000084172125,0.00003510072,0.00068288133,0.0000013944198,0.000001820534,0.0000010694995,0.0044758483,0.96431756,0.0000010282608,0.021346156,0.0003371571,0.008791581],"study_design_scores_gemma":[0.001750499,0.0003296109,0.25015804,0.000075919124,0.00018207716,6.225891e-7,0.0034015107,0.7141493,0.000030060832,0.008265793,0.020782935,0.0008736278],"about_ca_topic_score_codex":0.00009680217,"about_ca_topic_score_gemma":0.0003238844,"teacher_disagreement_score":0.64673567,"about_ca_system_score_codex":0.00006805741,"about_ca_system_score_gemma":0.00007682817,"threshold_uncertainty_score":0.30161887},"labels":[],"label_agreement":null},{"id":"W2331234998","doi":"10.14288/1.0094338","title":"Urban transportation policy and planning : |b an analysis of future directions for the metropolitan transportation decision-making process","year":2010,"lang":"en","type":"article","venue":"cIRcle (University of British Columbia)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Metropolitan area; Transportation planning; Process (computing); Decision-making; Policy making; Business; Urban planning; Transport engineering; Environmental planning; Computer science; Operations research; Process management; Engineering; Geography; Civil engineering; Marketing","score_opus":0.007963773647439065,"score_gpt":0.2577384483763326,"score_spread":0.24977467472889353,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2331234998","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9804829,0.00008398943,0.018274315,0.00010609978,0.00012498324,0.00022087108,0.00056128716,0.000043640946,0.00010195659],"genre_scores_gemma":[0.99729085,0.00008646479,0.0023075133,0.0000120670975,0.00008667489,0.0000015842294,0.00017133707,0.000006688051,0.000036833186],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.999255,0.000028669658,0.00014969535,0.00020311165,0.00023165967,0.00013189156],"domain_scores_gemma":[0.9990596,0.00022089777,0.0001840891,0.00010002259,0.00036673,0.00006866043],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028275035,0.000034226723,0.0001720644,0.00017090695,0.00077800214,0.000055535722,0.00015038671,0.000107442276,0.000027994389],"category_scores_gemma":[0.00006763038,0.00008184015,0.00010314315,0.0011076877,0.00024023272,0.00042875664,7.943106e-7,0.00008754927,5.7619985e-8],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008086365,0.00014327535,0.5503156,0.000073700394,0.0003766753,0.0000062083564,0.092125446,0.0075273123,0.00006343701,0.001004086,0.000150387,0.34813297],"study_design_scores_gemma":[0.0002273153,0.000024310582,0.92900264,0.000033404776,0.0005149703,2.093027e-7,0.06728893,0.002100711,2.885545e-7,0.00024163231,0.00048501935,0.00008058236],"about_ca_topic_score_codex":0.11428002,"about_ca_topic_score_gemma":0.8851287,"teacher_disagreement_score":0.77084863,"about_ca_system_score_codex":0.00002939993,"about_ca_system_score_gemma":0.0001256586,"threshold_uncertainty_score":0.8916181},"labels":[],"label_agreement":null},{"id":"W2333362585","doi":"10.2208/jscejipm.70.145","title":"SECOND BEST PRICING OF EXPRESSWAY WITH REFERENCE TO MAINTENANCE COSTS","year":2014,"lang":"en","type":"article","venue":"Journal of Japan Society of Civil Engineers Ser D3 (Infrastructure Planning and Management)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Transport engineering; Business; Computer science; Engineering","score_opus":0.006768124760176947,"score_gpt":0.2283547666670865,"score_spread":0.22158664190690958,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2333362585","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7593374,0.00030036207,0.20679702,0.000469174,0.00020541601,0.00028925564,0.000018317985,0.000038581602,0.032544453],"genre_scores_gemma":[0.9640326,0.00013437489,0.035222445,0.00012084818,0.000046798636,0.000001338089,0.000004671126,0.000011181926,0.0004257234],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9989257,0.00003310939,0.00034197574,0.00012869544,0.00037438283,0.00019613368],"domain_scores_gemma":[0.99909455,0.00008517784,0.00039882536,0.00009186027,0.00020772056,0.00012184222],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049638137,0.00012586215,0.00027305566,0.00010588392,0.000113597,0.000029828965,0.0001774834,0.000075511656,0.000026851256],"category_scores_gemma":[0.000026723977,0.0001060304,0.00006264118,0.00024495815,0.00009725214,0.0002131731,0.000013341766,0.00017747529,2.1485118e-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":[0.00026888523,0.00006098909,0.05207843,0.0011915448,0.00067141507,0.0000052636437,0.12151967,0.7775937,0.0016815315,0.009669178,0.02989508,0.0053643594],"study_design_scores_gemma":[0.0073881657,0.002146466,0.4236041,0.009355376,0.0008838305,0.0000427709,0.26490065,0.008148475,0.0018905483,0.00075993425,0.2793208,0.0015588915],"about_ca_topic_score_codex":0.000023998196,"about_ca_topic_score_gemma":0.000039019436,"teacher_disagreement_score":0.7694452,"about_ca_system_score_codex":0.00003960757,"about_ca_system_score_gemma":0.000037025035,"threshold_uncertainty_score":0.43237942},"labels":[],"label_agreement":null},{"id":"W2335460464","doi":"","title":"Probe Vehicle Bluetooth Study for Travel Time Savings Estimation","year":2016,"lang":"en","type":"article","venue":"Purdue e-Pubs (Purdue University System)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Bluetooth; Timestamp; Travel time; MAC address; Transport engineering; Computer science; Estimation; Real-time computing; Telecommunications; Engineering; Computer security; Wireless","score_opus":0.011915761952439107,"score_gpt":0.22898156356341187,"score_spread":0.21706580161097278,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2335460464","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6250555,0.000041816216,0.33774477,0.0029094792,0.00087205844,0.005459742,0.00036660084,0.0012352761,0.026314763],"genre_scores_gemma":[0.9797256,0.0000044050107,0.0032689546,0.000016737718,0.00010714248,0.000011533276,0.00004801569,0.000019605217,0.016797993],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.99839294,0.00023471826,0.00023498888,0.00038132843,0.00039976346,0.00035626776],"domain_scores_gemma":[0.99885166,0.000274598,0.00019824492,0.00020156601,0.00029973214,0.00017418773],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009886696,0.00015412566,0.00024426932,0.00019342154,0.00084964425,0.00008372482,0.00030560675,0.00013631949,0.000063490086],"category_scores_gemma":[0.00015666512,0.00014317578,0.000094595845,0.0003681205,0.000106243824,0.0007639545,0.000014862283,0.00006340193,0.00014358037],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0032640225,0.003369651,0.19090405,0.0017365353,0.0011019454,0.0003236723,0.42035767,0.009362286,0.009156445,0.2892921,0.02704816,0.04408346],"study_design_scores_gemma":[0.034201346,0.0025808665,0.3583813,0.0033543496,0.0018389694,0.000020093166,0.36194924,0.038291667,0.0015416696,0.001114709,0.19225089,0.004474897],"about_ca_topic_score_codex":0.0008275178,"about_ca_topic_score_gemma":0.00045244614,"teacher_disagreement_score":0.3546701,"about_ca_system_score_codex":0.0004245423,"about_ca_system_score_gemma":0.00021620566,"threshold_uncertainty_score":0.65348625},"labels":[],"label_agreement":null},{"id":"W2335478259","doi":"10.14288/1.0087009","title":"Factors and policies affecting demand for light vehicle transportation in the lower mainland of British Columbia","year":2009,"lang":"en","type":"article","venue":"cIRcle (University of British Columbia)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Mainland; Business; Mainland China; Transport engineering; Geography; Engineering; Archaeology; China","score_opus":0.010264077712472027,"score_gpt":0.2076121085618068,"score_spread":0.19734803084933478,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2335478259","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9984512,0.00007438148,0.0004063723,0.00015574312,0.000040781426,0.00029528924,0.00019077843,0.000021828271,0.0003636292],"genre_scores_gemma":[0.99942684,0.00009020566,0.00016864759,0.000028895396,0.000013841027,3.853206e-7,0.00003561839,0.0000037922941,0.00023177362],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99934065,0.00006237114,0.000116737145,0.00015488366,0.00017373585,0.00015162736],"domain_scores_gemma":[0.99958247,0.00011561206,0.00010825537,0.000055515735,0.00009681215,0.000041360134],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033553017,0.000024273819,0.00015252216,0.000023389197,0.00034668104,0.000126542,0.00011708687,0.00008313412,0.000013946861],"category_scores_gemma":[0.00003119009,0.00007717911,0.000056352827,0.0002481816,0.000142139,0.0002676432,0.0000016490703,0.000055588895,9.354689e-8],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010520329,0.0001570456,0.9165972,0.00005682699,0.000013849142,0.000015936585,0.02452097,0.00014651834,0.000072455725,0.00001025655,0.0016724727,0.056725964],"study_design_scores_gemma":[0.0005910157,0.000093181,0.9788294,0.000110372865,0.000026820862,8.9573797e-7,0.019768475,0.00003412512,4.3134875e-7,0.00025704713,0.00020695187,0.000081290746],"about_ca_topic_score_codex":0.49485847,"about_ca_topic_score_gemma":0.97073907,"teacher_disagreement_score":0.47588062,"about_ca_system_score_codex":0.00001761721,"about_ca_system_score_gemma":0.00003953174,"threshold_uncertainty_score":0.5085053},"labels":[],"label_agreement":null},{"id":"W2335790481","doi":"","title":"Implementation of a \"Next Generation\" Activity-Based Travel Demand Model: The Toronto Case","year":2015,"lang":"en","type":"article","venue":"TAC 2015: Getting You There Safely - 2015 Conference and Exhibition of the Transportation Association of Canada // ATC: Destination sécurité routière - 2015 Congrès et Exposition de l'Association des transports du Canada","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Metropolitan area; Transport engineering; Demand forecasting; Operations research; Transportation planning; Computer science; Investment (military); Documentation; Urban planning; Demand management; Regional science; Engineering; Economics; Geography; Civil engineering","score_opus":0.028007725163272212,"score_gpt":0.28180400500989083,"score_spread":0.2537962798466186,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2335790481","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9537371,0.00048545632,0.024470776,0.016054515,0.00063984207,0.0014087445,0.00213724,0.000086411346,0.000979905],"genre_scores_gemma":[0.99637634,0.00026028013,0.0009374278,0.0005565819,0.00011820571,0.0000897822,0.00083965104,0.00004633509,0.0007754053],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9939526,0.0010706421,0.0015452929,0.00047772055,0.002334374,0.00061941834],"domain_scores_gemma":[0.99181986,0.0005884311,0.0032608476,0.00031913503,0.0036638845,0.00034785742],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0040177545,0.00042307837,0.00061422924,0.00013935598,0.0009226787,0.00013806068,0.00032449447,0.00035844053,0.00028605963],"category_scores_gemma":[0.00047017308,0.00040917817,0.00018537403,0.0005521073,0.0001980555,0.0013580789,0.000008036227,0.00032964206,3.73809e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006983073,0.00066193566,0.50817573,0.0008005824,0.00095020473,0.000084416635,0.10851825,0.2641139,0.005433309,0.03503931,0.07188878,0.0036352377],"study_design_scores_gemma":[0.0144422315,0.0006163217,0.7637099,0.0011824536,0.0024230294,0.000044186436,0.091332816,0.085461535,0.02368356,0.0037686122,0.010241396,0.003093963],"about_ca_topic_score_codex":0.8078125,"about_ca_topic_score_gemma":0.9916807,"teacher_disagreement_score":0.25553414,"about_ca_system_score_codex":0.003287074,"about_ca_system_score_gemma":0.010584179,"threshold_uncertainty_score":0.999836},"labels":[],"label_agreement":null},{"id":"W2336318815","doi":"","title":"Gardiner Expressway East Planning Study Innovative Planning Techniques","year":2015,"lang":"en","type":"article","venue":"TAC 2015: Getting You There Safely - 2015 Conference and Exhibition of the Transportation Association of Canada // ATC: Destination sécurité routière - 2015 Congrès et Exposition de l'Association des transports du Canada","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Transport engineering; Downtown; Scope (computer science); Transportation planning; Microsimulation; Work (physics); Urban planning; Engineering; Computer science; Civil engineering; Geography","score_opus":0.02104704527285344,"score_gpt":0.2764141480836281,"score_spread":0.2553671028107746,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2336318815","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9644663,0.00039393993,0.0114341,0.013653708,0.0010231083,0.001977832,0.0015225548,0.00030340106,0.0052250684],"genre_scores_gemma":[0.99485964,0.00005910194,0.0011138626,0.0005896659,0.00017592982,0.00011452679,0.00096860615,0.00006232011,0.0020563423],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99282044,0.0010929024,0.00179729,0.000622385,0.0028706307,0.0007963793],"domain_scores_gemma":[0.9901681,0.00058729266,0.0033513322,0.00031822073,0.0051473207,0.0004277236],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.004513859,0.0005283565,0.0007726685,0.00030890974,0.000962435,0.00018752368,0.0004264372,0.00043233493,0.00020538052],"category_scores_gemma":[0.0011084375,0.00055808475,0.00014088873,0.0011148035,0.00018548437,0.0013273277,0.00001442577,0.0005722217,8.482578e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032614951,0.00031102615,0.84768724,0.00017106437,0.0003443223,0.00004104292,0.08100614,0.03228112,0.0004444513,0.004102354,0.03273183,0.00055324053],"study_design_scores_gemma":[0.0046883253,0.00032080838,0.9003566,0.001300183,0.0005467811,0.0000066526127,0.06139939,0.0013894769,0.0024505013,0.0013304998,0.024717927,0.0014928662],"about_ca_topic_score_codex":0.50788957,"about_ca_topic_score_gemma":0.84601355,"teacher_disagreement_score":0.338124,"about_ca_system_score_codex":0.0030201967,"about_ca_system_score_gemma":0.007629491,"threshold_uncertainty_score":0.9996871},"labels":[],"label_agreement":null},{"id":"W2337484830","doi":"10.1007/978-3-319-25082-3_6","title":"The Theory of Transit Assignment: Basic Modelling Frameworks","year":2016,"lang":"en","type":"book-chapter","venue":"Springer tracts on transportation and traffic","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":34,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Transit (satellite); Schedule; Computer science; State (computer science); Operations research; Transit system; Mathematical optimization; Management science; Transport engineering; Engineering; Mathematics; Public transport; Algorithm","score_opus":0.01942124693694855,"score_gpt":0.23993099934315693,"score_spread":0.22050975240620838,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2337484830","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019745346,0.0046963617,0.25695908,0.004769427,0.002038736,0.0024357783,0.00068188,0.00078078645,0.7078926],"genre_scores_gemma":[0.945708,0.0048142653,0.00029160417,0.00010521926,0.00012719903,0.000012777852,0.000079773294,0.000059879763,0.04880132],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.998122,0.000059154467,0.00056123105,0.00037565955,0.0006061354,0.0002758662],"domain_scores_gemma":[0.9985597,0.00062755105,0.0003530583,0.00019601049,0.00011754802,0.00014613809],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00068199344,0.00028664767,0.00030443005,0.00014097431,0.0005750022,0.00006302539,0.0001747258,0.00065096543,0.00017524768],"category_scores_gemma":[0.0000117608615,0.00021675695,0.00017455455,0.00005506424,0.00041072554,0.00015055311,4.4199646e-7,0.00057298,0.000012701154],"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":[0.00019205762,0.000034619254,0.000024224799,0.00004581231,0.000094934316,0.0000075763114,0.0067426804,0.0660864,0.0000046312116,0.87555414,0.00002217327,0.051190764],"study_design_scores_gemma":[0.006512788,0.0009236181,0.0137437945,0.008011653,0.0026078483,0.00000252172,0.006015614,0.002542022,0.00039131872,0.19804204,0.75675094,0.0044558565],"about_ca_topic_score_codex":0.000009482056,"about_ca_topic_score_gemma":0.00030484435,"teacher_disagreement_score":0.9259626,"about_ca_system_score_codex":0.00003352291,"about_ca_system_score_gemma":0.00013868538,"threshold_uncertainty_score":0.88390917},"labels":[],"label_agreement":null},{"id":"W2337831897","doi":"","title":"Understanding Complete Streets: How do Professionals Define a Complete Street Project?","year":2016,"lang":"en","type":"article","venue":"Transportation Research Board 95th Annual MeetingTransportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Transport engineering; Transportation planning; Process (computing); Land-use planning; Ambiguity; Land use; Environmental planning; Geography; Computer science; Engineering; Civil engineering","score_opus":0.2744961787373265,"score_gpt":0.4323435293860352,"score_spread":0.1578473506487087,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2337831897","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.86747855,0.00044041566,0.045236062,0.05303845,0.0010995863,0.009713118,0.008774868,0.0018384581,0.012380479],"genre_scores_gemma":[0.9879786,0.00085088215,0.0047630398,0.0001411354,0.00042846784,0.00071894366,0.001270524,0.00014745856,0.0037009998],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9820647,0.0036220688,0.0014701006,0.001718264,0.00813914,0.002985676],"domain_scores_gemma":[0.9884283,0.003912744,0.0004785017,0.0007453621,0.005346567,0.0010885241],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.011094565,0.0006214161,0.00077601726,0.0023889102,0.0042254147,0.0005605949,0.0012249658,0.00062072295,0.0008145846],"category_scores_gemma":[0.0013594079,0.0005320488,0.00033900686,0.004349569,0.0025845875,0.002165049,0.00001932245,0.0015871346,0.00014111787],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0030845841,0.0012816051,0.3749416,0.000885044,0.00044819148,0.0004898176,0.10605471,0.0011784936,0.005389631,0.42736876,0.07444892,0.004428651],"study_design_scores_gemma":[0.009233015,0.0015625972,0.4120692,0.003555372,0.00016186923,9.043779e-7,0.31844583,0.00027783104,0.0005653227,0.014017496,0.23765457,0.0024559773],"about_ca_topic_score_codex":0.0150482915,"about_ca_topic_score_gemma":0.11528838,"teacher_disagreement_score":0.41335127,"about_ca_system_score_codex":0.00093165186,"about_ca_system_score_gemma":0.0022497245,"threshold_uncertainty_score":0.9997131},"labels":[],"label_agreement":null},{"id":"W2338016777","doi":"10.14288/1.0100209","title":"An evaluation of a transit supply program : the case of British Columbia","year":2010,"lang":"en","type":"article","venue":"cIRcle (University of British Columbia)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Transit (satellite); Business; Operations management; Transport engineering; Public transport; Computer science; Engineering","score_opus":0.01301436214556535,"score_gpt":0.24079957310279662,"score_spread":0.22778521095723128,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2338016777","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9973355,0.00003391458,0.00020684903,0.000062066225,0.00012045088,0.0005930353,0.00043425022,0.00005498717,0.001158938],"genre_scores_gemma":[0.9983507,0.000034925026,0.0012528461,0.000007231668,0.000022173579,0.0000020733016,0.000067012676,0.000007081068,0.0002559724],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9988131,0.00021318093,0.00016519573,0.00020964586,0.00044323778,0.0001556628],"domain_scores_gemma":[0.9986071,0.000052915333,0.00020025912,0.00018436274,0.0008668212,0.000088514986],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011623115,0.000024091645,0.00016667933,0.000021942828,0.00043773302,0.0001325124,0.00025850092,0.00013103797,0.00045437968],"category_scores_gemma":[0.000053522726,0.00009212727,0.00008813176,0.0003956161,0.0006907277,0.0003896396,0.0000066440216,0.00012663976,7.8152243e-7],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000069045764,0.00034455338,0.053720597,0.000040225987,0.000029360015,0.00010345128,0.0061998316,0.00037330808,0.00017701693,0.000006312902,0.0012213172,0.9377771],"study_design_scores_gemma":[0.00077005406,0.00009686202,0.9804505,0.000064144486,0.00014636407,0.000058397287,0.015860701,0.002003178,7.2121946e-7,0.0002055164,0.0002447176,0.00009882128],"about_ca_topic_score_codex":0.90362215,"about_ca_topic_score_gemma":0.9975213,"teacher_disagreement_score":0.9376783,"about_ca_system_score_codex":0.000020887983,"about_ca_system_score_gemma":0.00029082672,"threshold_uncertainty_score":0.4975139},"labels":[],"label_agreement":null},{"id":"W2338864598","doi":"","title":"Survey of Transportation Agencies on the Current State of Transportation Data Collection Practice in Canada","year":2016,"lang":"en","type":"article","venue":"Transportation Research Board 95th Annual MeetingTransportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Data collection; Commission; Sampling frame; Sample (material); TRIPS architecture; Business; Baseline (sea); Survey data collection; Travel survey; Data quality; Transportation planning; Marketing; Travel behavior; Transport engineering; Service (business); Engineering; Political science; Environmental health; Medicine; Finance; Statistics; Population","score_opus":0.1347139505928163,"score_gpt":0.41742966217947053,"score_spread":0.2827157115866542,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2338864598","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97923815,0.00046019684,0.0019646042,0.0043246374,0.00045314265,0.0027777574,0.010062183,0.00008996258,0.000629357],"genre_scores_gemma":[0.9915099,0.003997883,0.0006135172,0.000054022385,0.000062094565,0.00026237222,0.0031608264,0.00007568436,0.00026368204],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9827982,0.0047395257,0.0026473952,0.0012664319,0.0070516476,0.0014967913],"domain_scores_gemma":[0.9783784,0.01132803,0.0009534564,0.0010175743,0.007874285,0.00044822003],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.018084286,0.00044519475,0.0007306608,0.0013302374,0.00096954394,0.00007259423,0.0015225807,0.00019744191,0.00020146473],"category_scores_gemma":[0.0034320431,0.00035875486,0.00014310319,0.0058407956,0.0013541256,0.0016864917,0.000005664491,0.0013503375,0.000014137506],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.008377693,0.0013546901,0.71242535,0.00093286706,0.0003427145,0.00014422333,0.12022505,0.020043623,0.0013231605,0.012752414,0.015925707,0.10615248],"study_design_scores_gemma":[0.0014339979,0.00032985045,0.9685978,0.00076177996,0.00005259805,4.8064525e-8,0.014054487,0.00025515538,0.0011795487,0.0003969773,0.012533205,0.00040451784],"about_ca_topic_score_codex":0.912247,"about_ca_topic_score_gemma":0.99512273,"teacher_disagreement_score":0.25617245,"about_ca_system_score_codex":0.0008818612,"about_ca_system_score_gemma":0.010001653,"threshold_uncertainty_score":0.99988645},"labels":[],"label_agreement":null},{"id":"W2339922202","doi":"10.1002/atr.1386","title":"Surrogate‐based optimization of cordon toll levels in congested traffic networks","year":2016,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Trafikverket","keywords":"Toll; Computer science; Traffic congestion; Surrogate model; Mathematical optimization; Congestion pricing; Scheme (mathematics); Optimization problem; Operations research; Transport engineering; Engineering; Algorithm; Mathematics","score_opus":0.019152297581963778,"score_gpt":0.285064463475897,"score_spread":0.2659121658939332,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2339922202","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.72727615,0.000104187915,0.27148196,0.0004728717,0.0003796812,0.0001733407,0.00002059447,0.000024384683,0.0000667935],"genre_scores_gemma":[0.98619086,0.0002570861,0.013393352,0.000026295296,0.000048200003,0.000002870063,0.00003392669,0.000012480569,0.0000349557],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99844414,0.00011409619,0.0007784456,0.00011657297,0.000374896,0.00017183679],"domain_scores_gemma":[0.998386,0.00023877833,0.00075086806,0.00006462366,0.00047957792,0.00008012573],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00055409916,0.00009995282,0.00024880868,0.00025742495,0.000065062566,0.000010188341,0.00011025844,0.00011421193,0.00007248843],"category_scores_gemma":[0.00008380812,0.00008271094,0.00008760777,0.00048175073,0.00009406268,0.000647766,2.6564186e-7,0.000096165226,3.5748738e-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":[0.00027338127,0.000071998475,0.014931826,0.0000122535575,0.000009628542,0.000010049516,0.0026612312,0.9741272,0.00032818434,0.00031782695,0.000006975962,0.0072494596],"study_design_scores_gemma":[0.010716202,0.00056302844,0.96186835,0.0013425722,0.00014105691,0.0000017961925,0.004496784,0.018088141,0.0012348506,0.00016864948,0.00092749007,0.0004510865],"about_ca_topic_score_codex":0.000030468806,"about_ca_topic_score_gemma":0.001269696,"teacher_disagreement_score":0.9560391,"about_ca_system_score_codex":0.0000721723,"about_ca_system_score_gemma":0.00020740733,"threshold_uncertainty_score":0.33728543},"labels":[],"label_agreement":null},{"id":"W2340458625","doi":"10.3141/2538-01","title":"The Farside Story","year":2015,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"McGill University","funders":"","keywords":"Transit (satellite); Transport engineering; Service (business); Transit system; Variation (astronomy); Level of service; Engineering; Public transport; Computer science; Operations research; Business","score_opus":0.1584950676147807,"score_gpt":0.4306246592062054,"score_spread":0.2721295915914247,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2340458625","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95075786,0.0011592044,0.003230041,0.037072577,0.002663107,0.0013843024,0.000048188467,0.000091724076,0.0035930155],"genre_scores_gemma":[0.9904991,0.0018469918,0.001248856,0.000084594176,0.00047762235,0.000055438642,0.000016205335,0.00004552585,0.005725706],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.98738444,0.0039044474,0.0011304952,0.0003235168,0.006205893,0.0010512192],"domain_scores_gemma":[0.9885036,0.0024968465,0.00051782955,0.00045198164,0.0073106997,0.00071906816],"candidate_categories":["sts","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.018378863,0.00019855103,0.0003076539,0.0006144032,0.0034329437,0.0003357769,0.0015975683,0.00022512036,0.00010245855],"category_scores_gemma":[0.0011882134,0.00013321891,0.00032395334,0.0026069067,0.0018331753,0.0008611991,0.0000044618982,0.002486871,0.000047478694],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0035787083,0.00046835435,0.5040371,0.0001186252,0.0003508737,0.00022815035,0.11336164,0.021408977,0.00032713878,0.10823634,0.21865962,0.029224485],"study_design_scores_gemma":[0.0012216563,0.00029421644,0.42505717,0.00014619807,0.00004201986,2.4127812e-7,0.034815032,0.000073340496,0.00013327475,0.006525499,0.5315113,0.00018004522],"about_ca_topic_score_codex":0.019246764,"about_ca_topic_score_gemma":0.19793905,"teacher_disagreement_score":0.31285167,"about_ca_system_score_codex":0.0004649025,"about_ca_system_score_gemma":0.002787955,"threshold_uncertainty_score":0.99981445},"labels":[],"label_agreement":null},{"id":"W2340980903","doi":"10.14288/1.0101507","title":"Mode choice determiniants of selected socioeconomic groups : an investigation of a planning and control mechanism to divert automobile drivers to public transportation","year":2011,"lang":"en","type":"article","venue":"cIRcle (University of British Columbia)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Socioeconomic status; Public transport; Mode choice; Control (management); Mode (computer interface); Mechanism (biology); Business; Operations management; Transport engineering; Economics; Engineering; Computer science; Environmental health; Medicine","score_opus":0.01635513644423143,"score_gpt":0.2098630031100275,"score_spread":0.19350786666579609,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2340980903","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9912234,0.0000059885597,0.008055573,0.00003793531,0.000041448795,0.000303203,0.00022565029,0.000046116063,0.000060714832],"genre_scores_gemma":[0.9970222,0.000008578613,0.002849496,0.000031567783,0.000006776248,0.0000012272923,0.000051447194,0.0000063631805,0.000022341741],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9992662,0.00007260854,0.00015492174,0.00021280626,0.000152214,0.00014122493],"domain_scores_gemma":[0.9992685,0.00003568042,0.00017702913,0.00007324945,0.00026670482,0.00017886868],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018502343,0.000035027726,0.00018559421,0.000095857285,0.00020713767,0.000021725207,0.00015136394,0.000090205576,0.000038557122],"category_scores_gemma":[0.00002163675,0.00010928396,0.000031243108,0.00024721387,0.00014513922,0.0005837725,0.000004717028,0.00004362874,0.000001004519],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000039058876,0.0000695511,0.8435541,0.00005078225,0.000046052188,0.0000072911444,0.13253239,0.0006071289,0.0023655223,0.00006093844,0.000055737266,0.020611463],"study_design_scores_gemma":[0.00063888525,0.0001515943,0.9889619,0.000060334965,0.000040308332,2.8533412e-7,0.009046646,0.0007716225,0.000022774399,0.00019626803,0.000005569323,0.000103842176],"about_ca_topic_score_codex":0.12572269,"about_ca_topic_score_gemma":0.396913,"teacher_disagreement_score":0.2711903,"about_ca_system_score_codex":0.00003865287,"about_ca_system_score_gemma":0.00013073508,"threshold_uncertainty_score":0.8800992},"labels":[],"label_agreement":null},{"id":"W2341121544","doi":"10.1002/atr.1377","title":"Characterizing, measuring, and managing transit service quality","year":2016,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":41,"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; Quality (philosophy); Service quality; Service (business); Transit (satellite); Quality of service; Level of service; Transport engineering; Computer science; Perspective (graphical); Process management; Operations research; Risk analysis (engineering); Operations management; Engineering; Business; Marketing; Telecommunications","score_opus":0.027441795588023257,"score_gpt":0.2987950579652637,"score_spread":0.27135326237724045,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2341121544","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95831496,0.00012829975,0.035210077,0.005577335,0.00028740664,0.00008985648,0.000011934796,0.00003797963,0.00034212865],"genre_scores_gemma":[0.9947704,0.0007553991,0.0040946654,0.00017986534,0.000096745025,0.0000015643353,0.0000070545934,0.000009194157,0.00008511858],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.998877,0.00007195869,0.00044836095,0.00010850093,0.0003544016,0.00013978146],"domain_scores_gemma":[0.9990326,0.000081825456,0.00039718123,0.000050606042,0.00033094204,0.000106855594],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062490156,0.00007920629,0.00015730174,0.00010073148,0.00016362441,0.000022354825,0.00007898939,0.00005283595,0.00003434094],"category_scores_gemma":[0.00003454057,0.00006269159,0.000050483435,0.0001697208,0.00004729542,0.0009774887,3.206266e-7,0.000073425246,0.0000014593609],"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":[0.001815462,0.00024038115,0.1917981,0.0003819578,0.00021519154,0.00008412182,0.31292483,0.020893103,0.13221444,0.029790843,0.000052971616,0.30958858],"study_design_scores_gemma":[0.0013995416,0.00005344821,0.9821313,0.00028599543,0.000054364296,0.0000021082158,0.004865434,0.0000035654723,0.0010539684,0.0011545871,0.008835722,0.00015995077],"about_ca_topic_score_codex":0.00007174898,"about_ca_topic_score_gemma":0.0008938697,"teacher_disagreement_score":0.7903332,"about_ca_system_score_codex":0.00003414952,"about_ca_system_score_gemma":0.00006558956,"threshold_uncertainty_score":0.25564888},"labels":[],"label_agreement":null},{"id":"W2341494042","doi":"10.3141/2546-08","title":"What Happened to Speed?: Scheduled Speeds and Travel Times of North American Passenger Trains, 1965 to 2015","year":2016,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Train; Traffic speed; Aeronautics; Transport engineering; Computer science; Engineering; History","score_opus":0.06729780561302984,"score_gpt":0.39551343468812844,"score_spread":0.3282156290750986,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2341494042","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9678107,0.00009213847,0.0028143588,0.027362691,0.00043917925,0.0011959982,0.00012270229,0.000028541921,0.0001336761],"genre_scores_gemma":[0.990665,0.0021925885,0.0046821274,0.00014186707,0.0001947002,0.000030601866,0.00001902566,0.000050463274,0.0020236298],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99194366,0.0013025815,0.0013837598,0.0004959334,0.00391496,0.00095912284],"domain_scores_gemma":[0.99291366,0.0012392647,0.0005050145,0.00037981113,0.003972014,0.0009902584],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0051961034,0.00026288428,0.0005994539,0.0014721229,0.00069110934,0.0001978977,0.0009399763,0.00012567207,0.00025688257],"category_scores_gemma":[0.00045302237,0.00018589407,0.00025483707,0.0036751803,0.0012804553,0.0012394298,0.0000063593643,0.00075693976,0.00003522108],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0037437896,0.0004245509,0.86623836,0.00017431364,0.00027570096,0.000076204175,0.04778176,0.0026798788,0.0071519106,0.007495843,0.008785708,0.055171974],"study_design_scores_gemma":[0.0011036206,0.00077229127,0.97229826,0.00050916953,0.000041500174,2.1741539e-7,0.009972353,0.000014767356,0.0012673682,0.00040198924,0.013381846,0.00023659813],"about_ca_topic_score_codex":0.009929994,"about_ca_topic_score_gemma":0.22153355,"teacher_disagreement_score":0.21160357,"about_ca_system_score_codex":0.00017676274,"about_ca_system_score_gemma":0.00091175514,"threshold_uncertainty_score":0.996663},"labels":[],"label_agreement":null},{"id":"W2344633803","doi":"10.1002/atr.1380","title":"Estimation of train dwell time at short stops based on track occupation event data: A study at a Dutch railway station","year":2016,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":71,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Dwell time; Train; Computer science; Track (disk drive); Parametric statistics; Simulation; Statistics; Mathematics; Geography","score_opus":0.02300113947283465,"score_gpt":0.3276062254133139,"score_spread":0.30460508594047925,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2344633803","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9106578,0.000020602103,0.08767111,0.00041460898,0.00026276856,0.0005849649,0.00025427534,0.000038601418,0.00009526956],"genre_scores_gemma":[0.99271005,0.00004502396,0.0061207013,0.00002850568,0.00007662901,0.000010630152,0.00070156116,0.00002179608,0.00028509134],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9970592,0.00024452247,0.0010890219,0.00029322194,0.0011117287,0.00020228754],"domain_scores_gemma":[0.99786365,0.00037663337,0.00095806556,0.00023446504,0.00044243215,0.00012475158],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014210667,0.00017442807,0.00031983628,0.00029243215,0.00024691154,0.000017228913,0.00023091181,0.00010191736,0.00020141149],"category_scores_gemma":[0.00019437927,0.00014212223,0.000106207444,0.00038242736,0.00007914824,0.0012402894,0.0000020079656,0.00009982156,0.000010586158],"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":[0.0023264275,0.00067860004,0.027960528,0.000036700054,0.000053169188,0.000017430995,0.021756869,0.89562094,0.004929436,0.000087533146,0.00022643221,0.04630592],"study_design_scores_gemma":[0.0063314782,0.0016766426,0.968129,0.00046999936,0.00036811514,0.0000018992362,0.0042357254,0.013448069,0.0037760565,0.00019169437,0.00095731585,0.0004140028],"about_ca_topic_score_codex":0.0000381063,"about_ca_topic_score_gemma":0.0013876407,"teacher_disagreement_score":0.9401685,"about_ca_system_score_codex":0.0003350233,"about_ca_system_score_gemma":0.00022321177,"threshold_uncertainty_score":0.5795576},"labels":[],"label_agreement":null},{"id":"W2345282952","doi":"10.1007/s12205-016-0705-0","title":"Public transportation and sustainability: A review","year":2016,"lang":"en","type":"review","venue":"KSCE Journal of Civil Engineering","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":225,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Sustainability; Public transport; Sustainable transport; Social sustainability; Business; Environmental planning; Transportation planning; Sustainable development; Sustainability organizations; Order (exchange); Key (lock); Environmental resource management; Transport engineering; Environmental economics; Engineering; Political science; Economics; Computer science; Geography; Ecology","score_opus":0.031805622037383914,"score_gpt":0.309125073134094,"score_spread":0.2773194510967101,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2345282952","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[6.2668863e-7,0.98698646,0.011703502,0.0005669356,0.00022010329,0.00022987838,0.0000099992085,0.000029959716,0.00025252101],"genre_scores_gemma":[0.00014031866,0.9992606,0.00022498702,0.000016282052,0.00019748164,0.000007392653,0.0000111190575,0.000021605294,0.00012020435],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99855417,0.000097429525,0.0007126892,0.000112890935,0.00032823428,0.00019456778],"domain_scores_gemma":[0.9985971,0.00021743115,0.0005447328,0.00007987463,0.00040322496,0.00015763842],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012463484,0.00016051396,0.0007643704,0.0002389171,0.00007724807,0.000047714908,0.00015350964,0.00014166525,0.00007071094],"category_scores_gemma":[0.0006746574,0.00012289199,0.00022935928,0.00033568114,0.00003839145,0.00036595715,0.0000017406148,0.00023409528,0.0000013250033],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000016728876,0.000017727118,0.00002955604,0.05322427,0.00010983157,0.00003441531,0.0009104392,0.00015931472,3.6536438e-8,0.0043454254,0.0009178245,0.9402495],"study_design_scores_gemma":[0.00007033264,0.000013918071,0.000017459815,0.045898084,0.00027358672,0.000010156495,0.000034951347,0.0000030908398,9.9932205e-9,0.00004899936,0.9535016,0.00012785828],"about_ca_topic_score_codex":0.000006815376,"about_ca_topic_score_gemma":0.00011949972,"teacher_disagreement_score":0.95258373,"about_ca_system_score_codex":0.00015717687,"about_ca_system_score_gemma":0.00071058405,"threshold_uncertainty_score":0.5011389},"labels":[],"label_agreement":null},{"id":"W2345612734","doi":"","title":"Identifying TDM Corridors in Large Metropolitan Regions","year":2016,"lang":"en","type":"article","venue":"Transportation Research Board 95th Annual MeetingTransportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Metropolitan area; Transport engineering; Transit (satellite); Demand management; Service (business); Business; Computer science; Public transport; Geography; Engineering; Economics; Marketing","score_opus":0.09206767954289677,"score_gpt":0.4279862255438653,"score_spread":0.3359185460009685,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2345612734","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9501625,0.00036911393,0.017915709,0.014340339,0.00065181125,0.0028057282,0.0011784337,0.0007298538,0.011846561],"genre_scores_gemma":[0.98865116,0.0017168069,0.0027171988,0.00011201129,0.00032082997,0.00050857465,0.0004424011,0.00011673041,0.0054142643],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.98512226,0.0031522198,0.0016850384,0.0014618288,0.005363672,0.0032149921],"domain_scores_gemma":[0.9911682,0.0026710639,0.00029885938,0.0006685759,0.004083774,0.0011095083],"candidate_categories":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.014967867,0.00045349193,0.0006236244,0.0033120846,0.0025832525,0.00029302112,0.0010835201,0.0005743248,0.0009697096],"category_scores_gemma":[0.0017352941,0.00042032314,0.00029058135,0.0057388428,0.001717084,0.0020797087,0.000013073134,0.0015745056,0.00029299955],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00095023285,0.0006126871,0.6004354,0.00021340708,0.00008379841,0.00031843863,0.06685758,0.0005698075,0.0016118243,0.31764767,0.008139798,0.002559377],"study_design_scores_gemma":[0.0033769377,0.00028685754,0.82125676,0.00083918776,0.000034713717,2.942928e-7,0.104478955,0.0000611012,0.00039385818,0.010267829,0.058264524,0.0007389833],"about_ca_topic_score_codex":0.037975647,"about_ca_topic_score_gemma":0.3070794,"teacher_disagreement_score":0.30737984,"about_ca_system_score_codex":0.0011787366,"about_ca_system_score_gemma":0.0014077709,"threshold_uncertainty_score":0.99994355},"labels":[],"label_agreement":null},{"id":"W2347069314","doi":"10.1002/atr.1360","title":"Integrated optimization of location and signal timings for midblock pedestrian crosswalk","year":2015,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Schema crosswalk; Pedestrian; Computer science; Transport engineering; Engineering","score_opus":0.02601037160384755,"score_gpt":0.30223016947513154,"score_spread":0.276219797871284,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2347069314","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.34276384,0.0002258479,0.65604645,0.00034185132,0.00025249264,0.0002580325,0.000022961474,0.000020617003,0.00006788008],"genre_scores_gemma":[0.9019436,0.00014062588,0.097619,0.00001938525,0.00007339617,0.0000045848433,0.00012898698,0.000010274576,0.00006009996],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9988831,0.00004341377,0.0005544275,0.00009828941,0.00030958696,0.00011117389],"domain_scores_gemma":[0.99742466,0.00009673437,0.0006558132,0.000039239087,0.0016636952,0.00011988357],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00058509834,0.00008309985,0.00018300938,0.00015640467,0.00011155748,0.000026938893,0.00006973362,0.000085093554,0.000009486729],"category_scores_gemma":[0.00017521692,0.00008228215,0.000049646143,0.00033713825,0.000092667025,0.00068526727,3.50989e-7,0.000075202806,1.486185e-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":[0.00064043014,0.00004681685,0.0031778072,0.000045279634,0.000017009108,0.0000012831969,0.0194993,0.9719447,0.00027941514,0.00087516836,0.000062087835,0.0034106972],"study_design_scores_gemma":[0.056296375,0.008784315,0.415019,0.003619906,0.002553416,0.00003944866,0.29255798,0.13147426,0.017108364,0.017849617,0.051749267,0.002948039],"about_ca_topic_score_codex":0.00008093927,"about_ca_topic_score_gemma":0.00023649918,"teacher_disagreement_score":0.84047043,"about_ca_system_score_codex":0.000048687172,"about_ca_system_score_gemma":0.0003619895,"threshold_uncertainty_score":0.3355369},"labels":[],"label_agreement":null},{"id":"W2348249203","doi":"","title":"Train Service Planning for Passenger Dedicated Railway Line Based on Analyzing Importance of Nodes","year":2010,"lang":"en","type":"article","venue":"Journal of Beijing Jiaotong University","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Transport Canada","funders":"","keywords":"Train; Analytic hierarchy process; Line (geometry); Service (business); Transport engineering; Dedicated line; Computer science; Hierarchy; Class (philosophy); Operations research; Level of service; Resource (disambiguation); Process (computing); Engineering; Computer network; Artificial intelligence; Geography","score_opus":0.022407063485536904,"score_gpt":0.28481842164389115,"score_spread":0.26241135815835426,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2348249203","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96128577,0.0000103496395,0.036127906,0.0013969289,0.00021787154,0.00009347532,0.000017120583,0.000023626202,0.00082693255],"genre_scores_gemma":[0.98605096,0.000005688557,0.013633702,0.00009833519,0.00010318823,1.0684529e-7,0.000011825827,0.0000064120236,0.00008976054],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9991965,0.000065815264,0.00024090469,0.00009661296,0.00025941126,0.00014072318],"domain_scores_gemma":[0.9984504,0.00035137375,0.000542787,0.00007142054,0.00048317885,0.000100828984],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010086021,0.000070924376,0.00017503674,0.00023325236,0.0003018281,0.000017203161,0.00019632281,0.00010337351,0.00003569154],"category_scores_gemma":[0.00020763373,0.00007346113,0.000096988995,0.00039618064,0.00006488224,0.00024513667,0.0000032288083,0.00024094724,2.9299648e-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":[0.00092794944,0.0002422491,0.7379039,0.00010658202,0.0001233853,0.00007847797,0.014888364,0.23123802,0.010358141,0.0024545253,0.00045623712,0.0012221924],"study_design_scores_gemma":[0.009258085,0.0006747346,0.78822213,0.0014047709,0.0007435586,0.0000075892567,0.028508384,0.141404,0.0039818995,0.00030687713,0.024571037,0.0009169294],"about_ca_topic_score_codex":0.000086691136,"about_ca_topic_score_gemma":0.00043601956,"teacher_disagreement_score":0.08983402,"about_ca_system_score_codex":0.000044380242,"about_ca_system_score_gemma":0.00026196602,"threshold_uncertainty_score":0.29956582},"labels":[],"label_agreement":null},{"id":"W2356668080","doi":"","title":"URBAN TRANSIT ASSIGNMENT MODEL BASED ON AUGMENTED NETWORK WITH IN-VEHICLE CONGESTION AND TRANSFER CONGESTION","year":2011,"lang":"en","type":"article","venue":"系统科学与系统工程学报(英文版)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Transit (satellite); Computer science; Urban transit; Traffic congestion; Transfer (computing); Transport engineering; Path (computing); Mathematical optimization; Public transport; Simulation; Computer network; Engineering; Mathematics","score_opus":0.02722335887137216,"score_gpt":0.23261545646793502,"score_spread":0.20539209759656285,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2356668080","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.72409576,0.0001248199,0.25142846,0.0013231147,0.00018772706,0.0009218905,0.000029902381,0.0003160544,0.021572286],"genre_scores_gemma":[0.9970884,0.00003868765,0.0021243803,0.00031398868,0.000040684146,0.00004264473,0.000052928477,0.000018132636,0.00028017937],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987103,0.00013978341,0.00021883345,0.00029615726,0.00033832472,0.00029663593],"domain_scores_gemma":[0.9995887,0.00006309786,0.000043049116,0.0001116061,0.00006428782,0.00012924206],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043179697,0.00015029963,0.00015963301,0.000095827105,0.00027997894,0.000037323298,0.00007864769,0.00013924555,0.000090442816],"category_scores_gemma":[0.000011074874,0.000141787,0.000027630098,0.00028492403,0.00014012647,0.0002177072,0.0000013777335,0.00015070057,0.0000045899537],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014240098,0.00040794417,0.12636323,0.00003380517,0.000033329386,0.000026670366,0.058976732,0.78227705,0.0001086764,0.02427808,0.00060488714,0.005465618],"study_design_scores_gemma":[0.0058241826,0.0008834397,0.45172012,0.00052548695,0.00016802328,0.0000010797717,0.0035997734,0.5339855,0.00045124948,0.00070254825,0.0013257428,0.0008128804],"about_ca_topic_score_codex":0.000853666,"about_ca_topic_score_gemma":0.0036297909,"teacher_disagreement_score":0.32535687,"about_ca_system_score_codex":0.000068997375,"about_ca_system_score_gemma":0.00010183595,"threshold_uncertainty_score":0.57819057},"labels":[],"label_agreement":null},{"id":"W2359224595","doi":"","title":"Smart Commute Workplace Program Business Case Review","year":2016,"lang":"en","type":"article","venue":"Transportation Research Board 95th Annual MeetingTransportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"TRIPS architecture; Liberian dollar; Business; Transport engineering; Business case; Traffic congestion; Finance; Investment (military); Operations management; Engineering","score_opus":0.08287430046476967,"score_gpt":0.43159021534163183,"score_spread":0.34871591487686215,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2359224595","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.84136134,0.006490864,0.010335971,0.0896589,0.0014718118,0.0175794,0.0024067722,0.0040023704,0.026692536],"genre_scores_gemma":[0.9484678,0.026903857,0.008887074,0.00047956026,0.00051715964,0.0024321787,0.0010027487,0.000202594,0.011107062],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9842813,0.0036516003,0.0017766048,0.0014634887,0.00610294,0.002724088],"domain_scores_gemma":[0.9836249,0.0028999213,0.0003848383,0.0009039049,0.010847605,0.0013388691],"candidate_categories":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.015333354,0.0005542901,0.0007700464,0.0014081148,0.003625833,0.00032532524,0.0010826874,0.0005497356,0.0015656493],"category_scores_gemma":[0.001978052,0.00047966174,0.0003069926,0.0068121674,0.0024462764,0.001891685,0.000014333157,0.0015338229,0.00041898421],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.003932378,0.0036229133,0.34824702,0.008437336,0.0006796285,0.012172915,0.08984309,0.0014448354,0.0011165638,0.07316588,0.22753255,0.22980489],"study_design_scores_gemma":[0.0036022002,0.00070392434,0.2588872,0.007900232,0.00018103232,0.000012024897,0.020924142,0.000055373563,0.00024297442,0.0012689378,0.7047645,0.001457454],"about_ca_topic_score_codex":0.03178493,"about_ca_topic_score_gemma":0.11306155,"teacher_disagreement_score":0.47723195,"about_ca_system_score_codex":0.0004408378,"about_ca_system_score_gemma":0.0017702495,"threshold_uncertainty_score":0.9997655},"labels":[],"label_agreement":null},{"id":"W2360880025","doi":"","title":"Experience of Development on BRT Abroad and Enlightenment to China Urban Transport","year":2004,"lang":"en","type":"article","venue":"Urban Transport of China","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"China; Scope (computer science); Enlightenment; Bus rapid transit; Transport engineering; Business; Economic growth; Regional science; Geography; Engineering; Economics; Computer science; Public transport","score_opus":0.00898944781050218,"score_gpt":0.2581884645420389,"score_spread":0.24919901673153674,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2360880025","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9862691,0.000120639605,0.0060411347,0.00039626635,0.00014168737,0.0003938621,0.000035088844,0.000068721194,0.006533472],"genre_scores_gemma":[0.9931918,0.00007539194,0.0059077274,0.00007511369,0.000038512168,0.000023684202,0.00006624342,0.000017961496,0.0006035675],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.99803567,0.000022118438,0.00058821536,0.00035253848,0.00070590526,0.00029557338],"domain_scores_gemma":[0.9993538,0.000015490616,0.00016030262,0.00018175087,0.00006067869,0.00022796476],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002975575,0.00020279044,0.00033037848,0.00016461189,0.00022431769,0.000007250304,0.00022395155,0.00010884535,0.00010219547],"category_scores_gemma":[0.00000907239,0.00019879104,0.00008128367,0.00035740837,0.00021035936,0.00016811118,0.000002202667,0.00010745671,0.0000030243639],"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":[0.00025286066,0.00047423344,0.3818305,0.00010468622,0.000045686465,0.0000250554,0.59663826,0.008346582,0.00064698205,0.009760897,0.000096967815,0.0017772801],"study_design_scores_gemma":[0.0007590665,0.00019237933,0.97464985,0.00022791093,0.000030667303,5.755775e-7,0.0014647824,0.0000031766483,0.011061332,0.000109100365,0.011232544,0.0002686227],"about_ca_topic_score_codex":0.0014741756,"about_ca_topic_score_gemma":0.0006857182,"teacher_disagreement_score":0.5951735,"about_ca_system_score_codex":0.000066166234,"about_ca_system_score_gemma":0.00026089215,"threshold_uncertainty_score":0.81064636},"labels":[],"label_agreement":null},{"id":"W2365263822","doi":"","title":"Discussions on the Promotion of Informationalization Construction in Guangxi Taxi Service Management","year":2013,"lang":"en","type":"article","venue":"Western China Communications Science & Technology","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Transport Canada","funders":"","keywords":"Promotion (chess); Safeguard; Business; Service (business); Scale (ratio); Transport engineering; Engineering; Marketing; Geography; Political science; International trade","score_opus":0.02369679016639558,"score_gpt":0.29989511712007294,"score_spread":0.27619832695367735,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2365263822","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.79877806,0.000018655881,0.013401603,0.13628435,0.00015137016,0.0014983923,0.000010674989,0.00025984494,0.049597044],"genre_scores_gemma":[0.9938948,0.00019784078,0.0055378955,0.000109360706,0.000004360907,0.00012587437,0.000029515004,0.0000038449766,0.000096516975],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99893296,0.00010222509,0.00031600954,0.00013762622,0.0003361845,0.0001749763],"domain_scores_gemma":[0.99874717,0.000060239716,0.00021881812,0.0006948723,0.00024936814,0.00002955755],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006676309,0.000071193244,0.000080476704,0.00077310886,0.0009091885,0.00006658218,0.0011631183,0.00007701605,0.000050863266],"category_scores_gemma":[0.00010631324,0.00005419727,0.000015895488,0.0036335618,0.0013055374,0.00069768017,0.000099898694,0.00015601786,0.000041062012],"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":[0.0000023991452,0.00015253121,0.05498267,0.000011586456,0.0000046026366,9.081826e-8,0.011877119,0.0038514135,0.00023063121,0.9102109,0.00003210783,0.01864391],"study_design_scores_gemma":[0.00069882994,0.0001191966,0.85350627,0.00045410887,0.000029578792,0.000005336799,0.061527565,0.020602819,0.0010393002,0.050454333,0.0111379875,0.0004246502],"about_ca_topic_score_codex":0.0005961013,"about_ca_topic_score_gemma":0.0017063785,"teacher_disagreement_score":0.8597566,"about_ca_system_score_codex":0.000100936195,"about_ca_system_score_gemma":0.0000964019,"threshold_uncertainty_score":0.6992835},"labels":[],"label_agreement":null},{"id":"W2367578684","doi":"","title":"Continuation:An Important Index for the Quality of Urban Mass Transit Service","year":2011,"lang":"en","type":"article","venue":"Urban Mass Transit","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Transport Canada","funders":"","keywords":"Continuation; Transport engineering; Transit (satellite); Index (typography); Engineering; Service (business); Construction engineering; Forensic engineering; Civil engineering; Computer science; Business; Public transport","score_opus":0.06424333854675914,"score_gpt":0.3067736958266096,"score_spread":0.2425303572798505,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2367578684","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13797544,0.00038392382,0.84693533,0.0033090343,0.00063781766,0.0017261611,0.00037708992,0.00031121893,0.008343973],"genre_scores_gemma":[0.9949612,0.000029678191,0.0037241615,0.00031860193,0.000133961,0.000049138118,0.000111752095,0.000021977998,0.00064950023],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.9981349,0.00019541153,0.00063066086,0.00027976476,0.0004603305,0.00029889675],"domain_scores_gemma":[0.9987306,0.00022163283,0.0002604871,0.00026987088,0.00039314985,0.00012425409],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014559305,0.00015731266,0.00026093068,0.00006509094,0.0004112224,0.000041201645,0.0003557293,0.00017099975,0.0003111445],"category_scores_gemma":[0.00003985259,0.00013418475,0.00014139675,0.00037937675,0.00015961361,0.00033877257,0.0000011811752,0.00012533403,0.000003462651],"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":[0.001200791,0.00035333924,0.42247018,0.0002497218,0.00029670075,0.000004718145,0.46024925,0.00481849,0.003600814,0.10363145,0.0013179928,0.0018065728],"study_design_scores_gemma":[0.0047331247,0.00035235172,0.894453,0.00010932664,0.0006435227,6.7226733e-7,0.041774433,0.012944489,0.003007952,0.0075616837,0.0332735,0.001145974],"about_ca_topic_score_codex":0.0037602799,"about_ca_topic_score_gemma":0.008939648,"teacher_disagreement_score":0.8569858,"about_ca_system_score_codex":0.000025352736,"about_ca_system_score_gemma":0.00016231005,"threshold_uncertainty_score":0.5684443},"labels":[],"label_agreement":null},{"id":"W2371549227","doi":"","title":"The Evolution of Worldwide Metro Systems: A Study on Their Scales and Network Indexes","year":2008,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Beijing; Scale (ratio); Population; Metro station; Transport engineering; Geography; Computer science; China; Engineering; Cartography; Demography","score_opus":0.020473330451792688,"score_gpt":0.26551769856591656,"score_spread":0.24504436811412386,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2371549227","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98991483,0.00059465115,0.002501664,0.00015650685,0.00014586953,0.000255297,0.0000017499646,0.00004922345,0.006380202],"genre_scores_gemma":[0.99895763,0.00007011697,0.00007382746,0.000009195431,0.00005838031,0.000008755536,0.0000010999245,0.0000025871764,0.0008183957],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9993596,0.00015228245,0.00013101561,0.00007839451,0.00018092978,0.00009779512],"domain_scores_gemma":[0.9995079,0.00028593102,0.00006257364,0.000063045365,0.000054647244,0.000025881658],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004942111,0.000041596228,0.00007479397,0.000030484529,0.0006864931,0.00002089056,0.000061020684,0.000024535148,0.0000021898252],"category_scores_gemma":[0.00004041004,0.000025277586,0.000014224329,0.0002476386,0.00013163322,0.000058057885,0.0000027105136,0.000036363792,0.0000010079278],"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":[0.000023194107,0.00004066299,0.95163757,0.000001840501,0.000017568951,5.005965e-7,0.010346594,0.014577069,0.0000015041613,0.022607246,0.0004955961,0.00025064326],"study_design_scores_gemma":[0.00015231605,0.00006850291,0.9441789,0.00001642688,0.0000073630504,1.603425e-7,0.054078728,0.0004618801,0.0000022251854,0.00020082948,0.0007854513,0.00004722364],"about_ca_topic_score_codex":0.0022363104,"about_ca_topic_score_gemma":0.00753564,"teacher_disagreement_score":0.043732133,"about_ca_system_score_codex":0.000027244008,"about_ca_system_score_gemma":0.000037660273,"threshold_uncertainty_score":0.52800196},"labels":[],"label_agreement":null},{"id":"W2372236441","doi":"","title":"Influencing Factors of City Bus Service Quality Based on SERVPERF Model","year":2013,"lang":"en","type":"article","venue":"Journal of Transportation Systems Engineering and Information Technology","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Reliability (semiconductor); Dimension (graph theory); Service quality; Service (business); Empathy; Perception; Scale (ratio); Quality (philosophy); Business; Transport engineering; Computer science; Marketing; Psychology; Engineering; Mathematics; Social psychology; Geography","score_opus":0.013622088642102815,"score_gpt":0.24017481400881746,"score_spread":0.22655272536671464,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2372236441","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.87420946,0.00002452799,0.124936216,0.00035101845,0.00013552017,0.00013509492,0.000020133331,0.00007091901,0.00011707758],"genre_scores_gemma":[0.9972958,0.000025227731,0.002577398,0.000049179176,0.000011588093,0.000005651876,0.000024846016,0.0000044472727,0.00000583748],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986993,0.00001957622,0.00080787146,0.000049085505,0.00031350108,0.00011067399],"domain_scores_gemma":[0.99845475,0.00006457417,0.0006048306,0.00007173975,0.00074132613,0.000062752726],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043488768,0.00008872408,0.00021399536,0.00055411964,0.00008139535,0.00003423457,0.00010359951,0.00017169726,0.000007650096],"category_scores_gemma":[0.00008414723,0.00008186315,0.000038898117,0.0004445675,0.000028403676,0.0013926566,7.2037494e-7,0.0001540009,0.0000010791865],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000093473245,0.000010229963,0.017759142,0.0001438683,0.000011804082,1.4926135e-7,0.0056958594,0.96893173,0.00019576563,0.0070028505,0.000012530275,0.00022670427],"study_design_scores_gemma":[0.0013931365,0.00019411117,0.24819301,0.0006055453,0.00005719609,0.000002593589,0.020674888,0.7249593,0.001324786,0.00012983769,0.002099804,0.00036575584],"about_ca_topic_score_codex":0.00046890328,"about_ca_topic_score_gemma":0.000019854207,"teacher_disagreement_score":0.24397242,"about_ca_system_score_codex":0.000039056635,"about_ca_system_score_gemma":0.00009736615,"threshold_uncertainty_score":0.33382824},"labels":[],"label_agreement":null},{"id":"W2372858550","doi":"10.1002/atr.1384","title":"Estimating freeway route travel time distributions with consideration to time‐of‐day, inclement weather, and traffic incidents","year":2016,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Travel time; Meteorology; Snow; Probabilistic logic; Environmental science; Computer science; Transport engineering; Statistics; Geography; Mathematics; Engineering","score_opus":0.008218024326571557,"score_gpt":0.26731093368140074,"score_spread":0.2590929093548292,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2372858550","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8143538,0.000026602816,0.1843848,0.0008246653,0.00008134072,0.00018906787,0.00008548305,0.000017384074,0.00003684297],"genre_scores_gemma":[0.95283556,0.00003196031,0.04689225,0.000027158374,0.000054259603,0.0000048849083,0.000049757833,0.000009923143,0.00009423011],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9987319,0.00006557534,0.00053502544,0.00012371993,0.00040366562,0.00014008148],"domain_scores_gemma":[0.99878,0.00016823322,0.0004786264,0.000056227327,0.00039141765,0.00012547901],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046081826,0.00009989391,0.0001999818,0.00012812215,0.00020193658,0.000023643957,0.00006815615,0.00005088698,0.00005787806],"category_scores_gemma":[0.00009701072,0.00007504934,0.000042566826,0.00021742351,0.00008150718,0.0006337637,7.713355e-7,0.000060894436,0.0000032106527],"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":[0.000781307,0.0003039873,0.02196627,0.000057186484,0.0001771844,0.000037998347,0.18046106,0.69234604,0.044186905,0.0023997298,0.0002421464,0.057040174],"study_design_scores_gemma":[0.007332397,0.0015571136,0.9691133,0.0020765392,0.00048634375,0.0000150849855,0.008391183,0.0016131634,0.00594204,0.0014957212,0.0012652616,0.00071187626],"about_ca_topic_score_codex":0.000038037917,"about_ca_topic_score_gemma":0.00048720732,"teacher_disagreement_score":0.947147,"about_ca_system_score_codex":0.00006917154,"about_ca_system_score_gemma":0.00015326393,"threshold_uncertainty_score":0.30604234},"labels":[],"label_agreement":null},{"id":"W2374593911","doi":"","title":"Thoughts on Reforming the Implementation of Unified Payment by National Treasury System in Railway Units","year":2002,"lang":"en","type":"article","venue":"Railway Transport and Economy","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Treasury; Payment; Finance; Payment system; Capital (architecture); Business; Authorization; Unitary state; Plan (archaeology); Resource (disambiguation); Quarter (Canadian coin); Actuarial science; Computer science; Computer security","score_opus":0.02622606255078551,"score_gpt":0.26120418556513453,"score_spread":0.234978123014349,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2374593911","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9459844,0.00022979671,0.00041993242,0.0012659569,0.0001573541,0.00049826986,0.00009612053,0.000059885224,0.05128828],"genre_scores_gemma":[0.9989059,0.00014272385,0.00003513821,0.00007630837,0.00006435937,0.000027696127,0.00013851756,0.0000065156364,0.00060281635],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991328,0.00006190272,0.00032613863,0.00015322278,0.00017202878,0.00015387722],"domain_scores_gemma":[0.9996469,0.00006111443,0.00011785068,0.00005885003,0.0000672865,0.00004797585],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062889187,0.000087397464,0.00012674739,0.000085279775,0.0002356514,0.00001581216,0.000078471356,0.00005735488,0.000093508235],"category_scores_gemma":[0.0000031826796,0.00007097971,0.000026831272,0.00023144815,0.00006704634,0.00020549691,9.710317e-7,0.00006735561,0.0000034341947],"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":[0.0001622643,0.00035653607,0.52547145,0.0003072325,0.0001884872,0.000014624013,0.2862888,0.008040295,0.00011884346,0.12743977,0.003736023,0.047875695],"study_design_scores_gemma":[0.0060300473,0.0002714355,0.31485423,0.00036192776,0.00011612037,0.000003460823,0.16681598,0.002079087,0.0018076043,0.0005780559,0.5061689,0.0009131011],"about_ca_topic_score_codex":0.0007413727,"about_ca_topic_score_gemma":0.0023826873,"teacher_disagreement_score":0.5024329,"about_ca_system_score_codex":0.000083996114,"about_ca_system_score_gemma":0.000055424203,"threshold_uncertainty_score":0.28944686},"labels":[],"label_agreement":null},{"id":"W2376176557","doi":"","title":"A Planning Model for Determining Optimal CMS Locations on a Freeway-Arterial Network","year":2005,"lang":"en","type":"article","venue":"Communication and Transportati0n Systems Engineering and Information","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Sensitivity (control systems); Computer science; Quality (philosophy); Network planning and design; Transport engineering; Operations research; Mathematical optimization; Simulation; Engineering; Mathematics; Computer network","score_opus":0.019396924510483124,"score_gpt":0.2555098824419335,"score_spread":0.23611295793145037,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2376176557","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13501312,0.0005327217,0.86107284,0.00054120144,0.00018267299,0.00069602125,0.00005987724,0.00030711005,0.0015944309],"genre_scores_gemma":[0.9779046,0.00021217809,0.021103665,0.000089350935,0.00008939195,0.00013831197,0.0003924954,0.000010188804,0.000059775044],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99918354,0.000024434945,0.00038631193,0.00009272,0.00013467648,0.00017834468],"domain_scores_gemma":[0.99942076,0.00012504535,0.00013181411,0.00013752599,0.00009380732,0.00009103784],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042411505,0.00011297386,0.00013557708,0.00010972704,0.00056344306,0.00016457721,0.000093730814,0.00009852097,0.0000019774504],"category_scores_gemma":[0.000029238263,0.00012459076,0.000025103524,0.00010522009,0.000040982395,0.0011989393,0.0000026465893,0.00009522379,0.0000014808538],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021530817,0.0000065461095,0.00023167081,0.000046013793,0.0000100307625,3.8990052e-8,0.027195511,0.9246425,0.0000043760565,0.04546184,0.00009610986,0.0022838153],"study_design_scores_gemma":[0.0005463202,0.000025083997,0.002292841,0.00018608329,0.000021313359,8.3041476e-7,0.0017315178,0.96731555,0.0000030449332,0.000019752759,0.02770671,0.00015093062],"about_ca_topic_score_codex":0.00005699228,"about_ca_topic_score_gemma":0.000050398678,"teacher_disagreement_score":0.8428915,"about_ca_system_score_codex":0.000025927899,"about_ca_system_score_gemma":0.000042468924,"threshold_uncertainty_score":0.50806636},"labels":[],"label_agreement":null},{"id":"W2376806263","doi":"","title":"Integrated Model of Urban Transportation and Land Use","year":2013,"lang":"en","type":"article","venue":"Journal of Inner Mongolia University","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Beijing; TRIPS architecture; Sample (material); Monte Carlo method; Transit (satellite); Transport engineering; Computer science; Econometrics; Operations research; Geography; Statistics; Engineering; Mathematics; Public transport","score_opus":0.017949654317406586,"score_gpt":0.2125217568094963,"score_spread":0.19457210249208973,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2376806263","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99011105,0.000010063601,0.009303831,0.00021169189,0.000032127053,0.000040677944,0.000014511073,0.0000068617896,0.00026916346],"genre_scores_gemma":[0.9946763,0.00009644524,0.0045300573,0.000013480368,0.000011444946,2.7720564e-8,0.0000058899445,0.0000020342263,0.0006643413],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9996069,0.00003715903,0.00012893365,0.000040568128,0.0001287601,0.000057662193],"domain_scores_gemma":[0.99932605,0.000029978894,0.00017999591,0.00002614579,0.00037254946,0.00006525887],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011042458,0.000036287624,0.00008797843,0.000107380365,0.000080138096,0.00001583574,0.000058933572,0.000048808757,0.00002384816],"category_scores_gemma":[0.000021056228,0.000033645414,0.000029910758,0.00014502542,0.0000755006,0.00071018573,8.9598007e-7,0.00007170299,3.8161272e-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":[0.00011566226,0.000067687455,0.8975676,0.0000126434325,0.000047103986,0.000009680148,0.035830118,0.05655991,0.00059660693,0.0057708966,0.002697048,0.00072507694],"study_design_scores_gemma":[0.0012984693,0.000097045966,0.9584852,0.00008662995,0.00012033587,0.0000010750077,0.015173091,0.01947447,0.00015170133,0.00044371744,0.0045047016,0.00016355299],"about_ca_topic_score_codex":0.0028188876,"about_ca_topic_score_gemma":0.0007829026,"teacher_disagreement_score":0.060917646,"about_ca_system_score_codex":0.00002343282,"about_ca_system_score_gemma":0.00008012388,"threshold_uncertainty_score":0.42613333},"labels":[],"label_agreement":null},{"id":"W2377494236","doi":"","title":"Inflation and Logistic Transportation Cost Control","year":2013,"lang":"en","type":"article","venue":"Journal of Eastern Liaoning University","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Inflation (cosmology); Quarter (Canadian coin); Economics; Logistic regression; Falling (accident); Control (management); Monetary economics; Medicine; Mathematics; Statistics; Geography","score_opus":0.01974747656365958,"score_gpt":0.24310112590644944,"score_spread":0.22335364934278987,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2377494236","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92160314,0.00002386492,0.07591325,0.0006674383,0.00013159754,0.00010110419,0.000004099355,0.000014598153,0.0015409],"genre_scores_gemma":[0.9984802,0.000031980006,0.00085451343,0.000035862744,0.000052469946,6.701764e-8,0.0000034918794,0.000002179189,0.00053925667],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9995417,0.00006455572,0.00012421448,0.000047651916,0.00014598042,0.00007587992],"domain_scores_gemma":[0.9993624,0.000070858434,0.00021803855,0.000022096707,0.0002465968,0.00008002523],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019613646,0.00003822531,0.000078378194,0.00008687135,0.0001796744,0.000036803955,0.00005481414,0.000044109478,0.00006419188],"category_scores_gemma":[0.00003958366,0.000040004797,0.00002596986,0.00009325565,0.000065483815,0.0006135625,6.989733e-7,0.00007758737,0.0000048219076],"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":[0.000079404825,0.0000234841,0.9618849,0.000008877883,0.000029866773,0.000019493666,0.014048801,0.015227336,0.00007384469,0.0043628723,0.0001702398,0.004070854],"study_design_scores_gemma":[0.0010498668,0.00005607464,0.9832867,0.000056659763,0.000063469015,0.0000014959152,0.0077059516,0.002045175,0.0000029291916,0.00018956389,0.0054665785,0.00007555798],"about_ca_topic_score_codex":0.00046262544,"about_ca_topic_score_gemma":0.00025844993,"teacher_disagreement_score":0.076877035,"about_ca_system_score_codex":0.00003473199,"about_ca_system_score_gemma":0.000060114824,"threshold_uncertainty_score":0.16313483},"labels":[],"label_agreement":null},{"id":"W2384623059","doi":"","title":"Study of the Planning and Developing Model of TOD Traffic Corridor","year":2008,"lang":"en","type":"article","venue":"Journal of Transportation Engineering and Information","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"WSP (Canada)","funders":"","keywords":"Traffic planning; Transport engineering; Traffic flow (computer networking); Urban planning; Transportation planning; Computer science; Engineering; Civil engineering; Computer security","score_opus":0.020755638251858943,"score_gpt":0.23634676384610576,"score_spread":0.2155911255942468,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2384623059","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9696148,0.000039499304,0.03009662,0.000038355054,0.000078395075,0.00007658139,0.0000047114863,0.000007460935,0.000043576547],"genre_scores_gemma":[0.9976263,0.00008235078,0.0022613336,0.000009014006,0.000009307368,7.5839256e-7,0.0000040782984,0.0000021838835,0.0000046494442],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99926645,0.000010750218,0.00043047493,0.000023578294,0.0002196685,0.00004908411],"domain_scores_gemma":[0.9994234,0.000029656672,0.0003269179,0.000024729785,0.00016983421,0.000025471772],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002236278,0.000044179145,0.000104482155,0.000116567186,0.00010109062,0.0000068842937,0.000043965232,0.00003236325,6.4071315e-7],"category_scores_gemma":[0.000029168295,0.000035912395,0.000021476037,0.00014708628,0.000027293125,0.0008292341,5.6475335e-7,0.0000648165,2.0922755e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014053583,0.000010608959,0.009715926,0.000030602234,0.000010203425,2.7337458e-7,0.09710029,0.89231056,0.000031498483,0.0004501393,0.000004676389,0.00032117957],"study_design_scores_gemma":[0.0014305274,0.00016967,0.8290576,0.0002666548,0.00006571904,0.000007968736,0.026858566,0.1413858,0.0002880156,0.000015514031,0.00031658425,0.00013740336],"about_ca_topic_score_codex":0.000016881198,"about_ca_topic_score_gemma":0.0000121736475,"teacher_disagreement_score":0.81934166,"about_ca_system_score_codex":0.000009554886,"about_ca_system_score_gemma":0.0000741346,"threshold_uncertainty_score":0.1464465},"labels":[],"label_agreement":null},{"id":"W2391265413","doi":"","title":"Impacts of the Introduction of an Express Transit Service in Waterloo Region","year":2008,"lang":"en","type":"dissertation","venue":"UWSpace (University of Waterloo)","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":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Transport engineering; Transit (satellite); Service (business); Geography; Regional science; Telecommunications; Public transport; Computer science; Business; Engineering; Marketing","score_opus":0.011553560352971982,"score_gpt":0.21636202615455066,"score_spread":0.2048084658015787,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2391265413","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9979841,0.000029870918,0.0000129806085,0.0012985175,0.00023503136,0.00024693806,0.000025243788,0.000021490145,0.00014582791],"genre_scores_gemma":[0.9708971,0.00027289666,0.0004404798,0.000007636075,0.000051884057,2.8814583e-7,0.0005906991,0.000012757902,0.027726289],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.99888706,0.00018212751,0.00018992618,0.0002171714,0.00036366054,0.00016006659],"domain_scores_gemma":[0.9989692,0.000017805109,0.00041058665,0.00022063871,0.00033053607,0.00005125544],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018353091,0.0001221796,0.00028369133,0.00022896398,0.00019218336,0.000005668115,0.0003415413,0.00027755753,0.000041779254],"category_scores_gemma":[0.000012910239,0.00012271244,0.00009647055,0.0005077331,0.00013711593,0.00039790195,0.0000052727437,0.00015447717,0.0000010137438],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003769872,0.000119114164,0.0048558787,0.0003049824,0.000027789058,0.000005832797,0.98338854,0.0053660423,0.0046696616,0.00017244236,0.0002697237,0.00044298155],"study_design_scores_gemma":[0.0011227143,0.00012618267,0.17234282,0.00048572285,0.00016556197,0.0000015742097,0.81183517,0.00024772427,0.012672145,0.00009662876,0.0005909396,0.0003128486],"about_ca_topic_score_codex":0.3030809,"about_ca_topic_score_gemma":0.57851404,"teacher_disagreement_score":0.27543315,"about_ca_system_score_codex":0.000049333154,"about_ca_system_score_gemma":0.00016164946,"threshold_uncertainty_score":0.70155996},"labels":[],"label_agreement":null},{"id":"W2392494442","doi":"","title":"Canadian Urban Transport","year":2000,"lang":"en","type":"article","venue":"Journal of Guangdong University of Technology","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Interurban; Transport engineering; Transit (satellite); Urban rail transit; Urban transit; Light rail transit; Public transport; Passenger transport; Rail transit; Geography; Engineering","score_opus":0.005998941901523456,"score_gpt":0.20434636453751204,"score_spread":0.1983474226359886,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2392494442","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9654104,0.00009243319,0.0004711853,0.006594409,0.00007679594,0.000036346035,0.0000079127285,0.000029875195,0.027280627],"genre_scores_gemma":[0.99460185,0.00019954964,0.0023280724,0.000020111447,0.000017660963,6.6416805e-9,0.000001779804,0.0000020956036,0.002828856],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996083,0.000016765263,0.000106432606,0.00004405075,0.00011390727,0.00011057436],"domain_scores_gemma":[0.99962217,0.000009495759,0.00010447211,0.000048064656,0.00013494986,0.00008087443],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015973808,0.000032132593,0.00010343656,0.00036904452,0.00016537802,0.0000020595828,0.00021819453,0.00012794703,0.0008650887],"category_scores_gemma":[0.000010653554,0.00003818393,0.000051195686,0.00033456224,0.00016339541,0.0001254421,8.306556e-7,0.00011103806,0.000006080578],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002221569,0.00014382358,0.82190835,0.00001924418,0.00017235421,0.0008407243,0.03677419,0.005986685,0.00015157474,0.047182214,0.0121809095,0.07441776],"study_design_scores_gemma":[0.0007412966,0.00015618284,0.07888999,0.00006628567,0.000085192914,0.000017461427,0.017608257,0.000028316801,0.00007064137,0.00087487523,0.9013265,0.00013505433],"about_ca_topic_score_codex":0.04525662,"about_ca_topic_score_gemma":0.14572752,"teacher_disagreement_score":0.88914555,"about_ca_system_score_codex":0.00008440622,"about_ca_system_score_gemma":0.0003869454,"threshold_uncertainty_score":0.9611011},"labels":[],"label_agreement":null},{"id":"W2393960724","doi":"","title":"The Recoverable Canadian Traveller Problem Based on Limited Provision Information","year":2009,"lang":"en","type":"article","venue":"Systems Engineering","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":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Intersection (aeronautics); Operations research; Point (geometry); Computer science; Process (computing); Path (computing); Transport engineering; Engineering; Mathematics","score_opus":0.005666534218604783,"score_gpt":0.1979597786716871,"score_spread":0.19229324445308232,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2393960724","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05098545,0.00056338904,0.45312467,0.019218886,0.007712302,0.0071535474,0.000103561055,0.0025283997,0.4586098],"genre_scores_gemma":[0.99876624,0.000010130007,0.0004049494,0.000080611426,0.0000553674,0.000013480189,0.000035394463,0.000003784357,0.0006300615],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99940056,0.000024765888,0.00014948369,0.000055052897,0.0001835746,0.00018654599],"domain_scores_gemma":[0.9996904,0.00004999839,0.00003889547,0.000071628354,0.00006140722,0.00008763503],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040524718,0.000055929853,0.000048801074,0.00010452767,0.00039558607,0.00023205052,0.00007048232,0.000059828762,0.0000032154596],"category_scores_gemma":[0.00006092668,0.00004520685,0.000017075121,0.00021361768,0.0000052554374,0.000329806,3.2173534e-7,0.000069708694,0.000012124604],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000003980545,0.0000022052152,0.00009085078,0.000008464922,0.0000016345923,3.9647335e-7,0.0017910076,0.986524,0.0000065705685,0.008697957,0.00055099354,0.002321949],"study_design_scores_gemma":[0.00021171864,0.00005543791,0.005164878,0.00015714951,0.000004077574,2.549332e-7,0.0007915867,0.5797906,0.000015888476,0.000013334444,0.41364673,0.00014838215],"about_ca_topic_score_codex":0.017745644,"about_ca_topic_score_gemma":0.005058248,"teacher_disagreement_score":0.9477808,"about_ca_system_score_codex":0.00012380537,"about_ca_system_score_gemma":0.00014992092,"threshold_uncertainty_score":0.9887953},"labels":[],"label_agreement":null},{"id":"W2394648377","doi":"10.5539/jmr.v8n3p94","title":"A New Heuristic Method for Transportation Network and Land Use Problem","year":2016,"lang":"en","type":"article","venue":"Journal of Mathematics Research","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":"Heuristic; Integer programming; Mathematical optimization; Flow network; Work (physics); Network planning and design; Computer science; Resolution (logic); Traffic network; Operations research; Transport engineering; Mathematics; Artificial intelligence; Engineering","score_opus":0.13359419341141912,"score_gpt":0.444129317565574,"score_spread":0.31053512415415485,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2394648377","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012862458,0.00008746306,0.9839647,0.00257125,0.000059035043,0.0002403373,0.000010012792,0.000009269324,0.00019551608],"genre_scores_gemma":[0.053451445,0.00040691273,0.9430903,0.000012967198,0.00029935304,0.0000054204947,0.0000023985579,0.000012529996,0.0027186293],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9988133,0.00013607286,0.00030442158,0.00006589087,0.0004883476,0.00019201776],"domain_scores_gemma":[0.9969311,0.002217681,0.00016571586,0.000046150744,0.0005030261,0.00013632506],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003771098,0.00004347207,0.0001292714,0.00010360874,0.00020540881,0.00008256968,0.00009132737,0.00005601429,0.000033194487],"category_scores_gemma":[0.0006244229,0.000028249757,0.000037927173,0.00016971803,0.00004881708,0.0002435396,0.0000015729252,0.000090957554,0.0000013396531],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008591244,0.00048587722,0.0704182,0.0010144167,0.0003689023,0.000061972656,0.14526935,0.011021894,0.0012743244,0.49091613,0.14557531,0.13273448],"study_design_scores_gemma":[0.0067493627,0.0012573748,0.03663179,0.0030299295,0.00028266732,0.000031837404,0.007332351,0.004927504,0.0002868298,0.71835035,0.22055055,0.0005694248],"about_ca_topic_score_codex":0.000103607046,"about_ca_topic_score_gemma":0.0004777699,"teacher_disagreement_score":0.22743423,"about_ca_system_score_codex":0.00002778909,"about_ca_system_score_gemma":0.00023789238,"threshold_uncertainty_score":0.15798593},"labels":[],"label_agreement":null},{"id":"W2395147108","doi":"10.11159/icte17.125","title":"Impact of Exclusive Bus Lanes on Traffic Performance in Urban Areas","year":2017,"lang":"en","type":"article","venue":"Proceedings of the World Congress on Civil, Structural, and Environmental Engineering","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Transport engineering; Computer science; Telecommunications; Civil engineering; Engineering","score_opus":0.007112875307855127,"score_gpt":0.22974312700625793,"score_spread":0.2226302516984028,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2395147108","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99828655,0.000033236545,3.0261216e-7,0.000047677157,0.00014740697,0.00010745875,0.000012026288,0.000011895014,0.0013534577],"genre_scores_gemma":[0.9994868,0.00010138656,0.000042886943,0.0000037931259,0.000026020527,0.0000033340511,0.0000026981409,0.000007589701,0.00032548283],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9994541,0.0000024684964,0.00012815496,0.000116104835,0.00016788002,0.00013132712],"domain_scores_gemma":[0.99971545,0.000023387267,0.00015412031,0.00006356038,0.0000055780665,0.000037902446],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006118221,0.0001003477,0.00012654002,0.00007643381,0.00021542977,0.00003451452,0.00019696503,0.00003260881,0.000020194593],"category_scores_gemma":[0.00002030897,0.000075831595,0.000041283933,0.0000521028,0.00014676567,0.00019938598,0.000016755312,0.000096428,2.2809158e-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":[0.00008950476,0.000022245778,0.81230783,0.00006914388,0.000027512928,5.6337143e-7,0.0029467207,0.18012787,0.0013311009,0.001362232,0.000038124937,0.0016771525],"study_design_scores_gemma":[0.00028500953,0.00005090199,0.9899265,0.00021549546,0.000008849596,5.3060603e-7,0.00026161433,0.007975453,0.0010778253,0.000013708608,0.000084078936,0.00010004104],"about_ca_topic_score_codex":0.00004678102,"about_ca_topic_score_gemma":0.000037422593,"teacher_disagreement_score":0.17761867,"about_ca_system_score_codex":0.000040041497,"about_ca_system_score_gemma":0.0000052502073,"threshold_uncertainty_score":0.30923226},"labels":[],"label_agreement":null},{"id":"W2395541482","doi":"10.1080/03155986.2006.11732742","title":"Choix De Sites D’Entrepôts Pour Les Autobus De Transport Urbain : Le Cas Du Reséau De Transport De La Capitale","year":2006,"lang":"fr","type":"article","venue":"INFOR Information Systems and Operational Research","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"Université Laval","funders":"","keywords":"Humanities; Physics; Political science; Art","score_opus":0.02605300835239865,"score_gpt":0.32419764484607444,"score_spread":0.2981446364936758,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2395541482","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.90307194,0.0016737782,0.046773013,0.006285603,0.00019255104,0.00079489313,0.0004996538,0.00011779622,0.040590744],"genre_scores_gemma":[0.98210424,0.0005485435,0.0022728716,0.00016005679,0.00050905714,0.00012759143,0.000924739,0.000018037168,0.013334847],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.996596,0.0005407065,0.00086072204,0.00019278032,0.001030996,0.0007788273],"domain_scores_gemma":[0.9980585,0.0005703615,0.0001641948,0.00013052106,0.00076676044,0.00030967005],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0061137625,0.00021002839,0.00023627156,0.00034276478,0.0015528653,0.0005597322,0.00022655034,0.00046700373,0.00019506356],"category_scores_gemma":[0.00021353725,0.00022583408,0.000093566894,0.0005045526,0.0005256593,0.0023363712,0.000008503312,0.00046069833,0.00005716698],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008153515,0.000103334074,0.32020223,0.00033772073,0.000041005176,0.000031934367,0.061148766,0.3350057,0.00011683937,0.27316582,0.008759382,0.0010057371],"study_design_scores_gemma":[0.0015079579,0.00008820429,0.5299212,0.00030091856,0.000036424743,0.00013525903,0.023252664,0.12774922,0.00017838014,0.0007287839,0.31567273,0.00042826025],"about_ca_topic_score_codex":0.10632688,"about_ca_topic_score_gemma":0.0064203506,"teacher_disagreement_score":0.30691335,"about_ca_system_score_codex":0.000678287,"about_ca_system_score_gemma":0.003948229,"threshold_uncertainty_score":0.999747},"labels":[],"label_agreement":null},{"id":"W2396843479","doi":"10.1080/03155986.2003.11732672","title":"L’Optimisation Des Fréquences D’un Réseau De Transport En Commun Moyennement Congestionné","year":2003,"lang":"fr","type":"article","venue":"INFOR Information Systems and Operational Research","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"Université de Montréal; Université du Québec à Montréal","funders":"","keywords":"Transit (satellite); Sensitivity (control systems); Transit time; Computer science; Optimization problem; Mathematical optimization; Bilevel optimization; Operator (biology); Public transport; Transport engineering; Mathematics; Engineering; Chemistry","score_opus":0.06096277160879435,"score_gpt":0.3515452994676507,"score_spread":0.2905825278588563,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2396843479","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.59508216,0.01026466,0.09499214,0.015946144,0.0031955864,0.0044557513,0.0007262033,0.00028433808,0.275053],"genre_scores_gemma":[0.9859936,0.0031391925,0.005127923,0.00024997297,0.00015817083,0.00020428258,0.000513976,0.000011255341,0.0046016118],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99605405,0.00095799466,0.0009016199,0.00017302769,0.0013514713,0.0005618238],"domain_scores_gemma":[0.997203,0.0006145743,0.00017527098,0.00015439575,0.0015599582,0.00029281815],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.007036824,0.0001944074,0.00021357361,0.00034633954,0.0022840882,0.000672636,0.00020088127,0.00032761664,0.00051083206],"category_scores_gemma":[0.00056735665,0.00019931245,0.00005526874,0.00075286854,0.00077306374,0.003957187,0.000011297838,0.00042417995,0.00014372],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003373831,0.000057572946,0.017312028,0.00027116676,0.00005443019,0.0000021315923,0.06492288,0.103439115,0.000010762394,0.80277497,0.0010994463,0.01002179],"study_design_scores_gemma":[0.0014421489,0.0002536249,0.03761869,0.0005388192,0.0000438021,0.000045688616,0.07198563,0.033126682,0.00013319925,0.0023731508,0.8519286,0.00050999265],"about_ca_topic_score_codex":0.006610256,"about_ca_topic_score_gemma":0.0004710009,"teacher_disagreement_score":0.8508291,"about_ca_system_score_codex":0.0005062168,"about_ca_system_score_gemma":0.0012180507,"threshold_uncertainty_score":0.9992774},"labels":[],"label_agreement":null},{"id":"W2400800028","doi":"10.1080/03155986.2005.11732713","title":"A Note on Postman Problems with Zigzag Service","year":2005,"lang":"en","type":"article","venue":"INFOR Information Systems and Operational Research","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Zigzag; Arc routing; Service (business); Generalization; Arc (geometry); Flexibility (engineering); Transformation (genetics); Computer science; Travelling salesman problem; Mathematics; Routing (electronic design automation); Mathematical optimization; Combinatorics; Operations research; Business; Computer network; Geometry; Marketing; Mathematical analysis; Chemistry","score_opus":0.050639587122370724,"score_gpt":0.36002794957678963,"score_spread":0.3093883624544189,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2400800028","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.281058,0.00017415399,0.014344121,0.03811292,0.00042086036,0.0044395104,0.00022409238,0.0004051473,0.6608212],"genre_scores_gemma":[0.9959926,0.000042413216,0.0007554621,0.0009040204,0.00018416945,0.00010656476,0.0002330378,0.000005227725,0.0017765188],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.998091,0.00009119121,0.00031426048,0.000093120674,0.0011718505,0.00023858264],"domain_scores_gemma":[0.9982585,0.00016227126,0.00006845764,0.00008958068,0.0012910418,0.00013015758],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015967017,0.00007779241,0.00008423945,0.00026716836,0.0010315583,0.0006801577,0.00010950094,0.000089569185,0.00005393038],"category_scores_gemma":[0.00010484864,0.000061781786,0.0000112391945,0.00047378897,0.0000840903,0.002346184,0.000008280897,0.00018107689,0.0002881755],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018666066,0.000042943586,0.003461855,0.00013140812,0.000022216553,8.37159e-7,0.09252211,0.34173232,0.000015034869,0.5482896,0.004503432,0.009091574],"study_design_scores_gemma":[0.00067414466,0.00014308488,0.005399348,0.00013410192,0.00000277976,0.0000040937357,0.0067522093,0.032487825,0.000015366686,0.000022776576,0.9542084,0.00015589714],"about_ca_topic_score_codex":0.0020825274,"about_ca_topic_score_gemma":0.0015963085,"teacher_disagreement_score":0.94970495,"about_ca_system_score_codex":0.00009551667,"about_ca_system_score_gemma":0.0004197541,"threshold_uncertainty_score":0.79340166},"labels":[],"label_agreement":null},{"id":"W2404185686","doi":"","title":"Developing a GIS-based Decision Support System for Transportation System Planning","year":2006,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Decision support system; Geographic information system; Environmental planning; Information system; Transportation planning; Decision system; Computer science; Business; Environmental resource management; Geography; Transport engineering; Operations research; Engineering; Environmental science; Remote sensing; Data mining","score_opus":0.02875873162220286,"score_gpt":0.30653566409129324,"score_spread":0.27777693246909035,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2404185686","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03764388,0.00001991528,0.9531053,0.00016850115,0.000409403,0.00045576505,0.000045579753,0.00064791366,0.0075037796],"genre_scores_gemma":[0.905624,4.9597065e-7,0.093138605,0.000040428287,0.0001262008,0.000065160624,0.0005912639,0.000015420688,0.00039847512],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99869883,0.000038258746,0.00041596536,0.00023157817,0.00035430153,0.00026106622],"domain_scores_gemma":[0.99928814,0.00021979227,0.0001443547,0.000078431265,0.00021052039,0.000058756497],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00059175765,0.00010964623,0.00015631996,0.00013096893,0.00062112394,0.000084632025,0.00010670473,0.00012290703,0.00001588674],"category_scores_gemma":[0.000018491557,0.000108748514,0.00007249079,0.00028962083,0.00003065399,0.00019568404,5.2654076e-7,0.00003960519,0.000010441802],"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":[0.00023043477,0.000038781593,0.04015312,0.00056661386,0.000018990644,0.000027786538,0.005537385,0.29669335,0.0000665033,0.6513147,0.003030265,0.0023220952],"study_design_scores_gemma":[0.01570051,0.00052368926,0.29236993,0.0066204714,0.0006643464,0.000009860407,0.17010412,0.20721522,0.007968607,0.0019217709,0.2928451,0.004056365],"about_ca_topic_score_codex":0.0010835897,"about_ca_topic_score_gemma":0.0015552192,"teacher_disagreement_score":0.86798006,"about_ca_system_score_codex":0.0002276299,"about_ca_system_score_gemma":0.00031560485,"threshold_uncertainty_score":0.47772458},"labels":[],"label_agreement":null},{"id":"W2412151726","doi":"10.13189/aeb.2016.040603","title":"Economic Evaluation of Climate Change Impacts on Road Transportation in Atlantic Canada","year":2016,"lang":"en","type":"article","venue":"Advances in Economics and Business","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Climate change; Economic impact analysis; Environmental resource management; Natural resource economics; Geography; Business; Environmental science; Economics; Oceanography; Engineering; Civil engineering","score_opus":0.017838552543540588,"score_gpt":0.28174815039441176,"score_spread":0.26390959785087115,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2412151726","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99777246,0.00024180769,0.000013979437,0.00048113376,0.00023337599,0.00015582956,0.000041560932,0.000003860949,0.0010559671],"genre_scores_gemma":[0.96971,0.030143425,0.00003344862,0.000031504107,0.000032774304,0.00001502401,0.000027570944,0.000004077687,0.0000021993126],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9994516,0.000028030394,0.00021352425,0.00012647387,0.000058729296,0.000121643905],"domain_scores_gemma":[0.99971944,0.000049695875,0.00012101259,0.000044533703,0.000041641386,0.000023690805],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037461612,0.000054984197,0.00010792055,0.000080056685,0.00003599882,0.000007532027,0.000040372775,0.000032447497,0.0000142201125],"category_scores_gemma":[0.00002034906,0.000048795857,0.0000073316337,0.00007413993,0.00003664866,0.0005045733,8.923065e-7,0.000017161336,5.6006263e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008093878,0.000020475147,0.65047663,0.000022063714,0.0000023542968,9.402804e-7,0.0014583812,0.06032057,0.0000025506986,0.010046358,0.0000015437402,0.2775672],"study_design_scores_gemma":[0.00070789683,0.000010599476,0.9950542,0.00012072913,0.000006179351,7.404616e-8,0.0002833284,0.0013776078,0.000010642512,0.0006023754,0.0017388964,0.000087499386],"about_ca_topic_score_codex":0.21832193,"about_ca_topic_score_gemma":0.97593856,"teacher_disagreement_score":0.75761664,"about_ca_system_score_codex":0.0001875617,"about_ca_system_score_gemma":0.000225896,"threshold_uncertainty_score":0.78688335},"labels":[],"label_agreement":null},{"id":"W2417045655","doi":"10.1007/978-3-319-13111-5_22","title":"The Design of Rapid Transit Networks","year":2015,"lang":"en","type":"book-chapter","venue":"Location Science","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":20,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Transit (satellite); Transit system; Network planning and design; Transport engineering; Computer science; Operations research; Systems design; Public transport; Engineering; Telecommunications","score_opus":0.07461130015797435,"score_gpt":0.30696205918083985,"score_spread":0.2323507590228655,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2417045655","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000002351554,0.0013299524,0.5965372,0.0006395167,0.0005369486,0.0003408549,0.0000033985555,0.00005501468,0.4005548],"genre_scores_gemma":[0.42683843,0.00350525,0.0051728915,0.00018012627,0.00043398215,0.000024148567,0.000047113383,0.000038653947,0.5637594],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99845546,0.000046869423,0.00026150228,0.00020623095,0.00085729704,0.00017264656],"domain_scores_gemma":[0.9982392,0.00020349775,0.00024566756,0.00020219843,0.0010133816,0.00009606194],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030375838,0.000089456415,0.000110552945,0.00009227532,0.00078190473,0.00008607596,0.0004851145,0.00012115437,0.000058245532],"category_scores_gemma":[0.000159136,0.000070830625,0.000028757047,0.0003151778,0.0015408972,0.00021169517,0.0000048864536,0.00011231809,0.000014940549],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018901459,0.0000052570076,0.0000090729,0.0000042915512,0.000006104042,3.9835464e-7,0.005458653,0.3730209,0.000005701424,0.58941364,0.0022423395,0.029814731],"study_design_scores_gemma":[0.0004446851,0.0001706482,0.00056222663,0.00029935903,0.00011217108,0.0000013134137,0.0019834412,0.06526267,0.000046308433,0.031315777,0.8991225,0.0006789208],"about_ca_topic_score_codex":0.00006132964,"about_ca_topic_score_gemma":0.00010832331,"teacher_disagreement_score":0.89688015,"about_ca_system_score_codex":0.00007539862,"about_ca_system_score_gemma":0.0012878474,"threshold_uncertainty_score":0.60138583},"labels":[],"label_agreement":null},{"id":"W2417339009","doi":"10.5555/2500004.2500014","title":"A collaborative multi-touch, multi-display, urban futures tool","year":2013,"lang":"en","type":"article","venue":"Annual Simulation Symposium","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Futures contract; Computer science; Dashboard; Architecture; Human–computer interaction; Software; Simple (philosophy); Work (physics); Data science; World Wide Web; Engineering","score_opus":0.01309606002792622,"score_gpt":0.305609293575502,"score_spread":0.29251323354757575,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2417339009","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7957173,0.00034691376,0.17419223,0.0062521608,0.0025637944,0.0044960016,0.0006149363,0.0015986937,0.014217981],"genre_scores_gemma":[0.98745084,0.00002852773,0.0051861773,0.0003964883,0.0002832245,0.00006546417,0.00016929487,0.000019471738,0.0064005367],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9984991,0.00018704141,0.0003297241,0.00029415183,0.00038388008,0.00030610836],"domain_scores_gemma":[0.99850446,0.00020363221,0.00016203188,0.000142749,0.00085027725,0.00013682709],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001921361,0.00016130273,0.00016095785,0.00010890254,0.0007890841,0.00019088807,0.0001518119,0.00018665915,0.00034072835],"category_scores_gemma":[0.00012701732,0.00015859022,0.000060311908,0.00051738304,0.000120726014,0.0011121882,0.000009962314,0.000113315524,0.00025654305],"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":[0.00004917807,0.0002619332,0.069617555,0.000014380846,0.0000510145,0.000004245297,0.18952261,0.7267097,0.0004042328,0.00444259,0.0073495633,0.0015729496],"study_design_scores_gemma":[0.0037759773,0.00015974602,0.36443216,0.000070937574,0.000090803536,4.2219858e-7,0.062365036,0.3600268,0.0003135343,0.0001367142,0.20741937,0.0012085035],"about_ca_topic_score_codex":0.0013732427,"about_ca_topic_score_gemma":0.0007523572,"teacher_disagreement_score":0.36668295,"about_ca_system_score_codex":0.00006643713,"about_ca_system_score_gemma":0.00013926727,"threshold_uncertainty_score":0.6467122},"labels":[],"label_agreement":null},{"id":"W2417785606","doi":"10.1080/21680566.2016.1190674","title":"Agent-based day-to-day adjustment process to evaluate dynamic flexible transport service policies","year":2016,"lang":"en","type":"article","venue":"Transportmetrica B Transport Dynamics","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":63,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Toronto Metropolitan University","funders":"University of Toronto; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Process (computing); Public transport; Computer science; Service (business); Transport engineering; Transit (satellite); Operations research; Simulation; Distributed computing; Engineering; Business","score_opus":0.023646396264594294,"score_gpt":0.3187395357384431,"score_spread":0.2950931394738488,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2417785606","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.41744143,0.00010805055,0.5587349,0.013447633,0.0009234904,0.002505772,0.0021698105,0.0013952891,0.0032736151],"genre_scores_gemma":[0.9872629,0.00025630122,0.004943647,0.0025059371,0.00013475894,0.00037192515,0.0013948034,0.00017564514,0.0029540844],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9930098,0.00020528711,0.0014455857,0.0013871912,0.0022682243,0.0016839358],"domain_scores_gemma":[0.99617314,0.00036726892,0.00029981966,0.00078053516,0.00094077043,0.0014384845],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0018965236,0.0008112918,0.0008967938,0.0017361314,0.0007800622,0.000050964198,0.0011917955,0.00046128625,0.00065429515],"category_scores_gemma":[0.000094957555,0.000719503,0.0004116894,0.00764365,0.00023246858,0.0006230819,0.0000045903084,0.0003044537,0.00023321118],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001725869,0.0020974497,0.40656668,0.00082451664,0.0005237568,0.00027240365,0.07399704,0.4494584,0.00081873155,0.0128846895,0.00030769824,0.05052279],"study_design_scores_gemma":[0.0044634566,0.0006040418,0.9592896,0.0008756115,0.0010359831,0.000002792101,0.0029451915,0.0044474825,0.00055309106,0.0005410922,0.022482434,0.002759194],"about_ca_topic_score_codex":0.0026495766,"about_ca_topic_score_gemma":0.04959069,"teacher_disagreement_score":0.5698215,"about_ca_system_score_codex":0.00085445173,"about_ca_system_score_gemma":0.0014602913,"threshold_uncertainty_score":0.9995256},"labels":[],"label_agreement":null},{"id":"W2440655608","doi":"","title":"Modeling And Optimizing Route Choice For Multimodal Transportation Networks","year":2016,"lang":"en","type":"dissertation","venue":"Spectrum Research Repository (Concordia University)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Transport engineering; Traffic congestion; Computer science; Schedule; Routing (electronic design automation); Probabilistic logic; Vehicle routing problem; Flow network; Dijkstra's algorithm; Operations research; Shortest path problem; Graph; Engineering; Computer network; Mathematical optimization","score_opus":0.029861922937649676,"score_gpt":0.3072234006994945,"score_spread":0.27736147776184483,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2440655608","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8581279,0.0005548149,0.07248936,0.0006864989,0.0020634164,0.002105567,0.00013344418,0.00041587997,0.06342313],"genre_scores_gemma":[0.9564819,0.0008928821,0.00054007815,0.0000060761768,0.0006623597,0.000014827337,0.0007134751,0.00005089765,0.040637553],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974972,0.00030805517,0.00028833922,0.00064364314,0.0005829078,0.0006798663],"domain_scores_gemma":[0.99838597,0.0004496636,0.00015808875,0.00019324996,0.00052104733,0.00029200705],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0006769012,0.00022981883,0.0002972544,0.0006560396,0.0023478416,0.00019618287,0.00033967514,0.00052430836,0.000018043265],"category_scores_gemma":[0.00008653661,0.00025963105,0.00014926867,0.0005269603,0.00018820156,0.0005894346,0.0000062669055,0.0005041266,0.0000017900929],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.011269685,0.00061058585,0.200428,0.001952368,0.0018567329,0.0008387891,0.107205346,0.51843,0.0039026903,0.11994365,0.0024583845,0.031103801],"study_design_scores_gemma":[0.015647441,0.0015766326,0.18604772,0.0037794162,0.0018137376,0.000006555084,0.124051645,0.44788408,0.00315786,0.0032801286,0.20636554,0.006389254],"about_ca_topic_score_codex":0.040071715,"about_ca_topic_score_gemma":0.1478762,"teacher_disagreement_score":0.20390715,"about_ca_system_score_codex":0.00046444125,"about_ca_system_score_gemma":0.000968048,"threshold_uncertainty_score":0.9999856},"labels":[],"label_agreement":null},{"id":"W2461241631","doi":"10.1002/atr.1394","title":"Urban development with financially sustainable rail service","year":2016,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","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":"Business; Service (business); Sustainable development; Transport engineering; Environmental planning; Engineering; Environmental science; Marketing; Political science","score_opus":0.007275393321081975,"score_gpt":0.23997336306429087,"score_spread":0.2326979697432089,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2461241631","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.91533595,0.000089986526,0.081488244,0.0018323064,0.00019387413,0.00018247069,0.000004121932,0.000045528526,0.0008274956],"genre_scores_gemma":[0.9741891,0.000083643994,0.023981959,0.000101431324,0.00009623411,0.000004779691,0.00001197989,0.000011701463,0.0015191754],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.99885243,0.00003087997,0.00038294398,0.000099016244,0.00042635427,0.00020835304],"domain_scores_gemma":[0.9984251,0.00006204727,0.00039264286,0.000050474682,0.00097236224,0.0000973761],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003616241,0.00008556465,0.00013624567,0.00011273308,0.00024022268,0.000019719175,0.0001089736,0.00005317532,0.000044399472],"category_scores_gemma":[0.000036383623,0.000058585432,0.00003297546,0.00034753853,0.00004053592,0.0009430701,3.746782e-7,0.00006519012,0.0000035900682],"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":[0.0044182558,0.0005452231,0.19013445,0.0003165745,0.00025052624,0.00063601247,0.43356243,0.16720605,0.0044353013,0.07375701,0.0020559712,0.12268223],"study_design_scores_gemma":[0.0036000307,0.0003321379,0.69037354,0.00044703105,0.00007425903,0.0000034215554,0.017405052,0.0000014507508,0.0016882305,0.0005745876,0.2851725,0.0003277422],"about_ca_topic_score_codex":0.000038822032,"about_ca_topic_score_gemma":0.001694692,"teacher_disagreement_score":0.50023913,"about_ca_system_score_codex":0.00010248526,"about_ca_system_score_gemma":0.00073709496,"threshold_uncertainty_score":0.23890446},"labels":[],"label_agreement":null},{"id":"W2462943687","doi":"10.1109/vtcspring.2016.7504509","title":"Traffic Assignment with Maximum Delay Constraint in Stochastic Network","year":2016,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Bottleneck; Constraint (computer-aided design); Mathematical optimization; Network delay; Flow network; Routing (electronic design automation); Path (computing); Set (abstract data type); Assignment problem; Maximum flow problem; Computer network; Mathematics","score_opus":0.013404009707752419,"score_gpt":0.24825156379615212,"score_spread":0.23484755408839972,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2462943687","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.122591,0.000018391369,0.85824406,0.0021921522,0.00012755414,0.00022892319,0.0000033483154,0.00012974902,0.016464818],"genre_scores_gemma":[0.99660134,0.0000060556126,0.0023191639,0.00007826212,0.00004145109,0.000010001403,0.0000028001136,0.0000040231407,0.000936907],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99938387,0.000043393346,0.00010542377,0.00011042881,0.0001670577,0.00018982207],"domain_scores_gemma":[0.9997506,0.000099349796,0.000030644554,0.000036607158,0.000023959825,0.00005881384],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024714987,0.000048887396,0.0000612978,0.000027860035,0.00009575615,0.000016501435,0.00004490478,0.000038636827,0.00039818155],"category_scores_gemma":[0.00001328315,0.000029796596,0.00001082771,0.00014628212,0.000116797804,0.000078861325,0.0000011034476,0.000029399214,0.00001741177],"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":[0.00005202283,0.00004875211,0.012035689,0.0000014633108,0.000011282766,0.00001547501,0.004611994,0.92735493,0.0000063297157,0.02459567,0.000713439,0.030552955],"study_design_scores_gemma":[0.043001268,0.0034644147,0.6167021,0.0050413935,0.00045659996,0.00005278755,0.11639647,0.076611504,0.0001371995,0.027132098,0.102890514,0.008113645],"about_ca_topic_score_codex":0.000098989585,"about_ca_topic_score_gemma":0.00416981,"teacher_disagreement_score":0.8740103,"about_ca_system_score_codex":0.000047467547,"about_ca_system_score_gemma":0.000103489576,"threshold_uncertainty_score":0.4359809},"labels":[],"label_agreement":null},{"id":"W2467881888","doi":"10.1139/cjce-2016-0119","title":"Bus service quality prediction and attribute ranking using probabilistic neural network and adaptive neuro fuzzy inference system","year":2016,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Bangladesh University of Engineering and Technology","keywords":"Adaptive neuro fuzzy inference system; Punctuality; Ranking (information retrieval); Computer science; Artificial neural network; Service quality; Data mining; Service (business); Reliability (semiconductor); Probabilistic neural network; Machine learning; Fuzzy logic; Artificial intelligence; Engineering; Fuzzy control system; Transport engineering; Time delay neural network","score_opus":0.0316253325352566,"score_gpt":0.2429792347982329,"score_spread":0.21135390226297632,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2467881888","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8991503,0.0003930603,0.09895771,0.0004470614,0.00071198813,0.00012404266,0.000031363943,0.000037267604,0.00014716876],"genre_scores_gemma":[0.999264,0.000016787735,0.00047763728,0.000021995023,0.00020665563,7.583535e-7,9.469125e-7,0.0000072435696,0.00000397094],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99927765,0.00006729089,0.00023937038,0.00008538016,0.00012623247,0.000204082],"domain_scores_gemma":[0.9991203,0.00021637836,0.00012991825,0.00003835064,0.00019791599,0.00029711655],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005361061,0.00007184087,0.00012790565,0.000089840614,0.00022065012,0.00006202656,0.00005686953,0.00005202399,0.000003834172],"category_scores_gemma":[0.00026082387,0.000063496074,0.000016476575,0.00019504812,0.000038055856,0.00031763557,0.0000024754174,0.00009169752,1.11576426e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009294722,0.0000010407906,0.072156064,0.000060714665,0.000016515507,0.000019308047,0.0028123928,0.9206425,0.000058194222,0.0037999358,0.000016244792,0.0004078109],"study_design_scores_gemma":[0.001363816,0.00012143161,0.693032,0.002859777,0.00016846645,0.00013262322,0.0022820928,0.2979439,0.000007616245,0.00038622462,0.0012094096,0.0004926237],"about_ca_topic_score_codex":0.0040478474,"about_ca_topic_score_gemma":0.109167404,"teacher_disagreement_score":0.6226986,"about_ca_system_score_codex":0.00013177797,"about_ca_system_score_gemma":0.00029178924,"threshold_uncertainty_score":0.907088},"labels":[],"label_agreement":null},{"id":"W2468222878","doi":"","title":"Bus Rapid Transit Applications Phase 2","year":2011,"lang":"en","type":"article","venue":"","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":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Bus rapid transit; Las vegas; Transport engineering; Metropolitan area; Transit (satellite); Public transport; Intelligent transportation system; Orange (colour); Telecommunications; Computer science; Engineering; Operations research; Geography","score_opus":0.05295762274255611,"score_gpt":0.3181527823776701,"score_spread":0.265195159635114,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2468222878","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0035258292,0.00003942988,0.41379297,0.00032891086,0.00005813073,0.00019664336,0.000009545277,0.00024622396,0.5818023],"genre_scores_gemma":[0.985026,0.000056861336,0.011587088,0.00015988547,0.000050601233,0.000035234338,0.000037259313,0.0000039594547,0.0030431042],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99965036,0.000019274072,0.000077687364,0.00007818792,0.00008717006,0.00008734842],"domain_scores_gemma":[0.99981284,0.00001294612,0.000018655945,0.00005608065,0.000042101685,0.000057400626],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00011105791,0.000030086172,0.00003506876,0.00002902361,0.00021920947,0.000010975209,0.000066851666,0.000033419146,0.0026147338],"category_scores_gemma":[0.000004027819,0.000029436513,0.00002010313,0.0001608541,0.000054324864,0.00010157542,4.817723e-7,0.000025669537,0.00008223896],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005294326,0.00065975625,0.00410939,0.0000081869985,0.000029070497,0.0000039854617,0.14002377,0.00021936739,0.00016215742,0.68543,0.004499165,0.16480221],"study_design_scores_gemma":[0.0013128989,0.000077592944,0.008177578,0.000006453638,0.00004951548,4.9358033e-7,0.0088466965,0.00028065668,0.0010257956,0.0035458324,0.97638005,0.00029641492],"about_ca_topic_score_codex":0.0005337503,"about_ca_topic_score_gemma":0.00040533527,"teacher_disagreement_score":0.98150015,"about_ca_system_score_codex":0.000005985362,"about_ca_system_score_gemma":0.000037143876,"threshold_uncertainty_score":0.99829704},"labels":[],"label_agreement":null},{"id":"W2469774933","doi":"10.1007/s12469-016-0126-y","title":"Have they bunched yet? An exploratory study of the impacts of bus bunching on dwell and running times","year":2016,"lang":"en","type":"article","venue":"Public Transport","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":42,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Dwell time; Transit (satellite); Local bus; Frame (networking); Computer science; Transport engineering; Engineering; Public transport; Real-time computing; Control bus; System bus; Computer network; Computer hardware","score_opus":0.030344116528274196,"score_gpt":0.27884460153961277,"score_spread":0.24850048501133856,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2469774933","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9963946,0.000026071175,0.00020136975,0.00052224705,0.0001160464,0.00026037762,0.000019782625,0.000051704817,0.0024078155],"genre_scores_gemma":[0.9996425,0.00002488208,0.000068076435,0.000033470606,0.00003016742,0.000008237701,0.0000066936636,0.000011900221,0.00017410376],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99877024,0.00012629024,0.00029042645,0.00019268296,0.00043146577,0.00018888559],"domain_scores_gemma":[0.9992776,0.00009664291,0.0001872328,0.00020209877,0.00013004284,0.000106417814],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00085267215,0.00010109886,0.00017059091,0.000096047814,0.0002205228,0.00001824557,0.00020215349,0.00006645812,0.000058121972],"category_scores_gemma":[0.000062357314,0.000061240906,0.000041767657,0.00016965382,0.00013873912,0.0004540544,0.000003478652,0.00007263432,9.189933e-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":[0.000039268158,0.000278544,0.84331095,0.00001099199,0.0000283065,0.0000012842459,0.15238088,0.00020006884,0.00038967768,0.002137862,0.000020176594,0.0012019775],"study_design_scores_gemma":[0.0009872905,0.00022305222,0.96272707,0.00009576587,0.000041845273,2.1075489e-7,0.034524396,0.000012958036,0.0004085117,0.00026131014,0.000580876,0.00013671389],"about_ca_topic_score_codex":0.0013482396,"about_ca_topic_score_gemma":0.012084483,"teacher_disagreement_score":0.11941611,"about_ca_system_score_codex":0.00001971394,"about_ca_system_score_gemma":0.00015840011,"threshold_uncertainty_score":0.6743426},"labels":[],"label_agreement":null},{"id":"W2470158534","doi":"10.1142/s0219198920500085","title":"Revenue Sharing in Airline Alliance Networks","year":2020,"lang":"en","type":"preprint","venue":"International Game Theory Review","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Revenue; Axiom; Revenue sharing; Simple (philosophy); Shapley value; Alliance; Mathematical economics; Mathematical optimization; Set (abstract data type); Computer science; Microeconomics; Mathematics; Economics; Game theory; Finance; Geography","score_opus":0.05593696669172733,"score_gpt":0.3674559966251273,"score_spread":0.31151902993339997,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2470158534","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0025742548,0.53864574,0.17194787,0.115293615,0.012688201,0.004853144,0.00063930074,0.0008944272,0.15246342],"genre_scores_gemma":[0.31342277,0.6647511,0.001961326,0.00833817,0.0016711806,0.0002084752,0.0011940284,0.000051251103,0.008401708],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99837756,0.000261194,0.0004826138,0.00038464667,0.0003341189,0.0001598656],"domain_scores_gemma":[0.99918747,0.00012150266,0.00030609433,0.00017868019,0.00013323424,0.000073039795],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015073746,0.00014829649,0.00030940544,0.00004406507,0.000043118627,0.00006505155,0.0007365697,0.00014366528,0.0005148227],"category_scores_gemma":[0.0005399817,0.00016255304,0.0001232078,0.00018218937,0.00006995038,0.00009891365,0.00008956586,0.0005316102,0.00006086442],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016486959,0.00016987539,0.014425883,0.0034871774,0.0002609143,0.00017509269,0.011762095,0.32266057,0.0000018345171,0.54435354,0.013437358,0.08910075],"study_design_scores_gemma":[0.000365744,0.000013277495,0.0062564965,0.038797297,0.0001232586,0.0000019062375,0.00011524778,0.012381115,0.0000012093744,0.096057706,0.8452294,0.00065737707],"about_ca_topic_score_codex":0.00013856584,"about_ca_topic_score_gemma":0.00029317592,"teacher_disagreement_score":0.831792,"about_ca_system_score_codex":0.00012539241,"about_ca_system_score_gemma":0.00012206101,"threshold_uncertainty_score":0.662872},"labels":[],"label_agreement":null},{"id":"W2473463908","doi":"10.1002/atr.1393","title":"Public transit service operation strategy under indifference thresholds‐based bi‐modal equilibrium","year":2016,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Public transport; Modal; Transit (satellite); Transport engineering; Service (business); Modal shift; Computer science; Business; Engineering; Chemistry; Marketing","score_opus":0.04782019072664978,"score_gpt":0.29696093174172017,"score_spread":0.2491407410150704,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2473463908","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8050673,0.00008554672,0.18659166,0.007414487,0.0003759827,0.00017882966,0.000030966938,0.00006109016,0.00019414553],"genre_scores_gemma":[0.9962007,0.00010292781,0.0030882626,0.000296335,0.00015309686,0.000006031122,0.00006355714,0.000019165242,0.00006995406],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9980713,0.00011480269,0.0006587841,0.00019421805,0.00067913835,0.00028173995],"domain_scores_gemma":[0.9983416,0.00010774632,0.00046094222,0.00010550832,0.0007911771,0.00019301625],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045722153,0.00015565855,0.00022337513,0.00022977512,0.0002141677,0.00008291936,0.00021699477,0.00014822098,0.00018714582],"category_scores_gemma":[0.000031783467,0.00012089333,0.0001037188,0.00046933038,0.00008800334,0.002115355,5.2255973e-7,0.00015161275,0.000007146179],"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":[0.00084115757,0.0003577278,0.03384266,0.000087648,0.00009681509,0.000042058295,0.024632255,0.82926565,0.064298786,0.02402926,0.000065649125,0.022440335],"study_design_scores_gemma":[0.008183076,0.0006785163,0.9606842,0.00062206964,0.00021665389,0.0000066031826,0.007885312,0.0007028341,0.0061144214,0.011903097,0.0021918588,0.0008113813],"about_ca_topic_score_codex":0.000055539018,"about_ca_topic_score_gemma":0.002585603,"teacher_disagreement_score":0.9268415,"about_ca_system_score_codex":0.00009835239,"about_ca_system_score_gemma":0.00060735346,"threshold_uncertainty_score":0.4929887},"labels":[],"label_agreement":null},{"id":"W2474007778","doi":"10.1287/trsc.2015.0628","title":"Network Pricing of Congestion-Free Networks: The Elastic and Linear Demand Case","year":2016,"lang":"en","type":"article","venue":"Transportation Science","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Toll; Price elasticity of demand; Mathematical optimization; Flow network; Quadratic equation; Path (computing); Elasticity (physics); Revenue; Computer science; Mathematics; Economics; Microeconomics; Computer network","score_opus":0.018577951861976468,"score_gpt":0.28004958604830726,"score_spread":0.2614716341863308,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2474007778","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6819051,0.00016346821,0.3153844,0.0015183804,0.00026292796,0.0002325441,0.000014814648,0.00006337828,0.00045503976],"genre_scores_gemma":[0.9973269,0.00012836167,0.00227536,0.0000717808,0.00007693812,0.000006469374,0.000002788494,0.0000045203547,0.00010689235],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99889547,0.000056803005,0.00025718854,0.00019554177,0.00037102134,0.00022398664],"domain_scores_gemma":[0.9989019,0.0004763595,0.0001484844,0.00014709344,0.00023596747,0.00009016931],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001390299,0.00006829388,0.00009100423,0.000055820106,0.00080942886,0.00003053359,0.00020015947,0.00004209626,0.000028272001],"category_scores_gemma":[0.00021914236,0.00004251239,0.000019206132,0.00075600104,0.0011731494,0.00042338634,0.0000024534602,0.000048754482,0.0000012289732],"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":[0.00008277018,0.00003921296,0.28853315,0.000027022837,0.000017373686,0.00006888779,0.037620798,0.5071193,0.00045326987,0.15582304,0.00028036808,0.009934818],"study_design_scores_gemma":[0.0014592039,0.00011455324,0.9728269,0.00032647976,0.00014164421,0.000026824147,0.0051414366,0.013036925,0.00019274655,0.0046423078,0.0016861544,0.00040483454],"about_ca_topic_score_codex":0.0003309042,"about_ca_topic_score_gemma":0.0023435436,"teacher_disagreement_score":0.68429375,"about_ca_system_score_codex":0.000016203241,"about_ca_system_score_gemma":0.00014908519,"threshold_uncertainty_score":0.62255543},"labels":[],"label_agreement":null},{"id":"W2493442683","doi":"10.1002/atr.1401","title":"Survey and empirical evaluation of nonhomogeneous arrival process models with taxi data","year":2016,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":44,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Canada Excellence Research Chairs, Government of Canada; Canada Research Chairs","keywords":"Autoregressive integrated moving average; Computer science; Process (computing); Count data; Operations research; Set (abstract data type); Piecewise linear function; Data set; Time series; Data mining; Engineering; Statistics; Mathematics; Machine learning; Artificial intelligence","score_opus":0.10057465866353979,"score_gpt":0.38435928611169856,"score_spread":0.2837846274481588,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2493442683","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93109596,0.00018303627,0.06803282,0.00024776786,0.00009064589,0.00015681233,0.00011247926,0.00000980092,0.00007067434],"genre_scores_gemma":[0.99445295,0.000239244,0.00513247,0.000011052324,0.000041477102,0.0000021721883,0.000098883764,0.000008972087,0.000012746458],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9982573,0.0001611996,0.00043121565,0.0001488249,0.0008924138,0.000109040775],"domain_scores_gemma":[0.99773335,0.00015776568,0.00055083336,0.00011051114,0.001364231,0.00008329985],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017891243,0.00007802566,0.0001784233,0.00009511839,0.00008765347,0.000011364529,0.00015430445,0.00005637945,0.000019462508],"category_scores_gemma":[0.00015267768,0.00005311898,0.00002040594,0.00022797483,0.00011396667,0.0012026934,7.605605e-7,0.000058429054,2.3318879e-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":[0.0010982646,0.00013578005,0.22144194,0.00004233822,0.0000857438,0.000009823116,0.023214564,0.7076369,0.00051846553,0.00032214602,0.000040189094,0.04545386],"study_design_scores_gemma":[0.0033739943,0.00024768678,0.9870571,0.00026641766,0.00027291873,0.0000049863643,0.0030549678,0.0027080595,0.0003783343,0.0022787761,0.0001553651,0.00020143122],"about_ca_topic_score_codex":0.0000533429,"about_ca_topic_score_gemma":0.0030577916,"teacher_disagreement_score":0.7656151,"about_ca_system_score_codex":0.00002981867,"about_ca_system_score_gemma":0.00047595863,"threshold_uncertainty_score":0.21661292},"labels":[],"label_agreement":null},{"id":"W2506850060","doi":"10.3141/2541-02","title":"Demand-Sensitive Candidate Route Generation Algorithm","year":2016,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Public transport; Computer science; Transport engineering; Transit (satellite); Minification; Quality (philosophy); Service (business); Set (abstract data type); Level of service; Function (biology); Demand patterns; Path (computing); Operations research; Demand management; Engineering; Computer network; Economics","score_opus":0.08440518504364633,"score_gpt":0.393056158829961,"score_spread":0.3086509737863147,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2506850060","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9019109,0.00015305419,0.07822948,0.016136866,0.0014298104,0.0011719248,0.00025732938,0.000079683705,0.00063091837],"genre_scores_gemma":[0.98854315,0.0024008267,0.0047026654,0.00008481311,0.0007324741,0.00005090137,0.000047152374,0.00005094136,0.0033870586],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9900584,0.00267227,0.0013220304,0.00046203984,0.004458723,0.0010265149],"domain_scores_gemma":[0.99176586,0.0010752575,0.0005615963,0.00037441638,0.005695043,0.00052783807],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0076393127,0.00024240767,0.0004000192,0.00095525064,0.0018102126,0.00019799576,0.0008449518,0.00028170855,0.00034892748],"category_scores_gemma":[0.00038393453,0.00016122917,0.000346463,0.0021394426,0.000998526,0.001289517,0.000003945757,0.0011094993,0.000048209746],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0019255868,0.00080894417,0.5547001,0.00017785402,0.0007179432,0.0005890359,0.069500774,0.005763395,0.021991212,0.039182536,0.054802876,0.24983978],"study_design_scores_gemma":[0.0031648353,0.0005194699,0.8910736,0.00063986075,0.000114700095,7.7008286e-7,0.007923888,0.00036361167,0.005154,0.0028336996,0.08774551,0.00046609322],"about_ca_topic_score_codex":0.022885017,"about_ca_topic_score_gemma":0.15076232,"teacher_disagreement_score":0.3363735,"about_ca_system_score_codex":0.00049278734,"about_ca_system_score_gemma":0.0014552062,"threshold_uncertainty_score":0.9994893},"labels":[],"label_agreement":null},{"id":"W2507414157","doi":"10.1139/cjce-2016-0091","title":"Conditions contributing to the attitudes for toll facilities in the United States with a specific focus on Virginia","year":2016,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Transportation Planning and Optimization","field":"Social Sciences","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":"U.S. Department of Transportation","keywords":"Toll; Revenue; Toll road; Popularity; Business; Consistency (knowledge bases); Economics; Marketing; Public economics; Finance; Political science","score_opus":0.014125233570082626,"score_gpt":0.23483032545328547,"score_spread":0.22070509188320284,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2507414157","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.75151205,0.0002479833,0.20280215,0.04253703,0.00046687343,0.00060239574,0.0005665021,0.000028020442,0.0012369574],"genre_scores_gemma":[0.99961436,0.000014424523,0.00009883711,0.00010664781,0.00007987027,0.000007981988,0.000005845734,0.0000045404345,0.00006750458],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994756,0.000028682216,0.00012867614,0.000041937375,0.00011043706,0.00021468016],"domain_scores_gemma":[0.9992413,0.00045559433,0.0000425486,0.000044251763,0.00010765666,0.00010865755],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00055273453,0.000048171467,0.00006609718,0.00018230315,0.00024041612,0.00006251362,0.00012802763,0.000019071205,0.00003083664],"category_scores_gemma":[0.00016881028,0.00002527291,0.000021474636,0.00021891415,0.00003576621,0.00007879595,5.3229564e-7,0.00006502911,0.0000011574826],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028754064,0.000006361543,0.012112389,0.00000784421,0.00002799889,0.000026758884,0.039340433,0.9112199,0.000019293986,0.033864997,0.00303308,0.0003122026],"study_design_scores_gemma":[0.0014466012,0.00033770603,0.13055702,0.0009124503,0.00002674144,0.000013901015,0.025782133,0.00090321497,0.00005931137,0.0006863961,0.8389777,0.00029680715],"about_ca_topic_score_codex":0.0026963966,"about_ca_topic_score_gemma":0.47204,"teacher_disagreement_score":0.91031665,"about_ca_system_score_codex":0.000086425054,"about_ca_system_score_gemma":0.000186466,"threshold_uncertainty_score":0.5375939},"labels":[],"label_agreement":null},{"id":"W2509064993","doi":"10.1038/srep32591","title":"The Effect of Next-Nearest Neighbour Hopping in the One, Two, and Three Dimensional Holstein Model","year":2016,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Universities Space Research Association","keywords":"Nearest neighbour; Computer science; Artificial intelligence","score_opus":0.02287161093247206,"score_gpt":0.27766965219555445,"score_spread":0.25479804126308236,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2509064993","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9956278,0.00012426592,0.0009495347,0.0010702997,0.00065402914,0.00024666497,0.0000016807348,0.000017465183,0.0013082285],"genre_scores_gemma":[0.99922705,0.0000070759384,0.0001651756,0.000013017195,0.000024145727,0.000011260076,0.00000404545,0.0000035110352,0.00054472074],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99866605,0.00014208033,0.0002480291,0.00023364795,0.0005345399,0.00017562551],"domain_scores_gemma":[0.99906176,0.00043427135,0.00016381987,0.00023373739,0.00006828309,0.000038100727],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004253126,0.000057565394,0.00008384032,0.000052761854,0.00073223124,0.00018598659,0.00011559221,0.000033155025,0.000008943718],"category_scores_gemma":[0.0003387832,0.000027169051,0.000031522934,0.00028338502,0.00049144274,0.00021849532,0.000010825521,0.000050847702,0.0000015230642],"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":[0.00012809031,0.00010319993,0.8078717,0.00004737703,0.000039537907,0.0001379001,0.02325543,0.07205631,0.017919188,0.024677133,0.004312665,0.049451455],"study_design_scores_gemma":[0.0040379562,0.00043005846,0.71219563,0.0017365114,0.00021571406,0.00007885999,0.004057154,0.10184894,0.009617964,0.1401736,0.024217226,0.0013904063],"about_ca_topic_score_codex":0.00033766852,"about_ca_topic_score_gemma":0.003437785,"teacher_disagreement_score":0.11549646,"about_ca_system_score_codex":0.000015217111,"about_ca_system_score_gemma":0.0001035336,"threshold_uncertainty_score":0.5631805},"labels":[],"label_agreement":null},{"id":"W2510256274","doi":"","title":"Benchmarking, Planning, and Promoting Transit- Oriented Intensification in Rapid Transit Station Areas: Project Key Indicators","year":2016,"lang":"en","type":"article","venue":"MacSphere (McMaster University)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Benchmarking; Transit (satellite); Key (lock); Business; Public transport; Computer science; Transport engineering; Engineering; Marketing; Computer security","score_opus":0.016233033556740727,"score_gpt":0.23500225088554616,"score_spread":0.21876921732880544,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2510256274","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.72221076,0.00012386772,0.1290855,0.0020663065,0.00029371813,0.0014079204,0.00006400664,0.00040242716,0.14434546],"genre_scores_gemma":[0.99032044,0.000076241806,0.001275204,0.000043349617,0.00003609394,0.0000024961344,0.000047141926,0.000013018699,0.008185991],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9987954,0.00020835575,0.00019204213,0.000330013,0.00022269672,0.0002515247],"domain_scores_gemma":[0.9995479,0.00007687376,0.00013176388,0.00007681034,0.000079893936,0.00008678197],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00029384115,0.000125446,0.000136068,0.00038471652,0.00025231016,0.000038860628,0.00011244924,0.00012261166,0.001836451],"category_scores_gemma":[0.00003609185,0.00011841377,0.000031293,0.0008967754,0.00014692386,0.00056118227,0.000006261702,0.000098943245,0.0000029634882],"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":[0.00024758186,0.000084784275,0.35762072,0.000051237468,0.000035515197,0.00004112363,0.14510232,0.00031317756,0.00035179756,0.0053891004,0.0001264396,0.4906362],"study_design_scores_gemma":[0.0053352043,0.00033135744,0.40684345,0.0011307098,0.00013981586,0.000003329945,0.0766832,0.00173789,0.0005166632,0.00014422931,0.50620973,0.00092443055],"about_ca_topic_score_codex":0.00056278094,"about_ca_topic_score_gemma":0.0019025457,"teacher_disagreement_score":0.5060833,"about_ca_system_score_codex":0.00012102409,"about_ca_system_score_gemma":0.00012032512,"threshold_uncertainty_score":0.999076},"labels":[],"label_agreement":null},{"id":"W2511724755","doi":"10.1007/978-3-319-31281-1_6","title":"Evolution Solutions of Equilibrium Problems: A Computational Approach","year":2016,"lang":"en","type":"book-chapter","venue":"Springer optimization and its applications","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University; University of Guelph","funders":"","keywords":"Equilibrium point; Computer science; Mathematical optimization; Set (abstract data type); Stability (learning theory); State space; Oligopoly; Equilibrium selection; Mathematical economics; Applied mathematics; Mathematics; Game theory","score_opus":0.029357792499946425,"score_gpt":0.2549532134174874,"score_spread":0.22559542091754098,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2511724755","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000003605962,0.00084881415,0.66405416,0.00034579873,0.00005090587,0.0008453668,0.00017703247,0.00014511123,0.33352917],"genre_scores_gemma":[0.15404394,0.005294416,0.20506006,0.00022169562,0.0011893172,0.0011136406,0.0035551756,0.00031233625,0.6292094],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9983699,0.00003691889,0.0005207878,0.0004235866,0.00041514862,0.00023369654],"domain_scores_gemma":[0.99844885,0.00010575493,0.00047276757,0.00019005252,0.00064058905,0.00014198775],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028936446,0.00021709649,0.00025914807,0.00029615706,0.0005050668,0.000050492763,0.00017397811,0.0003332959,0.00032106915],"category_scores_gemma":[0.000032275788,0.00022495625,0.000088128756,0.00015769625,0.00029129995,0.00027053957,0.000030672454,0.00014472063,0.000027615188],"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":[0.0000032724,0.000030174619,0.000019010993,0.000055983255,0.000024434208,5.0458866e-8,0.00047351382,0.27303916,0.000008920565,0.72582686,0.00011091765,0.00040769455],"study_design_scores_gemma":[0.0024727369,0.00014056388,0.00049614004,0.0011368523,0.00080274977,0.000009017415,0.0007575733,0.43330353,0.000016485938,0.121731855,0.43654063,0.0025918519],"about_ca_topic_score_codex":0.000017195161,"about_ca_topic_score_gemma":0.000013532435,"teacher_disagreement_score":0.60409504,"about_ca_system_score_codex":0.000116961586,"about_ca_system_score_gemma":0.00027483093,"threshold_uncertainty_score":0.9173449},"labels":[],"label_agreement":null},{"id":"W2514205401","doi":"10.3141/2565-11","title":"It’s a Matter of Time: Assessment of Additional Time Budgeted for Commuting to McGill University Across Modes","year":2016,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"McGill University","funders":"","keywords":"Transport engineering; Travel time; Traffic congestion; Real-time data; Reliability (semiconductor); Time allocation; Operations research; Pedestrian; Computer science; Variable (mathematics); Engineering; Economics; Mathematics","score_opus":0.0822076738554626,"score_gpt":0.4189072273175247,"score_spread":0.33669955346206215,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2514205401","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95061636,0.000012449932,0.016005792,0.018052256,0.00017261508,0.0016376259,0.012465793,0.000030750274,0.0010063739],"genre_scores_gemma":[0.9804235,0.00014443595,0.013970339,0.0000668755,0.00007574117,0.000028786944,0.00014222291,0.00003428346,0.005113825],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99427295,0.001108223,0.0010831392,0.00028600928,0.0026158565,0.00063384825],"domain_scores_gemma":[0.99014944,0.0030448397,0.0006216087,0.00026020428,0.005628119,0.00029577035],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.005163626,0.00015451902,0.00041192656,0.00062624516,0.00092616334,0.000025448067,0.00084701413,0.0001674169,0.002437828],"category_scores_gemma":[0.00031677893,0.0001182902,0.00033533122,0.0014833065,0.0006870435,0.00057809474,0.0000057095167,0.00047064034,0.000020562216],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0110501135,0.0019424737,0.46830803,0.0011536403,0.0011387409,0.00006848067,0.050696686,0.050693043,0.081777416,0.01896322,0.29739255,0.016815595],"study_design_scores_gemma":[0.0029718454,0.0007719041,0.91166675,0.0013840515,0.00008203328,1.7838919e-7,0.007492822,0.00047157242,0.0022278759,0.0011267305,0.07151388,0.00029034214],"about_ca_topic_score_codex":0.0043034223,"about_ca_topic_score_gemma":0.018177658,"teacher_disagreement_score":0.44335872,"about_ca_system_score_codex":0.00032694475,"about_ca_system_score_gemma":0.0008094675,"threshold_uncertainty_score":0.99973804},"labels":[],"label_agreement":null},{"id":"W2515448415","doi":"","title":"A new policy in congestion pricing. Why only toll? Why not subsidy?","year":2016,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Toll; Subsidy; Road pricing; Congestion pricing; Equity (law); Order (exchange); Scheme (mathematics); Business; Traffic congestion; Computer science; Economics; Public economics; Transport engineering; Operations research; Finance; Engineering","score_opus":0.021700281804137307,"score_gpt":0.2967917373181247,"score_spread":0.2750914555139874,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2515448415","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23946269,0.00007001231,0.31933972,0.24187742,0.0007215067,0.00072650175,0.000018764178,0.00078916247,0.19699423],"genre_scores_gemma":[0.97757095,0.000073792085,0.002774339,0.0021319878,0.00018888447,0.0000031320594,0.000006253786,0.0000066646153,0.017243974],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99924576,0.000060048427,0.00015465553,0.00014359037,0.00020723771,0.00018870803],"domain_scores_gemma":[0.99963176,0.00010883132,0.000048624195,0.00006841778,0.000045032164,0.00009731956],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023283831,0.000060048547,0.0000797191,0.00014993425,0.00009964647,0.00003648502,0.000085446656,0.00008050578,0.00038699745],"category_scores_gemma":[0.00018550166,0.000043800068,0.000022511313,0.00037461915,0.000040853476,0.00036489763,0.0000026977677,0.000037895672,0.000049391045],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010783409,0.00007078673,0.29696968,0.00000937087,0.000012047985,0.000009547189,0.023601132,0.0011139901,0.00055890647,0.5401994,0.03352931,0.10381796],"study_design_scores_gemma":[0.002258126,0.00009130005,0.6585031,0.00018453784,0.000016990176,0.0000015664683,0.0020707888,0.00014346538,0.0005435477,0.004857649,0.3308673,0.00046163224],"about_ca_topic_score_codex":0.034685925,"about_ca_topic_score_gemma":0.057965823,"teacher_disagreement_score":0.7381083,"about_ca_system_score_codex":0.000086421336,"about_ca_system_score_gemma":0.0003912154,"threshold_uncertainty_score":0.9717422},"labels":[],"label_agreement":null},{"id":"W2516249911","doi":"","title":"Bus rapid transit: theory and practice in the United States and abroad","year":2010,"lang":"en","type":"dissertation","venue":"SMARTech Repository (Georgia Institute of Technology)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Transit (satellite); Transport engineering; Political science; Engineering; Public transport","score_opus":0.006956317874293981,"score_gpt":0.2831245616965245,"score_spread":0.27616824382223054,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2516249911","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97584325,0.0025442517,0.0002941939,0.0021019098,0.0012756024,0.00078339345,0.00002333131,0.00027783937,0.016856235],"genre_scores_gemma":[0.9765355,0.009612999,0.009397535,0.00021457924,0.00014055677,0.00016747201,0.0012911288,0.00005374969,0.0025865007],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9984534,0.0002436928,0.00042320535,0.00034378612,0.00031252665,0.00022338577],"domain_scores_gemma":[0.99868596,0.00035405264,0.00038158038,0.0002813818,0.00025291956,0.000044112672],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00141858,0.00021342527,0.00027880823,0.00073359365,0.0005368216,0.00007804054,0.0003364663,0.0009745821,0.0000056388935],"category_scores_gemma":[0.0007468719,0.00018530714,0.00003925857,0.0009077369,0.0009516152,0.00036314456,0.0000075314365,0.00084975816,7.257049e-7],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00232737,0.0007861947,0.007440285,0.0009832382,0.00050691096,0.0006383735,0.31536338,0.0008396874,0.011409118,0.55221003,0.0006585877,0.10683681],"study_design_scores_gemma":[0.0016911057,0.00037174686,0.027695367,0.0008703311,0.0007711803,0.00014880179,0.17872733,0.000064232474,0.0049059987,0.010936416,0.7727792,0.0010382555],"about_ca_topic_score_codex":0.0019793713,"about_ca_topic_score_gemma":0.0040468182,"teacher_disagreement_score":0.77212065,"about_ca_system_score_codex":0.000027572865,"about_ca_system_score_gemma":0.00030871457,"threshold_uncertainty_score":0.7556606},"labels":[],"label_agreement":null},{"id":"W2518271336","doi":"10.1016/j.cor.2016.08.009","title":"Multi-objective rapid transit network design with modal competition: The case of Concepción, Chile","year":2016,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":57,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"Fondo Nacional de Desarrollo Científico y Tecnológico; Natural Sciences and Engineering Research Council of Canada; Comisión Nacional de Investigación Científica y Tecnológica; Instituto de Sistemas Complejos de Ingeniería; Consiglio Nazionale delle Ricerche","keywords":"Modal; Competition (biology); Computer science; Integer (computer science); Mathematical optimization; Network planning and design; Integer programming; Transit (satellite); Mathematics; Transport engineering; Engineering; Public transport; Telecommunications","score_opus":0.07772687742908109,"score_gpt":0.36079891512287005,"score_spread":0.28307203769378897,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2518271336","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0263043,0.00010579707,0.9688618,0.0035273985,0.00010503632,0.00066703185,0.000022460268,0.00004606657,0.00036006488],"genre_scores_gemma":[0.9507518,0.00007575307,0.0486354,0.000058144287,0.00015951217,0.00005620828,0.000014202257,0.000008773055,0.00024023713],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980047,0.001014637,0.0001770087,0.00020475306,0.00032434257,0.00027456917],"domain_scores_gemma":[0.99818945,0.000752422,0.000023817372,0.00017667635,0.0007736682,0.00008399197],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0013559016,0.000075442615,0.000110012414,0.00008285435,0.0016734486,0.00009672547,0.00021415402,0.000053933032,0.00013924767],"category_scores_gemma":[0.000057220943,0.00004487866,0.000031240546,0.00064615795,0.0008303866,0.00027617847,0.000010287277,0.00014304092,0.000011724936],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008283259,0.00008991846,0.0002988557,0.0000046945884,0.000046617053,0.00006521194,0.026555164,0.9165012,0.0001451416,0.048074856,0.0008949297,0.0072405343],"study_design_scores_gemma":[0.010651329,0.002467958,0.022320358,0.0011520002,0.00013610125,0.00027448943,0.07152681,0.8712895,0.0021607392,0.00088761933,0.015807148,0.001325938],"about_ca_topic_score_codex":0.0013992565,"about_ca_topic_score_gemma":0.0041360687,"teacher_disagreement_score":0.9244475,"about_ca_system_score_codex":0.000055225402,"about_ca_system_score_gemma":0.0003813931,"threshold_uncertainty_score":0.9996262},"labels":[],"label_agreement":null},{"id":"W2518788117","doi":"10.1002/atr.1404","title":"Time control point strategy coupled with transfer coordination in bus schedule design","year":2016,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Schedule; Transfer (computing); Point (geometry); Control (management); Coordination game; Computer science; Operations research; Control theory (sociology); Engineering; Mathematics; Mathematical economics","score_opus":0.010635473284144515,"score_gpt":0.25351140750360873,"score_spread":0.2428759342194642,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2518788117","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.53139144,0.000054528224,0.46685925,0.0012807891,0.00007479514,0.00024589,0.000009685239,0.000024881732,0.000058724556],"genre_scores_gemma":[0.99333775,0.00008426905,0.006319531,0.00003746048,0.00005230822,0.000008480512,0.000013517642,0.000014624277,0.00013205869],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9985853,0.00012408788,0.00052910356,0.00013176432,0.00043310568,0.00019663047],"domain_scores_gemma":[0.99895036,0.0002034732,0.00023512324,0.0000538328,0.0004607781,0.0000964072],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00071852683,0.000113140115,0.00022641988,0.00020469989,0.000098461256,0.000020609894,0.000093315684,0.00008574538,0.000118184595],"category_scores_gemma":[0.000042860447,0.00008074115,0.00005602792,0.00033576947,0.000099732555,0.001167511,1.0445799e-7,0.00010799785,0.0000046058317],"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":[0.0019463629,0.00014702,0.004675103,0.000012430546,0.00004260269,0.0000580305,0.009414031,0.9603089,0.015082231,0.00216504,0.000021866415,0.0061263703],"study_design_scores_gemma":[0.05307623,0.004109749,0.9057512,0.0019239644,0.0005201399,0.000020574973,0.015354401,0.0055532493,0.0063309288,0.0042690733,0.001729372,0.0013610981],"about_ca_topic_score_codex":0.00002848994,"about_ca_topic_score_gemma":0.00055166817,"teacher_disagreement_score":0.95475566,"about_ca_system_score_codex":0.00009916717,"about_ca_system_score_gemma":0.0002747818,"threshold_uncertainty_score":0.32925287},"labels":[],"label_agreement":null},{"id":"W2518921919","doi":"10.1002/atr.1408","title":"Efficient solution algorithm for finding spatially dependent reliable shortest path in road networks","year":2016,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":27,"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":"Hong Kong Polytechnic University; National Natural Science Foundation of China","keywords":"Shortest path problem; Computer science; Reliability (semiconductor); K shortest path routing; Mathematical optimization; Constraint (computer-aided design); Flow network; Travel time; Dominance (genetics); Constrained Shortest Path First; Path (computing); Dijkstra's algorithm; Yen's algorithm; Private Network-to-Network Interface; Algorithm; Routing (electronic design automation); Transport engineering; Mathematics; Engineering; Routing protocol; Link-state routing protocol; Graph; Computer network; Theoretical computer science","score_opus":0.012085235331973983,"score_gpt":0.2742313739504415,"score_spread":0.26214613861846753,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2518921919","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2287364,0.00011888369,0.769832,0.00030145713,0.00067013555,0.0002543929,0.000019985144,0.000021255071,0.000045476365],"genre_scores_gemma":[0.95191824,0.0002876484,0.047444623,0.00002179053,0.00018408979,0.000013066058,0.000034622903,0.00001317543,0.00008272717],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99854034,0.000041296957,0.0006295626,0.00014961202,0.00038265146,0.00025650917],"domain_scores_gemma":[0.998915,0.00012662676,0.00047790763,0.00005525496,0.00033603123,0.00008914509],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006989864,0.00009955514,0.00018432306,0.00017202854,0.00018126701,0.000020786994,0.00010014872,0.00010271104,0.000017382581],"category_scores_gemma":[0.00006211622,0.00008227812,0.00008999742,0.00024132215,0.000039518414,0.00033938055,5.0658235e-7,0.000100379184,7.467165e-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":[0.00011695739,0.000067908935,0.0059968256,0.000004413325,0.000008914666,0.00001067565,0.0033141351,0.8978609,0.0005382043,0.00032161336,0.000015014724,0.09174446],"study_design_scores_gemma":[0.010331578,0.0008034604,0.85979176,0.0015918894,0.00020788648,0.0000053875015,0.005372567,0.116315484,0.00076856755,0.0012269494,0.0028175395,0.0007669444],"about_ca_topic_score_codex":0.000107068234,"about_ca_topic_score_gemma":0.0017962935,"teacher_disagreement_score":0.85379493,"about_ca_system_score_codex":0.00016820435,"about_ca_system_score_gemma":0.00016161322,"threshold_uncertainty_score":0.33552045},"labels":[],"label_agreement":null},{"id":"W2519372973","doi":"10.1002/atr.1398","title":"Modelling public transport on‐board congestion: comparing schedule‐based and agent‐based assignment approaches and their implications","year":2016,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Public transport; Schedule; Operations research; Computer science; Transport engineering; Traffic congestion; Engineering","score_opus":0.09076321400691402,"score_gpt":0.2867310333849502,"score_spread":0.1959678193780362,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2519372973","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.49114308,0.000111630696,0.50448316,0.0038338313,0.000085049636,0.00015557485,0.000020601563,0.000033346907,0.00013371704],"genre_scores_gemma":[0.9826424,0.00018623602,0.016943801,0.00008343521,0.000055273715,0.000014304355,0.00004226807,0.000014735455,0.000017521063],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99879265,0.00006994215,0.00048317612,0.00020471412,0.00025842676,0.0001910934],"domain_scores_gemma":[0.9989108,0.00019970618,0.00038714637,0.00008832234,0.00021943785,0.0001946131],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004445944,0.00014612032,0.00022819269,0.0001900651,0.00032435227,0.00003944158,0.00009043371,0.00008574451,0.000013113165],"category_scores_gemma":[0.000019448424,0.00010965757,0.00007146945,0.00018929146,0.00016601257,0.00060242787,4.134658e-7,0.00012219865,4.6479855e-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":[0.00018168388,0.000117240845,0.05140696,0.000027944694,0.000033393302,0.0000024143662,0.0038676541,0.9217127,0.000540561,0.011939195,0.000004532376,0.010165705],"study_design_scores_gemma":[0.00989483,0.00066952314,0.93670154,0.0010664354,0.00029458114,0.000003983511,0.008760051,0.028179336,0.0019462743,0.0040214863,0.0075546,0.0009073522],"about_ca_topic_score_codex":0.000012611368,"about_ca_topic_score_gemma":0.00016457358,"teacher_disagreement_score":0.89353335,"about_ca_system_score_codex":0.00006696479,"about_ca_system_score_gemma":0.00016650379,"threshold_uncertainty_score":0.4471706},"labels":[],"label_agreement":null},{"id":"W2519478281","doi":"10.1002/atr.1407","title":"A dual control approach for repeated anticipatory traffic control with estimation of network flow sensitivity","year":2016,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Vlaamse Overheid","keywords":"Sensitivity (control systems); Dual (grammatical number); Computer science; Process (computing); Control (management); Traffic flow (computer networking); Flow network; SIGNAL (programming language); Function (biology); Control theory (sociology); Simulation; Real-time computing; Mathematical optimization; Engineering; Artificial intelligence; Mathematics","score_opus":0.01062986241608495,"score_gpt":0.2608418395759114,"score_spread":0.25021197715982646,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2519478281","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3656987,0.00003332274,0.633595,0.00016813962,0.000110670844,0.0002981044,0.000057846453,0.000022232989,0.000015999276],"genre_scores_gemma":[0.9215998,0.000029711891,0.078115284,0.000028052416,0.00013885062,0.000010817079,0.000048078335,0.0000133983385,0.000016013531],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99856764,0.00013731595,0.0005827051,0.00013318699,0.00038295198,0.00019622064],"domain_scores_gemma":[0.9979563,0.000378641,0.0008309804,0.00006566508,0.00068152713,0.00008689843],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010209895,0.00010929146,0.00031908895,0.00009277826,0.00015164251,0.00001072044,0.0000446092,0.000085582775,0.000005361513],"category_scores_gemma":[0.00010301509,0.000078299425,0.00010814269,0.0002144554,0.0001175406,0.0005748635,1.234943e-7,0.00007152819,1.5541437e-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":[0.0019597267,0.00007828331,0.003483629,0.000030917778,0.000074021766,0.000007665574,0.003516407,0.9795185,0.00075443374,0.0004029291,0.000021256345,0.010152267],"study_design_scores_gemma":[0.056686677,0.003154616,0.6666641,0.001603406,0.002201398,0.000026786813,0.006657922,0.25883687,0.0014540023,0.000739003,0.000881156,0.0010940193],"about_ca_topic_score_codex":0.000009670207,"about_ca_topic_score_gemma":0.00016721949,"teacher_disagreement_score":0.7206816,"about_ca_system_score_codex":0.000042271215,"about_ca_system_score_gemma":0.00019340908,"threshold_uncertainty_score":0.3192958},"labels":[],"label_agreement":null},{"id":"W2520439611","doi":"10.1287/trsc.2017.0805","title":"Network Learning via Multiagent Inverse Transportation Problems","year":2018,"lang":"en","type":"article","venue":"Transportation Science","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Transport engineering; Computer science; Flow network; Operations research; Engineering; Artificial intelligence; Mathematical optimization; Mathematics","score_opus":0.03091271684936079,"score_gpt":0.29899894925623943,"score_spread":0.2680862324068786,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2520439611","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6956579,0.000050076298,0.29102287,0.0008055477,0.0012492862,0.00075570197,0.00002070936,0.0007604771,0.009677393],"genre_scores_gemma":[0.98935825,0.000045189972,0.009162202,0.00027294434,0.00028185744,0.000028421391,0.00016139293,0.000016900487,0.00067283906],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9973689,0.00008531345,0.0004564796,0.00049467897,0.0010171706,0.00057750667],"domain_scores_gemma":[0.9986389,0.00006188192,0.00023100279,0.0001503583,0.00065875176,0.00025907453],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.00154088,0.00015745682,0.00015120194,0.00019366002,0.0020985317,0.00011765335,0.0003282758,0.00010239716,0.00043321602],"category_scores_gemma":[0.000059496917,0.00017031409,0.000059428516,0.0021576204,0.0013491416,0.0011910695,9.1865815e-7,0.00017572683,0.00011804608],"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":[0.00011218167,0.000140968,0.2853862,0.000044440258,0.000021524447,0.000014516381,0.23284373,0.43219903,0.0042763487,0.033956014,0.0009373015,0.01006774],"study_design_scores_gemma":[0.0012805137,0.0002820286,0.8936302,0.00014047204,0.000102425845,6.4223127e-7,0.0077764033,0.016075583,0.0013021235,0.002284357,0.07626421,0.0008610248],"about_ca_topic_score_codex":0.001460703,"about_ca_topic_score_gemma":0.016515171,"teacher_disagreement_score":0.608244,"about_ca_system_score_codex":0.00008199005,"about_ca_system_score_gemma":0.00038753793,"threshold_uncertainty_score":0.9992006},"labels":[],"label_agreement":null},{"id":"W2520654676","doi":"10.1002/atr.1402","title":"Modeling and solving discrete network design problem with stochastic user equilibrium","year":2016,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Mathematical optimization; Integer (computer science); Nonlinear system; Network planning and design; Computer science; Flow network; Optimization problem; Integer programming; Traffic network; Convex optimization; Regular polygon; Mathematics","score_opus":0.015434615273087912,"score_gpt":0.25981761538175985,"score_spread":0.24438300010867192,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2520654676","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21179585,0.00014622344,0.7873855,0.0004023271,0.00009139877,0.00013114318,0.0000022859906,0.000023488208,0.00002177615],"genre_scores_gemma":[0.8823804,0.000112480135,0.11729633,0.000018259976,0.00011855361,0.0000034639002,0.0000033439615,0.00001314305,0.000054028755],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9989647,0.00005455262,0.00035385662,0.00011652072,0.00031434163,0.00019597993],"domain_scores_gemma":[0.99922997,0.00010198294,0.00026048793,0.00004286735,0.00025969287,0.00010501631],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043688042,0.00009153648,0.00014954075,0.00006560008,0.00016443657,0.000030980013,0.0000650408,0.000049125094,0.000008758518],"category_scores_gemma":[0.000024964042,0.00006135751,0.000032717668,0.00016267203,0.00006407972,0.0010765543,4.688362e-7,0.000078862555,3.8512212e-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":[0.00030694867,0.000008001709,0.0012175074,0.000008817113,0.000017902144,0.0000064775186,0.0058482746,0.9890149,0.00046620073,0.0013309433,0.000005010395,0.0017690081],"study_design_scores_gemma":[0.07001122,0.011660268,0.3335368,0.037738167,0.0049548806,0.00017603008,0.088079005,0.27064413,0.0021476543,0.16690527,0.0053854147,0.008761169],"about_ca_topic_score_codex":0.000017526163,"about_ca_topic_score_gemma":0.0002462915,"teacher_disagreement_score":0.7183708,"about_ca_system_score_codex":0.000030627773,"about_ca_system_score_gemma":0.00012498508,"threshold_uncertainty_score":0.25020868},"labels":[],"label_agreement":null},{"id":"W2521740062","doi":"10.1108/9780857245670-012","title":"Transport Demand Elasticities","year":2007,"lang":"en","type":"book-chapter","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":24,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Economics; Econometrics","score_opus":0.03655736890740622,"score_gpt":0.28548891416697963,"score_spread":0.24893154525957342,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2521740062","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00002488486,0.00009670288,0.03172055,0.00010022627,0.00025757236,0.000108215565,0.000020353942,0.0001918422,0.96747965],"genre_scores_gemma":[0.007826023,0.0003602734,0.0014515561,0.00017056998,0.00025017821,0.0000012546125,0.00015175412,0.000021544762,0.98976684],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99909914,0.000005676891,0.0002143433,0.00016019258,0.00035763136,0.00016302995],"domain_scores_gemma":[0.9996012,0.0000719865,0.00007670118,0.00006742225,0.00009380886,0.00008889129],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00025203993,0.00012581219,0.00014938442,0.00011545447,0.00023440259,0.000018181167,0.00008814272,0.0002653002,0.00393721],"category_scores_gemma":[0.000006989023,0.00012687374,0.00007538362,0.000022681686,0.00017685545,0.00007308537,9.652252e-7,0.00013754681,0.00011836923],"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":[0.000010152995,0.000003856814,0.00022767438,0.000012203401,0.00002269521,0.000018679977,0.004123738,0.00040741853,1.2659592e-7,0.9919548,0.0016426403,0.001575999],"study_design_scores_gemma":[0.000096480166,0.000014301447,0.0007414196,0.000070609945,0.00006707591,3.635578e-7,0.00034741944,0.000008436307,0.000002108502,0.006051186,0.99235207,0.00024853047],"about_ca_topic_score_codex":0.00023689696,"about_ca_topic_score_gemma":0.0045971624,"teacher_disagreement_score":0.9907094,"about_ca_system_score_codex":0.000030684954,"about_ca_system_score_gemma":0.00012666611,"threshold_uncertainty_score":0.99697334},"labels":[],"label_agreement":null},{"id":"W2522203301","doi":"","title":"Le transport combiné réellement moins énergivore que la route ?","year":2009,"lang":"fr","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ministère des Transports","funders":"","keywords":"Combinatorics; Computer science; Mathematics","score_opus":0.012228634923834934,"score_gpt":0.2360962552533626,"score_spread":0.22386762032952767,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2522203301","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07566919,0.0036717057,0.31376237,0.10703005,0.0013817074,0.0013992739,0.00029281393,0.00078689953,0.49600598],"genre_scores_gemma":[0.88499755,0.009393199,0.03702104,0.00037186037,0.00006901988,0.000084871375,0.0032803568,0.00007519699,0.0647069],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.98794,0.008125858,0.0011147062,0.0011425429,0.0009743966,0.0007024759],"domain_scores_gemma":[0.9942202,0.0011215869,0.00085103797,0.001434987,0.001924924,0.00044727558],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0077875406,0.0005782155,0.0006375625,0.00021946701,0.0013938128,0.0006320697,0.0014749683,0.000724615,0.00035099153],"category_scores_gemma":[0.00027813236,0.0007095465,0.0004511017,0.00088003866,0.0008155591,0.0007037249,0.00012615984,0.0009711956,0.000087832304],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020710555,0.0011608634,0.004248119,0.000105848274,0.00010747062,0.000018897188,0.062217623,0.009954034,0.00008059958,0.8971993,0.0009697528,0.023916796],"study_design_scores_gemma":[0.0034408043,0.0000042266797,0.09424861,0.0089592235,0.00049630797,0.00001739225,0.009720231,0.020295389,0.0047324277,0.028506542,0.8269897,0.002589143],"about_ca_topic_score_codex":0.022873525,"about_ca_topic_score_gemma":0.035145145,"teacher_disagreement_score":0.86869276,"about_ca_system_score_codex":0.00023539265,"about_ca_system_score_gemma":0.0017720194,"threshold_uncertainty_score":0.99990624},"labels":[],"label_agreement":null},{"id":"W2523196464","doi":"10.7939/r3-rfgj-4e16","title":"Capacity planning and management for mesh survivable networks under demand uncertainty","year":2005,"lang":"en","type":"article","venue":"University of Alberta Library","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Network planning and design; Computer science; Robust optimization; Ambiguity; Operations research; Futures contract; Survivability; Robustness (evolution); Capacity planning; Stochastic programming; Mathematical optimization; Engineering; Economics","score_opus":0.01582851632338997,"score_gpt":0.21405842897667055,"score_spread":0.19822991265328058,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2523196464","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.694183,0.00028219284,0.13356455,0.007181492,0.00015387121,0.0006633905,0.000016696125,0.00015288503,0.16380194],"genre_scores_gemma":[0.97609097,0.00023076995,0.012289961,0.00014620168,0.000041952128,2.3163244e-7,0.000072213756,0.0000054381953,0.0111222705],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995974,0.00003470021,0.00006014565,0.00011483238,0.00007171706,0.0001212227],"domain_scores_gemma":[0.99967164,0.00015648997,0.000048984348,0.000049668844,0.000014075964,0.000059158574],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008854475,0.000048506514,0.00007816108,0.00004612815,0.0003200491,0.000019183064,0.00008967753,0.000056906934,0.000090754074],"category_scores_gemma":[0.0000036078488,0.000058744175,0.000026333457,0.00009597172,0.00010071736,0.000501853,0.000012041642,0.00003327281,8.5523544e-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":[0.0001855115,0.000055976067,0.10441793,0.000060361705,0.00010444509,0.000002400606,0.030849755,0.65222806,0.000001117859,0.19609562,0.01473825,0.0012605889],"study_design_scores_gemma":[0.003154893,0.00010018589,0.17838718,0.00024542716,0.0002299136,6.925496e-7,0.041023,0.121998064,0.000031978125,0.0026506034,0.65152997,0.00064807944],"about_ca_topic_score_codex":0.0014139811,"about_ca_topic_score_gemma":0.0021317382,"teacher_disagreement_score":0.6367917,"about_ca_system_score_codex":0.00001099719,"about_ca_system_score_gemma":0.000021314692,"threshold_uncertainty_score":0.24615912},"labels":[],"label_agreement":null},{"id":"W2523775127","doi":"10.1002/atr.1410","title":"A decomposition approach to determining fleet size and structure with network flow effects and demand uncertainty","year":2016,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","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":"European Commission; National Science Foundation","keywords":"Decomposition; Flow network; Flow (mathematics); Computer science; Environmental science; Econometrics; Mathematical optimization; Mathematics; Mechanics; Physics; Ecology; Biology","score_opus":0.004629056065464812,"score_gpt":0.2497753611556436,"score_spread":0.2451463050901788,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2523775127","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8698114,0.0000985441,0.12952329,0.0002369392,0.00009921164,0.00017002644,0.000008912968,0.000016185431,0.00003547908],"genre_scores_gemma":[0.87694746,0.00011677234,0.12273371,0.00006930426,0.00010139208,0.000003225322,0.000008571775,0.000008323924,0.000011240931],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99918467,0.00005925243,0.00024028173,0.00013548648,0.00023069464,0.00014961344],"domain_scores_gemma":[0.99924153,0.00021480673,0.0002148185,0.00003781497,0.0001503224,0.00014072159],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021203427,0.00009639481,0.00016877473,0.000056478864,0.00020327284,0.00003059514,0.000042817883,0.00005951301,0.0000027436624],"category_scores_gemma":[0.000040812083,0.000065424785,0.0000198552,0.00016530672,0.000052021995,0.00047751743,5.5185825e-7,0.00006708542,8.223968e-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":[0.00086561515,0.000031283766,0.047806904,0.000077555116,0.00005018638,0.000020166706,0.024977341,0.88681835,0.0036368486,0.0006607595,0.000024784866,0.035030186],"study_design_scores_gemma":[0.002805929,0.00051001494,0.99156547,0.0006387571,0.00014665685,0.000017425691,0.0018656966,0.00029205543,0.0002635809,0.0012860956,0.00036710143,0.00024123215],"about_ca_topic_score_codex":0.000008699648,"about_ca_topic_score_gemma":0.00036704814,"teacher_disagreement_score":0.94375855,"about_ca_system_score_codex":0.000026813035,"about_ca_system_score_gemma":0.000047820507,"threshold_uncertainty_score":0.26679453},"labels":[],"label_agreement":null},{"id":"W2525633492","doi":"","title":"Traffic Data： Bluetooth Sensors vs. Crowdsourcing——A Comparative Study to Calculate Travel Time Reliability in Calgary, Alberta, Canada","year":2015,"lang":"en","type":"article","venue":"Acta Scientiarum Naturalium Universitatis Sunyatseni","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Crowdsourcing; Bluetooth; Reliability (semiconductor); Computer science; Transport engineering; Engineering; Telecommunications; World Wide Web; Wireless","score_opus":0.026387084610394945,"score_gpt":0.28241833935999694,"score_spread":0.256031254749602,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2525633492","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9909072,0.000014868529,0.000050827603,0.0041797734,0.00064681435,0.00080084585,0.00011737842,0.000060570605,0.003221739],"genre_scores_gemma":[0.9917903,0.0000017122062,0.0011130858,0.00014631139,0.000033356035,0.0000018091782,0.00037202553,0.0000136608305,0.006527759],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.99646866,0.0006390622,0.00037762566,0.00081977254,0.0010890934,0.00060577324],"domain_scores_gemma":[0.9979616,0.0004071116,0.00015236474,0.0005287337,0.00038900983,0.000561185],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0012423773,0.00024403556,0.00038544548,0.00033361057,0.00057205657,0.00014653998,0.00090711704,0.00010122554,0.000114117334],"category_scores_gemma":[0.00037914523,0.00025806206,0.000043503427,0.0018505828,0.00022910688,0.0009248902,0.000093785515,0.00029985304,0.000039283987],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007598649,0.0007489033,0.06734847,0.000018846245,0.00013312756,0.0002882735,0.49779195,0.26858506,0.00008437568,0.00042607856,0.1636041,0.000210956],"study_design_scores_gemma":[0.0057757758,0.00057529425,0.4426811,0.00010740388,0.00029939626,0.000006800952,0.38671124,0.10380094,0.00008590236,0.000034183668,0.05794241,0.0019795657],"about_ca_topic_score_codex":0.8311312,"about_ca_topic_score_gemma":0.9746315,"teacher_disagreement_score":0.37533262,"about_ca_system_score_codex":0.00073657965,"about_ca_system_score_gemma":0.0015425121,"threshold_uncertainty_score":0.9999872},"labels":[],"label_agreement":null},{"id":"W2527730355","doi":"","title":"The Development and Deployment of a GIS tool for Transit Network Design","year":2010,"lang":"en","type":"dissertation","venue":"UWSpace (University of Waterloo)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Software deployment; Transit (satellite); Development (topology); Systems engineering; Computer science; Transport engineering; Engineering; Software engineering; Public transport","score_opus":0.016077535782911004,"score_gpt":0.2249072785619821,"score_spread":0.20882974277907107,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2527730355","genre_codex":"empirical","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99046564,0.00012702218,0.007864685,0.00047109413,0.00026719572,0.0006294694,0.000013979684,0.000031160147,0.00012974431],"genre_scores_gemma":[0.2678086,0.0010592418,0.2871839,0.000021704844,0.00013687539,0.000010260574,0.0009289293,0.0000532869,0.44279718],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.9993576,0.00004987044,0.000113059716,0.00013492527,0.00019090362,0.0001536564],"domain_scores_gemma":[0.9994042,0.00013762855,0.00018854458,0.000070770875,0.0001599982,0.000038895345],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045205155,0.00008597036,0.00015530916,0.000045437126,0.0007268552,0.000016702512,0.00014948854,0.00018194545,0.000015445417],"category_scores_gemma":[0.000013117662,0.000083778745,0.000051675,0.00009030707,0.00010141304,0.0000744497,0.000002733966,0.00008257347,6.4770836e-7],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004955125,0.000031313815,0.0007385908,0.00014495768,0.00011983418,0.0000012817097,0.9709438,0.0033688252,0.00021004773,0.0017728773,0.0008760311,0.021296933],"study_design_scores_gemma":[0.0017497694,0.00021871945,0.032318283,0.00046074644,0.0005291979,2.4054876e-7,0.91752595,0.0007358638,0.0025833363,0.0006973954,0.042526685,0.00065383577],"about_ca_topic_score_codex":0.0058658733,"about_ca_topic_score_gemma":0.2553316,"teacher_disagreement_score":0.722657,"about_ca_system_score_codex":0.000019066692,"about_ca_system_score_gemma":0.00020681348,"threshold_uncertainty_score":0.88674843},"labels":[],"label_agreement":null},{"id":"W2530059968","doi":"10.1002/atr.1418","title":"Tradable credits scheme and transit investment optimization for a two‐mode traffic network","year":2016,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"China National Funds for Distinguished Young Scientists; National Natural Science Foundation of China","keywords":"Investment (military); Public transport; Scheme (mathematics); Modal; Mode (computer interface); Transit (satellite); Mode choice; Transport engineering; Bus rapid transit; Marginal cost; Computer science; Operations research; Economics; Microeconomics; Business; Engineering","score_opus":0.013962608075229335,"score_gpt":0.2843395931100012,"score_spread":0.27037698503477187,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2530059968","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3949483,0.00036121873,0.6014836,0.0019729664,0.0005896679,0.0004422593,0.000043560365,0.000055193945,0.00010324207],"genre_scores_gemma":[0.77698696,0.00070112076,0.22158399,0.00014638896,0.000376906,0.000017517144,0.000052774885,0.00002064685,0.000113704504],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9987329,0.000050118626,0.000520413,0.00015336963,0.00031564882,0.00022754176],"domain_scores_gemma":[0.9988727,0.00016713458,0.0003934236,0.00005338736,0.00036566152,0.0001476816],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044228492,0.00011626289,0.00020944988,0.000100121964,0.00025584974,0.000030329837,0.00008075042,0.00007950543,0.000025860956],"category_scores_gemma":[0.00005213755,0.000093160284,0.00008855741,0.00021540685,0.00008079679,0.0007935475,2.6207994e-7,0.00006637587,2.762392e-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":[0.0003550851,0.00004121111,0.00048052467,0.000020422147,0.000030370244,0.0000030530089,0.007280186,0.97616583,0.00062707835,0.0038963298,0.00010289322,0.010997009],"study_design_scores_gemma":[0.1582238,0.010330325,0.27197394,0.009563167,0.0045407633,0.00008051079,0.052256707,0.1667809,0.007850135,0.046546582,0.26434916,0.007503993],"about_ca_topic_score_codex":0.000014124549,"about_ca_topic_score_gemma":0.0005745403,"teacher_disagreement_score":0.80938494,"about_ca_system_score_codex":0.000057579255,"about_ca_system_score_gemma":0.00015999483,"threshold_uncertainty_score":0.3798966},"labels":[],"label_agreement":null},{"id":"W2531401995","doi":"10.1016/j.tra.2016.10.005","title":"Time-dependent congestion pricing system for large networks: Integrating departure time choice, dynamic traffic assignment and regional travel surveys in the Greater Toronto Area","year":2016,"lang":"en","type":"article","venue":"Transportation Research Part A Policy and Practice","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":29,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Congestion pricing; Traffic congestion; Transport engineering; Road pricing; Variable (mathematics); Computer science; Travel time; Supply and demand; Christian ministry; Operations research; Business; Environmental economics; Economics; Microeconomics; Engineering","score_opus":0.061404710655034465,"score_gpt":0.38815895863739586,"score_spread":0.3267542479823614,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2531401995","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.61506414,0.001657713,0.30370224,0.063626155,0.0002947938,0.007584099,0.0009371415,0.00038412973,0.0067495643],"genre_scores_gemma":[0.9976442,0.00045744446,0.00021540599,0.0001325882,0.00014445835,0.00018137397,0.00018790521,0.0000135778755,0.0010230452],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.9960649,0.0023475378,0.00032177093,0.00029838952,0.00054689345,0.00042050975],"domain_scores_gemma":[0.9939674,0.0054502836,0.00013872657,0.000088710505,0.00024390718,0.000110980225],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010384855,0.00012443462,0.00015783151,0.00010140508,0.0006799411,0.00012828018,0.00010066632,0.00014249061,0.00003200768],"category_scores_gemma":[0.0007897475,0.000084435334,0.000029790925,0.00024684597,0.00013403142,0.000770019,0.0000018213944,0.00017501584,0.0000035003816],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.004810425,0.0015405144,0.044311885,0.0010658735,0.00066247716,0.00017192106,0.66466224,0.04937963,0.0020543525,0.108844,0.008300953,0.11419573],"study_design_scores_gemma":[0.0084529165,0.00087848294,0.71683866,0.0017861418,0.00030581895,0.000029000194,0.10801656,0.088555016,0.000024710738,0.0002144679,0.07378601,0.0011121936],"about_ca_topic_score_codex":0.0037343742,"about_ca_topic_score_gemma":0.04113679,"teacher_disagreement_score":0.6725268,"about_ca_system_score_codex":0.00018746716,"about_ca_system_score_gemma":0.00017281831,"threshold_uncertainty_score":0.97635996},"labels":[],"label_agreement":null},{"id":"W2531520206","doi":"10.1002/atr.1416","title":"Optimal design of a short‐turning strategy considering seat availability","year":2016,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Schedule; Markov chain; Markov model; Markov decision process; Service (business); Crowding; Operations research; Markov process; Process (computing); Computer science; Transport engineering; Engineering; Simulation","score_opus":0.033854644364905935,"score_gpt":0.30240205540608295,"score_spread":0.268547411041177,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2531520206","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6464936,0.000083135565,0.35281545,0.000171296,0.00016662006,0.00014806856,0.0000104322935,0.00002173062,0.00008965634],"genre_scores_gemma":[0.9433143,0.00025501417,0.056286708,0.00000965454,0.000055261935,0.0000033449878,0.000005085148,0.00000919317,0.00006142695],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9985444,0.00011115729,0.00066318683,0.000116481366,0.0004028829,0.00016192441],"domain_scores_gemma":[0.9985824,0.00030474397,0.0004370977,0.00006665552,0.00051175675,0.00009731838],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007502593,0.000091645146,0.00021615112,0.00011442041,0.00011384392,0.000013397827,0.000097547025,0.00010147408,0.000092471055],"category_scores_gemma":[0.00013944782,0.00006440786,0.000088977824,0.00019472562,0.00013298602,0.0008218956,4.386772e-7,0.00012769522,9.399557e-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":[0.00034368975,0.000051142837,0.023922598,0.000023123052,0.000033120486,0.00001744361,0.00876605,0.92621344,0.024733853,0.00057616347,0.000018282251,0.0153011],"study_design_scores_gemma":[0.005545728,0.0014498238,0.9121531,0.0014609067,0.00041962534,0.000015123018,0.029573193,0.0005933563,0.04127237,0.0024243863,0.004247625,0.0008447262],"about_ca_topic_score_codex":0.00001999899,"about_ca_topic_score_gemma":0.00010361828,"teacher_disagreement_score":0.9256201,"about_ca_system_score_codex":0.000056031568,"about_ca_system_score_gemma":0.00026036348,"threshold_uncertainty_score":0.26264763},"labels":[],"label_agreement":null},{"id":"W2535668829","doi":"10.1109/vnis.1995.518848","title":"Examining the delay and environmental impacts of toll plaza characteristics","year":2002,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Toll; Context (archaeology); Computer science; Toll road; Transport engineering; Sample (material); Yield (engineering); Service (business); Estimation; Fuel efficiency; Consumption (sociology); Track (disk drive); Operations research; Environmental economics; Automotive engineering; Engineering; Business; Systems engineering; Economics","score_opus":0.026563893808524617,"score_gpt":0.23690079261518357,"score_spread":0.21033689880665896,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2535668829","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99125654,0.000053336396,0.00040842357,0.00017384945,0.000035356352,0.000041605177,0.0000107920605,0.000013437009,0.00800664],"genre_scores_gemma":[0.9985779,0.0002197045,0.00028422385,0.000068636844,0.000020201116,7.1453826e-7,0.0000085558195,0.000001953275,0.0008181328],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9997001,0.00002266276,0.000076396784,0.00004502655,0.00009273559,0.00006304435],"domain_scores_gemma":[0.99983215,0.000063871776,0.000039427723,0.00003280956,0.0000037196428,0.000028047469],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011008728,0.00002646505,0.00003779564,0.0000114262675,0.00013431882,0.00001431158,0.000033183383,0.00002311342,0.00037350506],"category_scores_gemma":[0.000018127366,0.000018951327,0.0000063638568,0.000030274708,0.00008748848,0.00006385907,0.0000021566343,0.000024337172,0.000003639879],"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":[0.000008207069,0.00005220934,0.8950751,0.0000065556246,0.000016278354,0.0000035543708,0.087891534,0.00015423051,0.00048272373,0.0033460627,0.00084183656,0.012121747],"study_design_scores_gemma":[0.00022762842,0.0000484001,0.97139984,0.000012796897,0.000021032854,0.0000014805739,0.016645657,0.0034820687,0.00013537363,0.000024104947,0.007897424,0.000104175735],"about_ca_topic_score_codex":0.00014600437,"about_ca_topic_score_gemma":0.00009309537,"teacher_disagreement_score":0.0763248,"about_ca_system_score_codex":0.0000057646835,"about_ca_system_score_gemma":0.0000036183071,"threshold_uncertainty_score":0.40896186},"labels":[],"label_agreement":null},{"id":"W253972526","doi":"10.1177/0361198105192600106","title":"How Far in Advance Are Activities Planned?","year":2005,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Wilfrid Laurier University","funders":"","keywords":"Time horizon; Scheduling (production processes); Operations research; Recall; Computer science; Schedule; Operations management; Engineering; Psychology; Business; Cognitive psychology","score_opus":0.07815191939301032,"score_gpt":0.39339041724760576,"score_spread":0.31523849785459546,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W253972526","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96944875,0.0005290942,0.0020005624,0.025837542,0.0005199231,0.00086719694,0.000088031455,0.00005225514,0.0006566415],"genre_scores_gemma":[0.98942876,0.0026619982,0.0034858936,0.000095134026,0.0004030694,0.000060682447,0.000022624752,0.000043324075,0.003798486],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9917778,0.0017065009,0.0010360765,0.0003949662,0.004038316,0.0010463608],"domain_scores_gemma":[0.99548817,0.0011325349,0.0005788953,0.00032878484,0.0021005294,0.00037109404],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0052332613,0.00022860678,0.0004316293,0.001309046,0.001084241,0.00029755937,0.0010377271,0.00026124416,0.00018656252],"category_scores_gemma":[0.00036874812,0.00019389045,0.00027579546,0.0026885627,0.0009026502,0.0020311347,0.0000031378922,0.0021320386,0.000011852925],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0025186273,0.00059275207,0.874811,0.00019793378,0.000104423765,0.00019093453,0.04521399,0.025225623,0.0007346707,0.019247808,0.007751664,0.023410551],"study_design_scores_gemma":[0.001297532,0.00021212586,0.78708684,0.00038599363,0.000020641144,2.0632915e-7,0.036221348,0.00009584833,0.0005406588,0.0011688005,0.17274903,0.000220947],"about_ca_topic_score_codex":0.010299012,"about_ca_topic_score_gemma":0.5565206,"teacher_disagreement_score":0.54622155,"about_ca_system_score_codex":0.00045733323,"about_ca_system_score_gemma":0.00085246796,"threshold_uncertainty_score":0.9962915},"labels":[],"label_agreement":null},{"id":"W2541543146","doi":"10.1002/atr.1415","title":"Online calibration of an integrated framework for information‐based evacuation operations","year":2016,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","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":"National Center for Theoretical Sciences; U.S. Department of Transportation","keywords":"Calibration; Computer science; Transport engineering; Operations research; Systems engineering; Engineering; Mathematics; Statistics","score_opus":0.017264988070086782,"score_gpt":0.321553938805841,"score_spread":0.3042889507357542,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2541543146","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2555525,0.000011563227,0.7428694,0.00083608704,0.00025381704,0.0002270467,0.00021706334,0.000024659492,0.000007910852],"genre_scores_gemma":[0.724354,0.000041842977,0.27478346,0.00007523247,0.00008107977,0.000008684184,0.0006353521,0.0000070141855,0.0000133252415],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99856865,0.000073615,0.00078734674,0.00007683758,0.00038102164,0.00011250856],"domain_scores_gemma":[0.9976054,0.0002293241,0.00057140534,0.0000787781,0.001429036,0.000086094486],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004400491,0.00008668371,0.00016529595,0.00025657742,0.00016046174,0.000026984988,0.000113904774,0.00011398051,0.000044070497],"category_scores_gemma":[0.00043807973,0.000067220244,0.00008635248,0.00035564267,0.000064006694,0.0031752659,2.3456693e-7,0.00007337637,3.9713032e-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":[0.0005631562,0.0002186356,0.0013764366,0.000034282773,0.00002585458,6.137133e-7,0.012278773,0.90221745,0.0020524908,0.034103606,0.000025644611,0.047103077],"study_design_scores_gemma":[0.028198838,0.006000103,0.69889605,0.0041041425,0.0011541968,0.0000044651624,0.070382096,0.056287553,0.043268967,0.043541364,0.04614564,0.0020165835],"about_ca_topic_score_codex":0.000040410676,"about_ca_topic_score_gemma":0.0008761196,"teacher_disagreement_score":0.84592986,"about_ca_system_score_codex":0.00006180722,"about_ca_system_score_gemma":0.0004195816,"threshold_uncertainty_score":0.27411622},"labels":[],"label_agreement":null},{"id":"W2542514208","doi":"10.1109/vnis.1989.98781","title":"Development and potential of system optimized route guidance strategies","year":2003,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Context (archaeology); Network packet; Computer science; Traffic count; Traffic engineering; Traffic generation model; Simple (philosophy); Real-time computing; Computer network; Operations research; Transport engineering; Engineering; Traffic congestion","score_opus":0.014777408406283046,"score_gpt":0.25876234353322186,"score_spread":0.2439849351269388,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2542514208","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.41753027,0.00015456416,0.33373064,0.00006954614,0.00024239792,0.00018830146,0.0000032558896,0.0001591336,0.24792191],"genre_scores_gemma":[0.85374206,0.00001013163,0.14538278,0.000005914386,0.000005618993,0.0000025778156,0.0000039571355,0.0000019300173,0.0008450038],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","domain_scores_codex":[0.99952865,0.000044533026,0.00014405185,0.000073230134,0.00013141059,0.00007811158],"domain_scores_gemma":[0.99981517,0.000012584974,0.00005153416,0.000030774252,0.000058198508,0.000031734806],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027039007,0.00003652885,0.00006881881,0.000023243869,0.00014287376,0.000032845935,0.000032069198,0.000032171643,0.000027388283],"category_scores_gemma":[0.000009759899,0.000033428136,0.00001015557,0.00007615852,0.000044012282,0.00011938417,0.000001100192,0.000016061165,0.0000015104526],"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":[0.000019001063,0.000020458889,0.0030842056,0.000062600164,0.000022195427,0.0000035257121,0.018311063,0.03749137,0.00015129986,0.94007254,0.000068083355,0.0006936334],"study_design_scores_gemma":[0.006095261,0.000092726,0.08524672,0.0006228488,0.00015292561,0.000009707158,0.7881103,0.0075816386,0.011442644,0.0009623832,0.09830555,0.0013772983],"about_ca_topic_score_codex":0.00016079255,"about_ca_topic_score_gemma":0.00012934684,"teacher_disagreement_score":0.93911016,"about_ca_system_score_codex":0.000014728745,"about_ca_system_score_gemma":0.00017587683,"threshold_uncertainty_score":0.13631599},"labels":[],"label_agreement":null},{"id":"W2543705200","doi":"10.1109/vnis.1995.518839","title":"Estimation of expected minimum paths in dynamic and stochastic traffic networks","year":2002,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Shortest path problem; Computer science; Mathematical optimization; Heuristic; Path (computing); K shortest path routing; Computation; Constrained Shortest Path First; Yen's algorithm; Shortest Path Faster Algorithm; Floyd–Warshall algorithm; Longest path problem; Dijkstra's algorithm; Algorithm; Mathematics; Graph; Theoretical computer science","score_opus":0.011522217522496854,"score_gpt":0.25459756144346773,"score_spread":0.24307534392097088,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2543705200","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.87308145,0.00014366428,0.1252127,0.00015104072,0.000054783457,0.00011127304,0.0000012130203,0.00005258666,0.001191281],"genre_scores_gemma":[0.9983078,0.000034175875,0.0013668789,0.000010423254,0.0000056151302,0.0000035111063,0.0000113346005,0.0000027027163,0.00025753555],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99959564,0.000031609932,0.00012815048,0.000075105774,0.00008759231,0.00008188831],"domain_scores_gemma":[0.99981683,0.00006994471,0.000039309874,0.000029722441,0.000017992876,0.000026201229],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009165702,0.000034820856,0.00006188469,0.000062064035,0.000053403022,0.000010561999,0.000027116042,0.000047532816,0.0001017043],"category_scores_gemma":[0.000038251368,0.000035471487,0.000008684107,0.00021391344,0.000051296647,0.00008719212,9.57185e-7,0.00003219159,0.0000012498821],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000031413206,0.000022863816,0.00050990086,0.0000025331065,0.0000012527547,4.8882515e-7,0.011866146,0.97230947,0.000001846703,0.0004499549,0.000029721652,0.014802652],"study_design_scores_gemma":[0.00017691987,0.00001268589,0.012062004,0.00001601205,0.0000036927454,1.1440644e-7,0.0016963965,0.98595774,3.5282625e-7,0.000028432865,0.000004089242,0.00004154431],"about_ca_topic_score_codex":0.00010308893,"about_ca_topic_score_gemma":0.0012432217,"teacher_disagreement_score":0.12522636,"about_ca_system_score_codex":0.00001244806,"about_ca_system_score_gemma":0.000007640738,"threshold_uncertainty_score":0.14464852},"labels":[],"label_agreement":null},{"id":"W2543868641","doi":"10.1109/vnis.1995.518852","title":"Coupled evaluation of communication system loading and ATIS/ATMS efficiency","year":2002,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Communications system; Computer science; Telecommunications network; Intelligent transportation system; Network topology; Network congestion; Traffic congestion; Computer network; Network performance; Advanced Traffic Management System; Distributed computing; Engineering; Transport engineering","score_opus":0.053383286302739155,"score_gpt":0.3128981933668722,"score_spread":0.25951490706413305,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2543868641","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.82876027,0.0009979072,0.01675837,0.0004111173,0.000099056655,0.0004185472,0.000002689085,0.00014866189,0.15240338],"genre_scores_gemma":[0.99844044,0.00009992379,0.0011560302,0.000008131505,0.000008666625,0.0000066747566,0.000009539831,0.0000026004438,0.00026798993],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99921477,0.00013445356,0.00015523327,0.00007397333,0.00035371736,0.00006784978],"domain_scores_gemma":[0.99948215,0.00008796933,0.00009157187,0.00008626385,0.00022328926,0.000028772503],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011839226,0.00003399651,0.000062913365,0.00004468567,0.00025609092,0.0000224169,0.00006205533,0.000039503255,0.00020602637],"category_scores_gemma":[0.00008612273,0.000033367996,0.000011457009,0.00018545207,0.000073932366,0.00013137185,0.0000026630498,0.000026035581,0.00000647734],"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":[0.000022852282,0.00028774206,0.110637575,0.00017329976,0.00006418527,7.967829e-7,0.22743307,0.1424175,0.0008957528,0.46205288,0.0012300534,0.054784276],"study_design_scores_gemma":[0.0004832109,0.00001698075,0.013920083,0.00007635866,0.000056315715,2.945371e-7,0.01141252,0.97353286,0.00006376842,0.00009431895,0.0002602501,0.00008305731],"about_ca_topic_score_codex":0.00075497065,"about_ca_topic_score_gemma":0.00034525516,"teacher_disagreement_score":0.83111537,"about_ca_system_score_codex":0.000041587926,"about_ca_system_score_gemma":0.00002026201,"threshold_uncertainty_score":0.22558442},"labels":[],"label_agreement":null},{"id":"W2545826862","doi":"10.1002/atr.1425","title":"Multiclass fuzzy user equilibrium with endogenous membership functions and risk‐taking behaviors","year":2016,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Bounded rationality; Mathematical optimization; Fuzzy logic; Calibration; Bounded function; Data mining; Mathematics; Artificial intelligence","score_opus":0.024488751550269812,"score_gpt":0.2735082106845213,"score_spread":0.2490194591342515,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2545826862","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9881894,0.00010031401,0.01052428,0.00029511933,0.00026718265,0.00013417313,0.000028937848,0.000040322197,0.000420242],"genre_scores_gemma":[0.9914326,0.00031929498,0.007865361,0.000012607775,0.00009687017,0.0000044124395,0.000011298283,0.000015633932,0.00024189595],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9988671,0.00007414373,0.00036574414,0.00013450281,0.000372605,0.00018590828],"domain_scores_gemma":[0.9984443,0.00016830435,0.00086193625,0.00006341534,0.00032634506,0.00013568833],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033936245,0.00010296069,0.00015306758,0.00012804128,0.00024194022,0.00002868911,0.000069711314,0.00006802728,0.00004709395],"category_scores_gemma":[0.00008169687,0.000072557516,0.000054966393,0.00021316494,0.0001245095,0.0009421506,4.556648e-7,0.000119328266,0.0000016900319],"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":[0.0007998911,0.00012192478,0.85523325,0.000022522085,0.000101970334,0.00007652276,0.036722776,0.07257518,0.013443358,0.0014514226,0.00002202276,0.019429173],"study_design_scores_gemma":[0.0031666218,0.00038911888,0.9778546,0.00025274468,0.00043385132,0.000016710232,0.013556434,0.000006636197,0.0013081387,0.00031523328,0.0024282136,0.00027165955],"about_ca_topic_score_codex":0.0001340785,"about_ca_topic_score_gemma":0.0022490548,"teacher_disagreement_score":0.1226214,"about_ca_system_score_codex":0.000052672152,"about_ca_system_score_gemma":0.000111129724,"threshold_uncertainty_score":0.29588097},"labels":[],"label_agreement":null},{"id":"W2546570954","doi":"10.5539/mas.v11n1p159","title":"Investigating the Transit Oriented Development in New Urbanism, Case Study: Subway Stations Development in Mashhad","year":2016,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Transportation Planning and Optimization","field":"Social Sciences","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":"Transit-oriented development; SWOT analysis; Transport engineering; New Urbanism; Capitalization; Sustainable development; Urban planning; Pedestrian; Investment (military); Business; Land use; Public transport; Environmental planning; Civil engineering; Urbanism; Environmental science; Geography; Political science","score_opus":0.03966497041357605,"score_gpt":0.2914969751325875,"score_spread":0.25183200471901146,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2546570954","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.82777303,0.0000071611726,0.16992699,0.00053875835,0.000057907317,0.00054854795,0.0000010390019,0.00005491097,0.0010916834],"genre_scores_gemma":[0.98372006,0.0000015840676,0.015688358,0.00010096399,0.000011803353,0.00007193026,0.000002273853,0.0000073560664,0.00039564323],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.99804366,0.00006502615,0.00039583188,0.0003967537,0.00069251657,0.0004061858],"domain_scores_gemma":[0.9994222,0.00012686972,0.00008822962,0.00013739149,0.00007186882,0.0001534763],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018961511,0.00011421493,0.000110064044,0.00021617247,0.0010079596,0.00009118291,0.00031002235,0.000039729206,0.000019572515],"category_scores_gemma":[0.00008406088,0.00008025229,0.00000938826,0.0014095828,0.00034797157,0.00028704302,0.000018155908,0.00010371958,0.000012230978],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000886106,0.00014025865,0.08364982,0.000003909744,0.0000043754353,0.00006517129,0.849308,0.006831048,0.0037305471,0.008338001,0.000019051402,0.047900945],"study_design_scores_gemma":[0.0073957006,0.00008128354,0.6796631,0.0003525703,0.000030030158,0.00002679357,0.26733738,0.01598942,0.008190803,0.005190397,0.013876766,0.0018657116],"about_ca_topic_score_codex":0.001593038,"about_ca_topic_score_gemma":0.06821657,"teacher_disagreement_score":0.5960133,"about_ca_system_score_codex":0.00031292054,"about_ca_system_score_gemma":0.0026485329,"threshold_uncertainty_score":0.9487861},"labels":[],"label_agreement":null},{"id":"W2549797977","doi":"10.1080/02286203.2016.1219806","title":"A system dynamics based simulation model to evaluate regulatory policies for sustainable transportation planning","year":2016,"lang":"en","type":"article","venue":"International Journal of Modelling and Simulation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":46,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"TRIPS architecture; Sustainable transport; System dynamics; Transportation planning; Causal loop diagram; Public transport; Environmental economics; Transport engineering; Sustainable development; Business; Energy consumption; Computer science; Sustainability; Economics; Engineering","score_opus":0.04295007285412454,"score_gpt":0.3481625538414726,"score_spread":0.30521248098734804,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2549797977","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.31797507,0.00002029091,0.6810333,0.0005757693,0.00012431026,0.0001531727,0.000020063011,0.000028945069,0.00006904712],"genre_scores_gemma":[0.9874492,0.000007304869,0.011969932,0.00006449575,0.00019047342,0.000006156038,0.000032629665,0.00001471359,0.00026505726],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99859625,0.00004490169,0.00047609213,0.00013652086,0.0005793972,0.000166833],"domain_scores_gemma":[0.99713516,0.00044311924,0.00034722156,0.000052971365,0.0019203925,0.000101159385],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009991849,0.00009623052,0.00014017402,0.00035004583,0.00024107326,0.00009239877,0.00012125204,0.00008966684,0.0000025622817],"category_scores_gemma":[0.00015122828,0.00008218338,0.00007383038,0.000103620856,0.00003219367,0.0006085423,0.000002046133,0.000047346737,4.23289e-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":[0.00038181263,0.00001525051,0.0012422949,0.000017541683,0.000024403229,0.0000024769593,0.004284324,0.977957,0.000024374205,0.014584501,0.000010768571,0.001455218],"study_design_scores_gemma":[0.000919625,0.00004982477,0.00058457075,0.00024590406,0.0000423156,3.1111915e-7,0.0015238251,0.99432236,0.000029447569,0.0018603863,0.0003155106,0.00010594021],"about_ca_topic_score_codex":0.000069828224,"about_ca_topic_score_gemma":0.000025597337,"teacher_disagreement_score":0.6694742,"about_ca_system_score_codex":0.0003433627,"about_ca_system_score_gemma":0.00019475288,"threshold_uncertainty_score":0.33513412},"labels":[],"label_agreement":null},{"id":"W2550355463","doi":"10.1002/atr.1428","title":"A GIS‐based method to identify cost‐effective routes for rural deviated fixed route transit","year":2016,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; Tennessee Department of Transportation","keywords":"Transport engineering; Metropolitan area; Computer science; Heuristic; Total cost; Transit (satellite); Operating cost; Operations research; Public transport; Engineering; Business; Geography","score_opus":0.01781213698518653,"score_gpt":0.36293845039477995,"score_spread":0.34512631340959343,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2550355463","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1632332,0.000041023803,0.8326221,0.002430914,0.0003901395,0.0010819797,0.000121041725,0.00005022886,0.000029336352],"genre_scores_gemma":[0.8637421,0.000028681765,0.13562633,0.00018195756,0.00010358434,0.00010380154,0.00007025206,0.000021134967,0.00012216512],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9983558,0.00015622548,0.0006230771,0.00016829104,0.00043100608,0.00026561468],"domain_scores_gemma":[0.997822,0.0006107815,0.0004655199,0.00007432763,0.00083062646,0.00019676512],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009144322,0.00014760926,0.0002963334,0.00023747263,0.00023117191,0.000037978007,0.00015397213,0.00010518594,0.00003758476],"category_scores_gemma":[0.00017083439,0.00011661912,0.00021394284,0.00042082582,0.000042734096,0.0008512835,3.2607187e-7,0.00008974598,0.0000030837457],"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":[0.004185462,0.00020718685,0.014535038,0.0000860905,0.00019600328,0.00002604272,0.039874107,0.59337926,0.0851014,0.0032564981,0.00029754007,0.2588554],"study_design_scores_gemma":[0.0102707,0.00090708525,0.9209856,0.0009691616,0.00048101885,0.0000023124153,0.0062409965,0.00025975273,0.021892466,0.002035072,0.035338562,0.0006173178],"about_ca_topic_score_codex":0.00005944838,"about_ca_topic_score_gemma":0.00097090297,"teacher_disagreement_score":0.9064505,"about_ca_system_score_codex":0.00014325384,"about_ca_system_score_gemma":0.00019821466,"threshold_uncertainty_score":0.47555897},"labels":[],"label_agreement":null},{"id":"W2551954685","doi":"","title":"L'optimisation d'un projet de transport en commun en site propre : une proposition de méthode pour le cas du tramway de Montréal","year":2016,"lang":"fr","type":"article","venue":"Archipelago (Université du Québec à Montréal)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Humanities; Political science; Art","score_opus":0.007535970244001269,"score_gpt":0.19550487923695653,"score_spread":0.18796890899295526,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2551954685","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7142964,0.0012714132,0.22481258,0.057899643,0.00010442693,0.0005552464,0.00011768959,0.00022516468,0.0007174083],"genre_scores_gemma":[0.9607932,0.0023552186,0.032737073,0.000272,0.00024866793,0.000028262262,0.00012821793,0.00006251791,0.0033748334],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.9964022,0.0014090085,0.0004177815,0.0005003413,0.0004430434,0.0008276249],"domain_scores_gemma":[0.99823844,0.0005409511,0.0002965778,0.00031910994,0.0001851836,0.00041975733],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0013335013,0.00038481908,0.00036558506,0.000207723,0.0013574099,0.00005082649,0.00047517076,0.00042544407,0.00016310457],"category_scores_gemma":[0.00009265859,0.00038429437,0.00025159153,0.0005282462,0.00044206218,0.0009441861,0.0000307903,0.00040391987,0.00006264857],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005677068,0.0005523816,0.07077803,0.00009651289,0.00017226135,0.00039480045,0.79306376,0.02649971,0.0031481066,0.009112389,0.0007155993,0.09489875],"study_design_scores_gemma":[0.006995898,0.0006479021,0.76563966,0.0008206532,0.001134779,0.00033576225,0.14784165,0.046262357,0.001662613,0.008563782,0.018468427,0.0016264997],"about_ca_topic_score_codex":0.7730301,"about_ca_topic_score_gemma":0.7590891,"teacher_disagreement_score":0.69486165,"about_ca_system_score_codex":0.0019376995,"about_ca_system_score_gemma":0.002573572,"threshold_uncertainty_score":0.99994266},"labels":[],"label_agreement":null},{"id":"W2553663365","doi":"10.1002/atr.1363","title":"Special issue: Public transit and pedestrian studies","year":2015,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","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":"Pedestrian; Public transport; Transport engineering; Transit (satellite); Computer science; Engineering","score_opus":0.05232711610789458,"score_gpt":0.3378655697956557,"score_spread":0.2855384536877611,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2553663365","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97157,0.0023252484,0.0062978305,0.010572751,0.0041702874,0.00029410992,0.000019048548,0.00007257372,0.0046781586],"genre_scores_gemma":[0.98566777,0.002111832,0.008434628,0.00008504577,0.0033358457,0.0000019965037,0.000021699638,0.000010709915,0.0003304483],"study_design_codex":"qualitative","study_design_gemma":"not_applicable","domain_scores_codex":[0.99887043,0.00006461575,0.00040574084,0.00009338559,0.000426572,0.00013926228],"domain_scores_gemma":[0.9987545,0.00006345253,0.00029140143,0.00003787677,0.00065659184,0.00019618802],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00058492157,0.00008101709,0.00019108791,0.00013597414,0.00015963537,0.00003844551,0.000070986156,0.00005783394,0.000031467942],"category_scores_gemma":[0.00015182579,0.000076476616,0.000049387683,0.0002543293,0.00011794361,0.0009925175,3.15899e-7,0.00011219632,0.0000019878178],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010309442,0.00023740489,0.045816947,0.00012014644,0.00026292814,0.00017820428,0.68431306,0.108078785,0.00013990054,0.013583821,0.012576617,0.13366123],"study_design_scores_gemma":[0.0041229143,0.0004439976,0.11133313,0.0001276044,0.00018971895,0.000008608658,0.1829416,0.000012665831,0.000070798,0.003920901,0.6965204,0.0003076916],"about_ca_topic_score_codex":0.000021838527,"about_ca_topic_score_gemma":0.0012470897,"teacher_disagreement_score":0.68394375,"about_ca_system_score_codex":0.000050677427,"about_ca_system_score_gemma":0.0002204369,"threshold_uncertainty_score":0.3118626},"labels":[],"label_agreement":null},{"id":"W2554709116","doi":"","title":"Linking Bike Path to Train Track: Recommendations for Improved Bicycle Parking Facilities at the Future Baseline LRT Station, Ottawa","year":2015,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Baseline (sea); Track (disk drive); Transport engineering; Path (computing); Aeronautics; Computer science; Engineering; Political science","score_opus":0.04067540067052844,"score_gpt":0.3146297423078136,"score_spread":0.27395434163728516,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2554709116","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.055497143,0.0002003082,0.71601874,0.21262386,0.0022640305,0.0021600875,0.0016920939,0.00064521935,0.008898498],"genre_scores_gemma":[0.82184774,0.00010146474,0.11402625,0.006066701,0.0017302813,0.00036661746,0.0038405876,0.00004772653,0.051972654],"study_design_codex":"qualitative","study_design_gemma":"not_applicable","domain_scores_codex":[0.998939,0.00014746601,0.00028391046,0.00019848917,0.00019506918,0.00023606815],"domain_scores_gemma":[0.99907184,0.00031636402,0.00008888244,0.0001115937,0.00027394734,0.00013736723],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017036517,0.00009466961,0.00009802991,0.00006321753,0.0008960404,0.00011530924,0.00013094662,0.00007375526,0.00023651654],"category_scores_gemma":[0.00020426193,0.00007312614,0.000049520822,0.00025721648,0.000047484995,0.00023433218,0.000005699757,0.000068050605,0.000013679408],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013853652,0.00012275306,0.008474451,0.000046224537,0.00005296238,7.364728e-7,0.41722518,0.018093102,0.00023691004,0.02146453,0.32155362,0.21259099],"study_design_scores_gemma":[0.00035833678,0.000040074246,0.0006783962,0.000012495235,0.000013515487,1.6301782e-7,0.0706495,0.0052079656,0.00005792918,0.00085201184,0.92199147,0.00013812463],"about_ca_topic_score_codex":0.00087979814,"about_ca_topic_score_gemma":0.03136992,"teacher_disagreement_score":0.76635057,"about_ca_system_score_codex":0.00009142155,"about_ca_system_score_gemma":0.00018720604,"threshold_uncertainty_score":0.98630506},"labels":[],"label_agreement":null},{"id":"W2556016565","doi":"10.3141/2568-10","title":"Moving Beyond Evaluation to Transit Project Prioritization: Lessons from the Toronto, Ontario, Canada, Context","year":2016,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Dillon Consulting","funders":"","keywords":"Transportation planning; Context (archaeology); Interdependence; Agency (philosophy); Process (computing); Prioritization; Business; Process management; Transport engineering; Risk analysis (engineering); Management science; Computer science; Economics; Engineering; Political science","score_opus":0.11262999933357473,"score_gpt":0.4115247462752259,"score_spread":0.2988947469416512,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2556016565","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9268504,0.00048429397,0.0094027985,0.056958377,0.0015183553,0.0029853391,0.00039892478,0.000047145673,0.0013543703],"genre_scores_gemma":[0.99486953,0.0005716728,0.0010406176,0.0003357711,0.00044033164,0.00015871087,0.000060798087,0.00004705049,0.0024755124],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.98709977,0.0028324816,0.0013767086,0.0005285757,0.007121388,0.0010411029],"domain_scores_gemma":[0.98925674,0.0026654012,0.0004701563,0.00050664856,0.0066460436,0.00045503347],"candidate_categories":["sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.009117088,0.00026719648,0.0004016869,0.0003026167,0.0022848542,0.00024348151,0.0013875637,0.00021692897,0.0019585476],"category_scores_gemma":[0.0006832415,0.00016681652,0.0002623441,0.0016691695,0.00056669005,0.0013802586,0.0000056143,0.0010323299,0.000011197239],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002850099,0.00036007634,0.5112783,0.000075278134,0.0005031193,0.000093386545,0.1980652,0.006222832,0.0020451539,0.025120066,0.1193846,0.13400191],"study_design_scores_gemma":[0.0015733249,0.00018357472,0.83604723,0.00047016004,0.000108222,1.5060458e-7,0.022904249,0.000035394933,0.00024608066,0.0015093856,0.13667908,0.00024315616],"about_ca_topic_score_codex":0.9891241,"about_ca_topic_score_gemma":0.999824,"teacher_disagreement_score":0.32476893,"about_ca_system_score_codex":0.0026204432,"about_ca_system_score_gemma":0.0118411,"threshold_uncertainty_score":0.999014},"labels":[],"label_agreement":null},{"id":"W255749324","doi":"","title":"Technology-Driven Transit-Oriented Development","year":2010,"lang":"en","type":"article","venue":"Community Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Quarter (Canadian coin); Transit (satellite); Transit system; Twin cities; Public transport; Transit-oriented development; Capital city; Engineering; Transport engineering; Geography; Economic geography; Archaeology; Metropolitan area","score_opus":0.02024638762563492,"score_gpt":0.2851616331432733,"score_spread":0.2649152455176384,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W255749324","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96483284,0.000009310131,0.029506227,0.00078949524,0.00032170358,0.00019262612,0.000014966635,0.0004610224,0.0038718022],"genre_scores_gemma":[0.9787297,0.000014163853,0.020260513,0.00006343592,0.000026814756,0.00002395577,0.00064317044,0.000011993818,0.00022620529],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.999088,0.00010359525,0.0002552388,0.00010873128,0.00024416696,0.00020029515],"domain_scores_gemma":[0.9993545,0.00006491589,0.000078878664,0.00020614789,0.00020730184,0.000088300934],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0004714284,0.0000990859,0.00010620986,0.00018695179,0.0015190222,0.000026344896,0.0002488938,0.00020819243,0.0001428599],"category_scores_gemma":[0.000043933767,0.00011480831,0.000035510715,0.0005748143,0.00022699115,0.00023478863,0.0000011755353,0.00067814643,0.000021902573],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007727088,0.0006955766,0.328701,0.00006621758,0.00007207526,0.000013980365,0.45324868,0.0038018567,0.008551866,0.1635185,0.0002521442,0.041000817],"study_design_scores_gemma":[0.0012500783,0.000059344373,0.70369655,0.000071604074,0.000070682494,9.99256e-7,0.03259672,0.00020121236,0.004430892,0.0016121821,0.25542796,0.0005817542],"about_ca_topic_score_codex":0.00070986734,"about_ca_topic_score_gemma":0.067507334,"teacher_disagreement_score":0.42065197,"about_ca_system_score_codex":0.00002381172,"about_ca_system_score_gemma":0.0001970602,"threshold_uncertainty_score":0.9997809},"labels":[],"label_agreement":null},{"id":"W2558004593","doi":"10.5539/jsd.v9n6p103","title":"Traffic Congestion in Dar es Salaam: Implications for Workers’ Productivity","year":2016,"lang":"en","type":"article","venue":"Journal of Sustainable Development","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Dar es salaam; Traffic congestion; Tanzania; Productivity; Transport engineering; Work (physics); Business; Geography; Operations management; Economics; Economic growth; Engineering; Environmental planning","score_opus":0.021367636104702104,"score_gpt":0.29036108715082315,"score_spread":0.26899345104612105,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2558004593","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9688066,0.000069863425,0.017569654,0.012808492,0.00013556366,0.00036744526,0.0000015413243,0.000019291603,0.00022149489],"genre_scores_gemma":[0.9899437,0.000040759707,0.0076186312,0.00001565509,0.0000702524,0.000027593713,0.0000026689095,0.000005604377,0.0022751605],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99917126,0.000050121223,0.00031278576,0.00009480456,0.00015198172,0.00021905404],"domain_scores_gemma":[0.9989603,0.00013136216,0.00022065229,0.000044720582,0.0005786467,0.000064323016],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001319682,0.000055898534,0.00010776204,0.00019039755,0.00024239163,0.000030320194,0.00009606744,0.00004813334,0.000010354331],"category_scores_gemma":[0.00041854006,0.000042562446,0.000027860915,0.00028171475,0.000054906723,0.00041481922,0.000003405954,0.00004815129,8.95566e-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":[0.00049787306,0.00059942907,0.5788567,0.00013716212,0.0000995558,0.00004095846,0.085473664,0.028363016,0.00035263432,0.064376906,0.006590634,0.23461147],"study_design_scores_gemma":[0.00083673577,0.00004436763,0.892695,0.00011506245,0.0000121912035,0.000003045949,0.018223168,0.0000034754423,0.00012994248,0.0013115387,0.08649791,0.00012754352],"about_ca_topic_score_codex":0.000014551389,"about_ca_topic_score_gemma":0.00014300767,"teacher_disagreement_score":0.31383833,"about_ca_system_score_codex":0.0004249044,"about_ca_system_score_gemma":0.0011851895,"threshold_uncertainty_score":0.21024752},"labels":[],"label_agreement":null},{"id":"W2558921574","doi":"10.1007/s10109-016-0244-8","title":"Discovering the space–time dimensions of schedule padding and delay from GTFS and real-time transit data","year":2016,"lang":"en","type":"article","venue":"Journal of Geographical Systems","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":31,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Padding; Schedule; Transit (satellite); Prepayment of loan; Computer science; Transport engineering; Operations research; Computer security; Business; Public transport; Engineering; Finance","score_opus":0.017124844889920294,"score_gpt":0.26146707630100247,"score_spread":0.24434223141108216,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2558921574","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99300784,0.0008113877,0.003726643,0.0019422473,0.00016284535,0.000095598116,0.000098800476,0.000011797666,0.00014282198],"genre_scores_gemma":[0.9978511,0.001331711,0.00061188446,0.0000066004386,0.00012756247,5.5919946e-7,0.000005788964,0.000005605334,0.00005921365],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9989096,0.00017827655,0.00034699368,0.0001188741,0.00032425547,0.000121978796],"domain_scores_gemma":[0.99884754,0.00054340664,0.00027127506,0.00013423656,0.00008875991,0.00011480344],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010193979,0.00006706712,0.0002075821,0.00007459744,0.00022858877,0.00006444523,0.00017315576,0.000073308554,0.000018287426],"category_scores_gemma":[0.00010968446,0.0000330711,0.000042363456,0.00017777289,0.00022853242,0.00036078403,0.00001601103,0.00008154748,8.955488e-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":[0.00052173407,0.00024205567,0.86088073,0.00010664484,0.0010687425,0.00007338153,0.021681305,0.0038479618,0.08983172,0.013486739,0.0022899122,0.005969095],"study_design_scores_gemma":[0.008224226,0.0009719548,0.88078356,0.008425244,0.0019565618,0.00017277985,0.021153493,0.025997626,0.0004548659,0.0029586265,0.04741986,0.0014812024],"about_ca_topic_score_codex":0.0016805968,"about_ca_topic_score_gemma":0.00016261982,"teacher_disagreement_score":0.08937685,"about_ca_system_score_codex":0.000007093767,"about_ca_system_score_gemma":0.000038307615,"threshold_uncertainty_score":0.25405708},"labels":[],"label_agreement":null},{"id":"W2559370790","doi":"10.1016/j.seps.2016.11.003","title":"A quarter century of Data Envelopment Analysis applied to the transport sector: A bibliometric analysis","year":2016,"lang":"en","type":"article","venue":"Socio-Economic Planning Sciences","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":67,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Data envelopment analysis; Scope (computer science); Quarter (Canadian coin); Descriptive statistics; Field (mathematics); Regional science; Bibliometrics; Operations research; Data science; Computer science; Statistics; Geography; Engineering; Library science; Mathematics","score_opus":0.05684136479299786,"score_gpt":0.32871414328080745,"score_spread":0.2718727784878096,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2559370790","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94795424,0.00017702926,0.04155398,0.0031613873,0.00024882847,0.00027530856,0.0004286378,0.000086817825,0.0061137704],"genre_scores_gemma":[0.99736154,0.00009210033,0.0020887027,0.00012165212,0.00007330345,0.000014453988,0.00008961766,0.0000045118513,0.00015413488],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9980464,0.00009295883,0.0004484274,0.0005600871,0.00048990425,0.00036219638],"domain_scores_gemma":[0.9987449,0.00042453667,0.0002742989,0.00038209758,0.000044131033,0.0001300349],"candidate_categories":["bibliometrics"],"consensus_categories":["bibliometrics"],"category_scores_codex":[0.0030522114,0.00012685519,0.00033722364,0.013777637,0.0007299275,0.00008891116,0.0011979477,0.00007375962,0.0003211457],"category_scores_gemma":[0.0000413749,0.00008094735,0.00015362242,0.04624597,0.0004372275,0.0004384683,0.000025570364,0.00005466618,0.00003300677],"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":[0.000016750311,0.000023145574,0.8749165,0.0000029031073,0.0009568501,7.6043483e-7,0.031010536,0.08612599,0.00002925853,0.0013833786,0.0010837428,0.00445016],"study_design_scores_gemma":[0.00024893353,0.0000379453,0.96030664,0.000016181039,0.0012811202,1.4098842e-7,0.023064572,0.0014393537,0.000018327672,0.0001134068,0.013170617,0.0003027392],"about_ca_topic_score_codex":0.001315423,"about_ca_topic_score_gemma":0.0014347632,"teacher_disagreement_score":0.08539014,"about_ca_system_score_codex":0.00010092373,"about_ca_system_score_gemma":0.00034359732,"threshold_uncertainty_score":0.99740034},"labels":[],"label_agreement":null},{"id":"W2560987797","doi":"10.1002/atr.1433","title":"Integrated optimization of bus priority operations in connected vehicle environment","year":2016,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Natural Science Foundation of Hunan Province; National Natural Science Foundation of China","keywords":"Bus priority; Intersection (aeronautics); Dwell time; SIGNAL (programming language); Transit (satellite); Local bus; Computer science; Real-time computing; Control bus; Automotive engineering; Engineering; System bus; Transport engineering; Public transport; Computer hardware","score_opus":0.010346511714287233,"score_gpt":0.2587523232639675,"score_spread":0.24840581154968028,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2560987797","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.79195285,0.00006474599,0.20685789,0.000698175,0.00016299891,0.000156901,0.000025260244,0.000014787654,0.000066400775],"genre_scores_gemma":[0.9652535,0.00072832726,0.033879846,0.000015576445,0.000028601582,0.0000034659906,0.000038249476,0.0000075127014,0.000044942863],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99875087,0.0000931845,0.00063559227,0.00009722637,0.00030812042,0.00011504058],"domain_scores_gemma":[0.9991713,0.00009383141,0.00031235104,0.00005855693,0.00030310202,0.00006082561],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035886618,0.00007403336,0.00017098134,0.00017932705,0.000074659765,0.000008666609,0.00008471458,0.00007335746,0.00012762292],"category_scores_gemma":[0.00010899131,0.000059070182,0.0000519534,0.00030753252,0.00008077658,0.00070510665,4.272459e-7,0.00008040736,0.0000010439007],"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":[0.00014247304,0.00009434454,0.017133119,0.0000063489015,0.000010513703,0.000004789737,0.007468911,0.9628666,0.0070334715,0.0013493364,0.0000050932495,0.0038849898],"study_design_scores_gemma":[0.0079238545,0.00052275276,0.96215963,0.00073669036,0.00012863515,0.0000018567214,0.011544001,0.0037858519,0.008241577,0.0007787533,0.0037400783,0.00043629148],"about_ca_topic_score_codex":0.000129921,"about_ca_topic_score_gemma":0.00113316,"teacher_disagreement_score":0.95908076,"about_ca_system_score_codex":0.00010373978,"about_ca_system_score_gemma":0.00017035399,"threshold_uncertainty_score":0.2408812},"labels":[],"label_agreement":null},{"id":"W2562410910","doi":"10.1155/2017/9216864","title":"Three Extensions of Tong and Richardson’s Algorithm for Finding the Optimal Path in Schedule-Based Railway Networks","year":2017,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"University of Hong Kong; Jessie and George Ho Charitable Foundation","keywords":"Schedule; Computer science; Path (computing); Transport engineering; Operations research; Transit (satellite); Algorithm; Mathematical optimization; Public transport; Engineering; Computer network; Mathematics","score_opus":0.022109013169319288,"score_gpt":0.3091386937718797,"score_spread":0.2870296806025604,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2562410910","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.42331812,0.0001881598,0.57541245,0.0005822547,0.00025644776,0.00020277989,0.00001754897,0.0000056187405,0.000016637516],"genre_scores_gemma":[0.8929457,0.0001595766,0.1067492,0.000022122162,0.000083020816,0.000007105547,0.000017242712,0.0000075817297,0.000008427911],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9991074,0.00002619365,0.00040431172,0.000094938274,0.00022727503,0.00013983672],"domain_scores_gemma":[0.99866766,0.0001994472,0.0007060472,0.000088527624,0.00028598562,0.000052322528],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007518327,0.00007387153,0.00017488748,0.00008423637,0.00049034844,0.000040273277,0.00014285136,0.000068861686,0.0000049768446],"category_scores_gemma":[0.00010592541,0.000059172606,0.0000751506,0.000091670816,0.00012373662,0.0004823834,7.3114796e-7,0.00013643365,4.5526274e-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":[0.0001898973,0.000051618048,0.034395583,0.000017369277,0.000019576455,0.000009270883,0.006669444,0.9035926,0.0001678548,0.0016467278,0.000018574756,0.053221505],"study_design_scores_gemma":[0.0023220791,0.00014184517,0.9464791,0.00027844633,0.00007680393,7.319494e-7,0.0032494063,0.04619582,0.00014988515,0.0005639424,0.00042444057,0.00011750242],"about_ca_topic_score_codex":0.00007339093,"about_ca_topic_score_gemma":0.0010585813,"teacher_disagreement_score":0.9120835,"about_ca_system_score_codex":0.000022984595,"about_ca_system_score_gemma":0.00011291368,"threshold_uncertainty_score":0.37714133},"labels":[],"label_agreement":null},{"id":"W2562860796","doi":"10.3141/2544-04","title":"Nonadditive Public Transit Fare Pricing Under Congestion with Policy Lessons from a Case Study in Toronto, Ontario, Canada","year":2016,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Toronto Metropolitan University","funders":"Canada Research Chairs","keywords":"Downtown; Public transport; Pareto principle; Traffic congestion; Economics; Policy analysis; Congestion pricing; Price elasticity of demand; Microeconomics; Business; Operations research; Computer science; Public economics; Econometrics; Transport engineering; Operations management; Engineering","score_opus":0.10219917988957518,"score_gpt":0.40485464854999015,"score_spread":0.302655468660415,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2562860796","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97915083,0.000068549154,0.0023340392,0.016202953,0.00026231658,0.0013465447,0.00021050168,0.000028481238,0.000395787],"genre_scores_gemma":[0.99803853,0.000247166,0.00050245645,0.000063064435,0.00017119973,0.00009539141,0.00003369683,0.000044003824,0.00080451614],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.990984,0.0021958782,0.0011933529,0.0005245956,0.0040852134,0.0010169363],"domain_scores_gemma":[0.99332947,0.0018293926,0.00044333903,0.00034927193,0.0035120926,0.0005364228],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0035544073,0.00026887117,0.00045764155,0.00072110846,0.0012246053,0.00017566809,0.00070960517,0.00019329281,0.0010341102],"category_scores_gemma":[0.00021841667,0.00018159588,0.00014294885,0.002057301,0.00059679867,0.0014373258,0.0000030961335,0.0012567076,0.0000024758576],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":true,"study_design_scores_codex":[0.0012784053,0.00051941414,0.9097482,0.000028878183,0.0002242784,0.0025774997,0.06765013,0.0030775324,0.00014267342,0.0058756312,0.00064612465,0.008231252],"study_design_scores_gemma":[0.0027210952,0.00040751093,0.874015,0.00036281382,0.000048923343,0.0000019977942,0.118414916,0.000011827341,0.000033109103,0.000458658,0.0033033462,0.000220846],"about_ca_topic_score_codex":0.9986793,"about_ca_topic_score_gemma":0.9999875,"teacher_disagreement_score":0.050764788,"about_ca_system_score_codex":0.004539251,"about_ca_system_score_gemma":0.012104611,"threshold_uncertainty_score":0.99987906},"labels":[],"label_agreement":null},{"id":"W2563324897","doi":"10.1002/atr.1432","title":"Competition and regulation in a new integrated transit system across jurisdictional borders","year":2016,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","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":"National Natural Science Foundation of China","keywords":"Transit (satellite); Competition (biology); Oligopoly; Subsidy; Complementarity (molecular biology); Transit system; Industrial organization; Transport engineering; Public transport; Business; Vertical integration; Computer science; Economics; Microeconomics; Engineering; Market economy","score_opus":0.008220171857094281,"score_gpt":0.28278581534737507,"score_spread":0.2745656434902808,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2563324897","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7840625,0.000098765835,0.21386147,0.0013368827,0.0003166256,0.00013719304,0.000025883743,0.00004007238,0.000120605204],"genre_scores_gemma":[0.9938804,0.00025692285,0.00556364,0.000017903954,0.00009026373,0.000001797096,0.00003767797,0.0000084841895,0.00014291861],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9988471,0.00006671679,0.00050452497,0.00011607539,0.00032215513,0.00014339843],"domain_scores_gemma":[0.9992262,0.0000826196,0.00030998245,0.00003694174,0.0002446236,0.00009963155],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000406832,0.000083862986,0.00015825512,0.00014843141,0.0001298245,0.000022050095,0.000051231447,0.00008631601,0.000025960346],"category_scores_gemma":[0.000020244212,0.0000682482,0.00004823977,0.00030542616,0.000069937836,0.0009109665,2.3350334e-7,0.00008929156,0.0000010194196],"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":[0.004065824,0.00024615639,0.15801892,0.00019242708,0.00011961297,0.00009185192,0.15402617,0.34067884,0.016802592,0.12580955,0.00021590544,0.19973215],"study_design_scores_gemma":[0.0036444643,0.00014017898,0.97376275,0.00077582354,0.00003637266,0.0000064476685,0.012434345,0.00009327352,0.0002570675,0.00092412793,0.0077605415,0.00016458027],"about_ca_topic_score_codex":0.00019835512,"about_ca_topic_score_gemma":0.0047169654,"teacher_disagreement_score":0.81574386,"about_ca_system_score_codex":0.00011677202,"about_ca_system_score_gemma":0.00027482543,"threshold_uncertainty_score":0.2783081},"labels":[],"label_agreement":null},{"id":"W2564436999","doi":"10.3141/2544-09","title":"Bus Network Microsimulation with General Transit Feed Specification and Tap-in-Only Smart Card Data","year":2016,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Transport Canada; Polytechnique Montréal","funders":"","keywords":"Microsimulation; Public transport; Smart card; Transit (satellite); Computer science; Transport engineering; Data validation; Engineering; Database; Computer security","score_opus":0.11651868352805914,"score_gpt":0.3916704046501482,"score_spread":0.2751517211220891,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2564436999","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9784236,0.00026910985,0.010966341,0.008212213,0.00037803664,0.0012872398,0.00013217753,0.000042072454,0.0002891943],"genre_scores_gemma":[0.9903688,0.0021789204,0.0058371383,0.000043093816,0.0003799507,0.000041034327,0.000085735155,0.00004991829,0.0010154431],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9923826,0.0017054398,0.0012523888,0.00061407464,0.0030950226,0.00095051026],"domain_scores_gemma":[0.9949121,0.0012422604,0.000464089,0.00060275156,0.0023944192,0.0003843859],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0070321197,0.00024268142,0.000417923,0.00074300525,0.0010291757,0.00018938414,0.00109349,0.000243596,0.00013742392],"category_scores_gemma":[0.00018444394,0.00016352898,0.00012022579,0.0023914091,0.0010725578,0.0011961189,0.00000572822,0.0010089341,0.000008593854],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00184741,0.00015625343,0.9570696,0.00006678376,0.00008213051,0.00007236313,0.00669652,0.007911584,0.0012313677,0.0061956802,0.001929385,0.01674097],"study_design_scores_gemma":[0.0022353895,0.00023640518,0.966744,0.00044115563,0.000059030404,7.511695e-7,0.0017756375,0.00021185921,0.00009786391,0.0010232839,0.026952809,0.00022181337],"about_ca_topic_score_codex":0.016039746,"about_ca_topic_score_gemma":0.30128014,"teacher_disagreement_score":0.2852404,"about_ca_system_score_codex":0.00026352465,"about_ca_system_score_gemma":0.0011418841,"threshold_uncertainty_score":0.99051255},"labels":[],"label_agreement":null},{"id":"W2564904794","doi":"10.1109/itsc.2016.7795560","title":"A solution to the road network design problem for multimodal flow","year":2016,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Bilevel optimization; Mathematical optimization; Flow network; Integer programming; Computer science; Linear programming; Set (abstract data type); Network planning and design; Decomposition; Flow (mathematics); Traffic flow (computer networking); Optimization problem; Mathematics; Computer network","score_opus":0.03363565032115201,"score_gpt":0.2866529454312263,"score_spread":0.2530172951100743,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2564904794","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00020146382,0.000008858602,0.98348194,0.013576427,0.00015785148,0.0005757066,0.0000043086334,0.00009065647,0.0019027579],"genre_scores_gemma":[0.4107357,0.000012154976,0.5777904,0.00052295864,0.00045798387,0.00017832633,0.00000748232,0.000007648347,0.010287379],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994646,0.00006383577,0.00008481404,0.00009699949,0.00010960111,0.00018017738],"domain_scores_gemma":[0.9996623,0.0001422772,0.000024031484,0.00005183125,0.00007341519,0.0000461755],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063499704,0.00003761704,0.00003855745,0.000012380543,0.00045840902,0.000027712023,0.000082936676,0.00003715106,0.000066537985],"category_scores_gemma":[0.00006511103,0.000019862102,0.000023578837,0.00011852491,0.000025642426,0.000093123934,0.0000020216173,0.000013387298,0.000035931287],"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":[0.000108895954,0.000023535715,0.0011954972,0.0000027004244,0.000013478445,1.8128256e-7,0.012128951,0.6578028,0.00008504392,0.020427095,0.11770561,0.1905062],"study_design_scores_gemma":[0.0020756398,0.00033105302,0.019608136,0.0001521859,0.00006339243,3.8450085e-7,0.0018770944,0.25526452,0.0002370964,0.010166207,0.70960987,0.0006144051],"about_ca_topic_score_codex":0.0003815594,"about_ca_topic_score_gemma":0.002036794,"teacher_disagreement_score":0.5919043,"about_ca_system_score_codex":0.000023483772,"about_ca_system_score_gemma":0.00005893951,"threshold_uncertainty_score":0.35257578},"labels":[],"label_agreement":null},{"id":"W2565127175","doi":"10.3141/2544-11","title":"Perspectives on Transit: Potential Benefits of Visualizing Transit Data","year":2016,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto; McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Transit (satellite); Public transport; Transport engineering; Computer science; Schedule; Big data; Transportation planning; Data collection; Data science; Engineering; Data mining","score_opus":0.15798658556793943,"score_gpt":0.43621453976171376,"score_spread":0.27822795419377433,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2565127175","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9687561,0.000496828,0.015374629,0.012593822,0.00063270016,0.0009804502,0.0005594372,0.000055134613,0.0005509484],"genre_scores_gemma":[0.9927779,0.0043470752,0.00174683,0.000030536394,0.00033283184,0.000018980552,0.00004112229,0.000054871754,0.00064984395],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.98944616,0.0020445602,0.0015433613,0.00062639796,0.0054147816,0.00092471077],"domain_scores_gemma":[0.99210334,0.0015669819,0.0006188135,0.00076118915,0.0045253295,0.0004243715],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007667874,0.0002644505,0.0005202571,0.0011991394,0.0011298511,0.00010950826,0.0020699329,0.00028321604,0.00053055026],"category_scores_gemma":[0.00043889772,0.00018134408,0.0003766694,0.0022582775,0.0014939524,0.0013427424,0.000005494246,0.0011750385,0.000016115051],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.017157853,0.003571613,0.25067443,0.00085945596,0.0014548508,0.0003312435,0.27801806,0.053714693,0.028725853,0.21786048,0.016445452,0.13118607],"study_design_scores_gemma":[0.0037167708,0.0010671918,0.937799,0.0015824075,0.00017329094,4.855142e-7,0.03462363,0.00011010477,0.0023227392,0.0018642704,0.016319688,0.00042044508],"about_ca_topic_score_codex":0.008459194,"about_ca_topic_score_gemma":0.043234114,"teacher_disagreement_score":0.68712455,"about_ca_system_score_codex":0.0002207542,"about_ca_system_score_gemma":0.001113444,"threshold_uncertainty_score":0.99814355},"labels":[],"label_agreement":null},{"id":"W2565658586","doi":"10.1016/j.jtrangeo.2016.12.007","title":"Travel demand corridors: Modelling approach and relevance in the planning process","year":2016,"lang":"en","type":"article","venue":"Journal of Transport Geography","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":20,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Polytechnique Montréal","funders":"","keywords":"Process (computing); Computer science; Relevance (law); Cluster analysis; Transportation planning; Trajectory; Point (geometry); Travel behavior; Scale (ratio); Relation (database); Data mining; Operations research; Transport engineering; Data science; Machine learning; Geography; Engineering; Mathematics","score_opus":0.0228810408651606,"score_gpt":0.2722746200812746,"score_spread":0.24939357921611402,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2565658586","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.72328293,0.0007798354,0.27411017,0.0005175291,0.00007305382,0.00010330064,0.0000047328026,0.000009301433,0.001119153],"genre_scores_gemma":[0.9958831,0.00095944956,0.002990742,0.00006244208,0.000078652876,0.0000030852564,0.000002047016,0.000005879711,0.000014606906],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9989233,0.000064978034,0.00035058946,0.00010700105,0.00038306462,0.00017108764],"domain_scores_gemma":[0.9993905,0.00018540393,0.00020749887,0.000050519993,0.00010319258,0.00006292377],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011789335,0.000079528014,0.00015231245,0.00019541835,0.00017465402,0.000022775774,0.00017359394,0.00006586794,0.000004052541],"category_scores_gemma":[0.000022026581,0.00004613217,0.000074237665,0.00035025962,0.00014018636,0.00037880737,4.5304594e-7,0.00014666581,1.1444362e-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":[0.00012913867,0.00008041402,0.789742,0.000032477317,0.000023261206,0.0000341079,0.05128616,0.15686853,0.000016347854,0.0005916648,0.000022585713,0.001173321],"study_design_scores_gemma":[0.00227926,0.00016529908,0.9599616,0.0008620064,0.0001466869,0.000040620875,0.02674919,0.002657545,0.000035871748,0.0043291277,0.0024066241,0.00036619144],"about_ca_topic_score_codex":0.000053563937,"about_ca_topic_score_gemma":0.000027697424,"teacher_disagreement_score":0.27260017,"about_ca_system_score_codex":0.0000064553337,"about_ca_system_score_gemma":0.000057452176,"threshold_uncertainty_score":0.18812151},"labels":[],"label_agreement":null},{"id":"W2566057767","doi":"10.2495/sdp-v12-n3-570-579","title":"Cityopt planning tool for energy efficient cities","year":2016,"lang":"en","type":"article","venue":"International Journal of Sustainable Development and Planning","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":"Environmental planning; Energy planning; Environmental science; Environmental resource management; Architectural engineering; Business; Renewable energy; Engineering","score_opus":0.01698393199042022,"score_gpt":0.279248007064664,"score_spread":0.2622640750742438,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2566057767","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7519477,0.00032929776,0.24434122,0.0009822086,0.000619213,0.0000713514,0.0000040919263,0.000022999417,0.0016818903],"genre_scores_gemma":[0.9885124,0.000038458442,0.006822718,0.0001130789,0.00032553394,0.0000068161967,0.0000091916345,0.000007173571,0.0041646557],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99891657,0.000028902985,0.00033205238,0.00009325243,0.00041023907,0.00021898207],"domain_scores_gemma":[0.9984655,0.00031906014,0.00024626657,0.000022978958,0.0008796399,0.000066598455],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00077958417,0.00007907301,0.00011079925,0.00029740902,0.00031574324,0.00012122686,0.00016190909,0.000053728647,0.000024582363],"category_scores_gemma":[0.0003138847,0.00006105819,0.00003598902,0.000078380916,0.000054506734,0.00029874107,0.000013057554,0.000043733562,3.8616807e-7],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016985468,0.00015023946,0.17597993,0.00009113385,0.0007086375,0.0016881892,0.29951513,0.06134617,0.00023574509,0.3595715,0.0094575295,0.08955729],"study_design_scores_gemma":[0.0029809824,0.000103187034,0.037327223,0.00097323285,0.00003938851,0.000034728942,0.15234584,0.00043435954,0.001441752,0.00379302,0.80001384,0.0005124492],"about_ca_topic_score_codex":0.000021946562,"about_ca_topic_score_gemma":0.0000023365008,"teacher_disagreement_score":0.7905563,"about_ca_system_score_codex":0.00014010773,"about_ca_system_score_gemma":0.00036062943,"threshold_uncertainty_score":0.24898806},"labels":[],"label_agreement":null},{"id":"W2568308921","doi":"10.1016/j.trb.2016.12.018","title":"Multi-objective integration of timetables, vehicle schedules and user routings in a transit network","year":2017,"lang":"en","type":"article","venue":"Transportation Research Part B Methodological","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":56,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Scheduling (production processes); Transit (satellite); Computer science; Public transport; Operations research; Flow network; A priori and a posteriori; Transport engineering; Mathematical optimization; Engineering","score_opus":0.4082340842496169,"score_gpt":0.500687464540124,"score_spread":0.09245338029050715,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2568308921","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.87320334,0.00017435967,0.124617636,0.00073609134,0.00008171188,0.00050366635,0.000024546509,0.00004658888,0.00061205606],"genre_scores_gemma":[0.87171006,0.0003421414,0.12758128,0.000020037873,0.00005586103,0.000042632815,0.000046531517,0.000008033788,0.00019342986],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9966625,0.0016960723,0.0004117685,0.00033331147,0.00050696306,0.0003894151],"domain_scores_gemma":[0.9979982,0.0011933659,0.00017424795,0.00017602088,0.00034206145,0.000116123774],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00852726,0.000105077685,0.0002890216,0.00013744298,0.00085737207,0.000092222916,0.0002267622,0.00021330401,0.00011389836],"category_scores_gemma":[0.0015774823,0.000095063944,0.00005448345,0.00032320866,0.0009640132,0.0004289977,0.0000042699144,0.0003851161,0.0000030725244],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006504878,0.0002542087,0.8401306,0.000058780126,0.000041653228,0.000025724868,0.031781733,0.012920803,0.0029784897,0.101797745,0.00013416962,0.009225606],"study_design_scores_gemma":[0.0007311942,0.00007271886,0.9894803,0.000100390374,0.000015387,7.5117434e-8,0.0048511666,0.0014628624,0.0005900859,0.001690719,0.0008928871,0.00011221606],"about_ca_topic_score_codex":0.005291046,"about_ca_topic_score_gemma":0.022769194,"teacher_disagreement_score":0.1493497,"about_ca_system_score_codex":0.000029113507,"about_ca_system_score_gemma":0.00011269197,"threshold_uncertainty_score":0.9950627},"labels":[],"label_agreement":null},{"id":"W257286637","doi":"","title":"Transportation management associations: exploring public-private partnerships to enhance travel behaviour change programs","year":2006,"lang":"en","type":"article","venue":"Transport Research Forum","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":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"General partnership; Public transport; Private sector; Traffic congestion; Harbour; Strengths and weaknesses; Business; Public relations; Environmental planning; Political science; Transport engineering; Public administration; Geography; Engineering; Finance; Computer science","score_opus":0.3066298635308833,"score_gpt":0.4135464287444547,"score_spread":0.1069165652135714,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W257286637","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9246466,0.00017173414,0.026532177,0.026284583,0.00049318396,0.0047349255,0.00022190409,0.00083999475,0.016074881],"genre_scores_gemma":[0.99028045,0.00021834782,0.0028308982,0.00009360244,0.00021850203,0.0019338146,0.0016637188,0.00004065438,0.0027200168],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99559563,0.000202988,0.0005379568,0.0005493396,0.0017911218,0.0013229889],"domain_scores_gemma":[0.99882334,0.00006578003,0.00009962104,0.0002535892,0.00041474644,0.00034294665],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002449206,0.0001981003,0.00022480439,0.00050225377,0.001277962,0.00016604044,0.00041936085,0.00015217948,0.0001192426],"category_scores_gemma":[0.000021435666,0.00022395946,0.00012272809,0.0018782804,0.00016557057,0.0010795607,0.0000044372327,0.00034442008,0.00007406557],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000074085154,0.00058290985,0.6865557,0.00007235551,0.000047139452,0.000076113865,0.022699727,0.00027425823,0.000057514953,0.2679422,0.0007551328,0.020862902],"study_design_scores_gemma":[0.0003785863,0.00011730519,0.91503495,0.00011961508,0.00003971364,1.3376311e-7,0.013722073,0.000021837206,0.00046028543,0.0013537203,0.068339445,0.00041233888],"about_ca_topic_score_codex":0.004982879,"about_ca_topic_score_gemma":0.04236634,"teacher_disagreement_score":0.26658848,"about_ca_system_score_codex":0.00024764184,"about_ca_system_score_gemma":0.00010762704,"threshold_uncertainty_score":0.98291796},"labels":[],"label_agreement":null},{"id":"W2574594197","doi":"10.1155/2017/8436821","title":"A Model to Simulate Multimodality in a Mesoscopic Dynamic Network Loading Framework","year":2017,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","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":"Mesoscopic physics; Multimodal transport; Modal; Computer science; Multimodality; Mode (computer interface); Path (computing); Network model; Simulation; Simple (philosophy); Network packet; Flow network; Artificial intelligence; Engineering; Physics; Mathematical optimization; Computer network; Transport engineering; Mathematics; Human–computer interaction","score_opus":0.024486349486702266,"score_gpt":0.35765435276261676,"score_spread":0.3331680032759145,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2574594197","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.79067004,0.000054733773,0.20767269,0.00084000395,0.0003972101,0.00017310212,0.0000066253656,0.00001824526,0.00016737486],"genre_scores_gemma":[0.8885832,0.000111590634,0.111059606,0.0000859177,0.000076621756,0.0000036340498,0.000007729876,0.0000106335465,0.000061077066],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9987035,0.000045590008,0.00052315794,0.00014198678,0.00034687226,0.00023891467],"domain_scores_gemma":[0.99886656,0.00009199629,0.00055175717,0.0001448259,0.00020887719,0.0001359726],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006260334,0.0000984747,0.00022458883,0.00011773399,0.00044554638,0.00007222429,0.0002438731,0.000107190994,0.000012852974],"category_scores_gemma":[0.00022427556,0.00010396755,0.000076874145,0.00017892031,0.000049880167,0.0008522234,0.0000011835282,0.00023597857,0.0000017834186],"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":[0.00014811115,0.000028242464,0.03768886,0.000009804273,0.000008426734,0.000015567328,0.01950838,0.9381421,0.00008888243,0.0016216342,0.0000038381163,0.0027361119],"study_design_scores_gemma":[0.0011391485,0.000071024646,0.9430749,0.0006186806,0.000041006548,3.0514707e-7,0.0015857688,0.036761608,0.000023424274,0.01612944,0.00034863825,0.00020608207],"about_ca_topic_score_codex":0.00014418611,"about_ca_topic_score_gemma":0.0044773384,"teacher_disagreement_score":0.90538603,"about_ca_system_score_codex":0.00010117506,"about_ca_system_score_gemma":0.00014395421,"threshold_uncertainty_score":0.42396736},"labels":[],"label_agreement":null},{"id":"W2575241979","doi":"","title":"Leading Pedestrian Interval Assessment and Implementation Guidelines","year":2014,"lang":"en","type":"article","venue":"Transportation Research Board 94th Annual Meeting","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Toronto Public Health","funders":"","keywords":"Schema crosswalk; Pedestrian; Guideline; Transport engineering; Checklist; Interval (graph theory); Process (computing); Visibility; Computer science; Engineering; Operations research; Geography; Mathematics; Political science","score_opus":0.09224601309733371,"score_gpt":0.4782000613507584,"score_spread":0.38595404825342466,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2575241979","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9656389,0.00008143535,0.020117324,0.0052938657,0.00023046786,0.000573929,0.000062113155,0.00022754654,0.0077744257],"genre_scores_gemma":[0.98457885,0.00018170616,0.014146244,0.00013136865,0.00027608057,0.000047134425,0.00020407469,0.000020178903,0.00041434538],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99704415,0.00056121644,0.00054672995,0.00035573228,0.000984928,0.0005072276],"domain_scores_gemma":[0.9981391,0.0004142034,0.0001383923,0.00010549406,0.00096911576,0.00023372137],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004666142,0.00012690472,0.00017791016,0.00027006242,0.0010975163,0.00018529336,0.00015414764,0.00009566915,0.000121884936],"category_scores_gemma":[0.00029467783,0.00013568433,0.000047844213,0.00046529272,0.0001968966,0.0005624812,0.0000044221388,0.00027008305,0.0000114416725],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000102582664,0.000050127637,0.74454904,0.00017684222,0.00004900787,0.000010557557,0.083920546,0.003937278,0.0005230305,0.06250236,0.0057322225,0.09844642],"study_design_scores_gemma":[0.0017301424,0.0003239107,0.74654484,0.00028752405,0.000053605927,2.596018e-7,0.13791092,0.0026987747,0.0002586245,0.001698631,0.1079964,0.00049638574],"about_ca_topic_score_codex":0.0111641,"about_ca_topic_score_gemma":0.026106222,"teacher_disagreement_score":0.10226417,"about_ca_system_score_codex":0.000081291415,"about_ca_system_score_gemma":0.00021662783,"threshold_uncertainty_score":0.99542063},"labels":[],"label_agreement":null},{"id":"W2575316818","doi":"10.1007/978-3-319-50100-0_17","title":"Transportation Planning and the Urban Economy","year":2017,"lang":"en","type":"book-chapter","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"The Scarborough Hospital; University of Toronto","funders":"","keywords":"Urban sprawl; Complementarity (molecular biology); Planner; Argument (complex analysis); Urban planning; Transaction cost; Shadow price; Land use; Economics; Operations research; Environmental planning; Business; Computer science; Engineering; Geography; Microeconomics; Civil engineering; Mathematics","score_opus":0.02541948420040975,"score_gpt":0.26980685578215413,"score_spread":0.2443873715817444,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2575316818","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000180611,0.0004606951,0.0012201231,0.0011737654,0.00013083748,0.00021234414,0.000014216807,0.000067591696,0.9967024],"genre_scores_gemma":[0.04782632,0.00026483153,0.00023894936,0.00022151384,0.00024873792,0.000008045267,0.00017357898,0.000017130104,0.95100087],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994431,0.0000143151965,0.00016510958,0.00016245122,0.00011784179,0.00009714053],"domain_scores_gemma":[0.9994691,0.00010432841,0.00020162573,0.00012285351,0.000052417086,0.00004967681],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003188935,0.00010824995,0.00015554209,0.000041081574,0.00082180824,0.00014877201,0.00013011106,0.00017589153,0.0002918877],"category_scores_gemma":[0.0000122130405,0.000081591104,0.00005456077,0.00000416188,0.00043469694,0.00016092957,0.0000012113292,0.00013429149,0.000014634866],"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":[0.000020042897,8.100002e-7,0.00053973467,0.0000066493326,0.000020966138,0.0000026476482,0.012866872,0.00010056182,1.4479152e-8,0.98290884,0.0027170933,0.00081579015],"study_design_scores_gemma":[0.00046323083,0.0000057118527,0.0010677563,0.00006762839,0.000074531876,1.7237892e-7,0.0004294313,0.000048593465,2.8579913e-7,0.033755977,0.96392834,0.00015835415],"about_ca_topic_score_codex":0.00039091267,"about_ca_topic_score_gemma":0.0012409308,"teacher_disagreement_score":0.96121126,"about_ca_system_score_codex":0.000013156365,"about_ca_system_score_gemma":0.00008124484,"threshold_uncertainty_score":0.63207674},"labels":[],"label_agreement":null},{"id":"W2576574795","doi":"","title":"A Joint Econometric Analysis of Temporal and Spatial Flexibility of Activities, Vehicle Type Choice and Primary Driver Selection","year":2015,"lang":"en","type":"article","venue":"Journal of International Crisis and Risk Communication Research","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Selection (genetic algorithm); Flexibility (engineering); Joint (building); Computer science; Econometrics; Economics; Statistics; Artificial intelligence; Engineering; Mathematics","score_opus":0.10579164168355384,"score_gpt":0.40992355894423893,"score_spread":0.3041319172606851,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2576574795","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99717844,0.0009891632,0.0005891525,0.0005681832,0.00003525017,0.000056921614,0.000015868105,0.0000025797822,0.0005644181],"genre_scores_gemma":[0.99034035,0.007938882,0.001621763,0.000007743285,0.00002616423,8.636564e-7,0.000015515849,0.0000025120921,0.00004617887],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99854165,0.00048122095,0.00036196626,0.00007875867,0.00047222452,0.0000641596],"domain_scores_gemma":[0.9975862,0.00043290196,0.00042656652,0.0000852712,0.0013878238,0.000081245],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003367653,0.00003965356,0.00017736442,0.0007925391,0.00014919342,0.0000457568,0.00013232068,0.00005157969,0.000027288372],"category_scores_gemma":[0.0006089001,0.000035246758,0.000036777103,0.0007075121,0.00022316947,0.00029931913,0.000036989197,0.0002047009,9.230814e-8],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015072661,0.000094239615,0.9876709,0.000009730235,0.00027479517,1.2737239e-7,0.0053641363,0.0015745078,0.000054394386,0.0005070984,0.00021945272,0.00407987],"study_design_scores_gemma":[0.00038702853,0.00017264984,0.9849071,0.00001893166,0.000104297396,7.527697e-7,0.004777968,0.0076916935,0.00007627159,0.00039741318,0.0014265398,0.000039336013],"about_ca_topic_score_codex":0.01360024,"about_ca_topic_score_gemma":0.0032242602,"teacher_disagreement_score":0.01037598,"about_ca_system_score_codex":0.00008287757,"about_ca_system_score_gemma":0.00017842525,"threshold_uncertainty_score":0.99296826},"labels":[],"label_agreement":null},{"id":"W2579160740","doi":"","title":"Unifying Long and Short Distance Personal Travel in a Statewide Planning Model","year":2016,"lang":"en","type":"article","venue":"Transportation Research Board 95th Annual Meeting","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Travel behavior; Travel time; Microsimulation; Consistency (knowledge bases); Travel survey; Transport engineering; Duration (music); Key (lock); Computer science; Econometrics; Geography; Engineering; Economics; Artificial intelligence","score_opus":0.06700512215998321,"score_gpt":0.37859518150682564,"score_spread":0.3115900593468424,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2579160740","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93647957,0.00017140822,0.060128294,0.0011123442,0.000044858876,0.0003397355,0.00015055753,0.00009223476,0.001481],"genre_scores_gemma":[0.9962254,0.0002479503,0.002578629,0.000036483532,0.000054467793,0.00004325055,0.000051255152,0.000023460922,0.00073909224],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99717945,0.00026281946,0.00043069324,0.00043680117,0.0009832929,0.0007069158],"domain_scores_gemma":[0.9987523,0.0005058388,0.00006536962,0.00008335111,0.00034115836,0.00025198588],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023364406,0.000145639,0.00019347658,0.00032994454,0.0006495943,0.00008179031,0.00015151827,0.00012436535,0.000022178523],"category_scores_gemma":[0.0002263756,0.00013066866,0.000039301904,0.0005629114,0.0003540657,0.00076656573,0.000003436747,0.00029834264,0.000003120913],"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":[0.00027821367,0.000049436036,0.8040899,0.000079700316,0.000014461857,0.00009091871,0.15999536,0.02280896,0.00082397787,0.0049925633,0.000097630946,0.0066788686],"study_design_scores_gemma":[0.0012805572,0.00007901107,0.9043567,0.0010320232,0.00001701207,3.484804e-7,0.072393425,0.018569509,0.00028238242,0.000959248,0.00054852566,0.00048125314],"about_ca_topic_score_codex":0.0026650897,"about_ca_topic_score_gemma":0.015484256,"teacher_disagreement_score":0.10026679,"about_ca_system_score_codex":0.00012094667,"about_ca_system_score_gemma":0.00026709752,"threshold_uncertainty_score":0.86405784},"labels":[],"label_agreement":null},{"id":"W2580010530","doi":"","title":"Modeling Daily Activity Program Generation Considering Within-Day and Day-to-Day Dynamics in Activity-Travel Behavior","year":2007,"lang":"en","type":"article","venue":"Transportation Research Board 86th Annual MeetingTransportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Scheduling (production processes); Day to day; Duration (music); Computer science; Activities of daily living; Simulation; Operations research; Engineering; Operations management; Psychology","score_opus":0.08930012620168315,"score_gpt":0.41643531755891383,"score_spread":0.3271351913572307,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2580010530","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93940026,0.000061385894,0.05279457,0.0015821741,0.00035135815,0.004063567,0.00026517475,0.0003701643,0.0011113507],"genre_scores_gemma":[0.98885226,0.00028047475,0.008461967,0.00006173375,0.00029733506,0.00082215096,0.0005818832,0.000117299714,0.0005248755],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9876432,0.0023305279,0.0014242189,0.0015905586,0.004622421,0.0023890997],"domain_scores_gemma":[0.99315166,0.0018145071,0.00024617702,0.00047966052,0.00304292,0.0012650571],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.026955327,0.00052208774,0.000645618,0.0025681397,0.002275081,0.000509154,0.0005494774,0.0006705343,0.00007587713],"category_scores_gemma":[0.0013766654,0.0006128143,0.00015587689,0.0039341105,0.00094542187,0.0018183643,0.000015511787,0.0022845648,0.000025415606],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0020913729,0.0022647555,0.4906838,0.00057768176,0.00014412223,0.0007899486,0.2465494,0.14620475,0.0152319465,0.024712196,0.00037025896,0.07037977],"study_design_scores_gemma":[0.0022052892,0.00067535724,0.8563222,0.00043839597,0.00007379212,6.531372e-7,0.05746636,0.07761116,0.0028566371,0.00033091928,0.0009091558,0.0011100769],"about_ca_topic_score_codex":0.05247186,"about_ca_topic_score_gemma":0.58311516,"teacher_disagreement_score":0.53064334,"about_ca_system_score_codex":0.00090948463,"about_ca_system_score_gemma":0.0013276833,"threshold_uncertainty_score":0.9996323},"labels":[],"label_agreement":null},{"id":"W2580707802","doi":"10.5038/2375-0901.20.1.2","title":"A New Market Segmentation Approach: Evidence from Two Canadian Cities","year":2017,"lang":"en","type":"article","venue":"Journal of Public Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":49,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"McGill University","funders":"Social Sciences and Humanities Research Council of Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Market segmentation; Transit (satellite); Public transport; Cluster analysis; Business; Marketing; Segmentation; Conceptual framework; Geography; Transport engineering; Computer science; Engineering; Sociology; Artificial intelligence","score_opus":0.07061475341128132,"score_gpt":0.3196080945906307,"score_spread":0.2489933411793494,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2580707802","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6664065,0.00069984514,0.30242625,0.013865445,0.001865459,0.00039209603,0.00013412524,0.000070770846,0.014139532],"genre_scores_gemma":[0.9683549,0.00029422753,0.029741254,0.000070844144,0.00051011785,0.0000022053198,0.000088994944,0.000010066889,0.0009274273],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9985341,0.00011842916,0.00043194272,0.00013017893,0.0005633919,0.00022196701],"domain_scores_gemma":[0.9982156,0.00009644941,0.0007296553,0.0001442547,0.000393718,0.00042033094],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010751538,0.000095744734,0.00015655474,0.0002352433,0.0008288485,0.0005154314,0.00038111082,0.000086176966,0.0005630046],"category_scores_gemma":[0.00026687767,0.000096937605,0.00009265371,0.0001266048,0.00008916884,0.0029217398,7.438256e-7,0.00014950638,0.0000034509803],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001243563,0.00003357634,0.86103135,0.000019373125,0.000119053024,0.000038455622,0.07248377,0.0037511464,0.00011828837,0.0065600816,0.008748691,0.046971843],"study_design_scores_gemma":[0.0007825275,0.00003389181,0.9821008,0.00010783369,0.0000891024,0.0000010908424,0.0072496682,0.00022883015,0.000046722926,0.001023397,0.0081864325,0.00014972032],"about_ca_topic_score_codex":0.2913086,"about_ca_topic_score_gemma":0.56961024,"teacher_disagreement_score":0.30194837,"about_ca_system_score_codex":0.00014907146,"about_ca_system_score_gemma":0.0016962602,"threshold_uncertainty_score":0.7134106},"labels":[],"label_agreement":null},{"id":"W2580712508","doi":"10.1002/atr.1440","title":"Transfer demand prediction for timed transfer coordination in public transport operational control","year":2016,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Transfer (computing); Transfer station; Public transport; Variable (mathematics); Computer science; Control (management); Operations research; Computation; Simulation; Transport engineering; Engineering; Artificial intelligence; Algorithm","score_opus":0.013076529139102896,"score_gpt":0.26463802895888167,"score_spread":0.25156149981977877,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2580712508","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.42962164,0.000062511666,0.5638222,0.0052841585,0.00038941953,0.00051973347,0.00019637379,0.00003466495,0.000069263195],"genre_scores_gemma":[0.99657583,0.00019082025,0.0024683229,0.00012637554,0.00020344903,0.000059814458,0.00017267022,0.000022644967,0.0001800475],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99782777,0.00011357203,0.0009938502,0.0002117132,0.0005667263,0.00028634304],"domain_scores_gemma":[0.9985975,0.00026313355,0.00013557135,0.000065924025,0.00078991003,0.00014795134],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001123373,0.00015624687,0.0002978499,0.00036684086,0.00021494435,0.000025517185,0.0001353676,0.00017158051,0.0001021287],"category_scores_gemma":[0.0000813791,0.00012853897,0.0001804382,0.00036619868,0.000099772755,0.0024890504,1.1413276e-7,0.00012295647,0.0000012372288],"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":[0.00636029,0.0010826502,0.2655816,0.00018340764,0.00028220232,0.000048376973,0.050828718,0.43635076,0.06812927,0.12125188,0.0003160301,0.049584795],"study_design_scores_gemma":[0.026649518,0.0007580957,0.92821777,0.0004404854,0.00029194416,0.0000054205107,0.0030323658,0.00046406232,0.0027243972,0.0028114174,0.034073815,0.0005307002],"about_ca_topic_score_codex":0.00001986994,"about_ca_topic_score_gemma":0.0011703791,"teacher_disagreement_score":0.66263616,"about_ca_system_score_codex":0.00014700329,"about_ca_system_score_gemma":0.0003604021,"threshold_uncertainty_score":0.52416676},"labels":[],"label_agreement":null},{"id":"W2581699074","doi":"","title":"Analyzing Commuter Train User Behavior: Decision Framework for Access Mode and Station Choice","year":2013,"lang":"en","type":"article","venue":"Transportation Research Board 92nd Annual MeetingTransportation Research Board","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":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Mode (computer interface); Computer science; Hierarchy; Service (business); Function (biology); Mode choice; Transit (satellite); Transport engineering; Segmentation; Operations research; Public transport; Engineering; Artificial intelligence; Human–computer interaction","score_opus":0.09709440915048527,"score_gpt":0.46752351638946005,"score_spread":0.37042910723897476,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2581699074","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.88405216,0.00019803489,0.10410232,0.004723189,0.00026863345,0.0048993877,0.0008603903,0.00036059206,0.000535321],"genre_scores_gemma":[0.94115984,0.00084220426,0.052435473,0.00014406643,0.00034321763,0.002631563,0.0014596571,0.00012277381,0.00086120813],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9906034,0.0013242572,0.0013772422,0.0012592425,0.0035663962,0.0018695015],"domain_scores_gemma":[0.98617846,0.006074771,0.0003103875,0.00050527847,0.0060104877,0.00092063047],"candidate_categories":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0062411595,0.00044492085,0.0005638783,0.0015997406,0.0033901243,0.0012421821,0.0009395776,0.0006248172,0.00063544407],"category_scores_gemma":[0.0015377754,0.00047759776,0.00022719595,0.0025989641,0.0012438458,0.0035076395,0.000014875415,0.0015451713,0.00005285085],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011916982,0.0008873141,0.7123863,0.0006864359,0.00020528241,0.00004678112,0.10344079,0.010041327,0.0012672403,0.10531526,0.019213047,0.045318525],"study_design_scores_gemma":[0.0019804994,0.00037286722,0.9200342,0.00050069136,0.00009853633,1.8871502e-7,0.026035286,0.0013022998,0.0002979765,0.01872989,0.029895732,0.0007518323],"about_ca_topic_score_codex":0.053962924,"about_ca_topic_score_gemma":0.06382069,"teacher_disagreement_score":0.2076479,"about_ca_system_score_codex":0.0002537631,"about_ca_system_score_gemma":0.0006089765,"threshold_uncertainty_score":0.9997946},"labels":[],"label_agreement":null},{"id":"W2581799292","doi":"10.1002/atr.1437","title":"A congested and dwell time dependent transit corridor assignment model","year":2016,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","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":"Volvo Research and Educational Foundations","keywords":"Dwell time; Queueing theory; Public transport; Transport engineering; Transit (satellite); Computer science; Constraint (computer-aided design); Travel time; Operations research; Process (computing); Simulation; Engineering; Computer network","score_opus":0.011516885555506746,"score_gpt":0.2595286114119692,"score_spread":0.24801172585646244,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2581799292","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.70071363,0.00014451047,0.29674655,0.0015562332,0.00021982887,0.00018473138,0.000040682833,0.00003810366,0.00035572998],"genre_scores_gemma":[0.9915204,0.000534873,0.0070399833,0.00006147985,0.000053184558,0.0000029456896,0.000011631922,0.000010879904,0.0007646096],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99885446,0.000048015583,0.00041710908,0.000119254866,0.0004129917,0.00014819008],"domain_scores_gemma":[0.9991652,0.00009287161,0.00031695148,0.00004847136,0.00023905163,0.00013747714],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003490703,0.00009170984,0.00016835505,0.000097512144,0.00012945516,0.000019188075,0.00007332679,0.00007155986,0.00006765081],"category_scores_gemma":[0.000026281272,0.000069748785,0.000056546385,0.0000991522,0.000075500466,0.0006441064,3.0449903e-7,0.000072661474,0.0000037141886],"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":[0.000914966,0.00016570801,0.007831575,0.000030949723,0.00008514747,0.00006075084,0.036517553,0.86619717,0.041985348,0.0028075804,0.00017271751,0.043230515],"study_design_scores_gemma":[0.046152607,0.0029959707,0.8337353,0.0030710588,0.00207967,0.00006700831,0.026955051,0.011790024,0.023288006,0.02115069,0.02549145,0.0032231905],"about_ca_topic_score_codex":0.000010796232,"about_ca_topic_score_gemma":0.00019178707,"teacher_disagreement_score":0.8544072,"about_ca_system_score_codex":0.000056938257,"about_ca_system_score_gemma":0.00013011186,"threshold_uncertainty_score":0.2844273},"labels":[],"label_agreement":null},{"id":"W2583408848","doi":"10.1016/j.jtrangeo.2017.01.003","title":"Determinants of route choice behavior: A comparison of shop versus work trips using the Potential Path Area - Gateway (PPAG) algorithm and Path-Size Logit","year":2017,"lang":"en","type":"article","venue":"Journal of Transport Geography","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":25,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"TRIPS architecture; Mixed logit; Logit; Mode choice; Work (physics); Transport engineering; Computer science; Value of time; Path (computing); Travel behavior; Process (computing); Econometrics; Public transport; Logistic regression; Economics; Engineering; Travel time; Machine learning","score_opus":0.0453323243157589,"score_gpt":0.3352733633213817,"score_spread":0.2899410390056228,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2583408848","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9938722,0.00035974904,0.004812989,0.00006905449,0.0005941058,0.00015536802,0.000077719415,0.000008476318,0.000050284965],"genre_scores_gemma":[0.9913598,0.00025337478,0.00827547,0.000005170231,0.000085221494,0.0000016781655,0.000005385863,0.000008872231,0.0000050701665],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99828476,0.000074886164,0.0007302825,0.00013316855,0.00056911795,0.00020776782],"domain_scores_gemma":[0.9978306,0.000115579496,0.001449002,0.00018752307,0.00030377443,0.00011349052],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006822597,0.00013422476,0.00041311138,0.00012375273,0.0005894516,0.000060768507,0.0003862758,0.00013005432,0.000015758496],"category_scores_gemma":[0.000041949508,0.0001038493,0.00029495088,0.00019994215,0.0004883004,0.00034660252,0.0000065525383,0.00022648973,4.7926907e-8],"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":[0.00026705276,0.00014696576,0.98701537,0.000014123755,0.00005772123,0.000025564857,0.0038598203,0.002040093,0.0000819539,0.0000065402273,0.0000045031047,0.0064802784],"study_design_scores_gemma":[0.0012988182,0.00015979297,0.9951164,0.00017718974,0.00052580057,0.0000030749973,0.001858833,0.0005020149,0.00013061774,0.000028187382,0.000084670384,0.00011459911],"about_ca_topic_score_codex":0.00093053246,"about_ca_topic_score_gemma":0.00036325344,"teacher_disagreement_score":0.008101018,"about_ca_system_score_codex":0.000011379299,"about_ca_system_score_gemma":0.00011078175,"threshold_uncertainty_score":0.45336443},"labels":[],"label_agreement":null},{"id":"W2585074950","doi":"10.3141/2649-02","title":"Use of Automated Vehicle Location Data for Route- and Segment-Level Analyses of Bus Route Reliability and Speed","year":2017,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Reliability (semiconductor); Intersection (aeronautics); Transit (satellite); Transport engineering; Level of service; Computer science; Automatic vehicle location; Engineering; Public transport; Telecommunications","score_opus":0.40359370937977945,"score_gpt":0.4967179114765797,"score_spread":0.09312420209680022,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2585074950","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99122655,0.0001489168,0.0040001576,0.0025084962,0.00018267191,0.0010142146,0.00085604365,0.00002613808,0.0000368039],"genre_scores_gemma":[0.9881692,0.0013780153,0.00990055,0.000010553587,0.0000608068,0.000012807045,0.00015031017,0.000023490611,0.0002942743],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9949482,0.00083385356,0.0012717583,0.00040454316,0.0020943722,0.0004472359],"domain_scores_gemma":[0.99102265,0.0016069819,0.0011024559,0.000788958,0.0052235546,0.0002554327],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0065894956,0.00016096742,0.0004550807,0.00049628917,0.001284245,0.00022325068,0.0010367292,0.0001838511,0.000028557835],"category_scores_gemma":[0.0018169341,0.0001328548,0.00012646987,0.0007438355,0.0016238731,0.0018488015,0.000012437426,0.0005373463,4.3126587e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0022958014,0.00030003654,0.958244,0.0007361087,0.00024954896,0.000010090379,0.009103773,0.011762239,0.006109414,0.002739041,0.0016438758,0.0068060164],"study_design_scores_gemma":[0.0013123165,0.00025293595,0.9844216,0.00035865442,0.0001319526,1.0357312e-7,0.002208351,0.0072623626,0.0015064339,0.0006807137,0.0017405198,0.00012406516],"about_ca_topic_score_codex":0.06271728,"about_ca_topic_score_gemma":0.07256969,"teacher_disagreement_score":0.026177537,"about_ca_system_score_codex":0.00008507674,"about_ca_system_score_gemma":0.0008333496,"threshold_uncertainty_score":0.9877504},"labels":[],"label_agreement":null},{"id":"W25857627","doi":"10.1002/biof.1206","title":"Application of Dynameq in Montreal: Bridging the Gap between Regional Planning Models and Microsimulation","year":2007,"lang":"en","type":"article","venue":"ITE 2007 Annual Meeting and ExhibitInstitute of Transportation Engineers (ITE)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Kerala State Council for Science, Technology and Environment","keywords":"Microsimulation; Downtown; Transport engineering; Metropolitan area; VisSim; Computer science; Traffic simulation; Bridging (networking); Transportation planning; Simulation software; Software; Operations research; Engineering; Geography","score_opus":0.017262035728762993,"score_gpt":0.27463282683002377,"score_spread":0.25737079110126077,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W25857627","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.78433716,0.00039279409,0.213826,0.00018019234,0.000054598488,0.00023736966,0.000093516726,0.00004629231,0.0008320995],"genre_scores_gemma":[0.99622464,0.000109386,0.0031802827,0.000031343265,0.00009121912,0.000004979839,0.0003233765,0.000010635107,0.000024165307],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9986681,0.00003521378,0.00055690884,0.00021504221,0.00030116696,0.0002236112],"domain_scores_gemma":[0.99915755,0.0002652481,0.0002549374,0.00007480296,0.00016811087,0.0000793717],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011592645,0.00012851904,0.00020168257,0.00022392215,0.00018968683,0.000016768961,0.00007248649,0.00012415777,0.0000011086526],"category_scores_gemma":[0.00003667501,0.00012854967,0.000039553946,0.00036149687,0.00018873223,0.0003864496,0.0000017113443,0.00012893877,1.7819809e-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":[0.00006419106,0.000022944416,0.13913523,0.000091649184,0.000017211192,0.000002889331,0.05254796,0.79736,0.00032759717,0.0047341213,0.00002822689,0.005667989],"study_design_scores_gemma":[0.0012831704,0.000046095898,0.91328263,0.00064965856,0.000105726926,0.000001289986,0.01782934,0.06418271,0.0004685183,0.00050543353,0.0012812353,0.000364165],"about_ca_topic_score_codex":0.0040885797,"about_ca_topic_score_gemma":0.0020499814,"teacher_disagreement_score":0.77414745,"about_ca_system_score_codex":0.000023919407,"about_ca_system_score_gemma":0.000039931125,"threshold_uncertainty_score":0.61807364},"labels":[],"label_agreement":null},{"id":"W2586397369","doi":"","title":"Don’t stop just yet! A simple, effective, and socially responsible approach to bus-stop consolidation","year":2015,"lang":"en","type":"article","venue":"eScholarship@McGill (McGill)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"McGill University","funders":"","keywords":"Consolidation (business); Local bus; Public transport; Computer science; Transport engineering; Engineering; Operations research; Telecommunications; Business; Control bus; System bus; Finance","score_opus":0.03954987549976947,"score_gpt":0.29278620002478983,"score_spread":0.2532363245250204,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2586397369","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.85120344,0.000059465805,0.000093758994,0.0004031683,0.00040806067,0.0011608502,0.00028997497,0.00040846158,0.1459728],"genre_scores_gemma":[0.9880409,0.000037463316,0.009036975,0.0005497019,0.000058421985,0.00011743692,0.00014731604,0.000046136258,0.0019656322],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9968793,0.0008527512,0.00039494992,0.0006200428,0.00072553573,0.0005274384],"domain_scores_gemma":[0.9980745,0.0003790086,0.00018781287,0.00024500303,0.00051295565,0.0006007656],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0024969282,0.0002639642,0.00032102352,0.00027893682,0.0016232877,0.00015401604,0.00027004041,0.00030446646,0.00003871079],"category_scores_gemma":[0.0018656317,0.00029336574,0.00007339661,0.0009260638,0.00018004257,0.0009128392,0.000049093553,0.00036580648,0.000084114916],"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":[0.00050470704,0.00026718384,0.0022198556,0.000057637884,0.00007764774,0.000016270214,0.0023102846,0.003920941,0.0022987074,0.9448258,0.00020140424,0.043299504],"study_design_scores_gemma":[0.009322503,0.0011244913,0.059914567,0.00030165433,0.0005540528,0.000032294804,0.03715948,0.00085902173,0.010055902,0.15591517,0.7210603,0.0037005828],"about_ca_topic_score_codex":0.0013736571,"about_ca_topic_score_gemma":0.0021929059,"teacher_disagreement_score":0.7889107,"about_ca_system_score_codex":0.0004385477,"about_ca_system_score_gemma":0.00035843378,"threshold_uncertainty_score":0.99995184},"labels":[],"label_agreement":null},{"id":"W2586605122","doi":"","title":"Travel Mode Choice and Transit Route Choice Behavior in Montreal: Insights from McGill University Members Commute Patterns","year":2012,"lang":"en","type":"article","venue":"eScholarship@McGill (McGill)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"McGill University","funders":"","keywords":"Mode choice; Multinomial logistic regression; Metropolitan area; Public transport; Travel behavior; Transport engineering; Transit (satellite); Mode (computer interface); Work (physics); Business; Marketing; Computer science; Geography; Engineering","score_opus":0.023250478133941887,"score_gpt":0.25390787323624453,"score_spread":0.23065739510230265,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2586605122","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98447794,0.00008872654,0.000009941633,0.00008507873,0.00030779096,0.00043303773,0.0037239906,0.00018180328,0.010691693],"genre_scores_gemma":[0.9978107,0.00027073701,0.000528884,0.00022324239,0.00004467029,0.000013920112,0.00029653005,0.00004424388,0.00076705555],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99729186,0.0005727531,0.00041465377,0.00054695184,0.0005341632,0.00063963863],"domain_scores_gemma":[0.9984103,0.0004386161,0.00019505153,0.0003057208,0.00012026204,0.00053005124],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0003922586,0.00033827696,0.00039215127,0.00025506245,0.0015264389,0.00004149833,0.0003920259,0.00039384104,0.00011313925],"category_scores_gemma":[0.00010947595,0.00039541902,0.0001197394,0.000519929,0.00011992157,0.0021236816,0.00003509373,0.0006124427,0.000021566444],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00046457798,0.0024364921,0.8077802,0.0001453795,0.00041292695,0.00024631218,0.007197821,0.0074825557,0.008100607,0.07670473,0.000024116262,0.08900428],"study_design_scores_gemma":[0.0018915704,0.00004010343,0.96486586,0.000090131456,0.00022201551,0.0000017734484,0.0022455133,0.0002195382,0.0007571226,0.00043509682,0.028578633,0.00065262907],"about_ca_topic_score_codex":0.22938234,"about_ca_topic_score_gemma":0.4010066,"teacher_disagreement_score":0.17162426,"about_ca_system_score_codex":0.0005443318,"about_ca_system_score_gemma":0.000023805516,"threshold_uncertainty_score":0.9998498},"labels":[],"label_agreement":null},{"id":"W2588580793","doi":"","title":"Sustainable transport and the organisation of bus services in Manchester - Do markets, hierarchies or networks fail?","year":2008,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Transport Canada","funders":"","keywords":"Business; Industrial organization; Computer science","score_opus":0.008336951432486299,"score_gpt":0.22776394448304393,"score_spread":0.21942699305055763,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2588580793","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9894928,0.00017867742,0.002475431,0.00083507417,0.000032934153,0.00026244856,0.000001201725,0.00003630335,0.006685163],"genre_scores_gemma":[0.9941174,0.0008949891,0.000435294,0.0000988382,0.000024310064,0.000007680043,0.000013450484,0.0000046908594,0.004403395],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9993401,0.00009140506,0.0001797449,0.00009889369,0.00014696119,0.00014289256],"domain_scores_gemma":[0.9996674,0.00013714924,0.00005745588,0.0000587339,0.000053237007,0.000026016427],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046900412,0.000055273984,0.000104997795,0.000054258253,0.0002452348,0.000018838198,0.00007679413,0.000056883717,0.00017257623],"category_scores_gemma":[0.000010536518,0.000035356195,0.000015725738,0.00027032505,0.0002556306,0.00025594156,0.0000029968576,0.000055491328,4.813066e-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":[0.00062282046,0.000028446713,0.6331293,0.00008637284,0.000014266997,0.000021588578,0.32340017,0.009536156,0.000001274649,0.032557435,0.000101795384,0.0005003718],"study_design_scores_gemma":[0.0013979988,0.000019461428,0.86731166,0.00003151682,0.000018112618,0.0000021241483,0.12555408,0.0018052483,0.000011659015,0.0008290785,0.0029020132,0.00011707578],"about_ca_topic_score_codex":0.0029811757,"about_ca_topic_score_gemma":0.00450272,"teacher_disagreement_score":0.23418231,"about_ca_system_score_codex":0.000012264636,"about_ca_system_score_gemma":0.000065235115,"threshold_uncertainty_score":0.45066658},"labels":[],"label_agreement":null},{"id":"W2589054564","doi":"10.1007/s40534-016-0121-7","title":"Identifying Achilles-heel roads in real-sized networks","year":2017,"lang":"en","type":"article","venue":"Journal of Modern Transportation","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":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Closure (psychology); Heel; Index (typography); Transport engineering; Process (computing); Computer science; Engineering; Economics","score_opus":0.040341622136896065,"score_gpt":0.3374833895946778,"score_spread":0.2971417674577817,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2589054564","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8362499,0.00021368542,0.1596378,0.000768274,0.0006322484,0.00013313415,0.0000055968535,0.00002632948,0.002333056],"genre_scores_gemma":[0.9960669,0.0012277805,0.002281477,0.000026781945,0.00019156186,0.000002091546,0.000025358228,0.000011878691,0.0001661495],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9986203,0.00008057704,0.00056320487,0.00011502953,0.0004344014,0.00018649388],"domain_scores_gemma":[0.99873555,0.000049743623,0.000802861,0.00012497272,0.00019449311,0.00009237093],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001032458,0.00008845516,0.00020585902,0.00015147925,0.0005183389,0.0001513997,0.0002763304,0.00012602685,0.000029560177],"category_scores_gemma":[0.000060850103,0.00008961274,0.00010714081,0.00009092759,0.00007628687,0.0010127691,9.034452e-7,0.00022532417,0.0000011776243],"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":[0.00032136563,0.000117337026,0.55371135,0.000022974102,0.000046412446,0.00012091423,0.050910957,0.36552897,0.00023371846,0.0037269546,0.00012197108,0.025137065],"study_design_scores_gemma":[0.0011598893,0.000024359117,0.98986065,0.00013931231,0.000045052268,9.630476e-7,0.00093439384,0.0053727757,0.000019867302,0.0020988581,0.00023218669,0.000111676745],"about_ca_topic_score_codex":0.0011712519,"about_ca_topic_score_gemma":0.0148887085,"teacher_disagreement_score":0.43614933,"about_ca_system_score_codex":0.000059734222,"about_ca_system_score_gemma":0.00015814939,"threshold_uncertainty_score":0.83082485},"labels":[],"label_agreement":null},{"id":"W2590468962","doi":"","title":"Can Tax Increment Financing Support Transportation Infrastructure Investment","year":2016,"lang":"en","type":"preprint","venue":"TSpace (University of Toronto)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"University of Toronto; University of Ottawa; TD Bank","keywords":"Revenue; Real estate; Finance; Business; Tax revenue; Government (linguistics); Investment (military); Value (mathematics); Capital (architecture); Economics; Public economics; Geography; Political science","score_opus":0.012644464187906978,"score_gpt":0.24466295756067916,"score_spread":0.23201849337277217,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2590468962","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.66113,0.0005108038,0.037688218,0.016038261,0.0021201118,0.0019279333,0.0018406942,0.0005248061,0.27821916],"genre_scores_gemma":[0.98842216,0.00060675683,0.0052453387,0.00007052704,0.00006874775,9.48959e-7,0.00042023545,0.000011442158,0.005153855],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.9987877,0.000088099296,0.00015141773,0.00031114047,0.0004344752,0.00022715914],"domain_scores_gemma":[0.9990611,0.000025730582,0.00039032017,0.0002066683,0.00018091196,0.00013524812],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00024526316,0.00016324958,0.00025637905,0.000051995325,0.00033042312,0.000016955535,0.00030068532,0.0003017007,0.007349502],"category_scores_gemma":[0.000013999682,0.00019453527,0.00011607687,0.000048912065,0.00017177232,0.0002522763,0.00002254955,0.00014611057,0.000004116245],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024093242,0.0001254732,0.041279495,0.00044176268,0.00032363026,0.000064410306,0.85481423,0.015057407,0.00032737624,0.041215684,0.017747892,0.028361699],"study_design_scores_gemma":[0.0008480639,0.00008300615,0.9285704,0.0003360932,0.0002163587,1.6809604e-7,0.04398251,0.00010192805,0.00004925191,0.0011734364,0.024153808,0.00048501068],"about_ca_topic_score_codex":0.31804627,"about_ca_topic_score_gemma":0.50527376,"teacher_disagreement_score":0.8872909,"about_ca_system_score_codex":0.0006682766,"about_ca_system_score_gemma":0.00071917305,"threshold_uncertainty_score":0.99355793},"labels":[],"label_agreement":null},{"id":"W2591142659","doi":"10.1155/2017/5069824","title":"Bayesian Nonparametric Model for Estimating Multistate Travel Time Distribution","year":2017,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Log-normal distribution; Markov chain Monte Carlo; Posterior probability; Dirichlet distribution; Dirichlet process; Computer science; Bayesian probability; Probability distribution; Mathematics; Statistics; Algorithm","score_opus":0.019164323590413666,"score_gpt":0.3199269732163163,"score_spread":0.30076264962590266,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2591142659","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.075027175,0.000022794782,0.92369413,0.0003968696,0.00036110778,0.00022209196,0.00015223099,0.00002075308,0.00010287087],"genre_scores_gemma":[0.7429591,0.00003629645,0.25642022,0.000012752199,0.00012284666,0.0000060452926,0.000204156,0.000010623898,0.00022793729],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988194,0.000022873843,0.0005132211,0.0001241461,0.00033077813,0.00018956736],"domain_scores_gemma":[0.9981083,0.000110696084,0.0010553139,0.00010436388,0.0005103679,0.00011093285],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056391134,0.00009806587,0.00020078856,0.00009210672,0.0009138215,0.00010056447,0.00019342241,0.00007957505,0.000009154741],"category_scores_gemma":[0.00037351885,0.00010103458,0.00012536692,0.00010518498,0.000084170424,0.001136555,4.847599e-7,0.00011019951,0.0000013123575],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017071387,0.00004510482,0.0010121507,0.000023079614,0.000017088205,0.000004582814,0.0074103414,0.97373086,0.00038268015,0.0010536488,0.00008088029,0.01606889],"study_design_scores_gemma":[0.0025418953,0.000135763,0.107742645,0.0001651396,0.0001580246,0.0000013970702,0.0011562511,0.883319,0.00029723396,0.0037281387,0.0004974864,0.00025700126],"about_ca_topic_score_codex":0.00004116476,"about_ca_topic_score_gemma":0.00018766578,"teacher_disagreement_score":0.6679319,"about_ca_system_score_codex":0.000070386006,"about_ca_system_score_gemma":0.00015787616,"threshold_uncertainty_score":0.7028469},"labels":[],"label_agreement":null},{"id":"W2591664842","doi":"","title":"Evaluating the Relationship Between Socially (Dis)advantaged Neighbourhoods and Customer Satisfaction of Bus Service in London, U.K.","year":2017,"lang":"en","type":"article","venue":"eScholarship@McGill (McGill)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Customer satisfaction; Service quality; Public transport; Business; Marketing; Service (business); Quality (philosophy); Transport engineering; Engineering","score_opus":0.08098312559999467,"score_gpt":0.3642628791861405,"score_spread":0.2832797535861458,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2591664842","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98563826,0.000045864315,0.0000043372406,0.00050514523,0.00019426331,0.00036649097,0.00012071486,0.00006306232,0.0130618885],"genre_scores_gemma":[0.9983328,0.00006121197,0.0011386528,0.00010097156,0.00004774662,0.000020229449,0.000029952882,0.000024833169,0.00024362683],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9975902,0.0006654845,0.00050165993,0.00033205157,0.000596239,0.0003143868],"domain_scores_gemma":[0.99762505,0.0010732748,0.0005806248,0.00033477982,0.00027134884,0.000114948525],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0030595711,0.00016887445,0.00024581442,0.00013429676,0.0041377335,0.000129926,0.00033784658,0.00023355891,0.000048368693],"category_scores_gemma":[0.0028448838,0.0001605616,0.000060477305,0.00036444582,0.00020226433,0.0012213078,0.000041411582,0.0005155626,0.000020178148],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004191105,0.000020220574,0.859186,0.000041778756,0.000021933378,0.0000020130512,0.0004509555,0.000982746,0.0006894966,0.10263121,5.63075e-7,0.035931163],"study_design_scores_gemma":[0.0006873259,0.000032434116,0.98056436,0.000103297454,0.00006290255,6.121967e-7,0.0007934835,0.00007519656,0.00020083819,0.01689863,0.00039119524,0.0001897331],"about_ca_topic_score_codex":0.008818815,"about_ca_topic_score_gemma":0.031156935,"teacher_disagreement_score":0.12137834,"about_ca_system_score_codex":0.00017322773,"about_ca_system_score_gemma":0.00010172194,"threshold_uncertainty_score":0.9977816},"labels":[],"label_agreement":null},{"id":"W2592008816","doi":"","title":"Perceived Reality: Understanding the Relationship Between Customer Perceptions and Operational Characteristics","year":2017,"lang":"en","type":"article","venue":"Transportation Research Board 96th Annual MeetingTransportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Perception; Psychology; Business; Computer science","score_opus":0.23375629276992788,"score_gpt":0.4496783745627343,"score_spread":0.21592208179280642,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2592008816","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95940685,0.00005286621,0.006286893,0.02296142,0.00027062136,0.0019002203,0.0009604147,0.00025379562,0.007906922],"genre_scores_gemma":[0.99387455,0.00042675843,0.0008652185,0.00009915895,0.0006219731,0.00027290476,0.0011203693,0.000068368114,0.0026507098],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99044716,0.0021008195,0.0010749568,0.0009355445,0.0040597883,0.0013817162],"domain_scores_gemma":[0.9911642,0.0041639865,0.00037418012,0.0007728395,0.0027945947,0.00073020917],"candidate_categories":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":["sts"],"category_scores_codex":[0.010982163,0.00034207897,0.00042868732,0.00080010336,0.018169038,0.0015900442,0.0010253921,0.000452742,0.00037550548],"category_scores_gemma":[0.0029831254,0.00031860976,0.00016245696,0.0009895784,0.003971225,0.0020518624,0.000017878707,0.0020041273,0.00011433931],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013982267,0.000058609967,0.8280318,0.000078285346,0.000043782962,0.000019965819,0.050174493,0.00016281591,0.000039333558,0.11889154,0.0019019269,0.00045762787],"study_design_scores_gemma":[0.0007170324,0.00009038637,0.9327206,0.00018121324,0.000058720107,2.1653265e-7,0.052535903,0.00009346066,0.000004531426,0.0045821494,0.008684415,0.00033138506],"about_ca_topic_score_codex":0.020552853,"about_ca_topic_score_gemma":0.042954233,"teacher_disagreement_score":0.114309385,"about_ca_system_score_codex":0.00040711218,"about_ca_system_score_gemma":0.00097463676,"threshold_uncertainty_score":0.9999266},"labels":[],"label_agreement":null},{"id":"W2592268401","doi":"10.48550/arxiv.1702.07001","title":"Theoretical and Experimental Analysis of the Canadian Traveler Problem","year":2017,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Operations research; Regional science; History; Political science; Computer science; Geography; Engineering","score_opus":0.04559808339104005,"score_gpt":0.2208517785880324,"score_spread":0.17525369519699235,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2592268401","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9424661,0.00003097442,0.0007263924,0.00028850848,0.00011996368,0.00019621069,0.000065185406,0.000018327475,0.056088347],"genre_scores_gemma":[0.9991042,0.000030249239,0.00006589544,0.000020469626,0.000012407408,2.9425755e-7,0.00002110171,0.0000035562355,0.0007418537],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9994161,0.0000933165,0.00007478207,0.00022511862,0.000067919245,0.00012274078],"domain_scores_gemma":[0.99945277,0.00002111062,0.00012243185,0.0002220479,0.000069146765,0.0001125019],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019015954,0.00007327275,0.00013878029,0.0001465852,0.0005908385,0.000052858806,0.0003143489,0.00016792113,0.00018011921],"category_scores_gemma":[0.000018737388,0.000067793815,0.000102570906,0.00022279522,0.0007228181,0.00005381361,0.00003509567,0.0001391608,8.071648e-7],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008686738,0.000014653669,0.08476418,0.0000058998403,0.00015693154,0.0000063727807,0.007260986,0.113130175,0.0000018038446,0.7945945,0.000025676109,0.000030108367],"study_design_scores_gemma":[0.00087783614,0.000043026947,0.7510728,0.0001901256,0.0045383316,3.530221e-7,0.0108808875,0.16333246,0.00015956252,0.06600864,0.0019695233,0.00092644035],"about_ca_topic_score_codex":0.14906798,"about_ca_topic_score_gemma":0.44756892,"teacher_disagreement_score":0.7285859,"about_ca_system_score_codex":0.00009623225,"about_ca_system_score_gemma":0.0003863174,"threshold_uncertainty_score":0.85659844},"labels":[],"label_agreement":null},{"id":"W2594571064","doi":"10.1002/atr.1449","title":"A dynamic linear model for the estimation of time‐varying origin–destination matrices from link counts","year":2016,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico","keywords":"Link (geometry); Benchmark (surveying); Inference; Mathematics; Series (stratigraphy); Applied mathematics; Constant (computer programming); Linear model; Bayesian probability; Estimation; Statistics; Computer science; Artificial intelligence; Economics; Combinatorics; Geodesy","score_opus":0.015728616659450592,"score_gpt":0.30921835092771466,"score_spread":0.2934897342682641,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2594571064","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09484822,0.00015450647,0.9037257,0.0007396429,0.00021515887,0.00015435972,0.00007526382,0.000018100312,0.00006905194],"genre_scores_gemma":[0.91193324,0.00046179988,0.08723506,0.000014710023,0.00006498569,0.000004397265,0.000055276414,0.000008257578,0.00022228801],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992552,0.000017182238,0.00033867522,0.000063593034,0.00025457446,0.00007078984],"domain_scores_gemma":[0.9987788,0.00033258772,0.00047857908,0.000043625772,0.00034045117,0.000025947116],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027745927,0.000052504194,0.00009808581,0.000069045585,0.000110230685,0.000008613474,0.000082810024,0.000047942976,0.00002959265],"category_scores_gemma":[0.00008843357,0.000035028123,0.000057826088,0.00012573155,0.000038768398,0.0004467043,2.3664536e-7,0.000042097894,0.0000029125886],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010314711,0.0000131808265,0.00025404876,0.0000105419995,0.0000162765,3.8272256e-7,0.0037579152,0.9522124,0.0010728339,0.0006613198,0.000029141029,0.04186879],"study_design_scores_gemma":[0.0019867844,0.0001115972,0.021844128,0.0004371811,0.00028826745,5.21593e-7,0.0005444648,0.9619332,0.0005125149,0.010020196,0.0021334947,0.00018764279],"about_ca_topic_score_codex":0.000018076096,"about_ca_topic_score_gemma":0.000048618916,"teacher_disagreement_score":0.81708497,"about_ca_system_score_codex":0.000038746854,"about_ca_system_score_gemma":0.00011634362,"threshold_uncertainty_score":0.14284053},"labels":[],"label_agreement":null},{"id":"W2594651765","doi":"","title":"Role of Station Accessibility, Multimodal Competitions and Contextual Variables in Retaining Customer Base of Urban Commuter Rail: The Case of GO Rail in Greater Toronto and Hamilton Area","year":2017,"lang":"en","type":"article","venue":"Transportation Research Board 96th Annual MeetingTransportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Transport engineering; Customer base; Base (topology); Geography; Engineering; Business; Marketing; Mathematics","score_opus":0.06320486866751558,"score_gpt":0.38974564594406064,"score_spread":0.32654077727654507,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2594651765","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9952131,0.00026199987,0.00021013101,0.00047016732,0.00003755502,0.0013051264,0.00069689687,0.000024655166,0.0017803486],"genre_scores_gemma":[0.99749446,0.0005573531,0.0013964006,0.000012346664,0.000027602127,0.00013956078,0.00023381677,0.00002790192,0.000110565576],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99437636,0.0015924454,0.0013361133,0.00058059045,0.0014275683,0.00068690727],"domain_scores_gemma":[0.99470186,0.0018009306,0.000522186,0.0004890048,0.0022456108,0.00024040528],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008756712,0.00022902362,0.0005371984,0.0006517759,0.00104491,0.00013215785,0.00048738124,0.0002585255,0.00013894365],"category_scores_gemma":[0.0007284495,0.00021198622,0.00008474604,0.00067029765,0.0026328564,0.0013935674,0.00001493833,0.000681387,8.96771e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00090311404,0.00022833073,0.8042978,0.00032620688,0.0000370501,0.00007234105,0.17953312,0.0016063489,0.00095426454,0.010399785,0.000051265415,0.0015903598],"study_design_scores_gemma":[0.0020730188,0.00019354247,0.881781,0.00045246925,0.00002780699,6.061048e-7,0.11211679,0.0017302099,0.0006409931,0.00058361597,0.00021714537,0.00018279329],"about_ca_topic_score_codex":0.24332167,"about_ca_topic_score_gemma":0.58373696,"teacher_disagreement_score":0.34041527,"about_ca_system_score_codex":0.00011704992,"about_ca_system_score_gemma":0.00042379354,"threshold_uncertainty_score":0.97008693},"labels":[],"label_agreement":null},{"id":"W2594840230","doi":"10.1103/physreve.96.030101","title":"Random matrices and the New York City subway system","year":2017,"lang":"en","type":"article","venue":"Physical review. E","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"National Science Foundation","keywords":"Train; Poisson distribution; Random matrix; Statistics; Matrix (chemical analysis); Mathematics; Statistical physics; Computer science; Engineering; Geography; Physics; Cartography","score_opus":0.03162921395524351,"score_gpt":0.34967286992973523,"score_spread":0.3180436559744917,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2594840230","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25746655,0.18632096,0.017589638,0.09873262,0.001880105,0.005185221,0.000040275496,0.00078131194,0.43200332],"genre_scores_gemma":[0.99128985,0.007565405,0.000105019084,0.00014587585,0.00031694814,0.000010420788,0.0000025556633,0.0000030602218,0.0005608665],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99940276,0.000121817946,0.00010249331,0.0000991527,0.00018226531,0.000091514834],"domain_scores_gemma":[0.9993955,0.00020881477,0.0001442836,0.00015550038,0.000029116945,0.000066768844],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005542134,0.000051532967,0.00018926729,0.000005399624,0.0007747791,0.00012834444,0.00019035854,0.000015068104,0.000010762557],"category_scores_gemma":[0.0002786418,0.00003065845,0.00006326425,0.000053601412,0.00017387085,0.00011818076,0.000006998701,0.000055280463,0.000021947708],"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":[0.0001941466,0.00005427387,0.0131829595,0.0010297622,0.00006138123,0.0000037871664,0.016242642,0.000091113536,0.00000981788,0.90093386,0.026648583,0.041547664],"study_design_scores_gemma":[0.012913488,0.00006697139,0.09929239,0.008039303,0.0014338339,0.0000029090443,0.0026357297,0.0052317423,0.000087916305,0.02806915,0.8411777,0.0010488621],"about_ca_topic_score_codex":0.0013291442,"about_ca_topic_score_gemma":0.00013740515,"teacher_disagreement_score":0.8728647,"about_ca_system_score_codex":0.000008373762,"about_ca_system_score_gemma":0.000037578073,"threshold_uncertainty_score":0.5959053},"labels":[],"label_agreement":null},{"id":"W2595325507","doi":"","title":"Interconnected logistic networks and protocols: simulation-based efficiency assessment","year":2014,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Computer science; Modular design; Container (type theory); Asynchronous communication; Interconnectivity; Operations research; Risk analysis (engineering); Transport engineering; Computer network; Business; Engineering","score_opus":0.06361010160366126,"score_gpt":0.4138073499655822,"score_spread":0.35019724836192095,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2595325507","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.38288584,0.00017757958,0.26902205,0.0039807153,0.0023894103,0.07097007,0.00018130559,0.0010994264,0.2692936],"genre_scores_gemma":[0.99564993,0.00017486383,0.0015274491,0.00005532337,0.00014600392,0.0020614178,0.00013652693,0.00002361638,0.00022485365],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975484,0.0006565137,0.00047567667,0.0005832069,0.0002741462,0.00046203184],"domain_scores_gemma":[0.99691147,0.0022041695,0.00019892435,0.00033153352,0.00020053056,0.00015339616],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002859766,0.00017110357,0.00029627435,0.0003056898,0.00038739212,0.00027286416,0.00033352492,0.00039562874,0.00006723953],"category_scores_gemma":[0.0011180557,0.00019100448,0.00005761632,0.00015189615,0.00044204088,0.0000656189,0.00007604576,0.00089227577,0.0000013457446],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029793026,0.000058476795,0.017437758,0.00006460892,0.000009353967,0.0000027061117,0.0006047934,0.9630662,4.1906435e-7,0.00097387267,0.0000043650552,0.017747637],"study_design_scores_gemma":[0.0003882454,0.000055581866,0.014316906,0.00027471696,0.0000052385285,5.323307e-8,0.0004379781,0.9801059,6.0567646e-7,0.00034587708,0.0038650306,0.00020389254],"about_ca_topic_score_codex":0.00030434292,"about_ca_topic_score_gemma":0.001325138,"teacher_disagreement_score":0.6127641,"about_ca_system_score_codex":0.0004849668,"about_ca_system_score_gemma":0.0008962746,"threshold_uncertainty_score":0.77889365},"labels":[],"label_agreement":null},{"id":"W2597498835","doi":"10.1111/1475-4932.12330","title":"Light Rail, Land Values and Taxes","year":2017,"lang":"en","type":"article","venue":"Economic Record","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Quarter (Canadian coin); Investment (military); Scope (computer science); Statutory law; Value (mathematics); Economics; Capital investment; Land value; Government (linguistics); Agricultural economics; Light rail; Business; Finance; Geography; Transport engineering; Public transport","score_opus":0.01949418276633611,"score_gpt":0.28687269019029504,"score_spread":0.2673785074239589,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2597498835","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.90328693,0.000046747544,0.00008277423,0.002503532,0.000653549,0.000056658606,0.000008122724,0.000034652046,0.09332701],"genre_scores_gemma":[0.9887925,0.0002971697,0.00073789695,0.00003966871,0.00025007964,0.0000031839252,0.0000037832033,0.0000041968,0.009871516],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99969447,0.000017137536,0.00008011547,0.00010274971,0.0000240073,0.000081530576],"domain_scores_gemma":[0.9997352,0.00002610903,0.00008823164,0.000098061144,0.000007777928,0.00004464905],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018507995,0.00003608573,0.00006456182,0.00001874948,0.0006394086,0.00019082501,0.0000956202,0.000036952264,0.00019385698],"category_scores_gemma":[0.0000330134,0.00003668858,0.00001485326,0.0000045738,0.000055339024,0.00026668864,0.0000049595487,0.000026451493,0.000068399495],"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":[0.000011297309,0.0000049475784,0.95893294,0.0000034453567,0.000011451928,0.0000013735887,0.0050437134,0.000093167095,0.000005256663,0.003848253,0.010647918,0.021396255],"study_design_scores_gemma":[0.0004092088,0.000023737206,0.53722656,0.00001956699,0.0000128614,4.3214808e-7,0.00071944983,0.0004697569,0.00004996375,0.0067700734,0.45411336,0.00018500956],"about_ca_topic_score_codex":0.0029647432,"about_ca_topic_score_gemma":0.0068695145,"teacher_disagreement_score":0.44346544,"about_ca_system_score_codex":0.000015183357,"about_ca_system_score_gemma":0.00003178491,"threshold_uncertainty_score":0.49178785},"labels":[],"label_agreement":null},{"id":"W2598009976","doi":"10.1155/2017/3865701","title":"Large-Scale Demand Driven Design of a Customized Bus Network: A Methodological Framework and Beijing Case Study","year":2017,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":72,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National High-tech Research and Development Program","keywords":"Beijing; Scheduling (production processes); Transport engineering; Computer science; Public transport; Operations research; Traffic congestion; Network planning and design; Cluster analysis; Engineering; Computer network; China; Operations management","score_opus":0.07059861641799073,"score_gpt":0.386325595359092,"score_spread":0.31572697894110124,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2598009976","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5659661,0.000102800106,0.43346223,0.00007615762,0.00016482132,0.0002007374,0.000003557654,0.000009281739,0.000014269258],"genre_scores_gemma":[0.7574501,0.00021598571,0.24220382,0.000012165701,0.00009521153,0.000003924958,0.000002397327,0.0000068670643,0.000009512613],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99840426,0.00037680834,0.0005774583,0.0001400655,0.00031683256,0.00018459893],"domain_scores_gemma":[0.9977911,0.0005826896,0.0010940363,0.00011404667,0.00031082347,0.00010731112],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002277068,0.00010029238,0.0003659799,0.00007440031,0.00073483627,0.000051894516,0.00013543233,0.00011410375,0.000014986391],"category_scores_gemma":[0.00038473128,0.00008889915,0.000075641794,0.00011362725,0.00011693723,0.0005740163,0.0000018046082,0.00021228647,1.473934e-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":[0.000757049,0.00016991599,0.13713332,0.000017433198,0.00007185226,0.00059824623,0.09707326,0.76166147,0.00010019078,0.0005108072,0.000008079129,0.0018983781],"study_design_scores_gemma":[0.008842897,0.0010429097,0.8261291,0.0004949791,0.00081773347,0.00009388834,0.15313135,0.002969351,0.00006833742,0.0057517258,0.00025557377,0.00040219186],"about_ca_topic_score_codex":0.000066452594,"about_ca_topic_score_gemma":0.00084555236,"teacher_disagreement_score":0.75869215,"about_ca_system_score_codex":0.000018212913,"about_ca_system_score_gemma":0.00007926511,"threshold_uncertainty_score":0.56518406},"labels":[],"label_agreement":null},{"id":"W2599248517","doi":"10.1002/atr.1455","title":"Coordination of last train transfers using automated fare collection (AFC) system data","year":2016,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Train; Destinations; Computer science; Operations research; Line (geometry); Data collection; Transport engineering; Genetic algorithm; Real-time computing; Function (biology); Engineering; Tourism; Geography","score_opus":0.02917013716453193,"score_gpt":0.311969146878785,"score_spread":0.2827990097142531,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2599248517","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.82625246,0.000056915953,0.17192502,0.00039194958,0.00073897914,0.0002191692,0.00018273339,0.00012887143,0.00010392702],"genre_scores_gemma":[0.98959726,0.00009573665,0.010070431,0.0000053709855,0.00007840926,0.0000016129775,0.0001046352,0.000014854242,0.000031701937],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9982756,0.00012651512,0.0007693715,0.00015678274,0.00051797467,0.00015378186],"domain_scores_gemma":[0.9983134,0.00014868115,0.00069183594,0.000119567885,0.0006437685,0.00008271595],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007028164,0.00010474862,0.00024408197,0.00026335468,0.00020072027,0.000015811169,0.00020001452,0.000105290266,0.000018008759],"category_scores_gemma":[0.00009390825,0.000086995125,0.00007674786,0.00057126326,0.000087136425,0.0013752448,6.1572246e-7,0.00007482498,5.567565e-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":[0.0018077142,0.0004044703,0.04248341,0.0005803365,0.00036100633,0.00006384499,0.07304182,0.6785202,0.15376148,0.009400874,0.00055046997,0.039024398],"study_design_scores_gemma":[0.020834176,0.0016213332,0.78891575,0.006292479,0.001609086,0.00004881407,0.1222162,0.033646587,0.016450234,0.00044643632,0.0064776214,0.0014412561],"about_ca_topic_score_codex":0.0001286146,"about_ca_topic_score_gemma":0.0012518765,"teacher_disagreement_score":0.74643236,"about_ca_system_score_codex":0.00016382948,"about_ca_system_score_gemma":0.00028315868,"threshold_uncertainty_score":0.35475582},"labels":[],"label_agreement":null},{"id":"W2600032765","doi":"","title":"Parking fees as a substitute for roadpricing in a dynamic model of traffic congestion","year":2011,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Transport Canada","funders":"","keywords":"Traffic congestion; Transport engineering; Computer science; Engineering","score_opus":0.060928820555776995,"score_gpt":0.30910840638545484,"score_spread":0.24817958582967783,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2600032765","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92406714,0.000016155032,0.07002358,0.00005066509,0.000046573026,0.00015971917,0.0000021817475,0.000047094803,0.005586889],"genre_scores_gemma":[0.98436713,0.00002197042,0.015246812,0.000016020836,0.00000427722,0.000012694998,0.00000972084,0.000003524489,0.00031784188],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99957514,0.00001728111,0.00013966339,0.00008550164,0.00008389817,0.000098515084],"domain_scores_gemma":[0.9998059,0.00003703392,0.00005377174,0.000032279153,0.000048858943,0.000022129796],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002620184,0.0000365449,0.000069395755,0.000073775256,0.00007309708,0.000006635724,0.000049434948,0.00004666086,0.000016790573],"category_scores_gemma":[0.00004565206,0.00003702431,0.00002180123,0.00012794592,0.0000389389,0.00013318694,9.191996e-7,0.00002491425,9.2341435e-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":[0.000046535613,0.000033137807,0.005781616,0.000012496823,0.0000043669465,4.2700782e-7,0.054975197,0.9072233,0.00009903935,0.028376577,0.000004918027,0.0034423969],"study_design_scores_gemma":[0.00041320059,0.000027754611,0.023116652,0.000053361597,0.000014476975,1.0632822e-7,0.004681632,0.97008604,0.00009860062,0.0013843329,0.000024857147,0.00009896949],"about_ca_topic_score_codex":0.0012432075,"about_ca_topic_score_gemma":0.011056406,"teacher_disagreement_score":0.06286276,"about_ca_system_score_codex":0.000023296901,"about_ca_system_score_gemma":0.00007828674,"threshold_uncertainty_score":0.6169734},"labels":[],"label_agreement":null},{"id":"W2600871385","doi":"10.1680/jmuen.16.00064","title":"Reducing congestion during road works through travel demand management","year":2017,"lang":"en","type":"article","venue":"Proceedings of the Institution of Civil Engineers - Municipal Engineer","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Reputation; Quarter (Canadian coin); Work (physics); Transport engineering; Demand management; Traffic congestion; Control (management); Business; Computer science; Advertising; Engineering; Economics; Geography; Law","score_opus":0.017941782378782693,"score_gpt":0.2612495311613775,"score_spread":0.2433077487825948,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2600871385","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9145785,0.00028219185,0.005175906,0.00063969794,0.00094848394,0.00048496723,0.000008827196,0.00011654784,0.077764876],"genre_scores_gemma":[0.99693865,0.00038360964,0.0019788537,0.000007231515,0.00008255592,0.000015870712,0.000003286964,0.000012641185,0.00057732646],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9988542,0.0000068225136,0.00032902372,0.00016863039,0.0004149876,0.00022631066],"domain_scores_gemma":[0.9992152,0.0000160401,0.0003263385,0.00018189709,0.00020656249,0.00005396102],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044111087,0.00013735864,0.00019415657,0.000105161824,0.00064098893,0.00007158365,0.00052951823,0.00012867784,0.00001548412],"category_scores_gemma":[0.00024236774,0.00012497378,0.000098363686,0.00020545702,0.0003217328,0.00086565776,0.000036187987,0.00017281639,9.4492657e-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":[0.00006523809,0.000072463205,0.0017369726,0.00040338762,0.00013310449,0.0000011372151,0.02715688,0.78070635,0.002074447,0.18696538,0.00010176805,0.0005829037],"study_design_scores_gemma":[0.008294259,0.0001742553,0.7821252,0.012005673,0.0012734442,0.000016418864,0.038997516,0.08935328,0.054555282,0.002786968,0.008169219,0.0022484937],"about_ca_topic_score_codex":0.00047330972,"about_ca_topic_score_gemma":0.00008094078,"teacher_disagreement_score":0.78038824,"about_ca_system_score_codex":0.00007287673,"about_ca_system_score_gemma":0.00003303212,"threshold_uncertainty_score":0.5096283},"labels":[],"label_agreement":null},{"id":"W2602445700","doi":"","title":"Coordinated Land Use and Transportation Planning – A Sketch Modelling Approach","year":2010,"lang":"en","type":"dissertation","venue":"TSpace (University of Toronto)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Plan (archaeology); Transport engineering; Baseline (sea); Land use; Computer science; Sketch; Software; Land-use planning; Network planning and design; Mode (computer interface); Transportation planning; Operations research; Geography; Engineering; Civil engineering; Telecommunications","score_opus":0.025862174498950886,"score_gpt":0.2589398976359758,"score_spread":0.2330777231370249,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2602445700","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9572683,0.0003556072,0.015052288,0.000057371184,0.00019548096,0.00027328107,0.000054858094,0.00011386248,0.02662893],"genre_scores_gemma":[0.9768804,0.00053301227,0.011013626,0.0000036552817,0.0000293739,4.9183916e-7,0.0021363585,0.000016842674,0.009386271],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.9991365,0.000054915767,0.00011073843,0.0002688796,0.00025688446,0.00017210346],"domain_scores_gemma":[0.9992746,0.00005728373,0.0002547043,0.00009824697,0.00021215314,0.00010301745],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018319683,0.00014408992,0.00024280591,0.00006652784,0.00047883324,0.00004379565,0.00013416038,0.00044572103,0.0005570004],"category_scores_gemma":[0.000012994358,0.00019617115,0.00006262677,0.00008603795,0.00008830293,0.00075065915,0.0000017256962,0.00021602384,8.614202e-7],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007126968,0.00013371679,0.03067798,0.00039490432,0.00017633106,0.000023121722,0.9138759,0.041314192,0.0001676847,0.003102538,0.00025689328,0.009164037],"study_design_scores_gemma":[0.0012487877,0.00006565756,0.72398186,0.00031843592,0.00048121158,4.5929897e-7,0.24598768,0.024014808,0.000023664063,0.000046838555,0.0031442756,0.00068630674],"about_ca_topic_score_codex":0.3390768,"about_ca_topic_score_gemma":0.3761027,"teacher_disagreement_score":0.6933039,"about_ca_system_score_codex":0.000055359058,"about_ca_system_score_gemma":0.00012669916,"threshold_uncertainty_score":0.79996276},"labels":[],"label_agreement":null},{"id":"W2602611346","doi":"","title":"Horizontal Cooperation Among Freight Carriers: Request Allocation and Profit Sharing","year":2007,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Shapley value; Profit (economics); Cooperative game theory; Revenue; Business; Profit sharing; Microeconomics; Operations research; Scheduling (production processes); Cost allocation; Industrial organization; Computer science; Game theory; Economics; Operations management; Engineering; Finance","score_opus":0.01582577833628314,"score_gpt":0.28161954746924106,"score_spread":0.26579376913295794,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2602611346","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.90742487,0.000036207955,0.04407283,0.00032745022,0.00015410254,0.00020686102,0.0000015063595,0.00016014728,0.04761602],"genre_scores_gemma":[0.99545527,0.000025332594,0.0023719084,0.000047186702,0.0001116082,0.000004559821,0.000063109626,0.000005319146,0.0019157209],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99940836,0.000018101571,0.00013810217,0.00015005017,0.00015000218,0.00013536283],"domain_scores_gemma":[0.9997035,0.00002505219,0.000040229683,0.000050199153,0.00009843365,0.00008253621],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00059263565,0.000051131712,0.000050049213,0.000054123964,0.0003810525,0.000093002,0.000047664875,0.000075440024,0.000069430054],"category_scores_gemma":[0.000048822083,0.000050618015,0.000009947213,0.00016392996,0.00007774957,0.00048157974,0.0000030564854,0.00005205148,0.0000043835457],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001791438,0.0000115225375,0.7476462,0.000008554051,0.000009810145,0.000004115738,0.023202874,0.0004360432,0.00052526867,0.2242502,0.00023542836,0.0036520327],"study_design_scores_gemma":[0.00081352104,0.00011842578,0.95516247,0.00007903498,0.000048182017,0.0000017560935,0.022513129,0.005781686,0.004401924,0.00076704274,0.009751392,0.00056145573],"about_ca_topic_score_codex":0.0029864253,"about_ca_topic_score_gemma":0.020081766,"teacher_disagreement_score":0.22348316,"about_ca_system_score_codex":0.00004534128,"about_ca_system_score_gemma":0.000040646788,"threshold_uncertainty_score":0.9977992},"labels":[],"label_agreement":null},{"id":"W2602655165","doi":"","title":"Transport urbain à Montréal : d’hier à demain","year":2014,"lang":"fr","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Environmental science","score_opus":0.009751548454514234,"score_gpt":0.23417484611047895,"score_spread":0.22442329765596472,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2602655165","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02968828,0.003275805,0.39005148,0.023083493,0.002246107,0.00025892616,0.000030690553,0.0002984399,0.5510668],"genre_scores_gemma":[0.71151704,0.00022459848,0.008565752,0.00085294526,0.000365799,0.0000061936653,0.000053946434,0.000015699165,0.278398],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99860424,0.0001787133,0.0002913966,0.00024444898,0.00032203904,0.00035913946],"domain_scores_gemma":[0.99943215,0.000106675194,0.000073027215,0.00012329585,0.00007110853,0.0001937343],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00070855796,0.00013916085,0.00017064769,0.00005051886,0.00031865385,0.000017275344,0.00012782287,0.00021603874,0.0022640429],"category_scores_gemma":[0.00003349839,0.00014388477,0.00009872602,0.0003789738,0.0002293335,0.00026382032,0.000001705627,0.00013096175,0.00036741767],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018190489,0.0001117401,0.04332422,0.000029861341,0.000029573239,0.000008060587,0.015417055,0.024655186,0.0000053035296,0.8861292,0.010323089,0.019948492],"study_design_scores_gemma":[0.00042432063,0.00005774474,0.048799995,0.000048519825,0.00007688688,0.0000011487613,0.001567885,0.01103051,0.000018039684,0.0028107066,0.9348742,0.00029007078],"about_ca_topic_score_codex":0.009845016,"about_ca_topic_score_gemma":0.012761086,"teacher_disagreement_score":0.92455107,"about_ca_system_score_codex":0.000038325124,"about_ca_system_score_gemma":0.00007295287,"threshold_uncertainty_score":0.99864805},"labels":[],"label_agreement":null},{"id":"W2604206221","doi":"10.1155/2017/9814909","title":"Robust Evaluation for Transportation Network Capacity under Demand Uncertainty","year":2017,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","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":"Fundamental Research Funds for the Central Universities; Government of Jiangsu Province; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Flow network; Heuristic; Sensitivity (control systems); Mathematical optimization; Computer science; Set (abstract data type); Bounded function; Robust optimization; Operations research; Engineering; Mathematics","score_opus":0.06671552727532547,"score_gpt":0.33621661171195083,"score_spread":0.26950108443662535,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2604206221","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6651336,0.00017526666,0.33062214,0.0012835321,0.001638418,0.00073468365,0.000085014944,0.00003836974,0.00028898267],"genre_scores_gemma":[0.9632665,0.00022536816,0.035486937,0.00007087545,0.00053894246,0.000027759326,0.0002992997,0.000019149853,0.00006519123],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99796087,0.00010081807,0.0007175579,0.00019699261,0.0007469842,0.00027676567],"domain_scores_gemma":[0.99664325,0.00015760894,0.001450958,0.00016848091,0.0014411797,0.00013854486],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0020093815,0.00014788701,0.00027193403,0.000093991046,0.0013477358,0.00011496856,0.0002481222,0.00015192373,0.000049631286],"category_scores_gemma":[0.00019370114,0.00014991536,0.00019398009,0.00012793085,0.00015033482,0.0015257763,3.1454624e-7,0.00016350081,9.079377e-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":[0.0003168254,0.00004370376,0.007816391,0.0000250789,0.00004635557,0.000001957041,0.007894229,0.97087604,0.00013591486,0.008108674,0.00019615736,0.004538671],"study_design_scores_gemma":[0.004252136,0.00021894544,0.9593019,0.00024333644,0.0005682811,0.000001111977,0.004681929,0.0028644272,0.00013120002,0.020141775,0.0072562927,0.00033865767],"about_ca_topic_score_codex":0.00017024264,"about_ca_topic_score_gemma":0.009431193,"teacher_disagreement_score":0.9680116,"about_ca_system_score_codex":0.00013193514,"about_ca_system_score_gemma":0.00032240886,"threshold_uncertainty_score":0.9999524},"labels":[],"label_agreement":null},{"id":"W2604257928","doi":"10.1002/atr.1460","title":"Optimizing coordinated transfer with probabilistic vehicle arrivals and passengers' walking time","year":2016,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Transfer (computing); Probabilistic logic; Synchronizing; Transfer station; Transport engineering; Computer science; Arrival time; Reservation; Travel time; Operations research; Engineering; Telecommunications; Computer network","score_opus":0.008041463781197133,"score_gpt":0.24438895137234048,"score_spread":0.23634748759114335,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2604257928","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9411895,0.00009973876,0.057255987,0.00086253235,0.0001295419,0.00019007215,0.000011673252,0.000049782422,0.00021112316],"genre_scores_gemma":[0.99068123,0.0001879546,0.008892286,0.000026480491,0.00007075534,0.0000035963092,0.000008172266,0.000014929304,0.00011460998],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9989242,0.000069890884,0.00038032944,0.00013488985,0.00031409637,0.00017661028],"domain_scores_gemma":[0.99896765,0.00018720012,0.00018683386,0.000050767634,0.0004881621,0.00011937517],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003815749,0.00010382439,0.00019303829,0.00010976689,0.00018282802,0.000028575309,0.00007035999,0.000064279695,0.000052401294],"category_scores_gemma":[0.000079416175,0.00007159099,0.00004479747,0.0002235299,0.00012028072,0.0008442502,2.8334955e-7,0.00008906328,0.000001464652],"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":[0.004735123,0.0004431702,0.050276946,0.0002217621,0.00043467083,0.00028778313,0.2536752,0.37557003,0.141097,0.011230685,0.00011588026,0.16191177],"study_design_scores_gemma":[0.028980378,0.0032791772,0.8848296,0.0054808166,0.0014652761,0.000034530458,0.030719709,0.00061428885,0.021810425,0.004158482,0.016283223,0.0023440938],"about_ca_topic_score_codex":0.000012623248,"about_ca_topic_score_gemma":0.00015641432,"teacher_disagreement_score":0.83455265,"about_ca_system_score_codex":0.000046207686,"about_ca_system_score_gemma":0.000103294515,"threshold_uncertainty_score":0.29193962},"labels":[],"label_agreement":null},{"id":"W2604448029","doi":"","title":"Temporal & Spatial Analysis of Taxi Demand in Montréal, Using a Clustering Approach","year":2017,"lang":"en","type":"article","venue":"Transportation Research Board 96th Annual MeetingTransportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Cluster analysis; Geography; Computer science; Cartography; Artificial intelligence","score_opus":0.10162164277802808,"score_gpt":0.41829519925498954,"score_spread":0.31667355647696144,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2604448029","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96426654,0.00020621243,0.029034497,0.0007169279,0.00016264083,0.0016379835,0.0005011818,0.000121062025,0.0033529436],"genre_scores_gemma":[0.9876964,0.00044009465,0.010231693,0.000021784826,0.00014279604,0.00017323189,0.0007309945,0.00006228115,0.0005007209],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9892508,0.0016942761,0.0016478015,0.0011100612,0.004660925,0.0016361702],"domain_scores_gemma":[0.9937962,0.00070717727,0.00068897934,0.0008660804,0.00334072,0.0006008415],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.011702811,0.00036711374,0.0009034099,0.0036138864,0.0031295915,0.00041521498,0.0011989742,0.00048612172,0.00021344925],"category_scores_gemma":[0.0009011772,0.00041061811,0.0003683726,0.0038558608,0.001847822,0.0015000191,0.000017499697,0.0012976181,0.000009162907],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00088735187,0.00039577787,0.7867158,0.00023561963,0.0003196222,0.00009688293,0.049751934,0.15784849,0.0003751456,0.0019886405,0.00017422765,0.0012104689],"study_design_scores_gemma":[0.0015142844,0.00015491262,0.9378064,0.0002251745,0.0002300143,9.046416e-8,0.024249041,0.033526942,0.0001380367,0.00016245461,0.0015699521,0.00042268712],"about_ca_topic_score_codex":0.4585899,"about_ca_topic_score_gemma":0.61574954,"teacher_disagreement_score":0.15715961,"about_ca_system_score_codex":0.00030548303,"about_ca_system_score_gemma":0.0009817935,"threshold_uncertainty_score":0.99983454},"labels":[],"label_agreement":null},{"id":"W2605777084","doi":"10.11159/icte17.122","title":"Transport Planning, Organisation and Management","year":2017,"lang":"en","type":"article","venue":"Proceedings of the World Congress on Civil, Structural, and Environmental Engineering","topic":"Transportation Planning and Optimization","field":"Social Sciences","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":"Narodowe Centrum Badań i Rozwoju","keywords":"Computer science; Process management; Business; Knowledge management","score_opus":0.007905087216170023,"score_gpt":0.22213057985518045,"score_spread":0.21422549263901042,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2605777084","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99491066,0.00004999458,0.0000172595,0.00025973195,0.00025680405,0.00015829685,0.000008635546,0.000029668594,0.0043089297],"genre_scores_gemma":[0.9980342,0.00016762613,0.00040997512,0.000018300778,0.000026809612,0.0000047701114,0.000003363583,0.000009065129,0.0013258823],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9994575,0.0000012925652,0.00010758994,0.00013912497,0.00017780336,0.00011669501],"domain_scores_gemma":[0.99976945,0.000010075214,0.00011090519,0.00005799299,0.0000043506097,0.000047211637],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007625823,0.00009227286,0.00008669773,0.0000467193,0.00054595724,0.00006758503,0.0001516247,0.000028960525,0.000020639109],"category_scores_gemma":[0.0000057585826,0.000077482604,0.000019635396,0.00002703489,0.0001611596,0.00023907417,0.000019719686,0.000067257264,2.4730213e-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":[0.000071468115,0.000021955617,0.8899969,0.00033890345,0.00011716114,0.000003509627,0.009343375,0.011338236,0.0037632817,0.081459865,0.00023070702,0.0033146301],"study_design_scores_gemma":[0.00033100994,0.000013774857,0.9926327,0.00016914653,0.000039781207,0.0000011924906,0.0010563319,0.0013642875,0.0014218728,0.00033959092,0.0024776687,0.0001526779],"about_ca_topic_score_codex":0.00002886801,"about_ca_topic_score_gemma":0.000036122772,"teacher_disagreement_score":0.10263576,"about_ca_system_score_codex":0.000021696665,"about_ca_system_score_gemma":0.0000016107651,"threshold_uncertainty_score":0.4199117},"labels":[],"label_agreement":null},{"id":"W2606228312","doi":"10.11159/icte17.121","title":"Standardisation of Travel Planners and Use of a Return Channel","year":2017,"lang":"en","type":"article","venue":"Proceedings of the World Congress on Civil, Structural, and Environmental Engineering","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Channel (broadcasting); Computer science; Transport engineering; Business; Telecommunications; Engineering","score_opus":0.012457150892182358,"score_gpt":0.22438093389239236,"score_spread":0.21192378300021,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2606228312","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99920774,0.000039306073,0.000020428814,0.000110720735,0.00014666234,0.000107779226,0.000058188787,0.0000069667167,0.0003021872],"genre_scores_gemma":[0.9993924,0.00012483123,0.00026667496,0.0000043670834,0.000013829819,0.0000018392353,0.0000030733406,0.00000624592,0.00018676375],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99948424,0.000002541144,0.00014301474,0.00009840414,0.00018633342,0.00008548524],"domain_scores_gemma":[0.9996485,0.000029036843,0.0002231763,0.00005183105,0.0000126006935,0.00003483241],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008915702,0.00007573931,0.00012687962,0.000051052775,0.00019387068,0.000036222376,0.00010386902,0.00003242933,0.000005312134],"category_scores_gemma":[0.00004449807,0.00006178982,0.000025789432,0.00003152552,0.00025517508,0.0002765679,0.000018511839,0.00005793409,1.6031953e-8],"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":[0.000599467,0.00006370694,0.7523919,0.0011198535,0.00030714824,0.0000013091345,0.039231632,0.053421147,0.10315227,0.04422502,0.00040467156,0.0050818897],"study_design_scores_gemma":[0.000660958,0.00005914939,0.9533148,0.0004766361,0.000061533065,0.000001345507,0.0027935572,0.012658451,0.029144472,0.0003092172,0.00030854234,0.000211383],"about_ca_topic_score_codex":0.00009447584,"about_ca_topic_score_gemma":0.00005964687,"teacher_disagreement_score":0.20092286,"about_ca_system_score_codex":0.000015260985,"about_ca_system_score_gemma":0.000003865378,"threshold_uncertainty_score":0.25197157},"labels":[],"label_agreement":null},{"id":"W2606327058","doi":"10.1002/atr.1461","title":"A method for solving the multi‐objective transit frequency optimization problem","year":2016,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Fondo Nacional de Desarrollo Científico y Tecnológico; Comisión Sectorial de Investigación Científica; Volvo Research and Educational Foundations","keywords":"Computer science; Set (abstract data type); Operator (biology); Tabu search; Public transport; Mathematical optimization; Interval (graph theory); Work (physics); Operations research; Optimization problem; Line (geometry); Transport engineering; Algorithm; Mathematics; Engineering","score_opus":0.017077083850386563,"score_gpt":0.31652873730829373,"score_spread":0.29945165345790714,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2606327058","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006013774,0.00011501229,0.9902651,0.002673846,0.0002736876,0.0004775631,0.00003259974,0.000036313682,0.00011212557],"genre_scores_gemma":[0.3085644,0.00025197992,0.6907984,0.000067933746,0.00013310592,0.00002724581,0.000017381017,0.000016512966,0.00012305591],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99868536,0.00009476376,0.00054923503,0.00014301245,0.00033190646,0.00019570599],"domain_scores_gemma":[0.9980823,0.00042571948,0.00057211163,0.00006570626,0.00078012154,0.00007405111],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00097507803,0.000107449145,0.00017502195,0.00011380743,0.00039467865,0.000029568884,0.00015074473,0.00008488454,0.000033000815],"category_scores_gemma":[0.00011163863,0.00006718702,0.00015137186,0.00027228997,0.00007135395,0.0009547483,2.8841472e-7,0.00009442081,5.753812e-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":[0.00023698082,0.00006085181,0.0018417817,0.000021216387,0.000054513883,0.000003091146,0.0403554,0.92225534,0.0038282461,0.0059805266,0.000032652493,0.02532943],"study_design_scores_gemma":[0.06913573,0.004845137,0.5289589,0.0056495327,0.00427888,0.000057356017,0.17447557,0.0255309,0.024620224,0.09189708,0.065796174,0.0047545233],"about_ca_topic_score_codex":0.00004452504,"about_ca_topic_score_gemma":0.00090966414,"teacher_disagreement_score":0.8967244,"about_ca_system_score_codex":0.000083922234,"about_ca_system_score_gemma":0.00020974655,"threshold_uncertainty_score":0.3035589},"labels":[],"label_agreement":null},{"id":"W2606694194","doi":"10.1155/2017/7058789","title":"Integrated Optimization of Bus Line Fare and Operational Strategies Using Elastic Demand","year":2017,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":21,"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":"China Scholarship Council; Dalian University of Technology","keywords":"Maximization; Service (business); Operations research; Crowding; Computer science; Differential (mechanical device); Value (mathematics); Social Welfare; Transport engineering; Function (biology); Public transport; Mathematical optimization; Business; Mathematics; Marketing; Engineering","score_opus":0.022424201907672147,"score_gpt":0.3146028517249859,"score_spread":0.2921786498173138,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2606694194","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6348009,0.00014152701,0.36448216,0.0001524702,0.00021710683,0.000084311665,0.000020520574,0.000008600559,0.00009238796],"genre_scores_gemma":[0.91835773,0.00036738443,0.08111133,0.00000908661,0.0000703973,8.2131646e-7,0.000056329915,0.0000074961263,0.00001940478],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9990062,0.000041033378,0.00047567586,0.000091285525,0.00029179407,0.00009399437],"domain_scores_gemma":[0.9982893,0.00005151204,0.0007942775,0.000065033186,0.0007362479,0.000063626634],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002935363,0.00008359212,0.00017979131,0.00011486553,0.00045912227,0.000100912395,0.00010463552,0.00007292292,0.000029957238],"category_scores_gemma":[0.00012522591,0.00007858071,0.00004283574,0.00009517166,0.00015430918,0.0016193475,7.630994e-7,0.000100073325,9.7879145e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011972926,0.000026945068,0.009026621,0.000022138498,0.00002038138,0.0000052027053,0.0059794164,0.98042434,0.0010836716,0.0023920084,0.000001840644,0.0008977291],"study_design_scores_gemma":[0.006508209,0.0007552188,0.83585006,0.0013925906,0.00062866835,0.000017376224,0.05333428,0.094242394,0.0023036567,0.0031788456,0.0010898074,0.0006988979],"about_ca_topic_score_codex":0.00014272206,"about_ca_topic_score_gemma":0.0005351093,"teacher_disagreement_score":0.88618195,"about_ca_system_score_codex":0.000026534792,"about_ca_system_score_gemma":0.0002829017,"threshold_uncertainty_score":0.35312438},"labels":[],"label_agreement":null},{"id":"W2607208565","doi":"","title":"Econometric Investigation of the Influence of Transit Passes on Transit Users’ Behavior in Toronto, Canada","year":2017,"lang":"en","type":"article","venue":"Transportation Research Board 96th Annual MeetingTransportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Transit (satellite); Public transport; Transport engineering; Geography; Engineering","score_opus":0.061468862099483976,"score_gpt":0.3816425140472368,"score_spread":0.3201736519477528,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2607208565","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9925113,0.00009038625,0.00004113604,0.0019812647,0.00020164976,0.0018460553,0.000657308,0.00004040285,0.0026305425],"genre_scores_gemma":[0.99838275,0.00041716365,0.00032531028,0.000053944535,0.00006126378,0.0002624094,0.00014610478,0.000045019104,0.00030604302],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99096745,0.0013142143,0.0014882843,0.000741669,0.004402004,0.0010863678],"domain_scores_gemma":[0.99359924,0.001236173,0.00065523887,0.00084100745,0.003239134,0.00042920286],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.005527023,0.00030656578,0.0005531529,0.0007956409,0.0018799505,0.00013442169,0.0015064755,0.00033656933,0.0002688303],"category_scores_gemma":[0.0010099668,0.0002942907,0.00020040368,0.001861418,0.0019364295,0.001408164,0.000007155956,0.0010396517,0.0000036413103],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007192928,0.00021351794,0.9125796,0.00028858107,0.000043604454,0.00003284586,0.026673535,0.048349213,0.00087973336,0.008724666,0.0003002531,0.0011951356],"study_design_scores_gemma":[0.0010870543,0.00027911205,0.980674,0.00032798512,0.000035898523,5.8117802e-8,0.014025938,0.000047368772,0.0021716687,0.0001628016,0.0009269011,0.00026120065],"about_ca_topic_score_codex":0.93299985,"about_ca_topic_score_gemma":0.99046814,"teacher_disagreement_score":0.06809439,"about_ca_system_score_codex":0.00075337593,"about_ca_system_score_gemma":0.003647417,"threshold_uncertainty_score":0.99995095},"labels":[],"label_agreement":null},{"id":"W2607394547","doi":"10.1007/s40534-017-0128-8","title":"A modern congestion pricing policy for urban traffic: subsidy plus toll","year":2017,"lang":"en","type":"article","venue":"Journal of Modern Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Toll; Subsidy; Road pricing; Equity (law); Congestion pricing; Business; Traffic congestion; Transport engineering; Profit (economics); Economics; Public economics; Computer science; Operations research; Microeconomics; Engineering; Political science; Market economy","score_opus":0.035368385247817526,"score_gpt":0.3285264594862877,"score_spread":0.2931580742384702,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2607394547","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6684907,0.00018752669,0.327453,0.0023679985,0.00041405435,0.00034811895,0.00004592544,0.000048940547,0.0006437177],"genre_scores_gemma":[0.9942494,0.00014989579,0.004521552,0.000061947925,0.0005454237,0.000008615966,0.000059292026,0.00002078803,0.00038306482],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99855137,0.00004667101,0.0005426328,0.00015183199,0.0004712719,0.00023621393],"domain_scores_gemma":[0.99816036,0.000086956185,0.0009580697,0.00013783001,0.0005138615,0.00014290518],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00070649,0.00012251607,0.0002461156,0.00019832067,0.0009104728,0.00016841159,0.00026790376,0.00014592304,0.000007369488],"category_scores_gemma":[0.00017780624,0.00012311418,0.00016530111,0.00007727437,0.000098075856,0.0010004977,6.692788e-7,0.00014504699,8.9976567e-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":[0.00088259357,0.00014927746,0.016004914,0.00009799533,0.00010920544,0.000025049552,0.13594912,0.76999277,0.0008266317,0.01349453,0.0003194795,0.06214845],"study_design_scores_gemma":[0.013084664,0.00077239034,0.76405036,0.0008353869,0.0009300749,0.000013741284,0.0057327845,0.180312,0.00092520996,0.020661319,0.011495504,0.0011865642],"about_ca_topic_score_codex":0.00031371717,"about_ca_topic_score_gemma":0.0027578096,"teacher_disagreement_score":0.74804544,"about_ca_system_score_codex":0.00014248525,"about_ca_system_score_gemma":0.0006076784,"threshold_uncertainty_score":0.70027125},"labels":[],"label_agreement":null},{"id":"W2611503171","doi":"","title":"Piecewise solutions of evolutionary variational inequalities. Application to double-layered dynamics modelling of equilibrium problems.","year":2007,"lang":"en","type":"article","venue":"Journal of Inequalities in Pure & Applied Mathematics","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Mathematics; Variational inequality; Neighbourhood (mathematics); Piecewise; Context (archaeology); Constraint (computer-aided design); Mathematical optimization; Inequality; Applied mathematics; Mathematical economics; Mathematical analysis; Geometry","score_opus":0.056710599483579,"score_gpt":0.29841102164072836,"score_spread":0.24170042215714937,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2611503171","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.070567824,0.000105312094,0.92401916,0.00023696318,0.00013990278,0.00044045228,0.00007161027,0.000022593049,0.0043962062],"genre_scores_gemma":[0.84904194,0.00003471217,0.1506293,0.0000245753,0.00011856968,0.000016404285,0.000045573826,0.000017423608,0.00007152852],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9966103,0.00005823251,0.0019630876,0.00013257166,0.00092763425,0.00030821894],"domain_scores_gemma":[0.99684954,0.00063152215,0.0014268489,0.00017574505,0.0008013938,0.0001149581],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0040153363,0.00015026498,0.00045002348,0.000520422,0.00010957096,0.000020897027,0.00031041406,0.00018189324,0.000024231927],"category_scores_gemma":[0.00015533395,0.00015599343,0.0001005821,0.0007200118,0.00014208369,0.00025804856,0.000025787467,0.00021406752,0.0000012224651],"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":[0.00008250967,0.00019854777,0.00020623617,0.00020723739,0.00002192289,4.8724985e-7,0.03325695,0.47088233,0.0001909422,0.494711,0.000043743312,0.00019807652],"study_design_scores_gemma":[0.003968453,0.00029352072,0.0008580576,0.0013168942,0.00022784456,0.000015252331,0.16870996,0.43066683,0.0008022257,0.3913911,0.0008605161,0.0008893345],"about_ca_topic_score_codex":0.0001963593,"about_ca_topic_score_gemma":0.00032119206,"teacher_disagreement_score":0.7784741,"about_ca_system_score_codex":0.00028881247,"about_ca_system_score_gemma":0.0004345622,"threshold_uncertainty_score":0.6361227},"labels":[],"label_agreement":null},{"id":"W2612488123","doi":"10.1139/cjce-2017-0294","title":"Breaking into emergency shuttle service: Aspects and impacts of retracting buses from existing scheduled bus services","year":2018,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Software deployment; Service (business); Public transport; Transport engineering; Service quality; Bridging (networking); Bus rapid transit; Level of service; Quality of service; Computer science; Engineering; Business; Telecommunications; Computer security; Marketing","score_opus":0.013718535403851468,"score_gpt":0.2586550347320708,"score_spread":0.24493649932821934,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2612488123","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9940169,0.0007135445,0.0024364528,0.00021276297,0.0006498615,0.00003606481,0.000007273844,0.000018029134,0.0019091356],"genre_scores_gemma":[0.9968837,0.000042908192,0.0025753642,0.000022827073,0.00045232184,2.545579e-7,0.0000035119233,0.00001332621,0.0000058214528],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99919343,0.000021869208,0.0003186645,0.00008406977,0.00017018495,0.00021180384],"domain_scores_gemma":[0.99888474,0.00007287483,0.00026804328,0.000056968147,0.0003841663,0.00033322457],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037187815,0.000083652834,0.00014970546,0.00020631676,0.00023748286,0.000054097964,0.00013241894,0.000073236166,0.00017992713],"category_scores_gemma":[0.0002715231,0.00009252086,0.000028305836,0.00035592043,0.00003230176,0.00041086652,0.0000032837427,0.00013456706,0.0000010472733],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000044749388,0.000025596124,0.5292549,0.0006219992,0.00033186976,0.00015886175,0.36874694,0.07557869,0.016662048,0.0053931363,0.00017561794,0.0030055598],"study_design_scores_gemma":[0.0017159968,0.0002648521,0.88388413,0.0064159343,0.00035151117,0.000058650272,0.045779612,0.044321675,0.0044257776,0.002505315,0.00900899,0.0012675814],"about_ca_topic_score_codex":0.07674838,"about_ca_topic_score_gemma":0.79912776,"teacher_disagreement_score":0.7223794,"about_ca_system_score_codex":0.000065292814,"about_ca_system_score_gemma":0.0003844522,"threshold_uncertainty_score":0.9293996},"labels":[],"label_agreement":null},{"id":"W2612592966","doi":"10.4000/developpementdurable.11622","title":"La mobilité sur le campus Lyon Tech-la-Doua : approche systémique et scénarios en éco-mobilité","year":2017,"lang":"fr","type":"article","venue":"Développement durable et territoires","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Humanities; Sociology; Philosophy","score_opus":0.02414037943199658,"score_gpt":0.30058351473072015,"score_spread":0.27644313529872355,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2612592966","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6311015,0.0038303551,0.020380117,0.21842678,0.0044587706,0.0032730268,0.0004616619,0.0010093289,0.11705847],"genre_scores_gemma":[0.96251696,0.0034764837,0.0062991604,0.00051219336,0.00023244633,0.00029475323,0.00021967082,0.00007724301,0.026371105],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.99532545,0.0014956681,0.00079255697,0.00086897553,0.00074378174,0.0007735939],"domain_scores_gemma":[0.9972382,0.0008043549,0.0006136919,0.0008261192,0.0002494814,0.00026813324],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.003536644,0.00050207,0.00053234753,0.0001628119,0.0017024359,0.0009660919,0.0009728368,0.00065610354,0.00019374111],"category_scores_gemma":[0.0007471209,0.0005300999,0.00020673007,0.00025498212,0.0009585035,0.0014422612,0.000105083585,0.0006859171,0.00005494859],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00044481427,0.0045315735,0.29932678,0.0034424923,0.0009540444,0.0005410368,0.20859393,0.049756095,0.0026435286,0.24239276,0.09835948,0.08901348],"study_design_scores_gemma":[0.0028654898,0.00025783925,0.2049179,0.002286393,0.00020349388,0.000033455555,0.011513231,0.0032325415,0.0012772952,0.0050296322,0.76686794,0.0015147805],"about_ca_topic_score_codex":0.0321176,"about_ca_topic_score_gemma":0.027992522,"teacher_disagreement_score":0.66850847,"about_ca_system_score_codex":0.00044315445,"about_ca_system_score_gemma":0.0015963673,"threshold_uncertainty_score":0.99971503},"labels":[],"label_agreement":null},{"id":"W2613601825","doi":"","title":"The Effects of Proof of Payment on Streetcar Performance in the City of Toronto","year":2017,"lang":"en","type":"article","venue":"Transportation Research Board 96th Annual MeetingTransportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Payment; Business; Actuarial science; Finance","score_opus":0.0498059500599394,"score_gpt":0.39530462956461876,"score_spread":0.3454986795046794,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2613601825","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98978245,0.00033048177,0.000078829326,0.0012281346,0.00017169595,0.00215592,0.00013900545,0.000023075758,0.006090397],"genre_scores_gemma":[0.9970431,0.0018567794,0.00025562887,0.000014297803,0.00006822551,0.00027618613,0.00007561002,0.000024327512,0.00038586074],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99094933,0.001595646,0.0012242617,0.0004734875,0.00484872,0.00090855174],"domain_scores_gemma":[0.99197716,0.003433338,0.0006635363,0.00084302534,0.0028997685,0.00018315023],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.011915616,0.00021711395,0.00041625198,0.00028480627,0.002121273,0.00010287364,0.0015658893,0.00021644327,0.000054608798],"category_scores_gemma":[0.001642118,0.00015857458,0.0001733418,0.0006882034,0.0023741766,0.0007100766,0.000007764239,0.00078220165,0.000002039013],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0021048326,0.0008493309,0.8028605,0.0013413316,0.00009613485,0.000025994717,0.13792922,0.0025628689,0.0006942006,0.040359862,0.0005230712,0.010652641],"study_design_scores_gemma":[0.0010579844,0.0009462045,0.9608875,0.00061621744,0.000023167295,2.392636e-8,0.026378036,0.00006586468,0.0069291685,0.00034421665,0.0026037835,0.00014782013],"about_ca_topic_score_codex":0.07323229,"about_ca_topic_score_gemma":0.24642569,"teacher_disagreement_score":0.1731934,"about_ca_system_score_codex":0.00013430625,"about_ca_system_score_gemma":0.00069780427,"threshold_uncertainty_score":0.9991778},"labels":[],"label_agreement":null},{"id":"W2613793899","doi":"","title":"East Coast Urbanites vs West Coast hippies – understanding differences in attitudes towards public transport","year":2017,"lang":"en","type":"article","venue":"International Choice Modelling Conference 2017","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Demographics; Context (archaeology); Service (business); Geography; Public transport; Scale (ratio); Perception; Product (mathematics); Marketing; Business; Sociology; Transport engineering; Engineering; Cartography; Psychology; Demography","score_opus":0.24590194440102747,"score_gpt":0.34964000578288157,"score_spread":0.1037380613818541,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2613793899","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6664641,0.000078950005,0.2888743,0.011030684,0.0019149716,0.0003060408,0.00018355894,0.00020660096,0.030940788],"genre_scores_gemma":[0.997051,0.00035353808,0.000898434,0.000056099787,0.0002780535,0.000023112334,0.00013764245,0.000016309154,0.0011858045],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9979663,0.00006660986,0.00039744642,0.00041777213,0.00076270977,0.00038917712],"domain_scores_gemma":[0.9986684,0.00015001952,0.00035907413,0.00027008454,0.00039809497,0.00015435254],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00056048116,0.0002162712,0.00026850123,0.00025320376,0.0012848038,0.0010456437,0.0011547386,0.00016386984,0.00024287825],"category_scores_gemma":[0.00025889766,0.00022407748,0.00008033202,0.00008466781,0.00046874906,0.0015235405,0.000029073799,0.00027934357,0.000019466306],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000040846513,0.000104339764,0.9046885,0.000017236183,0.00004686889,0.000014497971,0.011695947,0.009598385,0.000012852476,0.07321369,0.000090760695,0.00047607705],"study_design_scores_gemma":[0.0013661025,0.000056348927,0.8439204,0.00076866045,0.00004215658,0.0000027233687,0.0100650145,0.13051264,0.000017177474,0.0076283263,0.0048189242,0.0008015333],"about_ca_topic_score_codex":0.018371038,"about_ca_topic_score_gemma":0.12423572,"teacher_disagreement_score":0.3305869,"about_ca_system_score_codex":0.00020337701,"about_ca_system_score_gemma":0.00042579375,"threshold_uncertainty_score":0.99999136},"labels":[],"label_agreement":null},{"id":"W2613865919","doi":"","title":"Bus transport in Greater Manchester: Sustainable Transport between Market, Hierarchy, and Network","year":2009,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Transport Canada","funders":"","keywords":"Hierarchy; Business; Computer science; Transport engineering; Economics; Engineering; Market economy","score_opus":0.011637594872292609,"score_gpt":0.25362220198521346,"score_spread":0.24198460711292086,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2613865919","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8459324,0.00016636528,0.006067703,0.0030270093,0.000066468325,0.0004904182,0.0000070872966,0.00022239382,0.14402014],"genre_scores_gemma":[0.9811344,0.00011330987,0.0015449147,0.0002591467,0.00012982298,0.0000067780124,0.000047655474,0.0000083799405,0.016755566],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99870855,0.000063778454,0.00027572544,0.00025098163,0.00021857498,0.00048239267],"domain_scores_gemma":[0.99964863,0.000041751733,0.000039746144,0.00008895917,0.000047732905,0.00013318339],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000682101,0.00012094861,0.00019906186,0.000102585254,0.00024934372,0.000028133312,0.00010225533,0.00013184508,0.0002555348],"category_scores_gemma":[0.000005571148,0.00011920562,0.000037922873,0.00042274228,0.00008849331,0.00035074883,0.0000011657155,0.00012038378,0.0000021331016],"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":[0.000042890184,0.000017530725,0.98089314,0.000013592159,0.0000062076465,0.00006485151,0.0101700835,0.0011308654,3.3378203e-7,0.004744419,0.00041281566,0.002503277],"study_design_scores_gemma":[0.00039845612,0.000038070466,0.9798417,0.000023794462,0.000019960768,4.7342814e-7,0.0016057275,0.000042332722,0.0000029306102,0.001425546,0.016437994,0.0001630237],"about_ca_topic_score_codex":0.0010376973,"about_ca_topic_score_gemma":0.0012346688,"teacher_disagreement_score":0.13520202,"about_ca_system_score_codex":0.000038780763,"about_ca_system_score_gemma":0.000079459925,"threshold_uncertainty_score":0.48610643},"labels":[],"label_agreement":null},{"id":"W2615025140","doi":"","title":"Variations annuelles des comportements des usagers de transport collectif","year":2016,"lang":"fr","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Political science; Humanities; Art","score_opus":0.022824549113849877,"score_gpt":0.2595207275556925,"score_spread":0.23669617844184263,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2615025140","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15421498,0.0009606,0.69094026,0.00797308,0.00044500772,0.000602816,0.0003517034,0.00032196718,0.14418957],"genre_scores_gemma":[0.81272894,0.0030876324,0.11784501,0.00009447584,0.000056876466,0.00011048471,0.00080899626,0.0000634684,0.06520414],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9909955,0.0058748513,0.0008809962,0.00083811674,0.00067332794,0.00073716266],"domain_scores_gemma":[0.9934753,0.001625914,0.00073587504,0.00090324343,0.0028098805,0.00044974915],"candidate_categories":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0067765843,0.00044328038,0.0004551569,0.00023811868,0.002589565,0.0003035706,0.0010287819,0.0004829452,0.0009520224],"category_scores_gemma":[0.0008721031,0.00048374434,0.0003032702,0.0010294493,0.0018275274,0.0004918658,0.000098149816,0.00044390038,0.0000787642],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000047537513,0.0014835172,0.28083435,0.0003400634,0.00041737058,0.000020625228,0.24303372,0.0039704926,0.00097494374,0.42227143,0.0015828932,0.04502303],"study_design_scores_gemma":[0.003273368,0.0000037880725,0.68348193,0.011258518,0.0008122652,0.000021064265,0.0050303712,0.016937671,0.008732983,0.102984674,0.16498296,0.0024804182],"about_ca_topic_score_codex":0.009235209,"about_ca_topic_score_gemma":0.020314874,"teacher_disagreement_score":0.65851396,"about_ca_system_score_codex":0.00058755936,"about_ca_system_score_gemma":0.0011459705,"threshold_uncertainty_score":0.99996126},"labels":[],"label_agreement":null},{"id":"W2615498866","doi":"10.1155/2017/9184891","title":"Dynamic Path Planning of Emergency Vehicles Based on Travel Time Prediction","year":2017,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities","keywords":"Computer science; Shortest path problem; Reliability (semiconductor); Path (computing); Cluster analysis; Algorithm; Data mining; Mathematical optimization; Artificial intelligence; Mathematics; Graph; Theoretical computer science","score_opus":0.014409113823992922,"score_gpt":0.3010009889531438,"score_spread":0.2865918751291509,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2615498866","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9653209,0.00006749422,0.032049183,0.0003856412,0.0008524202,0.0001475085,0.00007806467,0.000024670395,0.0010741543],"genre_scores_gemma":[0.99375284,0.0001272174,0.005839077,0.00001229386,0.00006417752,0.0000019339684,0.00006654889,0.0000112391845,0.00012467486],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9986822,0.000043395445,0.0005542271,0.00010598789,0.00048857403,0.00012561529],"domain_scores_gemma":[0.99832785,0.000049613354,0.0011130795,0.00011829093,0.00031714657,0.000074036805],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042671384,0.000089859335,0.00018402911,0.0001418027,0.00040716774,0.00001927377,0.00017296779,0.00008071345,0.000057433383],"category_scores_gemma":[0.00009179564,0.00008883408,0.00011347992,0.00009377777,0.000059831673,0.000637334,3.2723716e-7,0.00013198768,0.0000014790785],"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":[0.00048215882,0.0001348912,0.054899182,0.000039862894,0.000027414177,0.000017012377,0.012228035,0.921236,0.0057755816,0.00041500485,0.000084891515,0.0046599694],"study_design_scores_gemma":[0.00096439203,0.0002512139,0.99061817,0.00027190815,0.00006393745,2.4512065e-7,0.0015619294,0.005098568,0.00038224668,0.00038671866,0.00030590128,0.000094784795],"about_ca_topic_score_codex":0.000027019409,"about_ca_topic_score_gemma":0.00004470327,"teacher_disagreement_score":0.93571895,"about_ca_system_score_codex":0.000037708713,"about_ca_system_score_gemma":0.000116772106,"threshold_uncertainty_score":0.36225486},"labels":[],"label_agreement":null},{"id":"W2617449067","doi":"10.5539/jmr.v9n3p95","title":"A Fuzzy-stochastic Approach for Binary Linear Programming under Uncertainties","year":2017,"lang":"en","type":"article","venue":"Journal of Mathematics Research","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Interval (graph theory); Fuzzy logic; Mathematical optimization; Binary number; Linear programming; Artificial intelligence; Computer science","score_opus":0.22334645735637218,"score_gpt":0.46478812090428717,"score_spread":0.241441663547915,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2617449067","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08785969,0.00024139746,0.8977794,0.003336193,0.00032733005,0.0009392469,0.000007940401,0.00003510217,0.009473716],"genre_scores_gemma":[0.75493884,0.000041868076,0.24295038,0.000006831558,0.0003291586,0.000017277971,0.0000029155945,0.000014170638,0.0016985754],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","domain_scores_codex":[0.99839157,0.00008664336,0.00030717652,0.00008108919,0.00085610105,0.00027743905],"domain_scores_gemma":[0.997905,0.00047741501,0.00034705954,0.00016246934,0.0009971756,0.00011086109],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0048128744,0.000058757763,0.00016410006,0.00019467958,0.001460156,0.00029900804,0.000468658,0.00007949247,0.000010033717],"category_scores_gemma":[0.0017481124,0.0000475793,0.0000853306,0.00010507968,0.00033962794,0.00026752817,0.000017471779,0.00025580273,0.0000028266627],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005022159,0.0028170957,0.0016245045,0.0019675775,0.00048752315,0.000047171176,0.26669163,0.34210736,0.00056432793,0.3609467,0.007744947,0.014498933],"study_design_scores_gemma":[0.0061229793,0.0023486333,0.006636356,0.002228574,0.0003432687,0.00005975535,0.44030377,0.26262617,0.00016713348,0.25926417,0.01876795,0.0011312246],"about_ca_topic_score_codex":0.000070199356,"about_ca_topic_score_gemma":0.000029517993,"teacher_disagreement_score":0.66707915,"about_ca_system_score_codex":0.00006353649,"about_ca_system_score_gemma":0.00035159153,"threshold_uncertainty_score":0.9998398},"labels":[],"label_agreement":null},{"id":"W2618778333","doi":"10.5539/jms.v7n2p116","title":"Research on Influence of Community Opening on Road Capacity","year":2017,"lang":"en","type":"article","venue":"Journal of Management and Sustainability","topic":"Transportation Planning and Optimization","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":"Openness to experience; Intersection (aeronautics); Transport engineering; Block (permutation group theory); Computer science; Basis (linear algebra); Mathematics; Engineering","score_opus":0.0878416562222331,"score_gpt":0.4159961133463359,"score_spread":0.3281544571241028,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2618778333","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9825987,0.00000788214,0.000093773466,0.0014896222,0.000038598533,0.00014755706,8.384179e-7,0.0000037011155,0.015619318],"genre_scores_gemma":[0.9994415,0.00006191613,0.00012959057,0.000026597058,0.000020188105,0.0000012712301,3.0528548e-7,0.000001906105,0.00031674028],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9984546,0.0006556734,0.00021996,0.000056012854,0.00047818501,0.00013556978],"domain_scores_gemma":[0.99847674,0.00021358833,0.00026285308,0.00023159396,0.0007548939,0.000060355575],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.007844606,0.00004396939,0.00011651388,0.00013566102,0.0015857376,0.00010661599,0.00034796388,0.00004059442,0.0000067788055],"category_scores_gemma":[0.0010852993,0.000037851823,0.000032201835,0.00008917755,0.00044456814,0.00031906657,0.00003406146,0.00038557922,3.2147958e-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":[0.0010286965,0.0006200704,0.7181107,0.00048048736,0.000078418576,0.000032918153,0.052174535,0.048045717,0.00000211997,0.13751985,0.00061510934,0.041291386],"study_design_scores_gemma":[0.0002769562,0.0002468857,0.9574086,0.000061475475,0.000008976284,1.1212692e-7,0.032606967,0.000020260157,0.000009845808,0.007783632,0.0015407829,0.000035472433],"about_ca_topic_score_codex":0.0020712654,"about_ca_topic_score_gemma":0.00024447148,"teacher_disagreement_score":0.23929794,"about_ca_system_score_codex":0.000113137365,"about_ca_system_score_gemma":0.000080026206,"threshold_uncertainty_score":0.9997141},"labels":[],"label_agreement":null},{"id":"W2624775521","doi":"10.1016/j.procs.2017.05.366","title":"Baseline Synthesis and Microsimulation of Life-stage Transitions within an Agent-based Integrated Urban Model","year":2017,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Microsimulation; Baseline (sea); Multinomial logistic regression; Population; Computer science; Simulation; Demography; Machine learning; Transport engineering; Engineering","score_opus":0.035892888603351866,"score_gpt":0.2928031734263001,"score_spread":0.2569102848229482,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2624775521","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.36595783,0.000007455369,0.6333051,0.00036205433,0.000091938724,0.000109635796,0.000028845416,0.00005158231,0.00008557162],"genre_scores_gemma":[0.8987077,0.0000035714563,0.10110876,0.00009933363,0.000035059995,0.0000058738274,0.000011935449,0.0000042638126,0.000023539345],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990624,0.000041099353,0.0001904404,0.000262954,0.0002937867,0.00014929807],"domain_scores_gemma":[0.99910575,0.000059594582,0.00017833216,0.00018305209,0.00031474163,0.00015852043],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00097044115,0.0000746799,0.000102841295,0.00012072459,0.0011720932,0.00025277873,0.00033725594,0.000043345433,0.0000064115743],"category_scores_gemma":[0.00028296327,0.00007240174,0.000019020024,0.00021798267,0.00073466246,0.00089051103,0.000008445619,0.000053974425,6.581065e-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":[0.000034813558,0.00010830326,0.023613522,0.000039576385,0.000003911674,8.9047745e-7,0.03396355,0.9317155,0.0022206535,0.0036204904,0.000032939,0.004645856],"study_design_scores_gemma":[0.0001506612,0.000020144602,0.011032313,0.00003798895,0.0000094255,6.5183755e-8,0.00021750557,0.9869854,0.0013867152,0.00005206455,0.000018539147,0.00008918273],"about_ca_topic_score_codex":0.00025352483,"about_ca_topic_score_gemma":0.00043185503,"teacher_disagreement_score":0.53274983,"about_ca_system_score_codex":0.000019597424,"about_ca_system_score_gemma":0.00063965924,"threshold_uncertainty_score":0.90149117},"labels":[],"label_agreement":null},{"id":"W2626265344","doi":"","title":"Integrated Operations Planning and Revenue Management for Rail Freight Transportation","year":2010,"lang":"en","type":"article","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; HEC Montréal","funders":"","keywords":"Profitability index; Revenue management; Profit maximization; Yield management; Revenue; Profit (economics); Bilevel optimization; Maximization; Operations research; Scheduling (production processes); Computer science; Flow network; Integer programming; Business; Operations management; Mathematical optimization; Engineering; Economics; Optimization problem; Microeconomics; Finance; Mathematics","score_opus":0.012702274317797147,"score_gpt":0.2613530989019162,"score_spread":0.24865082458411908,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2626265344","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24722308,0.00015810358,0.7472579,0.0027574378,0.00021538917,0.0009420193,0.00022209337,0.00036470874,0.00085926394],"genre_scores_gemma":[0.7881699,0.000089087116,0.20842141,0.00042438373,0.00009555975,0.00043034804,0.00036346194,0.000023309918,0.0019824903],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.998976,0.00004836409,0.00025990757,0.00024476554,0.00017960269,0.00029135967],"domain_scores_gemma":[0.9994274,0.00006842022,0.00007138714,0.00016085056,0.00012175329,0.00015018109],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054176006,0.00013219716,0.00013432634,0.00019850682,0.0007123162,0.00015775753,0.00014715326,0.00018344435,0.00003113755],"category_scores_gemma":[0.000077995835,0.00014157145,0.000050648887,0.00029705462,0.000093755,0.0003169136,0.0000037594564,0.000211684,0.0000016848238],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020230658,0.00023898539,0.12456382,0.00011393491,0.00011701616,0.000041498966,0.029698554,0.045144763,0.006942255,0.7465127,0.008015919,0.038408246],"study_design_scores_gemma":[0.0030294573,0.00021752619,0.5358126,0.0003064194,0.0003313435,0.000011746894,0.010081628,0.13390043,0.0034810018,0.010776196,0.3006073,0.0014443656],"about_ca_topic_score_codex":0.0068413224,"about_ca_topic_score_gemma":0.053689852,"teacher_disagreement_score":0.7357365,"about_ca_system_score_codex":0.00005397556,"about_ca_system_score_gemma":0.00010925575,"threshold_uncertainty_score":0.9997722},"labels":[],"label_agreement":null},{"id":"W2703155739","doi":"","title":"Lokalbanernes trafikale begrænsninger","year":2007,"lang":"da","type":"article","venue":"Technical University of Denmark, DTU Orbit (Technical University of Denmark, DTU)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Transport Canada","funders":"","keywords":"Philosophy","score_opus":0.015732156094883275,"score_gpt":0.23528095112128555,"score_spread":0.2195487950264023,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2703155739","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.54510385,0.0021949892,0.1766862,0.005819129,0.001268412,0.0033234968,0.00094014226,0.001390683,0.26327312],"genre_scores_gemma":[0.9617938,0.0019434094,0.018947344,0.00008028814,0.00013041691,1.5595236e-7,0.000111032175,0.000055867895,0.016937682],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99384326,0.000477285,0.0010553786,0.001354086,0.0019361333,0.0013338741],"domain_scores_gemma":[0.99431586,0.0014470648,0.0013414536,0.0010143567,0.00095457316,0.0009266801],"candidate_categories":["metaepi_narrow","sts","research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0026523427,0.0007216592,0.0014762676,0.00092186005,0.0012771724,0.000047959056,0.0027209232,0.0020043417,0.005112819],"category_scores_gemma":[0.00039919978,0.0009815701,0.0010712374,0.0028213644,0.0043041026,0.0009138179,0.00045521508,0.001544201,0.00036022696],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.03923318,0.024869168,0.19658841,0.0047991355,0.005342384,0.013241128,0.059959393,0.011095771,0.087389864,0.20218283,0.27522016,0.08007857],"study_design_scores_gemma":[0.014691041,0.003317329,0.4246323,0.0034375337,0.0045687486,0.00016797446,0.061798956,0.0010821337,0.0027937028,0.0018905844,0.47566712,0.005952575],"about_ca_topic_score_codex":0.003964326,"about_ca_topic_score_gemma":0.011188847,"teacher_disagreement_score":0.41669,"about_ca_system_score_codex":0.00073702086,"about_ca_system_score_gemma":0.0007296084,"threshold_uncertainty_score":0.99929124},"labels":[],"label_agreement":null},{"id":"W2706436907","doi":"","title":"Temporal Transferability of Model Components within an Activity-Based Travel Demand Modelling Approach","year":2016,"lang":"en","type":"dissertation","venue":"TSpace (University of Toronto)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Transferability; Computer science; Machine learning","score_opus":0.04787489115911004,"score_gpt":0.2790892035323623,"score_spread":0.23121431237325224,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2706436907","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.59825134,0.0000317441,0.38380343,0.000043702476,0.000070118294,0.00025773456,0.00009808235,0.000039959057,0.017403917],"genre_scores_gemma":[0.97670114,0.00005450465,0.020210413,0.000002094442,0.000013878582,5.268744e-7,0.000656131,0.000015023467,0.0023462758],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99866354,0.00018957636,0.00016281272,0.0003331551,0.00047477768,0.00017615303],"domain_scores_gemma":[0.99906325,0.000040250157,0.0003421199,0.00019529354,0.00023358574,0.00012549799],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045215304,0.00017119234,0.0004066468,0.000064607215,0.0003636622,0.0000097752445,0.00028759788,0.00035333316,0.00033691956],"category_scores_gemma":[0.0000057081525,0.00020162153,0.00014705394,0.00006542571,0.00017112982,0.0006194773,0.0000023495563,0.00012295714,4.7714127e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017744665,0.0006223045,0.0017990591,0.00049431995,0.000096003605,0.0000015175989,0.3753734,0.61387396,0.0021889894,0.0018649143,0.000008636173,0.0019024422],"study_design_scores_gemma":[0.0014596902,0.00012040058,0.056383472,0.00023633639,0.0002514226,5.21194e-8,0.061029006,0.87946117,0.0004577581,0.0001300873,0.000006859316,0.00046372373],"about_ca_topic_score_codex":0.17953785,"about_ca_topic_score_gemma":0.13257855,"teacher_disagreement_score":0.37844983,"about_ca_system_score_codex":0.00016079955,"about_ca_system_score_gemma":0.0004339583,"threshold_uncertainty_score":0.88324964},"labels":[],"label_agreement":null},{"id":"W2727161997","doi":"10.1155/2017/9243039","title":"A Trial-and-Error Method with Autonomous Vehicle-to-Infrastructure Traffic Counts for Cordon-Based Congestion Pricing","year":2017,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Toll; Congestion pricing; Variational inequality; Computer science; Road pricing; Traffic congestion; Operations research; Flow network; Mathematical optimization; Singapore Area Licensing Scheme; Transport engineering; Engineering; Mathematics","score_opus":0.018370155875575073,"score_gpt":0.3354299535417004,"score_spread":0.31705979766612535,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2727161997","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.73674834,0.000034397246,0.26093617,0.0010475476,0.00051406334,0.00053812424,0.00002387966,0.000029876108,0.00012762117],"genre_scores_gemma":[0.88748044,0.000017382343,0.11223503,0.000058026493,0.000106313266,0.000009957927,0.000020262145,0.000010374622,0.00006224439],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9992887,0.000026970398,0.00025781494,0.00010463175,0.00021480344,0.000107088854],"domain_scores_gemma":[0.9989569,0.00010405422,0.00049707125,0.00006616302,0.00029155597,0.00008426511],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037870338,0.00007481305,0.00015800507,0.0000836918,0.00037721082,0.000056835743,0.00008774707,0.00006174537,0.000011026412],"category_scores_gemma":[0.000091984555,0.0000651575,0.000043206855,0.00006510355,0.000044438657,0.00031023554,2.2311278e-7,0.000085732354,4.3338918e-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":[0.006178083,0.000027917804,0.0013625119,0.00002947208,0.000023230914,0.000005986118,0.006011597,0.94003797,0.00014812103,0.00081815844,0.00005524734,0.045301702],"study_design_scores_gemma":[0.06850548,0.0033481752,0.8571415,0.00080290687,0.0007471429,0.0000058996116,0.0066736504,0.013957762,0.00050171453,0.0008994566,0.046613093,0.00080322113],"about_ca_topic_score_codex":0.00001599178,"about_ca_topic_score_gemma":0.00024660255,"teacher_disagreement_score":0.9260802,"about_ca_system_score_codex":0.000040165785,"about_ca_system_score_gemma":0.00021303243,"threshold_uncertainty_score":0.29012388},"labels":[],"label_agreement":null},{"id":"W2728164561","doi":"10.1155/2017/4950425","title":"A Mesoscopic Simulation Model for Airport Curbside Management","year":2017,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Dwell time; Transport engineering; Mesoscopic physics; Computer science; Operations research; Traffic congestion; International airport; Simulation; Engineering","score_opus":0.03220148140508471,"score_gpt":0.35916469441459203,"score_spread":0.3269632130095073,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2728164561","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2095076,0.00005169459,0.787891,0.0006065134,0.0005546283,0.0003886683,0.000018881727,0.000029815707,0.0009512158],"genre_scores_gemma":[0.9255243,0.00016302508,0.07350295,0.00003957811,0.0001061565,0.00000996162,0.00003608639,0.000011029409,0.00060692604],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99895555,0.000014633931,0.00043363613,0.00010986107,0.00034227347,0.00014405807],"domain_scores_gemma":[0.9985422,0.00005036777,0.00082006544,0.000122151,0.00038811966,0.00007713277],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042331498,0.000080540674,0.00015556447,0.000101965335,0.00065593846,0.00006396656,0.00018374722,0.000059014157,0.000009171994],"category_scores_gemma":[0.00006216878,0.00008255155,0.00010614238,0.000050938408,0.000054627602,0.0010762503,5.9355693e-7,0.000069543996,7.738293e-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":[0.00014983393,0.00003099982,0.0038425033,0.000029650877,0.000025984114,0.000005902138,0.0074091093,0.9743816,0.000027123278,0.0070073614,0.00003705753,0.0070528523],"study_design_scores_gemma":[0.006904161,0.00027193385,0.7729999,0.0004970265,0.00059185334,8.961697e-7,0.006540494,0.16557789,0.00020410906,0.02766666,0.018193474,0.00055159564],"about_ca_topic_score_codex":0.000015839405,"about_ca_topic_score_gemma":0.00035357874,"teacher_disagreement_score":0.80880374,"about_ca_system_score_codex":0.000046520137,"about_ca_system_score_gemma":0.00009214077,"threshold_uncertainty_score":0.50450146},"labels":[],"label_agreement":null},{"id":"W2728423853","doi":"10.1155/2017/9147356","title":"Travel Time Reliability for Urban Networks: Modelling and Empirics","year":2017,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China; Fundamental Research Funds for the Central Universities; National Science Foundation","keywords":"Reliability (semiconductor); Standard deviation; Skewness; Weighting; Travel time; Computer science; Statistics; Simulation; Transport engineering; Mathematics; Engineering","score_opus":0.022844470827689946,"score_gpt":0.3042691579868346,"score_spread":0.2814246871591447,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2728423853","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.41935503,0.00015640665,0.579139,0.0006332105,0.00032097753,0.00018941645,0.00001611547,0.0000142249955,0.00017558767],"genre_scores_gemma":[0.95663226,0.000410832,0.042516805,0.000027733548,0.00020047238,0.000003228062,0.000026768825,0.00000954215,0.000172348],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9991733,0.000023066625,0.00036603241,0.00010836179,0.00019823115,0.00013102517],"domain_scores_gemma":[0.9987655,0.00011009754,0.000574387,0.00008816377,0.00037266026,0.0000891929],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062487205,0.00007167902,0.00016185676,0.000047649435,0.00064957334,0.00006844254,0.000115591516,0.000076968274,0.000005918225],"category_scores_gemma":[0.00008823623,0.00007065761,0.000072614144,0.000044850287,0.000098573524,0.0008465294,3.8028696e-7,0.00009635381,2.4106316e-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":[0.00017294532,0.000023621164,0.009154209,0.000015118285,0.0000101962105,0.0000019555232,0.00904349,0.9781378,0.000035787427,0.0009140857,0.00005596144,0.002434824],"study_design_scores_gemma":[0.0039178682,0.0003865424,0.87456644,0.00028211385,0.00029966474,0.0000018619565,0.0047603124,0.09809058,0.00014263178,0.008890944,0.008202331,0.00045871479],"about_ca_topic_score_codex":0.000045902387,"about_ca_topic_score_gemma":0.00010006522,"teacher_disagreement_score":0.8800472,"about_ca_system_score_codex":0.000024581714,"about_ca_system_score_gemma":0.000075325195,"threshold_uncertainty_score":0.49960583},"labels":[],"label_agreement":null},{"id":"W27292761","doi":"","title":"The far side story: Measuring the benefits of bus stop location on transit performance","year":2015,"lang":"en","type":"article","venue":"eScholarship@McGill (McGill)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Far side of the Moon; Transit (satellite); Computer science; Business; Transport engineering; Public transport; Engineering","score_opus":0.043151013591490976,"score_gpt":0.24608910662358902,"score_spread":0.20293809303209803,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W27292761","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96594214,0.0002859914,0.000010393802,0.00052893104,0.0004921387,0.00034247592,0.000045874534,0.0001276378,0.032224406],"genre_scores_gemma":[0.99860543,0.00025879522,0.00015582763,0.00016584418,0.000040012354,0.000025820125,0.0000136434965,0.000023440643,0.0007111996],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.997772,0.00044912664,0.00031038138,0.00025607488,0.00088815665,0.0003242573],"domain_scores_gemma":[0.9984451,0.00033588777,0.00021875356,0.00030604342,0.000547688,0.000146555],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.002876484,0.00016030062,0.00015273945,0.00007341642,0.0026702522,0.00005546057,0.0004455129,0.00012722063,0.00000712615],"category_scores_gemma":[0.00065263285,0.00011614971,0.00006638934,0.00055252213,0.00019127644,0.00050433347,0.000011339146,0.00039269327,0.000039391944],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002562144,0.000113589136,0.0010567406,0.000037702193,0.00006727694,0.0000020240427,0.0017681866,0.284069,0.00049678644,0.39092785,0.00001437238,0.32119027],"study_design_scores_gemma":[0.0049389917,0.0011647841,0.15374708,0.0010303612,0.0004624254,0.000011478311,0.02447844,0.0040663467,0.05136048,0.010264625,0.74621475,0.0022602377],"about_ca_topic_score_codex":0.00048989046,"about_ca_topic_score_gemma":0.0024044549,"teacher_disagreement_score":0.7462004,"about_ca_system_score_codex":0.0002439568,"about_ca_system_score_gemma":0.000093380586,"threshold_uncertainty_score":0.99862814},"labels":[],"label_agreement":null},{"id":"W2732631104","doi":"10.1016/j.jue.2017.06.003","title":"The economics of crowding in rail transit","year":2017,"lang":"en","type":"article","venue":"Journal of Urban Economics","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":93,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Social Sciences and Humanities Research Council of Canada; KU Leuven; Agence Nationale de la Recherche","keywords":"Train; Revenue; Marginal cost; Crowding; Transit (satellite); Economics; Microeconomics; Transit system; Welfare; Transport engineering; Crowding out; Public transport; Finance; Monetary economics; Engineering","score_opus":0.024367542496637796,"score_gpt":0.2720718657428885,"score_spread":0.2477043232462507,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2732631104","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9902323,0.0001202734,0.00028794326,0.0025292581,0.00052372314,0.00003872891,0.0000039752963,0.0000020295117,0.0062617916],"genre_scores_gemma":[0.9975768,0.0015368897,0.0004579979,0.000024951252,0.00014250547,2.568115e-7,4.2546424e-7,0.000004399699,0.0002557922],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9994071,0.000025149699,0.00039471142,0.000044243676,0.000031763113,0.00009700234],"domain_scores_gemma":[0.9989833,0.00010697819,0.0007179254,0.00009798401,0.00005272839,0.00004105233],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009371166,0.000039055034,0.00012987881,0.0000511774,0.00035074697,0.00011708938,0.00030542247,0.00004603294,0.000009289869],"category_scores_gemma":[0.000095392774,0.0000343504,0.00006779177,0.000015488667,0.00013537492,0.00040796452,0.0000030998542,0.00008949028,9.217737e-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":[0.00029005858,0.000078545076,0.56750417,0.000015447991,0.00010947677,0.0000072023195,0.04120987,0.18806805,0.00003002025,0.18259582,0.0014385883,0.018652786],"study_design_scores_gemma":[0.0042080237,0.0002337491,0.6879694,0.00019906582,0.00009682111,0.000010662936,0.017382467,0.00793795,0.000588979,0.01879747,0.26208156,0.0004938772],"about_ca_topic_score_codex":0.00012388022,"about_ca_topic_score_gemma":0.0025600693,"teacher_disagreement_score":0.26064298,"about_ca_system_score_codex":0.000064293214,"about_ca_system_score_gemma":0.00018735991,"threshold_uncertainty_score":0.26976976},"labels":[],"label_agreement":null},{"id":"W2734699206","doi":"10.1155/2017/7696094","title":"Relieving the Impact of Transit Signal Priority on Passenger Cars through a Bilevel Model","year":2017,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Bilevel optimization; Intersection (aeronautics); Transit (satellite); Computer science; Constraint (computer-aided design); Urban rail transit; Transport engineering; SIGNAL (programming language); Simulation; Mathematical optimization; Public transport; Engineering; Mathematics; Optimization problem; Algorithm","score_opus":0.03505561587405304,"score_gpt":0.3547690068941816,"score_spread":0.3197133910201286,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2734699206","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93594664,0.000084284344,0.06202502,0.0007735504,0.00019812195,0.00018764564,0.00005195173,0.000014786656,0.0007179943],"genre_scores_gemma":[0.9920688,0.00036157333,0.007382418,0.000023554549,0.00009663845,0.0000021681385,0.000012485106,0.000011522815,0.000040816718],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99862355,0.000060000286,0.00051460677,0.000112524045,0.00052516436,0.0001641639],"domain_scores_gemma":[0.9982148,0.00011654877,0.001007378,0.00016368213,0.0004324333,0.00006518134],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054448063,0.00011354673,0.00023021395,0.00006173322,0.00061311567,0.000047422353,0.00027431184,0.00008507565,0.000021211132],"category_scores_gemma":[0.00007352493,0.00007942823,0.00025872767,0.00010473099,0.00015492202,0.0010041478,5.342652e-7,0.00023667276,5.376548e-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":[0.00026380824,0.00006766214,0.0073253834,0.00000950102,0.000044233624,0.0000066569883,0.033832155,0.9518933,0.0016633478,0.0016095968,0.000033681306,0.0032506308],"study_design_scores_gemma":[0.0016508219,0.00040220836,0.98455715,0.00029485373,0.00015610798,0.0000015899224,0.0034291002,0.0029719416,0.0008395081,0.005235021,0.00026552784,0.0001961679],"about_ca_topic_score_codex":0.0004157665,"about_ca_topic_score_gemma":0.00054577435,"teacher_disagreement_score":0.97723174,"about_ca_system_score_codex":0.00006668039,"about_ca_system_score_gemma":0.00033898867,"threshold_uncertainty_score":0.4715652},"labels":[],"label_agreement":null},{"id":"W2735063875","doi":"10.1155/2017/2960728","title":"Real-Time Integrated Limited-Stop and Short-Turning Bus Control with Stochastic Travel Time","year":2017,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China; National Science Foundation","keywords":"Randomness; Transit (satellite); Control (management); Real-time Control System; Service (business); Travel time; Arrival time; Computer science; Real-time computing; Running time; Transport engineering; Public transport; Engineering; Simulation","score_opus":0.009543163656589398,"score_gpt":0.2653012592230127,"score_spread":0.25575809556642326,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2735063875","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9556006,0.00005651525,0.042914197,0.0004105896,0.00014719664,0.0002592278,0.000033725635,0.000040559968,0.00053734146],"genre_scores_gemma":[0.99081516,0.00014297564,0.0085635055,0.000020060217,0.00009359959,0.000005066774,0.000053516596,0.000017682692,0.000288433],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9988083,0.00005180672,0.000417494,0.00014822114,0.00038649395,0.00018763643],"domain_scores_gemma":[0.9985364,0.00012128972,0.000609884,0.000103064885,0.0004778083,0.00015153008],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039982283,0.00013512513,0.000283756,0.00012720277,0.00060827367,0.00010937045,0.00014859477,0.00009122919,0.0000395564],"category_scores_gemma":[0.000101116144,0.000114308124,0.00005543515,0.0001062233,0.0001801362,0.000997309,4.4450016e-7,0.00019665607,0.000002488855],"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":[0.0033667965,0.00018262572,0.022067849,0.000047608373,0.00027639046,0.00021290772,0.0541205,0.85919714,0.034673113,0.0014983653,0.000088895176,0.024267783],"study_design_scores_gemma":[0.004686937,0.0006566209,0.9811985,0.0005980728,0.0004667228,0.000016927788,0.0070076804,0.0038870075,0.00029112853,0.00024913676,0.00050489034,0.00043638312],"about_ca_topic_score_codex":0.00011851368,"about_ca_topic_score_gemma":0.00030968594,"teacher_disagreement_score":0.95913064,"about_ca_system_score_codex":0.00004187855,"about_ca_system_score_gemma":0.00016851239,"threshold_uncertainty_score":0.4678411},"labels":[],"label_agreement":null},{"id":"W27352134","doi":"10.1371/journal.pone.0157902","title":"Green Infrastructure.A math model for optimizing economic and sustainable public investments in the urban area","year":2013,"lang":"en","type":"dissertation","venue":"PLoS ONE","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Centre for Blood Research, University of British Columbia; KWF Kankerbestrijding; Michael Smith Health Research BC","keywords":"Computer science; Simulated annealing; Sustainability; Reuse; Originality; Competence (human resources); Knapsack problem; Mathematical optimization; Management science; Operations research; Engineering; Mathematics; Machine learning; Economics; Algorithm","score_opus":0.04065871249818199,"score_gpt":0.2551945696948257,"score_spread":0.2145358571966437,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W27352134","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9891733,0.00018515097,0.0004207961,0.0005722995,0.00004978269,0.0015098951,0.000048787035,0.00005510464,0.0079848915],"genre_scores_gemma":[0.9347897,0.00028503343,0.012488104,0.0002994631,0.00013927087,0.0006874409,0.0032133644,0.000046321435,0.048051283],"study_design_codex":"qualitative","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999006,0.00004974822,0.00021963281,0.0002201541,0.00019683068,0.00030767883],"domain_scores_gemma":[0.99947405,0.00007245464,0.000170257,0.00010607638,0.000116282696,0.000060858183],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035888675,0.00013417854,0.00019182087,0.00015749075,0.00038151225,0.00024170669,0.0001991005,0.00020165795,0.00002887756],"category_scores_gemma":[0.000064113876,0.00011986175,0.000028756853,0.000097126096,0.000039621355,0.0004193195,0.000004602281,0.00014290895,0.0000030910057],"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":[0.00015099507,0.0006560465,0.027896635,0.0016513555,0.00044927478,0.0000055635483,0.45759383,0.05061151,0.0000688224,0.4521973,0.0077810856,0.0009375922],"study_design_scores_gemma":[0.0015169355,0.00009074292,0.025183441,0.00038889213,0.00040794665,2.3210183e-7,0.11593961,0.82255477,0.00003885516,0.031860292,0.0011554317,0.000862852],"about_ca_topic_score_codex":0.001436753,"about_ca_topic_score_gemma":0.0023909276,"teacher_disagreement_score":0.7719433,"about_ca_system_score_codex":0.00012265738,"about_ca_system_score_gemma":0.00032356064,"threshold_uncertainty_score":0.48878202},"labels":[],"label_agreement":null},{"id":"W2737533255","doi":"","title":"Travel Demand Modeling for London, Ontario, Canada","year":2017,"lang":"en","type":"article","venue":"Scholarship at UWindsor (University of Windsor)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Geography; Regional science","score_opus":0.03374627219801284,"score_gpt":0.24876180889583796,"score_spread":0.2150155366978251,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2737533255","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9826846,0.00003315053,0.008633683,0.0012524206,0.00025782132,0.00024190181,0.000067226785,0.000025401521,0.0068037775],"genre_scores_gemma":[0.98592466,0.000014967176,0.0027027412,0.000049414786,0.000046153855,4.3343266e-7,0.0000548675,0.000009416322,0.011197356],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99888855,0.000052913936,0.00013969526,0.00025176845,0.00038026008,0.0002867947],"domain_scores_gemma":[0.99900436,0.000060255818,0.00023652894,0.00027238732,0.00025301316,0.00017344579],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.000633393,0.000104921965,0.00018896958,0.00006659898,0.0034436209,0.0000703228,0.00057090085,0.00014797237,0.00028781238],"category_scores_gemma":[0.00013847924,0.00014139013,0.00009376692,0.000061255065,0.0001562461,0.0007229779,0.00002709278,0.00016159996,0.000003970772],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00048955827,0.00007122225,0.9330899,0.000059927173,0.000120855,0.00003189094,0.038997076,0.019881276,0.0003207655,0.0043975534,0.0017193961,0.0008205467],"study_design_scores_gemma":[0.002240986,0.000048123286,0.96951383,0.000093280934,0.0001407572,0.0000010083129,0.008864925,0.0030879043,0.00016226264,0.0008928275,0.0145298885,0.0004241824],"about_ca_topic_score_codex":0.913179,"about_ca_topic_score_gemma":0.99678916,"teacher_disagreement_score":0.08361016,"about_ca_system_score_codex":0.00032080332,"about_ca_system_score_gemma":0.0011879154,"threshold_uncertainty_score":0.99785376},"labels":[],"label_agreement":null},{"id":"W2739260306","doi":"10.3141/2668-05","title":"Identification of Representative Patterns of Time Use Activity Through Fuzzy <i>C</i> -Means Clustering","year":2017,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Saint Mary's University; Dalhousie University","funders":"","keywords":"Cluster analysis; Hierarchical clustering; Computer science; Data mining; Initialization; Identification (biology); Artificial intelligence; Machine learning","score_opus":0.13738401067945816,"score_gpt":0.43612152997798453,"score_spread":0.2987375192985264,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2739260306","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9811247,0.00003629687,0.0147058,0.002018426,0.00046621653,0.0009180633,0.00029263998,0.000022240307,0.00041559397],"genre_scores_gemma":[0.9953409,0.0013464616,0.0014697283,0.000010488217,0.00012552801,0.000028244014,0.000024627914,0.00003799786,0.0016160276],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9915256,0.001783189,0.0016890069,0.00039851732,0.0039752517,0.00062842097],"domain_scores_gemma":[0.9898181,0.0013912721,0.0022489093,0.0008089413,0.00550511,0.00022765719],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.006228499,0.00020576733,0.00053682463,0.0005787285,0.0017052608,0.0002549977,0.0015507623,0.00023135719,0.00016477823],"category_scores_gemma":[0.00096786564,0.00017928211,0.00041248262,0.00090005394,0.0014910342,0.0027008262,0.0000104112505,0.001141705,0.000007451466],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0019920813,0.0004777865,0.90540755,0.00038871574,0.0002584599,0.000039940824,0.047390986,0.012594827,0.022483652,0.004800024,0.001262291,0.0029036696],"study_design_scores_gemma":[0.0011459966,0.00023113901,0.97820914,0.00051258656,0.000080555736,1.425621e-7,0.0053145206,0.00032729303,0.011195941,0.0014740868,0.0013385916,0.00017002356],"about_ca_topic_score_codex":0.08318001,"about_ca_topic_score_gemma":0.10798851,"teacher_disagreement_score":0.07280156,"about_ca_system_score_codex":0.00016026125,"about_ca_system_score_gemma":0.00073192845,"threshold_uncertainty_score":0.9995944},"labels":[],"label_agreement":null},{"id":"W2739415183","doi":"10.20286/jeas.v2i1.13","title":"Commuters’ Perception on the Quality of Pace Setter Transport Services in Ibadan Metropolis","year":2016,"lang":"en","type":"article","venue":"Nova Journal of Engineering and Applied Sciences","topic":"Transportation Planning and Optimization","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":"Setter; Pace; Transport engineering; Quality (philosophy); Service (business); Business; Marketing; Engineering; Geography","score_opus":0.03729609052854683,"score_gpt":0.30262864951554624,"score_spread":0.2653325589869994,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2739415183","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9940693,0.000019737878,0.0027101992,0.0022292929,0.00007785863,0.00003694878,0.000002930886,0.0000063803845,0.00084738794],"genre_scores_gemma":[0.9986682,0.000050386567,0.00116022,0.00006858231,0.00003802797,6.033331e-7,2.836777e-7,0.0000020661957,0.000011607841],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9992464,0.000032899297,0.00024025953,0.00006701815,0.00031110743,0.00010231492],"domain_scores_gemma":[0.9995321,0.0002197803,0.00013704186,0.00003989504,0.00003843,0.000032743526],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017099519,0.000050433617,0.00011005363,0.00011077937,0.00010201483,0.000017468028,0.00014685243,0.000030593113,0.000026724103],"category_scores_gemma":[0.000020645362,0.000027459559,0.000025660058,0.00025173617,0.00015859625,0.00013045824,0.000001975112,0.000070249305,3.9637055e-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":[0.0002368205,0.00021802384,0.4338296,0.00018071097,0.000072646384,0.0000054897614,0.12405894,0.14614381,0.12579049,0.15412866,0.00019302517,0.015141791],"study_design_scores_gemma":[0.00064967526,0.00011865532,0.9764419,0.0003574995,0.000015587002,0.0000016958483,0.018564941,0.0003716658,0.0018814283,0.00037172693,0.0010409091,0.00018427726],"about_ca_topic_score_codex":0.00016589943,"about_ca_topic_score_gemma":0.00020856071,"teacher_disagreement_score":0.5426123,"about_ca_system_score_codex":0.000016727345,"about_ca_system_score_gemma":0.000025977002,"threshold_uncertainty_score":0.11197683},"labels":[],"label_agreement":null},{"id":"W2739450326","doi":"10.1155/2017/9575676","title":"A Least-Square Model to Estimate Historical Percentages of Itinerant General Aviation Operations by Aircraft Types and Flight Rules at an Airport","year":2017,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","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":"Federal Aviation Administration","keywords":"Aviation; General aviation; Operations research; Square (algebra); Aeronautics; Air traffic control; Engineering; Computer science; Transport engineering; Aerospace engineering; Mathematics","score_opus":0.018361173153697164,"score_gpt":0.32550750065585504,"score_spread":0.30714632750215787,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2739450326","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93490803,0.00012363413,0.06309667,0.0013688327,0.00019644432,0.0001452633,0.00007347052,0.000017942628,0.00006972269],"genre_scores_gemma":[0.93224084,0.00023761191,0.06664352,0.00003495452,0.00007887832,0.000005142527,0.00016092829,0.00001245153,0.00058566255],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9988152,0.000034951863,0.00048617978,0.00015021591,0.0003807616,0.00013273755],"domain_scores_gemma":[0.9989167,0.00001731996,0.0003906931,0.00010834039,0.00040362962,0.00016332048],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025519653,0.00010538726,0.00021391132,0.000101265265,0.00063617557,0.000046001147,0.00012475009,0.00006269273,0.00001621787],"category_scores_gemma":[0.000054502798,0.0001021133,0.000058039655,0.000053879106,0.00005901792,0.0011275884,0.0000015528502,0.00008448098,7.5805275e-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":[0.00018567873,0.000105877334,0.049309194,0.000026341091,0.000018827825,0.0000056096087,0.020913126,0.9112284,0.01385424,0.0022564745,0.00024055231,0.0018556414],"study_design_scores_gemma":[0.0018019313,0.00039725294,0.9726898,0.00018300643,0.00021233223,0.000005030036,0.0015441144,0.017427063,0.0015068841,0.0004659546,0.0033820788,0.00038453948],"about_ca_topic_score_codex":0.00038529647,"about_ca_topic_score_gemma":0.0041670986,"teacher_disagreement_score":0.9233806,"about_ca_system_score_codex":0.00013979798,"about_ca_system_score_gemma":0.0001055868,"threshold_uncertainty_score":0.48930126},"labels":[],"label_agreement":null},{"id":"W2740049762","doi":"","title":"School Travel Mode Choice Behaviour in Toronto, Canada","year":2013,"lang":"en","type":"dissertation","venue":"TSpace (University of Toronto)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Canadian Institutes of Health Research; University of Toronto; Social Sciences and Humanities Research Council of Canada; Heart and Stroke Foundation of Canada","keywords":"Mode choice; Transport engineering; Travel behavior; Mode (computer interface); Geography; Engineering; Computer science; Public transport; Human–computer interaction","score_opus":0.009775790463244761,"score_gpt":0.26591346958644346,"score_spread":0.2561376791231987,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2740049762","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5093372,0.00090313767,0.000084827145,0.000268001,0.0006466615,0.00044480944,0.00007849919,0.000050732968,0.48818615],"genre_scores_gemma":[0.8845137,0.00059859327,0.0005973112,0.00001473069,0.00004612783,9.966772e-7,0.0006359127,0.000013755122,0.11357887],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.99884945,0.000076764176,0.00013104313,0.00025059766,0.00044434326,0.00024781292],"domain_scores_gemma":[0.99921393,0.000041557778,0.00023584973,0.00014456862,0.0001894644,0.0001746261],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00010533575,0.00015040257,0.00026693713,0.00002982679,0.00025606406,0.000018400664,0.00032199634,0.00029420402,0.026739482],"category_scores_gemma":[0.00003599821,0.00021787784,0.00006488243,0.000061967476,0.00003938756,0.0006788482,0.000004944974,0.00014540748,0.000006148017],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006107757,0.0005904787,0.14059164,0.00056759495,0.00032905588,0.00011389234,0.7476085,0.0042736903,0.0004509307,0.010718869,0.05398058,0.040163986],"study_design_scores_gemma":[0.00032233453,0.000013129331,0.76140475,0.000075695796,0.000046920904,4.222399e-8,0.23679043,0.00014611511,0.000005209975,0.000002731852,0.0009987873,0.00019383286],"about_ca_topic_score_codex":0.9999306,"about_ca_topic_score_gemma":0.99999315,"teacher_disagreement_score":0.62081313,"about_ca_system_score_codex":0.002171321,"about_ca_system_score_gemma":0.001634367,"threshold_uncertainty_score":0.9741502},"labels":[],"label_agreement":null},{"id":"W2741994132","doi":"","title":"A Fuzzy Evacuation Management Model Oriented Toward the Mitigation of Vehicular Emissions","year":2016,"lang":"en","type":"article","venue":"Journal of Environmental Informatics","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Fuzzy logic; Risk analysis (engineering); Process (computing); Transport engineering; Computer science; Operations research; Engineering; Business","score_opus":0.013613539851381787,"score_gpt":0.25259580635364803,"score_spread":0.23898226650226626,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2741994132","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8324004,0.000044374276,0.16080703,0.0016847996,0.00014458473,0.00019577066,0.000030403588,0.00000941005,0.0046832385],"genre_scores_gemma":[0.9900449,0.00047160304,0.009166626,0.00006571879,0.00002168647,0.0000014294242,0.0000070568635,0.0000030086328,0.00021799115],"study_design_codex":"simulation_or_modeling","study_design_gemma":"qualitative","domain_scores_codex":[0.9989286,0.000033668915,0.00046653507,0.0000231779,0.0004693615,0.000078645375],"domain_scores_gemma":[0.99932116,0.0000420304,0.00049445045,0.000062139574,0.00002954158,0.000050686398],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004626559,0.00004792771,0.00007315505,0.000058698006,0.00012043444,0.000010857865,0.0001077565,0.00003772785,0.000041788247],"category_scores_gemma":[0.00002795559,0.000027354658,0.000056257762,0.00007333267,0.000099678626,0.00044074413,0.000007935972,0.0000486728,0.000004786978],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002140326,0.0005031558,0.021692265,0.00013561487,0.00035674922,0.000006985221,0.3092066,0.5578087,0.00604958,0.03956588,0.0034898748,0.060970526],"study_design_scores_gemma":[0.011860487,0.0009917343,0.26528034,0.0022984718,0.001886529,0.00004232444,0.45281017,0.1607004,0.011737827,0.028767554,0.062244225,0.0013799295],"about_ca_topic_score_codex":0.0000015108051,"about_ca_topic_score_gemma":0.000001081406,"teacher_disagreement_score":0.39710832,"about_ca_system_score_codex":0.00008461438,"about_ca_system_score_gemma":0.000029496943,"threshold_uncertainty_score":0.11154906},"labels":[],"label_agreement":null},{"id":"W2743043137","doi":"","title":"Ghost Simulation Model for the Optimization of an Urban Subway System","year":2003,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Bishop's University","funders":"","keywords":"Headway; Computer science; Overhead (engineering); Function (biology); Filter (signal processing); Line (geometry); Control (management); Event (particle physics); Service (business); Mathematical optimization; Operations research; Simulation; Engineering; Artificial intelligence; Mathematics","score_opus":0.03332560279421142,"score_gpt":0.3033880659614066,"score_spread":0.2700624631671952,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2743043137","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0033427903,0.000017355054,0.98883784,0.00006167932,0.0000878634,0.000316782,0.000009058908,0.00007318865,0.007253464],"genre_scores_gemma":[0.9647137,0.00000424734,0.034127913,0.000024851914,0.000024247121,0.0000133920985,0.00003668445,0.0000061592114,0.0010488164],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994565,0.000061000388,0.00015431443,0.00008781623,0.00015223684,0.000088105335],"domain_scores_gemma":[0.9994452,0.00016765799,0.00008191167,0.000082958824,0.00019145661,0.00003079957],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048340234,0.000042131087,0.00005766875,0.000029825873,0.00033434498,0.00002694748,0.000065693006,0.000052021634,0.000023860659],"category_scores_gemma":[0.00008613509,0.000032104854,0.000027117945,0.00014647485,0.000036608006,0.00020738001,5.850638e-7,0.0000185833,5.849888e-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":[0.000007505097,0.00001067778,0.000484937,0.0000066915513,0.000003108629,1.7076836e-8,0.0045064087,0.8825486,0.0000020236619,0.11232568,0.00003167227,0.00007270521],"study_design_scores_gemma":[0.00015650659,0.000011780674,0.00011845585,0.000005661155,0.000017752072,2.7806136e-8,0.0035194561,0.9956587,0.00003375332,0.00006262532,0.00037112954,0.000044150504],"about_ca_topic_score_codex":0.00011433259,"about_ca_topic_score_gemma":0.0001840735,"teacher_disagreement_score":0.9613709,"about_ca_system_score_codex":0.000023319177,"about_ca_system_score_gemma":0.0000735651,"threshold_uncertainty_score":0.25715452},"labels":[],"label_agreement":null},{"id":"W2748695092","doi":"10.3141/2652-10","title":"Perceived Reality","year":2017,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"McGill University","funders":"","keywords":"Customer satisfaction; Public transport; Context (archaeology); Perception; Crowding; Service quality; Service (business); Transport engineering; Marketing; Business; Computer science; Engineering; Psychology; Geography","score_opus":0.1858991918718371,"score_gpt":0.46431243110659937,"score_spread":0.27841323923476224,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2748695092","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9707933,0.00009608214,0.001452392,0.020200646,0.0012558168,0.0009184905,0.000076477365,0.000054434688,0.005152348],"genre_scores_gemma":[0.99292463,0.0015258811,0.0015394388,0.000052564792,0.0004713638,0.0000367833,0.000020283416,0.00004108791,0.0033879587],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9903153,0.0020603503,0.001242979,0.00041536827,0.0049648276,0.001001176],"domain_scores_gemma":[0.9910323,0.0009258715,0.00090641476,0.0008637829,0.0057207374,0.0005509023],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.009647871,0.00021375045,0.00041462886,0.00066190545,0.0062313695,0.00062765495,0.002395634,0.00029114724,0.00056575844],"category_scores_gemma":[0.0010572812,0.00017237972,0.00042207105,0.0009429719,0.0023123205,0.0015284778,0.0000065290596,0.0020886299,0.000037444483],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012569547,0.00035823399,0.8884218,0.00015274367,0.00017350956,0.00017011868,0.036609598,0.002265782,0.0008924096,0.037471067,0.021510042,0.010717753],"study_design_scores_gemma":[0.0009964887,0.00015855087,0.9121026,0.0002512572,0.00004170884,1.2151207e-7,0.0059058713,0.00004373525,0.000119375465,0.004245328,0.075966276,0.0001687028],"about_ca_topic_score_codex":0.08440566,"about_ca_topic_score_gemma":0.29825678,"teacher_disagreement_score":0.21385112,"about_ca_system_score_codex":0.0002942822,"about_ca_system_score_gemma":0.0014170874,"threshold_uncertainty_score":0.9950624},"labels":[],"label_agreement":null},{"id":"W2750213341","doi":"10.1155/2017/5824051","title":"Estimating Train Choices of Rail Transit Passengers with Real Timetable and Automatic Fare Collection Data","year":2017,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; Tongji University; Ministry of Education of the People's Republic of China; National Natural Science Foundation of China","keywords":"Transport engineering; Schedule; Rail transit; Transit (satellite); Data collection; Operations research; Urban rail transit; Computer science; Rail freight transport; Public transport; Engineering","score_opus":0.02489743301578891,"score_gpt":0.31574841368021794,"score_spread":0.29085098066442905,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2750213341","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9467821,0.00011176101,0.051147126,0.00065027975,0.00026921084,0.00023187888,0.00005481433,0.000033478882,0.0007193354],"genre_scores_gemma":[0.9056227,0.00021357549,0.093957216,0.0000060861653,0.00006041138,0.0000018832926,0.00006948987,0.00001002933,0.000058612317],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99883425,0.000050198305,0.00045989896,0.00014110174,0.00038914286,0.00012541776],"domain_scores_gemma":[0.998325,0.00009434353,0.0010679081,0.00016908288,0.0002666902,0.000076994525],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006165441,0.00009313507,0.0002447445,0.000106714564,0.00056861754,0.00007135249,0.00022344915,0.00006048811,0.000019391338],"category_scores_gemma":[0.0000911987,0.000082836734,0.00003355257,0.00013830612,0.00015067807,0.0018674426,8.7524035e-7,0.00011201405,1.3721923e-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":[0.0010581368,0.00023932224,0.09129104,0.0005636003,0.0003432885,0.000059918137,0.11870953,0.69564223,0.0029123747,0.0016176566,0.000092347895,0.087470576],"study_design_scores_gemma":[0.0023414276,0.00026725893,0.9762567,0.00047610374,0.00033388127,0.0000050530484,0.009284759,0.009841597,0.00026675212,0.00029048495,0.00044524067,0.00019070129],"about_ca_topic_score_codex":0.00057417736,"about_ca_topic_score_gemma":0.0051833093,"teacher_disagreement_score":0.8849657,"about_ca_system_score_codex":0.00002197664,"about_ca_system_score_gemma":0.00017679218,"threshold_uncertainty_score":0.4373404},"labels":[],"label_agreement":null},{"id":"W2752315259","doi":"10.1155/2017/6495861","title":"Time Coefficient Estimation for Hourly Origin-Destination Demand from Observed Link Flow Based on Semidynamic Traffic Assignment","year":2017,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Japan Society for the Promotion of Science","keywords":"Traffic flow (computer networking); Demand forecasting; Residual; Reliability (semiconductor); Computer science; Trip distribution; Scale (ratio); Travel time; Simulation; Econometrics; Mathematical optimization; Statistics; Transport engineering; Mathematics; Operations research; Algorithm; Engineering","score_opus":0.02287955933900722,"score_gpt":0.2994043277222572,"score_spread":0.27652476838325,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2752315259","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.52568406,0.000026506885,0.47174692,0.0012909458,0.00060620945,0.00040278494,0.00013103735,0.00004618495,0.00006531984],"genre_scores_gemma":[0.9279173,0.00003159815,0.07094725,0.000058585258,0.00020062694,0.000016453112,0.0007227822,0.000020863898,0.00008455793],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982664,0.00007430278,0.00060621946,0.00021045156,0.00064347225,0.00019917062],"domain_scores_gemma":[0.9977032,0.00032127302,0.0011954911,0.00015139514,0.00050313346,0.00012550195],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00073164614,0.00015658354,0.00024913746,0.00014728722,0.00081723754,0.00013618375,0.00021298294,0.00012666281,0.00003675517],"category_scores_gemma":[0.00027497395,0.00015588767,0.00014646113,0.00010478639,0.000092504924,0.0007742548,4.9103943e-7,0.00014688853,0.00000442558],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004056572,0.000103905026,0.0007344857,0.000017062113,0.000018955901,0.0000058335545,0.003225248,0.9682964,0.00039982455,0.00011980494,0.000047627746,0.026625166],"study_design_scores_gemma":[0.00349106,0.0004354833,0.19443372,0.00040620923,0.00018675101,2.6880653e-7,0.00047238477,0.7985732,0.00032446225,0.00045557378,0.0009707868,0.00025012516],"about_ca_topic_score_codex":0.00003458925,"about_ca_topic_score_gemma":0.00027715162,"teacher_disagreement_score":0.4022332,"about_ca_system_score_codex":0.00018498336,"about_ca_system_score_gemma":0.00024815893,"threshold_uncertainty_score":0.63569146},"labels":[],"label_agreement":null},{"id":"W2753257100","doi":"10.3141/2658-01","title":"Modeling the Demand for New Transportation Services and Technologies","year":2017,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Microsimulation; Exploit; Scope (computer science); Computer science; Field (mathematics); Demand forecasting; Big data; Data science; Software; Operations research; Transport engineering; Engineering; Computer security","score_opus":0.1267950407008829,"score_gpt":0.42060378402988946,"score_spread":0.2938087433290065,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2753257100","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94830114,0.0007100479,0.021246014,0.027004428,0.00065398164,0.0017249149,0.00009930808,0.000084958396,0.00017519262],"genre_scores_gemma":[0.99141353,0.0035268785,0.0037545352,0.00004666471,0.00029306658,0.000086896536,0.00003169811,0.00004295075,0.000803776],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9944915,0.0005742963,0.0010955035,0.00042596651,0.002592067,0.0008206708],"domain_scores_gemma":[0.9945438,0.00095288665,0.0006330335,0.00059292704,0.0029922207,0.00028515165],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0060891835,0.00023680317,0.00038082202,0.0005156809,0.0058485325,0.000707627,0.0018438881,0.00030712577,0.000040443],"category_scores_gemma":[0.00038047734,0.00016438871,0.00029261518,0.00069806335,0.0013290439,0.0014394809,0.0000046431023,0.001295121,0.0000033345304],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.003904473,0.00033334925,0.6429633,0.0009990176,0.0005349657,0.00006853303,0.112824835,0.065293625,0.0010893091,0.10078989,0.0069780005,0.06422072],"study_design_scores_gemma":[0.0027794263,0.00039270194,0.87864053,0.0007246389,0.000205146,3.112371e-7,0.04348192,0.004129433,0.0003924854,0.031513043,0.0373618,0.00037854342],"about_ca_topic_score_codex":0.04192843,"about_ca_topic_score_gemma":0.32709712,"teacher_disagreement_score":0.28516868,"about_ca_system_score_codex":0.00010930005,"about_ca_system_score_gemma":0.00083836296,"threshold_uncertainty_score":0.9954457},"labels":[],"label_agreement":null},{"id":"W2754068171","doi":"","title":"Mass Transit: Toronto Plans Network of Light-Rail Systems","year":2007,"lang":"en","type":"article","venue":"Civil engineering","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Light rail transit; Transport engineering; Transit (satellite); Light rail; Transit system; Rail transit; Rapid transit; Mass transportation; Public transport; Engineering; Civil engineering; Business","score_opus":0.00779878824709195,"score_gpt":0.22359727219575254,"score_spread":0.21579848394866058,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2754068171","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017091265,0.0016121116,0.9122677,0.0000707813,0.0015992285,0.00018182355,0.000010313456,0.0003076596,0.066859104],"genre_scores_gemma":[0.9980817,0.000052834694,0.0012960287,0.0000058986802,0.000294073,0.0000025585787,0.000009395599,0.000010235523,0.00024729167],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99932253,0.000010619754,0.000187558,0.00008457369,0.00017799663,0.00021670831],"domain_scores_gemma":[0.99971855,0.00006153489,0.000039435334,0.000065280896,0.000044244767,0.000070972965],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004757413,0.00006218153,0.00010527903,0.00001867195,0.000075475706,0.000016045418,0.00007970569,0.000074988915,0.000033942782],"category_scores_gemma":[0.000018494653,0.00006849598,0.000031392916,0.00013698875,0.000011444091,0.00012609668,9.66533e-7,0.00004565073,0.0000018969896],"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":[0.0000063088755,0.0000064531105,0.0029216642,0.00004309618,0.000015567775,0.0000043188497,0.008912899,0.97017074,0.00054729247,0.016883329,0.00031125048,0.00017708991],"study_design_scores_gemma":[0.0028363685,0.0002467942,0.14967236,0.002022804,0.0002724984,0.0000072212083,0.034002315,0.14654823,0.0023889677,0.00025732635,0.6593295,0.0024156426],"about_ca_topic_score_codex":0.0007201165,"about_ca_topic_score_gemma":0.007998051,"teacher_disagreement_score":0.9809904,"about_ca_system_score_codex":0.000056057233,"about_ca_system_score_gemma":0.000024118963,"threshold_uncertainty_score":0.44631},"labels":[],"label_agreement":null},{"id":"W2754119428","doi":"","title":"Integrated agent-based transport simulation and air pollution modelling in urban areas - the example of Munich","year":2014,"lang":"en","type":"article","venue":"mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Technische Universität Berlin; Technische Universität München; Deutsche Forschungsgemeinschaft; McGill University","keywords":"Air quality index; Air pollution; Environmental science; Scale (ratio); Pollution; Meteorology; Environmental engineering; Transport engineering; Engineering; Geography","score_opus":0.02043061834728622,"score_gpt":0.22094562271322166,"score_spread":0.20051500436593545,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2754119428","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9257788,0.00023135067,0.050780393,0.014678748,0.00024864438,0.0012280458,0.00009362038,0.00035039795,0.006610009],"genre_scores_gemma":[0.99790686,0.00019816608,0.0011667876,0.000057756894,0.000023773451,0.0000011432392,0.000062532636,0.000009904371,0.00057307613],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99766743,0.0009407744,0.0003635366,0.0003115117,0.00047598162,0.00024074262],"domain_scores_gemma":[0.99696517,0.0014555755,0.00035534907,0.0007906447,0.00029564844,0.00013763347],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015034869,0.00016358698,0.0002734529,0.00028595014,0.0011689567,0.000019638823,0.001250953,0.00030814114,0.000007964971],"category_scores_gemma":[0.0003879238,0.00012417645,0.00013451822,0.0015049785,0.0018516593,0.00031271845,0.0001289058,0.00056192296,3.334981e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008408505,0.0011429948,0.08240154,0.00013548946,0.00015783266,0.000008945754,0.035846245,0.6069105,0.0018718359,0.26754358,0.0010123898,0.0021277508],"study_design_scores_gemma":[0.0034381293,0.00021892699,0.4894576,0.00041431,0.00091985264,0.000008721665,0.019306114,0.27089953,0.00032260575,0.0014424078,0.21270727,0.0008645348],"about_ca_topic_score_codex":0.007977348,"about_ca_topic_score_gemma":0.0061416044,"teacher_disagreement_score":0.40705606,"about_ca_system_score_codex":0.00017408656,"about_ca_system_score_gemma":0.00026377052,"threshold_uncertainty_score":0.9986286},"labels":[],"label_agreement":null},{"id":"W2758646404","doi":"10.1609/icaps.v27i1.13824","title":"Online Repositioning in Bike Sharing Systems","year":2017,"lang":"en","type":"article","venue":"Proceedings of the International Conference on Automated Planning and Scheduling","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"National Research Foundation Singapore; National Research Foundation","keywords":"Bike sharing; Computer science; Benchmark (surveying); Heuristic; Routing (electronic design automation); Greedy algorithm; Set (abstract data type); Matching (statistics); Operations research; Mathematical optimization; Transport engineering; Computer network; Engineering; Artificial intelligence","score_opus":0.06569628425107436,"score_gpt":0.3493977974602436,"score_spread":0.2837015132091692,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2758646404","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9726871,0.000031387375,0.00003428518,0.0015829714,0.0003798915,0.000092322,0.000008191781,0.00012891355,0.025054907],"genre_scores_gemma":[0.998517,0.00002804921,0.001001087,0.00003134447,0.00006204623,0.0000052561704,0.00000782957,0.000006085951,0.00034132806],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991122,0.000007126154,0.0002484302,0.00018734753,0.00031486168,0.0001300537],"domain_scores_gemma":[0.99922144,0.0000340043,0.00038595853,0.000066192144,0.0002549171,0.000037461738],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051861495,0.00007852031,0.00011357203,0.00011644799,0.00064213056,0.0005429026,0.00049937645,0.00007399082,0.0000061376863],"category_scores_gemma":[0.00043467255,0.000068362366,0.00002368346,0.000067177,0.00010209678,0.0003393795,0.00004164374,0.00017063753,6.6563257e-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":[0.000060552607,0.000063538355,0.7518892,0.00004812018,0.000036212678,0.000004272599,0.009618909,0.03245571,0.0028130184,0.20271203,0.000048916634,0.00024949125],"study_design_scores_gemma":[0.00039890336,0.000019334142,0.22980112,0.002723902,0.00001208451,0.0000043337654,0.00896515,0.75656253,0.0003923786,0.0008774158,0.000088002846,0.00015484862],"about_ca_topic_score_codex":0.0007697236,"about_ca_topic_score_gemma":0.00003757307,"teacher_disagreement_score":0.72410685,"about_ca_system_score_codex":0.000039028713,"about_ca_system_score_gemma":0.000047008198,"threshold_uncertainty_score":0.52352226},"labels":[],"label_agreement":null},{"id":"W2761574541","doi":"10.1155/2017/1712325","title":"Highway Passenger Transport Based Express Parcel Service Network Design: Model and Algorithm","year":2017,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Jilin Scientific and Technological Development Program; Specialized Research Fund for the Doctoral Program of Higher Education of China; British Academy","keywords":"Network planning and design; Robustness (evolution); Flow network; Computer science; Integer programming; Service (business); Flexibility (engineering); Level of service; Network model; Operations research; Transport network; Algorithm; Mathematical optimization; Transport engineering; Engineering; Data mining; Computer network; Mathematics","score_opus":0.027378339137725742,"score_gpt":0.29002222381011517,"score_spread":0.2626438846723894,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2761574541","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1515599,0.00016501601,0.8456619,0.0016780043,0.00045368192,0.00024961488,0.000031420954,0.00004888522,0.00015158174],"genre_scores_gemma":[0.7243993,0.00027559485,0.27483144,0.00015970395,0.00019910741,0.000006873332,0.000039065224,0.0000176027,0.0000713088],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9984711,0.000068319765,0.00053352857,0.00018052528,0.00049381895,0.00025273368],"domain_scores_gemma":[0.9981446,0.00010927264,0.0007761837,0.0001599085,0.0006244388,0.00018555317],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00072428683,0.00014911476,0.00026026106,0.0000784475,0.00085888366,0.000079324716,0.00024056555,0.0001292057,0.00001947268],"category_scores_gemma":[0.00003220946,0.00014755876,0.000075481825,0.0001420618,0.00010645593,0.0012707649,6.6584954e-7,0.0001968866,7.2321774e-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":[0.0001857453,0.000040885912,0.0044956445,0.000022795128,0.000021599595,0.000028109702,0.0074201645,0.9808386,0.00025047528,0.0003114035,0.000056608675,0.0063279527],"study_design_scores_gemma":[0.0057379752,0.00030733136,0.8659734,0.00074334524,0.0005657802,0.0000044986855,0.0038799585,0.10595028,0.0008458734,0.005305543,0.009729241,0.00095673185],"about_ca_topic_score_codex":0.000094009156,"about_ca_topic_score_gemma":0.0007566733,"teacher_disagreement_score":0.87488836,"about_ca_system_score_codex":0.000029587995,"about_ca_system_score_gemma":0.00027003544,"threshold_uncertainty_score":0.66059256},"labels":[],"label_agreement":null},{"id":"W2764234815","doi":"10.1155/2017/5095021","title":"Last-Train Timetabling under Transfer Demand Uncertainty: Mean-Variance Model and Heuristic Solution","year":2017,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":30,"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":"State Key Laboratory of Rail Traffic Control and Safety; Beijing Jiaotong University; National Natural Science Foundation of China","keywords":"Train; Heuristic; Variance (accounting); Computer science; Measure (data warehouse); Tabu search; Mathematical optimization; Beijing; Process (computing); Transfer (computing); Operations research; Decision maker; Artificial intelligence; Mathematics; Data mining","score_opus":0.02440616011168996,"score_gpt":0.30100640002979423,"score_spread":0.2766002399181043,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2764234815","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4707533,0.0002508561,0.5274236,0.00087197433,0.00028226507,0.00012520542,0.000018640898,0.000023634793,0.00025048843],"genre_scores_gemma":[0.9816792,0.00078783213,0.0171617,0.00006547252,0.000116159754,0.000002867485,0.000025279594,0.000014035238,0.00014746265],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99870527,0.0000563326,0.00046764797,0.00016370062,0.00040267702,0.00020435639],"domain_scores_gemma":[0.9989769,0.00007897687,0.00037135556,0.00011023761,0.00033037402,0.00013216746],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006970902,0.000121627105,0.00022517341,0.0001096307,0.0008708133,0.00011506775,0.000155429,0.000101352685,0.000014481478],"category_scores_gemma":[0.00007749844,0.000121126,0.00008060259,0.00008701537,0.00016973834,0.0013176665,7.3702057e-7,0.00018368554,8.4272494e-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":[0.00016795887,0.000036573438,0.0014283429,0.000023016397,0.000031167,0.000010431321,0.014780101,0.967619,0.0019409114,0.009421327,0.000012461676,0.00452873],"study_design_scores_gemma":[0.008113127,0.0003496751,0.8883736,0.00079581013,0.00083974097,0.000016995462,0.012067328,0.05054568,0.0005350763,0.034783192,0.002602204,0.0009775823],"about_ca_topic_score_codex":0.00010594836,"about_ca_topic_score_gemma":0.0036532704,"teacher_disagreement_score":0.9170733,"about_ca_system_score_codex":0.000045588746,"about_ca_system_score_gemma":0.0001789523,"threshold_uncertainty_score":0.66976804},"labels":[],"label_agreement":null},{"id":"W2765548372","doi":"10.1061/jtepbs.0000093","title":"Concurrent Estimation of Origin-Destination Flows and Calibration of Microscopic Traffic Simulation Parameters in a High-Performance Computing Cluster","year":2017,"lang":"en","type":"article","venue":"Journal of Transportation Engineering Part A Systems","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Calgary; CIMA+ (Canada)","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Computer science; Calibration; Traffic simulation; Genetic algorithm; Reliability (semiconductor); Scale (ratio); A priori and a posteriori; Convergence (economics); Population; Simulation; Process (computing); Traffic flow (computer networking); Real-time computing; Mathematical optimization; Engineering; Microsimulation; Transport engineering; Mathematics; Statistics; Machine learning","score_opus":0.020042391736660822,"score_gpt":0.2845909833843672,"score_spread":0.2645485916477064,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2765548372","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.84842616,0.000064409454,0.15067382,0.000034029938,0.0005937864,0.0001844067,0.000009344233,0.0000111612935,0.0000029010278],"genre_scores_gemma":[0.9968024,0.0000418452,0.0030613034,0.0000014222633,0.000045549583,0.000002421927,0.000032313746,0.000008309805,0.000004463823],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99863666,0.000049522216,0.0008306798,0.00008809298,0.00029449747,0.000100536905],"domain_scores_gemma":[0.9985009,0.00013980173,0.0010388116,0.000070582144,0.00020639326,0.00004352172],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00071597786,0.000087293,0.00025437845,0.00021571369,0.00010503445,0.000048564878,0.0000870001,0.00007676135,9.752088e-7],"category_scores_gemma":[0.000099284676,0.00009155284,0.00003369439,0.000121731086,0.000044744484,0.000658917,8.332779e-7,0.000090180794,7.670981e-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":[0.000018151113,0.000022956789,0.033699617,0.0002664893,0.000012560545,8.179819e-7,0.0054467553,0.958933,0.00015610132,0.00022899806,0.0000016165202,0.0012129674],"study_design_scores_gemma":[0.00068355247,0.000057075486,0.16591713,0.0007536661,0.000030755033,6.296395e-7,0.0001968113,0.8320758,0.0001907369,0.0000016704282,0.000023657763,0.000068564754],"about_ca_topic_score_codex":0.00016814192,"about_ca_topic_score_gemma":0.00010022705,"teacher_disagreement_score":0.14837623,"about_ca_system_score_codex":0.000043712967,"about_ca_system_score_gemma":0.00007213041,"threshold_uncertainty_score":0.37334168},"labels":[],"label_agreement":null},{"id":"W2765778286","doi":"10.1016/j.trc.2017.10.004","title":"A bi-level distributed approach for optimizing time-dependent congestion pricing in large networks: A simulation-based case study in the Greater Toronto Area","year":2017,"lang":"en","type":"article","venue":"Transportation Research Part C Emerging Technologies","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":25,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Toll; Congestion pricing; Computer science; Traffic congestion; Schedule; Revenue; Testbed; Road pricing; Operations research; Scheduling (production processes); Network congestion; Pricing strategies; Transport engineering; Computer network; Economics; Operations management; Engineering; Microeconomics","score_opus":0.15684516885549155,"score_gpt":0.41603912380168473,"score_spread":0.2591939549461932,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2765778286","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5753324,0.00008706898,0.41945723,0.001159285,0.00006240494,0.0032001769,0.0001407859,0.00043343534,0.00012723076],"genre_scores_gemma":[0.9957104,0.0000361144,0.0028870618,0.000010155737,0.000023853298,0.00091669825,0.00035867837,0.000017311902,0.000039760682],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99752235,0.00032419316,0.00047372724,0.00043817193,0.00063487916,0.00060665654],"domain_scores_gemma":[0.9982616,0.00074737164,0.00019461858,0.00044131794,0.0003175152,0.000037595088],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0044750166,0.00016381382,0.00022354518,0.00027848277,0.0019147077,0.00026675948,0.0005432431,0.00020381389,0.000016119893],"category_scores_gemma":[0.00084477855,0.00014195035,0.00005325732,0.00047666414,0.00022387132,0.0005468268,0.0000088405495,0.00035916443,7.251512e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000820317,0.0002268245,0.18756135,0.000018963243,0.000011613253,0.00012652804,0.014446697,0.79645985,0.0000021001836,0.00026180057,0.000031707514,0.00077054364],"study_design_scores_gemma":[0.001973163,0.000120805016,0.09304681,0.00010515359,0.00002750993,5.5702435e-7,0.17479879,0.7294061,0.000010550483,0.00008866329,0.00018439988,0.00023748404],"about_ca_topic_score_codex":0.010378022,"about_ca_topic_score_gemma":0.09726545,"teacher_disagreement_score":0.42037797,"about_ca_system_score_codex":0.0001990135,"about_ca_system_score_gemma":0.00011641069,"threshold_uncertainty_score":0.99938464},"labels":[],"label_agreement":null},{"id":"W2765782229","doi":"10.3141/2664-07","title":"Two-Level, Dynamic, Week-Long Work Episode Scheduling Model","year":2017,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Scheduling (production processes); Schedule; Duration (music); Work (physics); Interdependence; Operations research; Work schedule; Work time; Operations management; Computer science; Mathematics; Engineering","score_opus":0.16840712947845643,"score_gpt":0.44062933207532107,"score_spread":0.27222220259686464,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2765782229","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9354874,0.0002345961,0.0495277,0.011365693,0.0010173852,0.0010378903,0.00009870027,0.00007139777,0.0011592595],"genre_scores_gemma":[0.9740784,0.0013235128,0.020447146,0.00005714747,0.00029152515,0.000058340083,0.000037318343,0.00007308021,0.0036335],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.98981124,0.0014177886,0.0015362015,0.0005753421,0.0053244205,0.0013350021],"domain_scores_gemma":[0.9912859,0.0009346515,0.0010829996,0.0009338273,0.00509033,0.0006722933],"candidate_categories":["metaepi_narrow","sts","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0099543715,0.00031037026,0.0005306571,0.0009376562,0.0060810964,0.0007962933,0.0026266556,0.00032787345,0.00028207514],"category_scores_gemma":[0.0008599213,0.0002656443,0.00049722986,0.001417309,0.0018702003,0.0019290787,0.000010676512,0.002696441,0.000038730916],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017139479,0.000437586,0.7724814,0.00015790072,0.0002213041,0.00018493165,0.018896941,0.17502603,0.00042621946,0.013129013,0.0016603329,0.01566442],"study_design_scores_gemma":[0.0021124012,0.00018727106,0.97929585,0.00071328564,0.0000904874,2.479421e-7,0.0035665946,0.0039523696,0.0002227457,0.006705979,0.0027821732,0.00037059464],"about_ca_topic_score_codex":0.025830323,"about_ca_topic_score_gemma":0.29866046,"teacher_disagreement_score":0.27283013,"about_ca_system_score_codex":0.00039937755,"about_ca_system_score_gemma":0.0015631367,"threshold_uncertainty_score":0.99997956},"labels":[],"label_agreement":null},{"id":"W2765948587","doi":"10.3141/2651-02","title":"Automated Tool for Geographic Information Systems That Supports Transit Network Design by Identifying Urban Activity Centers","year":2017,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Geographic information system; Computer science; Transport engineering; Python (programming language); Public transport; Geospatial analysis; Population; Digital mapping; Flow network; Street network; TRIPS architecture; Transit (satellite); Geography; Engineering; Cartography","score_opus":0.10448106499359759,"score_gpt":0.3932837786365918,"score_spread":0.2888027136429942,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2765948587","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7667575,0.00025789367,0.22124547,0.003933386,0.0026514442,0.0043275347,0.00041783857,0.00023163814,0.00017731842],"genre_scores_gemma":[0.99478424,0.0011378803,0.0029041125,0.000038187798,0.00028257674,0.00020543176,0.00014222745,0.00004952026,0.00045581965],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99095374,0.0018574728,0.0013556724,0.00039983747,0.00413021,0.0013030778],"domain_scores_gemma":[0.99245113,0.0012418503,0.0014144034,0.0005962076,0.003853859,0.0004425293],"candidate_categories":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.011892786,0.00030156912,0.00052776386,0.0007671858,0.005567697,0.0013904182,0.0015854788,0.00037064738,0.0000683753],"category_scores_gemma":[0.00038933297,0.00026384785,0.00047631178,0.0009893172,0.0009988691,0.004215607,0.0000046462947,0.0013314654,0.000008379812],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0047270344,0.0003807347,0.7440636,0.0007932416,0.00055631326,0.000057976813,0.026760593,0.10286326,0.00088350027,0.0046894113,0.10770947,0.006514835],"study_design_scores_gemma":[0.0029039702,0.00042804464,0.9331685,0.0007794024,0.00016431036,4.0632406e-7,0.005860993,0.003495364,0.00047397395,0.0006070079,0.051671658,0.00044637275],"about_ca_topic_score_codex":0.028678881,"about_ca_topic_score_gemma":0.021844225,"teacher_disagreement_score":0.22802676,"about_ca_system_score_codex":0.000282302,"about_ca_system_score_gemma":0.00095281383,"threshold_uncertainty_score":0.99998134},"labels":[],"label_agreement":null},{"id":"W2766341542","doi":"10.1155/2017/5062984","title":"Numerical Bounds on the Price of Anarchy","year":2017,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Fondo Nacional de Desarrollo Científico y Tecnológico","keywords":"Price of anarchy; Mathematical economics; Upper and lower bounds; Economics; Price of stability; Mathematical optimization; Computer science; Mathematics; Keynesian economics; Mathematical analysis","score_opus":0.02114701201477726,"score_gpt":0.32049688005587895,"score_spread":0.2993498680411017,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2766341542","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9788627,0.000056464225,0.011062895,0.004835149,0.00054370385,0.00013224501,0.000009233866,0.000012546509,0.00448509],"genre_scores_gemma":[0.9971003,0.00014857399,0.002487819,0.000063436266,0.00009254341,0.0000013710277,0.000005273029,0.000005504615,0.00009515763],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9990238,0.0000471765,0.00033654313,0.00006306644,0.0004292296,0.00010019172],"domain_scores_gemma":[0.99856544,0.00013890091,0.00083185156,0.000120377415,0.00028973242,0.000053688495],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048931455,0.00005545944,0.00012431573,0.000056327117,0.0005103515,0.00003558423,0.00024194682,0.00004084774,0.000043208845],"category_scores_gemma":[0.00018982438,0.000040292358,0.00008378025,0.00009380242,0.0001444017,0.00044024928,3.2775165e-7,0.00013221517,0.0000012312777],"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":[0.0014813154,0.000447742,0.11469838,0.00006870056,0.00015353976,0.000058587928,0.1164661,0.5707117,0.0019483408,0.16802442,0.00072504877,0.025216158],"study_design_scores_gemma":[0.00057270296,0.00020318718,0.9828283,0.000113029455,0.000035833513,6.0106635e-7,0.00342207,0.00003862291,0.000701157,0.0021127986,0.009890214,0.00008148413],"about_ca_topic_score_codex":0.000072294926,"about_ca_topic_score_gemma":0.00012899475,"teacher_disagreement_score":0.8681299,"about_ca_system_score_codex":0.000022094648,"about_ca_system_score_gemma":0.00011390758,"threshold_uncertainty_score":0.39252627},"labels":[],"label_agreement":null},{"id":"W2766366461","doi":"10.1111/itor.12465","title":"Intelligent sleeping car berth inventory control system in the case of variable capacity of railcars","year":2017,"lang":"en","type":"article","venue":"International Transactions in Operational Research","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Science and Engineering Research Board; Ministarstvo Prosvete, Nauke i Tehnološkog Razvoja; McGill University","keywords":"Ticket; Computer science; Operations research; Revenue; Variable (mathematics); Task (project management); Control (management); Inventory control; Revenue management; Table (database); Stochastic game; Artificial intelligence; Business; Data mining; Engineering; Economics; Computer security","score_opus":0.11783673378057634,"score_gpt":0.4120435586554246,"score_spread":0.2942068248748482,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2766366461","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8262055,0.00007924572,0.12587878,0.0036195437,0.0009816033,0.0010973704,0.0003133648,0.00002108136,0.041803543],"genre_scores_gemma":[0.9985336,0.00004366174,0.000992446,0.00002021845,0.000058654987,0.00007872315,0.00001576889,0.000005131857,0.00025181027],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979645,0.00051464175,0.00043113664,0.00016158231,0.00075744133,0.0001706933],"domain_scores_gemma":[0.99835545,0.00062001246,0.00010892799,0.00018730412,0.0006909759,0.00003732533],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0041469065,0.00006200711,0.00012666428,0.0003645834,0.00063182245,0.00009318086,0.00050660776,0.00008245626,0.0001834583],"category_scores_gemma":[0.0004560139,0.000057183686,0.00004910661,0.0002568528,0.0004242332,0.00041344916,0.0000044031754,0.000313423,0.0000025577394],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015279869,0.00039688643,0.04692117,0.00007383722,0.000088512505,0.00010523397,0.032399196,0.4021658,0.00036087417,0.515417,0.00004329309,0.0018754327],"study_design_scores_gemma":[0.008869414,0.00035138827,0.15641655,0.0022703647,0.00010336426,0.00024820535,0.17707321,0.627585,0.0029611718,0.00770378,0.015466925,0.00095064245],"about_ca_topic_score_codex":0.043697994,"about_ca_topic_score_gemma":0.02500958,"teacher_disagreement_score":0.5077132,"about_ca_system_score_codex":0.00025511364,"about_ca_system_score_gemma":0.00039384424,"threshold_uncertainty_score":0.99278146},"labels":[],"label_agreement":null},{"id":"W2766672157","doi":"10.1155/2017/3127398","title":"A Road Pricing Model for Congested Highways Based on Link Densities","year":2017,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","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":"Fondo Nacional de Desarrollo Científico y Tecnológico","keywords":"Toll; Road pricing; Transport engineering; Revenue; Toll road; Traffic congestion; Traffic flow (computer networking); Congestion pricing; Extension (predicate logic); Function (biology); Computer science; Mathematical optimization; Economics; Engineering; Mathematics; Finance; Computer network","score_opus":0.030919441324399048,"score_gpt":0.3150143408715671,"score_spread":0.28409489954716804,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2766672157","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5676802,0.000039013135,0.42774191,0.0027512503,0.00073594245,0.00034645703,0.000046461962,0.00007366445,0.0005851451],"genre_scores_gemma":[0.9651035,0.000051608135,0.03433838,0.0001284497,0.0001694241,0.0000070920264,0.00004282186,0.000013216936,0.00014550053],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99897724,0.0000238468,0.0003848129,0.00011027306,0.00035061303,0.0001532344],"domain_scores_gemma":[0.99836046,0.000113414964,0.00076803524,0.00011415734,0.00056144135,0.0000825068],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041996944,0.0000949267,0.00018545036,0.00011983732,0.00073926634,0.00007812619,0.00015903356,0.00008244682,0.0000040527166],"category_scores_gemma":[0.00017005132,0.0000893708,0.00011564959,0.000052575062,0.00007527018,0.0006809651,2.729976e-7,0.00011356594,5.333786e-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":[0.00039796237,0.000029114346,0.0018160784,0.000020782581,0.000012311445,0.0000062562394,0.013245529,0.9761616,0.00025369597,0.002263619,0.000040782736,0.005752242],"study_design_scores_gemma":[0.0068255207,0.00060299126,0.4664391,0.0007360053,0.00028607502,9.377613e-7,0.006766519,0.5114517,0.0010180273,0.002909786,0.0024654896,0.00049785204],"about_ca_topic_score_codex":0.000037929738,"about_ca_topic_score_gemma":0.0006872818,"teacher_disagreement_score":0.46470994,"about_ca_system_score_codex":0.00005119629,"about_ca_system_score_gemma":0.00023485137,"threshold_uncertainty_score":0.56859136},"labels":[],"label_agreement":null},{"id":"W2766996256","doi":"10.1155/2017/7164790","title":"Impact Analysis of Land Use on Traffic Congestion Using Real-Time Traffic and POI","year":2017,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":72,"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":"Funding Project for Academic Human Resources Development in Institutions of Higher Learning under the Jurisdiction of Beijing Municipality; Beijing Nova Program; National Natural Science Foundation of China","keywords":"Beijing; Traffic congestion; Traffic congestion reconstruction with Kerner's three-phase theory; Ring road; Computer science; Transport engineering; Land use; Regression analysis; Linear regression; Geography; China; Engineering; Civil engineering","score_opus":0.025471429914099116,"score_gpt":0.33729365484904905,"score_spread":0.31182222493494993,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2766996256","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9991805,0.00002656112,0.00042083225,0.0000767458,0.00010700344,0.00008989555,0.000050631705,0.000013936141,0.000033901222],"genre_scores_gemma":[0.99626714,0.0004558466,0.00313386,0.000004792295,0.000040735664,4.5298356e-7,0.00006097077,0.000008469,0.00002772036],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9989114,0.000059560414,0.0004458158,0.000115412775,0.0003465036,0.0001213402],"domain_scores_gemma":[0.9983368,0.00013280693,0.0010284357,0.000105592895,0.00029673174,0.00009963792],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038038357,0.000096916134,0.00031101907,0.00035006856,0.00032207376,0.00007280461,0.00009191781,0.00008174022,0.000024877294],"category_scores_gemma":[0.00009708694,0.00008787599,0.0001569914,0.0002484477,0.00011440458,0.0010178295,3.5149958e-7,0.00009356399,2.543951e-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":[0.00031458668,0.000052831398,0.07324608,0.0000066763578,0.00018813947,0.000008707664,0.00748736,0.9145619,0.0008104158,0.00006604342,0.00000215565,0.0032550967],"study_design_scores_gemma":[0.0007463173,0.00016960889,0.99323696,0.0000842772,0.0008034287,6.969297e-7,0.00052543095,0.0042824387,0.000026032132,0.000012768699,0.000021259275,0.00009078133],"about_ca_topic_score_codex":0.0002973002,"about_ca_topic_score_gemma":0.0012880511,"teacher_disagreement_score":0.9199909,"about_ca_system_score_codex":0.000047051202,"about_ca_system_score_gemma":0.00009959353,"threshold_uncertainty_score":0.3583479},"labels":[],"label_agreement":null},{"id":"W2767162268","doi":"10.28989/senatik.v2i0.40","title":"KONSEP ECO-AIRPORT UNTUK MEMINIMALISASI EMISI BANDARA KULON PROGO","year":2016,"lang":"id","type":"article","venue":"Conference SENATIK STT Adisutjipto Yogyakarta","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Fraser Institute","funders":"","keywords":"Component (thermodynamics); Environmental science; Quality (philosophy); Floor area ratio; Transport engineering; Civil engineering; Engineering; Business; Environmental engineering","score_opus":0.030342013566461944,"score_gpt":0.2899843215552122,"score_spread":0.25964230798875026,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2767162268","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6681969,0.0018008,0.013610114,0.036323376,0.0072890497,0.003799698,0.0021600786,0.0017495978,0.26507035],"genre_scores_gemma":[0.9289311,0.0013903332,0.00071054033,0.00036011072,0.00057656056,0.000097147014,0.00035397196,0.00009027211,0.06748993],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.99388355,0.00052659266,0.0013233902,0.001436035,0.0013919472,0.0014384788],"domain_scores_gemma":[0.99578273,0.00061444636,0.0008538949,0.0008247333,0.0010914869,0.00083270285],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0013134029,0.0008059817,0.00093680475,0.0003428402,0.0011509081,0.0004964741,0.0010570772,0.0007833897,0.008238577],"category_scores_gemma":[0.00074119295,0.0006629442,0.0003425819,0.0008945245,0.0013245399,0.001214883,0.000075379794,0.00048464342,0.0013617596],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015898095,0.0023108497,0.25876686,0.00090887817,0.0014366354,0.00060022715,0.19518355,0.0002667715,0.008611629,0.2242613,0.1691691,0.13689439],"study_design_scores_gemma":[0.007033697,0.0011857293,0.09978228,0.0033505906,0.00093804667,0.0000427544,0.016302109,0.0012721361,0.008542523,0.0050565037,0.851893,0.004600595],"about_ca_topic_score_codex":0.0007202344,"about_ca_topic_score_gemma":0.0035152135,"teacher_disagreement_score":0.68272394,"about_ca_system_score_codex":0.0003204227,"about_ca_system_score_gemma":0.0021788217,"threshold_uncertainty_score":0.9995822},"labels":[],"label_agreement":null},{"id":"W2769159316","doi":"10.1155/2017/3097681","title":"Optimized Skip-Stop Metro Line Operation Using Smart Card Data","year":2017,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Scheme (mathematics); Smart card; Line (geometry); Genetic algorithm; Computer science; Transport engineering; Transit (satellite); Real-time computing; Travel time; Dedicated line; Simulation; Engineering; Public transport; Computer network; Computer security; Mathematics","score_opus":0.06741672062605786,"score_gpt":0.3756127434004958,"score_spread":0.3081960227744379,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2769159316","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.73346156,0.0003280802,0.26226354,0.00095238624,0.0021035718,0.00026409765,0.000096980926,0.000041991156,0.0004878302],"genre_scores_gemma":[0.8546838,0.00039813027,0.1441165,0.000045581583,0.00038954022,0.0000012471729,0.00026083706,0.000015000358,0.00008933812],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9984019,0.00008132475,0.0006056068,0.00016606247,0.00053964363,0.00020550913],"domain_scores_gemma":[0.99800164,0.00006332095,0.0010039231,0.00034227606,0.00044625087,0.00014258632],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010997494,0.000116613526,0.000260613,0.00013540284,0.0009143601,0.00015328596,0.00048275714,0.000096961034,0.00003452918],"category_scores_gemma":[0.0002864467,0.00011506705,0.00008542732,0.000109110224,0.0000983975,0.003768665,0.0000030300148,0.0001779845,0.0000017854635],"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":[0.00034870626,0.000094973235,0.02345907,0.000019657502,0.00007313496,0.000023554234,0.012801607,0.95631653,0.0010329913,0.00085110293,0.000096191914,0.0048824586],"study_design_scores_gemma":[0.012858114,0.00047461514,0.8914314,0.0007234223,0.001378419,0.0000132937985,0.029697811,0.021796368,0.0030873881,0.0008040868,0.036466733,0.0012683822],"about_ca_topic_score_codex":0.00032746926,"about_ca_topic_score_gemma":0.00077159144,"teacher_disagreement_score":0.9345202,"about_ca_system_score_codex":0.000074890304,"about_ca_system_score_gemma":0.0002545864,"threshold_uncertainty_score":0.70326114},"labels":[],"label_agreement":null},{"id":"W2769284531","doi":"10.1016/j.eswa.2017.11.039","title":"A hybrid machine-learning and optimization method to solve bi-level problems","year":2017,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":63,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Computer science; Solver; Mathematical optimization; MATLAB; Optimization problem; Set (abstract data type); Nonlinear system; Integer (computer science); Integer programming; Nonlinear programming; Artificial intelligence; Machine learning; Algorithm; Mathematics","score_opus":0.0343912978799911,"score_gpt":0.33373801364444877,"score_spread":0.29934671576445765,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2769284531","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00015343605,0.000268942,0.99180996,0.0013504411,0.000057604895,0.0011144425,0.000032836342,0.00016919954,0.005043113],"genre_scores_gemma":[0.41278976,0.00019265537,0.58069825,0.000117091484,0.00027834604,0.002288736,0.00016602967,0.00004058168,0.003428507],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9989794,0.000071064555,0.00019866889,0.00030847645,0.000246136,0.00019628338],"domain_scores_gemma":[0.9991034,0.00007148577,0.00021975143,0.00024303929,0.00018546567,0.00017685827],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.00043195026,0.000112977075,0.00014724309,0.000085737796,0.0023690911,0.00040689696,0.0002014765,0.000055208293,0.000017788276],"category_scores_gemma":[0.000055193144,0.000101900216,0.00001835099,0.00011746739,0.000081650505,0.00026237115,0.000014463515,0.00008624035,0.000013903721],"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":[0.00004277438,0.00010755669,0.013571328,0.000075601194,0.00007932812,0.0000033421902,0.044889573,0.86570567,0.00031407684,0.062344298,0.0014934421,0.011373004],"study_design_scores_gemma":[0.0009427879,0.000107014996,0.002481482,0.00024921307,0.000048192807,0.000016098606,0.0054898076,0.18437134,0.00012155661,0.00013794743,0.805334,0.0007005886],"about_ca_topic_score_codex":0.0055988706,"about_ca_topic_score_gemma":0.0005218685,"teacher_disagreement_score":0.8038405,"about_ca_system_score_codex":0.00003881871,"about_ca_system_score_gemma":0.00006317735,"threshold_uncertainty_score":0.9989297},"labels":[],"label_agreement":null},{"id":"W2770281990","doi":"","title":"Land Use and Transport Modeling to Support Transportation Asset Management: Case Study of the New Brunswick Road Network and its Regional Interactions","year":2011,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Asset management; Transport engineering; Business; Land use; Asset (computer security); Environmental planning; Finance; Computer science; Geography; Engineering; Computer security; Civil engineering","score_opus":0.08214146005342972,"score_gpt":0.307441417987929,"score_spread":0.2252999579344993,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2770281990","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99121433,0.000010935981,0.0073731546,0.00015054723,0.00010169974,0.00047676906,0.000005950682,0.000035808087,0.0006308017],"genre_scores_gemma":[0.99693877,0.00004450992,0.0016344847,0.00009008567,0.000028296512,0.000005870178,0.00001766422,0.000007258089,0.0012330827],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99921185,0.000045995486,0.00024694516,0.00018237419,0.00018422719,0.00012863212],"domain_scores_gemma":[0.99964964,0.000023047089,0.000064527754,0.00008008897,0.00007260952,0.000110092114],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019554347,0.000080686346,0.000099527075,0.000057242356,0.00030644392,0.000023089211,0.00006104903,0.00003584018,0.00006641156],"category_scores_gemma":[0.0000043582595,0.00006704679,0.00002349808,0.00024843897,0.000021364152,0.00038633688,0.0000024268422,0.000062898944,6.8664474e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009536057,0.00016447829,0.78039384,0.000018210929,0.00007611051,0.00009564273,0.15883522,0.053764895,0.0000018194191,0.005493258,0.0002844614,0.00077671476],"study_design_scores_gemma":[0.0009862785,0.00014546605,0.9451596,0.000055963792,0.00026311862,0.0000244059,0.04532251,0.0069635357,0.0000027339784,0.00014462229,0.0007145018,0.00021724554],"about_ca_topic_score_codex":0.044849053,"about_ca_topic_score_gemma":0.36409855,"teacher_disagreement_score":0.3192495,"about_ca_system_score_codex":0.000007717789,"about_ca_system_score_gemma":0.000066954664,"threshold_uncertainty_score":0.9615114},"labels":[],"label_agreement":null},{"id":"W2770817875","doi":"10.1155/2017/4941535","title":"A Simplified Network Model for Travel Time Reliability Analysis in a Road Network","year":2017,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Variance (accounting); Path (computing); Reliability (semiconductor); Travel time; Covariance; Computer science; Network model; Flow network; Simulation; Mathematics; Mathematical optimization; Statistics; Engineering; Transport engineering; Economics; Data mining","score_opus":0.01861398262623821,"score_gpt":0.3124241374168011,"score_spread":0.2938101547905629,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2770817875","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4960574,0.00008111579,0.5019822,0.00077527383,0.00029765227,0.0003922614,0.000036500885,0.000021251006,0.00035636924],"genre_scores_gemma":[0.94429904,0.00011076771,0.054998904,0.00004450076,0.00022082632,0.000010977391,0.00007669122,0.000010483758,0.0002278317],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9984712,0.000054267774,0.0007084492,0.00017232569,0.0003168754,0.00027685866],"domain_scores_gemma":[0.99828166,0.00012023267,0.00095038523,0.0001694482,0.0003758876,0.000102392354],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013677159,0.00010689526,0.00036838398,0.000120263416,0.00055577594,0.0000623324,0.0002340988,0.000113886126,0.000016403777],"category_scores_gemma":[0.00015309615,0.000108277585,0.00025344364,0.00035927657,0.000077461555,0.00078363915,6.7661506e-7,0.00014522621,6.026305e-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":[0.00039958805,0.000040304476,0.030874526,0.00000886072,0.0000608103,0.000003329562,0.006827385,0.9589339,0.00001959484,0.000869159,0.00007031255,0.00189226],"study_design_scores_gemma":[0.0012286706,0.000055939006,0.8518287,0.000059472364,0.00036601833,1.2018147e-7,0.0004822769,0.1380786,0.0000044444896,0.0075080716,0.00024433632,0.00014337306],"about_ca_topic_score_codex":0.00012992999,"about_ca_topic_score_gemma":0.004556111,"teacher_disagreement_score":0.82095414,"about_ca_system_score_codex":0.00006608634,"about_ca_system_score_gemma":0.00018320345,"threshold_uncertainty_score":0.44154316},"labels":[],"label_agreement":null},{"id":"W2772377432","doi":"10.1155/2017/8483750","title":"Improvement of Network Performance by In-Vehicle Routing Using Floating Car Data","year":2017,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Rijkswaterstaat; Nederlandse Organisatie voor Toegepast Natuurwetenschappelijk Onderzoek; Nederlandse Organisatie voor Wetenschappelijk Onderzoek; Technische Universiteit Delft","keywords":"Routing (electronic design automation); Computer science; Vehicle routing problem; Simulation; Real-time computing; Transport engineering; Operations research; Computer network; Automotive engineering; Engineering","score_opus":0.03177751629421966,"score_gpt":0.3224904652426671,"score_spread":0.29071294894844746,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2772377432","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.991437,0.00010108388,0.0077522327,0.00009274318,0.00038460828,0.00009451513,0.00001575271,0.0000063243833,0.00011573682],"genre_scores_gemma":[0.98396003,0.00022557074,0.0156096015,0.00001255294,0.0001220525,3.6306295e-7,0.000045188317,0.000007848653,0.000016761192],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9987728,0.000024625453,0.000594316,0.000111935864,0.00032883525,0.0001674876],"domain_scores_gemma":[0.9983547,0.00004097532,0.0011903492,0.00017513652,0.00019149375,0.000047371002],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009081455,0.00006757496,0.00016967831,0.000047953075,0.00040369463,0.000033168868,0.00032083635,0.0000489861,0.0000066523758],"category_scores_gemma":[0.000075422584,0.000070790025,0.000032168486,0.00011034943,0.00006133176,0.0015404556,0.0000025977943,0.00012805787,1.1691338e-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":[0.00007801968,0.00003102706,0.24770057,0.000028808952,0.000013449746,0.0000038541743,0.0074705556,0.723313,0.007158952,0.00010622153,0.000017948527,0.014077638],"study_design_scores_gemma":[0.0022930957,0.00018149609,0.9632821,0.000865128,0.0001020529,7.0071604e-7,0.008223635,0.02071754,0.002942001,0.00015520958,0.0009809744,0.00025603062],"about_ca_topic_score_codex":0.00045016542,"about_ca_topic_score_gemma":0.00092542387,"teacher_disagreement_score":0.7155816,"about_ca_system_score_codex":0.000044661883,"about_ca_system_score_gemma":0.000128141,"threshold_uncertainty_score":0.31049335},"labels":[],"label_agreement":null},{"id":"W2772890757","doi":"10.1061/jtepbs.0000114","title":"Optimization of Bus Depot Location with Consideration of Maintenance Center Availability","year":2017,"lang":"en","type":"article","venue":"Journal of Transportation Engineering Part A Systems","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Transport engineering; Public transport; Integer programming; Computer science; Operations research; Linear programming; Engineering","score_opus":0.016433501441207973,"score_gpt":0.2467841029912673,"score_spread":0.23035060155005932,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2772890757","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3277316,0.00014210178,0.6705814,0.0001641916,0.0008102523,0.00029638724,0.000042159714,0.000025191872,0.0002066688],"genre_scores_gemma":[0.99268615,0.000074727475,0.007050639,0.0000024301078,0.00008048221,0.000004696669,0.00004266457,0.00001094566,0.000047287624],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9985196,0.00004623212,0.00079786964,0.00009567647,0.00042852023,0.00011208208],"domain_scores_gemma":[0.9970099,0.000063252715,0.0014591145,0.0001535312,0.0012497478,0.00006446583],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006963781,0.000091883616,0.00026663885,0.00011845595,0.00014669668,0.000047116562,0.0001338698,0.000077425415,0.000010467448],"category_scores_gemma":[0.00016070889,0.00008350981,0.000057633162,0.00012842454,0.000095768555,0.0006042949,6.0565657e-7,0.00008324067,3.3886448e-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":[0.000072841314,0.000046838017,0.05884908,0.00018750092,0.000039167913,0.0000032227026,0.0030216707,0.93618655,0.00014919239,0.0013648388,0.0000479564,0.000031154046],"study_design_scores_gemma":[0.007133254,0.0008183487,0.77287203,0.006539247,0.00057170994,0.00003719618,0.0071582827,0.19272375,0.0039278604,0.000035800924,0.0072957063,0.0008868096],"about_ca_topic_score_codex":0.00022625274,"about_ca_topic_score_gemma":0.00022160358,"teacher_disagreement_score":0.7434628,"about_ca_system_score_codex":0.00004116941,"about_ca_system_score_gemma":0.00017864411,"threshold_uncertainty_score":0.34054315},"labels":[],"label_agreement":null},{"id":"W2773996763","doi":"10.17559/tv-20170317215248","title":"An extended car-following model considering the influence of bus","year":2017,"lang":"en","type":"article","venue":"Tehnicki vjesnik - Technical Gazette","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"Fundamental Research Funds for the Central Universities; Jiangsu Planned Projects for Postdoctoral Research Funds; Natural Science Foundation of Jiangsu Province; Government of Jiangsu Province","keywords":"Automotive engineering; Car model; Computer science; Engineering","score_opus":0.03129802166619165,"score_gpt":0.34130056681779636,"score_spread":0.3100025451516047,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2773996763","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9819101,0.000054530283,0.0077071213,0.0016240617,0.00010729875,0.00029643916,0.000010871961,0.00033956245,0.0079500275],"genre_scores_gemma":[0.9940869,0.00004519443,0.0053235893,0.0002356906,0.000048546077,0.000020793495,0.000005626492,0.000015971957,0.00021765163],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9985413,0.00008942991,0.0003360593,0.00026732878,0.00046318542,0.00030270283],"domain_scores_gemma":[0.99861,0.00014234748,0.0002723891,0.00074600737,0.00011314459,0.00011612057],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00096268026,0.00012618689,0.00020759198,0.000047037567,0.0012481853,0.0001342875,0.0008467519,0.00016130095,0.00001530039],"category_scores_gemma":[0.00063641375,0.000104141836,0.000111191075,0.00011876753,0.00060285395,0.00053899136,0.00003600661,0.00024855556,0.0000056557697],"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":[0.00010001889,0.00027840334,0.038991652,0.000050293307,0.00007559379,0.00006148752,0.015133248,0.78022647,0.06622867,0.090862416,0.000930875,0.007060847],"study_design_scores_gemma":[0.0018847208,0.00021891038,0.92842597,0.00048755168,0.0003602813,0.000009596857,0.002501496,0.03300298,0.0041670087,0.022407388,0.0051694605,0.0013646547],"about_ca_topic_score_codex":0.0012208618,"about_ca_topic_score_gemma":0.00094194734,"teacher_disagreement_score":0.8894343,"about_ca_system_score_codex":0.000036304802,"about_ca_system_score_gemma":0.0001748884,"threshold_uncertainty_score":0.96001583},"labels":[],"label_agreement":null},{"id":"W2774635833","doi":"10.1007/978-3-032-00906-7_15","title":"Optimization Using Surrogates and Models","year":2017,"lang":"en","type":"book-chapter","venue":"Springer series in operations research/Springer series in operations research and financial engineering","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia; Polytechnique Montréal","funders":"","keywords":"Surrogate model; Mathematical optimization; Computer science; Constraint (computer-aided design); Optimization problem; Artificial intelligence; Machine learning; Algorithm; Mathematics","score_opus":0.08521016068397114,"score_gpt":0.3549851214973027,"score_spread":0.2697749608133316,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2774635833","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.29950112,0.034145847,0.12627779,0.016106762,0.0071253926,0.0267175,0.0017342984,0.0020241544,0.4863671],"genre_scores_gemma":[0.6373948,0.064857386,0.100113206,0.00003324706,0.001475122,0.0009898824,0.0004951995,0.00042519352,0.19421597],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9957397,0.00023722158,0.0008003752,0.00092071306,0.0011888205,0.001113164],"domain_scores_gemma":[0.9975521,0.000226301,0.00004435682,0.00062236754,0.0012386926,0.0003161827],"candidate_categories":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0037654715,0.00043856725,0.000562817,0.0021887948,0.0035030297,0.0017140739,0.0005696623,0.0007040002,0.00012163856],"category_scores_gemma":[0.0018761631,0.0005281869,0.000058320296,0.00050187774,0.0014265388,0.0033454874,0.00035089828,0.0019965786,0.0000068823665],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000050686263,0.000027890248,0.00020091343,0.000083851024,0.000012480154,0.000033385404,0.0057021915,0.6875981,0.000048432852,0.30583194,0.000012125853,0.0003980069],"study_design_scores_gemma":[0.0014599226,0.0004637184,0.0018677098,0.002847267,0.000030423662,0.000028001034,0.0039556287,0.93603724,0.00016498774,0.006022309,0.045111723,0.0020110405],"about_ca_topic_score_codex":0.00560465,"about_ca_topic_score_gemma":0.07601064,"teacher_disagreement_score":0.33789366,"about_ca_system_score_codex":0.0005668932,"about_ca_system_score_gemma":0.0013712468,"threshold_uncertainty_score":0.999717},"labels":[],"label_agreement":null},{"id":"W2778571179","doi":"10.1016/j.trc.2017.11.023","title":"Analysis of bus travel time distributions for varying horizons and real-time applications","year":2017,"lang":"en","type":"article","venue":"Transportation Research Part C Emerging Technologies","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":93,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Medical Association; Alberta Innovates - Technology Futures; University of Calgary","keywords":"Global Positioning System; Horizon; Time horizon; Arrival time; Travel time; Log-normal distribution; Point (geometry); Real-time computing; Computer science; Range (aeronautics); Time point; Interval (graph theory); Probability distribution; Statistics; Engineering; Transport engineering; Mathematical optimization; Mathematics; Telecommunications","score_opus":0.0643322450338887,"score_gpt":0.40294240193033515,"score_spread":0.33861015689644647,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2778571179","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7040072,0.00042562227,0.2573691,0.017788151,0.00012848344,0.0042787786,0.0053873346,0.0024440428,0.008171319],"genre_scores_gemma":[0.9908029,0.0011946836,0.005778029,0.0000016441651,0.00002041701,0.0004671972,0.001033045,0.00001189639,0.0006901883],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9985172,0.000054594628,0.00034608154,0.0003179342,0.00041030472,0.00035388346],"domain_scores_gemma":[0.9983992,0.0003854766,0.00022141298,0.0004071749,0.0005247266,0.00006198318],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0010860418,0.00010123507,0.00025836824,0.0005710396,0.0026224619,0.00009935228,0.00042331507,0.00016649828,0.000051809136],"category_scores_gemma":[0.00036819742,0.000109312576,0.000101510064,0.0009693521,0.0010101811,0.00028875473,0.000007915003,0.0001426027,0.0000047375984],"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":[0.00022078263,0.0005306559,0.25794616,0.00031759756,0.002042947,0.000008750989,0.02293088,0.00789737,0.018230882,0.6355129,0.003528603,0.050832506],"study_design_scores_gemma":[0.0020008879,0.00034315858,0.87267745,0.00027857115,0.00258329,2.2935531e-7,0.025666794,0.016526341,0.0071434327,0.022754798,0.048836663,0.0011883734],"about_ca_topic_score_codex":0.0015351813,"about_ca_topic_score_gemma":0.001263019,"teacher_disagreement_score":0.6147313,"about_ca_system_score_codex":0.000033942324,"about_ca_system_score_gemma":0.0001115663,"threshold_uncertainty_score":0.998676},"labels":[],"label_agreement":null},{"id":"W2779031717","doi":"10.1155/2017/7392962","title":"A Dynamic Short-Turning Bus Control for Uncertain Demand","year":2017,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China; National Science Foundation","keywords":"Reliability (semiconductor); Transit (satellite); Service (business); Control (management); Engineering; Computer science; Real-time computing; Public transport; Simulation; Transport engineering","score_opus":0.015516936929758368,"score_gpt":0.32498555522067096,"score_spread":0.3094686182909126,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2779031717","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5257195,0.00023011187,0.4712907,0.0011211834,0.00086610706,0.0004427438,0.000045894445,0.000030491057,0.00025322926],"genre_scores_gemma":[0.97504,0.00020006347,0.024414198,0.00004374147,0.00011235841,0.000015178004,0.000037893693,0.000012145402,0.00012443156],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9989403,0.000033622007,0.00043703913,0.0001101338,0.00029997423,0.00017893358],"domain_scores_gemma":[0.99856526,0.0001362785,0.000638405,0.000096549775,0.0004659156,0.00009761022],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00061837694,0.00008971759,0.00020304603,0.00008816431,0.00083366624,0.000087030385,0.00019639709,0.000078668854,0.000011916374],"category_scores_gemma":[0.000206342,0.000086898224,0.00012909695,0.000054755506,0.00008807975,0.000904877,2.857164e-7,0.00011480541,4.8668323e-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":[0.00094500673,0.000073470066,0.03533313,0.00005529208,0.000099269295,0.000037608366,0.018119136,0.91098684,0.0024560029,0.0039434703,0.000074235635,0.02787651],"study_design_scores_gemma":[0.006012947,0.0003850056,0.9589863,0.00034559518,0.0003790362,0.0000039492284,0.007981968,0.0034461874,0.00024543446,0.0040314384,0.017791698,0.00039045044],"about_ca_topic_score_codex":0.000037048467,"about_ca_topic_score_gemma":0.00139449,"teacher_disagreement_score":0.9236532,"about_ca_system_score_codex":0.000052877775,"about_ca_system_score_gemma":0.00014487223,"threshold_uncertainty_score":0.6411971},"labels":[],"label_agreement":null},{"id":"W2781836232","doi":"10.18510/ijsrtm.2018.612","title":"QUANTITATIVE APPROACH TO DETERMINE PEDESTRIAN DELAY AND LEVEL OF SERVICE AT SIGNALIZED INTERSECTION","year":2018,"lang":"en","type":"article","venue":"International Journal of Students Research in Technology & Management","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Transport Canada","funders":"","keywords":"Pedestrian; Intersection (aeronautics); Level of service; Computer science; Transport engineering; Range (aeronautics); Simulation; Engineering","score_opus":0.22219436228208278,"score_gpt":0.4870713502548925,"score_spread":0.26487698797280973,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2781836232","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9824339,0.000039630537,0.010015193,0.003346478,0.00020848855,0.0002645828,0.0000057418,0.000010999838,0.0036749465],"genre_scores_gemma":[0.9902388,0.00016993165,0.009148744,0.00006382864,0.000040820436,0.000012474257,0.0000026339662,0.0000050366984,0.00031774217],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9981182,0.00013281438,0.00036865025,0.00014337484,0.0010733721,0.00016353819],"domain_scores_gemma":[0.99846977,0.000078715515,0.00017418036,0.000066182794,0.0011572712,0.00005386071],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018157463,0.00005837323,0.0001253559,0.0015752882,0.000114194314,0.000042887696,0.00066526886,0.000066011395,0.000023344173],"category_scores_gemma":[0.0001273574,0.000056439687,0.00002125052,0.0007911845,0.00026282558,0.0001516944,0.00017250623,0.00016343917,0.0000056205813],"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":[0.008547355,0.0033496935,0.64942133,0.0002475333,0.0022984827,0.0005668306,0.09961863,0.0045544547,0.0036954116,0.10522877,0.00294506,0.11952642],"study_design_scores_gemma":[0.01181756,0.003273446,0.7797585,0.0019670534,0.00012330853,0.00007924739,0.15252684,0.002373549,0.003246762,0.021713428,0.022505896,0.0006144143],"about_ca_topic_score_codex":0.0007523301,"about_ca_topic_score_gemma":0.0031832112,"teacher_disagreement_score":0.13033713,"about_ca_system_score_codex":0.0002499207,"about_ca_system_score_gemma":0.000039214145,"threshold_uncertainty_score":0.23015437},"labels":[],"label_agreement":null},{"id":"W2783033691","doi":"10.1109/mits.2017.2776123","title":"Harnessing the Power of HPC in Simulation and Optimization of Large Transportation Networks: Spatio-Temporal Traffic Management in the Greater Toronto Area","year":2018,"lang":"en","type":"article","venue":"IEEE Intelligent Transportation Systems Magazine","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Soar; Traffic congestion; Computer science; Advanced Traffic Management System; Traffic optimization; Intelligent transportation system; Scale (ratio); Constraint (computer-aided design); Distributed computing; Transport engineering; Operations research; Engineering; Floating car data; Artificial intelligence","score_opus":0.02688243060841645,"score_gpt":0.2954523262839123,"score_spread":0.2685698956754959,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2783033691","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.62235075,0.00026618788,0.37528804,0.00012095215,0.0003787852,0.0011179808,0.000044622044,0.000034244866,0.00039843874],"genre_scores_gemma":[0.9987262,0.00017805755,0.0005026354,0.000037015732,0.000056948633,0.000053990865,0.00035186825,0.000017013877,0.00007628568],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976545,0.00027382647,0.0010516959,0.00026609356,0.0005152963,0.00023861398],"domain_scores_gemma":[0.9989132,0.00015449736,0.00043888527,0.00017799511,0.00027779822,0.000037618265],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014061352,0.00017081537,0.00026148118,0.00016936033,0.00016274466,0.000049132777,0.00017662035,0.00012806579,0.00008207362],"category_scores_gemma":[0.000011893077,0.00013073046,0.000060812417,0.0006406297,0.00017824155,0.00045277315,7.209514e-7,0.000093368544,0.0000012563552],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013543062,0.000087315384,0.03067504,0.00006691239,0.000019957795,0.000003799989,0.07979017,0.8876224,0.0000038172634,0.0011204703,0.000026262549,0.00044840996],"study_design_scores_gemma":[0.0012091964,0.00014298997,0.3169727,0.000513684,0.000116558076,4.7476613e-7,0.042961676,0.6360332,0.000041913845,0.000028850767,0.0016873905,0.00029135286],"about_ca_topic_score_codex":0.0015318898,"about_ca_topic_score_gemma":0.04787293,"teacher_disagreement_score":0.37637544,"about_ca_system_score_codex":0.00006596558,"about_ca_system_score_gemma":0.000037407277,"threshold_uncertainty_score":0.9695009},"labels":[],"label_agreement":null},{"id":"W2783905471","doi":"10.1061/jtepbs.0000124","title":"Examining the Relationship between Drivers’ Anticipated Travel Time and Previous Experienced Travel Times","year":2018,"lang":"en","type":"article","venue":"Journal of Transportation Engineering Part A Systems","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Travel time; Time travel; Transport engineering; Psychology; Computer science; Engineering; Artificial intelligence","score_opus":0.05080946354533479,"score_gpt":0.2809122790018922,"score_spread":0.23010281545655742,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2783905471","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96608037,0.00017419399,0.0326354,0.00013243537,0.0004978051,0.00020683718,0.000021225458,0.000044496,0.00020724976],"genre_scores_gemma":[0.9985908,0.000023135855,0.0005712479,0.000007778728,0.00041701493,0.00000567101,0.000026118498,0.000014491005,0.00034370832],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9986492,0.00009214675,0.000581771,0.00011485959,0.0003828842,0.00017916423],"domain_scores_gemma":[0.99884313,0.00040372345,0.00033896565,0.000074221,0.00022407388,0.00011589892],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009149184,0.000112313355,0.00022813717,0.00014003493,0.00032198406,0.0000766014,0.00012944393,0.0000987773,0.000025924302],"category_scores_gemma":[0.00013619318,0.00009255944,0.00004766907,0.00031556078,0.00013004131,0.00034784168,7.039767e-7,0.00015008415,0.000005031764],"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":[0.00005255887,0.000039546765,0.6262515,0.00008867833,0.00017671988,0.000022029628,0.29641134,0.068282016,0.0011634115,0.006689405,0.0005912279,0.00023156177],"study_design_scores_gemma":[0.00046120828,0.00013590071,0.98642457,0.00031021552,0.00012625432,0.0000054378315,0.0076533854,0.0028803586,0.00011272592,0.000009158216,0.001715688,0.00016508122],"about_ca_topic_score_codex":0.000075457494,"about_ca_topic_score_gemma":0.000024581295,"teacher_disagreement_score":0.36017308,"about_ca_system_score_codex":0.000029150175,"about_ca_system_score_gemma":0.000079693935,"threshold_uncertainty_score":0.37744644},"labels":[],"label_agreement":null},{"id":"W2783972495","doi":"10.1139/cjce-2017-0559","title":"LRT passengers’ responses to advanced passenger information system (APIS) in case of information inconsistency and train crowding","year":2018,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Public transport; Transport engineering; Train; Transit (satellite); Multinomial logistic regression; Travel behavior; Service (business); Crowding; Passenger information; Geography; Computer science; Business; Engineering; Marketing; Psychology","score_opus":0.008050662225839797,"score_gpt":0.22576381251516361,"score_spread":0.2177131502893238,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2783972495","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9801416,0.000052082767,0.017702412,0.00030040706,0.0003638666,0.00012347376,0.000018829007,0.000014983254,0.0012823557],"genre_scores_gemma":[0.9980067,0.0000056921012,0.0018993444,0.000030206305,0.000044817,0.0000018805486,0.0000021872986,0.0000036715637,0.000005479644],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.9992303,0.00003356705,0.00042677886,0.0000339862,0.00011605229,0.00015928791],"domain_scores_gemma":[0.99918973,0.000072209376,0.00016378624,0.00004385356,0.00027660205,0.00025383386],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007412793,0.0000618846,0.00012460802,0.000775952,0.00011105793,0.00005971963,0.000058863403,0.000058662154,0.00001216963],"category_scores_gemma":[0.0005639912,0.00006885009,0.000021331582,0.0003860978,0.000034321307,0.0013377869,0.000001602516,0.000086372136,0.0000012265398],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020739558,0.000009831935,0.042405356,0.00063716073,0.000071822404,0.00044824032,0.5943993,0.32234108,0.00066154316,0.01734215,0.0006554512,0.020820713],"study_design_scores_gemma":[0.0056286263,0.001071461,0.47052193,0.0067393254,0.00016437256,0.0018246415,0.36901188,0.0625556,0.0010501627,0.00011031158,0.0795398,0.0017818989],"about_ca_topic_score_codex":0.00262548,"about_ca_topic_score_gemma":0.15127556,"teacher_disagreement_score":0.42811656,"about_ca_system_score_codex":0.00016475501,"about_ca_system_score_gemma":0.00045055302,"threshold_uncertainty_score":0.86421144},"labels":[],"label_agreement":null},{"id":"W2784009078","doi":"","title":"Positioning Toronto's Transit vehicles: Integrating low-cost GPS, odometers, gyros and signposts","year":2003,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Odometer; Global Positioning System; Transport engineering; Aeronautics; Transit (satellite); Computer science; Geography; Engineering; Telecommunications; Public transport; Artificial intelligence","score_opus":0.011999293989615165,"score_gpt":0.2690984173242866,"score_spread":0.2570991233346714,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2784009078","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.41821682,0.0006726386,0.22928442,0.0009401097,0.00046461116,0.000746704,0.000023487844,0.0005160439,0.34913516],"genre_scores_gemma":[0.98838484,0.00008316566,0.010243939,0.00023184066,0.000029854371,0.000007914187,0.000024776138,0.000008125984,0.000985514],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","domain_scores_codex":[0.9991957,0.00011366324,0.00015958922,0.00016679158,0.00017598589,0.00018826588],"domain_scores_gemma":[0.9996285,0.00011152644,0.000042422555,0.000052805717,0.00006627144,0.000098500765],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035512075,0.000081620165,0.00009114193,0.00002864074,0.00051585416,0.0001314087,0.000050802908,0.00007394739,0.00037526307],"category_scores_gemma":[0.00007990541,0.00007930494,0.000028300989,0.00012674218,0.00007072817,0.00044406188,0.0000011885398,0.00006427878,0.0000054353536],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010266052,0.00026929422,0.09637356,0.00009623464,0.000116077055,0.00003506424,0.3342063,0.0077122264,0.0076410673,0.47597042,0.002365168,0.07511194],"study_design_scores_gemma":[0.011126222,0.00086354173,0.15639293,0.0017455468,0.0005331026,0.000040502924,0.6568172,0.013864809,0.023510104,0.005889631,0.12385325,0.0053631733],"about_ca_topic_score_codex":0.0028436505,"about_ca_topic_score_gemma":0.00719262,"teacher_disagreement_score":0.570168,"about_ca_system_score_codex":0.00005884268,"about_ca_system_score_gemma":0.00005494953,"threshold_uncertainty_score":0.4298768},"labels":[],"label_agreement":null},{"id":"W2784068364","doi":"10.1155/2018/2738930","title":"Time-Dependent Transportation Network Design considering Construction Impact","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","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":"Lloyd's Register; National Natural Science Foundation of China; Mountain-Plains Consortium; U.S. Department of Transportation","keywords":"Schedule; Flexibility (engineering); Time horizon; Duration (music); Computer science; Network planning and design; Process (computing); Dimension (graph theory); Flow network; Operations research; Set (abstract data type); Transport engineering; Work (physics); Mathematical optimization; Engineering; Computer network","score_opus":0.01681640468223627,"score_gpt":0.29248129162209163,"score_spread":0.27566488693985536,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2784068364","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.59099716,0.0001507036,0.4066556,0.00023816772,0.0012208049,0.00031903063,0.000025005846,0.0001018046,0.00029168138],"genre_scores_gemma":[0.88596153,0.00019741226,0.11296259,0.00004831879,0.0006663736,0.0000037101768,0.00006673022,0.000021992893,0.00007133767],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99804837,0.0001474479,0.00077199,0.00017017339,0.00056900614,0.00029302295],"domain_scores_gemma":[0.99797475,0.00015531667,0.000795056,0.00008450195,0.00082270376,0.00016766234],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008586315,0.00015805927,0.00026800277,0.00017091785,0.00042767986,0.00004645773,0.00011887982,0.00013020342,0.00032762997],"category_scores_gemma":[0.000057546775,0.00015635169,0.00015129919,0.00044594955,0.0002721523,0.0011044941,2.553335e-7,0.00018299141,0.000014877012],"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":[0.000589276,0.000038740218,0.005865876,0.0000094050065,0.00007092654,0.00002999057,0.01856814,0.9666752,0.0013678233,0.0008001322,0.00027995076,0.0057045114],"study_design_scores_gemma":[0.014822447,0.005043216,0.86892724,0.0016507228,0.0017793904,0.00011249618,0.042309206,0.0037468288,0.010960274,0.023631223,0.024360854,0.0026561094],"about_ca_topic_score_codex":0.00006317111,"about_ca_topic_score_gemma":0.00052960136,"teacher_disagreement_score":0.9629284,"about_ca_system_score_codex":0.00011613287,"about_ca_system_score_gemma":0.00035648694,"threshold_uncertainty_score":0.6375837},"labels":[],"label_agreement":null},{"id":"W2785732460","doi":"10.20381/ruor-21526","title":"Stochastic Dynamic Model of Urban Traffic and Optimum Management of Its Flow and Congestion","year":2018,"lang":"en","type":"dissertation","venue":"uO Research (University of Ottawa)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Traffic congestion; Transport engineering; Computer science; Operations research; Engineering","score_opus":0.03543668905318402,"score_gpt":0.317082870180937,"score_spread":0.28164618112775297,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2785732460","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9850827,0.00019215657,0.0049190177,0.00011089277,0.00006667361,0.0005098469,0.00013523668,0.000021898179,0.008961576],"genre_scores_gemma":[0.9821573,0.0009884896,0.0047832453,4.800368e-7,0.0000061931614,5.6631484e-7,0.00030812898,0.000007905067,0.011747707],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998848,0.00009401584,0.00012606483,0.00020783299,0.0005666049,0.00015745666],"domain_scores_gemma":[0.9990643,0.00006944989,0.00015859834,0.000088813846,0.0005395009,0.0000792961],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006293965,0.00007717181,0.00018749587,0.00037817526,0.0002817167,0.000010339588,0.00016961452,0.00016660584,0.000040875075],"category_scores_gemma":[0.000030577157,0.000101384445,0.000032252537,0.0002867445,0.00040281267,0.00015371646,0.000013295924,0.0001327582,0.0000011277655],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005865435,0.0005855286,0.0022386743,0.008574699,0.0007570695,0.0000314981,0.25045606,0.5924593,0.0007748055,0.091110684,0.0021767197,0.05024838],"study_design_scores_gemma":[0.0012261588,0.00032348454,0.03937956,0.0010859326,0.00023140397,2.2069813e-7,0.08425372,0.8725886,0.000022262442,0.00032757412,0.00028816436,0.00027295024],"about_ca_topic_score_codex":0.00009410316,"about_ca_topic_score_gemma":0.0037590652,"teacher_disagreement_score":0.28012922,"about_ca_system_score_codex":0.00003942021,"about_ca_system_score_gemma":0.000131117,"threshold_uncertainty_score":0.41343376},"labels":[],"label_agreement":null},{"id":"W2786362895","doi":"","title":"Trajectory-Based Weather-ATM Integration at the Florida NextGen Testbed","year":2013,"lang":"en","type":"article","venue":"93rd American Meteorological Society Annual Meeting","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lockheed Martin (Canada)","funders":"","keywords":"Testbed; Computer science; Meteorology; Trajectory; Air traffic control; Aeronautics; Real-time computing; Geography; Computer network; Engineering; Cartography","score_opus":0.015880617521834495,"score_gpt":0.2659066363299272,"score_spread":0.2500260188080927,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2786362895","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9730885,0.00011319675,0.009620484,0.009682492,0.00021533723,0.0005562464,0.000031009335,0.00041861364,0.00627413],"genre_scores_gemma":[0.9759666,0.000052343494,0.019364318,0.0034946776,0.00029800594,0.00012930209,0.00006319823,0.000017453287,0.00061412854],"study_design_codex":"design_other","study_design_gemma":"qualitative","domain_scores_codex":[0.99770766,0.0005782212,0.00033715644,0.0003742057,0.0005016697,0.00050111115],"domain_scores_gemma":[0.99811995,0.0010205155,0.00028240873,0.00016680373,0.00024595237,0.00016434517],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0011544008,0.00019689006,0.0002507329,0.000020435431,0.0014602434,0.00012894392,0.00031243358,0.00014768647,0.0008315996],"category_scores_gemma":[0.00063909986,0.00013060462,0.00025867228,0.00066876586,0.0010993646,0.00024259234,0.00002120465,0.0002960315,0.00012568156],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036711962,0.00064744934,0.2272394,0.00004683516,0.0004127126,0.000013015695,0.24773726,0.111738116,0.020098735,0.0058417874,0.07118792,0.31466964],"study_design_scores_gemma":[0.0023190621,0.0018859603,0.35954514,0.00014232755,0.00037483624,0.0000032839068,0.4445915,0.046769157,0.0028883298,0.0018528677,0.13719493,0.0024326004],"about_ca_topic_score_codex":0.0045404807,"about_ca_topic_score_gemma":0.0005816209,"teacher_disagreement_score":0.31223705,"about_ca_system_score_codex":0.00014517954,"about_ca_system_score_gemma":0.00009088193,"threshold_uncertainty_score":0.9998397},"labels":[],"label_agreement":null},{"id":"W2786382743","doi":"10.2495/sdp-v13-n4-594-604","title":"Sustainable urban mobility plan at La Manga Del Mar Menor (Spain)","year":2018,"lang":"en","type":"article","venue":"International Journal of Sustainable Development and Planning","topic":"Transportation Planning and Optimization","field":"Social Sciences","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":"Plan (archaeology); Humanities; Geography; Art; Archaeology","score_opus":0.015319838214068264,"score_gpt":0.27956040578211094,"score_spread":0.2642405675680427,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2786382743","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97619325,0.00028745923,0.0036227952,0.0005537256,0.00034271847,0.0001366608,0.000002668486,0.000033432818,0.018827267],"genre_scores_gemma":[0.9692521,0.000040101164,0.004233035,0.00013980856,0.00043378142,0.0000047331555,0.000028917546,0.000010394881,0.025857097],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9984062,0.0001080375,0.00040552265,0.00014996,0.00057749404,0.00035283447],"domain_scores_gemma":[0.9978296,0.00019287661,0.00029886252,0.000048269565,0.0014841604,0.00014621117],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018630153,0.00011769727,0.00014907295,0.00032554465,0.0007178263,0.00020720958,0.00026886573,0.000095868476,0.00014193758],"category_scores_gemma":[0.00030919758,0.00011395884,0.000036440262,0.00018123652,0.00016369963,0.0006097062,0.000050007144,0.00014607204,0.0000047981525],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001426279,0.00021289372,0.4567553,0.00012260742,0.00045495896,0.005433807,0.43600634,0.0018797388,0.000039546114,0.07217645,0.020079581,0.0054124766],"study_design_scores_gemma":[0.0009479561,0.00008182137,0.079492174,0.00012210186,0.000025613004,0.000047402864,0.23877886,0.00013879605,0.0002676078,0.0009449751,0.6788892,0.0002634958],"about_ca_topic_score_codex":0.00023833907,"about_ca_topic_score_gemma":0.00004380126,"teacher_disagreement_score":0.6588096,"about_ca_system_score_codex":0.00041667907,"about_ca_system_score_gemma":0.0005662217,"threshold_uncertainty_score":0.55210125},"labels":[],"label_agreement":null},{"id":"W2788823684","doi":"10.1155/2018/6931025","title":"Managing Recurrent Congestion of Subway Network in Peak Hours with Station Inflow Control","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Bottleneck; Beijing; Inflow; Computer science; Constraint (computer-aided design); Control (management); Transport engineering; Plan (archaeology); Traffic congestion; Scheme (mathematics); Scale (ratio); Real-time computing; Simulation; Operations research; Engineering","score_opus":0.008415804367261314,"score_gpt":0.2709010180898273,"score_spread":0.262485213722566,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2788823684","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8879882,0.00016875035,0.11044718,0.00041162455,0.00045891525,0.00022797837,0.0000123694435,0.00001929773,0.00026565467],"genre_scores_gemma":[0.99001133,0.0002768394,0.009436087,0.000031112482,0.00017529675,0.0000033329113,0.000043172276,0.000009724389,0.000013085183],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99865484,0.00009782852,0.0005646636,0.000102278595,0.00041248905,0.00016787891],"domain_scores_gemma":[0.9983986,0.00009829358,0.0007382489,0.000051203377,0.00065017736,0.000063448795],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00064726843,0.00008660837,0.00020041394,0.00018336214,0.00010380776,0.00001455566,0.00007701855,0.00005674179,0.000020449366],"category_scores_gemma":[0.00003938245,0.000080980884,0.000042637985,0.00047922885,0.00012066869,0.00067867665,2.2272425e-7,0.00013264868,5.630953e-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":[0.0013174275,0.000054266373,0.097707905,0.000021654962,0.000020078105,0.000010071045,0.019854566,0.86478406,0.00017777835,0.0024471935,0.000021428017,0.013583571],"study_design_scores_gemma":[0.0027189618,0.0006218437,0.9884536,0.0005496897,0.00008216329,0.0000010736369,0.00482153,0.00040181313,0.00014773176,0.0015542584,0.00051334454,0.00013396234],"about_ca_topic_score_codex":0.00009478338,"about_ca_topic_score_gemma":0.0066886563,"teacher_disagreement_score":0.8907457,"about_ca_system_score_codex":0.00006104893,"about_ca_system_score_gemma":0.00014816102,"threshold_uncertainty_score":0.37324274},"labels":[],"label_agreement":null},{"id":"W2789620195","doi":"","title":"Simulation-Based Dynamic Traffic and Transit Assignment Model for the Greater Toronto Area","year":2018,"lang":"en","type":"article","venue":"Transportation Research Board 97th Annual MeetingTransportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Transport engineering; Computer science; Transit (satellite); Public transport; Simulation; Engineering","score_opus":0.08442528802970198,"score_gpt":0.409827862524938,"score_spread":0.325402574495236,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2789620195","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.49661976,0.00066931936,0.48341432,0.009575077,0.0003652119,0.006546313,0.0012675383,0.0005779152,0.0009645509],"genre_scores_gemma":[0.99025124,0.00034915292,0.005460376,0.00021848212,0.00024337332,0.0010058392,0.0005562161,0.00009744578,0.0018178604],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99148923,0.00091581955,0.0010479587,0.001130509,0.0037143952,0.001702115],"domain_scores_gemma":[0.9884289,0.005087034,0.00020417977,0.00050609827,0.005112489,0.00066132576],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.007972337,0.0004139592,0.00042579285,0.0005495479,0.004738948,0.00039279406,0.0006781899,0.00042401406,0.0004337611],"category_scores_gemma":[0.0006809731,0.00036768022,0.00023070026,0.0012742396,0.0023666131,0.0009912497,0.000004573483,0.0007660626,0.000020902795],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015217911,0.00017666945,0.005146295,0.0001583699,0.00009645311,0.000009835307,0.09289503,0.8895088,0.00013178377,0.003056357,0.0010289865,0.0062696445],"study_design_scores_gemma":[0.0022737344,0.00060805754,0.062166017,0.00017785939,0.000120084966,5.0075947e-8,0.024145123,0.8922113,0.00009228901,0.00057528337,0.017084086,0.0005460752],"about_ca_topic_score_codex":0.010561671,"about_ca_topic_score_gemma":0.20496942,"teacher_disagreement_score":0.49363148,"about_ca_system_score_codex":0.0004051383,"about_ca_system_score_gemma":0.0011282528,"threshold_uncertainty_score":0.9998775},"labels":[],"label_agreement":null},{"id":"W2789968962","doi":"","title":"Capacity Analysis of a Complex Railway Station Using Integrated Crowd and Transit Simulation: a Case Study of Toronto’s Union Station","year":2018,"lang":"en","type":"article","venue":"Transportation Research Board 97th Annual MeetingTransportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Transport engineering; Transit (satellite); Engineering; Telecommunications; Computer science; Public transport","score_opus":0.15620639538157852,"score_gpt":0.4486979136542875,"score_spread":0.29249151827270903,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2789968962","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97000635,0.00008932905,0.025625944,0.00016840761,0.00010457311,0.0023438032,0.0011667237,0.00013472898,0.0003601676],"genre_scores_gemma":[0.9931788,0.00014943817,0.005283199,0.00001451588,0.00009340877,0.000095656615,0.0010337031,0.00005510001,0.000096203425],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.9883112,0.0037330466,0.0019713463,0.0009899372,0.00399471,0.0009998062],"domain_scores_gemma":[0.9850658,0.0017614762,0.0006577532,0.0004688951,0.01153285,0.0005131791],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0092834765,0.00037715994,0.00085502124,0.0020450738,0.0018449379,0.00014470422,0.00037530178,0.0003611235,0.0006323554],"category_scores_gemma":[0.0005047226,0.00041325588,0.0002028084,0.006048242,0.001985441,0.0015324773,0.000007052311,0.0006699582,0.0000024565686],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016941503,0.0012999875,0.2468252,0.00035376803,0.0010386058,0.00016249169,0.5291417,0.20866328,0.0026447654,0.002651785,0.000105835636,0.005418387],"study_design_scores_gemma":[0.0026412208,0.0017230087,0.594885,0.0001671814,0.00082993164,8.902689e-7,0.33835414,0.059431132,0.00035426137,0.00014735962,0.0009892299,0.0004766807],"about_ca_topic_score_codex":0.5349316,"about_ca_topic_score_gemma":0.7240287,"teacher_disagreement_score":0.34805977,"about_ca_system_score_codex":0.00033962415,"about_ca_system_score_gemma":0.00075988326,"threshold_uncertainty_score":0.9998319},"labels":[],"label_agreement":null},{"id":"W2790455659","doi":"10.1080/01441647.2018.1426651","title":"Designing computable general equilibrium models for transportation applications","year":2018,"lang":"en","type":"article","venue":"Transport Reviews","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computable general equilibrium; Computer science; Operations research; Management science; Economics; Transport engineering; Macroeconomics; Engineering","score_opus":0.07612948014089387,"score_gpt":0.34609150312834364,"score_spread":0.26996202298744976,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2790455659","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0035938018,0.0011403247,0.9875705,0.00023757732,0.00016569131,0.0018960215,0.00008232512,0.00021324465,0.0051004738],"genre_scores_gemma":[0.52502894,0.0026971172,0.46054506,0.00078122754,0.0016208162,0.0016956256,0.00269892,0.00007701211,0.0048552616],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9985262,0.0000642618,0.0005451439,0.00032009787,0.00021176659,0.00033252334],"domain_scores_gemma":[0.9992571,0.000046152443,0.00017259667,0.00016810614,0.0002217626,0.00013427346],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00083004753,0.00014960537,0.0002902057,0.000069210444,0.00047969035,0.000030548592,0.00019402883,0.000105999796,0.0001275383],"category_scores_gemma":[0.0000069324005,0.00015196731,0.00015618588,0.00039365885,0.00016073305,0.00043204473,5.2838567e-7,0.00006755446,0.00003417487],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003874434,0.00066882884,0.01932207,0.0013977947,0.00017552658,0.000006361106,0.10910687,0.3011015,0.0039003033,0.41494733,0.02054946,0.1284365],"study_design_scores_gemma":[0.00046995387,0.000065430955,0.0008514572,0.0000945469,0.00014294201,2.678197e-7,0.000246715,0.009726284,0.00034760457,0.0038326995,0.983879,0.0003430536],"about_ca_topic_score_codex":0.00026196553,"about_ca_topic_score_gemma":0.00082814734,"teacher_disagreement_score":0.9633296,"about_ca_system_score_codex":0.00003281045,"about_ca_system_score_gemma":0.00013047343,"threshold_uncertainty_score":0.61970466},"labels":[],"label_agreement":null},{"id":"W2790753605","doi":"","title":"ADDRESSING DRIVER CONCERNS: THE NETWORK ROBUSTNESS INDEX APPROACH TO PLANNING CITY CYCLING INFRASTUCTURE","year":2017,"lang":"en","type":"dissertation","venue":"MacSphere (McMaster University)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Cycling; Robustness (evolution); Computer science; Transport engineering; Geography; Engineering; Forestry","score_opus":0.05427199531913092,"score_gpt":0.29350689047119866,"score_spread":0.23923489515206775,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2790753605","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012205639,0.00016943093,0.025390547,0.000117065116,0.0011952056,0.000618448,0.000022885297,0.00018389992,0.9600969],"genre_scores_gemma":[0.35082614,0.000044528326,0.0019711144,0.00015604045,0.0010602465,0.000004950668,0.0005943142,0.000053174736,0.6452895],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9980176,0.0002292297,0.00022239242,0.0005180442,0.00052245584,0.000490241],"domain_scores_gemma":[0.9987257,0.00010506627,0.00047089593,0.00030555937,0.0002144082,0.00017837732],"candidate_categories":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00032372292,0.0003126653,0.0003369881,0.00009864313,0.0029923005,0.00045855937,0.0009788846,0.0005433306,0.004276064],"category_scores_gemma":[0.000058620724,0.00029988622,0.00012798347,0.0005245071,0.00016281947,0.00046017024,0.00003406104,0.0006362499,0.000007819998],"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":[0.00024054323,0.000027897748,0.035253458,0.00007386742,0.00011193869,0.000035568835,0.04604496,0.86639476,0.0000023842144,0.0013607038,0.004441855,0.046012055],"study_design_scores_gemma":[0.00092811,0.00002896308,0.072811,0.0013397812,0.00036788397,9.922459e-7,0.101583734,0.0045192833,0.000008275533,0.00010705037,0.81721765,0.0010872515],"about_ca_topic_score_codex":0.00088340277,"about_ca_topic_score_gemma":0.0039315457,"teacher_disagreement_score":0.8618755,"about_ca_system_score_codex":0.0001686941,"about_ca_system_score_gemma":0.0003219658,"threshold_uncertainty_score":0.99994534},"labels":[],"label_agreement":null},{"id":"W2792450897","doi":"","title":"The sources of efficiency change in the Canadian urban transit systems : a data envelopment analysis (DEA) approach","year":2002,"lang":"en","type":"dissertation","venue":"Mspace (University of Manitoba)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Data envelopment analysis; Transit (satellite); Regional science; Transport engineering; Operations research; Computer science; Business; Geography; Engineering; Statistics; Mathematics; Public transport","score_opus":0.04373097362964748,"score_gpt":0.23502129069368247,"score_spread":0.191290317064035,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2792450897","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.89404947,0.0050116936,0.0018901174,0.0038296904,0.0008008568,0.003051869,0.0008351286,0.000088345914,0.09044285],"genre_scores_gemma":[0.99693435,0.0005114173,0.00011172184,0.000007194881,0.000029949999,0.0000015560412,0.0012779343,0.000005693903,0.0011201937],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.998565,0.00025151268,0.00013906894,0.00024191308,0.0005847713,0.00021774306],"domain_scores_gemma":[0.99912125,0.00009133305,0.00027422555,0.00033558838,0.00011495321,0.000062621606],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009184804,0.00010983299,0.00023604886,0.00041381016,0.001023028,0.00005844894,0.0011029603,0.00017969529,0.000005084244],"category_scores_gemma":[0.000017863755,0.00009870393,0.00008317017,0.0012357939,0.00014707404,0.0001473153,0.0000066754883,0.00016126396,0.000001840758],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000076826684,0.00021850949,0.28836685,0.00030668196,0.0007306682,0.000033073997,0.68192476,0.017420588,0.0000012060863,0.0063202437,0.0023062222,0.0022943486],"study_design_scores_gemma":[0.00014265062,0.000014024201,0.50181276,0.000054601293,0.00051023276,1.4414728e-7,0.47172996,0.010155431,2.975917e-7,0.0000018881556,0.0154360365,0.00014195812],"about_ca_topic_score_codex":0.8092787,"about_ca_topic_score_gemma":0.9990678,"teacher_disagreement_score":0.2134459,"about_ca_system_score_codex":0.00011403024,"about_ca_system_score_gemma":0.00026024648,"threshold_uncertainty_score":0.7868408},"labels":[],"label_agreement":null},{"id":"W2794077441","doi":"10.1080/01605682.2018.1438761","title":"Applications of logic constrained equilibria to traffic networks and to power systems with storage","year":2018,"lang":"en","type":"article","venue":"Journal of the Operational Research Society","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis","funders":"","keywords":"Computer science; Operations research; Information system; Scheduling (production processes); Computer data storage; Mathematical optimization; Engineering; Mathematics; Electrical engineering; Operating system","score_opus":0.03878335184551547,"score_gpt":0.3702936247805295,"score_spread":0.33151027293501406,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2794077441","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.76513934,0.00021461444,0.21118177,0.018992824,0.00026475347,0.0013301792,0.000034299155,0.00001532778,0.0028268513],"genre_scores_gemma":[0.9938594,0.00002159921,0.005027783,0.00019489757,0.00019577614,0.000011940116,0.0000016996962,0.0000045119746,0.000682401],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9985835,0.00019898292,0.00021540053,0.00008577,0.00076482986,0.00015153705],"domain_scores_gemma":[0.9979473,0.00023167366,0.000087593406,0.00007784214,0.0015078686,0.00014775025],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023830957,0.000044575467,0.00009770258,0.000046798283,0.00059107004,0.00010779163,0.00021038247,0.00005002949,0.000041407842],"category_scores_gemma":[0.00017319572,0.000028557712,0.000043038202,0.0005891824,0.00040260665,0.00013184285,0.00001575128,0.00016507303,0.0000018936997],"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":[0.00022978192,0.00013742519,0.0025078559,0.00002231321,0.0001323029,0.0000016715269,0.04323804,0.8699947,0.0015935818,0.055055913,0.026467154,0.0006192334],"study_design_scores_gemma":[0.0069933794,0.009571355,0.1309627,0.002202282,0.00025637358,0.00014474923,0.24074942,0.2413277,0.00044152598,0.0016736159,0.36407164,0.0016052687],"about_ca_topic_score_codex":0.0000441544,"about_ca_topic_score_gemma":0.000079692974,"teacher_disagreement_score":0.628667,"about_ca_system_score_codex":0.00006748636,"about_ca_system_score_gemma":0.0005272663,"threshold_uncertainty_score":0.45460927},"labels":[],"label_agreement":null},{"id":"W2794421988","doi":"10.1155/2018/2524838","title":"Flexible Emergency Vehicle Network Design considering Stochastic Demands and Inverse-Direction Lanes","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","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":"National Natural Science Foundation of China","keywords":"Inverse; Network planning and design; Computer science; Mathematical optimization; Genetic algorithm; Bilevel optimization; Optimal design; Value (mathematics); Simulation; Transport engineering; Optimization problem; Algorithm; Engineering; Computer network; Mathematics","score_opus":0.02612639134455331,"score_gpt":0.2918888663597427,"score_spread":0.2657624750151894,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2794421988","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5219231,0.00031974365,0.4759485,0.00023736132,0.0011433546,0.00014495208,0.000002127915,0.000066436754,0.00021436534],"genre_scores_gemma":[0.96944296,0.000379255,0.029573066,0.000031721032,0.0004272187,0.0000023976932,0.0000075262733,0.000010093959,0.00012578022],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99907196,0.00006571198,0.00037406562,0.00010081522,0.00023116995,0.00015631065],"domain_scores_gemma":[0.9991712,0.00007527596,0.00032300642,0.000037892212,0.00030070983,0.000091913804],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004648938,0.00007808735,0.00013353537,0.00009044377,0.00036509038,0.000019321811,0.000046633235,0.00006174667,0.0000695661],"category_scores_gemma":[0.000053715405,0.00007961599,0.000037315796,0.00025862857,0.00008756102,0.0005622725,4.3979338e-7,0.00009201722,0.0000022443833],"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":[0.0002389073,0.000021019483,0.010954525,0.000014030858,0.000028514442,0.000006270288,0.017872976,0.96492946,0.00080439623,0.00090549845,0.0003264938,0.0038978895],"study_design_scores_gemma":[0.005142328,0.0016762469,0.9194171,0.00073184713,0.00060714607,0.00002224809,0.023382973,0.009572281,0.0023641402,0.022250565,0.01382278,0.0010103876],"about_ca_topic_score_codex":0.000046538145,"about_ca_topic_score_gemma":0.00088485476,"teacher_disagreement_score":0.9553572,"about_ca_system_score_codex":0.000025208292,"about_ca_system_score_gemma":0.00007665773,"threshold_uncertainty_score":0.32466456},"labels":[],"label_agreement":null},{"id":"W2794833823","doi":"10.11575/prism/31593","title":"Total Societal Costs for Different Commuting Options in the City of Calgary: Comparing Transit, Automobile, and Cycling Costs","year":2006,"lang":"en","type":"article","venue":"Open MIND","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Cycling; Transit (satellite); Transport engineering; Journey to work; Public transport; Business; Economics; Geography; Engineering","score_opus":0.04887750295513505,"score_gpt":0.3414568074875383,"score_spread":0.2925793045324033,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2794833823","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98517567,0.000037701364,0.005903591,0.00025855683,0.000038811697,0.0005176174,0.000019450741,0.0000026338796,0.008045989],"genre_scores_gemma":[0.9966115,0.0000062345302,0.0031375117,0.000009493695,0.000021081452,0.000018893026,0.00008310599,0.000003308538,0.0001088984],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99935067,0.00009122524,0.00019576399,0.00011360801,0.00011928893,0.00012944806],"domain_scores_gemma":[0.9995908,0.00022809778,0.00007321696,0.00005490102,0.000031255167,0.000021686785],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006033519,0.000056150664,0.00012591551,0.000023576586,0.00037550475,0.00010587128,0.00014192493,0.000044961096,0.000022353577],"category_scores_gemma":[0.000017598593,0.000047149122,0.000031044827,0.00010739426,0.00009691894,0.00014097049,0.000009051465,0.00007517416,3.7640737e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011671507,0.0006036702,0.8260677,0.000030851457,0.00003886131,0.000003977574,0.091095306,0.024001773,0.00081026397,0.01237878,0.00019384832,0.044658296],"study_design_scores_gemma":[0.0013829564,0.00004360846,0.9498621,0.0001234783,0.000047234833,0.0000012714132,0.0125938365,0.034668796,0.00024797287,0.00016168196,0.0006934258,0.00017362312],"about_ca_topic_score_codex":0.0049819816,"about_ca_topic_score_gemma":0.019003555,"teacher_disagreement_score":0.12379445,"about_ca_system_score_codex":0.000038158312,"about_ca_system_score_gemma":0.00003323285,"threshold_uncertainty_score":0.9988971},"labels":[],"label_agreement":null},{"id":"W2795184361","doi":"10.1371/journal.pone.0192454","title":"An outer approximation method for the road network design problem","year":2018,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Benchmark (surveying); Mathematical optimization; Network planning and design; Heuristic; Bilevel optimization; Relaxation (psychology); Computer science; Integer programming; Linear programming; Constraint (computer-aided design); Integer (computer science); Function (biology); Set (abstract data type); Optimization problem; Mathematics","score_opus":0.09598481210997836,"score_gpt":0.3233842029370459,"score_spread":0.22739939082706753,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2795184361","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001075541,0.000021087757,0.9957141,0.0011697876,0.000052251264,0.0006470191,0.000002963596,0.0000979387,0.0012192747],"genre_scores_gemma":[0.0851702,0.00001180598,0.9130349,0.00024148534,0.000742082,0.00012118632,0.00002595149,0.000008383255,0.00064401684],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993097,0.00015401536,0.000102628976,0.00010903305,0.0001810724,0.0001435103],"domain_scores_gemma":[0.99948156,0.0001700554,0.000060897793,0.0000887543,0.00016485224,0.000033906224],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011394961,0.00004252277,0.00006193185,0.000014564938,0.0005838133,0.00006592914,0.000110582994,0.00004891676,0.000047007194],"category_scores_gemma":[0.000049471146,0.000032542914,0.000016073576,0.00014099706,0.000052995656,0.00017064737,0.0000014226714,0.000033581273,0.000011010267],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00075187493,0.0026081249,0.011289584,0.00017895928,0.0008181885,0.0000012204091,0.31763875,0.32754987,0.0032591517,0.14510323,0.01802345,0.17277758],"study_design_scores_gemma":[0.00063878717,0.00043067386,0.005478797,0.00011635286,0.0003591054,1.2474621e-7,0.0020879053,0.9658091,0.0023486482,0.016543511,0.0058707613,0.00031623043],"about_ca_topic_score_codex":0.00013445417,"about_ca_topic_score_gemma":0.0002301542,"teacher_disagreement_score":0.63825923,"about_ca_system_score_codex":0.000012705698,"about_ca_system_score_gemma":0.000039416656,"threshold_uncertainty_score":0.4490279},"labels":[],"label_agreement":null},{"id":"W2795222087","doi":"","title":"The law of the taxing power in Canada","year":2016,"lang":"en","type":"article","venue":"Books | Encyclopedia of Canadian Laws","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Law; Power (physics); Political science; Law and economics; Economics; Physics","score_opus":0.006954997720411811,"score_gpt":0.19997149647635382,"score_spread":0.193016498755942,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2795222087","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16903208,0.00010137781,0.0000069105968,0.0051097753,0.00049284816,0.00019022798,0.000075003176,0.000005899355,0.82498586],"genre_scores_gemma":[0.9944,0.000111986905,0.000031224918,0.00018060926,0.000019568131,0.000004106707,8.816261e-7,0.0000045639804,0.0052470197],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992525,0.00006672802,0.0001893908,0.00007037271,0.00021803392,0.00020296748],"domain_scores_gemma":[0.99946237,0.00017156632,0.00008780581,0.00012530196,0.000052308187,0.00010063731],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023113773,0.0000453774,0.00007223844,0.00004788333,0.00021742491,0.0000065306185,0.00022503264,0.000032421958,0.00004779865],"category_scores_gemma":[0.00009646585,0.000025859776,0.000023213086,0.00019280172,0.00015525855,0.000057265363,0.0000032482,0.000042062013,6.488445e-7],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000086527625,0.0000045841925,0.36812273,0.000006266068,0.000012340974,0.0000063705943,0.006950205,0.00024161812,0.00001460681,0.61155957,0.00822838,0.004844693],"study_design_scores_gemma":[0.00011090953,0.0000037407876,0.19321531,0.000047123962,0.0000038112385,1.0508548e-7,0.001155558,0.0000015115537,0.000028910494,0.00054313336,0.8048286,0.00006133003],"about_ca_topic_score_codex":0.9994304,"about_ca_topic_score_gemma":0.9999953,"teacher_disagreement_score":0.8253679,"about_ca_system_score_codex":0.00021522763,"about_ca_system_score_gemma":0.0033691272,"threshold_uncertainty_score":0.5976687},"labels":[],"label_agreement":null},{"id":"W2796033311","doi":"10.1177/0361198118758295","title":"Assessment of Canada’s Transportation System under the Comprehensive Economic and Trade Agreement","year":2018,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Canadian Heritage; Government of Canada; University of Waterloo; University of Toronto","funders":"Ministère des Transports; Innovation, Science and Economic Development Canada","keywords":"Computable general equilibrium; Parliament; European union; Commodity; Truck; International trade; Economic impact analysis; Trade agreement; Business; Government (linguistics); Economics; International economics; Engineering; Finance; Free trade; Political science","score_opus":0.0807949206633625,"score_gpt":0.3935504388399774,"score_spread":0.31275551817661496,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2796033311","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98335516,0.0001817094,0.0027189644,0.010351563,0.00092975644,0.0011429919,0.00020024768,0.000024141464,0.0010954659],"genre_scores_gemma":[0.9975173,0.0005787199,0.0010568172,0.000088193745,0.00028230055,0.0000380766,0.000034102668,0.000031749092,0.000372778],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9921901,0.0016361326,0.0015258251,0.0003751134,0.0035202329,0.0007525869],"domain_scores_gemma":[0.99488515,0.0012253339,0.0007685481,0.0003614815,0.0024029817,0.0003564883],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.004487778,0.00023010737,0.00045131607,0.00046281453,0.0018860196,0.00011708673,0.0008372605,0.00017355109,0.0002032299],"category_scores_gemma":[0.000038332273,0.00016706355,0.00021316981,0.0010615073,0.0017192615,0.00047793292,0.0000026529385,0.001083036,0.000002387716],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015989246,0.0003620817,0.5885328,0.00088723766,0.0008380321,0.000090442074,0.052674882,0.04089602,0.0018988583,0.28297406,0.025233528,0.0040131095],"study_design_scores_gemma":[0.000988819,0.00031091488,0.9362278,0.00028886096,0.00008556799,3.0048255e-7,0.03917233,0.00041039212,0.0002972475,0.00050936546,0.02154818,0.0001602208],"about_ca_topic_score_codex":0.51409775,"about_ca_topic_score_gemma":0.9483074,"teacher_disagreement_score":0.43420964,"about_ca_system_score_codex":0.0007058689,"about_ca_system_score_gemma":0.004365754,"threshold_uncertainty_score":0.9994134},"labels":[],"label_agreement":null},{"id":"W2800070170","doi":"10.1155/2018/3747632","title":"Exploring Travel Time Distribution and Variability Patterns Using Probe Vehicle Data: Case Study in Beijing","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":45,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Beijing; Punctuality; Weibull distribution; Transport engineering; Statistics; Ring road; Environmental science; Goodness of fit; Meteorology; Geography; Mathematics; Engineering; China","score_opus":0.09174198835492058,"score_gpt":0.3424739006581013,"score_spread":0.2507319123031807,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2800070170","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9565619,0.000010217994,0.042894486,0.000054152697,0.00018090472,0.00022629053,0.000051818755,0.000012851388,0.0000073256933],"genre_scores_gemma":[0.99626297,0.000040953288,0.003471114,0.0000051496527,0.00013498664,0.0000027384551,0.00007229726,0.0000073929464,0.0000024297292],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9987856,0.00013735809,0.0004961129,0.0001773763,0.00026133357,0.00014219357],"domain_scores_gemma":[0.9992339,0.00007853432,0.00028182505,0.00009949573,0.00023517599,0.00007106516],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012590184,0.000079396756,0.00016057814,0.00008360157,0.00024571657,0.000033483306,0.00008617934,0.000037798123,0.000009376297],"category_scores_gemma":[0.00007958025,0.00008210575,0.000019981057,0.00027884854,0.00007286172,0.0023733496,0.00000174909,0.00012992679,2.809549e-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":[0.00021357102,0.00035792656,0.8555493,0.000037384983,0.000029177178,0.0005186954,0.10858834,0.026021682,0.0012722203,0.00017235069,0.000001028258,0.007238335],"study_design_scores_gemma":[0.0012084864,0.00018831133,0.9549579,0.000115213465,0.00008340043,0.00002516947,0.04101898,0.0020966702,0.00006994609,0.00007160101,0.00003502142,0.00012928338],"about_ca_topic_score_codex":0.000664682,"about_ca_topic_score_gemma":0.004292462,"teacher_disagreement_score":0.09940862,"about_ca_system_score_codex":0.00007737204,"about_ca_system_score_gemma":0.0000911673,"threshold_uncertainty_score":0.3348175},"labels":[],"label_agreement":null},{"id":"W2800914662","doi":"10.1155/2018/8169036","title":"Performance Comparison of Mode Choice Optimization Algorithm with Simulated Discrete Choice Modeling","year":2018,"lang":"en","type":"article","venue":"Modelling and Simulation in Engineering","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"Korea Transport Institute","keywords":"Broyden–Fletcher–Goldfarb–Shanno algorithm; Computer science; Software; Discrete choice; Mathematical optimization; Algorithm; Mode (computer interface); Visual Basic for Applications; GSM; Estimation; Function (biology); Machine learning; Mathematics; Engineering","score_opus":0.024629122591813036,"score_gpt":0.2988504385179149,"score_spread":0.2742213159261018,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2800914662","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.41105518,0.00002471885,0.5886703,0.000007932637,0.000035043115,0.00007450727,0.0000016106945,0.000054737404,0.00007595037],"genre_scores_gemma":[0.9336651,0.000027714703,0.06616327,0.0000035029268,0.000073177805,0.0000022921458,0.00003083814,0.000015260859,0.000018816156],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99911994,0.00002183302,0.00030021433,0.0001737921,0.0002129838,0.00017121226],"domain_scores_gemma":[0.9994879,0.00013420278,0.00007895731,0.00007425015,0.00017445393,0.000050235285],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018361624,0.00010212599,0.00015437997,0.00016086701,0.0001832884,0.000030830284,0.000055239227,0.00008585496,0.0000049401965],"category_scores_gemma":[0.000032263608,0.000106289226,0.0000135431,0.00035182547,0.000041114054,0.00035619555,0.0000039083684,0.000097144555,3.090205e-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":[0.000015751526,0.000011551114,0.0140132345,0.000022747568,0.0000054987413,7.922193e-8,0.006761266,0.97846967,0.000003722965,0.000031345287,9.396721e-8,0.00066502293],"study_design_scores_gemma":[0.00036910683,0.00003477888,0.00020514037,0.0001391807,0.000012094139,4.4057586e-8,0.00018664089,0.99886733,0.000021679138,0.0000049377863,0.000026099082,0.00013296676],"about_ca_topic_score_codex":0.0007436843,"about_ca_topic_score_gemma":0.00008328544,"teacher_disagreement_score":0.52260995,"about_ca_system_score_codex":0.000031034066,"about_ca_system_score_gemma":0.00002438759,"threshold_uncertainty_score":0.43343487},"labels":[],"label_agreement":null},{"id":"W2800929362","doi":"","title":"Cross Border Transportation Patterns at the Western Cascade Gateway: Implications for Mitigating the Impact of Delay on Regional Supply Chains","year":2008,"lang":"en","type":"article","venue":"Western CEDAR (Western Washington University)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Gateway (web page); Cascade; Business; Supply chain; Economic geography; Computer science; Economics; Engineering; Marketing","score_opus":0.036784488686427044,"score_gpt":0.3292933781049757,"score_spread":0.2925088894185487,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2800929362","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9895863,0.000020061505,0.0057755644,0.0026216533,0.000101668,0.0006544118,0.0007379405,0.00010062603,0.00040180227],"genre_scores_gemma":[0.98968995,0.00012519844,0.00006137151,0.00025803896,0.000113820606,0.000010505143,0.00050300546,0.000033880155,0.009204252],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9982652,0.0001840916,0.00035790246,0.00038945538,0.00038230402,0.00042101182],"domain_scores_gemma":[0.9983012,0.00050536584,0.0004232429,0.00034860795,0.00028266956,0.00013891885],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0003314522,0.00023748989,0.00023587713,0.0001576285,0.0019404374,0.00008359277,0.00051027135,0.00017188242,0.000034530996],"category_scores_gemma":[0.00002801569,0.00018127525,0.00029054008,0.00042406414,0.0004718023,0.00047999684,0.000016749713,0.00021132079,0.000007845089],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010321858,0.00006200701,0.9491092,0.000015992322,0.000065613356,0.000007659446,0.029918145,0.0197633,0.00009646426,0.00054795714,0.00010664235,0.0002038138],"study_design_scores_gemma":[0.00085981184,0.00012575462,0.9878494,0.000079929225,0.00006184591,0.000008887762,0.0017500792,0.00005685983,0.00016475169,0.000012556118,0.008799961,0.00023015073],"about_ca_topic_score_codex":0.003881515,"about_ca_topic_score_gemma":0.019841742,"teacher_disagreement_score":0.038740225,"about_ca_system_score_codex":0.000255648,"about_ca_system_score_gemma":0.00024500198,"threshold_uncertainty_score":0.9993589},"labels":[],"label_agreement":null},{"id":"W2801088686","doi":"10.1287/trsc.2018.0820","title":"A Shortest-Path Algorithm for the Departure Time and Speed Optimization Problem","year":2018,"lang":"en","type":"article","venue":"Transportation Science","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"TKI DINALOG","keywords":"Shortest path problem; K shortest path routing; Schedule; Mathematical optimization; Constrained Shortest Path First; Path (computing); Computer science; Yen's algorithm; Optimization problem; Sequence (biology); Function (biology); Traffic congestion; Dijkstra's algorithm; Algorithm; Mathematics; Engineering; Transport engineering; Computer network","score_opus":0.016527466800714506,"score_gpt":0.29505118612984405,"score_spread":0.27852371932912956,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2801088686","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015596169,0.00006644137,0.97900367,0.0017658033,0.00024937416,0.0009952036,0.000093490424,0.00019078077,0.002039089],"genre_scores_gemma":[0.7344754,0.0001082286,0.26301742,0.00042594696,0.0002920106,0.000047007892,0.00016353674,0.000018289185,0.0014521653],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987364,0.000024243263,0.00021074637,0.00029902929,0.0004678958,0.0002616906],"domain_scores_gemma":[0.9989933,0.00014834023,0.000101635815,0.00010788044,0.00053987943,0.00010900053],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0010620963,0.00009122255,0.000086668544,0.00008565141,0.0016334429,0.0001645049,0.00022072163,0.0000580091,0.00011788391],"category_scores_gemma":[0.000059837486,0.00007236336,0.000028200491,0.0008342237,0.001120276,0.0006306189,0.0000014013975,0.000052537947,0.0000068474583],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019450701,0.0002380454,0.019359322,0.00003633545,0.0000703081,0.000006545637,0.2230566,0.5251244,0.002201041,0.04559277,0.0031360602,0.18098408],"study_design_scores_gemma":[0.00068320043,0.0001527522,0.04310632,0.000038875714,0.00009768064,0.0000011836187,0.0024930593,0.9372225,0.00037180563,0.00058746134,0.014915928,0.00032925542],"about_ca_topic_score_codex":0.00023177713,"about_ca_topic_score_gemma":0.0006948399,"teacher_disagreement_score":0.7188792,"about_ca_system_score_codex":0.00002478287,"about_ca_system_score_gemma":0.0002651311,"threshold_uncertainty_score":0.9996663},"labels":[],"label_agreement":null},{"id":"W28021512","doi":"","title":"Manitoba Capital Region Master Plan (Poster)","year":2014,"lang":"fr","type":"article","venue":"Transportation 2014: Past, Present, Future - 2014 Conference and Exhibition of the Transportation Association of Canada // Transport 2014 : Du passé vers l'avenir - 2014 Congrès et Exposition de 'Association des transports du Canada","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Plan (archaeology); Sustainability; Master plan; Transportation planning; Business; Strategic planning; Transport engineering; Environmental planning; Land use; Capital (architecture); Engineering; Marketing; Geography; Civil engineering","score_opus":0.010117338265737448,"score_gpt":0.19912101280372593,"score_spread":0.1890036745379885,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W28021512","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8979662,0.0011115508,0.02027882,0.058501292,0.0071462416,0.002574272,0.0092879,0.00024201845,0.0028916893],"genre_scores_gemma":[0.98161125,0.0053889165,0.00039998032,0.001352614,0.0011900838,0.00013763833,0.0064057126,0.00014701068,0.0033667781],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.98895586,0.0014101542,0.0032243042,0.0012757963,0.0035759795,0.001557912],"domain_scores_gemma":[0.9897904,0.00082709535,0.0047452296,0.00067573565,0.0030855085,0.0008760419],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0025388938,0.0011826536,0.0015807141,0.00035004233,0.0012140333,0.00016684562,0.000758041,0.0012565729,0.0012775888],"category_scores_gemma":[0.00007893524,0.0012975993,0.00072398764,0.00073696434,0.0005612389,0.0020805928,0.000006539627,0.0010286241,0.000010577573],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00069722,0.0004336958,0.7686542,0.0012296675,0.0008712075,0.00006463244,0.019907879,0.018770404,0.0003104722,0.016680142,0.17148088,0.00089956634],"study_design_scores_gemma":[0.004448241,0.0002582129,0.8164023,0.0007383439,0.0018225395,0.0000108625445,0.004940442,0.00050434016,0.001123866,0.000818638,0.16749622,0.0014359643],"about_ca_topic_score_codex":0.7442495,"about_ca_topic_score_gemma":0.98831576,"teacher_disagreement_score":0.24406625,"about_ca_system_score_codex":0.003093305,"about_ca_system_score_gemma":0.0055247573,"threshold_uncertainty_score":0.9996354},"labels":[],"label_agreement":null},{"id":"W2802506416","doi":"","title":"Stated Preference Survey for Passenger Sensitivities to Travel Costs in the Greater Toronto Hamilton Area","year":2017,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Preference; Economics; Geography; Transport engineering; Engineering; Microeconomics","score_opus":0.1278016272735658,"score_gpt":0.34439872651039993,"score_spread":0.21659709923683412,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2802506416","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9515921,0.0000057442703,0.010747221,0.00090316776,0.0001606938,0.0006284283,0.00014044606,0.000039796814,0.0357824],"genre_scores_gemma":[0.995732,0.000015666086,0.00041515808,0.0002295198,0.00002326813,0.00003270338,0.00007195889,0.0000043939353,0.0034753282],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.9992908,0.00014583014,0.00010401425,0.00013205037,0.00015763375,0.00016963773],"domain_scores_gemma":[0.9994218,0.0002327178,0.000044349134,0.00014884637,0.00011359299,0.000038733393],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00091746263,0.00006135258,0.00008085226,0.000015854825,0.00047993596,0.00019800563,0.00016179157,0.00004734753,0.0000477696],"category_scores_gemma":[0.000121204255,0.000043450582,0.000016741265,0.000032033364,0.000052776806,0.00026700075,0.0000026131413,0.000022888913,0.000003230316],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035796053,0.00010504407,0.4076483,0.000018215225,0.000028126016,0.000008909437,0.5654636,0.0010154691,0.00018121453,0.009488836,0.010067201,0.0056171627],"study_design_scores_gemma":[0.00018379473,0.000024258661,0.9845655,0.000016318243,0.000003370981,6.6434694e-8,0.013765892,0.00025539665,0.000084633575,0.00003210912,0.0009887321,0.00007993691],"about_ca_topic_score_codex":0.07863251,"about_ca_topic_score_gemma":0.66423666,"teacher_disagreement_score":0.58560413,"about_ca_system_score_codex":0.000052159783,"about_ca_system_score_gemma":0.000043370783,"threshold_uncertainty_score":0.92750293},"labels":[],"label_agreement":null},{"id":"W2802620160","doi":"10.1139/cjce-2017-0518","title":"Reliability estimation of public bus routes: Applicability of multivariate adaptive regression splines approach","year":2018,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Türkiye Bilimsel ve Teknolojik Araştırma Kurumu","keywords":"Reliability (semiconductor); Roundabout; Multivariate adaptive regression splines; Computer science; Multivariate statistics; Reliability engineering; Nonparametric statistics; Nonparametric regression; Engineering; Transport engineering; Statistics; Mathematics","score_opus":0.024154751022935482,"score_gpt":0.25658772438338295,"score_spread":0.23243297336044746,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2802620160","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24262744,0.00013363433,0.7543025,0.00014386007,0.00031894658,0.00016629632,0.000019947976,0.000018437784,0.0022689584],"genre_scores_gemma":[0.9769844,0.0000051789334,0.022880351,0.0000030665692,0.00009781693,0.0000016250947,0.0000052377213,0.000006934685,0.000015405238],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99905425,0.00005950367,0.0004199012,0.00009181955,0.0002095676,0.00016493567],"domain_scores_gemma":[0.9985519,0.00009802685,0.00031252578,0.00010486381,0.0006826945,0.00024998037],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011286404,0.000074142845,0.00019193644,0.00019886265,0.000099903744,0.00001638373,0.00015062981,0.0000853718,0.000033909928],"category_scores_gemma":[0.0009879392,0.00006826387,0.000059508136,0.00037132733,0.0001717552,0.00028686476,0.000002412201,0.00010996117,3.087483e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025979643,0.00004280383,0.011699685,0.00010067431,0.000032840122,0.0000017467105,0.015610972,0.9621261,0.00028614595,0.0069332304,0.00008639156,0.003053447],"study_design_scores_gemma":[0.0011382551,0.000316941,0.22855368,0.00077652454,0.00010295624,0.000007411344,0.0046605067,0.7584117,0.0011436511,0.0012381581,0.0032114598,0.00043876667],"about_ca_topic_score_codex":0.0045305905,"about_ca_topic_score_gemma":0.031013777,"teacher_disagreement_score":0.73435694,"about_ca_system_score_codex":0.00011095177,"about_ca_system_score_gemma":0.0006663614,"threshold_uncertainty_score":0.9866677},"labels":[],"label_agreement":null},{"id":"W2804178601","doi":"10.1155/2018/3935852","title":"Research on the Impact Scope of Bus Stations Based on the Application of Bus Lanes","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","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":"Bus network; Scope (computer science); Transport engineering; Transfer (computing); Local bus; Computer science; Control bus; Scheduling (production processes); Bus priority; Public transport; Real-time computing; System bus; Engineering; Operations management","score_opus":0.04694716772665152,"score_gpt":0.4086193067106265,"score_spread":0.361672138983975,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2804178601","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9715044,0.000056344346,0.02324909,0.0025936193,0.00013802061,0.0004366077,0.000061706814,0.00001288524,0.0019473137],"genre_scores_gemma":[0.9983027,0.00010239498,0.0013501452,0.000055821725,0.00010305071,0.000010360338,0.000035488312,0.0000091301845,0.00003090868],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9981786,0.00025356354,0.0004855632,0.00009840618,0.0008375899,0.00014629251],"domain_scores_gemma":[0.99683607,0.0010415517,0.0006045627,0.0001559027,0.0013118872,0.00005002378],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017601516,0.00007841865,0.00014369794,0.00021605798,0.00036914405,0.000017156925,0.00022257135,0.000056599456,0.00009205312],"category_scores_gemma":[0.00018673077,0.00004665826,0.00009257977,0.0007784418,0.0003252692,0.00019969812,4.6613496e-7,0.00021316392,0.000004106199],"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":[0.0009430906,0.000263996,0.0054685483,0.00002558831,0.00004216918,0.0000017191841,0.019545112,0.92796206,0.003805982,0.036495905,0.00042753102,0.0050182776],"study_design_scores_gemma":[0.0015729006,0.0022994177,0.93784386,0.0005735,0.000096720774,5.732472e-7,0.026740111,0.003055909,0.01796537,0.006361968,0.0032717544,0.00021792852],"about_ca_topic_score_codex":0.00036432952,"about_ca_topic_score_gemma":0.0007577598,"teacher_disagreement_score":0.9323753,"about_ca_system_score_codex":0.00004981221,"about_ca_system_score_gemma":0.00028106302,"threshold_uncertainty_score":0.28391948},"labels":[],"label_agreement":null},{"id":"W2804803202","doi":"10.1155/2018/2085625","title":"Dynamic O-D Demand Estimation: Application of SPSA AD-PI Method in Conjunction with Different Assignment Strategies","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Simultaneous perturbation stochastic approximation; Mathematical optimization; Computer science; Mathematics; Stochastic process","score_opus":0.008645491438010917,"score_gpt":0.3130036693379098,"score_spread":0.3043581778998989,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2804803202","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.38875905,0.000046462468,0.6106352,0.00016619186,0.000109776585,0.00016608583,0.000004579316,0.0000123929585,0.00010024584],"genre_scores_gemma":[0.9231882,0.00010372849,0.076567754,0.000014350726,0.000029466057,0.000010274104,0.00005801037,0.000009137016,0.000019044912],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.998567,0.00009040261,0.00061553513,0.00013628257,0.0004623843,0.00012842346],"domain_scores_gemma":[0.99859333,0.00008771023,0.0007576629,0.000073481744,0.00042822852,0.000059570422],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000462066,0.00010786978,0.00022649579,0.00020131418,0.00011238386,0.000022304679,0.000084381594,0.000074388394,0.000023782966],"category_scores_gemma":[0.000017590348,0.00009203606,0.00004718538,0.00036076255,0.000114427545,0.00077301596,4.1192715e-7,0.000114083916,6.329084e-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":[0.0007095187,0.00015900595,0.01180256,0.000055952834,0.000036565627,0.000004399078,0.021955397,0.9337668,0.0035257384,0.0037997498,0.0000040305986,0.02418027],"study_design_scores_gemma":[0.002617609,0.0008690001,0.95637065,0.00031606463,0.00015822236,0.0000032592623,0.018992405,0.0132714305,0.002349489,0.0043610237,0.00045588805,0.00023498049],"about_ca_topic_score_codex":0.00006911908,"about_ca_topic_score_gemma":0.0040909443,"teacher_disagreement_score":0.94456804,"about_ca_system_score_codex":0.00010113146,"about_ca_system_score_gemma":0.00015159819,"threshold_uncertainty_score":0.37531215},"labels":[],"label_agreement":null},{"id":"W2806581673","doi":"10.1007/s40864-018-0082-8","title":"Profit Model of Metro Enterprises and Quasi-Market Based Practice of Shenzhen Metro","year":2018,"lang":"en","type":"article","venue":"Urban Rail Transit","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Business; Industrial organization; Externality; Profit (economics); Finance; Marketing; Economics; Microeconomics","score_opus":0.01888511410638541,"score_gpt":0.2855400225077768,"score_spread":0.2666549084013914,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2806581673","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.29974362,0.00045971206,0.6435248,0.0023696749,0.0001873413,0.0005740768,0.0001820693,0.0001340832,0.052824587],"genre_scores_gemma":[0.97291666,0.000021648064,0.026379777,0.00012848084,0.000034504286,0.0000050965873,0.000011927241,0.000009564445,0.000492358],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989279,0.00014542026,0.00026548843,0.00017185179,0.00034222862,0.00014714569],"domain_scores_gemma":[0.9990873,0.00027883565,0.00015917902,0.00011482091,0.00028859774,0.00007131106],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006665196,0.00008635373,0.00018461508,0.00012037695,0.00012256656,0.000017435545,0.000112860886,0.00007732698,0.00014496507],"category_scores_gemma":[0.00021869995,0.000085824824,0.00005267822,0.000263257,0.00029408882,0.00029247694,0.000002186266,0.000058642363,0.00000120896],"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":[0.0046728486,0.001492956,0.5910247,0.00084433396,0.000700542,0.000011035004,0.32330623,0.019575132,0.00731515,0.03168112,0.01390419,0.0054717776],"study_design_scores_gemma":[0.01487315,0.0040619094,0.09926673,0.0011868248,0.0035702891,0.0000020778334,0.07341782,0.673648,0.058842216,0.0033868854,0.06491389,0.0028302327],"about_ca_topic_score_codex":0.0005249783,"about_ca_topic_score_gemma":0.00051285524,"teacher_disagreement_score":0.673173,"about_ca_system_score_codex":0.00001514695,"about_ca_system_score_gemma":0.00013340071,"threshold_uncertainty_score":0.34998348},"labels":[],"label_agreement":null},{"id":"W2807252041","doi":"10.1609/aaai.v32i1.11413","title":"Optimal Pricing for Distance-Based Transit Fares","year":2018,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Quest University Canada","funders":"","keywords":"Metropolitan area; Transit (satellite); Revenue; Equity (law); Transport engineering; Business; Pricing strategies; Service (business); Public transport; Computer science; Microeconomics; Economics; Finance; Geography; Engineering; Marketing","score_opus":0.08141576696269703,"score_gpt":0.3332916122753961,"score_spread":0.2518758453126991,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2807252041","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.38448495,0.000022756942,0.5675637,0.010567482,0.00093839807,0.001428908,0.000059317652,0.0002381413,0.034696348],"genre_scores_gemma":[0.9934393,0.000008695694,0.005851989,0.00014779139,0.00016004598,0.000029685769,0.000002505104,0.000010396698,0.0003495708],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988107,0.000012556748,0.00031945368,0.00024865454,0.00034533336,0.00026327075],"domain_scores_gemma":[0.9986232,0.00010515065,0.00021435232,0.00007788726,0.0009169198,0.00006251744],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004818063,0.000115555384,0.0001439423,0.00007039888,0.0006327298,0.00011869802,0.00046319148,0.00008073368,0.00011153457],"category_scores_gemma":[0.00035994765,0.00009431431,0.00008821227,0.0003889023,0.0006056052,0.00017213989,0.000007181788,0.00009583516,0.000012331265],"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":[0.00041445854,0.00010865571,0.0011301616,0.000049768027,0.000012633848,8.76603e-8,0.01834439,0.002956948,0.005276799,0.95540464,0.00025886606,0.016042596],"study_design_scores_gemma":[0.00020120211,0.0008675968,0.0014342017,0.00086830434,0.00010720739,3.2956066e-7,0.021536916,0.12693386,0.7829555,0.05536712,0.009013365,0.00071436417],"about_ca_topic_score_codex":0.00012117887,"about_ca_topic_score_gemma":0.00024866936,"teacher_disagreement_score":0.9000375,"about_ca_system_score_codex":0.00003558912,"about_ca_system_score_gemma":0.0001583319,"threshold_uncertainty_score":0.48665097},"labels":[],"label_agreement":null},{"id":"W2807896297","doi":"10.1139/cjce-2017-0186","title":"Improved multiple discreet-continuous extreme value model","year":2018,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Carleton University; Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada; King Abdulaziz University; University of Toronto","keywords":"Flexibility (engineering); Constant (computer programming); Model parameter; Duration (music); Computer science; Value (mathematics); Extreme value theory; Econometrics; Statistics; Mathematics; Data mining; Machine learning","score_opus":0.016097255148084706,"score_gpt":0.2175405769767383,"score_spread":0.20144332182865357,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2807896297","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.086896874,0.00036671959,0.8974035,0.00076788414,0.0014887305,0.00013244728,0.000024055445,0.000060422168,0.012859359],"genre_scores_gemma":[0.9943132,0.000007632242,0.0049016215,0.000042247524,0.00038175323,7.135629e-7,0.000002181232,0.000012384888,0.0003382593],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993625,0.000013163182,0.00019601978,0.00006300162,0.000120397715,0.00024487797],"domain_scores_gemma":[0.9992328,0.000030269053,0.00008120335,0.000057117297,0.00018527466,0.000413301],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029797334,0.00006568934,0.00010325825,0.00017158123,0.000186091,0.000058512087,0.00014371367,0.00005702812,0.000077381344],"category_scores_gemma":[0.00025009413,0.00006915346,0.00004924022,0.00015258935,0.000055991775,0.00022490982,0.0000011355195,0.000107678425,0.0000020540924],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009397553,0.0000062374147,0.0075461958,0.000008957268,0.000031525386,0.000028372493,0.016602324,0.96805,0.0007010512,0.0046577556,0.0015318564,0.0008263251],"study_design_scores_gemma":[0.00052904437,0.000066166634,0.0052634752,0.00012447659,0.000033585762,0.000007936971,0.0010523638,0.96779555,0.00013074299,0.00021165749,0.024530457,0.00025451265],"about_ca_topic_score_codex":0.006318285,"about_ca_topic_score_gemma":0.542493,"teacher_disagreement_score":0.90741634,"about_ca_system_score_codex":0.0000885983,"about_ca_system_score_gemma":0.00065573974,"threshold_uncertainty_score":0.95513994},"labels":[],"label_agreement":null},{"id":"W2808019791","doi":"","title":"2005 public transportation fact book","year":2005,"lang":"en","type":"article","venue":"Rosa P: A digital library for transportation research (United States Department of Transportation)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Public transport; Business; Transport engineering; Engineering","score_opus":0.04274277738749137,"score_gpt":0.3171174202945407,"score_spread":0.2743746429070493,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2808019791","genre_codex":"dataset","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.459805,0.0010037542,0.032847542,0.016466545,0.00028275143,0.004187889,0.48350298,0.0012328832,0.0006706508],"genre_scores_gemma":[0.5354136,0.0009972719,0.0037924433,0.00031127935,0.0001175454,0.00037737127,0.45705384,0.00008844416,0.0018481576],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9946028,0.00017711145,0.0014451476,0.00074680924,0.0018154804,0.0012126347],"domain_scores_gemma":[0.99693274,0.00074042246,0.0004513809,0.0003230888,0.00088050676,0.00067183096],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007494358,0.000433335,0.00049652235,0.001185113,0.0009798406,0.00047167978,0.0005593111,0.0003104919,0.0010988126],"category_scores_gemma":[0.000051070478,0.0004824286,0.00041412443,0.0022376433,0.0005900244,0.0074603,0.0000014904095,0.00038659197,0.000051567986],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0036690568,0.0045340206,0.07138939,0.0013903222,0.0011107911,0.0000958161,0.061559822,0.055346925,0.000051375002,0.32383156,0.45632577,0.02069513],"study_design_scores_gemma":[0.0018113529,0.00027906868,0.015550964,0.00009775628,0.000088496556,1.7317186e-7,0.004248514,0.00078187353,0.00056620664,0.0014083709,0.9746652,0.0005020208],"about_ca_topic_score_codex":0.00006148528,"about_ca_topic_score_gemma":0.0030267374,"teacher_disagreement_score":0.5183394,"about_ca_system_score_codex":0.000115702984,"about_ca_system_score_gemma":0.0006638575,"threshold_uncertainty_score":0.99981433},"labels":[],"label_agreement":null},{"id":"W2808030106","doi":"10.1016/j.retrec.2018.06.004","title":"The usage of location based big data and trip planning services for the estimation of a long-distance travel demand model. Predicting the impacts of a new high speed rail corridor","year":2018,"lang":"en","type":"article","venue":"Research in Transportation Economics","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Seventh Framework Programme; Technische Universität München; European Commission","keywords":"Transport engineering; TRIPS architecture; Multinomial logistic regression; Destinations; Trip generation; Travel survey; Modal; Mode choice; Estimation; Travel behavior; Service (business); Level of service; Computer science; Public transport; Business; Geography; Engineering; Tourism; Marketing","score_opus":0.133145669900912,"score_gpt":0.38219448582511867,"score_spread":0.24904881592420666,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2808030106","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7724583,0.00024114952,0.22532575,0.00093727047,0.00007446299,0.00068226724,0.00024723774,0.0000062949907,0.000027273358],"genre_scores_gemma":[0.9977866,0.0002986122,0.0015505961,0.000013879897,0.00006007907,0.000012891922,0.0002444964,0.000008760903,0.00002411314],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99877506,0.00010975668,0.00051738997,0.00017614465,0.00023424336,0.00018742046],"domain_scores_gemma":[0.9975221,0.0015041779,0.00037096298,0.00024922044,0.00030704594,0.00004645294],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003823592,0.00006929683,0.00013849014,0.000099100034,0.00042758303,0.000048678237,0.00038656936,0.00006280279,0.0000021804842],"category_scores_gemma":[0.00018250894,0.000048304995,0.00002212174,0.0003684889,0.00045470538,0.00030057144,0.0000036779923,0.00009734871,1.0146325e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00061452016,0.000026432268,0.038618,0.00020638581,0.000024212157,1.17931734e-7,0.031187309,0.917903,0.000038056987,0.0024785928,0.000024335353,0.008879017],"study_design_scores_gemma":[0.00062770303,0.00004951001,0.20201877,0.00014284841,0.000023754508,4.811913e-8,0.0055491677,0.79030097,0.00046582086,0.00075246237,0.000028936318,0.000040038718],"about_ca_topic_score_codex":0.0033765729,"about_ca_topic_score_gemma":0.024811106,"teacher_disagreement_score":0.22532828,"about_ca_system_score_codex":0.000031179603,"about_ca_system_score_gemma":0.00059703283,"threshold_uncertainty_score":0.9929835},"labels":[],"label_agreement":null},{"id":"W2808134243","doi":"","title":"Le coût de la congestion provoquée par le stationnement en double-file : estimations exploratoires dans les rues de Paris","year":2018,"lang":"fr","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ministère des Transports","funders":"","keywords":"Humanities; Computer science; Art","score_opus":0.01810665499056427,"score_gpt":0.26362580408868214,"score_spread":0.24551914909811787,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2808134243","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15673332,0.0006257697,0.7352639,0.027552484,0.00029585138,0.0009649862,0.00062606076,0.0005342096,0.07740341],"genre_scores_gemma":[0.81856734,0.0013224767,0.16507024,0.00006398072,0.000077885205,0.00047428723,0.0043710293,0.000060468552,0.009992275],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.987622,0.0097018415,0.00077857595,0.0007569649,0.0005866364,0.00055397255],"domain_scores_gemma":[0.992959,0.0029445486,0.00081288273,0.00086625916,0.0021031788,0.00031409424],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0072929305,0.000394268,0.00038218053,0.00018948535,0.0024766442,0.00071863807,0.0008753093,0.0005560396,0.0005377512],"category_scores_gemma":[0.0013698105,0.0004880824,0.00018887178,0.00060954876,0.0014302146,0.00053703325,0.0001737323,0.00056994037,0.00005813972],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004861834,0.0013157192,0.02905666,0.00019338638,0.0001622637,0.000005774475,0.2699712,0.03548042,0.0007651124,0.64205533,0.008500058,0.012445461],"study_design_scores_gemma":[0.0053645154,0.000011229969,0.25020564,0.008117595,0.00070989627,0.000030728264,0.044664856,0.23628446,0.04490855,0.038932946,0.36781916,0.0029504306],"about_ca_topic_score_codex":0.039772384,"about_ca_topic_score_gemma":0.031413842,"teacher_disagreement_score":0.66183406,"about_ca_system_score_codex":0.00043242483,"about_ca_system_score_gemma":0.0029151354,"threshold_uncertainty_score":0.9997571},"labels":[],"label_agreement":null},{"id":"W2808302217","doi":"10.5430/afr.v7n3p119","title":"Excess Capacity and the Economics of Public Transit Investment: A Study of a Growing American City","year":2018,"lang":"en","type":"article","venue":"Accounting and Finance Research","topic":"Transportation Planning and Optimization","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":"Public transport; Investment (military); Service (business); Transit (satellite); Business; Falling (accident); Control (management); Economics; Transport engineering; Demographic economics; Agricultural economics; Econometrics; Marketing; Engineering; Political science","score_opus":0.1031523061566802,"score_gpt":0.3592180582168698,"score_spread":0.2560657520601896,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2808302217","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99776804,0.000070447175,0.00011110288,0.00055497955,0.000018025601,0.00017618612,0.0000030309548,0.000006380185,0.0012917763],"genre_scores_gemma":[0.9992943,0.0004263039,0.0001788077,0.000025078107,0.000036310536,0.000009229199,6.543787e-7,0.0000029361647,0.000026404503],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99911714,0.00023084521,0.00015808262,0.00013203223,0.00019021628,0.00017169876],"domain_scores_gemma":[0.99927413,0.0002780729,0.000100459416,0.00008442488,0.00024268767,0.000020193162],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034206326,0.000038676113,0.00013182336,0.00008101761,0.0005475341,0.000057202622,0.000115892486,0.000023951074,0.0000021789601],"category_scores_gemma":[0.0002300549,0.000030942087,0.000011977186,0.00042122803,0.0019293155,0.00025973347,0.000013630414,0.00010348912,1.631874e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000110211724,0.00016186919,0.6810009,0.000038470487,0.00003722737,4.399114e-7,0.2169608,0.0001194709,0.000036138525,0.07817325,0.000022032626,0.02333919],"study_design_scores_gemma":[0.002521332,0.00051489787,0.8625265,0.0000921829,0.00002806171,6.5660697e-7,0.12104238,0.0042075086,0.00017565176,0.005872946,0.0028290597,0.00018886285],"about_ca_topic_score_codex":0.01581969,"about_ca_topic_score_gemma":0.0056590466,"teacher_disagreement_score":0.18152556,"about_ca_system_score_codex":0.000010663093,"about_ca_system_score_gemma":0.00007059823,"threshold_uncertainty_score":0.99073404},"labels":[],"label_agreement":null},{"id":"W2808854400","doi":"10.1155/2018/4264528","title":"On Individual Repositioning Distance along Platform during Train Waiting","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Conseil Régional, Île-de-France","keywords":"TRIPS architecture; Position (finance); Computer science; Computation; Service (business); Line (geometry); Probability distribution; Transport engineering; Random variable; Simulation; Traverse; Operations research; Real-time computing; Engineering; Statistics; Geography; Mathematics; Geodesy; Algorithm","score_opus":0.015075557456086401,"score_gpt":0.2831966072882952,"score_spread":0.2681210498322088,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2808854400","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98256534,0.000037867278,0.015633645,0.0002132862,0.00045498423,0.0000954984,0.000015294889,0.0000454038,0.000938653],"genre_scores_gemma":[0.9916584,0.000035836205,0.007735495,0.000059001683,0.00037389086,0.0000023887728,0.000049979466,0.000013397736,0.00007161353],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99848026,0.000037083337,0.0005796729,0.00013279861,0.00056677405,0.00020342866],"domain_scores_gemma":[0.99879634,0.000106655665,0.00060536177,0.00005932975,0.0003338123,0.00009852343],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048985786,0.00009794549,0.00014971169,0.00015653083,0.0006276925,0.00005457156,0.00011518114,0.000070189155,0.000044533954],"category_scores_gemma":[0.00009661964,0.00010031088,0.000081026556,0.0003033473,0.00010667107,0.0009396694,4.2233046e-7,0.00018370479,0.0000025921677],"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":[0.0022076962,0.0002928016,0.037481938,0.00010820023,0.00019011874,0.0003073005,0.37756544,0.44748762,0.006255865,0.09965723,0.00013846718,0.028307324],"study_design_scores_gemma":[0.0029724315,0.00057977263,0.9573996,0.0009104749,0.00013454202,0.000014634605,0.025821254,0.000161381,0.0056179236,0.0043244404,0.0016407835,0.00042276192],"about_ca_topic_score_codex":0.000027723903,"about_ca_topic_score_gemma":0.0007124653,"teacher_disagreement_score":0.91991764,"about_ca_system_score_codex":0.00007686318,"about_ca_system_score_gemma":0.00009858243,"threshold_uncertainty_score":0.48277667},"labels":[],"label_agreement":null},{"id":"W2809005054","doi":"10.1177/0361198118781646","title":"Urban Services on Regional Rail: Local and Network-Based Methodologies for Evaluating New Stations in Toronto","year":2018,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Transport engineering; Upgrade; Hierarchy; Service (business); Urban hierarchy; Street network; Land use; Investment (military); Urban rail transit; Computer science; Business; Civil engineering; Engineering","score_opus":0.27264791457406296,"score_gpt":0.5120970800850926,"score_spread":0.23944916551102963,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2809005054","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.87375516,0.0006446634,0.1084926,0.012893376,0.00086979463,0.0025062938,0.00008621666,0.00007756316,0.00067433337],"genre_scores_gemma":[0.95330495,0.0005613958,0.044528183,0.00024356857,0.00052988343,0.00009438727,0.000057591493,0.000042156513,0.00063790585],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99286234,0.0021561633,0.0010812328,0.00041570573,0.0026431435,0.0008413842],"domain_scores_gemma":[0.9917567,0.0039993445,0.00042918746,0.0002430975,0.003218295,0.00035341457],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.010583463,0.00019902128,0.00036706222,0.0005081453,0.0013879288,0.00016336066,0.0006325942,0.00022597513,0.00021829008],"category_scores_gemma":[0.0004052717,0.00016643398,0.00018600289,0.0013767635,0.0010744286,0.00072585826,0.000002634269,0.00082255976,0.0000036741042],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.011744817,0.00048615,0.5649236,0.0005771018,0.00027078929,0.000038314436,0.10688277,0.12319107,0.0006213712,0.09027565,0.041362755,0.059625596],"study_design_scores_gemma":[0.003010557,0.0016976706,0.9273533,0.00073389435,0.00005716411,9.6098624e-8,0.024747577,0.003099352,0.00013703904,0.010640133,0.02826881,0.0002543721],"about_ca_topic_score_codex":0.068135425,"about_ca_topic_score_gemma":0.6053628,"teacher_disagreement_score":0.53722733,"about_ca_system_score_codex":0.00044995992,"about_ca_system_score_gemma":0.0015803627,"threshold_uncertainty_score":0.99991214},"labels":[],"label_agreement":null},{"id":"W2809731182","doi":"10.1155/2018/4539324","title":"Road Maintenance Optimization Model Based on Dynamic Programming in Urban Traffic Network","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":37,"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":"Scientific Research Foundation of the Graduate School of Southeast University; Government of Jiangsu Province; Southeast University; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Scheme (mathematics); Computer science; Scale (ratio); Optimal maintenance; Dynamic programming; Transport engineering; Order (exchange); Operations research; Programming paradigm; Work (physics); Traffic congestion; Flow network; Engineering; Mathematical optimization; Reliability engineering; Mathematics; Geography; Business; Algorithm","score_opus":0.008469394450406594,"score_gpt":0.2714123992718558,"score_spread":0.26294300482144917,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2809731182","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25665966,0.000063027524,0.74145824,0.0005080267,0.00053750735,0.00032846545,0.0000072992334,0.00006404517,0.0003737512],"genre_scores_gemma":[0.85370934,0.000072852905,0.14582364,0.00011210725,0.0001389154,0.000006714215,0.00005360086,0.00001654467,0.000066262],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984337,0.00007274704,0.0005960986,0.00016751762,0.00044739115,0.00028253108],"domain_scores_gemma":[0.9988844,0.000052071635,0.0004899886,0.00007959666,0.0004002789,0.00009365531],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006178332,0.0001228006,0.00019350369,0.00021884586,0.00021416327,0.000033264485,0.00013058634,0.00010159519,0.000020012247],"category_scores_gemma":[0.000057286208,0.0001237903,0.00008262666,0.0006524276,0.000103559476,0.0005610741,3.001426e-7,0.00018819075,0.0000012689225],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036213375,0.00008090137,0.0018506657,0.0000086197,0.000005034601,0.0000091620805,0.007956398,0.9755855,0.000008421606,0.00034162097,0.000034867102,0.01375664],"study_design_scores_gemma":[0.0015824286,0.00031525284,0.03152063,0.00035094845,0.000031956406,4.5147948e-7,0.001372056,0.9636257,0.00000521232,0.00014745533,0.00086150225,0.00018637867],"about_ca_topic_score_codex":0.000016854323,"about_ca_topic_score_gemma":0.0015044911,"teacher_disagreement_score":0.5970497,"about_ca_system_score_codex":0.00013936294,"about_ca_system_score_gemma":0.00023143979,"threshold_uncertainty_score":0.5048022},"labels":[],"label_agreement":null},{"id":"W2809889752","doi":"10.1155/2018/7498594","title":"Optimization Method for Transit Signal Priority considering Multirequest under Connected Vehicle Environment","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"VisSim; Intersection (aeronautics); Transit (satellite); Arrival time; MATLAB; Computer science; Real-time computing; SIGNAL (programming language); Bus priority; Travel time; Signal timing; Simulation; Transmission (telecommunications); Traffic signal; Public transport; Transport engineering; Engineering; Telecommunications","score_opus":0.021475846241272144,"score_gpt":0.31393992870885074,"score_spread":0.2924640824675786,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2809889752","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08354607,0.000060647413,0.91493094,0.00069106516,0.0003163874,0.0003159047,0.00002525689,0.00004225281,0.000071483955],"genre_scores_gemma":[0.607256,0.00010642696,0.3922605,0.000078166406,0.00019439841,0.0000063128464,0.000056347908,0.0000137929555,0.000028035261],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.998593,0.000103544044,0.0005768853,0.00016688548,0.000358265,0.00020145148],"domain_scores_gemma":[0.99861526,0.00023326761,0.00047015888,0.00006259456,0.0005000007,0.000118696924],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062987005,0.000117636366,0.00021224012,0.00011737523,0.0003721833,0.000032724452,0.00009061224,0.00010852532,0.00013303781],"category_scores_gemma":[0.00004740414,0.00012384198,0.000107207634,0.00017712054,0.00011970942,0.0006488878,4.1243763e-7,0.00011475029,0.0000014066568],"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":[0.0003249843,0.00006346668,0.0006990357,0.000016131606,0.00003576162,0.0000031234288,0.013314306,0.974039,0.0045463718,0.0017746283,0.000013894604,0.0051692636],"study_design_scores_gemma":[0.04473399,0.0063930787,0.3747833,0.0013454205,0.002697445,0.000031620835,0.08399133,0.31235078,0.07701797,0.02170559,0.07121007,0.003739399],"about_ca_topic_score_codex":0.000051568586,"about_ca_topic_score_gemma":0.00039180985,"teacher_disagreement_score":0.66168827,"about_ca_system_score_codex":0.00009231935,"about_ca_system_score_gemma":0.00015814387,"threshold_uncertainty_score":0.5050129},"labels":[],"label_agreement":null},{"id":"W2810120777","doi":"10.1155/2018/1645475","title":"Design of Signal Timing Plan for Urban Signalized Networks including Left Turn Prohibition","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","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":"Deutsche Forschungsgemeinschaft","keywords":"Signal timing; Intersection (aeronautics); SIGNAL (programming language); Plan (archaeology); Turn (biochemistry); Computer science; Travel time; Sequence (biology); Transport engineering; Simulation; Mathematical optimization; Real-time computing; Traffic signal; Engineering; Mathematics; Geography","score_opus":0.04479607134893407,"score_gpt":0.31533811767959935,"score_spread":0.27054204633066525,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2810120777","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11185874,0.00010170798,0.88713783,0.00010181797,0.0003344809,0.00036632206,0.000012657124,0.00002682471,0.00005963794],"genre_scores_gemma":[0.93821657,0.0000701528,0.061121468,0.000036249578,0.00042928103,0.0000053760214,0.00007451947,0.00001616725,0.000030218314],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99849623,0.0000960292,0.00070599455,0.00012539688,0.00037580048,0.00020056037],"domain_scores_gemma":[0.9978263,0.00025602666,0.00093211344,0.000048300917,0.00085259415,0.000084659165],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009060346,0.00010841631,0.00023936924,0.0001732981,0.00031434782,0.000022890572,0.00011356565,0.0001061747,0.000048207497],"category_scores_gemma":[0.00005968222,0.00010793585,0.00010321097,0.0002270884,0.00011809976,0.0006735567,5.1133674e-7,0.00011461731,3.9713245e-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":[0.0019593574,0.000074202246,0.0031265048,0.00004437436,0.00005253856,0.0000044476333,0.040021814,0.9401952,0.010155791,0.0010091058,0.00019123907,0.0031653927],"study_design_scores_gemma":[0.056373067,0.020081494,0.37886706,0.010008235,0.0041845795,0.00005064258,0.09126284,0.19813854,0.15324342,0.0478164,0.03478441,0.0051893108],"about_ca_topic_score_codex":0.000016972364,"about_ca_topic_score_gemma":0.00017270517,"teacher_disagreement_score":0.82635784,"about_ca_system_score_codex":0.000051242423,"about_ca_system_score_gemma":0.00017825224,"threshold_uncertainty_score":0.44014964},"labels":[],"label_agreement":null},{"id":"W2810304902","doi":"10.1155/2018/6060898","title":"How Well Does the Traffic System Protect Transit from Congestion? Measuring Route-Level Costs That Congestion Imposes on Transit Operators and Users","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","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":"Traffic congestion; Transport engineering; Transit (satellite); Sample (material); Level of service; Rush hour; Reliability (semiconductor); Computer science; Public transport; Service (business); Operations research; Business; Engineering","score_opus":0.02443059163374138,"score_gpt":0.2524927983088426,"score_spread":0.2280622066751012,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2810304902","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.982568,0.00016836426,0.012874868,0.0026730062,0.00085738284,0.00058546744,0.000106186104,0.00008532306,0.00008135721],"genre_scores_gemma":[0.99717927,0.00026862219,0.0020360216,0.000064983455,0.0003041253,0.000015341433,0.000053226166,0.000025187572,0.00005322299],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9980948,0.00023222576,0.0004716608,0.00025870698,0.0007075884,0.00023504975],"domain_scores_gemma":[0.9985485,0.00021419326,0.00047247674,0.00011284249,0.00049791107,0.0001540813],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063990644,0.00021378389,0.00029881473,0.00015017024,0.00068769144,0.00019725181,0.00017515472,0.00015491042,0.000012797506],"category_scores_gemma":[0.000046313573,0.00014797627,0.00011089669,0.0002679084,0.00025249057,0.001097468,4.8291315e-7,0.00028334832,0.000001553787],"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":[0.004855497,0.0002963807,0.037555818,0.00025575925,0.000561659,0.00012320203,0.1689691,0.68881625,0.009761012,0.006324422,0.000072189214,0.08240874],"study_design_scores_gemma":[0.0048170607,0.00085506454,0.90303564,0.0016344121,0.00063046993,0.00001212301,0.074292116,0.0005953647,0.009866952,0.00010846427,0.0034984702,0.0006538899],"about_ca_topic_score_codex":0.00023355827,"about_ca_topic_score_gemma":0.0070128534,"teacher_disagreement_score":0.8654798,"about_ca_system_score_codex":0.00015395752,"about_ca_system_score_gemma":0.000143173,"threshold_uncertainty_score":0.60342973},"labels":[],"label_agreement":null},{"id":"W2810710152","doi":"10.1155/2018/3435720","title":"Optimal Design of Transportation Networks with Automated Vehicle Links and Congestion Pricing","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":35,"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":"Shanghai Shuguang Program; Tongji University; National Natural Science Foundation of China","keywords":"Congestion pricing; Traffic congestion; Singapore Area Licensing Scheme; Computer science; Network traffic control; Network congestion; Market penetration; Travel time; Network planning and design; Road pricing; Flow network; Transport engineering; Operations research; Simulation; Computer network; Mathematical optimization; Engineering","score_opus":0.01222874373365461,"score_gpt":0.27169518813113935,"score_spread":0.25946644439748473,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2810710152","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6465926,0.00009316462,0.35283327,0.000106128755,0.00013064982,0.00015813587,0.0000059746358,0.000057485206,0.000022584689],"genre_scores_gemma":[0.9371978,0.00025966374,0.062340762,0.00002404533,0.00010297927,0.0000027051494,0.000045651876,0.0000139554895,0.000012459886],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99871504,0.00007933522,0.00055725547,0.00012864055,0.00036161763,0.00015813617],"domain_scores_gemma":[0.9982418,0.000119043376,0.00070142833,0.000051909643,0.0007919961,0.0000938116],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048675895,0.0001084931,0.0002161208,0.00014444634,0.0001951709,0.000020593856,0.00006964082,0.00015141774,0.000012863283],"category_scores_gemma":[0.000024088167,0.0000991169,0.000037377864,0.0003922387,0.00021581573,0.0007172175,1.9148113e-7,0.00018397093,2.6925417e-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":[0.0007590242,0.000039924278,0.010219898,0.000020045854,0.00003292516,0.000009214045,0.02060918,0.9646175,0.0013659364,0.0004114754,0.000014151042,0.0019007642],"study_design_scores_gemma":[0.0031584294,0.0017155564,0.9431271,0.00054858864,0.00032120317,0.000004672144,0.0076092235,0.04101592,0.0018025826,0.00009026614,0.00031044218,0.00029600054],"about_ca_topic_score_codex":0.000047367117,"about_ca_topic_score_gemma":0.00053649663,"teacher_disagreement_score":0.9329072,"about_ca_system_score_codex":0.000028937107,"about_ca_system_score_gemma":0.0001363065,"threshold_uncertainty_score":0.404187},"labels":[],"label_agreement":null},{"id":"W2811037449","doi":"","title":"Characterisation of Intercity Bus Travel Demand in Quebec","year":2017,"lang":"en","type":"article","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Competition (biology); Welfare economics; Public transport; Business; Humanities; Economy; Political science; Transport engineering; Economics; Engineering; Art","score_opus":0.01601266808435708,"score_gpt":0.2677274792171916,"score_spread":0.2517148111328345,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2811037449","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.84803563,0.00006738748,0.14644471,0.0026691915,0.00012123961,0.00033883087,0.00002346882,0.00010425085,0.002195291],"genre_scores_gemma":[0.99237514,0.00014178414,0.005940049,0.00014121407,0.000058955902,0.00005574206,0.000029073688,0.000012909866,0.0012451223],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99885356,0.00011205782,0.00033730967,0.00018935007,0.0002364953,0.00027121563],"domain_scores_gemma":[0.9990573,0.000052858562,0.00035097584,0.00034147003,0.00009658915,0.00010079649],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008218873,0.00011071066,0.00020452739,0.00021132607,0.0004116328,0.00016083202,0.0003626479,0.00018997407,0.000037078942],"category_scores_gemma":[0.00031899338,0.00012223463,0.00006207823,0.0001352301,0.00017956526,0.0006404632,0.000019338455,0.00015523125,0.0000029514185],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010195491,0.00016003892,0.93081677,0.00002842913,0.000013700938,0.000014116255,0.016923785,0.0015047288,0.0030346864,0.028806807,0.0001732951,0.018421665],"study_design_scores_gemma":[0.00030100765,0.000020489295,0.99012405,0.00006854613,0.0000093292165,7.0783824e-7,0.00085667,0.0052559585,0.002283043,0.00054059504,0.0004064223,0.00013315922],"about_ca_topic_score_codex":0.45603934,"about_ca_topic_score_gemma":0.5661108,"teacher_disagreement_score":0.14433952,"about_ca_system_score_codex":0.00019706682,"about_ca_system_score_gemma":0.00022683362,"threshold_uncertainty_score":0.5475829},"labels":[],"label_agreement":null},{"id":"W2811057492","doi":"10.5539/cis.v11n3p57","title":"The Feasibility of Opening Communities -- Based on the Wiedemann Car-Following Model","year":2018,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"Transportation Planning and Optimization","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":"VisSim; Computer science; Queue; Traffic flow (computer networking); Transport engineering; Traffic congestion; Measure (data warehouse); Urbanization; Software; Traffic simulation; Scale (ratio); Simulation; Microsimulation; Computer network; Data mining; Geography","score_opus":0.060416791167453625,"score_gpt":0.32456106151206493,"score_spread":0.2641442703446113,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2811057492","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.61366814,0.0000061953774,0.35663643,0.0015014341,0.0003038333,0.00021537178,0.000003647173,0.000038133636,0.02762681],"genre_scores_gemma":[0.9969394,0.00000646787,0.0023171937,0.0007019354,0.000017048496,0.0000023368607,0.0000023242533,7.052349e-7,0.000012555552],"study_design_codex":"qualitative","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993008,0.000053786178,0.00015458303,0.000041167772,0.00034669478,0.00010299058],"domain_scores_gemma":[0.9992961,0.00022850552,0.00008028393,0.00013950697,0.00022621326,0.000029332616],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0021295396,0.000036263416,0.000042014526,0.000049105696,0.002477092,0.00027413867,0.00031736464,0.000015314903,0.0000032065511],"category_scores_gemma":[0.00007477576,0.000021408563,0.000017272016,0.000317619,0.0009905465,0.0016815045,0.000020598307,0.00004633889,0.0000022554182],"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":[0.000044500768,0.000021172362,0.025791597,0.000016138025,0.000005215593,6.007374e-8,0.33711466,0.29840696,0.000025894551,0.3199915,0.0008152358,0.01776706],"study_design_scores_gemma":[0.000083801184,0.0000316282,0.011306809,0.00002136535,0.0000018741011,4.570052e-8,0.004898179,0.9824677,0.00005226057,0.000236986,0.0008617673,0.00003756296],"about_ca_topic_score_codex":0.00011434536,"about_ca_topic_score_gemma":0.000051981056,"teacher_disagreement_score":0.68406075,"about_ca_system_score_codex":0.000016229178,"about_ca_system_score_gemma":0.00015921147,"threshold_uncertainty_score":0.99882156},"labels":[],"label_agreement":null},{"id":"W2811318053","doi":"10.1061/jtepbs.0000151","title":"Beyond Standard Zonal Congestion Pricing: A Detailed Impact Analysis","year":2018,"lang":"en","type":"article","venue":"Journal of Transportation Engineering Part A Systems","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"University of British Columbia","keywords":"Economics; Financial economics; Business","score_opus":0.012460678249320258,"score_gpt":0.27737625621358,"score_spread":0.26491557796425974,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2811318053","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.69372946,0.0002329682,0.30409837,0.00010610204,0.0012796657,0.00015786027,0.00007129129,0.0000770292,0.00024727348],"genre_scores_gemma":[0.99751943,0.000037228372,0.0016580968,0.00000771259,0.00058489357,0.000004037989,0.00007090712,0.000013512278,0.00010416321],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9982478,0.00006713461,0.00070781505,0.00011718655,0.0006514498,0.00020858576],"domain_scores_gemma":[0.9983599,0.00008848364,0.0004874689,0.00007913687,0.00080483773,0.00018013116],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010571356,0.00012521933,0.00034653817,0.00049330445,0.0001676528,0.000089304915,0.00011379905,0.00010316097,0.00009578848],"category_scores_gemma":[0.00006602736,0.000114551105,0.00023394542,0.0010134452,0.00005202257,0.00040047432,3.952244e-7,0.0001323928,0.0000036174813],"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":[0.00011128255,0.00002323367,0.10686361,0.000026235939,0.0005752653,0.000014877515,0.0095570255,0.88050157,0.00014279112,0.0017507835,0.000349723,0.00008363186],"study_design_scores_gemma":[0.0021146133,0.00083689386,0.90386707,0.0004474808,0.0027236827,0.000014426033,0.0034613984,0.045490235,0.00022014389,0.000043033884,0.040118627,0.0006624031],"about_ca_topic_score_codex":0.0001475391,"about_ca_topic_score_gemma":0.00049676833,"teacher_disagreement_score":0.8350113,"about_ca_system_score_codex":0.0001314827,"about_ca_system_score_gemma":0.00021177038,"threshold_uncertainty_score":0.46712583},"labels":[],"label_agreement":null},{"id":"W2811411091","doi":"10.1155/2018/5914276","title":"A Practical Method for Timetable Rescheduling in Subway Networks during the End-of-Service Period","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Operations research; Linearization; Beijing; Constraint (computer-aided design); Service (business); Computer science; Transfer (computing); Pareto principle; Mathematical optimization; Transport engineering; Engineering; Mathematics; Economics","score_opus":0.023526410594293744,"score_gpt":0.3594585040079726,"score_spread":0.33593209341367886,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2811411091","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4354449,0.00011794341,0.5611128,0.0025430976,0.00037723815,0.00025927683,0.000007158523,0.000012660366,0.00012494257],"genre_scores_gemma":[0.75904715,0.00006632595,0.24056791,0.000058444934,0.00020372166,0.0000058801493,0.000010296296,0.000008414816,0.0000318652],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99881095,0.00010647447,0.00054150587,0.000097756536,0.0002737005,0.00016959438],"domain_scores_gemma":[0.99846125,0.00032089333,0.000514135,0.000061550585,0.0005931727,0.000049025868],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012811482,0.000069998525,0.00017534463,0.00009855464,0.00022813672,0.00002062409,0.00010329036,0.00007247391,0.000037544953],"category_scores_gemma":[0.00015803243,0.000057244863,0.000073713236,0.0004727302,0.00007019671,0.00056747324,5.522356e-7,0.00016366567,3.5233793e-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":[0.0012275563,0.000081914455,0.0076484387,0.000055923607,0.000039944152,0.000008685604,0.048624303,0.93145406,0.0024616988,0.0057748957,0.000015800755,0.0026067705],"study_design_scores_gemma":[0.008603618,0.0008080345,0.85279167,0.0008609077,0.00053136394,0.000021765512,0.06735984,0.040272824,0.00873185,0.0033206698,0.016046729,0.00065075135],"about_ca_topic_score_codex":0.00013468714,"about_ca_topic_score_gemma":0.004628879,"teacher_disagreement_score":0.89118123,"about_ca_system_score_codex":0.000036981204,"about_ca_system_score_gemma":0.00018723542,"threshold_uncertainty_score":0.25830233},"labels":[],"label_agreement":null},{"id":"W2811432766","doi":"10.1155/2018/1675967","title":"Planning for Operation: Can Line Extension Planning Mitigate Capacity Mismatch on an Existing Rail Network?","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Natural Science Foundation of Beijing Municipality","keywords":"Overcrowding; Bottleneck; Beijing; Headway; Line (geometry); Transport engineering; Transportation planning; Computer science; Network planning and design; Transfer (computing); Operations research; Engineering; China; Telecommunications; Operations management; Economics","score_opus":0.06606589139274574,"score_gpt":0.3534621906064894,"score_spread":0.28739629921374366,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2811432766","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8900795,0.00009538643,0.10754061,0.0005506295,0.0010804594,0.00026691024,0.000025701727,0.000065854,0.00029494386],"genre_scores_gemma":[0.9397122,0.000022848895,0.05835395,0.00029181974,0.0013303283,0.0000074834315,0.00018099195,0.000023343888,0.00007699794],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9982783,0.00009681796,0.00067125825,0.00021971352,0.00043523678,0.00029864535],"domain_scores_gemma":[0.9981148,0.00017138812,0.0006246976,0.00009399139,0.0008443155,0.0001507954],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009521924,0.00015270108,0.0002491452,0.00012426957,0.0009057643,0.000069386864,0.00013106689,0.000119993994,0.000018998659],"category_scores_gemma":[0.000119262775,0.00015178943,0.00008898184,0.00024643613,0.00010687754,0.0007560827,5.431512e-7,0.0001999374,9.643541e-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":[0.0005573199,0.00004770676,0.0029805028,0.000018779092,0.000019892397,0.000015173407,0.05020354,0.9396364,0.0013009734,0.001925808,0.00010714356,0.0031867654],"study_design_scores_gemma":[0.01667201,0.010063189,0.7565892,0.006104576,0.0007916666,0.000039106526,0.08125992,0.03314115,0.013628153,0.01694878,0.062102452,0.0026598265],"about_ca_topic_score_codex":0.000055526376,"about_ca_topic_score_gemma":0.000496634,"teacher_disagreement_score":0.9064953,"about_ca_system_score_codex":0.00006784305,"about_ca_system_score_gemma":0.00015199177,"threshold_uncertainty_score":0.69664985},"labels":[],"label_agreement":null},{"id":"W2846328685","doi":"10.1155/2018/2758652","title":"Optimal Bus-Bridging Service under a Metro Station Disruption","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; State Key Laboratory of Rail Traffic Control and Safety; Beijing Jiaotong University; National Natural Science Foundation of China","keywords":"Bridging (networking); Computer science; Metro station; Algorithm; Operations research; Simulation; Transport engineering; Mathematics; Engineering; Computer network","score_opus":0.017158678464612973,"score_gpt":0.3142532447673551,"score_spread":0.2970945663027421,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2846328685","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7383334,0.00005248338,0.25931185,0.0013490703,0.0005758866,0.00010722292,0.000012598458,0.000044283846,0.0002132003],"genre_scores_gemma":[0.96181977,0.00010401337,0.037120283,0.00027860957,0.00048047033,0.0000028901954,0.00010200399,0.00001578443,0.00007617884],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99847025,0.00007775916,0.00054823735,0.00013869035,0.00056138716,0.00020370175],"domain_scores_gemma":[0.99800843,0.00006988014,0.00058593194,0.00006872113,0.0011487716,0.00011826068],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00053352787,0.00010867434,0.00016618641,0.00021865759,0.000347814,0.00005027345,0.00011584273,0.00007971881,0.000099983954],"category_scores_gemma":[0.000046597608,0.000111133166,0.00007767453,0.0006392829,0.00008137159,0.001363245,5.609682e-7,0.00014220912,0.000010178395],"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":[0.0005820133,0.000106574174,0.0067018913,0.000033622906,0.00006086992,0.00001377076,0.06318764,0.9084744,0.003736778,0.0058566085,0.0001245226,0.011121325],"study_design_scores_gemma":[0.0037193887,0.0006862734,0.9120417,0.0003563305,0.00035642245,0.000006319555,0.06294639,0.0023536065,0.0030648173,0.002442329,0.011441946,0.0005844549],"about_ca_topic_score_codex":0.00014842463,"about_ca_topic_score_gemma":0.002836295,"teacher_disagreement_score":0.9061208,"about_ca_system_score_codex":0.00010370993,"about_ca_system_score_gemma":0.00018326554,"threshold_uncertainty_score":0.45318788},"labels":[],"label_agreement":null},{"id":"W287183341","doi":"","title":"Implementing Customer Service Training","year":2008,"lang":"en","type":"article","venue":"Mass transit","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Transit (satellite); Public transport; Customer service; Business; Service (business); Training (meteorology); Upgrade; Transport engineering; Marketing; Operations management; Engineering; Computer science","score_opus":0.054263678095406324,"score_gpt":0.2959724435521982,"score_spread":0.24170876545679187,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W287183341","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5728835,0.00008260712,0.09850701,0.0070016556,0.00046116184,0.00032906534,0.000028108721,0.00070905976,0.31999788],"genre_scores_gemma":[0.9944036,0.000027055614,0.003953011,0.00043244025,0.0000969611,0.00000536636,0.000031378357,0.000008559534,0.0010416041],"study_design_codex":"qualitative","study_design_gemma":"not_applicable","domain_scores_codex":[0.99919134,0.00004290123,0.00013223571,0.00011798781,0.0002222452,0.00029327872],"domain_scores_gemma":[0.99975574,0.000030720315,0.000031614694,0.000051309205,0.00006424555,0.00006638035],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028010985,0.0000575694,0.000076651675,0.00004653776,0.0007892258,0.000020250967,0.00008307264,0.000047005626,0.0004814943],"category_scores_gemma":[0.000007354757,0.00006381059,0.000032270444,0.00030829196,0.000039390867,0.00016502841,0.0000010114896,0.00006578161,0.00006782199],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003593789,0.000051516385,0.06376467,0.000040673036,0.000059700862,0.00007496825,0.8818216,0.021230359,0.001710251,0.017248746,0.0018371055,0.012124519],"study_design_scores_gemma":[0.0012371778,0.00001905764,0.056649838,0.000042498385,0.000064487554,0.0000054253132,0.04321024,0.0015801763,0.00036918526,0.00029520324,0.8959834,0.0005432944],"about_ca_topic_score_codex":0.00072171964,"about_ca_topic_score_gemma":0.0020336432,"teacher_disagreement_score":0.8941463,"about_ca_system_score_codex":0.000015250624,"about_ca_system_score_gemma":0.00008356821,"threshold_uncertainty_score":0.6070166},"labels":[],"label_agreement":null},{"id":"W2883430347","doi":"10.1007/s11116-018-9911-6","title":"A novel approach for systematically calibrating transport planning model systems","year":2018,"lang":"en","type":"article","venue":"Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Calibration; Process (computing); Computer science; Scale (ratio); Industrial engineering; Simulation; Systems engineering; Engineering; Mathematics","score_opus":0.0602943615527471,"score_gpt":0.3147329805005458,"score_spread":0.25443861894779873,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2883430347","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.033369932,0.000036261787,0.9584268,0.00007005009,0.00020835937,0.0010014282,0.00015448194,0.00032221476,0.0064104935],"genre_scores_gemma":[0.8740424,0.0000025595227,0.12384979,0.00005027048,0.00025054807,0.00019397885,0.00085198274,0.000030280624,0.0007282314],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982774,0.00002904153,0.0005829346,0.00032894392,0.00043677058,0.0003448847],"domain_scores_gemma":[0.99910927,0.00008768723,0.00020362937,0.0001267941,0.00033870767,0.00013393031],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00071790034,0.00016040132,0.0002494284,0.00010671743,0.0006848471,0.000069604605,0.00015876834,0.00017813063,0.000010168239],"category_scores_gemma":[0.000028457182,0.00016610745,0.00009089112,0.00029743472,0.00014130957,0.0004397181,3.6213916e-7,0.00008248419,0.0000026655964],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000106025094,0.000094738876,0.0044792006,0.0004916334,0.000039159815,0.0000011232179,0.06374931,0.782956,0.000813128,0.14710695,0.0001055066,0.000057192905],"study_design_scores_gemma":[0.0010773512,0.00008620018,0.0050527407,0.0002735993,0.00016582457,8.5936784e-7,0.010884359,0.98112124,0.000100463294,0.00034017468,0.000492504,0.00040465867],"about_ca_topic_score_codex":0.00046110235,"about_ca_topic_score_gemma":0.0002634708,"teacher_disagreement_score":0.84067243,"about_ca_system_score_codex":0.000042582215,"about_ca_system_score_gemma":0.00017178438,"threshold_uncertainty_score":0.6773665},"labels":[],"label_agreement":null},{"id":"W2883542709","doi":"10.1111/rsp3.12141","title":"An urban metric system based on space‐economy: Foundations and implementation","year":2018,"lang":"en","type":"article","venue":"Regional Science Policy & Practice","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique; Université du Québec à Montréal","funders":"","keywords":"Metric (unit); Space (punctuation); Urban space; Metric system; Metric space; Computer science; Economic geography; Architectural engineering; Geography; Mathematics; Economics; Engineering; Operations management; Pure mathematics; Physics","score_opus":0.04042809596619454,"score_gpt":0.4243493097205117,"score_spread":0.3839212137543172,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2883542709","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16916052,0.000073421936,0.080168635,0.19268821,0.00089070085,0.0017359606,0.00005060565,0.00083352195,0.5543984],"genre_scores_gemma":[0.9918366,0.000011413448,0.0055302116,0.0019694963,0.0005061687,0.000014727358,0.000019867595,0.0000058879973,0.000105648425],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99856967,0.0001807576,0.00015894757,0.00030983356,0.00050558883,0.00027522625],"domain_scores_gemma":[0.9986001,0.00038106844,0.00019988742,0.00015234314,0.0004380995,0.00022845966],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0018213292,0.0000790474,0.000072051,0.00052851555,0.0019020489,0.00037563124,0.00019716159,0.00003958668,0.00003755127],"category_scores_gemma":[0.0006054681,0.00008022864,0.000018106797,0.0019159035,0.00074715796,0.0022400161,0.0000054778893,0.000063823616,0.0000361769],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003384433,0.000055004773,0.005943016,0.000004974025,0.000004498812,0.0000010299584,0.007989806,0.00073578465,0.000059255162,0.982477,0.000633222,0.0020625787],"study_design_scores_gemma":[0.00125477,0.0008667863,0.14331901,0.00006999905,0.00011730597,0.000016489392,0.06594533,0.050438534,0.00028078613,0.0017395185,0.73526794,0.00068350945],"about_ca_topic_score_codex":0.0075529595,"about_ca_topic_score_gemma":0.00066444866,"teacher_disagreement_score":0.98073745,"about_ca_system_score_codex":0.00035054117,"about_ca_system_score_gemma":0.0015838296,"threshold_uncertainty_score":0.99939734},"labels":[],"label_agreement":null},{"id":"W2883936857","doi":"","title":"Breaking into Emergency Shuttle Service: Aspects and Impacts of Retracting Buses from Existing Scheduled Bus Services","year":2017,"lang":"en","type":"article","venue":"Transportation Research Board 96th Annual MeetingTransportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Public transport; Service (business); Bus rapid transit; Transport engineering; Service quality; Transit (satellite); Business; Engineering; Marketing","score_opus":0.08394168208219437,"score_gpt":0.43547016032844404,"score_spread":0.35152847824624966,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2883936857","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.989631,0.00043286337,0.00043993953,0.002624982,0.0003681726,0.0011633574,0.00041912706,0.0002459235,0.0046746368],"genre_scores_gemma":[0.9910933,0.0013547195,0.0059814826,0.00004092239,0.0004324377,0.000111912595,0.0006319054,0.0000932505,0.00026008833],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9901948,0.0011380779,0.0015385671,0.0011959985,0.004360487,0.0015720504],"domain_scores_gemma":[0.9895359,0.0014238752,0.0009856782,0.0008133767,0.0063902936,0.00085086894],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0077702384,0.0004345068,0.0006579666,0.0009691151,0.0060691647,0.00059595535,0.0012391325,0.00052733603,0.00048360057],"category_scores_gemma":[0.0017004035,0.00048203667,0.00016954816,0.0015894427,0.0011860607,0.0026950988,0.00002712507,0.0014821277,0.000036478123],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008383124,0.00025196132,0.80917287,0.0010865884,0.0001729948,0.00011024982,0.16403869,0.0009785896,0.0067712264,0.013248046,0.00021273477,0.003117724],"study_design_scores_gemma":[0.0013123775,0.00017651124,0.9205677,0.0010589529,0.0000750294,1.8760932e-7,0.068219125,0.00047319583,0.0020023524,0.0034948406,0.002115797,0.0005039233],"about_ca_topic_score_codex":0.356914,"about_ca_topic_score_gemma":0.42849317,"teacher_disagreement_score":0.111394815,"about_ca_system_score_codex":0.00017101334,"about_ca_system_score_gemma":0.0008953806,"threshold_uncertainty_score":0.99976313},"labels":[],"label_agreement":null},{"id":"W2884033034","doi":"10.1155/2018/2710608","title":"Using Smart Card Data Trimmed by Train Schedule to Analyze Metro Passenger Route Choice with Synchronous Clustering","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Natural Science Foundation of Guangdong Province; Ministry of Education of the People's Republic of China","keywords":"Schedule; Cluster analysis; Train; Computer science; Smart card; sync; Real-time computing; Data collection; Transport engineering; Operations research; Simulation; Engineering; Computer network; Computer security; Channel (broadcasting); Artificial intelligence","score_opus":0.03534802489280268,"score_gpt":0.3376798643645353,"score_spread":0.3023318394717326,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2884033034","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6429759,0.000109050874,0.35589108,0.00030926074,0.00035014533,0.00016400358,0.00008152828,0.000027108606,0.00009189799],"genre_scores_gemma":[0.89523095,0.00003439518,0.104032,0.000109287415,0.0003445931,0.0000021335109,0.0001870173,0.000021181097,0.00003845653],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9983056,0.00008797657,0.00053069886,0.00021796333,0.0005616632,0.0002960787],"domain_scores_gemma":[0.99860567,0.00009475757,0.00045092806,0.00017917906,0.0004368409,0.00023263943],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00071384804,0.00014150358,0.00027004478,0.00019773867,0.00030486032,0.000057204674,0.00026674807,0.00007668049,0.000024441139],"category_scores_gemma":[0.00009951729,0.00013143028,0.000053911568,0.0006470499,0.0000819914,0.0014985249,0.000001866965,0.00015719501,0.000001650231],"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":[0.0016894011,0.00034636332,0.16606566,0.000087665205,0.00047641734,0.000049861195,0.066028126,0.72545534,0.018472934,0.00014710477,0.00069781666,0.020483272],"study_design_scores_gemma":[0.009033265,0.0019170364,0.8404528,0.0011054118,0.0016047776,0.000018018813,0.04639953,0.009297102,0.0054141805,0.00006754465,0.08303984,0.0016504991],"about_ca_topic_score_codex":0.00059098704,"about_ca_topic_score_gemma":0.011694248,"teacher_disagreement_score":0.7161583,"about_ca_system_score_codex":0.00013955428,"about_ca_system_score_gemma":0.00025236415,"threshold_uncertainty_score":0.6525665},"labels":[],"label_agreement":null},{"id":"W2884292415","doi":"10.21949/1404519","title":"Review of the transportation planning process in the Portland, Orgeon, metropolitan area","year":2010,"lang":"en","type":"article","venue":"ROSA P","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Metropolitan area; Process (computing); Environmental planning; Business; Geography; Archaeology; Computer science","score_opus":0.019792096585591255,"score_gpt":0.3207537865571042,"score_spread":0.3009616899715129,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2884292415","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98010373,0.002013827,0.00039770993,0.0049011055,0.0004010556,0.0006024827,0.00006209074,0.000046671666,0.011471353],"genre_scores_gemma":[0.99891293,0.00035542226,0.000090513044,0.00047359554,0.000032358563,0.000012975946,0.00006324593,0.0000035194917,0.000055421704],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9992944,0.00006520276,0.0001996813,0.000080791724,0.0002548908,0.00010505534],"domain_scores_gemma":[0.99961835,0.0000597882,0.00012232563,0.0001026079,0.00007745926,0.000019466965],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00066198956,0.00004751661,0.00008397358,0.000025811527,0.00015895315,0.000011395714,0.00019593908,0.000045069755,0.00006279365],"category_scores_gemma":[0.00013021468,0.00002815629,0.00003713263,0.00045615117,0.00007642411,0.000092747476,5.7964985e-7,0.00014259302,8.912784e-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":[0.000012465963,0.00007207731,0.9008376,0.00067908445,0.000008659604,0.0000037387595,0.06285853,0.0007449842,0.000071505114,0.030756198,0.0034445785,0.0005105628],"study_design_scores_gemma":[0.00037435885,0.00002311391,0.90756834,0.0024521556,0.00007900568,0.000001111055,0.02564121,0.000105456566,0.00023420033,0.0016276693,0.06168226,0.00021112217],"about_ca_topic_score_codex":0.0006101242,"about_ca_topic_score_gemma":0.0049613263,"teacher_disagreement_score":0.058237683,"about_ca_system_score_codex":0.000010330981,"about_ca_system_score_gemma":0.00009243139,"threshold_uncertainty_score":0.27685365},"labels":[],"label_agreement":null},{"id":"W2884524204","doi":"10.1177/0361198118781665","title":"A Bi-Level Passenger Preference-Oriented Line Planning Model for High-Speed Railway Operations","year":2018,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Economic shortage; Computer science; Operations research; Line (geometry); Transport engineering; Service level; Dedicated line; Service (business); Block (permutation group theory); Preference; Engineering; Business; Computer network; Mathematics","score_opus":0.26646933104353104,"score_gpt":0.43895124522090534,"score_spread":0.1724819141773743,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2884524204","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.77187866,0.000081291255,0.21923696,0.0054157414,0.0008822613,0.0017452067,0.00037186884,0.00007705712,0.0003109641],"genre_scores_gemma":[0.96679157,0.00034451162,0.027069544,0.000099998026,0.000733395,0.00012544397,0.00016397944,0.00006610015,0.004605467],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99188787,0.0011693004,0.0015612175,0.0005343669,0.0037125146,0.0011347196],"domain_scores_gemma":[0.9868653,0.0011579556,0.0004285744,0.0004181004,0.010611336,0.00051872316],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.006506632,0.000285107,0.0004726414,0.0011517624,0.0029958435,0.0002682889,0.0010617243,0.0003440063,0.00031875548],"category_scores_gemma":[0.0006352006,0.0002338964,0.0003263387,0.0026241997,0.0013240074,0.0011225872,0.0000049567357,0.001457772,0.000019379515],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.008532464,0.0013835062,0.1529593,0.00045032316,0.00070816535,0.00007297158,0.18853728,0.4394821,0.007918831,0.15373807,0.035633467,0.010583523],"study_design_scores_gemma":[0.009584951,0.0028708426,0.79608154,0.001597121,0.0003645141,6.228262e-7,0.02676917,0.09485406,0.003980782,0.015387202,0.04730575,0.0012034739],"about_ca_topic_score_codex":0.011745765,"about_ca_topic_score_gemma":0.14088283,"teacher_disagreement_score":0.6431222,"about_ca_system_score_codex":0.00027951147,"about_ca_system_score_gemma":0.0020389922,"threshold_uncertainty_score":0.9983021},"labels":[],"label_agreement":null},{"id":"W2884697353","doi":"10.1155/2018/7051789","title":"Modeling the Satisfaction of Bus Traffic Transfer Service Quality at a High-Speed Railway Station","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":43,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; Natural Science Foundation of Shaanxi Province","keywords":"Transport engineering; Structural equation modeling; Service (business); Service quality; Transfer (computing); Level of service; Public transport; Quality (philosophy); Process (computing); Computer science; Engineering; Business; Marketing","score_opus":0.026012921227503778,"score_gpt":0.3106724359451086,"score_spread":0.28465951471760487,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2884697353","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92134595,0.000061856445,0.07690707,0.0007877324,0.00058712513,0.0002011372,0.00003598575,0.000029048986,0.00004408231],"genre_scores_gemma":[0.9958164,0.0001954603,0.0035534152,0.000094976,0.00022093617,0.0000023859693,0.000072043425,0.000014047975,0.000030333702],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9979297,0.0001798633,0.0008963328,0.00013730355,0.00068804267,0.00016874199],"domain_scores_gemma":[0.9977961,0.00013537452,0.00041866148,0.00009801407,0.001475369,0.00007645391],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00093286135,0.000115544266,0.00022990975,0.00012432976,0.00039497227,0.000020369767,0.00012482164,0.000098414166,0.00008497156],"category_scores_gemma":[0.00006142,0.00009532414,0.00010674554,0.0005193451,0.00011197643,0.0009557339,4.909613e-7,0.0001485178,0.0000028619716],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00066134473,0.00004627381,0.0017102393,0.00003233392,0.000033906108,9.886493e-7,0.06967687,0.9170603,0.004256548,0.0015368279,0.000008251339,0.0049761045],"study_design_scores_gemma":[0.005946873,0.00079599145,0.9184064,0.00037142413,0.00049544714,0.0000038427047,0.048642654,0.01683876,0.0048358385,0.0019357911,0.0011534761,0.00057345204],"about_ca_topic_score_codex":0.0010639554,"about_ca_topic_score_gemma":0.025897527,"teacher_disagreement_score":0.9166962,"about_ca_system_score_codex":0.00009892402,"about_ca_system_score_gemma":0.0001679211,"threshold_uncertainty_score":0.9918773},"labels":[],"label_agreement":null},{"id":"W2884747536","doi":"10.1155/2018/9407801","title":"Integrating Bus Holding Control Strategies and Schedule Recovery: Simulation-Based Comparison and Recommendation","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China; China Postdoctoral Science Foundation; Royal Academy of Engineering; Fundamental Research Funds for the Central Universities; National Science Foundation","keywords":"Schedule; Headway; Context (archaeology); Control (management); Computer science; Constraint (computer-aided design); Operations research; Simulation; Transport engineering; Real-time computing; Engineering","score_opus":0.019958211470090165,"score_gpt":0.3308396047088584,"score_spread":0.3108813932387683,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2884747536","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6037306,0.00008575155,0.39530525,0.0004050412,0.0002188297,0.000104879444,0.000007730509,0.000022908585,0.00011901815],"genre_scores_gemma":[0.96911466,0.000054780554,0.030558884,0.00007333254,0.0001408778,0.0000018529305,0.000041340336,0.000008530364,0.00000571434],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99901485,0.00008133596,0.00047764447,0.00011797114,0.0001879323,0.00012023363],"domain_scores_gemma":[0.9984994,0.00037672193,0.00054099417,0.000033439683,0.00047166328,0.00007779588],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000522564,0.00009115265,0.00018110141,0.00014777314,0.00035417074,0.00012476342,0.00004196453,0.00007070311,0.000029232722],"category_scores_gemma":[0.00015517413,0.00009017404,0.000032922602,0.00018916451,0.00012050319,0.0014558508,3.255381e-7,0.00012836522,3.897041e-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":[0.00037964594,0.000038249567,0.031891312,0.000024131163,0.000025954145,0.0000016592867,0.01587255,0.9123027,0.0009553829,0.0025794907,0.0000071193026,0.03592185],"study_design_scores_gemma":[0.009082747,0.0019006049,0.68043476,0.0007866837,0.00039211614,0.000002138294,0.11969167,0.17442153,0.0011822077,0.0049741063,0.00639155,0.0007398481],"about_ca_topic_score_codex":0.000054189546,"about_ca_topic_score_gemma":0.0009533192,"teacher_disagreement_score":0.7378811,"about_ca_system_score_codex":0.000035323937,"about_ca_system_score_gemma":0.000139469,"threshold_uncertainty_score":0.36771908},"labels":[],"label_agreement":null},{"id":"W2885340014","doi":"10.1177/0361198118790132","title":"Does Roadway Performance Affect Transit Headway Unreliability? Evidence from Mixed-Traffic Transit Corridors in Toronto, Canada","year":2018,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Headway; Transport engineering; Traffic congestion; Downtown; Public transport; Transit (satellite); Reliability (semiconductor); Level of service; Computer science; Engineering; Geography","score_opus":0.05664077586958917,"score_gpt":0.37677063537750166,"score_spread":0.3201298595079125,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2885340014","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98905855,0.0004174325,0.0011591446,0.0054154387,0.0021171428,0.0012881838,0.00015360127,0.000052798765,0.00033769038],"genre_scores_gemma":[0.994727,0.002777801,0.0010755875,0.00008713472,0.0005287752,0.0000680145,0.000047208785,0.000056782,0.0006316987],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9874548,0.0030090418,0.0018737399,0.00073084334,0.005552183,0.0013794363],"domain_scores_gemma":[0.9917787,0.0026048322,0.00048649978,0.0005711569,0.003928368,0.00063044956],"candidate_categories":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.008036254,0.00037144666,0.0006617264,0.0005574193,0.0015568709,0.00020073824,0.001586425,0.00034617697,0.0014066997],"category_scores_gemma":[0.0004793794,0.00026216815,0.00033135636,0.0026553392,0.0015174359,0.0019227942,0.0000038381636,0.0019556852,0.000014131828],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0031403645,0.00037553746,0.88427216,0.0002783164,0.0001559343,0.00013516084,0.051659927,0.038989644,0.0009610498,0.00040253784,0.0051371683,0.014492211],"study_design_scores_gemma":[0.0013231164,0.0004890479,0.96840173,0.0011996749,0.00006457873,1.3394404e-7,0.015009591,0.0009972305,0.0011336294,0.00017763661,0.01085653,0.00034708076],"about_ca_topic_score_codex":0.9254345,"about_ca_topic_score_gemma":0.9987091,"teacher_disagreement_score":0.0841296,"about_ca_system_score_codex":0.0017868737,"about_ca_system_score_gemma":0.004471877,"threshold_uncertainty_score":0.9999831},"labels":[],"label_agreement":null},{"id":"W2885554195","doi":"10.1016/j.tranpol.2018.07.011","title":"What about the dynamics in daily travel mode choices? A dynamic discrete choice approach for tour-based mode choice modelling","year":2018,"lang":"en","type":"article","venue":"Transport Policy","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":41,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mode choice; Discrete choice; TRIPS architecture; Mode (computer interface); Travel behavior; Computer science; Heteroscedasticity; Sensitivity (control systems); Econometrics; Operations research; Simulation; Economics; Transport engineering; Engineering; Microeconomics; Public transport; Human–computer interaction","score_opus":0.03195737206400141,"score_gpt":0.34085149455628155,"score_spread":0.30889412249228015,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2885554195","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10753488,0.000101932426,0.8828027,0.0023240843,0.00023278137,0.0011197645,0.0003071306,0.00016541572,0.0054112603],"genre_scores_gemma":[0.9856079,0.00016164631,0.009739754,0.00066643464,0.00053137424,0.00019883113,0.0010200999,0.000058816124,0.0020151476],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976071,0.00009662526,0.0005427213,0.00052854605,0.000479431,0.0007455506],"domain_scores_gemma":[0.9988488,0.00024259572,0.0002045745,0.00035303133,0.00017104391,0.00017994057],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00055393233,0.00029067273,0.00032220862,0.00027118574,0.0007898341,0.0001721528,0.0005801243,0.00025396052,0.000018962242],"category_scores_gemma":[0.000045163084,0.00025652614,0.00017922948,0.00088588474,0.00043027787,0.00088424457,0.0000033263586,0.00027401614,0.0000036143394],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011390608,0.00011048648,0.008597761,0.00006929222,0.000024680732,8.065038e-7,0.021625526,0.9494054,0.000012889176,0.019160863,0.00001853416,0.00085991214],"study_design_scores_gemma":[0.0010672887,0.00004336586,0.013978476,0.000089303816,0.000060990867,2.2858816e-7,0.0030931141,0.9793442,0.000013533973,0.000533647,0.0014429903,0.00033288455],"about_ca_topic_score_codex":0.045920856,"about_ca_topic_score_gemma":0.16095184,"teacher_disagreement_score":0.87807304,"about_ca_system_score_codex":0.0003413885,"about_ca_system_score_gemma":0.0005867444,"threshold_uncertainty_score":0.9999887},"labels":[],"label_agreement":null},{"id":"W2886211105","doi":"10.1139/cjce-2018-0079","title":"Lane-based signal optimization with left turn prohibition in urban road networks","year":2018,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Transportation Planning and Optimization","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":"Deutsche Forschungsgemeinschaft","keywords":"Signal timing; Randomness; SIGNAL (programming language); Travel time; Computer science; Turn (biochemistry); Left behind; Plan (archaeology); Traffic congestion; Transport engineering; Simulation; Real-time computing; Mathematical optimization; Traffic signal; Mathematics; Engineering; Statistics; Geography","score_opus":0.005952061660512238,"score_gpt":0.19280738534156333,"score_spread":0.1868553236810511,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2886211105","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029876618,0.00015579998,0.9663232,0.00037240607,0.00033812402,0.00012245569,0.0000043214422,0.000029120842,0.0027779518],"genre_scores_gemma":[0.9964846,0.000004214221,0.0030765606,0.00004687651,0.00033013374,8.335594e-7,0.000012184507,0.00001148712,0.00003312208],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99936485,0.000029093126,0.0001858073,0.00006469538,0.00014383356,0.00021172938],"domain_scores_gemma":[0.9994262,0.000021945716,0.00009334957,0.000036760262,0.00018416508,0.0002375701],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032329938,0.00006670318,0.00009193806,0.00029520396,0.000115037736,0.000060251426,0.00007799598,0.000065971486,0.00021443242],"category_scores_gemma":[0.00004301672,0.00006674772,0.00002081027,0.00028395696,0.000049895494,0.00022472104,4.160926e-7,0.00013781943,7.326571e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000096751,0.0000030034491,0.016496778,0.0000038668463,0.000004326334,0.000025123318,0.0021076985,0.98092854,0.0000031261616,0.00012596,0.00017755645,0.00011432567],"study_design_scores_gemma":[0.00080986327,0.00018817722,0.03210366,0.0005165527,0.000023225468,0.000012829158,0.0003944775,0.9610875,0.000031263902,0.000010846943,0.0045759897,0.0002456091],"about_ca_topic_score_codex":0.002484889,"about_ca_topic_score_gemma":0.46762615,"teacher_disagreement_score":0.966608,"about_ca_system_score_codex":0.00013436555,"about_ca_system_score_gemma":0.0006848457,"threshold_uncertainty_score":0.54208827},"labels":[],"label_agreement":null},{"id":"W2886796913","doi":"10.1155/2018/4805250","title":"A Simulation-Based Dynamic Programming Method for Interchange Scheduling of Port Collecting and Distributing Network","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Dalian University of Technology; National Natural Science Foundation of China","keywords":"Port (circuit theory); Computer science; Scheduling (production processes); Intersection (aeronautics); Operations research; Flow network; Traffic simulation; Simulation modeling; Network model; Traffic congestion; Dynamic programming; Mathematical optimization; Simulation; Transport engineering; Engineering; Data mining; Algorithm","score_opus":0.024533973776029925,"score_gpt":0.36758018567481054,"score_spread":0.3430462118987806,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2886796913","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26981944,0.00007365668,0.72957575,0.000102054866,0.00018769153,0.0002056334,0.000010706289,0.000015478105,0.0000095966325],"genre_scores_gemma":[0.619095,0.0000074807494,0.38073197,0.000012894172,0.000102621416,0.000003614685,0.00003404424,0.000006953542,0.0000054317698],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988975,0.000053855547,0.0005540646,0.000108164364,0.00021690648,0.00016950844],"domain_scores_gemma":[0.9975174,0.00077412627,0.0007797612,0.000036255333,0.00083176256,0.000060678594],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011204602,0.00007716866,0.00019246382,0.00010420146,0.00033197278,0.000021806469,0.00005697195,0.000063773376,0.000005171101],"category_scores_gemma":[0.0004257935,0.00007991215,0.00007424154,0.0003810804,0.00007334157,0.00028806753,4.9207705e-7,0.000084148436,4.1161503e-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":[0.00024618403,0.000021145797,0.010600024,0.0000515519,0.000021040893,0.000001071925,0.011603181,0.95550036,0.00022187886,0.00030172616,8.0352595e-7,0.021431],"study_design_scores_gemma":[0.005803572,0.0016708144,0.13709919,0.0019970145,0.00059229444,0.0000018264259,0.032888316,0.8108901,0.0013764965,0.0029423484,0.004128143,0.00060993404],"about_ca_topic_score_codex":0.000031590862,"about_ca_topic_score_gemma":0.0007942972,"teacher_disagreement_score":0.34927556,"about_ca_system_score_codex":0.00004534097,"about_ca_system_score_gemma":0.00014432598,"threshold_uncertainty_score":0.3258723},"labels":[],"label_agreement":null},{"id":"W288693672","doi":"","title":"GIS Toolkit Design and Application for Route Choice Analysis:A Comparison of Observed Routes and Shortest Paths","year":2009,"lang":"en","type":"article","venue":"Transportation Research Board 88th Annual MeetingTransportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Global Positioning System; Sample (material); Data collection; Shortest path problem; Travel time; Operations research; Transport engineering; Data mining; Engineering; Telecommunications","score_opus":0.11576561673310792,"score_gpt":0.427362876713756,"score_spread":0.3115972599806481,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W288693672","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7961281,0.0009030536,0.19326346,0.0036789034,0.00007591259,0.004359796,0.00095379865,0.000268611,0.0003683936],"genre_scores_gemma":[0.9718836,0.0006731996,0.02441137,0.000053753993,0.00012548298,0.00031021473,0.0022063626,0.000040879237,0.00029513706],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9921143,0.0012436239,0.0014167028,0.0012546733,0.002811275,0.0011594403],"domain_scores_gemma":[0.99085736,0.003599209,0.00040725857,0.0005958437,0.003915724,0.00062458194],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.009770697,0.00033918032,0.0007685022,0.0013673563,0.0016951418,0.00023977783,0.0007222552,0.00040517037,0.000034966808],"category_scores_gemma":[0.0010246187,0.00037080544,0.00016017782,0.0035789877,0.0010322741,0.0011923073,0.000008423718,0.0007425632,0.0000031600948],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014778359,0.00064484234,0.84070635,0.00033494917,0.00043018875,0.000009850799,0.06160882,0.040830318,0.0015568777,0.03142881,0.0011294829,0.0198417],"study_design_scores_gemma":[0.001357115,0.0007356434,0.95195323,0.00011699424,0.00038978562,5.700021e-8,0.016103424,0.020262482,0.0004664648,0.0021365376,0.006049137,0.00042913618],"about_ca_topic_score_codex":0.015850877,"about_ca_topic_score_gemma":0.042991083,"teacher_disagreement_score":0.17575552,"about_ca_system_score_codex":0.00010263798,"about_ca_system_score_gemma":0.00061515736,"threshold_uncertainty_score":0.9998744},"labels":[],"label_agreement":null},{"id":"W2887645288","doi":"10.1155/2018/6292410","title":"A Methodology for Choosing between Route Deviation and Point Deviation Policies for Flexible Transit Services","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"National Natural Science Foundation of China","keywords":"Transit (satellite); Point (geometry); Service (business); Computer science; Standard deviation; Absolute deviation; Transport engineering; Function (biology); Operations research; Mathematical optimization; Public transport; Economics; Engineering; Mathematics; Statistics","score_opus":0.05637042915589989,"score_gpt":0.37734877884535767,"score_spread":0.32097834968945776,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2887645288","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3951669,0.00010851307,0.6027494,0.0012844338,0.00023909823,0.00033989266,0.000051526393,0.000032129185,0.000028101662],"genre_scores_gemma":[0.76420504,0.00013543485,0.23481576,0.00011934167,0.00048797828,0.000014450331,0.00017027678,0.00001380614,0.000037921847],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.9987988,0.00007808835,0.000575256,0.00014414155,0.00020925143,0.00019446257],"domain_scores_gemma":[0.99804515,0.00040138012,0.0006122969,0.000045574638,0.0008114503,0.00008415424],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010564397,0.000106060266,0.00025236164,0.00019783968,0.0004169392,0.000046351546,0.000081199534,0.000113737144,0.000005064108],"category_scores_gemma":[0.00007783558,0.00010709415,0.00009451416,0.00021938111,0.00007668443,0.0009836452,3.913403e-7,0.00006498326,3.1911335e-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":[0.0038007183,0.00016133976,0.13453552,0.0010447316,0.00045229887,0.0000023214664,0.540897,0.08236011,0.018742954,0.057829827,0.00017293851,0.16000022],"study_design_scores_gemma":[0.0063430634,0.00136746,0.87564087,0.00047899238,0.00093831035,0.0000032000476,0.026168106,0.0006183677,0.0145859,0.035532527,0.037763055,0.0005601629],"about_ca_topic_score_codex":0.00009569989,"about_ca_topic_score_gemma":0.001614209,"teacher_disagreement_score":0.7411053,"about_ca_system_score_codex":0.000054372405,"about_ca_system_score_gemma":0.00010931009,"threshold_uncertainty_score":0.43671727},"labels":[],"label_agreement":null},{"id":"W2887844928","doi":"10.1139/cjce-2018-0306","title":"Metaheuristic approach to optimize placement of detectors in transport networks — case study of Serbia","year":2018,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Transportation Planning and Optimization","field":"Social Sciences","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":"Detector; Minification; Computer science; Metaheuristic; Sample (material); Mathematical optimization; Set (abstract data type); Flow network; Traffic flow (computer networking); Real-time computing; Algorithm; Mathematics; Computer network; Telecommunications","score_opus":0.01627476251198811,"score_gpt":0.23598047115194337,"score_spread":0.21970570863995526,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2887844928","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.89514166,0.00007656319,0.103630655,0.000010870415,0.00025118946,0.00016728454,0.000004145381,0.000004844697,0.00071277213],"genre_scores_gemma":[0.9978749,0.0000032788234,0.0020255917,0.000005223792,0.00006684942,0.0000021859057,9.0630704e-7,0.000009128264,0.0000119026145],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99912715,0.00003478162,0.00041692046,0.00007177382,0.00016657253,0.00018282863],"domain_scores_gemma":[0.999317,0.00004026615,0.00012427031,0.00006366018,0.00016417322,0.00029062046],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006123544,0.00007084323,0.00020729884,0.00037944067,0.000055602894,0.000008456511,0.00011736375,0.000044055287,0.000034297518],"category_scores_gemma":[0.000067444984,0.00007588935,0.000036403373,0.0004881519,0.000032254353,0.00008207319,9.831202e-7,0.00010558082,9.6035926e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010859282,0.000026279187,0.01577241,0.000012105494,0.000026334556,0.00014689993,0.030659309,0.95323694,0.0000049038504,0.000032567106,0.000019229032,0.000052187323],"study_design_scores_gemma":[0.008959897,0.0038272592,0.27184454,0.0015227825,0.00096043415,0.000542245,0.23646487,0.47080806,0.00026873892,0.000023808194,0.0030380227,0.0017393421],"about_ca_topic_score_codex":0.025070105,"about_ca_topic_score_gemma":0.63767225,"teacher_disagreement_score":0.6126021,"about_ca_system_score_codex":0.000075924625,"about_ca_system_score_gemma":0.00030096725,"threshold_uncertainty_score":0.98142207},"labels":[],"label_agreement":null},{"id":"W2888071855","doi":"","title":"Toward Sustainable Transportation: Exploring Transportation Decision Making in Teleworking Households in a Mid-Sized Canadian City","year":2004,"lang":"en","type":"article","venue":"Canadian journal of urban research","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":24,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Environmental planning; Sustainable transport; Business; Regional science; Transport engineering; Geography; Engineering; Sustainability","score_opus":0.14454489101170137,"score_gpt":0.35347134652288137,"score_spread":0.20892645551118,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2888071855","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9951042,0.00046731913,0.0010330102,0.0018044312,0.00024447343,0.00033022682,0.000019086405,0.000011233235,0.0009860477],"genre_scores_gemma":[0.9981524,0.00013051955,0.001425003,0.000044594934,0.0001270177,0.000016642174,0.0000179779,0.000022089642,0.00006380185],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9970696,0.00024202756,0.00065966515,0.00022283882,0.0007647868,0.0010410991],"domain_scores_gemma":[0.99804956,0.00025277367,0.000120518474,0.00010690817,0.00060975295,0.0008605113],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004177972,0.00012198754,0.00024802278,0.0023141857,0.0005954243,0.00020042558,0.0003713905,0.00018636623,0.00012064878],"category_scores_gemma":[0.00046174528,0.00014280777,0.000076635995,0.002566564,0.0001645355,0.00092269585,7.611285e-7,0.0007759989,0.0000024713393],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017835593,0.00003215892,0.7394886,0.00005811191,0.000012845382,0.0048721563,0.14899115,0.091667354,0.000009666632,0.011596503,0.0002625629,0.0028305496],"study_design_scores_gemma":[0.0017415454,0.00007816934,0.9387983,0.0012214344,0.000009398242,0.0000040684486,0.041718636,0.000020725718,0.000039966606,0.005471098,0.0106603615,0.00023630475],"about_ca_topic_score_codex":0.8084958,"about_ca_topic_score_gemma":0.9953458,"teacher_disagreement_score":0.19930972,"about_ca_system_score_codex":0.0025868686,"about_ca_system_score_gemma":0.013269838,"threshold_uncertainty_score":0.992324},"labels":[],"label_agreement":null},{"id":"W2888956109","doi":"10.1177/0361198118791665","title":"Subway Service Down Again? Assessing the Effects of Subway Service Interruptions on Local Surface Transit Performance","year":2018,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada; Connaught Fund","keywords":"Transport engineering; Transit (satellite); Service (business); Public transport; Level of service; Service quality; Business; Engineering; Marketing","score_opus":0.07460023934273372,"score_gpt":0.4088372526254113,"score_spread":0.33423701328267763,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2888956109","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97847825,0.00009421432,0.008783157,0.009102207,0.000995661,0.0011998239,0.00004545762,0.000053668795,0.0012475803],"genre_scores_gemma":[0.99681276,0.0007661374,0.0011311494,0.0003183341,0.00032529703,0.00003639235,0.00003518195,0.000056086024,0.00051867764],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9898014,0.0026196374,0.0014860985,0.00046549866,0.004614585,0.0010127584],"domain_scores_gemma":[0.98732376,0.0029165759,0.0006890285,0.00056676543,0.008113544,0.0003902992],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0071988897,0.0003093628,0.0004915172,0.0006750421,0.0024884534,0.00023877379,0.0016390377,0.0003036043,0.00027900463],"category_scores_gemma":[0.0002809732,0.0002193328,0.0003232584,0.004874434,0.0017216078,0.0011820327,0.0000058683745,0.0021455565,0.00004343712],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.006509064,0.0022793233,0.38605827,0.0039231693,0.0010450532,0.00017258011,0.37298512,0.13509235,0.019937024,0.026612734,0.009809768,0.035575528],"study_design_scores_gemma":[0.0019348299,0.0011018063,0.95162827,0.0018063185,0.000147059,5.492546e-7,0.022557294,0.001693602,0.0066387397,0.0006891503,0.011468445,0.00033394014],"about_ca_topic_score_codex":0.02120167,"about_ca_topic_score_gemma":0.13954523,"teacher_disagreement_score":0.56557,"about_ca_system_score_codex":0.00031291202,"about_ca_system_score_gemma":0.0013731503,"threshold_uncertainty_score":0.9988102},"labels":[],"label_agreement":null},{"id":"W2889347675","doi":"10.1155/2018/8796250","title":"Tram-Oriented Traffic Signal Timing Resynchronization","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Science and Technology Commission of Shanghai Municipality; National Natural Science Foundation of China","keywords":"Transport engineering; SIGNAL (programming language); Reduction (mathematics); Computer science; Travel time; Traffic signal; Signal timing; Simulation; Real-time computing; Engineering; Mathematics","score_opus":0.013961830434380386,"score_gpt":0.29306658026301524,"score_spread":0.27910474982863487,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2889347675","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.85213953,0.00010487325,0.14555459,0.00043531373,0.0008487218,0.00015081001,0.000009021049,0.000069099464,0.0006880387],"genre_scores_gemma":[0.9787778,0.00011860471,0.020291768,0.00006147899,0.0005369753,0.0000018226599,0.000064659325,0.000014790144,0.00013207858],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99852747,0.0000742027,0.0005616304,0.00012703089,0.00051564595,0.00019405164],"domain_scores_gemma":[0.9984195,0.00006642396,0.0004943516,0.000058510992,0.0008434969,0.00011767527],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004609335,0.00009777325,0.00015880937,0.00019470803,0.00036722104,0.00002950862,0.000108160064,0.00009143506,0.00020090684],"category_scores_gemma":[0.000059071608,0.00009982702,0.00008422777,0.0005373965,0.00015041702,0.00087213103,3.1172547e-7,0.00013620549,0.0000065168915],"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":[0.00072145776,0.0002068931,0.005226831,0.000034222536,0.00006696343,0.000039373645,0.13204627,0.7800339,0.0025226278,0.0068158545,0.00042736626,0.07185826],"study_design_scores_gemma":[0.013544271,0.0036502904,0.6338322,0.0014165301,0.0008794065,0.000027835582,0.084088646,0.006631937,0.009381362,0.002368176,0.24236442,0.0018148965],"about_ca_topic_score_codex":0.000017744123,"about_ca_topic_score_gemma":0.0006233363,"teacher_disagreement_score":0.773402,"about_ca_system_score_codex":0.0000788244,"about_ca_system_score_gemma":0.00023808406,"threshold_uncertainty_score":0.40708277},"labels":[],"label_agreement":null},{"id":"W2889382438","doi":"10.1007/s10589-018-0032-0","title":"Pricing and revenue maximization over a multicommodity transportation network: the nonlinear demand case","year":2018,"lang":"en","type":"article","venue":"Computational Optimization and Applications","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Polytechnique Montréal","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematical optimization; Heuristic; Bilevel optimization; Mathematics; Revenue management; Maximization; Piecewise linear function; Nonlinear system; Flow network; Function (biology); Piecewise; Integer programming; Revenue; Optimization problem; Economics","score_opus":0.016547017935155797,"score_gpt":0.29307292499669324,"score_spread":0.27652590706153746,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2889382438","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01619542,0.000119123055,0.9811862,0.0010815621,0.00006748287,0.0006279536,0.000063911866,0.00009573011,0.0005626243],"genre_scores_gemma":[0.8682658,0.00033447356,0.12936792,0.0006164696,0.00056492444,0.00011459982,0.00056449993,0.00001956585,0.000151738],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989786,0.00011494676,0.0002813886,0.00025401634,0.00020347428,0.00016756672],"domain_scores_gemma":[0.99907535,0.0003040634,0.00016523468,0.00010172397,0.00025354067,0.000100110425],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0003706673,0.000111816196,0.0001029948,0.00006330439,0.002067687,0.00014462663,0.0000710706,0.00008510745,0.00006057204],"category_scores_gemma":[0.00004676482,0.00010470558,0.00002360762,0.00046985442,0.00038508725,0.00026628934,0.000007109487,0.00009095037,0.0000035917278],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010436517,0.000030748633,0.0034756898,0.000009997222,0.000013289018,0.0000022798886,0.0039018095,0.9601225,0.0000010687072,0.028024951,0.00030724527,0.0040999395],"study_design_scores_gemma":[0.0004980587,0.000021833475,0.0126418285,0.000021139387,0.000065821594,0.000023252316,0.0007549482,0.9574206,0.0000023425878,0.0027984178,0.02556436,0.00018739917],"about_ca_topic_score_codex":0.00021310007,"about_ca_topic_score_gemma":0.0008530274,"teacher_disagreement_score":0.8520704,"about_ca_system_score_codex":0.000024952229,"about_ca_system_score_gemma":0.00006303785,"threshold_uncertainty_score":0.99923146},"labels":[],"label_agreement":null},{"id":"W2889724758","doi":"10.1109/itsc.2018.8569581","title":"Modelling Latent Travel Behaviour Characteristics with Generative Machine Learning","year":2018,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Latent variable; Computer science; Bayesian network; Latent variable model; Machine learning; Generative grammar; Artificial intelligence; Generative model; Restricted Boltzmann machine; Graphical model; Mode (computer interface); Dynamic Bayesian network; Preference; Latent class model; Artificial neural network; Statistics; Mathematics; Human–computer interaction","score_opus":0.03299488692954841,"score_gpt":0.27039244801658574,"score_spread":0.23739756108703733,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2889724758","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32340938,0.000006583489,0.66767955,0.0002323595,0.00007337914,0.00007378983,0.000006360823,0.00009065271,0.008427939],"genre_scores_gemma":[0.9739858,0.000027305357,0.019571774,0.00006490163,0.00013185816,0.0000029334979,0.000068165944,0.000007486479,0.0061398004],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994611,0.000041037667,0.0000966786,0.00011280247,0.00016033034,0.00012806329],"domain_scores_gemma":[0.99970514,0.000014585285,0.000050303544,0.000034670764,0.0001401337,0.00005517728],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014918223,0.00005645929,0.00006628205,0.00002995823,0.00048701034,0.000048212118,0.000044627683,0.000039432907,0.00024763442],"category_scores_gemma":[0.0000068239733,0.000045696856,0.000012621642,0.00010472011,0.00008625092,0.00009814416,0.0000015710544,0.00007573049,0.000017224485],"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":[0.00010209517,0.00010274617,0.53941983,0.0000056466047,0.000037364774,0.000013666769,0.11349814,0.3152007,0.00011949116,0.029408585,0.00011110472,0.0019806463],"study_design_scores_gemma":[0.00046424416,0.00024859284,0.042712778,0.000031522297,0.000054721848,0.0000011182296,0.003995343,0.94917333,0.00046436544,0.00007635994,0.0024478196,0.00032978252],"about_ca_topic_score_codex":0.0015577852,"about_ca_topic_score_gemma":0.00091126235,"teacher_disagreement_score":0.6505764,"about_ca_system_score_codex":0.000016049751,"about_ca_system_score_gemma":0.00004633794,"threshold_uncertainty_score":0.37457392},"labels":[],"label_agreement":null},{"id":"W2890118987","doi":"10.1155/2018/8797328","title":"A Subjective Optimal Strategy for Transit Simulation Models","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Scope (computer science); Computer science; Transit (satellite); Path (computing); Operations research; Public transport; Transport engineering; Engineering","score_opus":0.034592677451389636,"score_gpt":0.34082752861336496,"score_spread":0.30623485116197535,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2890118987","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.37907457,0.000045771987,0.6198946,0.0001234848,0.00027818044,0.00022093939,0.000023411334,0.000021839993,0.00031716484],"genre_scores_gemma":[0.96606946,0.000051593703,0.033346575,0.000035825113,0.00035949878,0.0000063261527,0.000041127187,0.000014216697,0.00007535812],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9988428,0.00004267559,0.00047552964,0.00012601913,0.0003348854,0.00017812547],"domain_scores_gemma":[0.99812627,0.00014250119,0.0003932789,0.000049135844,0.0011974204,0.00009138658],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004033753,0.00009543945,0.0001701128,0.0001409455,0.0002934518,0.000030940744,0.000092275775,0.00009275251,0.00002873182],"category_scores_gemma":[0.000037519592,0.000097269025,0.000120392186,0.000259491,0.0001013976,0.0009808359,1.5533293e-7,0.0000905926,9.824635e-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":[0.0006588046,0.00004017355,0.00024332761,0.000010012217,0.000023781924,0.0000021210083,0.032547228,0.955957,0.00032541875,0.0050817085,0.00000973471,0.005100702],"study_design_scores_gemma":[0.024436967,0.009013114,0.36068067,0.00086550764,0.0014903697,0.0000096722,0.12641175,0.34906977,0.007187916,0.0845827,0.033926006,0.0023255688],"about_ca_topic_score_codex":0.000030508807,"about_ca_topic_score_gemma":0.0005981682,"teacher_disagreement_score":0.6068872,"about_ca_system_score_codex":0.000054176075,"about_ca_system_score_gemma":0.00020896892,"threshold_uncertainty_score":0.3966516},"labels":[],"label_agreement":null},{"id":"W2890138613","doi":"10.1155/2018/8963234","title":"Mining Connected Vehicle Data for Beneficial Patterns in Dubai Taxi Operations","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Taxis; Outlier; Identifier; Computer science; Global Positioning System; Quality (philosophy); Data mining; Filter (signal processing); Scale (ratio); Data cleansing; Data quality; Transport engineering; Engineering; Geography; Artificial intelligence; Operations management; Telecommunications","score_opus":0.036248292238126194,"score_gpt":0.3355626252465439,"score_spread":0.2993143330084177,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2890138613","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9427929,0.000036228983,0.055602055,0.00062295917,0.00051491015,0.0002014306,0.00015310876,0.000019560346,0.000056840363],"genre_scores_gemma":[0.975049,0.000067573215,0.02389529,0.00007153844,0.00038901926,0.000005596631,0.0004812617,0.000010804955,0.00002992418],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99889475,0.000042210635,0.0005139302,0.00014417994,0.00024458245,0.00016032996],"domain_scores_gemma":[0.999015,0.00010900184,0.00022090183,0.000105226456,0.0004870171,0.0000628969],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046829385,0.00007267032,0.00014756517,0.00014853147,0.00024653482,0.000035318386,0.00020039795,0.000067395544,0.000045503013],"category_scores_gemma":[0.00015393493,0.000076482866,0.000037635436,0.00027805983,0.000051532643,0.0009986612,9.240591e-7,0.00008103458,8.466831e-7],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012576465,0.00039426403,0.13088815,0.00006975342,0.00008331504,0.000034366334,0.16928408,0.64808935,0.005421889,0.009098469,0.0003517139,0.03502704],"study_design_scores_gemma":[0.0047046444,0.00055577164,0.9427139,0.00036322727,0.00012912787,0.0000014731676,0.031886376,0.006291298,0.00091185217,0.00042876648,0.011646999,0.00036654994],"about_ca_topic_score_codex":0.00017561943,"about_ca_topic_score_gemma":0.031183183,"teacher_disagreement_score":0.81182575,"about_ca_system_score_codex":0.0000375822,"about_ca_system_score_gemma":0.00018004554,"threshold_uncertainty_score":0.9864952},"labels":[],"label_agreement":null},{"id":"W2890829050","doi":"10.1177/0361198118797219","title":"The Impact of Various Streetcar Types on Passenger Activity and Running Times","year":2018,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Saskatchewan; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Transport engineering; Service (business); Attractiveness; Type of service; Public transport; Doors; Level of service; Descriptive statistics; Regression analysis; Business; Computer science; Engineering; Statistics; Marketing; Mathematics","score_opus":0.07164258686567086,"score_gpt":0.42572162545275655,"score_spread":0.35407903858708567,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2890829050","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9947752,0.0001512922,0.0005094561,0.0024516743,0.00029986553,0.00054675824,0.000041904776,0.000022567949,0.0012013129],"genre_scores_gemma":[0.9972107,0.0012365069,0.00045312883,0.000011562659,0.00024337774,0.000015841582,0.0000061550877,0.000027743028,0.00079498935],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99407613,0.001502822,0.0007598958,0.00027603615,0.0027219232,0.00066317845],"domain_scores_gemma":[0.9930576,0.002220834,0.0004918193,0.00030830235,0.0036365194,0.0002849062],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0067393514,0.0001784162,0.00030851358,0.00051945704,0.0020422328,0.00017047931,0.00067025,0.00017667272,0.00019667797],"category_scores_gemma":[0.00041142703,0.00011151122,0.0002748078,0.0015767792,0.0019581134,0.00048812598,0.000003572132,0.001199271,0.000008491559],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0063669896,0.00054269604,0.8513652,0.00012615637,0.0006543225,0.00005300718,0.050565805,0.008190421,0.0036191768,0.02750687,0.010197795,0.040811554],"study_design_scores_gemma":[0.0006963331,0.0010190002,0.98701394,0.00020626892,0.000038317976,1.4537827e-7,0.0031927614,0.00028756785,0.0007831139,0.0022905576,0.004346819,0.00012516363],"about_ca_topic_score_codex":0.036415152,"about_ca_topic_score_gemma":0.08501739,"teacher_disagreement_score":0.13564874,"about_ca_system_score_codex":0.0001735647,"about_ca_system_score_gemma":0.0010214708,"threshold_uncertainty_score":0.99925697},"labels":[],"label_agreement":null},{"id":"W2891014681","doi":"10.1016/j.trb.2018.08.020","title":"Public transit service frequency and fares with heterogeneous users under monopoly and alternative regulatory policies","year":2018,"lang":"en","type":"article","venue":"Transportation Research Part B Methodological","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":39,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Leibniz-Gemeinschaft; Social Sciences and Humanities Research Council of Canada","keywords":"Monopoly; Subsidy; Economic surplus; Willingness to pay; Microeconomics; Economics; Profit (economics); Public transport; Value (mathematics); Service (business); Public economics; Business; Computer science; Transport engineering; Engineering; Welfare","score_opus":0.4684840518001212,"score_gpt":0.4652411621009373,"score_spread":0.003242889699183904,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2891014681","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96643555,0.00019584277,0.025268208,0.006494809,0.000056709734,0.00037576273,0.000042233278,0.00012880683,0.0010020724],"genre_scores_gemma":[0.9819055,0.00038596976,0.016900728,0.0004270861,0.00012800831,0.00004713028,0.00005882292,0.000015622456,0.00013113704],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99654937,0.0015180161,0.0002712964,0.0004437797,0.00072693976,0.00049062946],"domain_scores_gemma":[0.9978527,0.00092219387,0.00007555083,0.00013506811,0.0007058532,0.0003086453],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026393866,0.00015055828,0.00022194156,0.000204118,0.0010369826,0.00013393915,0.00016204607,0.00017707898,0.0002034218],"category_scores_gemma":[0.00015921367,0.00012244254,0.00002759851,0.00062897743,0.0017219185,0.0003618928,0.0000035098803,0.00026131698,0.000005035294],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012445587,0.0002789809,0.40714434,0.00016943918,0.00035181027,0.00010747642,0.17339835,0.0051970547,0.0024115664,0.40055084,0.00033318825,0.008812387],"study_design_scores_gemma":[0.0010671916,0.0007337017,0.95927763,0.000071366914,0.000054302407,0.0000047226636,0.015037214,0.00032407933,0.0010617826,0.017555118,0.0043871226,0.0004257537],"about_ca_topic_score_codex":0.004027637,"about_ca_topic_score_gemma":0.025755584,"teacher_disagreement_score":0.5521333,"about_ca_system_score_codex":0.00003397909,"about_ca_system_score_gemma":0.00015421021,"threshold_uncertainty_score":0.99202186},"labels":[],"label_agreement":null},{"id":"W2891494901","doi":"10.1155/2018/9876598","title":"Effects of Users’ Bounded Rationality on a Traffic Network Performance: A Simulation Study","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"H2020 European Research Council; National Natural Science Foundation of China; European Commission","keywords":"Bounded rationality; Bounded function; Benchmark (surveying); Inflow; Computer science; Monte Carlo method; Mathematical optimization; Mathematics; Physics","score_opus":0.014229499927962715,"score_gpt":0.31074244787047267,"score_spread":0.29651294794250993,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2891494901","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99224466,0.000029963447,0.0065310793,0.00006137836,0.00059673184,0.00040297778,0.0000028386728,0.000025746445,0.000104625215],"genre_scores_gemma":[0.9967541,0.00004231127,0.0027308848,0.000037919788,0.00036568567,0.000005102031,0.000019176501,0.000010226068,0.000034565972],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9983135,0.00014144715,0.0006245031,0.00012291339,0.00064402504,0.00015364576],"domain_scores_gemma":[0.9981752,0.00029976037,0.0006788445,0.000068245805,0.00070667465,0.000071276765],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006736423,0.000101868085,0.00021660913,0.00012983216,0.0003063736,0.000019672867,0.00010063324,0.00006814763,0.00002414953],"category_scores_gemma":[0.00009262536,0.00009777939,0.00008185553,0.00047097032,0.000104578656,0.0006011396,3.828541e-7,0.0001241248,0.000001870659],"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":[0.000784474,0.00032037534,0.01880968,0.000031780688,0.000038503706,0.0000037890647,0.033778958,0.9425161,0.000085079024,0.00039838767,0.000015816495,0.0032170794],"study_design_scores_gemma":[0.002321518,0.0016781774,0.98942995,0.00017573593,0.00013092262,2.2665564e-7,0.002997828,0.0020200652,0.00029694915,0.00022226041,0.00059908855,0.00012730317],"about_ca_topic_score_codex":0.000013504515,"about_ca_topic_score_gemma":0.00035916883,"teacher_disagreement_score":0.9706203,"about_ca_system_score_codex":0.000057439032,"about_ca_system_score_gemma":0.00016398479,"threshold_uncertainty_score":0.39873278},"labels":[],"label_agreement":null},{"id":"W2892976651","doi":"10.1155/2018/9714046","title":"Improving Bus Operations through Integrated Dynamic Holding Control and Schedule Optimization","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","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":"Department of Science and Technology of Sichuan Province","keywords":"Schedule; Control (management); Computer science; Operations research; Travel time; Limit (mathematics); Simulation; Transport engineering; Mathematical optimization; Engineering","score_opus":0.008404690240787865,"score_gpt":0.2830709333349312,"score_spread":0.27466624309414334,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2892976651","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3475456,0.00014995546,0.65125173,0.00039980627,0.00036446474,0.0001407747,0.000014894672,0.000036237772,0.00009649749],"genre_scores_gemma":[0.8220187,0.00024949881,0.17742425,0.00008469763,0.00011288403,0.0000032564137,0.000054076238,0.0000119713595,0.00004066471],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988666,0.00007155334,0.0004883315,0.00013761126,0.00027558708,0.0001602738],"domain_scores_gemma":[0.9986544,0.00006010409,0.00031357098,0.000056368885,0.00083525316,0.000080286656],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037671684,0.00010384631,0.0001712086,0.00012994747,0.00048123806,0.00007677476,0.00008155601,0.00008869326,0.000044304015],"category_scores_gemma":[0.00013406869,0.000100897065,0.00004803973,0.00035647315,0.00015668158,0.00159688,5.0869403e-7,0.00014547406,0.0000010716553],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014122785,0.000035720117,0.0014319709,0.000011401816,0.000026266282,0.0000050628805,0.017914822,0.96964073,0.0034262594,0.0021774035,0.000006994196,0.005182159],"study_design_scores_gemma":[0.026133938,0.0031684993,0.25207382,0.001583498,0.0015669841,0.000047400325,0.13066085,0.5637928,0.0047895014,0.0030724967,0.010784738,0.00232542],"about_ca_topic_score_codex":0.0001435654,"about_ca_topic_score_gemma":0.0014426257,"teacher_disagreement_score":0.4744731,"about_ca_system_score_codex":0.00006668429,"about_ca_system_score_gemma":0.00018316197,"threshold_uncertainty_score":0.4114463},"labels":[],"label_agreement":null},{"id":"W2893390091","doi":"10.1155/2018/9825327","title":"Predicting and Visualizing the Uncertainty Propagations in Traffic Assignments Model Using Monte Carlo Simulation Method","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Monte Carlo method; Traffic flow (computer networking); Computer science; Stochastic simulation; Traffic generation model; Uncertainty analysis; Traffic simulation; Transport network; Matrix (chemical analysis); Standard deviation; Uncertainty quantification; Process (computing); Simulation; Mathematical optimization; Statistics; Mathematics; Engineering; Machine learning; Real-time computing; Transport engineering","score_opus":0.038888358245893574,"score_gpt":0.37956540356020235,"score_spread":0.34067704531430876,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2893390091","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8193097,0.00004968822,0.18013665,0.0001509454,0.00012888684,0.00018275368,0.000005474162,0.00001537831,0.000020500353],"genre_scores_gemma":[0.96190363,0.000042401636,0.037885465,0.00003368765,0.00010558007,0.0000029313906,0.0000049378964,0.000009544558,0.0000118483385],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99872017,0.00013965793,0.0005084141,0.000117953176,0.00036685003,0.0001469797],"domain_scores_gemma":[0.99892986,0.00017488594,0.00045287842,0.000047342004,0.00033927168,0.000055738798],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00093711214,0.00008055878,0.00013472597,0.00014050253,0.0006040032,0.000032033036,0.00007222611,0.00006302912,0.0000032075893],"category_scores_gemma":[0.00009837017,0.00006943615,0.000040654606,0.00034832215,0.00008937108,0.00077845884,9.331835e-7,0.00014406593,9.0766385e-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":[0.00007144918,0.000020071397,0.007917329,0.000006779338,0.000009516348,0.0000015583779,0.062393483,0.9253903,0.0003103079,0.00015320045,4.5309542e-7,0.003725547],"study_design_scores_gemma":[0.0006259719,0.000058634618,0.050442845,0.00011956178,0.00005904678,7.921532e-7,0.008884478,0.9393139,0.000041144787,0.00031926023,0.000052754887,0.00008162452],"about_ca_topic_score_codex":0.00016270937,"about_ca_topic_score_gemma":0.002320514,"teacher_disagreement_score":0.14259388,"about_ca_system_score_codex":0.00009271579,"about_ca_system_score_gemma":0.0001513432,"threshold_uncertainty_score":0.46455652},"labels":[],"label_agreement":null},{"id":"W2893573364","doi":"10.1155/2018/2051606","title":"Study on the Equilibrium Discriminant Model of Urban Agglomeration Transport Supply and Demand Structure","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","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":"National Natural Science Foundation of China","keywords":"Economies of agglomeration; Urban agglomeration; Entropy (arrow of time); Supply and demand; Urban structure; Economics; General equilibrium theory; Microeconomics; Economic geography; Engineering; Urban planning; Civil engineering; Thermodynamics; Physics","score_opus":0.020388314775229063,"score_gpt":0.2902826529607797,"score_spread":0.26989433818555064,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2893573364","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99000216,0.00005007791,0.008783536,0.00050275837,0.0002029886,0.0002970969,0.0000442973,0.0000113850365,0.000105733096],"genre_scores_gemma":[0.9976501,0.000057033016,0.0020402586,0.000046413534,0.00012320837,0.0000026846988,0.000024885385,0.000010929713,0.00004448968],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.9986271,0.00007598847,0.0005399214,0.00012992365,0.0004954663,0.00013158182],"domain_scores_gemma":[0.99890256,0.00007717928,0.00047294758,0.000088609224,0.00039037346,0.00006833379],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044049352,0.00011028991,0.00019660887,0.00010973471,0.00022327346,0.000017763963,0.00011348962,0.000060060673,0.00001865107],"category_scores_gemma":[0.00003209027,0.00007818401,0.00005858974,0.00021237042,0.00018263914,0.00050834083,4.7549256e-7,0.00013461988,2.097745e-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":[0.001140433,0.00034337657,0.15131424,0.00003991228,0.000099350924,0.000014378923,0.43743795,0.374586,0.021558542,0.0115580335,0.00009476674,0.0018130273],"study_design_scores_gemma":[0.0016926862,0.0013038738,0.9471387,0.00015627357,0.00027431437,0.0000016228793,0.041174967,0.0006876135,0.0050691096,0.0020982295,0.0001965103,0.00020605129],"about_ca_topic_score_codex":0.00003140442,"about_ca_topic_score_gemma":0.000975318,"teacher_disagreement_score":0.7958245,"about_ca_system_score_codex":0.000020081101,"about_ca_system_score_gemma":0.00011188555,"threshold_uncertainty_score":0.31882516},"labels":[],"label_agreement":null},{"id":"W2894611379","doi":"10.1155/2018/2310905","title":"Modelling Rail-Based Park and Ride with Environmental Constraints in a Multimodal Transport Network","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Park and ride; Multimodal transport; Complementarity (molecular biology); Computer science; Public transport; Flexibility (engineering); Flow network; Transport engineering; Transport network; Sustainability; Adaptability; Constraint (computer-aided design); Mathematical optimization; Operations research; Engineering; Mathematics; Ecology","score_opus":0.009178807828327184,"score_gpt":0.24413185506250384,"score_spread":0.23495304723417665,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2894611379","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8197899,0.00006794278,0.17967404,0.00011551368,0.00009046899,0.00013955034,0.00001338738,0.000013743333,0.00009545019],"genre_scores_gemma":[0.94728065,0.00011952166,0.05236551,0.000054997665,0.0001195738,0.0000031217562,0.000035725967,0.000011642644,0.000009247827],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9988285,0.000046750436,0.00044265625,0.00014235075,0.00033471384,0.00020507019],"domain_scores_gemma":[0.9994116,0.00006899149,0.000296048,0.000038141632,0.00007741214,0.00010785096],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039407413,0.00010831535,0.00018704409,0.00010637605,0.00017201288,0.000012635251,0.000061885956,0.00007177991,0.00004378824],"category_scores_gemma":[0.000005093035,0.00010185234,0.000041508694,0.00019408157,0.00035534415,0.00045807933,1.7214259e-7,0.00014954626,5.81086e-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":[0.000539913,0.00004677906,0.10364491,0.0000074523336,0.000011421052,0.00003416108,0.011501269,0.88197696,0.000097207194,0.00017051872,0.0000016269332,0.0019677994],"study_design_scores_gemma":[0.0067298915,0.00064339786,0.9582171,0.0005136494,0.00013649689,0.000006939244,0.011229609,0.019921152,0.00021748443,0.0006009655,0.0013638433,0.00041949857],"about_ca_topic_score_codex":0.00009210245,"about_ca_topic_score_gemma":0.0030562414,"teacher_disagreement_score":0.8620558,"about_ca_system_score_codex":0.000042865093,"about_ca_system_score_gemma":0.00013523336,"threshold_uncertainty_score":0.4153418},"labels":[],"label_agreement":null},{"id":"W2895118251","doi":"10.1155/2018/4218625","title":"Minimizing Metro Transfer Waiting Time with AFCS Data Using Simulated Annealing with Parallel Computing","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Headway; Simulated annealing; Transfer (computing); Computer science; Transfer station; Train; Travel time; Real-time computing; Transfer efficiency; Simulation; Operations research; Engineering; Transport engineering; Algorithm","score_opus":0.0370050801069123,"score_gpt":0.31731611135393845,"score_spread":0.28031103124702617,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2895118251","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.71330506,0.000054962355,0.2861616,0.000101656835,0.00009070297,0.00012483686,0.00001895683,0.000044070697,0.000098118464],"genre_scores_gemma":[0.8285348,0.000018451354,0.17098865,0.00004288206,0.0002346031,1.3268891e-7,0.00014589976,0.000023733988,0.000010888134],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99820465,0.000081521255,0.0006015512,0.00023593499,0.0005894905,0.00028684206],"domain_scores_gemma":[0.9984127,0.00015396439,0.0004420818,0.0001400365,0.0007261961,0.00012497391],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00068894424,0.00015723947,0.0002844484,0.00017371326,0.0005484406,0.00006647286,0.00023570888,0.00007685493,0.000035852878],"category_scores_gemma":[0.000035855992,0.00013184191,0.00004314053,0.00059023825,0.00015950107,0.0013989107,0.0000011597367,0.00019713842,9.618232e-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":[0.00061498466,0.000029912128,0.0075123515,0.00001547077,0.000072489485,0.000036331272,0.019111013,0.9702014,0.00056383316,0.00007094255,0.0000036073695,0.001767687],"study_design_scores_gemma":[0.010440001,0.0016381651,0.05305475,0.0026462045,0.0013216763,0.000036012756,0.045758788,0.8805703,0.0012959344,0.00013780885,0.0018660341,0.001234353],"about_ca_topic_score_codex":0.00009251384,"about_ca_topic_score_gemma":0.0005900618,"teacher_disagreement_score":0.11522967,"about_ca_system_score_codex":0.000053526142,"about_ca_system_score_gemma":0.00024371858,"threshold_uncertainty_score":0.5376357},"labels":[],"label_agreement":null},{"id":"W2895427262","doi":"10.5383/juspn.10.01.005","title":"Agent–based Modeling of Social Behavioral Challenges in Transportation","year":2018,"lang":"en","type":"article","venue":"Journal of Ubiquitous Systems and Pervasive Networks","topic":"Transportation Planning and Optimization","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":"Computer science; Psychology; Transport engineering; Engineering","score_opus":0.07532753955216039,"score_gpt":0.32332228394025325,"score_spread":0.24799474438809288,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2895427262","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97645193,0.002849298,0.018902745,0.00031233422,0.0008131327,0.00015151188,0.000007864192,0.000010771258,0.0005003949],"genre_scores_gemma":[0.9981867,0.00078327383,0.0001972089,0.000012808792,0.00078743533,0.0000021374747,0.0000063602474,0.0000074868212,0.000016608576],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988724,0.00013558952,0.00049324724,0.00008687299,0.0002667042,0.00014520588],"domain_scores_gemma":[0.9990525,0.000045502828,0.0003772587,0.00003348953,0.0004336921,0.00005753467],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007254293,0.000072798684,0.00023409587,0.000118946075,0.00016521857,0.00002558713,0.00007481557,0.00014079438,0.000009563977],"category_scores_gemma":[0.00000970318,0.00006868694,0.0000626821,0.0001359523,0.00008601834,0.00016486905,9.346767e-7,0.00012152605,1.5325232e-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":[0.0002780737,0.00016911654,0.027008334,0.00012211277,0.000037866892,0.000034041375,0.130509,0.8331239,0.000053793206,0.0035755902,0.00017027195,0.004917857],"study_design_scores_gemma":[0.0051191514,0.0020087876,0.11749568,0.0022113572,0.0003997849,0.000017443534,0.17102095,0.6942845,0.000037617934,0.00027608805,0.0063374364,0.00079123146],"about_ca_topic_score_codex":0.0007614215,"about_ca_topic_score_gemma":0.0039762435,"teacher_disagreement_score":0.13883945,"about_ca_system_score_codex":0.00003145756,"about_ca_system_score_gemma":0.00010407407,"threshold_uncertainty_score":0.28009722},"labels":[],"label_agreement":null},{"id":"W2895797859","doi":"10.1145/3283207.3283212","title":"Optimizing Bus Stop Spacing Using the Simulated Annealing Algorithm with Spatial Interaction Coverage Model","year":2018,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Simulated annealing; Computer science; Public transport; System bus; Attractiveness; Population; Transport engineering; Simulation; Algorithm; Engineering","score_opus":0.03361435638642847,"score_gpt":0.3181541330618906,"score_spread":0.28453977667546215,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2895797859","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14677538,0.000005406499,0.8478284,0.0001568995,0.00018671724,0.0001295672,0.0000029799328,0.00012572363,0.0047889533],"genre_scores_gemma":[0.9310014,0.000007407134,0.06826678,0.0001453121,0.0002322919,0.0000010181785,0.0000151255035,0.000012161758,0.000318489],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99914366,0.00006823031,0.00015201885,0.00016952775,0.00026027256,0.00020627031],"domain_scores_gemma":[0.99946165,0.00007168708,0.0001043032,0.00009066277,0.00021989386,0.000051777217],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030025147,0.00008952295,0.000085255495,0.000057577767,0.0010607115,0.00015168,0.00008334402,0.00006205839,0.00007628664],"category_scores_gemma":[0.000025858377,0.000064665815,0.000024471607,0.0002564901,0.00011988134,0.00042673596,0.0000057170564,0.000109779896,0.0000038239464],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002770508,0.000008041806,0.00028700565,0.0000012445761,0.0000113096885,0.0000015122378,0.02307593,0.9730222,0.00008002428,0.00021316271,0.000010971312,0.003260863],"study_design_scores_gemma":[0.00022506714,0.000028943596,0.000059760692,0.000035575093,0.000023900733,0.0000010244083,0.005094491,0.99380946,0.0003586019,0.000058914942,0.00019652568,0.00010773776],"about_ca_topic_score_codex":0.0057227174,"about_ca_topic_score_gemma":0.0019935225,"teacher_disagreement_score":0.78422606,"about_ca_system_score_codex":0.00007365007,"about_ca_system_score_gemma":0.00011223587,"threshold_uncertainty_score":0.8651075},"labels":[],"label_agreement":null},{"id":"W2898050127","doi":"","title":"Factors Affecting Road Capacity Under non-Ideal Conditions in Egypt","year":2018,"lang":"en","type":"article","venue":"Nova Journal of Engineering and Applied Sciences","topic":"Transportation Planning and Optimization","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":"","keywords":"Population; Transport engineering; Highway Capacity Manual; Synchro; Variable (mathematics); Reduction (mathematics); Calibration; Carrying capacity; Traffic congestion; Engineering; Simulation; Computer science; Statistics; Mathematics; Level of service","score_opus":0.04898184739924424,"score_gpt":0.3032983631326488,"score_spread":0.2543165157334046,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2898050127","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9909975,0.000016116408,0.0064247902,0.000085757194,0.00027832168,0.00003509507,0.0000012315097,0.000013624258,0.0021475437],"genre_scores_gemma":[0.99772286,0.0000062280933,0.002112862,0.000015506572,0.00013080884,3.106282e-7,4.993584e-7,0.0000026207108,0.000008322864],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9993645,0.00001158574,0.00017318415,0.000082968036,0.00021439126,0.00015337195],"domain_scores_gemma":[0.999657,0.00010754451,0.00009346354,0.000022094458,0.000053018157,0.00006692713],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007860725,0.00005816056,0.000102536804,0.00017891628,0.00029220153,0.00006419894,0.00009089876,0.000041393992,0.000023513327],"category_scores_gemma":[0.00006124385,0.00004828029,0.000019023382,0.0004216288,0.00023137742,0.00018181922,0.0000030931276,0.00011396381,7.672501e-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":[0.000016201288,0.000083188635,0.14327388,0.000030225607,0.000034775476,0.0000041741546,0.072645076,0.72939783,0.014238135,0.038580075,0.00017515913,0.0015212573],"study_design_scores_gemma":[0.00047062992,0.00012918499,0.97203505,0.00014217698,0.000016182268,0.0000046622963,0.01883172,0.00476391,0.0026275262,0.00047594437,0.0002595675,0.00024343785],"about_ca_topic_score_codex":0.0001442957,"about_ca_topic_score_gemma":0.00023891758,"teacher_disagreement_score":0.82876116,"about_ca_system_score_codex":0.000021690434,"about_ca_system_score_gemma":0.00006461606,"threshold_uncertainty_score":0.22474076},"labels":[],"label_agreement":null},{"id":"W2898056314","doi":"10.1155/2018/6809304","title":"Impact of Capacity Drop on Commuting Systems under Uncertainty","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Beijing Municipal Natural Science Foundation; National Natural Science Foundation of China","keywords":"Toll; Bottleneck; Queue; Drop (telecommunication); Queueing theory; Monotonic function; Traffic congestion; Computer science; Mathematical optimization; Simulation; Operations research; Transport engineering; Mathematics; Engineering; Statistics","score_opus":0.02913363573858669,"score_gpt":0.3323826050733651,"score_spread":0.3032489693347784,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2898056314","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9815984,0.000036716418,0.017054835,0.000076633805,0.0005044098,0.00010694739,0.00002471224,0.00001614276,0.00058121316],"genre_scores_gemma":[0.99817437,0.000052678744,0.0015009313,0.00001791606,0.00019300544,9.287892e-7,0.000021874643,0.0000088554125,0.00002941452],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9986773,0.00009933771,0.00054837466,0.00008424077,0.00043962317,0.00015113021],"domain_scores_gemma":[0.9981544,0.00012983255,0.00075956626,0.000076470686,0.00079179095,0.00008791573],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00053008494,0.00008940476,0.000211074,0.00014213062,0.0002063744,0.000017961092,0.000112179216,0.00007417198,0.00003395441],"category_scores_gemma":[0.000058678026,0.000076470875,0.00013588904,0.00030350796,0.00015811047,0.00037087884,2.885257e-7,0.0001437651,0.0000016957831],"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":[0.0002647991,0.00007531427,0.024420213,0.000014701325,0.00004982451,0.0000023642747,0.018701987,0.95025647,0.0015513748,0.0040331115,0.00003683051,0.00059299415],"study_design_scores_gemma":[0.0016014093,0.0013142524,0.9799044,0.00044503473,0.000096210955,0.0000023562493,0.013605125,0.00089052,0.00045478114,0.00087470753,0.00060490705,0.00020630765],"about_ca_topic_score_codex":0.0008807212,"about_ca_topic_score_gemma":0.00081087695,"teacher_disagreement_score":0.95548415,"about_ca_system_score_codex":0.0001398845,"about_ca_system_score_gemma":0.0001988754,"threshold_uncertainty_score":0.31183916},"labels":[],"label_agreement":null},{"id":"W2898929540","doi":"10.1016/j.trpro.2018.10.009","title":"Workshop Synthesis: Validation under \"ground truth\" in surveys","year":2018,"lang":"en","type":"article","venue":"Transportation research procedia","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ministère des Transports","funders":"","keywords":"Quality (philosophy); Data collection; Order (exchange); Data quality; Point (geometry); Computer science; Ground truth; Transport engineering; Operations research; Survey data collection; Risk analysis (engineering); Data science; Business; Engineering; Operations management; Mathematics; Statistics","score_opus":0.1425053003700412,"score_gpt":0.4283320677163855,"score_spread":0.28582676734634427,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2898929540","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9708912,0.00004651799,0.017538156,0.0019185849,0.00033038514,0.0007090547,0.00003361582,0.00034782576,0.008184695],"genre_scores_gemma":[0.997511,0.00012512045,0.0008404471,0.000036030367,0.000251664,0.00015578998,0.00015372732,0.00002866823,0.00089753186],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99688107,0.00077055575,0.00038776692,0.00037297435,0.0010503664,0.0005372815],"domain_scores_gemma":[0.9979258,0.0009178656,0.00007683496,0.00014275283,0.0007769004,0.0001598066],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0058056815,0.00011398535,0.00014607672,0.0004648037,0.0005817306,0.00012718682,0.00023541076,0.00019219301,0.0005710292],"category_scores_gemma":[0.00077595346,0.0001244588,0.000038864022,0.0018143218,0.00047568718,0.0006217998,0.0000016600519,0.00029392846,0.0001474946],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039199254,0.0007865507,0.4023741,0.00025032417,0.00008435648,0.000047382437,0.20588078,0.010394883,0.0006102649,0.35424274,0.0042331372,0.020703493],"study_design_scores_gemma":[0.0004376521,0.000045219917,0.9757638,0.0001241482,0.000012160263,9.559966e-8,0.016460748,0.00027755104,0.0006437429,0.0036601578,0.0023509571,0.0002237854],"about_ca_topic_score_codex":0.0028203921,"about_ca_topic_score_gemma":0.04271569,"teacher_disagreement_score":0.5733897,"about_ca_system_score_codex":0.00012327278,"about_ca_system_score_gemma":0.0004663163,"threshold_uncertainty_score":0.97475225},"labels":[],"label_agreement":null},{"id":"W2899774098","doi":"10.1155/2018/1605717","title":"Chaotic Behavior of Traffic-Flow Evolution with Two Departure Intervals in Two-Link Transportation Network","year":2018,"lang":"en","type":"article","venue":"Discrete Dynamics in Nature and Society","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"National Natural Science Foundation of China","keywords":"Algorithm; Computer science; Flow (mathematics); Traffic flow (computer networking); Chaotic; Artificial intelligence; Physics; Mechanics","score_opus":0.005593550590576028,"score_gpt":0.2859138549959135,"score_spread":0.28032030440533745,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2899774098","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9923838,0.00020927934,0.006055703,0.00034607045,0.00019234793,0.00030478954,0.00007161157,0.00003696581,0.0003994405],"genre_scores_gemma":[0.9911957,0.000122041856,0.00797432,0.00007971555,0.00013541445,0.000015170214,0.0004207313,0.000011434048,0.00004546756],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9989883,0.000073620125,0.00025962104,0.00022308239,0.00022811063,0.00022728181],"domain_scores_gemma":[0.9995889,0.000054905995,0.000117538046,0.0000823211,0.00010885713,0.00004745277],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044924795,0.000114841125,0.00018218078,0.000046163543,0.00016980202,0.000021855587,0.000091039095,0.00024034354,0.0000076129672],"category_scores_gemma":[0.000012619293,0.000105918465,0.00006420648,0.0005488828,0.00027235912,0.00019322542,0.0000022578415,0.00038146324,2.0236088e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020447247,0.00011795765,0.6430783,0.000079919446,0.000045520566,0.000011259522,0.103589304,0.18314235,0.000013530904,0.066366225,0.00010635958,0.0032447514],"study_design_scores_gemma":[0.0022052273,0.00014719249,0.5867086,0.00037865445,0.00011235724,9.620157e-7,0.022566821,0.38628736,0.0000047660783,0.0010692338,0.00014037821,0.000378419],"about_ca_topic_score_codex":0.0004914836,"about_ca_topic_score_gemma":0.12328407,"teacher_disagreement_score":0.20314503,"about_ca_system_score_codex":0.00012541556,"about_ca_system_score_gemma":0.00008044982,"threshold_uncertainty_score":0.8927137},"labels":[],"label_agreement":null},{"id":"W2900531378","doi":"10.1155/2018/8414069","title":"A Global Sensitivity Analysis of Dynamic Loading and Route Selection Parameters on Network Performances","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","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":"Horizon 2020 Framework Programme; European Commission","keywords":"Gridlock; Sensitivity (control systems); Flow network; Mathematical optimization; Flow (mathematics); Computer science; Range (aeronautics); Variance (accounting); Routing (electronic design automation); Selection (genetic algorithm); Dynamic network analysis; Mathematics; Engineering","score_opus":0.008525368933967213,"score_gpt":0.2882964108555675,"score_spread":0.2797710419216003,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2900531378","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96122384,0.000040215724,0.038215008,0.000078956255,0.0002274587,0.00006139429,0.000016103773,0.000014120487,0.00012288561],"genre_scores_gemma":[0.9862462,0.00019067153,0.013432905,0.000029527713,0.000062577295,5.933869e-7,0.000025438325,0.0000035029366,0.000008550455],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9990416,0.00007398913,0.00036029893,0.000104082275,0.00029258276,0.00012744592],"domain_scores_gemma":[0.998991,0.00008720144,0.0005079435,0.000034672772,0.00031983823,0.00005933597],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00052409945,0.00007287327,0.00021351094,0.00017168587,0.00020029824,0.000017011289,0.000034169043,0.000054942124,0.0000074147647],"category_scores_gemma":[0.000031322375,0.00007056194,0.000089055764,0.0011194416,0.00011003465,0.00044496843,2.97828e-7,0.00006709452,2.2094099e-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":[0.0002975143,0.000021932265,0.19348532,0.000007075217,0.00017897041,0.000002491268,0.00548463,0.7931371,0.000115238516,0.0006955413,0.0000049857413,0.006569219],"study_design_scores_gemma":[0.00032342595,0.00025115392,0.98858404,0.00006940459,0.00050920417,9.108791e-7,0.0014310012,0.008403266,0.00006524164,0.0001836787,0.000101457554,0.00007722947],"about_ca_topic_score_codex":0.00014269249,"about_ca_topic_score_gemma":0.009748362,"teacher_disagreement_score":0.7950987,"about_ca_system_score_codex":0.00007079565,"about_ca_system_score_gemma":0.000074614036,"threshold_uncertainty_score":0.5439815},"labels":[],"label_agreement":null},{"id":"W2901039232","doi":"10.22459/rpp.07.2018","title":"Road Pricing and Provision: Changed Traffic Conditions Ahead","year":2018,"lang":"en","type":"book","venue":"ANU Press eBooks","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"Albert Einstein College of Medicine, Yeshiva University; Organisation de Coopération et de Développement Économiques; Griffith University; Chartered Institute of Logistics and Transport; Australian National University; Australian Government","keywords":"Toll; Governor; Road pricing; Transport engineering; Toll road; Business; Advertising; Finance; Economics; Engineering; Traffic congestion","score_opus":0.03940826774589377,"score_gpt":0.30306985585435947,"score_spread":0.2636615881084657,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2901039232","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0029865156,0.00041653472,0.00017770617,0.000060525497,0.00042589346,0.0007653088,0.00011219163,0.00031048732,0.99474484],"genre_scores_gemma":[0.008606229,0.0001027984,0.00026435056,0.00011442682,0.00063372316,0.000049701073,0.00033305134,0.000028980476,0.98986673],"study_design_codex":"qualitative","study_design_gemma":"not_applicable","domain_scores_codex":[0.9988662,0.00008296524,0.00021668819,0.00030594703,0.00030486714,0.00022332306],"domain_scores_gemma":[0.9993515,0.000077997654,0.00017487396,0.00014607083,0.00013543888,0.00011414271],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025263356,0.00016332952,0.00020496985,0.00009759446,0.0005310702,0.000116550975,0.00013332792,0.00031333964,0.00005896792],"category_scores_gemma":[0.000026348158,0.00017041968,0.000049047914,0.000016930877,0.00033924307,0.000091639704,0.000011333658,0.00018334911,0.000011858675],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010982052,0.0001059294,0.00003742563,0.0006188291,0.00031046924,0.00008033554,0.49290177,0.0006082255,0.000030026316,0.06741556,0.3399096,0.09787201],"study_design_scores_gemma":[0.000483107,0.00011507437,0.00023331454,0.0006288172,0.0001812563,0.0000023813143,0.00050968153,0.00031310535,0.000025134435,0.0026210847,0.9943439,0.0005431898],"about_ca_topic_score_codex":0.00072088605,"about_ca_topic_score_gemma":0.000949753,"teacher_disagreement_score":0.65443426,"about_ca_system_score_codex":0.000044413886,"about_ca_system_score_gemma":0.00027077043,"threshold_uncertainty_score":0.6949513},"labels":[],"label_agreement":null},{"id":"W2901553425","doi":"10.1080/23249935.2018.1546780","title":"Penalization and augmented Lagrangian for O-D demand matrix estimation from transit segment counts","year":2018,"lang":"en","type":"article","venue":"Transportmetrica A Transport Science","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Consejo Nacional de Ciencia y Tecnología","keywords":"Augmented Lagrangian method; Matrix (chemical analysis); Quadratic equation; Mathematical optimization; Transit (satellite); Applied mathematics; Convergence (economics); Computer science; Mathematics; Public transport; Geometry; Engineering; Economics","score_opus":0.017921893810494447,"score_gpt":0.314050242466595,"score_spread":0.29612834865610055,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2901553425","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13300054,0.0002063908,0.8607027,0.00045532023,0.00037282088,0.00070484413,0.00022994286,0.00021304657,0.004114408],"genre_scores_gemma":[0.9786193,0.0001433323,0.020292645,0.000092772374,0.00009200806,0.000024019024,0.00028455793,0.00001419564,0.00043716552],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9982774,0.000021545544,0.00031507717,0.0004211984,0.00062945124,0.0003353296],"domain_scores_gemma":[0.99922603,0.00007173526,0.00009635213,0.0001299597,0.0002788179,0.00019710293],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007564682,0.00013803958,0.00016820137,0.00035458492,0.0008418955,0.00006299804,0.0002091453,0.00009434097,0.0003259067],"category_scores_gemma":[0.0000319586,0.00014074425,0.000055183536,0.0016725389,0.00072333246,0.00049377483,0.0000010300166,0.00005842618,0.00001779846],"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":[0.0020246936,0.0014815885,0.40043515,0.0005981476,0.00040016402,0.0000706013,0.24492821,0.031051047,0.012046614,0.16594963,0.002706352,0.13830778],"study_design_scores_gemma":[0.0053109843,0.0006341556,0.80007106,0.00031591314,0.00092064956,0.000003679782,0.002408435,0.061425466,0.0052247127,0.0045119165,0.11740136,0.0017716526],"about_ca_topic_score_codex":0.00046512147,"about_ca_topic_score_gemma":0.00052122615,"teacher_disagreement_score":0.8456188,"about_ca_system_score_codex":0.000060121325,"about_ca_system_score_gemma":0.00025260518,"threshold_uncertainty_score":0.6475265},"labels":[],"label_agreement":null},{"id":"W2902480336","doi":"10.1016/j.retrec.2018.11.005","title":"Journal of the Transportation Research Forum (JTRF) / Research in Transportation Economics (RETREC)","year":2018,"lang":"en","type":"article","venue":"Research in Transportation Economics","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Transport engineering; Regional science; Business; Engineering; Geography","score_opus":0.16526978082326296,"score_gpt":0.4314651904232614,"score_spread":0.26619540959999843,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2902480336","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98760104,0.00017216112,0.00026179006,0.0075918655,0.0007477291,0.0011519006,0.0001799576,0.00002701949,0.0022665402],"genre_scores_gemma":[0.99491394,0.003185016,0.00081060414,0.00006105088,0.0004179646,0.00008651187,0.00019265943,0.000056861805,0.00027542244],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9927935,0.0018388752,0.0019076926,0.0006567209,0.001355319,0.0014479198],"domain_scores_gemma":[0.9950558,0.0014293026,0.00041466937,0.0004450736,0.002339915,0.00031526474],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.018012913,0.00023258552,0.0004512468,0.002504039,0.0012072006,0.00021267854,0.001114399,0.00048535023,0.00017260549],"category_scores_gemma":[0.00022702597,0.00023667255,0.00019548072,0.0036723432,0.0018671056,0.0014548813,0.0000041771764,0.0020444894,0.000035880064],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013324505,0.00062076515,0.63210386,0.00017776353,0.00006431457,0.000051548846,0.11117969,0.057154085,0.00013344391,0.18314672,0.0013659726,0.012669368],"study_design_scores_gemma":[0.0027866554,0.000453595,0.86685354,0.00049205334,0.000019172561,8.902268e-7,0.051473163,0.0016063883,0.0008878284,0.03185943,0.043143734,0.00042357182],"about_ca_topic_score_codex":0.011026266,"about_ca_topic_score_gemma":0.43900433,"teacher_disagreement_score":0.42797804,"about_ca_system_score_codex":0.001099285,"about_ca_system_score_gemma":0.0025239445,"threshold_uncertainty_score":0.9955594},"labels":[],"label_agreement":null},{"id":"W2902624855","doi":"10.1155/2018/4378364","title":"Dynamic Evolution of Traveler’s Bounded-Rational Route Choice Behavior","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","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":"Fundamental Research Funds for the Central Universities; Chang'an University","keywords":"Bounded rationality; Bounded function; Rationality; Mathematical economics; Convergence (economics); Computer science; Mathematics; Economics; Mathematical analysis; Artificial intelligence","score_opus":0.01169228731540614,"score_gpt":0.3063337853467747,"score_spread":0.2946414980313686,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2902624855","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93238115,0.00011530143,0.065959886,0.0002049546,0.0007338646,0.00016083285,0.000040535735,0.000024043293,0.00037941925],"genre_scores_gemma":[0.9781069,0.000072228126,0.02137978,0.000018327377,0.00017204488,0.000004354291,0.0000765951,0.000010715756,0.00015902537],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9984331,0.000055282042,0.00066455704,0.000114653616,0.0005798796,0.00015251352],"domain_scores_gemma":[0.99802804,0.00007096538,0.00073679956,0.00006703822,0.0010112902,0.000085870255],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037470763,0.000092582384,0.00017921066,0.00019528161,0.00023234254,0.00001853404,0.00012707336,0.00009442222,0.00009384752],"category_scores_gemma":[0.00006679466,0.00009632476,0.00010714516,0.00037994812,0.00019694379,0.00088408444,3.851105e-7,0.0001220616,0.000002499641],"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":[0.001723369,0.0013212147,0.45154548,0.00015503865,0.00026505443,0.000044276705,0.12819003,0.23306437,0.050080653,0.08935645,0.00024653456,0.044007547],"study_design_scores_gemma":[0.0008976347,0.00024037025,0.990844,0.000073349,0.00011582338,0.0000018943982,0.002870668,0.00012980582,0.00047168537,0.0011191359,0.0031193527,0.00011625551],"about_ca_topic_score_codex":0.00018364305,"about_ca_topic_score_gemma":0.0050977073,"teacher_disagreement_score":0.53929853,"about_ca_system_score_codex":0.00016386693,"about_ca_system_score_gemma":0.00040457066,"threshold_uncertainty_score":0.392801},"labels":[],"label_agreement":null},{"id":"W2902665519","doi":"10.1016/j.ijtst.2018.11.002","title":"Metropolis-Hasting based Expanded Path Size Logit model for cyclists’ route choice using GPS data","year":2018,"lang":"en","type":"article","venue":"International Journal of Transportation Science and Technology","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Polytechnique Montréal; Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Logit; Global Positioning System; Logistic regression; Transport engineering; Mixed logit; Computer science; Geography; Statistics; Econometrics; Mathematics; Engineering; Telecommunications","score_opus":0.09012465269356584,"score_gpt":0.4016408995392342,"score_spread":0.31151624684566837,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2902665519","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.48635238,0.000019448931,0.5107108,0.002285488,0.00035842558,0.00008324019,0.00008787284,0.000035105684,0.000067265624],"genre_scores_gemma":[0.89247864,0.00001686179,0.107128434,0.00018179651,0.00015182042,0.0000021650162,0.000023791321,0.00000565939,0.000010854202],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99834996,0.000014169667,0.00043757996,0.00024514672,0.00075097044,0.00020219729],"domain_scores_gemma":[0.99640113,0.00016879616,0.0004572374,0.0001314492,0.0027590666,0.00008230509],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013243356,0.00008443024,0.00013740861,0.00048971653,0.0004478664,0.00012158642,0.0009144927,0.00009550417,0.000012677049],"category_scores_gemma":[0.0011827016,0.00008101947,0.00003079467,0.00063898234,0.0009701552,0.0010738674,0.000010986958,0.00011428101,2.8123003e-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":[0.0008352308,0.00057783024,0.531532,0.000060014732,0.00030201933,0.00006465928,0.027186815,0.15196314,0.05590492,0.18114364,0.0005791971,0.04985052],"study_design_scores_gemma":[0.0020336,0.00014086638,0.02508474,0.00014480781,0.00011394403,0.000010128231,0.003836395,0.9523983,0.0026757747,0.011332469,0.0019407615,0.00028824332],"about_ca_topic_score_codex":0.00017695801,"about_ca_topic_score_gemma":0.0007112299,"teacher_disagreement_score":0.8004351,"about_ca_system_score_codex":0.00009403573,"about_ca_system_score_gemma":0.00090665085,"threshold_uncertainty_score":0.3574577},"labels":[],"label_agreement":null},{"id":"W2902906171","doi":"10.1287/inte.2018.0959","title":"Metro Uses a Simulation-Optimization Approach to Improve Fare-Collection Shift Scheduling","year":2018,"lang":"en","type":"article","venue":"INFORMS Journal on Applied Analytics","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Integer programming; Scheduling (production processes); Discrete event simulation; Computer science; Operations research; Mathematical optimization; Simulation; Engineering; Mathematics; Algorithm","score_opus":0.026314478494525704,"score_gpt":0.30246534036897915,"score_spread":0.2761508618744534,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2902906171","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016818628,0.0000041176745,0.9310983,0.00024197064,0.00035576845,0.0002828231,0.000004898056,0.00011131751,0.051082194],"genre_scores_gemma":[0.9130614,0.000016629101,0.08522644,0.0006487643,0.0006581506,0.000008220616,0.00002630641,0.000017680086,0.0003364235],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983385,0.000026718324,0.0004887357,0.00018881017,0.0006400191,0.0003171716],"domain_scores_gemma":[0.9987949,0.00010904875,0.00031885988,0.00012436091,0.00040056097,0.00025225224],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00074634945,0.00015387704,0.0001817439,0.0004962003,0.0012188788,0.00041644927,0.00018274746,0.00015345565,0.00008856366],"category_scores_gemma":[0.00025456893,0.00013743243,0.000076726305,0.0011851484,0.00008624846,0.00040332534,0.0000082536935,0.00025672925,0.000044809844],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009440189,0.000048787675,0.0007383068,0.0000041372678,0.00003365667,3.8715453e-7,0.0064960574,0.9824785,0.000005308117,0.008519519,0.0000844541,0.0014964952],"study_design_scores_gemma":[0.00074963394,0.00021430416,0.0013660726,0.000031528267,0.000076528086,8.748121e-7,0.0043912046,0.9862216,0.00009700397,0.00075967703,0.0057397564,0.0003518186],"about_ca_topic_score_codex":0.000045826848,"about_ca_topic_score_gemma":0.000073559546,"teacher_disagreement_score":0.89624274,"about_ca_system_score_codex":0.00022858357,"about_ca_system_score_gemma":0.00022817297,"threshold_uncertainty_score":0.9374753},"labels":[],"label_agreement":null},{"id":"W2903633831","doi":"10.1155/2018/4702517","title":"Evaluating the Impacts of Bus Stop Design and Bus Dwelling on Operations of Multitype Road Users","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":39,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; Southeast University; National Natural Science Foundation of China","keywords":"Transport engineering; Vehicle type; Computer science; Engineering","score_opus":0.054864772548820356,"score_gpt":0.3785333534985255,"score_spread":0.3236685809497052,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2903633831","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9497327,0.0001045866,0.049519088,0.00020227756,0.00020202537,0.00018351943,0.000005975884,0.000007086395,0.00004269896],"genre_scores_gemma":[0.95268655,0.00023031245,0.04695594,0.000026241729,0.00006959429,0.0000014189612,0.0000071502714,0.0000066120597,0.000016184185],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99881744,0.00012679753,0.00048412362,0.00007894503,0.0003943097,0.00009837833],"domain_scores_gemma":[0.99849916,0.0002025994,0.00045535856,0.0000612156,0.00073050894,0.000051186475],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001003012,0.000068117886,0.00014692737,0.00010234725,0.00024614565,0.000014705667,0.000084056664,0.00004833348,0.000015724578],"category_scores_gemma":[0.00020645815,0.000052727733,0.00004554645,0.00026044904,0.00015598915,0.0003819718,3.799505e-7,0.00009881359,3.851684e-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":[0.00033356456,0.00004007456,0.0025502436,0.000013719726,0.000023404038,9.597522e-7,0.046609797,0.9341399,0.008949742,0.00080249883,0.0000033873164,0.0065326956],"study_design_scores_gemma":[0.0041789426,0.0043932986,0.8966273,0.0010004687,0.00046148812,0.000003344485,0.033000432,0.027518338,0.030966401,0.001266609,0.00021315049,0.0003701995],"about_ca_topic_score_codex":0.00020598604,"about_ca_topic_score_gemma":0.0005910416,"teacher_disagreement_score":0.9066216,"about_ca_system_score_codex":0.000022090187,"about_ca_system_score_gemma":0.00017187805,"threshold_uncertainty_score":0.21501745},"labels":[],"label_agreement":null},{"id":"W2903903106","doi":"10.1287/ijoc.2018.0819","title":"Stochastic Network Design for Planning Scheduled Transportation Services: The Value of Deterministic Solutions","year":2018,"lang":"en","type":"article","venue":"INFORMS journal on computing","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":51,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Computer science; Mathematical optimization; Network planning and design; Stochastic optimization; Path (computing); Stochastic programming; Quality (philosophy); Stochastic process; Mathematics","score_opus":0.04904796322602469,"score_gpt":0.3218126665640246,"score_spread":0.2727647033379999,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2903903106","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.088437356,0.000030123234,0.9102914,0.00013597596,0.000588732,0.00023550233,0.0000043995897,0.000037710204,0.00023881548],"genre_scores_gemma":[0.96904737,0.0000028647246,0.02994224,0.00021518116,0.0007532774,0.000003033272,0.000011908529,0.0000086193,0.000015509859],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99881816,0.0000632517,0.00045080294,0.00008188982,0.00028477793,0.00030111877],"domain_scores_gemma":[0.9983963,0.0006820136,0.00046208387,0.0000659406,0.000322179,0.00007150242],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0015819003,0.00008710614,0.0001259489,0.00007082021,0.0020197663,0.00010027819,0.000203978,0.00006500034,0.000008591737],"category_scores_gemma":[0.00010234069,0.00006440735,0.00007175138,0.00025263673,0.00012959339,0.00020308456,0.0000023774098,0.00015131081,0.0000029479172],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000772488,0.000009597107,0.0005273893,0.000010957873,0.000020929621,7.071328e-7,0.020337695,0.9666178,0.000003389893,0.010171626,0.000056324847,0.002166301],"study_design_scores_gemma":[0.0008903667,0.00050660863,0.016032254,0.0008528352,0.000104132465,0.000006759164,0.0066591566,0.9684996,0.00002099247,0.0049143555,0.0012992395,0.00021371472],"about_ca_topic_score_codex":0.000033301018,"about_ca_topic_score_gemma":0.00005013842,"teacher_disagreement_score":0.88061,"about_ca_system_score_codex":0.00003502253,"about_ca_system_score_gemma":0.00019021428,"threshold_uncertainty_score":0.99927944},"labels":[],"label_agreement":null},{"id":"W2904193894","doi":"","title":"Wind transport in an idealised urban canopy","year":2000,"lang":"en","type":"article","venue":"Davis Meeting","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Meteorology; Environmental science; Atmospheric sciences; Geography; Geology","score_opus":0.017080122762141083,"score_gpt":0.28680074175118997,"score_spread":0.26972061898904887,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2904193894","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9445124,0.000027322254,0.000029807163,0.00022146289,0.00005816322,0.00009175409,0.000010319076,0.00009301602,0.054955725],"genre_scores_gemma":[0.99785334,0.000022234617,0.00076602574,0.00013445498,0.000100428806,0.0000031379416,0.0001301894,0.000008936718,0.0009812341],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9991339,0.00009287004,0.00019262396,0.00016302202,0.00020318781,0.00021440104],"domain_scores_gemma":[0.9997479,0.000030978805,0.000036143363,0.00007340922,0.000027487042,0.00008408816],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004950127,0.000062230145,0.000087136206,0.00005699411,0.00022993873,0.000030349345,0.000104563274,0.00006547236,0.00052483135],"category_scores_gemma":[0.000017699065,0.00007035082,0.000022038532,0.00029412814,0.000044622386,0.00024164653,4.8709404e-7,0.00007510566,0.000013300343],"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":[0.000028801756,0.000042561303,0.8810775,0.000005493814,0.0000027186786,0.000019606157,0.083421074,0.028885191,0.00011018323,0.0007950254,0.00016487879,0.0054469323],"study_design_scores_gemma":[0.0009736896,0.00004671729,0.913202,0.00014220184,0.000022244656,4.3279582e-7,0.008567205,0.0018248637,0.00012411394,0.0001697782,0.07449656,0.00043021777],"about_ca_topic_score_codex":0.006500683,"about_ca_topic_score_gemma":0.011721813,"teacher_disagreement_score":0.07485387,"about_ca_system_score_codex":0.000031244897,"about_ca_system_score_gemma":0.0000734808,"threshold_uncertainty_score":0.9827131},"labels":[],"label_agreement":null},{"id":"W2904533698","doi":"10.1155/2018/9789316","title":"Hybrid Random Regret Minimization and Random Utility Maximization in the Context of Schedule-Based Urban Rail Transit Assignment","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; National Key Research and Development Program of China","keywords":"Regret; Maximization; Minification; Computer science; Schedule; Public transport; Mathematical optimization; Context (archaeology); Operations research; Utility maximization; Transport engineering; Engineering; Mathematics; Mathematical economics; Machine learning; Geography","score_opus":0.014949589341944266,"score_gpt":0.26990422158499466,"score_spread":0.25495463224305037,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2904533698","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.62898564,0.00026427288,0.36861524,0.0011540623,0.0002648124,0.0004955099,0.000031028645,0.000015930154,0.00017347386],"genre_scores_gemma":[0.99385685,0.00020207276,0.0055712704,0.00014379586,0.000090651396,0.000007767093,0.000105508254,0.000010055928,0.000012059109],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9979257,0.0003405997,0.0008446752,0.00015469918,0.0005774407,0.00015687375],"domain_scores_gemma":[0.9982876,0.0003062195,0.0007064883,0.00009690312,0.00053719233,0.00006555531],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014206546,0.00012495,0.0003018952,0.00019140242,0.00019717123,0.00003103681,0.00013563446,0.00007811509,0.000045928413],"category_scores_gemma":[0.00015646793,0.00010172248,0.00011108756,0.0003570796,0.00027073824,0.0005693887,3.8410977e-7,0.00014430282,3.3491708e-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":[0.03467873,0.0011598968,0.14425379,0.00032215778,0.00020851457,0.00006050922,0.35155728,0.4139435,0.0035232957,0.008175449,0.00056606665,0.041550785],"study_design_scores_gemma":[0.0725735,0.0015878461,0.82511127,0.0010777874,0.0007785838,0.000009335434,0.056295615,0.015985249,0.013463256,0.003373135,0.008952209,0.0007922075],"about_ca_topic_score_codex":0.000063635904,"about_ca_topic_score_gemma":0.00077993446,"teacher_disagreement_score":0.6808575,"about_ca_system_score_codex":0.000038616043,"about_ca_system_score_gemma":0.00019012869,"threshold_uncertainty_score":0.4148122},"labels":[],"label_agreement":null},{"id":"W2904713659","doi":"10.1080/23302674.2018.1554168","title":"An integrated approach based on system dynamics and ANP for evaluating sustainable transportation policies","year":2018,"lang":"en","type":"article","venue":"International Journal of Systems Science Operations & Logistics","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":156,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Sustainability; Sustainable transport; System dynamics; Environmental economics; Analytic network process; Computer science; Fuel efficiency; Traffic congestion; Transport engineering; Process (computing); Intelligent transportation system; Business; Operations research; Economics; Engineering; Automotive engineering; Analytic hierarchy process","score_opus":0.040323797772258654,"score_gpt":0.37477507560342527,"score_spread":0.3344512778311666,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2904713659","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04460043,0.00001276502,0.9518949,0.00041114917,0.0010140347,0.00035423518,0.00014172867,0.000033134558,0.0015376632],"genre_scores_gemma":[0.9598526,0.000005383467,0.039348286,0.0000671859,0.00036852923,0.0000160643,0.00009910604,0.000008483168,0.00023437283],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99799055,0.00011393917,0.000540917,0.0001893734,0.00094736397,0.0002178383],"domain_scores_gemma":[0.99151665,0.0001277322,0.00028626432,0.00009744487,0.007841418,0.00013047356],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003057477,0.00010023349,0.00014769386,0.00047255945,0.0010537979,0.0007587442,0.00043393174,0.00006907216,0.0000034797883],"category_scores_gemma":[0.0008310485,0.000087925626,0.000034068067,0.000470085,0.0006593682,0.0008520735,0.0000027764574,0.00009253541,6.6284105e-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":[0.000053712174,0.000060293714,0.0011151667,0.000017674596,0.000010125175,0.000002624527,0.0043683588,0.6273275,0.00009068167,0.36647356,0.000040656138,0.00043965928],"study_design_scores_gemma":[0.0003921276,0.00040187553,0.0015175687,0.00010402514,0.000029086215,0.000006574184,0.06478112,0.9322536,0.00003475909,0.00005023037,0.0003278496,0.00010115978],"about_ca_topic_score_codex":0.0016171938,"about_ca_topic_score_gemma":0.0006298112,"teacher_disagreement_score":0.91525215,"about_ca_system_score_codex":0.0005409946,"about_ca_system_score_gemma":0.0012446602,"threshold_uncertainty_score":0.81050676},"labels":[],"label_agreement":null},{"id":"W2905158425","doi":"10.2139/ssrn.3219767","title":"Efficiency of Correlation in a Bottleneck Game","year":2018,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Bottleneck; Nash equilibrium; Computer science; Constraint (computer-aided design); Cover (algebra); Best response; Strategy; Task (project management); Order (exchange); Mathematical optimization; Train; Operations research; Mathematical economics; Game theory; Mathematics; Economics; Engineering","score_opus":0.007692570655109867,"score_gpt":0.273210090738234,"score_spread":0.2655175200831241,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2905158425","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8495624,0.00045825023,0.13380982,0.00032627268,0.00030387353,0.00009412365,8.7665677e-7,0.000025587815,0.015418797],"genre_scores_gemma":[0.9984835,0.0005180087,0.00014443911,0.000012018527,0.000119525816,6.470045e-7,0.0000019844094,0.0000038202124,0.00071610603],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9988817,0.00007464018,0.00018532756,0.00006672324,0.0001992356,0.00059240195],"domain_scores_gemma":[0.9996832,0.00003071163,0.00011750563,0.000036455527,0.000104299026,0.000027816057],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013755818,0.000038090195,0.00006510746,0.00011420698,0.0001305294,0.000012384866,0.00009253501,0.00004992367,0.000044716617],"category_scores_gemma":[0.000093982664,0.00003768209,0.000026231073,0.0003318761,0.0000974847,0.00013758645,0.0000015161905,0.0003071752,0.0000106320385],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011807787,0.00012261333,0.15907232,0.0000045224842,0.00002543458,0.0000016967427,0.03823308,0.014160566,0.0002730949,0.77396524,0.00006619824,0.013957129],"study_design_scores_gemma":[0.0065124626,0.0028848534,0.20221199,0.00041013685,0.00012856502,0.00007429091,0.12892452,0.027711382,0.0008670989,0.61503434,0.014139349,0.0011010411],"about_ca_topic_score_codex":0.0005739835,"about_ca_topic_score_gemma":0.01067074,"teacher_disagreement_score":0.15893094,"about_ca_system_score_codex":0.0002926711,"about_ca_system_score_gemma":0.0015787374,"threshold_uncertainty_score":0.59545237},"labels":[],"label_agreement":null},{"id":"W2906663089","doi":"10.32920/ryerson.14661327","title":"Active transportation demand modeling and infrastructure performance assessment","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Transport engineering; TRIPS architecture; Service (business); Level of service; Quality (philosophy); Pedestrian; Supply and demand; Business; Environmental economics; Computer science; Engineering; Marketing","score_opus":0.015801382962547454,"score_gpt":0.2991570956066479,"score_spread":0.2833557126441004,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2906663089","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.83997047,0.00007456103,0.1539767,0.00023109517,0.00025110226,0.000195554,0.000022358015,0.00008788093,0.0051902873],"genre_scores_gemma":[0.977655,0.0013415364,0.019911375,0.00006116226,0.00007098077,0.000019191404,0.0007417088,0.000009760529,0.0001892681],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989937,0.00006247542,0.000206014,0.000300054,0.0002946295,0.0001431539],"domain_scores_gemma":[0.99953306,0.000023333007,0.00009772916,0.00008875048,0.00018224213,0.000074873504],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019709385,0.0001298267,0.00016461324,0.000061938474,0.00032767773,0.00014870356,0.00007805172,0.0002645883,0.00014396229],"category_scores_gemma":[0.000008946551,0.00013430843,0.000038280716,0.00009461927,0.00005070854,0.0002623305,0.0000072387875,0.0003332489,2.9583452e-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":[0.000008806871,0.000010176136,0.02108941,0.00008008388,0.00002851202,0.0000020240705,0.029892378,0.94329435,0.000007096322,0.0011914944,0.000020278949,0.0043753902],"study_design_scores_gemma":[0.00034774432,0.000017813018,0.36385232,0.00027116653,0.0001296056,3.6618096e-7,0.023295682,0.61044216,0.000050180162,0.0008089662,0.00033921856,0.00044477452],"about_ca_topic_score_codex":0.00079436257,"about_ca_topic_score_gemma":0.0019169073,"teacher_disagreement_score":0.34276292,"about_ca_system_score_codex":0.00006343003,"about_ca_system_score_gemma":0.00043628164,"threshold_uncertainty_score":0.5476939},"labels":[],"label_agreement":null},{"id":"W2907393893","doi":"10.3390/su11010170","title":"A Dynamic Traffic Assignment Model for the Sustainability of Pavement Performance","year":2018,"lang":"en","type":"article","venue":"Sustainability","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Fundamental Research Funds for the Central Universities","keywords":"Serviceability (structure); Transport engineering; Traffic flow (computer networking); Sustainability; Fuel efficiency; Computer science; Shortest path problem; Travel time; Engineering; Civil engineering; Automotive engineering","score_opus":0.013973980901371311,"score_gpt":0.31173354685248694,"score_spread":0.29775956595111563,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2907393893","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.70766413,0.000028797653,0.28665006,0.0038130733,0.00010062563,0.0015541874,0.000015605123,0.000060558457,0.000112997215],"genre_scores_gemma":[0.9978217,0.0000140662505,0.0007735359,0.00004061372,0.000029532519,0.00016726593,0.000010068439,0.0000068899844,0.0011363407],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99872494,0.00010513345,0.00029418356,0.00023676836,0.00030849746,0.00033047277],"domain_scores_gemma":[0.9972939,0.00023750348,0.0001191066,0.0002534469,0.0020382477,0.000057789402],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020522978,0.00009270237,0.00012651927,0.000036169746,0.0007638924,0.000023931816,0.00020195838,0.00006917642,0.00002872684],"category_scores_gemma":[0.0006767271,0.00007162967,0.00008215725,0.00024167355,0.00080325495,0.00014402157,0.0000123874415,0.00006558905,4.4148567e-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":[0.00025336107,0.0003692715,0.02302996,0.00050817453,0.000031953507,2.6059962e-7,0.09827741,0.8109621,0.0000041024764,0.017366081,0.00023906256,0.048958298],"study_design_scores_gemma":[0.0003347775,0.00016846489,0.030263402,0.000007513514,0.00003660088,3.8777298e-8,0.019699093,0.9401557,0.00003282441,0.0074100383,0.0017742127,0.00011736806],"about_ca_topic_score_codex":0.00024484197,"about_ca_topic_score_gemma":0.00087495794,"teacher_disagreement_score":0.2901576,"about_ca_system_score_codex":0.0010213766,"about_ca_system_score_gemma":0.0013513659,"threshold_uncertainty_score":0.587532},"labels":[],"label_agreement":null},{"id":"W2908302515","doi":"10.1155/2018/5294185","title":"A Reliability-Based Network Equilibrium Model with Adaptive Risk-Averse Travelers","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Travel time; Reliability (semiconductor); Variational inequality; Computer science; Boundary (topology); Flow network; Mathematical optimization; Distribution (mathematics); Operations research; Mathematics; Transport engineering; Engineering","score_opus":0.013567084012094015,"score_gpt":0.26522833737796103,"score_spread":0.25166125336586703,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2908302515","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.59671706,0.00002355185,0.40207666,0.00018573496,0.00030510614,0.00017359301,0.000022481565,0.0000447881,0.00045098658],"genre_scores_gemma":[0.8136238,0.00003656168,0.18591528,0.000076871125,0.0002225517,0.0000031716716,0.000022680288,0.000014845296,0.000084274616],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9984854,0.00009819903,0.0004655577,0.00016695497,0.00053583074,0.00024805046],"domain_scores_gemma":[0.99805963,0.000101923484,0.00070462236,0.00009651301,0.0008895225,0.00014778333],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062526605,0.00012500548,0.0002038172,0.00010145306,0.00027968502,0.00002284266,0.00012432944,0.000089063054,0.000035261666],"category_scores_gemma":[0.00004003332,0.00010918132,0.000098371806,0.0004559854,0.00025748767,0.0007361747,2.9660274e-7,0.00021030252,0.0000020924622],"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":[0.0014673204,0.000047264217,0.0065662633,0.000006760296,0.000021900165,0.000007831089,0.013462912,0.97675914,0.00009447494,0.00065708085,0.00011121788,0.0007978287],"study_design_scores_gemma":[0.017694741,0.006930903,0.5813556,0.0012881209,0.0015702748,0.0000061525107,0.038517527,0.3208398,0.0019082126,0.015073456,0.012935906,0.0018793498],"about_ca_topic_score_codex":0.00006737924,"about_ca_topic_score_gemma":0.0014856259,"teacher_disagreement_score":0.6559194,"about_ca_system_score_codex":0.00007662418,"about_ca_system_score_gemma":0.00049148273,"threshold_uncertainty_score":0.4452285},"labels":[],"label_agreement":null},{"id":"W2908934362","doi":"10.1155/2019/2452348","title":"Multiobjective Optimal Formulations for Bus Fleet Size of Public Transit under Headway-Based Holding Control","year":2019,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China; Natural Science Foundation of Shanghai","keywords":"Headway; Schedule; Public transport; Control (management); Train; Engineering; Optimal control; Reliability (semiconductor); Randomness; Computer science; Transit (satellite); Automotive engineering; Simulation; Transport engineering; Mathematical optimization","score_opus":0.019270752302061753,"score_gpt":0.3013253181152217,"score_spread":0.2820545658131599,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2908934362","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5177242,0.000045831348,0.48089135,0.0005332033,0.00024758215,0.00039922324,0.00007428348,0.000019676418,0.00006464531],"genre_scores_gemma":[0.94889826,0.000022438076,0.050788146,0.000076950186,0.00006623098,0.000011844176,0.0000750192,0.000016992011,0.000044109147],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99844915,0.0000829928,0.00068149046,0.0001400902,0.000427532,0.0002187433],"domain_scores_gemma":[0.9973847,0.0007149987,0.0006950983,0.00007330142,0.0010277744,0.00010412729],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000705728,0.00011717135,0.00030153632,0.0001950811,0.00021286112,0.00002581185,0.00011190633,0.00010774154,0.000057947804],"category_scores_gemma":[0.00013277736,0.00011873856,0.00022521595,0.00033815016,0.00006379607,0.0009713801,1.7857006e-7,0.00014455443,7.824394e-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":[0.0005017969,0.000098161916,0.011737807,0.000042236083,0.00004785316,9.4304494e-7,0.007813256,0.9695463,0.0038896904,0.0051777875,0.000004883837,0.001139291],"study_design_scores_gemma":[0.022504205,0.0012382224,0.9271746,0.0003779629,0.00049568084,0.000001613893,0.026773266,0.012787256,0.0034222861,0.00207649,0.0025682987,0.0005800763],"about_ca_topic_score_codex":0.00003138939,"about_ca_topic_score_gemma":0.00039532562,"teacher_disagreement_score":0.95675904,"about_ca_system_score_codex":0.000083209256,"about_ca_system_score_gemma":0.00035667568,"threshold_uncertainty_score":0.48420176},"labels":[],"label_agreement":null},{"id":"W2909821332","doi":"","title":"Integrated Transport - Economics Model for Ontario","year":2017,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Economics; Computer science","score_opus":0.05941209796407472,"score_gpt":0.29981598236912094,"score_spread":0.24040388440504623,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2909821332","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.35392934,0.0000019548697,0.47698948,0.0013093872,0.0002611669,0.00025333246,0.000035007462,0.00009957037,0.16712077],"genre_scores_gemma":[0.9123605,0.000009691673,0.021199042,0.00008097295,0.000023357748,0.000010385917,0.000066389104,0.0000041667367,0.06624548],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996852,0.0000029413745,0.00008788404,0.00009084632,0.000034393754,0.00009867709],"domain_scores_gemma":[0.9997451,0.00001081104,0.000051322582,0.000096669006,0.00005222354,0.000043873762],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001531042,0.000038227216,0.00005707165,0.000015210018,0.00072613236,0.00006316767,0.0001365848,0.000051782445,0.0001194888],"category_scores_gemma":[0.000013956929,0.0000371259,0.000035112196,0.00000768492,0.00005692395,0.00025060982,7.131236e-7,0.000030319972,0.000003460521],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010312521,0.00006128684,0.2081515,0.000006877639,0.00003351452,8.0827164e-7,0.08409051,0.27110228,0.0000057073257,0.42841172,0.0022356645,0.0057969857],"study_design_scores_gemma":[0.0013585511,0.000035662106,0.1105865,0.00001873915,0.000060432678,1.2306454e-7,0.002644624,0.63494545,0.000056806435,0.014732872,0.23512174,0.00043849266],"about_ca_topic_score_codex":0.078135975,"about_ca_topic_score_gemma":0.852179,"teacher_disagreement_score":0.77404296,"about_ca_system_score_codex":0.00005213169,"about_ca_system_score_gemma":0.00035630082,"threshold_uncertainty_score":0.9280028},"labels":[],"label_agreement":null},{"id":"W2910121745","doi":"10.1155/2019/6964943","title":"Models and Technologies for Transport System Flow Analysis","year":2019,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","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":"Transport engineering; Computer science; Traffic flow (computer networking); Public transport; Systems analysis; Transportation planning; Transport system; Transport network; Cover (algebra); Software; Operations research; Engineering; Computer security","score_opus":0.011581811614571476,"score_gpt":0.2703486559299337,"score_spread":0.2587668443153622,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2910121745","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.59016615,0.00029859235,0.4083052,0.00028076037,0.0002602427,0.0003078962,0.00004598179,0.00010084099,0.00023435427],"genre_scores_gemma":[0.9533403,0.00026197365,0.046208378,0.000009297924,0.000025405969,0.000007081514,0.000065283966,0.000008435939,0.00007387746],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9989726,0.000018639103,0.00044524303,0.00013120209,0.00029441772,0.00013791097],"domain_scores_gemma":[0.99908906,0.000079053156,0.0003570075,0.00006705829,0.00035934872,0.000048448474],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040011844,0.00008577633,0.00027377924,0.0003052481,0.00012576043,0.000016146001,0.00009189824,0.000100208665,0.0000059991216],"category_scores_gemma":[0.000011598845,0.00008082994,0.00015511867,0.00049333024,0.000045722354,0.0007169182,2.1649525e-7,0.00008452136,3.9344457e-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":[0.00016335375,0.000017415667,0.022526244,0.00007357969,0.00014498706,0.0000035020964,0.01005249,0.9507161,0.00010559694,0.011746592,0.0000043330724,0.004445819],"study_design_scores_gemma":[0.00942412,0.0010482216,0.6659571,0.0007646472,0.0059949243,0.0000078706435,0.22489738,0.06893965,0.0009853401,0.010742691,0.009986423,0.0012516222],"about_ca_topic_score_codex":0.00003054438,"about_ca_topic_score_gemma":0.0004317852,"teacher_disagreement_score":0.88177645,"about_ca_system_score_codex":0.00003972987,"about_ca_system_score_gemma":0.00007151869,"threshold_uncertainty_score":0.3296149},"labels":[],"label_agreement":null},{"id":"W2910854604","doi":"10.1177/0361198118823005","title":"Analysis of Private Participation Effects in Bus Rapid Transit Projects in Ecuador","year":2019,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Bus rapid transit; Public transport; Business; Private enterprise; Sustainable transport; Transport engineering; Function (biology); Transit (satellite); Private transport; Private sector; Finance; Transit system; Engineering; Economic growth; Economics; Sustainability","score_opus":0.07172605999381805,"score_gpt":0.40349535672372233,"score_spread":0.3317692967299043,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2910854604","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9957928,0.00015073817,0.0005944343,0.0010496153,0.0003220421,0.0017039747,0.00003942272,0.00001905714,0.00032790937],"genre_scores_gemma":[0.9979533,0.00072615227,0.00087877194,0.000025178897,0.000043241933,0.000076217635,0.000053878575,0.000029095023,0.00021415207],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9907419,0.0026317427,0.0017961997,0.00042422448,0.003534847,0.00087107316],"domain_scores_gemma":[0.9951775,0.0017309744,0.00059480604,0.00036213634,0.0018933866,0.00024120261],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008212534,0.00020578766,0.000722477,0.0038978513,0.00026497614,0.00007170366,0.0007788514,0.00026634452,0.00032689556],"category_scores_gemma":[0.00034368585,0.00017757271,0.00040404507,0.01060451,0.0004780422,0.0008348972,0.000002589441,0.0014120687,0.000009262698],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010663487,0.00035681788,0.8978268,0.00024775468,0.00019420008,0.00005575001,0.027194792,0.0661571,0.0008220977,0.0022766024,0.000055281143,0.003746482],"study_design_scores_gemma":[0.0019305369,0.00034993578,0.9894233,0.0004706987,0.00015645211,2.7110362e-8,0.0044394657,0.0010522067,0.00083397655,0.000520803,0.0006581183,0.00016452833],"about_ca_topic_score_codex":0.026868764,"about_ca_topic_score_gemma":0.27984023,"teacher_disagreement_score":0.25297147,"about_ca_system_score_codex":0.00031582327,"about_ca_system_score_gemma":0.00085967756,"threshold_uncertainty_score":0.9796114},"labels":[],"label_agreement":null},{"id":"W2911723578","doi":"10.1155/2019/1896261","title":"Departure Time Choice (DTC) Behavior for Intercity Travel during a Long-Holiday in Bangkok, Thailand","year":2019,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Multinomial logistic regression; Travel time; Travel behavior; Transport engineering; Traffic congestion; Advertising; Business; Computer science; Engineering","score_opus":0.010054903161946573,"score_gpt":0.2869400603010007,"score_spread":0.27688515713905415,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2911723578","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9959394,0.00007588165,0.0028295147,0.00016914372,0.0003560066,0.00044334005,0.000031838714,0.000018365317,0.00013656508],"genre_scores_gemma":[0.9963956,0.000059375325,0.0027812594,0.0000289961,0.00010812383,0.000015256666,0.00008387238,0.000014246885,0.00051327207],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99882656,0.000039859104,0.0004960737,0.00014225976,0.00029584716,0.0001993744],"domain_scores_gemma":[0.99916977,0.00009664593,0.00036474314,0.00006157316,0.00022108448,0.00008621577],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032857707,0.00010569831,0.00023112731,0.00017493477,0.00009398674,0.000027687161,0.000119061355,0.00010841987,0.00006232309],"category_scores_gemma":[0.000044828048,0.00010426329,0.0001167928,0.00023103954,0.00002791057,0.00076316885,4.2130304e-7,0.00017737877,0.000004314457],"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":[0.00085867237,0.00023013071,0.829437,0.0001129305,0.000028642204,0.000041985466,0.028967235,0.13097996,0.007130153,0.00014120083,0.000034568184,0.0020375184],"study_design_scores_gemma":[0.0020871416,0.00009624054,0.9949725,0.00014161335,0.00004306602,0.000002006098,0.0013795511,0.00004054598,0.00061597326,0.000047428926,0.00044927967,0.00012465523],"about_ca_topic_score_codex":0.00007438429,"about_ca_topic_score_gemma":0.0037588256,"teacher_disagreement_score":0.16553548,"about_ca_system_score_codex":0.00007604481,"about_ca_system_score_gemma":0.00009411329,"threshold_uncertainty_score":0.42517334},"labels":[],"label_agreement":null},{"id":"W2911811893","doi":"10.1109/cdc.2018.8619448","title":"A mean field route choice game model","year":2018,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis","funders":"","keywords":"Computer science; Nash equilibrium; Mathematical optimization; Limit (mathematics); Travel time; Scheme (mathematics); Function (biology); Game theory; Field (mathematics); Simulation; Transport engineering; Mathematics; Engineering; Mathematical economics","score_opus":0.030997370562385183,"score_gpt":0.3199551812055499,"score_spread":0.2889578106431647,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2911811893","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07693089,0.000008946668,0.29647473,0.003134612,0.00019978863,0.00008109018,0.000002712338,0.00025260283,0.6229146],"genre_scores_gemma":[0.96996653,0.000006504422,0.005847614,0.0010841793,0.00015920623,0.000001858099,0.000004541146,0.0000026867385,0.02292686],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996237,0.000014400012,0.00006218921,0.00007915142,0.000120031444,0.00010051847],"domain_scores_gemma":[0.99977744,0.000041099738,0.000017941473,0.00005289266,0.00006547316,0.0000451411],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010703865,0.000029093218,0.000033451073,0.000020132014,0.0001581124,0.000028729737,0.00006822847,0.000042443702,0.00045750267],"category_scores_gemma":[0.000049587503,0.000027100245,0.000015418722,0.00009897803,0.000043385455,0.00012495948,0.0000018229019,0.000028212917,0.000059418686],"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":[0.000044156754,0.00008835874,0.06787182,0.0000084756075,0.000028965584,0.000002193005,0.19759582,0.03202759,0.00012501155,0.63503206,0.053864367,0.013311196],"study_design_scores_gemma":[0.0010006438,0.0002023406,0.022292465,0.000044713986,0.000052041905,3.756485e-7,0.0068258136,0.50436646,0.0012888011,0.010994725,0.4522549,0.00067677215],"about_ca_topic_score_codex":0.0025214197,"about_ca_topic_score_gemma":0.013689219,"teacher_disagreement_score":0.89303565,"about_ca_system_score_codex":0.000009437339,"about_ca_system_score_gemma":0.000051386876,"threshold_uncertainty_score":0.76389056},"labels":[],"label_agreement":null},{"id":"W2912857351","doi":"10.3166/ria.32.197-221","title":"Simulation de l’activité humaine. Une étude sur le réalisme multi-niveau","year":2018,"lang":"fr","type":"article","venue":"Revue d intelligence artificielle","topic":"Transportation Planning and Optimization","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":"Humanities; Realism; Philosophy; Computer science; Epistemology","score_opus":0.08651915562771233,"score_gpt":0.32401264726347273,"score_spread":0.2374934916357604,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2912857351","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15426399,0.00084905856,0.8304468,0.0074125705,0.0014008338,0.00033731083,0.000028764527,0.00023750533,0.0050232043],"genre_scores_gemma":[0.9754774,0.00027639966,0.006456232,0.00022210232,0.0006671817,0.000010656293,0.000041552485,0.000033808912,0.016814696],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99793726,0.00023518511,0.00051498716,0.0004674559,0.00026232968,0.0005827884],"domain_scores_gemma":[0.99841124,0.0004568285,0.00024413859,0.00029176395,0.00040073518,0.00019528012],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00073843484,0.00022161492,0.00023656021,0.0001172632,0.0009024928,0.0001110044,0.00028622278,0.0002706712,0.000543249],"category_scores_gemma":[0.0003147813,0.00027337778,0.0001311096,0.0011421653,0.0007039991,0.0005522353,0.000018085448,0.00022833496,0.0007808596],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003435752,0.00035977634,0.0022153945,0.000041619547,0.00002406856,0.000010838974,0.046875928,0.91626656,0.000542499,0.018545017,0.00044848057,0.0146354465],"study_design_scores_gemma":[0.00010507338,0.00011032872,0.0018867637,0.00018666082,0.000042483924,0.0000020109644,0.0060995463,0.9530639,0.0050811,0.0008161979,0.032282863,0.00032309548],"about_ca_topic_score_codex":0.008425064,"about_ca_topic_score_gemma":0.007185444,"teacher_disagreement_score":0.8239905,"about_ca_system_score_codex":0.00016417097,"about_ca_system_score_gemma":0.00031712712,"threshold_uncertainty_score":0.99999714},"labels":[],"label_agreement":null},{"id":"W2913278627","doi":"10.11575/prism/35752","title":"Mode and Departure Time Choice Behavior of Non-Work Related Trips","year":2019,"lang":"en","type":"dissertation","venue":"Open MIND","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"TRIPS architecture; Mode choice; Mode (computer interface); Work (physics); Transport engineering; Psychology; Engineering; Computer science; Public transport; Mechanical engineering; Human–computer interaction","score_opus":0.018669680327027174,"score_gpt":0.3426527006611482,"score_spread":0.3239830203341211,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2913278627","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9152136,0.00010543483,0.000007900605,0.00003958793,0.00029605473,0.0007443699,0.00008026887,0.0000032863315,0.08350952],"genre_scores_gemma":[0.71197677,0.000074299816,0.0026537539,0.000007619259,0.000039218514,0.000027963733,0.0052078906,0.00002603525,0.27998644],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.99910945,0.00005288206,0.000258435,0.00024041449,0.00021656921,0.00012225853],"domain_scores_gemma":[0.9993583,0.000077626435,0.00027968982,0.00011350367,0.000112505055,0.000058400892],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00022243703,0.0001136061,0.00023944136,0.00006771366,0.00014873261,0.00009000494,0.00022770496,0.0003946155,0.001263872],"category_scores_gemma":[0.000049666025,0.00011826935,0.00003840551,0.00023586527,0.000035339763,0.00019770356,0.0000053438566,0.00015916796,0.0001130671],"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":[0.0012110142,0.00076047506,0.14003874,0.0002568697,0.0004150876,0.00004264727,0.4669518,0.0068615144,0.0023021458,0.0002689741,0.009744832,0.3711459],"study_design_scores_gemma":[0.0052692075,0.0003505698,0.7752734,0.0026457224,0.002465433,0.0000021096737,0.028479643,0.0019572463,0.002820761,0.00009470371,0.17819567,0.0024455532],"about_ca_topic_score_codex":0.0008137204,"about_ca_topic_score_gemma":0.0019154411,"teacher_disagreement_score":0.63523465,"about_ca_system_score_codex":0.000017278973,"about_ca_system_score_gemma":0.00023158896,"threshold_uncertainty_score":0.9996491},"labels":[],"label_agreement":null},{"id":"W2914174993","doi":"10.5539/jmr.v11n1p42","title":"A Dichotomic Algorithm for Transportation Network and Land Use Problem","year":2019,"lang":"en","type":"article","venue":"Journal of Mathematics Research","topic":"Transportation Planning and Optimization","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":"Mathematical optimization; Mathematics; Integer programming; Class (philosophy); Flow network; Nonlinear system; Nonlinear programming; Linear programming; Transportation theory; Algorithm; Computer science; Artificial intelligence","score_opus":0.0757064628409176,"score_gpt":0.38825050788624144,"score_spread":0.31254404504532385,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2914174993","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8578609,0.0001925742,0.13939592,0.0006809022,0.00018697527,0.00094146386,0.000027988015,0.000018025894,0.00069520826],"genre_scores_gemma":[0.4112955,0.00074553775,0.5847372,0.000029711617,0.00036229205,0.000017263526,0.000022064864,0.00002620943,0.0027642436],"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99888366,0.0000782298,0.00027962087,0.00006396428,0.0005072459,0.00018727254],"domain_scores_gemma":[0.9984832,0.0007320051,0.00015638195,0.000047057005,0.000500091,0.00008128093],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029027392,0.00004210137,0.00013170848,0.00010996994,0.00017890768,0.000111348745,0.000089271605,0.00006053802,0.000029469207],"category_scores_gemma":[0.000095605654,0.00003511746,0.00003829334,0.0001802238,0.00005669689,0.00027437066,0.0000015385039,0.00015481835,0.0000030923604],"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":[0.0005519874,0.0013263159,0.38630125,0.0021172976,0.00058210635,0.000067409615,0.31133226,0.053781193,0.0006819085,0.13009965,0.028341154,0.08481747],"study_design_scores_gemma":[0.01538306,0.003426645,0.14979935,0.0045526577,0.00044095257,0.000057404854,0.059604526,0.26399165,0.00034075062,0.266835,0.23412052,0.0014474791],"about_ca_topic_score_codex":0.000058442383,"about_ca_topic_score_gemma":0.00016574013,"teacher_disagreement_score":0.44656542,"about_ca_system_score_codex":0.000027145235,"about_ca_system_score_gemma":0.00014372966,"threshold_uncertainty_score":0.14320484},"labels":[],"label_agreement":null},{"id":"W2916481862","doi":"","title":"Urban road pricing and sustainable transportation systems","year":2008,"lang":"en","type":"article","venue":"International Journal of Sustainable Development and Planning","topic":"Transportation Planning and Optimization","field":"Social Sciences","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":"Transport engineering; Road pricing; Sustainable transport; Business; City logistics; Environmental planning; Environmental economics; Sustainability; Environmental science; Engineering; Traffic congestion; Economics","score_opus":0.015345812425373556,"score_gpt":0.25980471324927046,"score_spread":0.2444589008238969,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2916481862","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98850507,0.0012890822,0.0073947166,0.00029316868,0.00028847405,0.00012078193,0.0000010683211,0.000023204651,0.0020844387],"genre_scores_gemma":[0.99258256,0.00023340077,0.0016876152,0.000047807323,0.00019369184,0.0000033457366,0.000020295533,0.000007265767,0.005224017],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.9987437,0.000045305915,0.0003848463,0.000103723134,0.0004651333,0.00025725365],"domain_scores_gemma":[0.99849385,0.000069631344,0.00027796737,0.000022916345,0.00100244,0.00013317728],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00081944646,0.00008862594,0.00013794808,0.0003464804,0.00062384235,0.00013810827,0.00011376216,0.00006294112,0.000007824522],"category_scores_gemma":[0.00016252953,0.00008780279,0.000020458814,0.0001577502,0.000069380054,0.0007782351,0.000006629951,0.000117836535,3.8967892e-7],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027174526,0.00006264669,0.39021668,0.00013244431,0.00022354414,0.007212466,0.51526916,0.011464345,0.000016550428,0.06845809,0.0043589156,0.0023133964],"study_design_scores_gemma":[0.0011622496,0.000054956894,0.29085857,0.00024467625,0.000030976673,0.000111669026,0.52675104,0.0002727546,0.00005446018,0.00025564324,0.17991179,0.00029122873],"about_ca_topic_score_codex":0.00043685857,"about_ca_topic_score_gemma":0.000006997884,"teacher_disagreement_score":0.17555287,"about_ca_system_score_codex":0.00012884871,"about_ca_system_score_gemma":0.00053420174,"threshold_uncertainty_score":0.4798154},"labels":[],"label_agreement":null},{"id":"W2917340184","doi":"","title":"Testing the benefits of on-street and off-street rapid transit alignments: implications for Winnipeg's Southwest Rapid Transit Corridor","year":2010,"lang":"en","type":"dissertation","venue":"Mspace (University of Manitoba)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Transit (satellite); Transport engineering; Geography; Rapid transit; Light rail transit; Cartography; Public transport; Engineering","score_opus":0.029257765658678568,"score_gpt":0.24008872378628807,"score_spread":0.2108309581276095,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2917340184","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98697597,0.00038889798,0.0015145752,0.0025133954,0.0003965124,0.0013092133,0.0019817774,0.00010222702,0.004817411],"genre_scores_gemma":[0.9949681,0.000402472,0.0025338943,0.000041234223,0.00008741733,0.000004855695,0.0014820225,0.000028397571,0.00045161232],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9988434,0.0000761832,0.00019936787,0.00033736342,0.00030970274,0.00023403592],"domain_scores_gemma":[0.9983413,0.00045753873,0.0004545806,0.00023474189,0.00041629747,0.000095520074],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003244237,0.00020485662,0.00031909673,0.0001741714,0.00096672826,0.000028598866,0.00037626256,0.00033487778,0.000012989257],"category_scores_gemma":[0.00008265015,0.00022730796,0.00013928073,0.00033324928,0.0002613403,0.00017885913,0.000005040189,0.00022068927,0.0000013543554],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0024137862,0.0013763327,0.47717333,0.0019526625,0.0013585486,0.000010804882,0.18995596,0.020965658,0.0067531173,0.041706994,0.008339296,0.24799351],"study_design_scores_gemma":[0.0008384651,0.00020754064,0.88583094,0.00031381508,0.00046772062,3.2805872e-7,0.106321886,0.00011806998,0.00014731727,0.00008877379,0.00536137,0.00030378267],"about_ca_topic_score_codex":0.0048936233,"about_ca_topic_score_gemma":0.579224,"teacher_disagreement_score":0.57433033,"about_ca_system_score_codex":0.00003301068,"about_ca_system_score_gemma":0.00018153385,"threshold_uncertainty_score":0.92693496},"labels":[],"label_agreement":null},{"id":"W2918549544","doi":"10.1155/2019/8203081","title":"Quantifying the Impact of Weather Events on Travel Time and Reliability","year":2019,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":34,"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":"Federal Highway Administration; Kentucky Transportation Cabinet","keywords":"Reliability (semiconductor); Quantile regression; Adverse weather; Computer science; Extreme weather; Quantile; Environmental science; Climate change; Econometrics; Meteorology; Geography; Machine learning","score_opus":0.016171975452619135,"score_gpt":0.3194818182209034,"score_spread":0.30330984276828427,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2918549544","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9986161,0.000042427655,0.00060203153,0.00018518078,0.000112410344,0.00014559971,0.000012879969,0.0000056187423,0.00027776792],"genre_scores_gemma":[0.99867344,0.0001166067,0.0010485878,0.000011251001,0.000025717556,5.874808e-7,0.00000957695,0.000006039648,0.00010820849],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99916136,0.00006506184,0.00033033208,0.00007492534,0.0002819728,0.00008632091],"domain_scores_gemma":[0.9991878,0.00014616858,0.00038235125,0.000062871455,0.00017924233,0.000041533902],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005370841,0.000063222746,0.00015088827,0.0000589957,0.00007942885,0.000007219114,0.0000712415,0.000044393804,0.000073392584],"category_scores_gemma":[0.0000378782,0.00004178287,0.000102267375,0.00014613078,0.000043923596,0.00029783213,2.3269335e-7,0.00010626323,0.000002687077],"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":[0.0005444737,0.00010347222,0.22164243,0.000020349233,0.000039252514,0.0000016274041,0.029571334,0.74085593,0.003922581,0.0007936857,0.000017283866,0.0024875924],"study_design_scores_gemma":[0.00056251854,0.000233325,0.99647814,0.000083809835,0.000024338828,4.5012615e-7,0.0017942131,0.00014237002,0.00017838167,0.00039347023,0.000058115784,0.000050852246],"about_ca_topic_score_codex":0.00010946743,"about_ca_topic_score_gemma":0.00004906651,"teacher_disagreement_score":0.7748357,"about_ca_system_score_codex":0.0000309903,"about_ca_system_score_gemma":0.0000811314,"threshold_uncertainty_score":0.1703856},"labels":[],"label_agreement":null},{"id":"W2919930136","doi":"10.1155/2019/9095279","title":"Exploring Passengers’ Travel Behaviors Based on Elaboration Likelihood Model under the Impact of Intelligent Bus Information","year":2019,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; Natural Science Foundation of Heilongjiang Province; China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Metropolitan area; Computer science; Intelligent transportation system; Transport engineering; Travel time; Public transport; Traffic congestion; Passenger information; Information system; Engineering","score_opus":0.03467711967850259,"score_gpt":0.30459670406181816,"score_spread":0.2699195843833156,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2919930136","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.91925347,0.0000085742795,0.079705894,0.00020333844,0.00035938277,0.00024231899,0.000027036785,0.000014334625,0.00018564695],"genre_scores_gemma":[0.99705607,0.00010198009,0.0026435351,0.000036676945,0.000038537655,0.000007403789,0.00009815149,0.0000095306805,0.000008121724],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99849844,0.000051232226,0.000647837,0.00007731658,0.0005781287,0.00014704376],"domain_scores_gemma":[0.9983339,0.00012784089,0.0007449431,0.00010229249,0.0006181856,0.00007288754],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005301248,0.00011138459,0.00017068778,0.00023012672,0.00011694788,0.00003186199,0.00012387233,0.00006165089,0.000034384782],"category_scores_gemma":[0.000036247315,0.00008405801,0.00016344278,0.00040528452,0.000039026007,0.0019553243,3.329392e-7,0.00020417049,0.0000029148111],"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":[0.00022590958,0.00006725495,0.004262353,0.000010726949,0.000017099597,4.6123495e-7,0.01946007,0.9701186,0.002132158,0.0014375114,0.0000075807357,0.002260265],"study_design_scores_gemma":[0.0017201223,0.0006896372,0.91482496,0.00020170769,0.00013270331,5.3771447e-7,0.03554182,0.04256708,0.0033876908,0.00060919125,0.00006932152,0.0002552462],"about_ca_topic_score_codex":0.00013659615,"about_ca_topic_score_gemma":0.00014738717,"teacher_disagreement_score":0.9275515,"about_ca_system_score_codex":0.00010326242,"about_ca_system_score_gemma":0.0003850088,"threshold_uncertainty_score":0.34277862},"labels":[],"label_agreement":null},{"id":"W2921833023","doi":"10.1155/2019/1370159","title":"Evaluation of Transportation Network Reliability under Emergency Based on Reserve Capacity","year":2019,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Natural Science Foundation of Guangdong Province; Shenzhen Technology University","keywords":"Bilevel optimization; Reliability (semiconductor); Computer science; Mathematical optimization; Programming paradigm; Operations research; Emergency evacuation; Optimization problem; Engineering; Mathematics; Algorithm","score_opus":0.035010459791982945,"score_gpt":0.3228926415681837,"score_spread":0.28788218177620073,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2921833023","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9847885,0.00007172966,0.012287938,0.0003638703,0.0011701023,0.00047941992,0.000043293276,0.000026012995,0.00076915213],"genre_scores_gemma":[0.99337494,0.00009433003,0.0061531886,0.00003614632,0.00011822707,0.0000076270044,0.00016208734,0.000015840573,0.00003762072],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9961884,0.00037794214,0.0010423174,0.00020568227,0.001978538,0.00020709181],"domain_scores_gemma":[0.99596834,0.00015944161,0.0011263947,0.00017313976,0.0024740603,0.00009861051],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004039601,0.00013947474,0.00028908227,0.00016183089,0.00013937143,0.000009595358,0.00016052931,0.00014329278,0.00032950015],"category_scores_gemma":[0.0001428246,0.00013744901,0.00020331613,0.00061189453,0.00006297491,0.0007147111,2.281874e-7,0.00022065015,0.0000034702443],"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":[0.00035561842,0.00012621393,0.05340213,0.000040506082,0.000023772507,7.023642e-7,0.0048180916,0.93601155,0.0002701634,0.0037604417,0.00004410166,0.0011467073],"study_design_scores_gemma":[0.0022111745,0.00038916417,0.979381,0.00022766863,0.00032895163,1.0901658e-7,0.0025879017,0.0075694574,0.00030701965,0.0061006015,0.000707772,0.00018913524],"about_ca_topic_score_codex":0.0001596653,"about_ca_topic_score_gemma":0.002306074,"teacher_disagreement_score":0.9284421,"about_ca_system_score_codex":0.0001577014,"about_ca_system_score_gemma":0.0004815754,"threshold_uncertainty_score":0.5605008},"labels":[],"label_agreement":null},{"id":"W2922570500","doi":"10.1287/opre.2018.1781","title":"Competitive Facility Location with Selfish Users and Queues","year":2019,"lang":"en","type":"article","venue":"Operations Research","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Facility location problem; Queue; Computer science; Operations research; Business; Operations management; Computer network; Economics; Mathematics","score_opus":0.0462678411129308,"score_gpt":0.37622606208826637,"score_spread":0.32995822097533556,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2922570500","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9561405,0.00003556961,0.0013613211,0.0020958863,0.000033865545,0.00048488396,0.000021971857,0.00005184357,0.039774112],"genre_scores_gemma":[0.9933439,0.00006353146,0.001069579,0.000027523187,0.000016889633,0.000023378989,0.000089152876,0.0000029051564,0.0053631356],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99900866,0.00024110948,0.000073760464,0.00015776606,0.00037220342,0.00014648495],"domain_scores_gemma":[0.9990848,0.00010481544,0.000006523696,0.0000869385,0.0006545735,0.00006239209],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00077356683,0.000037123908,0.000050883616,0.00008347716,0.00067711715,0.0001818556,0.00006141283,0.00004058209,0.00024402786],"category_scores_gemma":[0.00009254174,0.000033153214,0.000005453162,0.00044268646,0.00021390927,0.00032665444,0.0000051540574,0.0001183545,0.00009711204],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009854648,0.00014699191,0.2755983,0.0000517744,0.00003747379,0.0000031428392,0.14046836,0.12381192,0.00031415824,0.45617986,0.00087714696,0.0024123543],"study_design_scores_gemma":[0.0019874165,0.0005857838,0.50754493,0.00020813411,0.000022966151,0.0000025010695,0.28615174,0.028626451,0.00062891166,0.0002951654,0.17329386,0.00065216224],"about_ca_topic_score_codex":0.003306244,"about_ca_topic_score_gemma":0.022216372,"teacher_disagreement_score":0.45588467,"about_ca_system_score_codex":0.000047393743,"about_ca_system_score_gemma":0.00028478584,"threshold_uncertainty_score":0.9956256},"labels":[],"label_agreement":null},{"id":"W2922901819","doi":"10.1155/2019/1493206","title":"Exploring the Node Importance and Its Influencing Factors in the Railway Freight Transportation Network in China","year":2019,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Office for Philosophy and Social Sciences; Central South University","keywords":"China; PageRank; Transport engineering; Node (physics); Asset (computer security); Distribution (mathematics); Geography; Population; Economic geography; Business; Regional science; Computer science; Engineering; Mathematics; Demography","score_opus":0.024195045043942796,"score_gpt":0.2611122926741125,"score_spread":0.23691724763016972,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2922901819","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99788,0.00037096385,0.00028024125,0.00061321014,0.00037955638,0.0003395722,0.000006510317,0.0000126478135,0.0001173144],"genre_scores_gemma":[0.99826425,0.0009670492,0.0005170593,0.00009145948,0.00008872079,0.000011959845,0.000034611512,0.000011798322,0.000013081864],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99829566,0.0001320791,0.0007028123,0.0001512328,0.00047546648,0.00024276089],"domain_scores_gemma":[0.9990701,0.00023876407,0.00045760002,0.00008346332,0.000102051315,0.000047986006],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010821169,0.00012964412,0.00021261025,0.00013942165,0.0001849046,0.00003841925,0.00019026149,0.000058640344,0.000018630692],"category_scores_gemma":[0.000040729115,0.00008829389,0.000066834786,0.00071998313,0.000038577487,0.0016085958,3.5030124e-7,0.000361938,7.2890447e-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":[0.00006406319,0.000016272219,0.4609287,0.000015849713,0.0000056618774,0.0000108081085,0.107240535,0.429224,0.00013508007,0.0020001517,0.0000025834972,0.00035628068],"study_design_scores_gemma":[0.0006867313,0.00004031991,0.9783314,0.00015970321,0.000021247297,6.3104204e-7,0.019581774,0.000080986996,0.00005615819,0.00043886932,0.0004964348,0.000105779065],"about_ca_topic_score_codex":0.0002130648,"about_ca_topic_score_gemma":0.010235592,"teacher_disagreement_score":0.51740265,"about_ca_system_score_codex":0.000055472126,"about_ca_system_score_gemma":0.00008802293,"threshold_uncertainty_score":0.57117003},"labels":[],"label_agreement":null},{"id":"W2924488638","doi":"10.1139/cjce-2018-0505","title":"Performance evaluation and alternative optimization model of light rail transit network projects: A real case perspective","year":2019,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"China Scholarship Council; National Natural Science Foundation of China","keywords":"Computer science; Weighting; Transportation planning; Operations research; Decision support system; Management science; Risk analysis (engineering); Transport engineering; Data mining; Engineering","score_opus":0.01587217138296274,"score_gpt":0.2396295750903453,"score_spread":0.22375740370738256,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2924488638","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8455226,0.0005251373,0.1433105,0.0002504565,0.00043101722,0.0003826115,0.000011873007,0.00001473139,0.009551097],"genre_scores_gemma":[0.99698603,0.00010453684,0.002763835,0.000006856381,0.00008957086,0.000001864193,0.000002395254,0.000008577119,0.000036332684],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999358,0.00003372508,0.00019728628,0.000073857154,0.00019346506,0.0001436919],"domain_scores_gemma":[0.9991376,0.000028109514,0.00013610146,0.00003954569,0.0005075356,0.00015111329],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060894474,0.0000634145,0.000119631826,0.00018045366,0.000096107244,0.00002445314,0.000053542215,0.000049968883,0.00004315533],"category_scores_gemma":[0.00006291,0.000067554996,0.0000275578,0.00020604063,0.000023525981,0.00035485058,7.629988e-7,0.000095646086,1.940421e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000866889,0.0000020278608,0.0019361257,0.00001218908,0.000019337205,0.000011357079,0.036692895,0.9600329,0.000012930402,0.0011451305,0.000018269393,0.000108161265],"study_design_scores_gemma":[0.00032579794,0.000045370653,0.00044819826,0.00012545062,0.0000389489,0.000025802277,0.0039863847,0.9948531,0.000011947443,0.00003295916,0.00003149909,0.00007455681],"about_ca_topic_score_codex":0.0043246676,"about_ca_topic_score_gemma":0.086876445,"teacher_disagreement_score":0.15146345,"about_ca_system_score_codex":0.00018487366,"about_ca_system_score_gemma":0.0007963166,"threshold_uncertainty_score":0.92978567},"labels":[],"label_agreement":null},{"id":"W2927012229","doi":"10.1155/2019/6184827","title":"Modeling the Morning Commute Problem in a Bottleneck Model Based on Personal Perception","year":2019,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","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":"National Natural Science Foundation of China","keywords":"Bottleneck; Perception; Rush hour; Travel time; Travel behavior; Morning; Transport engineering; Work (physics); Advertising; Psychology; Computer science; Simulation; Engineering; Business; Operations management; Medicine","score_opus":0.016502716974009374,"score_gpt":0.2807235875379578,"score_spread":0.2642208705639484,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2927012229","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9046523,0.000024885392,0.093426034,0.0010066469,0.00013421972,0.00021736439,0.0000058481382,0.000018198352,0.0005144926],"genre_scores_gemma":[0.97906613,0.00006650521,0.020528415,0.00018518143,0.00004339869,0.0000052522355,0.000033288357,0.000011669518,0.00006015574],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987133,0.00008114493,0.00042353195,0.000112359565,0.00051202974,0.00015762563],"domain_scores_gemma":[0.9993827,0.00007442254,0.00023237492,0.000063171705,0.00019918424,0.000048172755],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007025746,0.000090869624,0.00014601312,0.0001591481,0.00017013973,0.000028322942,0.00012008191,0.00006728582,0.000033930723],"category_scores_gemma":[0.000017536691,0.00007398968,0.00009019718,0.00024729944,0.000028817287,0.00051539484,3.4523796e-7,0.00028749814,0.0000031965808],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020678894,0.00004307779,0.007026822,0.000012733679,0.0000039879696,0.0000022240058,0.034507938,0.956055,0.0003249029,0.00047746848,0.0000039891866,0.0013350914],"study_design_scores_gemma":[0.0010842374,0.00009521688,0.03758324,0.00022397004,0.000020501342,3.693865e-7,0.010010434,0.9503657,0.0000069285907,0.0004294332,0.00007854814,0.000101396414],"about_ca_topic_score_codex":0.00006503209,"about_ca_topic_score_gemma":0.00050479977,"teacher_disagreement_score":0.07441383,"about_ca_system_score_codex":0.00010232212,"about_ca_system_score_gemma":0.0001839291,"threshold_uncertainty_score":0.30172116},"labels":[],"label_agreement":null},{"id":"W2927243835","doi":"10.1155/2019/9408595","title":"Integrating Frequency Setting, Timetabling, and Route Assignment to Synchronize Transit Lines","year":2019,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Centro de Desarrollo Urbano Sustentable; Volvo Research and Educational Foundations","keywords":"Headway; Heuristic; Solver; Transit (satellite); Synchronization (alternating current); Computer science; Service (business); Line (geometry); Service level; Transport engineering; Operations research; Public transport; Transfer (computing); Mathematical optimization; Real-time computing; Simulation; Engineering; Computer network; Mathematics","score_opus":0.0073609932851211755,"score_gpt":0.27339371183221334,"score_spread":0.26603271854709215,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2927243835","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9002865,0.00035595626,0.096642196,0.0011713727,0.00055292743,0.0002937934,0.00001616005,0.000038022376,0.0006431059],"genre_scores_gemma":[0.91840065,0.00018426833,0.08090464,0.00012607615,0.00012368875,0.0000031457762,0.00002828559,0.000012976764,0.00021627892],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99865615,0.000057518904,0.0005415929,0.00015311892,0.0004128427,0.00017877389],"domain_scores_gemma":[0.99902356,0.000090418675,0.00037169195,0.000060964678,0.00031070854,0.00014264879],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054367655,0.00011390388,0.00022156604,0.00016097126,0.00015278587,0.000050089973,0.00009784599,0.00007121415,0.00009109259],"category_scores_gemma":[0.000060294293,0.00010643995,0.00007063345,0.00027541176,0.000029345463,0.0007132058,5.0963575e-7,0.00016647326,0.0000051558204],"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":[0.00034030722,0.00015359846,0.17886761,0.00014273259,0.00013363457,0.000048215097,0.16262417,0.520487,0.028328443,0.01028598,0.00012638418,0.09846194],"study_design_scores_gemma":[0.004772557,0.0012170714,0.9047527,0.0015097264,0.0003987077,0.000014649656,0.045660313,0.0005645731,0.0026212058,0.002583364,0.03495486,0.00095027784],"about_ca_topic_score_codex":0.000107145686,"about_ca_topic_score_gemma":0.0007146339,"teacher_disagreement_score":0.7258851,"about_ca_system_score_codex":0.00006486793,"about_ca_system_score_gemma":0.00014833496,"threshold_uncertainty_score":0.43404952},"labels":[],"label_agreement":null},{"id":"W2927454765","doi":"10.1155/2019/6830450","title":"Data-Driven Approaches to Mining Passenger Travel Patterns: “Left-Behinds” in a Congested Urban Rail Transit Network","year":2019,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities","keywords":"Trajectory; Beijing; Database transaction; Urban rail transit; Transit (satellite); Transport engineering; Automatic vehicle location; Transaction data; Computer science; Real-time computing; Public transport; Engineering; Telecommunications; Geography; Database; Global Positioning System","score_opus":0.06323593414167458,"score_gpt":0.2846661239022699,"score_spread":0.22143018976059534,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2927454765","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97227556,0.000121172205,0.025490645,0.00081080204,0.00055570295,0.0004165803,0.00008346635,0.000028022894,0.00021804795],"genre_scores_gemma":[0.9831878,0.00010872036,0.015850294,0.00012926827,0.00021940909,0.0000045535044,0.00037476982,0.000023077937,0.00010208824],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99797815,0.00012695695,0.00078455935,0.0002802154,0.00051634276,0.00031378752],"domain_scores_gemma":[0.9989423,0.00015360459,0.0004223999,0.00018034369,0.00014161863,0.00015974556],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007686076,0.00015758803,0.00035988772,0.00021249507,0.000103812985,0.000045439818,0.00032923283,0.00012668066,0.00007307662],"category_scores_gemma":[0.000030678162,0.00016076589,0.000077869656,0.000404532,0.000030932155,0.0011002712,0.0000014195876,0.00024419423,0.000004431099],"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":[0.0002793117,0.00006785134,0.18775053,0.00003387842,0.000033680186,0.000026439206,0.07444812,0.73401505,0.00019056918,0.00044621908,0.00008479684,0.0026235806],"study_design_scores_gemma":[0.002036991,0.00017857547,0.9588057,0.00042649085,0.00008132812,0.0000022609815,0.03368877,0.0011946263,0.00004263746,0.00006996406,0.003182237,0.00029042896],"about_ca_topic_score_codex":0.000073876705,"about_ca_topic_score_gemma":0.008841138,"teacher_disagreement_score":0.77105516,"about_ca_system_score_codex":0.00006275816,"about_ca_system_score_gemma":0.00017177957,"threshold_uncertainty_score":0.6555843},"labels":[],"label_agreement":null},{"id":"W2938687239","doi":"10.29173/mocs29","title":"A study on small-world characteristics of Chinaäó»s high-speed railway networks","year":2016,"lang":"en","type":"article","venue":"Modular and Offsite Construction (MOC) Summit Proceedings","topic":"Transportation Planning and Optimization","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":"Adjacency matrix; China; Clustering coefficient; Adjacency list; Average path length; Small-world network; Economic geography; Social network analysis; Cluster analysis; Computer science; Geography; Topology (electrical circuits); Complex network; Shortest path problem; Mathematics; Artificial intelligence; Theoretical computer science; Algorithm","score_opus":0.01590018906603718,"score_gpt":0.2412934774210046,"score_spread":0.2253932883549674,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2938687239","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9921275,0.000024949042,0.004734968,0.0004237866,0.00047678477,0.0003904169,0.000020467618,0.00012683883,0.0016743087],"genre_scores_gemma":[0.997525,0.00010083235,0.0009068945,0.00005012757,0.00024582932,0.0000099653835,0.000014136346,0.000017460961,0.0011297645],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99865746,0.00003781288,0.00038345176,0.0003543635,0.0003116901,0.0002551961],"domain_scores_gemma":[0.9991223,0.000060768096,0.00029296137,0.000087990644,0.00030694777,0.00012899905],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043592296,0.00017462194,0.00028410266,0.00023402923,0.00040113553,0.000091336144,0.00012397267,0.000114841074,0.00006237442],"category_scores_gemma":[0.00008478586,0.00014273457,0.0000556104,0.0004278577,0.00030148067,0.00028679665,0.000013920492,0.00012517159,0.00000491925],"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":[0.00019566363,0.00015454189,0.9167702,0.000026336264,0.00007225178,0.0000047139274,0.0049118414,0.00007638754,0.0004758469,0.029022688,0.00018013646,0.048109367],"study_design_scores_gemma":[0.0024951666,0.00039790192,0.97882724,0.00030627562,0.00016395826,0.0000035407795,0.007408955,0.00045732775,0.0005994909,0.00052257243,0.008283572,0.00053400215],"about_ca_topic_score_codex":0.00016353822,"about_ca_topic_score_gemma":0.00019472573,"teacher_disagreement_score":0.06205701,"about_ca_system_score_codex":0.000044230368,"about_ca_system_score_gemma":0.000037841743,"threshold_uncertainty_score":0.58205473},"labels":[],"label_agreement":null},{"id":"W2938951244","doi":"10.29007/gx3h","title":"Assessment of Potential Commercial Corridors for Hyperloop Systems","year":2019,"lang":"en","type":"paratext","venue":"EasyChair preprint","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"ASL Environmental Sciences (Canada)","funders":"","keywords":"Ranking (information retrieval); Transport engineering; Complement (music); Order (exchange); Computer science; Rank (graph theory); Operations research; Selection (genetic algorithm); Geography; Engineering; Business; Mathematics; Information retrieval","score_opus":0.028051486679339373,"score_gpt":0.34263546432150876,"score_spread":0.3145839776421694,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2938951244","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0024983338,0.00019321103,0.8086279,0.00024170749,0.017098684,0.0024154962,0.0007041294,0.00007320179,0.16814731],"genre_scores_gemma":[0.9050861,0.00014001912,0.001502789,0.000031814376,0.0006248845,0.00014636182,0.0009074026,0.000029570885,0.09153108],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99813634,0.0001953246,0.00050357665,0.00040256948,0.0004945727,0.0002676174],"domain_scores_gemma":[0.99860173,0.00010069735,0.0005296083,0.0003138994,0.00036326516,0.00009077669],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007700782,0.00018693284,0.00045038303,0.00011917438,0.00023581761,0.00006989271,0.0003407765,0.00037576442,0.0006250139],"category_scores_gemma":[0.000037656733,0.00020058935,0.00020266595,0.00011965739,0.00010672965,0.0000757809,0.000021003496,0.00021211835,0.0002804551],"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":[0.000035264024,0.00009629297,0.0015950318,0.00029283282,0.000073186864,4.99676e-7,0.0023826524,0.9500958,0.00002103053,0.005557944,0.039389253,0.00046024792],"study_design_scores_gemma":[0.002365549,0.000319348,0.05191188,0.0011064758,0.00010859837,0.0000017502475,0.0056775166,0.05602088,0.000043750144,0.00009992817,0.881136,0.001208346],"about_ca_topic_score_codex":0.002056734,"about_ca_topic_score_gemma":0.00011957945,"teacher_disagreement_score":0.9025877,"about_ca_system_score_codex":0.0001129453,"about_ca_system_score_gemma":0.0007659274,"threshold_uncertainty_score":0.81797963},"labels":[],"label_agreement":null},{"id":"W2940137465","doi":"10.1177/0361198119838838","title":"Managing Unplanned Rail Disruptions: Policy Implications and Guidelines Towards an Effective Bus Bridging Strategy","year":2019,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Saskatchewan; University of Toronto","funders":"Ontario Centres of Excellence","keywords":"Bridging (networking); Transport engineering; Public transport; Service (business); Operations research; Computer science; Engineering; Business; Computer security","score_opus":0.10635806311754954,"score_gpt":0.46030010971340807,"score_spread":0.35394204659585854,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2940137465","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97113323,0.00035853242,0.005037875,0.019830616,0.0003786021,0.0017384622,0.00011334115,0.00008002749,0.0013293143],"genre_scores_gemma":[0.99286616,0.0031138612,0.0020799537,0.00011497711,0.00041646586,0.0000938885,0.00007579781,0.00005104672,0.0011878341],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9928806,0.0017783317,0.001265466,0.00052007206,0.0026380385,0.0009175294],"domain_scores_gemma":[0.99270934,0.00083082693,0.00048442214,0.00043604395,0.004991795,0.0005475956],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0063945763,0.00025245256,0.00042047125,0.0014078189,0.00163822,0.0003734452,0.0008631133,0.00022549432,0.0001863662],"category_scores_gemma":[0.0004199728,0.00021342981,0.0002292073,0.0028890865,0.0007586685,0.001572026,0.0000059258714,0.0013686615,0.000019453157],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014349658,0.00038780764,0.63391495,0.00035627154,0.0002732361,0.000054026415,0.034551237,0.044224016,0.002442871,0.1005668,0.002377769,0.17941603],"study_design_scores_gemma":[0.0012327321,0.00043050473,0.96769506,0.0003913116,0.00005189983,7.5072467e-7,0.011702535,0.00065721106,0.000116573785,0.0082634,0.0092142,0.00024381519],"about_ca_topic_score_codex":0.047446206,"about_ca_topic_score_gemma":0.07352942,"teacher_disagreement_score":0.33378008,"about_ca_system_score_codex":0.00025426334,"about_ca_system_score_gemma":0.0013131681,"threshold_uncertainty_score":0.9996615},"labels":[],"label_agreement":null},{"id":"W2941768360","doi":"10.1007/s11116-019-10003-z","title":"Investigating the influence of assigning a higher priority to scheduling work and school activities in the activity-based models on the simulated travel/activity patterns","year":2019,"lang":"en","type":"article","venue":"Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; IBI Group (Canada)","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Replicate; Scheduling (production processes); Computer science; Schedule; Operations research; Work schedule; Operations management; Statistics; Engineering; Mathematics","score_opus":0.03193950925843538,"score_gpt":0.2850820433650313,"score_spread":0.25314253410659593,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2941768360","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9921624,0.0000045296347,0.00428571,0.0026424995,0.000042264684,0.0006820365,0.000014266568,0.000031103373,0.00013519348],"genre_scores_gemma":[0.9992659,0.0000035985786,0.00022904221,0.00041684674,0.000017220853,0.000026282645,0.000009750134,0.00000948932,0.000021849824],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99863344,0.00035157026,0.00019620892,0.00019944247,0.0004391238,0.000180208],"domain_scores_gemma":[0.99860233,0.0009681172,0.00016867844,0.00015473962,0.000060763294,0.000045374658],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010433382,0.00011729429,0.00013750762,0.000070227215,0.00032503414,0.00007223139,0.00016878007,0.00008301019,0.000016665039],"category_scores_gemma":[0.00006760937,0.000074289506,0.000035897734,0.00055993133,0.00009985604,0.00040636188,0.0000012255246,0.0003063802,0.0000013995749],"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":[0.000050462524,0.000025857284,0.21665822,0.000019146284,0.0000059670892,3.8128698e-7,0.026775554,0.7533996,0.0010574813,0.0017984798,9.4098914e-7,0.00020791765],"study_design_scores_gemma":[0.00025918154,0.000036329246,0.98665285,0.00019507788,0.000018403616,1.4442269e-8,0.0032011818,0.008574397,0.00069329614,0.00025947377,0.000008045569,0.00010175406],"about_ca_topic_score_codex":0.0023031738,"about_ca_topic_score_gemma":0.0015637353,"teacher_disagreement_score":0.7699946,"about_ca_system_score_codex":0.000034063967,"about_ca_system_score_gemma":0.00012427274,"threshold_uncertainty_score":0.34817252},"labels":[],"label_agreement":null},{"id":"W2943771175","doi":"10.1155/2019/4212631","title":"Vehicle Scheduling Optimization considering the Passenger Waiting Cost","year":2019,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Beijing Municipal Natural Science Foundation; National Natural Science Foundation of China","keywords":"Beijing; Scheduling (production processes); Schedule; Public transport; Computer science; Operations research; Transport engineering; Bus priority; Engineering; Operations management","score_opus":0.015603472143761054,"score_gpt":0.2787820910500913,"score_spread":0.2631786189063302,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2943771175","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93138665,0.0001567939,0.06523616,0.001455609,0.00074980495,0.00028855985,0.000003821248,0.00004113207,0.00068150007],"genre_scores_gemma":[0.97304785,0.00019734299,0.026391285,0.00011367989,0.00013881481,0.0000031687453,0.000021334396,0.000012993117,0.00007350477],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9987947,0.00007411323,0.00046470916,0.00010034651,0.00040076458,0.00016539043],"domain_scores_gemma":[0.9987527,0.00020931983,0.0005066406,0.00007041849,0.0003971138,0.00006377201],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060518325,0.00008120782,0.00014014663,0.0000842627,0.00028893183,0.00005536026,0.000110808454,0.00006196699,0.00011088165],"category_scores_gemma":[0.00008581711,0.00006748761,0.00007832241,0.00028266924,0.000050358976,0.0008186903,6.038725e-7,0.0001953652,0.0000051513657],"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":[0.000046767083,0.0000139858175,0.01249005,0.000010246311,0.000014652636,0.0000044274707,0.010677093,0.9722092,0.0006974507,0.0018054786,0.000007833754,0.0020227865],"study_design_scores_gemma":[0.010933717,0.0005505988,0.52257276,0.0017200502,0.00061757525,0.0000238115,0.23318382,0.1750998,0.0052821008,0.001970723,0.046489164,0.0015558812],"about_ca_topic_score_codex":0.00002172492,"about_ca_topic_score_gemma":0.00012416116,"teacher_disagreement_score":0.7971094,"about_ca_system_score_codex":0.000049359714,"about_ca_system_score_gemma":0.0001358062,"threshold_uncertainty_score":0.27520648},"labels":[],"label_agreement":null},{"id":"W2943778955","doi":"10.1016/j.ejtl.2020.100004","title":"A tutorial on recursive models for analyzing and predicting path choice behavior","year":2020,"lang":"en","type":"preprint","venue":"EURO Journal on Transportation and Logistics","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Intuition; Computer science; Path (computing); Discrete choice; Inverse; Theoretical computer science; Mathematical optimization; Artificial intelligence; Machine learning; Mathematics; Cognitive science","score_opus":0.08301966753873334,"score_gpt":0.33858687997178055,"score_spread":0.2555672124330472,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2943778955","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20121342,0.00042399752,0.78414714,0.0036814644,0.0053616106,0.0018010068,0.0019281077,0.00026143258,0.0011818341],"genre_scores_gemma":[0.9896263,0.0013759494,0.006766367,0.00031686312,0.0012869155,0.00002790705,0.0004884389,0.00003083759,0.000080427664],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9984524,0.00011738545,0.00047912874,0.0003537881,0.000389729,0.00020756433],"domain_scores_gemma":[0.9985028,0.00042029252,0.00047690573,0.00006939529,0.00027483178,0.00025579924],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045678727,0.00020872046,0.00027702088,0.00012724326,0.00065346074,0.00023818995,0.00010847516,0.00023952001,0.00000886138],"category_scores_gemma":[0.000373749,0.00021241322,0.00009512868,0.000098856166,0.00010344041,0.00012241444,0.0000019359152,0.0007131643,4.326259e-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":[0.0011591868,0.00031285526,0.046892837,0.00041674942,0.00021209788,0.00022719322,0.077909455,0.71208334,0.00003638958,0.14981675,0.0012557756,0.009677338],"study_design_scores_gemma":[0.015540437,0.0051309858,0.67418116,0.0049412423,0.0068005347,0.000019739964,0.016991623,0.09607141,0.000090289774,0.11408385,0.06149936,0.00464938],"about_ca_topic_score_codex":0.000095003525,"about_ca_topic_score_gemma":0.00017326526,"teacher_disagreement_score":0.78841287,"about_ca_system_score_codex":0.000046567853,"about_ca_system_score_gemma":0.00019615247,"threshold_uncertainty_score":0.8661959},"labels":[],"label_agreement":null},{"id":"W2944134452","doi":"10.1177/0361198119844964","title":"Transferring Matters: Analysis of the Influence of Transfers on Trip Satisfaction","year":2019,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; McGill University","keywords":"TRIPS architecture; Transfer (computing); Public transport; Mode choice; Transport engineering; Perception; Mode (computer interface); Customer satisfaction; Statistical analysis; Affect (linguistics); Marketing; Business; Psychology; Computer science; Engineering; Statistics; Mathematics","score_opus":0.059646155183368084,"score_gpt":0.3849669773120816,"score_spread":0.32532082212871355,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2944134452","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9936837,0.000036159374,0.00068755477,0.0036943934,0.00040039123,0.0010890962,0.00014641491,0.000018507693,0.00024376738],"genre_scores_gemma":[0.9986717,0.0005248134,0.00027639346,0.00007696612,0.000042714764,0.000021920892,0.000014866048,0.00002956142,0.00034103397],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9906187,0.0018678278,0.0016771295,0.000355557,0.004866455,0.00061432103],"domain_scores_gemma":[0.99406385,0.0014659897,0.00066142523,0.00051020127,0.0030820444,0.00021650395],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005230125,0.00021139185,0.000626013,0.001756107,0.00060780713,0.000059598522,0.0010972404,0.00023354849,0.00041220724],"category_scores_gemma":[0.00016745448,0.000151616,0.00082116463,0.006682519,0.00094133033,0.00066987274,0.000002270085,0.0014187458,0.0000064756878],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010751672,0.0001413132,0.71487945,0.00017451325,0.0004981171,0.0000041356047,0.018383093,0.25323245,0.004847473,0.005144602,0.00019659141,0.0014230895],"study_design_scores_gemma":[0.0010499231,0.0003913103,0.98838246,0.000482364,0.00035644657,4.8897615e-8,0.0056500556,0.00016093167,0.0018826964,0.00038810947,0.0011163676,0.0001393012],"about_ca_topic_score_codex":0.035613377,"about_ca_topic_score_gemma":0.10399753,"teacher_disagreement_score":0.27350298,"about_ca_system_score_codex":0.00021055111,"about_ca_system_score_gemma":0.00079869933,"threshold_uncertainty_score":0.97080857},"labels":[],"label_agreement":null},{"id":"W2944959425","doi":"10.1016/j.procs.2019.04.099","title":"Analysis of Activity Location and Trip Mode Choice – A Study on Hierarchical Ordering","year":2019,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Nested logit; Computer science; Mode (computer interface); Logit; Mode choice; Operations research; Travel behavior; Work (physics); Logistic regression; Simulation; Machine learning; Transport engineering; Econometrics; Human–computer interaction; Mathematics; Public transport","score_opus":0.019591172751433534,"score_gpt":0.3185318900925721,"score_spread":0.2989407173411386,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2944959425","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8987673,0.0000028222225,0.100579545,0.00009953265,0.00009505269,0.00018619891,9.2057235e-7,0.000031421485,0.00023721243],"genre_scores_gemma":[0.99796486,0.0000026596467,0.0019526392,0.00002857994,0.000023774504,0.000004411065,0.0000011478738,0.0000018118855,0.000020107942],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99903774,0.000039075327,0.00010139358,0.00026933814,0.0004260658,0.00012635591],"domain_scores_gemma":[0.99947643,0.00013243375,0.0000708371,0.00010130974,0.00015648738,0.00006251217],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054406456,0.000049814636,0.000116165305,0.0002974792,0.00018919859,0.000063925014,0.00016984514,0.000021187425,0.000003679018],"category_scores_gemma":[0.00007761844,0.00004719271,0.000016102238,0.0023038753,0.0001537456,0.00032213828,0.000015785283,0.000057832884,0.0000014865763],"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":[0.000020656204,0.00019822855,0.78947556,0.000014523328,0.00003151701,2.646458e-7,0.03702585,0.15406987,0.00022559712,0.001636571,0.000001972523,0.017299404],"study_design_scores_gemma":[0.00011481368,0.00007261067,0.6557808,0.00000713401,0.000025321951,2.8716013e-8,0.00016676137,0.3437155,0.000041485888,0.000016457681,0.000013603885,0.00004547289],"about_ca_topic_score_codex":0.00044368676,"about_ca_topic_score_gemma":0.00036466116,"teacher_disagreement_score":0.18964563,"about_ca_system_score_codex":0.000023949766,"about_ca_system_score_gemma":0.00016121755,"threshold_uncertainty_score":0.19244628},"labels":[],"label_agreement":null},{"id":"W2945603111","doi":"10.1007/s11518-019-5413-0","title":"Evolutional Attitude Based on Option Prioritization for Conflict Analysis of Urban Transport Planning in Pakistan","year":2019,"lang":"en","type":"article","venue":"Journal of Systems Science and Systems Engineering","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":23,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Nanjing University of Aeronautics and Astronautics; Memorial University of Newfoundland; National Natural Science Foundation of China","keywords":"Prioritization; Government (linguistics); Preference; Conflict analysis; Operations research; Management science; Conflict avoidance; Conflict resolution; Political science; Computer science; Engineering; Psychology; Social psychology; Economics; Microeconomics","score_opus":0.018622271175420542,"score_gpt":0.3023524018955889,"score_spread":0.28373013072016834,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2945603111","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8452609,0.00046059716,0.15269494,0.000029459516,0.0008518286,0.0004009899,0.000018198827,0.000015069032,0.00026797564],"genre_scores_gemma":[0.99947625,0.000011156226,0.00038065092,0.0000034160769,0.000069011396,0.000005451214,0.0000072091984,0.000004752848,0.00004211694],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983739,0.000027326952,0.0005813396,0.00012824188,0.00072932703,0.00015983533],"domain_scores_gemma":[0.998854,0.00014745934,0.00034546768,0.00006278392,0.0005141613,0.00007615171],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029772015,0.00007281719,0.00029720285,0.0010214716,0.000098576114,0.000060392238,0.00011716931,0.000060856244,9.613173e-7],"category_scores_gemma":[0.00006461716,0.0000686603,0.000058121426,0.0012030525,0.000049782087,0.0003834869,0.0000010955638,0.000067645065,1.2818957e-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":[0.000017170663,0.000011098414,0.15244518,0.000098251556,0.000022093325,0.0000010682813,0.0024401727,0.8366348,0.00093782105,0.0073821764,0.0000027056606,0.000007453169],"study_design_scores_gemma":[0.00039922018,0.00007986911,0.19890077,0.00059570547,0.00006081526,0.0000012060357,0.0020120344,0.79735404,0.000011216247,9.701794e-7,0.00050281646,0.00008132863],"about_ca_topic_score_codex":0.00018723488,"about_ca_topic_score_gemma":0.0000126428595,"teacher_disagreement_score":0.15421529,"about_ca_system_score_codex":0.00017641873,"about_ca_system_score_gemma":0.00023844333,"threshold_uncertainty_score":0.2799886},"labels":[],"label_agreement":null},{"id":"W2947684706","doi":"10.1109/ccsse.2018.8724747","title":"Stochastic Dynamic Model of City Bus Routes and Their Optimum Management","year":2018,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Revenue; Computer science; Mathematical optimization; Operations research; Revenue management; Stochastic modelling; Engineering; Mathematics; Finance","score_opus":0.019349756672277323,"score_gpt":0.2738869176320855,"score_spread":0.25453716095980816,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2947684706","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18788801,0.000015102317,0.78838605,0.00019542695,0.00004368421,0.000089957626,0.0000071238815,0.00004812374,0.023326544],"genre_scores_gemma":[0.98523736,0.000019972382,0.012303425,0.000035959853,0.000009593206,0.000002226524,0.0000053071167,0.0000025027532,0.0023836354],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996653,0.000011277896,0.0000785489,0.00008437367,0.00008062121,0.000079835234],"domain_scores_gemma":[0.99981934,0.000019976733,0.000030241907,0.000048175563,0.000052582167,0.000029669525],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012844552,0.000038153197,0.00005200328,0.000034246565,0.00013277013,0.000013627061,0.00005203325,0.000025098547,0.000046993264],"category_scores_gemma":[0.0000067356236,0.000031367093,0.00001120929,0.00008120809,0.0001398614,0.00007440584,0.0000052291634,0.000016410517,0.0000018004444],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006622137,0.00013154442,0.0044630277,0.000046676574,0.0000880679,6.7334696e-7,0.07969217,0.69513214,0.00018918123,0.20418689,0.00044086043,0.015562565],"study_design_scores_gemma":[0.00018450085,0.000027300852,0.004689725,0.000016616277,0.000014128282,5.7613157e-8,0.0048486013,0.98542345,0.000027275431,0.004635348,0.000051342475,0.00008163869],"about_ca_topic_score_codex":0.000185495,"about_ca_topic_score_gemma":0.00065371156,"teacher_disagreement_score":0.7973494,"about_ca_system_score_codex":0.000009405713,"about_ca_system_score_gemma":0.000014652682,"threshold_uncertainty_score":0.1279113},"labels":[],"label_agreement":null},{"id":"W2947687498","doi":"10.3929/ethz-b-000286496","title":"Modelling intermodal travel in Switzerland: A recursive logit approach","year":2018,"lang":"en","type":"article","venue":"Repository for Publications and Research Data (ETH Zurich)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Mixed logit; Computer science; Mode (computer interface); Logit; Mode choice; Estimation; Choice set; Discrete choice; Econometrics; Nested logit; Sample (material); Process (computing); Sampling (signal processing); Travel behavior; Public transport; Path (computing); Operations research; Logistic regression; Transport engineering; Economics; Engineering; Machine learning","score_opus":0.24071444215018858,"score_gpt":0.43035989288059856,"score_spread":0.18964545073040998,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2947687498","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034467097,0.00050513237,0.87843287,0.0046475567,0.0002758177,0.0018208866,0.00041105226,0.00014346582,0.07929611],"genre_scores_gemma":[0.9680512,0.00021393382,0.02588132,0.00001982125,0.0003592349,0.00015521898,0.001196388,0.000014211449,0.004108694],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983405,0.0002547365,0.0002464338,0.00045360462,0.00036140834,0.00034334898],"domain_scores_gemma":[0.9984027,0.0003193398,0.000067949535,0.00044854305,0.0006192834,0.00014215636],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027176144,0.00007040661,0.00010514307,0.0003547269,0.0012307352,0.00033560436,0.000561665,0.00011953303,0.000007350967],"category_scores_gemma":[0.00040453358,0.00006516564,0.000019242812,0.0007229974,0.00047594722,0.0007462305,0.000057711073,0.0002086947,0.0000025714023],"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":[0.0005513052,0.0017456262,0.025789412,0.0003224807,0.00018569491,0.000007642923,0.13020243,0.009673013,0.000505317,0.71016186,0.10510102,0.015754176],"study_design_scores_gemma":[0.0016277209,0.0003383711,0.013594624,0.0001519766,0.000045871584,0.000013617703,0.02829416,0.5700734,0.00018607892,0.008681072,0.37637314,0.0006199629],"about_ca_topic_score_codex":0.0019823415,"about_ca_topic_score_gemma":0.00034855268,"teacher_disagreement_score":0.9335841,"about_ca_system_score_codex":0.000060862003,"about_ca_system_score_gemma":0.00029408885,"threshold_uncertainty_score":0.9465944},"labels":[],"label_agreement":null},{"id":"W2948161603","doi":"","title":"Analyzing Passenger Incidence Behavior in Heterogeneous Transit Services Using Smartcard Data and Schedule-Based Assignment","year":2012,"lang":"en","type":"article","venue":"DSpace@MIT (Massachusetts Institute of Technology)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vancouver Community College; University of British Columbia","funders":"","keywords":"Transit (satellite); Incidence (geometry); Schedule; Service (business); Public transport; Reliability (semiconductor); Computer science; Transport engineering; Engineering; Business; Mathematics","score_opus":0.03776807724151047,"score_gpt":0.3130372065032603,"score_spread":0.2752691292617498,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2948161603","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9842665,0.0010829691,0.012405513,0.0012912584,0.0002736029,0.00033947077,0.00008788089,0.00017564699,0.00007714711],"genre_scores_gemma":[0.9671874,0.00008977783,0.03251971,0.000044102784,0.000037889193,0.000020037409,0.00007626498,0.000015487807,0.000009311514],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99839485,0.00007014867,0.00037361245,0.00037964512,0.0003365517,0.00044521998],"domain_scores_gemma":[0.99906427,0.000033230484,0.0002238621,0.00048326992,0.000079639496,0.00011574179],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006227775,0.00018845682,0.0002972034,0.00057237566,0.000285084,0.000043334578,0.00050523184,0.00036161373,0.000015619167],"category_scores_gemma":[0.000068785725,0.00020365312,0.000039605027,0.00082162424,0.0004500844,0.0009253539,0.000057431494,0.00024241599,0.0000019643453],"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":[0.000020072319,0.00015791232,0.98244554,0.00005851273,0.000036556914,0.000025737732,0.0014753371,0.009209438,0.0024805749,0.000545946,0.000006997817,0.003537361],"study_design_scores_gemma":[0.009844511,0.0006009294,0.70583016,0.0033938156,0.0028816317,0.00008349547,0.017149085,0.09834657,0.03866469,0.0004610697,0.11750512,0.0052388962],"about_ca_topic_score_codex":0.0009178547,"about_ca_topic_score_gemma":0.0054996265,"teacher_disagreement_score":0.27661538,"about_ca_system_score_codex":0.000109018074,"about_ca_system_score_gemma":0.00015421573,"threshold_uncertainty_score":0.83047336},"labels":[],"label_agreement":null},{"id":"W2948485289","doi":"10.1007/s11116-019-10018-6","title":"Calibration of large-scale transport planning models: a structured approach","year":2019,"lang":"en","type":"article","venue":"Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":18,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Calibration; Computer science; Focus (optics); Key (lock); Scale (ratio); Transport network; Trip distribution; Industrial engineering; Operations research; Simulation; Mathematical optimization; Engineering; Mathematics; Statistics","score_opus":0.02099461346418064,"score_gpt":0.271046508612467,"score_spread":0.25005189514828635,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2948485289","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.693821,0.00005380001,0.29740593,0.00005149977,0.00015252893,0.00040407456,0.00010817512,0.00014011026,0.007862893],"genre_scores_gemma":[0.9876956,0.000019564985,0.009950344,0.000043603664,0.00003874891,0.000014577383,0.0016931965,0.000018962239,0.0005254053],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.998593,0.00004379541,0.000407716,0.00025684468,0.00046637494,0.00023231577],"domain_scores_gemma":[0.99945694,0.000028514092,0.00018622489,0.00013143112,0.00012085272,0.000076062825],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031405163,0.00012241649,0.00020774417,0.00012412832,0.0001499248,0.000013499498,0.00012259865,0.00016314084,0.00016368259],"category_scores_gemma":[0.0000027968429,0.00013198535,0.00008608873,0.00038725976,0.00005287005,0.0006773357,3.859343e-7,0.000107572414,0.000002213116],"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":[0.00011460781,0.000083808416,0.14016372,0.000100631194,0.000021832026,0.0000012935238,0.13242865,0.6839577,0.00042986852,0.042531867,0.000026824402,0.00013919742],"study_design_scores_gemma":[0.0040465235,0.00016750183,0.6397791,0.00022900231,0.0002657691,7.4698113e-7,0.064199395,0.2779152,0.0015742988,0.008676556,0.0021830713,0.0009628177],"about_ca_topic_score_codex":0.00046381407,"about_ca_topic_score_gemma":0.00092842826,"teacher_disagreement_score":0.4996154,"about_ca_system_score_codex":0.000022582155,"about_ca_system_score_gemma":0.00010600324,"threshold_uncertainty_score":0.5382206},"labels":[],"label_agreement":null},{"id":"W2948674847","doi":"10.1155/2019/6873912","title":"Measuring Retiming Responses of Passengers to a Prepeak Discount Fare by Tracing Smart Card Data: A Practical Experiment in the Beijing Subway","year":2019,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Retiming; Beijing; Elasticity (physics); Tracing; Computer science; Econometrics; Economics; China; Parallel computing; Operating system","score_opus":0.049923807117180245,"score_gpt":0.3503088364430138,"score_spread":0.30038502932583355,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2948674847","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9886657,0.00018839148,0.008933133,0.0014842661,0.00023072501,0.00032176712,0.00003259133,0.000009882113,0.00013355096],"genre_scores_gemma":[0.9904426,0.00009702586,0.009276399,0.000050382296,0.000040741033,0.000006373632,0.000052378942,0.000010326123,0.000023805198],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.99802357,0.00021510485,0.0006538271,0.00017918991,0.0007510985,0.0001772105],"domain_scores_gemma":[0.99874014,0.00036851666,0.0004633526,0.00016999489,0.0001929248,0.000065081214],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017954337,0.00009565081,0.00022131467,0.00014099941,0.00011453046,0.000038769896,0.00021837173,0.00005455463,0.00001078317],"category_scores_gemma":[0.00025171816,0.00007945959,0.00005774578,0.00033203777,0.000034597986,0.0011223358,0.0000016444244,0.00020140788,6.3751645e-7],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0027967466,0.00031980922,0.12092201,0.00015317967,0.0000832475,0.00007256655,0.468224,0.36661905,0.035559185,0.0009036744,0.00018946388,0.004157067],"study_design_scores_gemma":[0.0026958997,0.00072331383,0.5800289,0.0018200435,0.00022515531,0.000010826877,0.3975302,0.0003354585,0.007374355,0.00008389062,0.008645802,0.0005261325],"about_ca_topic_score_codex":0.00016343535,"about_ca_topic_score_gemma":0.0011330534,"teacher_disagreement_score":0.45910692,"about_ca_system_score_codex":0.00008304333,"about_ca_system_score_gemma":0.0001905111,"threshold_uncertainty_score":0.3240268},"labels":[],"label_agreement":null},{"id":"W2949085663","doi":"10.1155/2019/6510396","title":"Evaluation of Shared Space to Reduce Traffic Congestion","year":2019,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"VisSim; Shared space; Pedestrian; Traffic congestion; Transport engineering; Microsimulation; Space (punctuation); Computer science; Street network; Perception; Traffic simulation; Engineering","score_opus":0.0263205145763013,"score_gpt":0.3340910091675261,"score_spread":0.3077704945912248,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2949085663","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9957663,0.00011989716,0.0018032605,0.0006645943,0.00066478865,0.00039710657,0.000013489588,0.000018536373,0.00055206445],"genre_scores_gemma":[0.9897262,0.00006556113,0.009955704,0.000021801916,0.00007181895,0.0000044398935,0.00004438652,0.000009608642,0.000100507146],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9979142,0.0001400401,0.00051495613,0.00011643674,0.0011931541,0.00012121616],"domain_scores_gemma":[0.99731576,0.000072840114,0.00056394225,0.00007557453,0.0018766436,0.00009521106],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017854964,0.00007784209,0.00018610079,0.00021030809,0.00006413911,0.0000150650485,0.00010227108,0.00007138705,0.00017414252],"category_scores_gemma":[0.00018247937,0.000081103026,0.000078507146,0.00043447103,0.000027510056,0.0006555093,3.1521262e-7,0.0000956597,0.000007528413],"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":[0.00018630877,0.000045812994,0.0019979917,0.000015308016,0.000020368192,9.3915764e-7,0.022630163,0.95465845,0.004843509,0.0007741004,0.00004995639,0.014777088],"study_design_scores_gemma":[0.0036789076,0.0005370833,0.96953803,0.00048031213,0.0004517724,0.0000016189767,0.017133832,0.0010039804,0.0022653255,0.0005872187,0.004052361,0.00026954128],"about_ca_topic_score_codex":0.000027940334,"about_ca_topic_score_gemma":0.0003466639,"teacher_disagreement_score":0.96754,"about_ca_system_score_codex":0.00010647346,"about_ca_system_score_gemma":0.00037138668,"threshold_uncertainty_score":0.33072853},"labels":[],"label_agreement":null},{"id":"W2949950221","doi":"10.48550/arxiv.1809.05781","title":"Modelling Latent Travel Behaviour Characteristics with Generative Machine Learning","year":2018,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Latent variable; Latent variable model; Computer science; Bayesian network; Generative grammar; Machine learning; Artificial intelligence; Generative model; Restricted Boltzmann machine; Mode (computer interface); Latent class model; Graphical model; Preference; Dynamic Bayesian network; Bayesian probability; Artificial neural network; Statistics; Mathematics; Human–computer interaction","score_opus":0.08457697967768811,"score_gpt":0.20803507682437156,"score_spread":0.12345809714668345,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2949950221","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.52291757,0.0000117287855,0.4752397,0.00003661104,0.00015818664,0.0001495109,0.000049647337,0.000103829894,0.0013332122],"genre_scores_gemma":[0.99199355,0.00034104928,0.0021813747,0.000020996576,0.0001454713,7.9049835e-7,0.00040930073,0.000021954418,0.004885515],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988456,0.00015821368,0.0001475991,0.00048820852,0.00011867513,0.00024175183],"domain_scores_gemma":[0.9991404,0.00003445251,0.00025585023,0.00015958941,0.0002793447,0.00013039137],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023303123,0.00019328455,0.0002177205,0.00012110549,0.0005946914,0.00008153804,0.00023453985,0.00024336918,0.00010516988],"category_scores_gemma":[0.000011659481,0.000210042,0.00007030041,0.00022224501,0.00019093866,0.00013545409,0.00003580904,0.000472411,0.000018612156],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000052091866,0.00003384384,0.09818388,0.000011325512,0.000038196406,0.00005977399,0.0055423495,0.8904768,0.0000011614792,0.005565601,0.000011346125,0.000023621775],"study_design_scores_gemma":[0.00032313744,0.00006483684,0.008350771,0.00010829542,0.00020596028,4.6245637e-7,0.0010647106,0.9886908,0.000012415618,0.00055331126,0.00023401287,0.00039128552],"about_ca_topic_score_codex":0.002323596,"about_ca_topic_score_gemma":0.00064078503,"teacher_disagreement_score":0.4730583,"about_ca_system_score_codex":0.00012643768,"about_ca_system_score_gemma":0.00022149035,"threshold_uncertainty_score":0.8565264},"labels":[],"label_agreement":null},{"id":"W2950123127","doi":"10.17615/z4bb-4c59","title":"Plan evaluation and monitoring in ten U.S. cities, and an assay of land use and transportation integration indicators","year":2019,"lang":"en","type":"article","venue":"Carolina Digital Repository (University of North Carolina at Chapel Hill)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Gillings School of Public Health; Queensland Government; Vancouver Foundation; U.S. Department of Transportation","keywords":"Environmental planning; Land use; Plan (archaeology); Environmental resource management; Geography; Business; Environmental protection; Environmental science; Civil engineering; Engineering","score_opus":0.015585733305770554,"score_gpt":0.22545856818744026,"score_spread":0.2098728348816697,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2950123127","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9989905,0.00013266644,0.00004843797,0.000016051012,0.000088240435,0.00027149118,0.00006631519,0.00002133639,0.0003649757],"genre_scores_gemma":[0.9993615,0.00018547454,0.000110943394,0.0000021684666,0.000022061447,5.9877175e-7,0.00018092009,0.0000068382333,0.00012950155],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99903977,0.00009940503,0.00018799184,0.00024226167,0.00031594787,0.00011462513],"domain_scores_gemma":[0.9993269,0.00010555647,0.00021177766,0.00008795224,0.00016152232,0.00010630699],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002678917,0.000098717275,0.00018757604,0.00020229862,0.00017436969,0.000028583676,0.0000620776,0.00010046236,0.0000018436699],"category_scores_gemma":[0.000037141108,0.00011964213,0.000026564632,0.00019888312,0.00020764332,0.0012320076,0.000008651582,0.00008175172,1.9529675e-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":[0.00008765276,0.000019396306,0.9708871,0.000036536516,0.000011061136,0.0000050016706,0.021599539,0.0005005063,0.00010413354,0.000074716234,0.0000010428737,0.0066732992],"study_design_scores_gemma":[0.0005634065,0.00008260618,0.99393123,0.00006364211,0.000046173875,0.0000019769434,0.003583156,0.0014901878,0.00005773503,0.00002512867,0.000041715826,0.00011302316],"about_ca_topic_score_codex":0.001872571,"about_ca_topic_score_gemma":0.004858723,"teacher_disagreement_score":0.023044134,"about_ca_system_score_codex":0.000077385186,"about_ca_system_score_gemma":0.00007898457,"threshold_uncertainty_score":0.48788646},"labels":[],"label_agreement":null},{"id":"W2951432389","doi":"10.1155/2019/1837628","title":"Explanatory Factors for Public Transportation Financing Needs in Spain","year":2019,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Subsidy; Public transport; Business; Finance; Sustainability; Principal (computer security); Population; Public fund; Public economics; Economics; Transport engineering; Engineering; Computer science","score_opus":0.01918897751508266,"score_gpt":0.27429836622742204,"score_spread":0.2551093887123394,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2951432389","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97593117,0.00011888015,0.022228844,0.00042821473,0.00073407934,0.0003792642,0.00003476135,0.000029537227,0.00011526967],"genre_scores_gemma":[0.9945836,0.00013932864,0.0047615236,0.000050484738,0.00007116442,0.000009463554,0.00024708212,0.000018157174,0.000119180324],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99844515,0.000055383545,0.00071371195,0.00013201994,0.0004022821,0.00025144345],"domain_scores_gemma":[0.9987134,0.00024646914,0.0005397238,0.00006701313,0.0003341971,0.00009920077],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000701126,0.00012329106,0.0002587406,0.0005135368,0.00011491793,0.000028508537,0.00013061315,0.000121588244,0.00005267381],"category_scores_gemma":[0.00008210407,0.00012673122,0.00013966988,0.0005872221,0.000033250173,0.0013358769,1.3815074e-7,0.00016617848,0.0000015468559],"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":[0.0003946065,0.00014008,0.56709546,0.00012205851,0.000032861673,0.000011816751,0.12334437,0.27992022,0.0021072403,0.022002095,0.000041181534,0.004788015],"study_design_scores_gemma":[0.0024997415,0.00020242155,0.93154293,0.00017851526,0.000041226158,3.083599e-7,0.052698504,0.0001031277,0.0002995178,0.00090352993,0.011286911,0.00024323979],"about_ca_topic_score_codex":0.00007504467,"about_ca_topic_score_gemma":0.0028301487,"teacher_disagreement_score":0.3644475,"about_ca_system_score_codex":0.00011124971,"about_ca_system_score_gemma":0.00027389725,"threshold_uncertainty_score":0.5167949},"labels":[],"label_agreement":null},{"id":"W295149922","doi":"","title":"Synthesizing Population for Agent-Based Microsimulation Modeling in Atlantic Canada","year":2015,"lang":"en","type":"article","venue":"Transportation Research Board 94th Annual MeetingTransportation Research Board","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":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Microsimulation; Population; Computer science; Weighting; Econometrics; Operations research; Economics; Engineering; Transport engineering","score_opus":0.14358126749174696,"score_gpt":0.4060807760266452,"score_spread":0.26249950853489823,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W295149922","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96718603,0.0001527289,0.02429302,0.0035770568,0.00041249642,0.003097364,0.00035557352,0.00025133262,0.00067439926],"genre_scores_gemma":[0.989109,0.00006792763,0.0067746895,0.00012656034,0.00023869821,0.0005697036,0.0026919513,0.00009279373,0.0003286771],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.98945075,0.0016124797,0.0014296775,0.0010234124,0.0046473257,0.001836351],"domain_scores_gemma":[0.99163836,0.0018967703,0.00024168214,0.0003706441,0.0050027464,0.0008498169],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.011196093,0.00035948312,0.00050597906,0.001525979,0.0015466369,0.00026065705,0.0005676044,0.00039948578,0.000060578364],"category_scores_gemma":[0.001650509,0.00042327444,0.00015643101,0.0028181348,0.00032597216,0.0010039716,0.00000433129,0.0009756283,0.000013853207],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012827833,0.00019126381,0.334633,0.00026235607,0.000027663367,0.000053836677,0.01880788,0.63554156,0.000111247304,0.0057193567,0.002233231,0.0011358348],"study_design_scores_gemma":[0.01010976,0.0007216052,0.54490197,0.0013097401,0.00012705878,2.8154918e-7,0.08914772,0.29200444,0.00046334806,0.005632696,0.05349387,0.0020875363],"about_ca_topic_score_codex":0.9096573,"about_ca_topic_score_gemma":0.978521,"teacher_disagreement_score":0.34353712,"about_ca_system_score_codex":0.0013508078,"about_ca_system_score_gemma":0.0043499987,"threshold_uncertainty_score":0.9998219},"labels":[],"label_agreement":null},{"id":"W2951951756","doi":"10.1002/net.20074","title":"An approximation algorithm for Stackelberg network pricing","year":2005,"lang":"en","type":"article","venue":"Networks","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":84,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Stackelberg competition; Toll; Relaxation (psychology); Linear programming relaxation; Set (abstract data type); Approximation algorithm; Computer science; Constraint (computer-aided design); Flow network; Algorithm; Combinatorics; Mathematics; Mathematical optimization; Linear programming; Mathematical economics","score_opus":0.01407393180372267,"score_gpt":0.29096143431612065,"score_spread":0.27688750251239796,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2951951756","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015723694,0.00012257838,0.99413645,0.00028684412,0.00031005158,0.00028768205,0.0000031410507,0.00018257729,0.0030983312],"genre_scores_gemma":[0.5610772,0.00009597276,0.43205303,0.00047983095,0.004973244,0.000059074508,0.00038823127,0.000022396087,0.0008510597],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992139,0.000060728085,0.00015750967,0.0001509281,0.00014467591,0.0002722832],"domain_scores_gemma":[0.9995839,0.000084628875,0.00007859165,0.000083321946,0.00008960664,0.00007996707],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00055867934,0.000065118074,0.00007946973,0.000024223302,0.0005302889,0.00007559093,0.00009313862,0.00011043428,0.000040504565],"category_scores_gemma":[0.000012546972,0.000070199596,0.000031181287,0.00021213456,0.000032913395,0.0003424009,0.0000016330354,0.00007008598,0.000003898533],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000047554545,0.000015799613,0.0006423025,0.000001111387,0.000003718482,9.779611e-8,0.0019688602,0.7272051,3.570216e-7,0.0045675626,0.0021975823,0.26339278],"study_design_scores_gemma":[0.00019819448,0.000024396524,0.0011560294,0.000012326378,0.000012348443,9.397627e-8,0.00035441323,0.93320245,0.000002306436,0.0002997573,0.064637914,0.00009976031],"about_ca_topic_score_codex":0.000070547154,"about_ca_topic_score_gemma":0.0006087623,"teacher_disagreement_score":0.5620834,"about_ca_system_score_codex":0.000042711243,"about_ca_system_score_gemma":0.0000391773,"threshold_uncertainty_score":0.40786073},"labels":[],"label_agreement":null},{"id":"W2952066860","doi":"10.1186/s40537-019-0218-z","title":"STVG: an evolutionary graph framework for analyzing fast-evolving networks","year":2019,"lang":"en","type":"article","venue":"Journal Of Big Data","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada; Tertiary Education Trust Fund; Cisco Systems","keywords":"Computer science; Theoretical computer science; Graph; Snapshot (computer storage); Graph database; Database","score_opus":0.0752406044275541,"score_gpt":0.3422732496091298,"score_spread":0.2670326451815757,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2952066860","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024601365,0.0009017043,0.9714366,0.0006795396,0.0018232219,0.00013120851,0.00008274918,0.000023505467,0.00032007345],"genre_scores_gemma":[0.91752076,0.00024493845,0.08044328,0.00008616572,0.0013277322,6.5263106e-7,0.00025330854,0.000009633359,0.00011353162],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9990853,0.000075254306,0.00028027184,0.00012827196,0.00026701705,0.00016388886],"domain_scores_gemma":[0.99887395,0.00022851264,0.00032780558,0.00023170617,0.0002368114,0.00010124334],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010446877,0.000057293582,0.00013166512,0.00011945174,0.0002466196,0.00008437791,0.00047910013,0.00009844081,0.00005838626],"category_scores_gemma":[0.00024835984,0.000053829106,0.000051398903,0.0002814386,0.000036145608,0.00096724596,0.000014264556,0.00018223783,0.0000022529202],"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":[0.00031491034,0.0002561891,0.49368018,0.000050338243,0.0002532807,0.000017592396,0.01155723,0.36862954,0.000045314886,0.024182092,0.026793066,0.074220255],"study_design_scores_gemma":[0.0031343428,0.0010011684,0.4360406,0.0016290649,0.0006579864,0.000027818109,0.023168007,0.29133865,0.000009872745,0.035936344,0.20580019,0.0012559306],"about_ca_topic_score_codex":0.000047159778,"about_ca_topic_score_gemma":0.00012944837,"teacher_disagreement_score":0.8929194,"about_ca_system_score_codex":0.000027289972,"about_ca_system_score_gemma":0.0001516346,"threshold_uncertainty_score":0.21950872},"labels":[],"label_agreement":null},{"id":"W2953902999","doi":"10.1155/2019/9692024","title":"Optimal Coordination of Last Trains for Maximum Transfer Accessibility with Heterogeneous Walking Time","year":2019,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Train; Dwell time; Transfer (computing); Computer science; Variable (mathematics); Extension (predicate logic); Nonlinear system; Transfer function; Mathematical optimization; Sensitivity (control systems); Operations research; Simulation; Engineering; Mathematics","score_opus":0.00987360422513034,"score_gpt":0.27470691256046703,"score_spread":0.2648333083353367,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2953902999","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8759187,0.000036929116,0.12321125,0.00012928735,0.0001566619,0.0003803754,0.000040194707,0.000019418854,0.000107182794],"genre_scores_gemma":[0.9797239,0.000024148783,0.019989215,0.000016957718,0.00005104509,0.000006048723,0.000087540175,0.0000158419,0.00008532785],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9986563,0.000057745307,0.000565562,0.00014685471,0.00040998278,0.0001635805],"domain_scores_gemma":[0.9986956,0.00014531726,0.00037476557,0.00007011427,0.000643887,0.00007030771],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00058001553,0.00010690675,0.0002637051,0.00013464027,0.000104747436,0.000020030211,0.000121754754,0.00008213852,0.00007658676],"category_scores_gemma":[0.000026578877,0.00009564052,0.00012894937,0.00025017155,0.00006459997,0.000783371,2.5711265e-7,0.000095003634,9.74441e-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":[0.0014505096,0.00015172483,0.014568326,0.00012221094,0.00007152165,0.0000050619715,0.025618486,0.9386745,0.009995515,0.0005724305,0.000006724187,0.008762973],"study_design_scores_gemma":[0.023689646,0.0055539445,0.8865428,0.0014152006,0.0010533887,0.000020362442,0.026193827,0.0048986683,0.04050181,0.0024085518,0.0062899287,0.0014318553],"about_ca_topic_score_codex":0.000010204773,"about_ca_topic_score_gemma":0.00023003168,"teacher_disagreement_score":0.93377584,"about_ca_system_score_codex":0.000049471917,"about_ca_system_score_gemma":0.00016492448,"threshold_uncertainty_score":0.39001074},"labels":[],"label_agreement":null},{"id":"W2954566054","doi":"10.1155/2019/5282879","title":"A Traffic Assignment Model Based on Link Densities","year":2019,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","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":"Link (geometry); Queue; Traffic flow (computer networking); Microscopic traffic flow model; Traffic generation model; Computer science; Mathematical optimization; Flow (mathematics); Traffic congestion reconstruction with Kerner's three-phase theory; Flow network; Traffic congestion; Traffic model; Applied mathematics; Mathematics; Real-time computing; Transport engineering; Computer network; Engineering","score_opus":0.0115997764684693,"score_gpt":0.2682627389870154,"score_spread":0.2566629625185461,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2954566054","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.91808856,0.000036696412,0.07905263,0.0011202194,0.00048750063,0.00018643387,0.000011112839,0.00003911693,0.0009777086],"genre_scores_gemma":[0.98702425,0.00005480952,0.012281149,0.00022315253,0.00008294123,0.0000023635146,0.000032932152,0.000011390815,0.0002869875],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99877137,0.000043873883,0.0003775375,0.000104519706,0.00056301535,0.0001396847],"domain_scores_gemma":[0.99919415,0.000093108814,0.00033269153,0.000058001147,0.00023964311,0.00008243981],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003341885,0.00009087175,0.00016383005,0.00015374999,0.00010992991,0.00002260536,0.00007927598,0.00007164037,0.00006172293],"category_scores_gemma":[0.000019151663,0.0000836605,0.000105835294,0.00016496598,0.000031987824,0.00038782452,1.4087992e-7,0.00014650114,0.0000070049687],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021717139,0.00004471421,0.0010802062,0.000012231992,0.0000084136145,0.0000063810285,0.01137206,0.9816874,0.0002172691,0.0008638072,0.00002909,0.0044612437],"study_design_scores_gemma":[0.013317563,0.0025400275,0.20942146,0.0014785758,0.00043424452,0.0000031500056,0.04943437,0.70239556,0.0015295924,0.0024360223,0.015637409,0.0013720341],"about_ca_topic_score_codex":0.0000051688016,"about_ca_topic_score_gemma":0.00008677223,"teacher_disagreement_score":0.27929187,"about_ca_system_score_codex":0.00007557263,"about_ca_system_score_gemma":0.00023859327,"threshold_uncertainty_score":0.34115762},"labels":[],"label_agreement":null},{"id":"W2955448449","doi":"10.1155/2019/5745870","title":"Limited-Stop High-Frequency Service Design: Reducing In-Vehicle Congestion","year":2019,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Service (business); Computer science; Transport engineering; Level of service; Work (physics); Travel time; Operations research; Engineering; Business; Marketing","score_opus":0.017257406998630757,"score_gpt":0.27379741785446193,"score_spread":0.25654001085583117,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2955448449","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98855597,0.00012550369,0.008921036,0.0011663913,0.0006998866,0.00023916132,0.0000045947986,0.000033717886,0.000253722],"genre_scores_gemma":[0.974292,0.00023381818,0.02513845,0.00011849345,0.000085681146,0.000003348271,0.00003893242,0.000012989772,0.00007627801],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.998597,0.000113619084,0.00056138966,0.00014110543,0.00040316308,0.00018366764],"domain_scores_gemma":[0.99876565,0.00014176195,0.00046682253,0.00007768606,0.0004657316,0.00008235747],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062224135,0.00009639978,0.00019336816,0.00022047467,0.00009708849,0.000026317997,0.00012393708,0.00010475086,0.00008636318],"category_scores_gemma":[0.000052960648,0.00010193289,0.000049918435,0.00063305575,0.000025025538,0.0011489608,3.873005e-7,0.0002078086,0.000011411227],"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":[0.00019185284,0.000055269546,0.030526875,0.000027473732,0.00001074893,0.000018216188,0.022951916,0.9338026,0.007062017,0.002494411,0.00000953624,0.0028490794],"study_design_scores_gemma":[0.0022422187,0.0001934411,0.9857134,0.0004005536,0.000051491083,0.0000021432752,0.0076351347,0.00037458973,0.00093986175,0.0017657578,0.0004556466,0.00022572796],"about_ca_topic_score_codex":0.00043532453,"about_ca_topic_score_gemma":0.0009317796,"teacher_disagreement_score":0.95518655,"about_ca_system_score_codex":0.000106655134,"about_ca_system_score_gemma":0.00021177027,"threshold_uncertainty_score":0.41567028},"labels":[],"label_agreement":null},{"id":"W2955507252","doi":"10.33423/jabe.v20i4.348","title":"The Application of Data Envelopment Analysis for Transportation Planning based on the Viewpoint of Economic Efficiency","year":2018,"lang":"en","type":"article","venue":"Journal of Applied Business and Economics","topic":"Transportation Planning and Optimization","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":"Data envelopment analysis; Delphi method; Delphi; Computer science; Process (computing); Analytic hierarchy process; Fuzzy logic; Quality (philosophy); Transport engineering; Urban planning; Operations research; Measure (data warehouse); Engineering; Artificial intelligence; Data mining; Civil engineering","score_opus":0.034380678736990154,"score_gpt":0.28671503717498603,"score_spread":0.2523343584379959,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2955507252","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.69441855,0.000044002703,0.3025174,0.0018783021,0.00012809361,0.00029639792,0.00008006068,0.0000039141446,0.0006332638],"genre_scores_gemma":[0.99720186,0.0001656128,0.0024491353,0.000050522558,0.000074429765,0.0000050572844,0.000047216297,0.0000035687406,0.0000025771303],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993418,0.000009950124,0.0004302783,0.00008838956,0.00006116159,0.0000684486],"domain_scores_gemma":[0.9987634,0.00025133038,0.0007134707,0.00012539829,0.00012562251,0.00002081806],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001139336,0.000049183265,0.00015231004,0.00007994574,0.00025122036,0.000025901665,0.00021279533,0.000032258282,0.00000474405],"category_scores_gemma":[0.000013940784,0.000032605545,0.00003697811,0.00014381032,0.00014074,0.000092437724,0.0000024887747,0.000030851912,2.2692493e-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":[0.00056706497,0.000061014962,0.0073882174,0.000030192637,0.00019667095,5.397208e-8,0.005674037,0.88469064,0.00006116099,0.07741093,0.00010483722,0.023815183],"study_design_scores_gemma":[0.001895412,0.0001961054,0.33425638,0.00008598267,0.00090100965,3.1177234e-7,0.009902908,0.5946663,0.0012327472,0.0054014605,0.051117156,0.0003442397],"about_ca_topic_score_codex":0.000055304536,"about_ca_topic_score_gemma":0.00038673615,"teacher_disagreement_score":0.32686818,"about_ca_system_score_codex":0.000022821627,"about_ca_system_score_gemma":0.00012813593,"threshold_uncertainty_score":0.19322094},"labels":[],"label_agreement":null},{"id":"W2955588865","doi":"10.1155/2019/4576961","title":"Route Redundancy-Based Network Topology Measure of Metro Networks","year":2019,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; Central University Basic Research Fund of China; National Natural Science Foundation of China","keywords":"Redundancy (engineering); Computer science; Network topology; Topology (electrical circuits); Transport engineering; Computer network; Engineering","score_opus":0.00932223124188927,"score_gpt":0.2691065161747729,"score_spread":0.25978428493288364,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2955588865","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8341196,0.00084200216,0.16068748,0.0006552233,0.002191938,0.00029831723,0.000013830025,0.00004043902,0.0011511587],"genre_scores_gemma":[0.9783498,0.000120636796,0.021096837,0.00008199495,0.00019662174,0.0000016387869,0.000053536965,0.000012171082,0.000086744854],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9985037,0.00010757772,0.0006385438,0.000113232054,0.00043228743,0.00020467895],"domain_scores_gemma":[0.9983259,0.00014229519,0.00083171966,0.00008923447,0.00052757835,0.00008328731],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060548395,0.00009598947,0.00029109934,0.00011694362,0.000103387,0.000010781972,0.00013976713,0.00012551401,0.000109048575],"category_scores_gemma":[0.00003842812,0.000094286006,0.00014770302,0.0004533509,0.000074162715,0.00038379017,3.459436e-7,0.00018794199,0.0000014527039],"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":[0.00030675792,0.00003179444,0.09599625,0.00001588731,0.000028856639,0.000004660897,0.0021347757,0.8960464,0.00011561886,0.003454065,0.0000579268,0.0018070315],"study_design_scores_gemma":[0.0044905944,0.0007660237,0.96756434,0.00058252376,0.00031040926,0.0000015514952,0.0061774715,0.0021561824,0.0005041318,0.0021231645,0.01490704,0.00041659054],"about_ca_topic_score_codex":0.000056606717,"about_ca_topic_score_gemma":0.00082490745,"teacher_disagreement_score":0.8938902,"about_ca_system_score_codex":0.00004878021,"about_ca_system_score_gemma":0.00025515966,"threshold_uncertainty_score":0.38448718},"labels":[],"label_agreement":null},{"id":"W2955720331","doi":"10.1016/j.ins.2019.07.014","title":"Facing robustness as a multi-objective problem: A bi-objective shortest path problem in smart regions","year":2019,"lang":"en","type":"article","venue":"Information Sciences","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Ministerio de Economía y Competitividad; FPInnovations","keywords":"Robustness (evolution); Computer science; Shortest path problem; Mathematical optimization; Robust optimization; Optimization problem; Algorithm; Mathematics; Theoretical computer science","score_opus":0.025034858079815734,"score_gpt":0.29515520111695054,"score_spread":0.2701203430371348,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2955720331","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.66953343,0.0000419883,0.08054498,0.0018140358,0.0006421177,0.0028650737,0.000037538182,0.00045956185,0.24406129],"genre_scores_gemma":[0.9886994,0.000025941325,0.0103980275,0.00018210628,0.00001756845,0.00007965303,0.000033742486,0.000004316016,0.00055924605],"study_design_codex":"simulation_or_modeling","study_design_gemma":"qualitative","domain_scores_codex":[0.9981608,0.00012053348,0.00045329912,0.0002355565,0.00066117296,0.00036864652],"domain_scores_gemma":[0.99908483,0.00014998003,0.00027667778,0.000102939586,0.00029555458,0.00009003861],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014919329,0.00013100228,0.00016282477,0.00047558456,0.0006819605,0.00033447798,0.0002817696,0.00011031324,0.00007826099],"category_scores_gemma":[0.00017848001,0.00011972598,0.000045290853,0.0018584463,0.00029287423,0.0044206283,0.000016400101,0.00016168892,0.00015858766],"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":[0.0000267331,0.0000800855,0.20059763,0.00002835085,0.0000104199735,0.0000018021277,0.2414229,0.5309017,0.000024582045,0.023265611,0.00016753048,0.0034726677],"study_design_scores_gemma":[0.003320042,0.00057937694,0.28894076,0.0009914725,0.000038620925,0.000015236842,0.44285688,0.23821664,0.00023387485,0.0042846412,0.018916216,0.0016062319],"about_ca_topic_score_codex":0.0062015927,"about_ca_topic_score_gemma":0.004707542,"teacher_disagreement_score":0.31916597,"about_ca_system_score_codex":0.00013892226,"about_ca_system_score_gemma":0.00070091395,"threshold_uncertainty_score":0.93749946},"labels":[],"label_agreement":null},{"id":"W2956264009","doi":"10.1061/9780784482292.507","title":"A Bilevel Programming Framework for Determining the Optimal Incentive-Based Traffic Demand Management Strategy","year":2019,"lang":"en","type":"article","venue":"CICTP 2019","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ministry of Education and Child Care","funders":"","keywords":"Incentive; Bilevel optimization; Constraint (computer-aided design); Budget constraint; Computer science; Operations research; Demand management; Set (abstract data type); Linear programming; Variable (mathematics); Mathematical optimization; Microeconomics; Economics; Optimization problem; Engineering; Mathematics","score_opus":0.02562748218088755,"score_gpt":0.31067457180110863,"score_spread":0.2850470896202211,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2956264009","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.75738126,0.000091858616,0.2375241,0.00043057956,0.0005317123,0.001923394,0.00002281515,0.000177225,0.0019170286],"genre_scores_gemma":[0.9558149,0.000011005763,0.04292874,0.00010957039,0.00007010522,0.000070225826,0.00005965631,0.000013169462,0.00092263886],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9991167,0.000053377116,0.00015208333,0.00019848492,0.00021018602,0.00026914765],"domain_scores_gemma":[0.9994442,0.00021828114,0.000095426905,0.00012301923,0.00007227005,0.00004682433],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004064832,0.00008910056,0.00009520429,0.000048290713,0.00038441856,0.00013871786,0.00018934056,0.000085661886,0.000052678955],"category_scores_gemma":[0.000023932384,0.00007155618,0.000060854873,0.00018414043,0.0000685815,0.0001227597,0.000005116171,0.000085524225,0.000030594532],"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":[0.00019283345,0.00025479714,0.06840329,0.00018410057,0.00011781177,0.000004335608,0.022602761,0.59419847,0.000007303864,0.06388839,0.0012576068,0.24888833],"study_design_scores_gemma":[0.008978866,0.0011611492,0.4621768,0.0013099613,0.0006054853,6.2236404e-7,0.049772598,0.18587333,0.00017793475,0.0026555755,0.2849901,0.002297583],"about_ca_topic_score_codex":0.000015406555,"about_ca_topic_score_gemma":0.000030312747,"teacher_disagreement_score":0.4083251,"about_ca_system_score_codex":0.000031116517,"about_ca_system_score_gemma":0.000075471224,"threshold_uncertainty_score":0.29566756},"labels":[],"label_agreement":null},{"id":"W2958400684","doi":"10.1061/9780784482292.269","title":"Managing the Congestion and Emissions with Road Pricing Scheme Based on Practical Case Study","year":2019,"lang":"en","type":"article","venue":"CICTP 2019","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Transport Canada","funders":"","keywords":"Singapore Area Licensing Scheme; Road pricing; Scheme (mathematics); Congestion pricing; Computer science; Traffic congestion; Road traffic; Transport engineering; Environmental science; Automotive engineering; Engineering; Mathematics","score_opus":0.022946188612889433,"score_gpt":0.3269036790508258,"score_spread":0.30395749043793635,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2958400684","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9888437,0.000011985303,0.0014357597,0.0029111316,0.00008185141,0.00042695063,0.0000013142204,0.00005382275,0.0062335306],"genre_scores_gemma":[0.9981407,0.000004524055,0.0007900063,0.00014547526,0.000024779043,0.0000058346677,0.0000050023755,0.000005552519,0.000878152],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99939185,0.00010484128,0.00007182308,0.00013689215,0.00019217763,0.000102394784],"domain_scores_gemma":[0.99955064,0.00019891764,0.000050724113,0.00010604108,0.000044510158,0.000049185426],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004020942,0.000052447667,0.000056890316,0.00003491976,0.00038462286,0.00006381263,0.000034954122,0.00003087188,0.000042558215],"category_scores_gemma":[0.000056315854,0.000034516375,0.000008346192,0.00013415569,0.00004348826,0.00012183239,0.0000031383638,0.000107316846,0.000015589923],"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":[0.0001763367,0.000328328,0.8906757,0.000022181945,0.000039903698,0.0004448189,0.05694584,0.040280417,0.00004409154,0.0041389666,0.0009493176,0.0059540784],"study_design_scores_gemma":[0.0037669288,0.001025547,0.55997264,0.00034952673,0.00022406779,0.00010641186,0.26771617,0.15843752,0.000026372696,0.000078655336,0.0076529137,0.00064327585],"about_ca_topic_score_codex":0.0008898534,"about_ca_topic_score_gemma":0.00029730864,"teacher_disagreement_score":0.33070308,"about_ca_system_score_codex":0.000016689111,"about_ca_system_score_gemma":0.0000773615,"threshold_uncertainty_score":0.2958247},"labels":[],"label_agreement":null},{"id":"W2959336552","doi":"10.5198/jtlu.2020.1600","title":"A multiple-path gradient projection method for solving the logit-based stochastic user equilibrium model","year":2020,"lang":"en","type":"article","venue":"Journal of Transport and Land Use","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Path (computing); Mathematical optimization; Variational inequality; Projection (relational algebra); Gradient descent; Logit; Set (abstract data type); Computer science; Descent direction; Mathematics; Algorithm; Artificial neural network; Artificial intelligence; Statistics","score_opus":0.05990711854896726,"score_gpt":0.30908669320667964,"score_spread":0.24917957465771237,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2959336552","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.29474294,0.000044004377,0.7024224,0.002481888,0.00008396984,0.00017971406,0.000020901973,0.000015664005,0.000008554633],"genre_scores_gemma":[0.97507125,0.000018034701,0.024397481,0.00031528488,0.00013353825,0.0000049854984,0.000012557813,0.000007908225,0.000038953447],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99931574,0.000032162898,0.0002450984,0.00009049548,0.0001909501,0.00012555644],"domain_scores_gemma":[0.99935985,0.00020429533,0.00016423922,0.000032638705,0.00013809263,0.00010088769],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049313455,0.00007013369,0.0001356078,0.000040212097,0.00020154293,0.00004576494,0.000071792914,0.000051635845,0.0000036771728],"category_scores_gemma":[0.000092220085,0.000045678455,0.000091099784,0.00010296137,0.000034354176,0.00024742237,8.303936e-7,0.00010940024,8.2262e-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":[0.00072642375,0.000049589096,0.07408057,0.000043644777,0.00003866972,0.0000067179762,0.021857841,0.9019146,0.00020747205,0.0004224785,0.00021231882,0.0004396653],"study_design_scores_gemma":[0.0019468382,0.00024140252,0.015305829,0.00007326542,0.00024419537,0.0000032274959,0.001269676,0.9772246,0.000049967344,0.00016370731,0.0033250945,0.00015220107],"about_ca_topic_score_codex":0.000112803005,"about_ca_topic_score_gemma":0.00030555017,"teacher_disagreement_score":0.6803283,"about_ca_system_score_codex":0.000011104201,"about_ca_system_score_gemma":0.00013820175,"threshold_uncertainty_score":0.18627134},"labels":[],"label_agreement":null},{"id":"W2960699488","doi":"10.1155/2019/4802967","title":"Integrated Optimization of Tram Schedule and Signal Priority at Intersections to Minimize Person Delay","year":2019,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Punctuality; Schedule; Bus priority; Transport engineering; Public transport; SIGNAL (programming language); Signal timing; Engineering; Computer science; Train; Simulation; Real-time computing; Traffic signal","score_opus":0.012141993814011032,"score_gpt":0.27005717681722485,"score_spread":0.2579151830032138,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2960699488","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.86221427,0.00006148679,0.13671531,0.00025315495,0.0003137592,0.00020791813,0.000021526894,0.000018438932,0.00019410877],"genre_scores_gemma":[0.92022824,0.00009077385,0.0793875,0.000031314274,0.000035423596,0.0000024687658,0.00004960363,0.000009821202,0.00016487789],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9989158,0.000061352366,0.00044706362,0.00013136464,0.00031899477,0.00012544141],"domain_scores_gemma":[0.9988321,0.00008028029,0.00040770104,0.000052386233,0.00050730247,0.00012023736],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003138267,0.00009449985,0.00021247244,0.00021375356,0.00012439475,0.000017240425,0.00007487079,0.00008633415,0.00017913696],"category_scores_gemma":[0.000044910463,0.00009336583,0.00007666887,0.0003866242,0.000054507353,0.00055421813,7.0371283e-7,0.00012787285,0.0000018033037],"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":[0.00049777835,0.00006103306,0.030323751,0.000026758524,0.000030037683,0.000002919281,0.035064723,0.92685056,0.004790585,0.00029868924,0.000018038458,0.0020351366],"study_design_scores_gemma":[0.007743916,0.001821462,0.8358157,0.0009975042,0.00047139285,0.000018646691,0.12765497,0.01186989,0.008174715,0.00017147203,0.0044425167,0.0008178176],"about_ca_topic_score_codex":0.00010839307,"about_ca_topic_score_gemma":0.00073190493,"teacher_disagreement_score":0.91498065,"about_ca_system_score_codex":0.00009112169,"about_ca_system_score_gemma":0.00012355675,"threshold_uncertainty_score":0.38073483},"labels":[],"label_agreement":null},{"id":"W2963420714","doi":"10.1155/2019/6965728","title":"Optimization of Bus Bridging Service under Unexpected Metro Disruptions with Dynamic Passenger Flows","year":2019,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":44,"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":"Bridging (networking); Transport engineering; Service (business); Computer science; Service level; Public transport; Operations research; Engineering; Computer network; Business","score_opus":0.0076549655960331765,"score_gpt":0.2680974664408825,"score_spread":0.2604425008448493,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2963420714","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.713508,0.000067460256,0.28521004,0.00063320046,0.0002314556,0.00017577423,0.000017405875,0.000030547857,0.0001260669],"genre_scores_gemma":[0.9389503,0.0001323006,0.060567696,0.000059411905,0.00003776119,0.0000030008705,0.00014591457,0.00001936812,0.00008425625],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9985221,0.00006661132,0.00056537637,0.00013994757,0.0005397826,0.00016618766],"domain_scores_gemma":[0.9980538,0.000089841255,0.0007283654,0.00009716317,0.0009470408,0.000083794715],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025057187,0.00011758369,0.00024731434,0.00024994527,0.00012835287,0.00002321325,0.00011356526,0.000079629084,0.00012097957],"category_scores_gemma":[0.000018645016,0.00010786071,0.000077692086,0.00090495107,0.00003613107,0.00094881107,5.380766e-7,0.00015164504,0.0000021249464],"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":[0.00020667557,0.000063367756,0.0078121396,0.00004006361,0.000048636317,0.000003181136,0.008097737,0.9801862,0.0024653212,0.000632769,0.0000017942975,0.00044211102],"study_design_scores_gemma":[0.0059689386,0.0006084477,0.8887897,0.00083175686,0.00054136716,0.0000073665774,0.054697774,0.045879826,0.0007213193,0.00043132677,0.00085205503,0.0006701268],"about_ca_topic_score_codex":0.000102985476,"about_ca_topic_score_gemma":0.002882577,"teacher_disagreement_score":0.9343064,"about_ca_system_score_codex":0.00008268277,"about_ca_system_score_gemma":0.00019932825,"threshold_uncertainty_score":0.4398432},"labels":[],"label_agreement":null},{"id":"W2963602993","doi":"10.1139/cjce-2018-0796","title":"Capacity estimation of unsignalized intersections under heterogeneous traffic conditions","year":2019,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Rice Research Institute, Indian Council of Agricultural Research","keywords":"Transport engineering; Estimation; Highway Capacity Manual; Computer science; Engineering; Level of service","score_opus":0.01323544079067477,"score_gpt":0.22787268115382342,"score_spread":0.21463724036314866,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2963602993","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93712837,0.00007327762,0.060730036,0.00016965243,0.0006851829,0.00007266395,0.000019026496,0.000016355358,0.0011054542],"genre_scores_gemma":[0.9994348,0.0000047724216,0.00044267383,0.000015375219,0.0000334681,6.660033e-7,0.0000067401543,0.000006496905,0.000054998003],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99950254,0.000022373266,0.00020739537,0.000042022086,0.00010953559,0.00011610729],"domain_scores_gemma":[0.9994966,0.000051298088,0.0001041029,0.00003798448,0.0001288457,0.00018121461],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016777327,0.000046984696,0.00010222956,0.00026167362,0.00007922025,0.000021882784,0.00006735902,0.000046956473,0.0004597534],"category_scores_gemma":[0.00005972218,0.00005345357,0.00005841453,0.00019102175,0.000036099293,0.00016439431,4.365949e-7,0.00009077462,0.0000033598644],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000013413678,0.0000029250093,0.00042002625,0.000008788143,0.00001981235,0.0000024404633,0.003356581,0.99438494,0.00010040372,0.0015925767,0.00006785628,0.000042285254],"study_design_scores_gemma":[0.0039787833,0.0005716318,0.06787384,0.0020114966,0.00045859863,0.00026273666,0.012636644,0.8915091,0.0018445839,0.0020589144,0.015455589,0.0013380483],"about_ca_topic_score_codex":0.0018263103,"about_ca_topic_score_gemma":0.14374591,"teacher_disagreement_score":0.14191961,"about_ca_system_score_codex":0.000103968145,"about_ca_system_score_gemma":0.0003858235,"threshold_uncertainty_score":0.8718785},"labels":[],"label_agreement":null},{"id":"W2965064847","doi":"10.1155/2019/4165942","title":"Multimodal Green Logistics Network Design of Urban Agglomeration with Stochastic Demand","year":2019,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; Central South University; National Natural Science Foundation of China","keywords":"Economies of agglomeration; Particle swarm optimization; Node (physics); Computer science; Multimodal transport; Operations research; Mathematical optimization; Business; Microeconomics; Economics; Engineering; Marketing; Mathematics","score_opus":0.013763362810408552,"score_gpt":0.260346540240505,"score_spread":0.24658317743009647,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2965064847","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20764725,0.0001101833,0.79159427,0.000073737094,0.0002509399,0.00025066087,0.00000727034,0.000014791252,0.000050925177],"genre_scores_gemma":[0.934214,0.00005626047,0.06544297,0.000019914576,0.00012826468,0.0000020428279,0.000039171246,0.0000110475985,0.00008631375],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.998736,0.0000774515,0.00048848864,0.000101602905,0.00044555948,0.0001508525],"domain_scores_gemma":[0.99847144,0.00016656841,0.000716504,0.000063066436,0.0005113646,0.000071080285],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004406455,0.000093638344,0.00021841077,0.000093816634,0.00010365433,0.000013519638,0.00008873968,0.0000720348,0.000031965854],"category_scores_gemma":[0.000029412335,0.000082424674,0.000049574955,0.00029489747,0.00007076089,0.00049086753,2.9419988e-7,0.000119368466,0.0000015665395],"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":[0.00052934815,0.000036354482,0.012227823,0.000021777792,0.000029771305,0.000004926173,0.009286862,0.97551817,0.00029654126,0.0013548384,0.00001817313,0.0006754206],"study_design_scores_gemma":[0.014226749,0.0048026084,0.89778733,0.0018585267,0.0011109553,0.000013500374,0.01825273,0.053240612,0.00074965635,0.0052334205,0.0014678091,0.0012560859],"about_ca_topic_score_codex":0.000058921796,"about_ca_topic_score_gemma":0.00025915456,"teacher_disagreement_score":0.92227757,"about_ca_system_score_codex":0.000034584176,"about_ca_system_score_gemma":0.00018460152,"threshold_uncertainty_score":0.33611807},"labels":[],"label_agreement":null},{"id":"W2965136572","doi":"10.1016/j.trb.2019.07.013","title":"Constrained optimization for bottleneck coarse tolling","year":2019,"lang":"en","type":"article","venue":"Transportation Research Part B Methodological","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Toll; Bottleneck; Duration (music); Queue; Schedule; Moment (physics); Queueing theory; Computer science; Mathematical optimization; Mathematics; Operations management; Economics; Computer network; Physics; Medicine","score_opus":0.45407691550965135,"score_gpt":0.5177534129549297,"score_spread":0.06367649744527837,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2965136572","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16972011,0.0000701774,0.81803834,0.002256726,0.00056074164,0.0021316875,0.00013561304,0.00032802383,0.0067585725],"genre_scores_gemma":[0.49641627,0.00026728102,0.49896038,0.00019175593,0.00024060307,0.0002709655,0.00103389,0.000027764048,0.002591106],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9964341,0.0013450732,0.00044701807,0.00044133494,0.00077109237,0.00056140666],"domain_scores_gemma":[0.9951901,0.0036196688,0.000109402674,0.00015722644,0.00072493515,0.00019870748],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.007783141,0.00012665216,0.00027005878,0.00018983951,0.0006238114,0.000085582236,0.00021296977,0.00026525772,0.002431708],"category_scores_gemma":[0.0011704495,0.000121792706,0.000118134616,0.000619999,0.00033441593,0.00027505998,0.0000019283862,0.00027634253,0.000050227158],"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":[0.0007306679,0.0001797067,0.025755323,0.00011759277,0.00005129913,0.000010181822,0.009794771,0.7416181,0.00052783865,0.21431427,0.001963985,0.0049362616],"study_design_scores_gemma":[0.011782529,0.0022659637,0.07003912,0.0005258384,0.00025090406,0.000002012761,0.051015183,0.28352234,0.0025046493,0.023213154,0.55242443,0.0024538566],"about_ca_topic_score_codex":0.00021298436,"about_ca_topic_score_gemma":0.00034731298,"teacher_disagreement_score":0.55046046,"about_ca_system_score_codex":0.00005304234,"about_ca_system_score_gemma":0.00026368926,"threshold_uncertainty_score":0.9984802},"labels":[],"label_agreement":null},{"id":"W2965382830","doi":"10.18280/jesa.520205","title":"Solution of Combined Economic Emission Dispatch with Demand Side Management Using Meta-heuristic Algorithms","year":2019,"lang":"fr","type":"article","venue":"Journal Européen des Systèmes Automatisés","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Meta heuristic; Demand side; Heuristic; Computer science; Economic dispatch; Algorithm; Demand management; Mathematical optimization; Economics; Mathematics; Microeconomics; Artificial intelligence; Macroeconomics; Electric power system","score_opus":0.02828031376775122,"score_gpt":0.2752288234843016,"score_spread":0.24694850971655039,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2965382830","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8296774,0.006135596,0.15367825,0.00070792856,0.002434892,0.0007488338,0.0000646583,0.00011953262,0.00643292],"genre_scores_gemma":[0.943425,0.0012239156,0.04576024,0.000027976263,0.00015299262,0.0000044401404,0.000019497636,0.00004958667,0.009336364],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972412,0.00057240424,0.00089048705,0.00029957952,0.0005629438,0.00043336826],"domain_scores_gemma":[0.99815,0.00013827665,0.0011040176,0.00019727944,0.00018888203,0.00022155281],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010293087,0.0002870623,0.00063345226,0.00020615844,0.00052617915,0.00018025935,0.00025038785,0.00012957085,0.00057168485],"category_scores_gemma":[0.000021414213,0.00024081832,0.00022346062,0.00036073057,0.00027167483,0.0007113466,0.000025269015,0.00023214954,0.00006876351],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002737736,0.00041367457,0.044783704,0.0020573458,0.0043231733,0.00025502208,0.01016646,0.8488798,0.00037415794,0.011881976,0.0012574549,0.07533345],"study_design_scores_gemma":[0.0025836478,0.0007581206,0.3082312,0.0027931018,0.005687355,0.00028539536,0.0022860384,0.6712191,0.00015866528,0.003138077,0.0021121656,0.00074711663],"about_ca_topic_score_codex":0.00066473504,"about_ca_topic_score_gemma":0.000079061974,"teacher_disagreement_score":0.26344752,"about_ca_system_score_codex":0.000426221,"about_ca_system_score_gemma":0.00021564006,"threshold_uncertainty_score":0.98202866},"labels":[],"label_agreement":null},{"id":"W2966427413","doi":"10.1155/2019/9540386","title":"Generating a Spatiotemporal Dynamic Map for Traffic Analysis Using Macroscopic Fundamental Diagram","year":2019,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","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":"Fundamental Research Funds for the Central Universities; Science and Technology Commission of Shanghai Municipality","keywords":"Computer science; Abstraction; Fidelity; Diagram; Macro; Resource (disambiguation); Data mining; Cluster analysis; Time horizon; Algorithm; Artificial intelligence; Mathematical optimization; Mathematics","score_opus":0.012662677452907029,"score_gpt":0.3180262435336976,"score_spread":0.3053635660807905,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2966427413","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8927571,0.00012298638,0.10606702,0.00010594834,0.00059176004,0.00028663516,0.000038507816,0.000022563769,0.000007462314],"genre_scores_gemma":[0.9212252,0.000038239203,0.078265995,0.000032744363,0.000080041296,0.0000040540394,0.00023670685,0.000013013873,0.00010400117],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986699,0.00004735208,0.00058033824,0.00014882427,0.00036264595,0.00019092955],"domain_scores_gemma":[0.9988742,0.00006965866,0.0006313666,0.00006282296,0.00027990792,0.000082042636],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003779095,0.0001057704,0.00026571707,0.00026577796,0.00022672355,0.000053643143,0.00009142275,0.00007734015,0.000058462992],"category_scores_gemma":[0.00001670163,0.000109546614,0.00023368043,0.0004838453,0.00003621334,0.00062737236,3.5554774e-7,0.00010166197,0.0000012535348],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000095127965,0.00003994168,0.024393974,0.000031608783,0.0001186426,0.0000026887342,0.010296768,0.9603173,0.0016282229,0.00009002011,0.000003138979,0.0029825305],"study_design_scores_gemma":[0.008403659,0.0010108071,0.45178947,0.00040699952,0.0032001187,0.000003049719,0.040886525,0.48927712,0.000583976,0.00035895113,0.0030164898,0.0010628274],"about_ca_topic_score_codex":0.00004064365,"about_ca_topic_score_gemma":0.0016236303,"teacher_disagreement_score":0.47104022,"about_ca_system_score_codex":0.000121715355,"about_ca_system_score_gemma":0.000160988,"threshold_uncertainty_score":0.44671813},"labels":[],"label_agreement":null},{"id":"W2968641664","doi":"10.1155/2019/8198346","title":"Modeling and Analysis of Ticketing Channel Choice for Intercity Bus Passengers: A Case Study in Beijing, China","year":2019,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Beijing; Multinomial logistic regression; Structural equation modeling; Transport engineering; Mode choice; Mixed logit; Latent variable; Channel (broadcasting); Discrete choice; China; Preference; Socioeconomic status; Computer science; Logistic regression; Econometrics; Engineering; Public transport; Telecommunications; Geography; Economics; Microeconomics","score_opus":0.02143609278708157,"score_gpt":0.32718814498822546,"score_spread":0.3057520522011439,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2968641664","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96639967,0.00007379347,0.03303988,0.00004318987,0.00011345518,0.00029964876,0.000011376771,0.000007147945,0.000011858063],"genre_scores_gemma":[0.99608344,0.000057607143,0.0037917616,0.000006254738,0.000023966815,0.0000043263617,0.000018808312,0.0000065603804,0.0000072554863],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9989304,0.000055883367,0.000570713,0.000120585166,0.00021133089,0.000111107125],"domain_scores_gemma":[0.99911976,0.0001464236,0.00038344742,0.000046986184,0.00025611583,0.000047260648],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00076550886,0.000072368945,0.0002866529,0.0004096481,0.000076123666,0.000014984056,0.00005285243,0.000045621993,0.000005142753],"category_scores_gemma":[0.000081255734,0.00007283229,0.00008828356,0.0005603842,0.000017540471,0.00048515893,5.145359e-7,0.00010167248,3.831183e-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":[0.000096320386,0.00007674568,0.20942077,0.000025812215,0.00008783085,0.000020355154,0.06749574,0.72183025,0.00006140984,0.000028124445,1.17431654e-7,0.00085651537],"study_design_scores_gemma":[0.0026686222,0.00037593555,0.68772095,0.00018013507,0.00095237663,0.0000039312063,0.14937131,0.15844323,0.000019034398,0.00008171089,0.000012020012,0.00017075257],"about_ca_topic_score_codex":0.0009187523,"about_ca_topic_score_gemma":0.016884562,"teacher_disagreement_score":0.56338704,"about_ca_system_score_codex":0.000032655542,"about_ca_system_score_gemma":0.000046834437,"threshold_uncertainty_score":0.9421981},"labels":[],"label_agreement":null},{"id":"W2969223211","doi":"10.1007/s11116-019-10047-1","title":"Transit network design using a genetic algorithm with integrated road network and disaggregated O–D demand data","year":2019,"lang":"en","type":"article","venue":"Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":31,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ministère des Transports; Polytechnique Montréal","funders":"","keywords":"Representativeness heuristic; Genetic algorithm; Transit (satellite); Computer science; Public transport; Network planning and design; Mathematical optimization; Data mining; Algorithm; Transport engineering; Engineering; Machine learning; Computer network; Mathematics","score_opus":0.032603151490064944,"score_gpt":0.2704640064009359,"score_spread":0.23786085491087094,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2969223211","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25463006,0.0003690294,0.7440577,0.000056238077,0.00012849194,0.0005260291,0.000048287988,0.00012216027,0.00006201175],"genre_scores_gemma":[0.723184,0.00023032712,0.2748566,0.000066298075,0.00013382961,0.000007963466,0.0013887668,0.000029415905,0.0001028155],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99852574,0.00016131824,0.00028686287,0.00039264598,0.00030016416,0.00033325725],"domain_scores_gemma":[0.9993693,0.00006987236,0.00013593494,0.00020929429,0.000104371386,0.000111174864],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000479646,0.00016209042,0.00020338742,0.000044297085,0.0003418517,0.00007898759,0.0001642386,0.00012432746,0.00008784145],"category_scores_gemma":[0.0000048182387,0.00014949698,0.000019659066,0.00062076026,0.000108268316,0.00047772934,0.0000011741487,0.00012009757,0.0000047617636],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012984234,0.000018623588,0.042647764,0.000015863197,0.000046756457,0.000011783487,0.006149309,0.937371,0.000015178954,0.00014834532,0.000060115006,0.013385413],"study_design_scores_gemma":[0.0018953175,0.0001788501,0.33816516,0.00039764066,0.0004322402,0.0000037729037,0.0029264353,0.65311325,0.00001600872,0.0002970541,0.0019712928,0.00060298643],"about_ca_topic_score_codex":0.0021966384,"about_ca_topic_score_gemma":0.0035622306,"teacher_disagreement_score":0.4692011,"about_ca_system_score_codex":0.000023377575,"about_ca_system_score_gemma":0.00017908707,"threshold_uncertainty_score":0.609631},"labels":[],"label_agreement":null},{"id":"W2969408439","doi":"10.1155/2019/6171532","title":"Role of Customized Bus Services in the Transportation System: Insight from Actual Performance","year":2019,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":49,"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":"Institute of Materials and Systems for Sustainability, Nagoya University; Fundamental Research Funds for the Central Universities; China Scholarship Council; Natural Science Foundation of Liaoning Province; National Natural Science Foundation of China","keywords":"Taxis; Multinomial logistic regression; Service (business); Public transport; Transport engineering; TRIPS architecture; Business; Level of service; Computer science; Marketing; Engineering","score_opus":0.005332767454069947,"score_gpt":0.23257253981173112,"score_spread":0.22723977235766118,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2969408439","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9979381,0.0002707614,0.00046750833,0.00008606324,0.00031270497,0.0002907967,0.000036740614,0.000018362705,0.00057899515],"genre_scores_gemma":[0.99670213,0.00031230276,0.0027028588,0.00003256168,0.00007431423,0.0000056228196,0.00014197452,0.000010597417,0.000017608141],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9981718,0.00011513088,0.00080445,0.00012715944,0.00063235394,0.00014911118],"domain_scores_gemma":[0.9985767,0.0001726003,0.0008095184,0.00010077486,0.0002952155,0.00004520331],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051941327,0.00011378804,0.00029066295,0.00017609909,0.000089774185,0.000020503934,0.00024112735,0.00010189737,0.000055011198],"category_scores_gemma":[0.000007301044,0.00009017939,0.00010200941,0.00043485072,0.000042133324,0.0010530121,2.2524705e-7,0.00018785307,0.0000050223066],"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":[0.0013812645,0.00013671437,0.19437766,0.00021369543,0.000049226073,0.000010653006,0.3351079,0.4554552,0.005723283,0.002931206,0.0000039918355,0.004609211],"study_design_scores_gemma":[0.0021859498,0.000107878455,0.91485494,0.0003775632,0.00008538122,4.4152907e-7,0.08000497,0.00033428692,0.0006541877,0.00010064255,0.0011820961,0.000111680754],"about_ca_topic_score_codex":0.0006521064,"about_ca_topic_score_gemma":0.0034592296,"teacher_disagreement_score":0.7204773,"about_ca_system_score_codex":0.000051031366,"about_ca_system_score_gemma":0.00012401374,"threshold_uncertainty_score":0.36774087},"labels":[],"label_agreement":null},{"id":"W2970777207","doi":"10.1155/2019/2607402","title":"The Energy-Efficient Dynamic Route Planning for Electric Vehicles","year":2019,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"NonStop; Energy consumption; Energy (signal processing); Acceleration; Computer science; SIGNAL (programming language); Mathematical optimization; Dynamic programming; Electric vehicle; Mode (computer interface); Real-time computing; Simulation; Engineering; Algorithm; Mathematics; Power (physics)","score_opus":0.007503886106123213,"score_gpt":0.27607583141784914,"score_spread":0.2685719453117259,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2970777207","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9254498,0.00075554923,0.07217015,0.00049146515,0.0007031568,0.00019453675,0.000008564034,0.000027408714,0.00019940209],"genre_scores_gemma":[0.99670446,0.0002741117,0.0025153535,0.00004369533,0.000068595305,0.0000064743276,0.000030439864,0.000011081417,0.00034579507],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9989486,0.00003808053,0.00039423167,0.000093162234,0.00033707835,0.00018885477],"domain_scores_gemma":[0.9988516,0.00028222337,0.0004360375,0.00005708109,0.0003173364,0.000055730772],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004727558,0.00007379494,0.00012244863,0.00009300327,0.00037008873,0.00004062766,0.00013296121,0.000053068317,0.0000047514422],"category_scores_gemma":[0.000037311944,0.000057870267,0.00010308128,0.0002609273,0.000026944022,0.0002389705,2.6593023e-7,0.00009484837,9.780367e-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":[0.00027294713,0.000024163512,0.0029448383,0.000009181691,0.000022442955,0.0000022254405,0.007376848,0.96786946,0.0026923725,0.008956448,0.000034857432,0.009794243],"study_design_scores_gemma":[0.00745436,0.0011932601,0.73491675,0.0005385667,0.00036867138,0.0000065342238,0.040805504,0.048254285,0.0035589763,0.008550098,0.1534788,0.0008742279],"about_ca_topic_score_codex":0.000016924896,"about_ca_topic_score_gemma":0.00014671205,"teacher_disagreement_score":0.91961515,"about_ca_system_score_codex":0.00006929173,"about_ca_system_score_gemma":0.00015589791,"threshold_uncertainty_score":0.28464606},"labels":[],"label_agreement":null},{"id":"W2971592801","doi":"10.1007/s11067-020-09505-6","title":"Spatial Aggregation Issues in Traffic Assignment Models","year":2020,"lang":"en","type":"article","venue":"Networks and Spatial Economics","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Association Nationale de la Recherche et de la Technologie","keywords":"Computer science; Operations research; TRIPS architecture; Transport engineering; Trip distribution; Order (exchange); Engineering; Economics","score_opus":0.02091499488984167,"score_gpt":0.23966460374474158,"score_spread":0.2187496088548999,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2971592801","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.55339456,0.00040846807,0.43429986,0.007974911,0.00039531593,0.00034986853,0.00001064003,0.00009530504,0.0030710746],"genre_scores_gemma":[0.9970835,0.001745597,0.00031094206,0.0003435325,0.00042521665,0.0000062434024,0.000044924367,0.0000072728208,0.00003280361],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994036,0.000043978325,0.0001932622,0.00016831206,0.000050976487,0.00013985076],"domain_scores_gemma":[0.9997619,0.00003275536,0.000067454326,0.000030663487,0.000014405785,0.00009282298],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015378378,0.00006835508,0.00011851333,0.00002350331,0.00019619576,0.000057981753,0.000049209382,0.000095075855,0.0000382751],"category_scores_gemma":[0.000010402624,0.00007880413,0.000020889249,0.000056186625,0.00004357587,0.00018264074,0.000006882145,0.000078940066,0.0000016929612],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024730296,0.0000085622405,0.0027160652,0.0000019010849,0.0000030668973,7.192861e-7,0.008008077,0.9209895,1.2414668e-7,0.0020837924,0.00005248272,0.06611097],"study_design_scores_gemma":[0.00028613332,0.00003008424,0.0020490626,0.000008738993,0.000005011511,4.8745015e-8,0.0004000412,0.99207324,0.0000012892953,0.00021660578,0.0048356513,0.00009409803],"about_ca_topic_score_codex":0.0043581924,"about_ca_topic_score_gemma":0.021843774,"teacher_disagreement_score":0.4436889,"about_ca_system_score_codex":0.000033663884,"about_ca_system_score_gemma":0.000043390803,"threshold_uncertainty_score":0.996005},"labels":[],"label_agreement":null},{"id":"W2971694425","doi":"10.1007/s12469-019-00203-2","title":"Modelling bus bunching under variable transit demand using cellular automata","year":2019,"lang":"en","type":"article","venue":"Public Transport","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":18,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Headway; Computer science; Service (business); Simulation; Cellular automaton; Control theory (sociology); Automotive engineering; Engineering; Algorithm; Control (management)","score_opus":0.037518269457391215,"score_gpt":0.256476575003594,"score_spread":0.21895830554620277,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2971694425","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.38043433,0.00008294976,0.6104816,0.00041511917,0.0002771685,0.00020370516,0.0000150569,0.0002610665,0.007828997],"genre_scores_gemma":[0.9837483,0.000035866506,0.014859625,0.0001533513,0.00010111547,0.000005194195,0.00016675954,0.000030788302,0.00089900714],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981894,0.00007620909,0.00034787666,0.00036208032,0.0005368036,0.00048761573],"domain_scores_gemma":[0.9993276,0.00005108016,0.00009402746,0.00021175224,0.00012777049,0.00018779437],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00090596935,0.00016180237,0.0002236011,0.00015523206,0.0004970949,0.00011691077,0.00024172227,0.00018598192,0.0005672363],"category_scores_gemma":[0.0000054929887,0.00017705801,0.00008705808,0.0005721028,0.00006654067,0.000898952,0.0000020608982,0.00018475665,0.00002752861],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000107814785,0.000051601997,0.019359812,0.000031528998,0.000029371418,0.00000423409,0.006548559,0.9312542,0.00023321885,0.042325694,0.000010691193,0.00014028659],"study_design_scores_gemma":[0.0018721392,0.000039675266,0.0048358543,0.00016058778,0.0002075423,0.0000037961515,0.006079459,0.9267794,0.00019008924,0.00316156,0.055700004,0.0009699307],"about_ca_topic_score_codex":0.0041138562,"about_ca_topic_score_gemma":0.00041540407,"teacher_disagreement_score":0.603314,"about_ca_system_score_codex":0.00007502386,"about_ca_system_score_gemma":0.00043224604,"threshold_uncertainty_score":0.7220216},"labels":[],"label_agreement":null},{"id":"W2972152794","doi":"10.11575/prism/32323","title":"Calgary Region’s New Regional Transit System: On-It or Love-It?","year":2016,"lang":"en","type":"article","venue":"PRISM (University of Calgary)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Transit (satellite); Regional science; Geography; Transport engineering; Engineering; Public transport","score_opus":0.029357282403384987,"score_gpt":0.23292585237827665,"score_spread":0.20356856997489167,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2972152794","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008026292,0.000047859074,0.8638964,0.03843842,0.00022382375,0.0002616621,0.0000014494426,0.00015901942,0.08894504],"genre_scores_gemma":[0.46180338,0.0011378162,0.044444572,0.001370486,0.00021185755,6.8688416e-7,0.00005022704,0.00004314127,0.49093786],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.998924,0.000099777935,0.00013259333,0.00023462916,0.00040470506,0.00020428191],"domain_scores_gemma":[0.99928427,0.00014892829,0.00012643183,0.00015370455,0.000084135914,0.00020251269],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017336984,0.000099190635,0.00017257509,0.0001211505,0.00023947223,0.000010660758,0.00026306964,0.00015648596,0.00048615583],"category_scores_gemma":[0.000024523591,0.0000876188,0.000099377576,0.00019657015,0.00021226559,0.0002820708,0.0000071799377,0.000080069476,0.00013925422],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0028008148,0.00026114023,0.0051508355,0.0001766268,0.00021146433,0.00062100147,0.1116433,0.00007985698,0.000127152,0.12970439,0.5584217,0.19080174],"study_design_scores_gemma":[0.00459439,0.00037165068,0.02561841,0.0012611443,0.00021035118,0.000011884397,0.010069776,0.002408235,0.000038084014,0.0006110971,0.95411235,0.000692604],"about_ca_topic_score_codex":0.0043286537,"about_ca_topic_score_gemma":0.0010137714,"teacher_disagreement_score":0.81945187,"about_ca_system_score_codex":0.00012219316,"about_ca_system_score_gemma":0.00039133217,"threshold_uncertainty_score":0.65436584},"labels":[],"label_agreement":null},{"id":"W2972564555","doi":"10.1093/jeg/lbaa037","title":"The congestion relief benefit of public transit: evidence from Rome","year":2020,"lang":"en","type":"article","venue":"Journal of Economic Geography","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Danmarks Tekniske Universitet; Vysoké Učení Technické v Brně; University of California, Irvine; University of Toronto; Newcastle University; Institut d'Economia de Barcelona; London School of Economics and Political Science","keywords":"Public transport; Transport engineering; Public authority; Transit (satellite); Business; Passenger transport; Service (business); Travel time; Advertising; Marketing; Engineering; Political science; Public administration","score_opus":0.04120234224685214,"score_gpt":0.2681091484339756,"score_spread":0.22690680618712344,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2972564555","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97359383,0.001843688,0.0016898736,0.021886854,0.00038453168,0.00006513043,0.000016247312,0.000012248152,0.00050757726],"genre_scores_gemma":[0.99663734,0.0024525567,0.0006031929,0.00010997388,0.00018065913,6.630725e-7,0.0000029603336,0.0000036882598,0.00000894989],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9992022,0.000070738635,0.0004128565,0.00007531298,0.00013561703,0.00010329751],"domain_scores_gemma":[0.9989085,0.00031699947,0.00046914612,0.00005561254,0.00013374626,0.00011596243],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00066078926,0.000050267696,0.00013643537,0.00007090073,0.00018041898,0.00007199896,0.00022837929,0.000049646074,0.000088180874],"category_scores_gemma":[0.000118315635,0.000040750434,0.00014320112,0.00013342168,0.00012065676,0.0004337009,0.0000024467472,0.000104798695,0.000004458121],"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":[0.00023970642,0.000048897666,0.8599209,0.000014610611,0.0002952174,0.0000054158136,0.026082905,0.08316678,0.00021678024,0.011774451,0.0017353336,0.016498955],"study_design_scores_gemma":[0.0005799273,0.0002412871,0.9707407,0.00011269163,0.00010132434,0.0000011207145,0.0039003873,0.0008231403,0.00008744138,0.0034583372,0.019812148,0.00014153024],"about_ca_topic_score_codex":0.00042290037,"about_ca_topic_score_gemma":0.0006074373,"teacher_disagreement_score":0.11081972,"about_ca_system_score_codex":0.000017195787,"about_ca_system_score_gemma":0.00011721944,"threshold_uncertainty_score":0.16617545},"labels":[],"label_agreement":null},{"id":"W2973827088","doi":"10.1016/j.trb.2020.09.007","title":"Locally optimal routes for route choice sets","year":2020,"lang":"en","type":"article","venue":"Transportation Research Part B Methodological","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Path (computing); Shortest path problem; Destinations; Mathematical optimization; Mathematics; Theoretical computer science; Geography; Graph; Tourism","score_opus":0.5669865806869429,"score_gpt":0.5322893261408838,"score_spread":0.0346972545460591,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2973827088","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16366747,0.00013736277,0.8039968,0.026896605,0.0002496478,0.0016728649,0.00031638713,0.00055086124,0.002512005],"genre_scores_gemma":[0.6226382,0.00020943533,0.3730326,0.0013308838,0.00054041017,0.00038317393,0.0009963217,0.000034625344,0.0008343438],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99593925,0.0015574537,0.00045060957,0.0005134903,0.0009090333,0.00063012895],"domain_scores_gemma":[0.99442744,0.0042799674,0.00009276141,0.00011361779,0.0006305537,0.00045564305],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0050006662,0.0001438704,0.00028568963,0.00008932173,0.00081142725,0.00010281192,0.00033514644,0.0002489382,0.00055599125],"category_scores_gemma":[0.0034817527,0.00013414316,0.0001401095,0.0007264821,0.00039757034,0.00028990925,0.0000035023058,0.00039576268,0.000042180738],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0039731506,0.0006110943,0.22632405,0.0004592806,0.00028010888,0.0001255363,0.1193696,0.23443224,0.0016908123,0.32346606,0.056081578,0.033186484],"study_design_scores_gemma":[0.0021890076,0.0008866611,0.22681478,0.000059521437,0.000079286925,2.5449356e-7,0.008073819,0.008437532,0.0006784638,0.002595531,0.7495799,0.00060524733],"about_ca_topic_score_codex":0.0004710563,"about_ca_topic_score_gemma":0.00090826204,"teacher_disagreement_score":0.6934983,"about_ca_system_score_codex":0.000040651823,"about_ca_system_score_gemma":0.00028088872,"threshold_uncertainty_score":0.62409246},"labels":[],"label_agreement":null},{"id":"W2974581475","doi":"10.1139/cjce-2018-0706","title":"Integrated simulation-based dynamic traffic and transit assignment model for large-scale network","year":2019,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Transit (satellite); Computer science; Schedule; Traffic simulation; Transport engineering; Scale (ratio); Simulation; Public transport; Operations research; Engineering; Microsimulation; Geography","score_opus":0.008047965901125425,"score_gpt":0.2188533679920104,"score_spread":0.210805402090885,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2974581475","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07412426,0.00013455877,0.9250634,0.00019629064,0.00022518862,0.000121253106,0.000022133943,0.00001311902,0.00009980271],"genre_scores_gemma":[0.99605596,0.0000043537807,0.0037306459,0.000041342777,0.000038077287,0.00000150309,0.0000138172345,0.00001065983,0.00010366926],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994721,0.0000111597565,0.00016407388,0.000060238886,0.00009288407,0.00019954551],"domain_scores_gemma":[0.99949163,0.00010185536,0.0000541159,0.000031481937,0.00009433861,0.00022659161],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003093869,0.000059497153,0.00010487669,0.00010842052,0.00010904486,0.000039520408,0.00005519256,0.000056257777,0.000043499764],"category_scores_gemma":[0.00003299565,0.00006325427,0.00004079755,0.00012271556,0.000009983115,0.00010771706,2.245973e-7,0.0000820069,3.5415832e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000583088,0.0000021979133,0.00071608514,0.000011118366,0.000008152002,0.0000011478338,0.0034296613,0.99543405,0.000006234457,0.00016447788,0.000057315752,0.00016373205],"study_design_scores_gemma":[0.00035915198,0.00001998068,0.0006652001,0.00007444252,0.000016176804,2.223981e-7,0.00023844534,0.99512964,5.691478e-7,0.000021041495,0.0034029137,0.000072226656],"about_ca_topic_score_codex":0.00012550694,"about_ca_topic_score_gemma":0.19685912,"teacher_disagreement_score":0.9219317,"about_ca_system_score_codex":0.00009951431,"about_ca_system_score_gemma":0.0005450411,"threshold_uncertainty_score":0.8177961},"labels":[],"label_agreement":null},{"id":"W2975864903","doi":"","title":"Barriers to Implementing Multi-Modal Transportation Strategies in Two New Brunswick Case Studies: An Exploratory Research","year":2019,"lang":"en","type":"dissertation","venue":"The Atrium (University of Guelph)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Modal; Exploratory research; Transport engineering; Engineering; Regional science; Business; Sociology; Social science; Materials science","score_opus":0.07898799879116723,"score_gpt":0.3810118668680123,"score_spread":0.30202386807684506,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2975864903","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9970411,0.00019490447,0.00068116654,0.00034943537,0.00028999188,0.00070521364,0.000039279555,0.00005305868,0.00064581825],"genre_scores_gemma":[0.9944977,0.00019424058,0.0012409986,0.00001632249,0.000069366055,9.572852e-7,0.00044683547,0.000019190089,0.0035143744],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.99790144,0.0004959042,0.00026093205,0.0003590965,0.0005801277,0.00040252865],"domain_scores_gemma":[0.99855286,0.00020164394,0.00023224863,0.00023406831,0.0005514776,0.0002276766],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020750049,0.00015858596,0.00028627875,0.0004647147,0.001116212,0.000053458713,0.0004405288,0.00014405651,0.0001370945],"category_scores_gemma":[0.000099939556,0.00018011249,0.000085571184,0.0009423552,0.00014593826,0.0007760717,0.000009171491,0.00037048277,0.000016768563],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030846646,0.00002989338,0.0055478835,0.00012541667,0.00007214881,0.00017657246,0.97070706,0.016468717,0.00072018395,0.0046177413,0.0003624691,0.0008634772],"study_design_scores_gemma":[0.00074588414,0.00006599301,0.03275978,0.00010026518,0.00007282138,5.631829e-7,0.96494997,0.00020885638,0.000014813246,0.00012944426,0.00076341565,0.00018822006],"about_ca_topic_score_codex":0.12395147,"about_ca_topic_score_gemma":0.8564958,"teacher_disagreement_score":0.73254436,"about_ca_system_score_codex":0.00016531585,"about_ca_system_score_gemma":0.0029843254,"threshold_uncertainty_score":0.8818822},"labels":[],"label_agreement":null},{"id":"W2976711075","doi":"10.1061/jtepbs.0000273","title":"Assessment of Motorcycle Ownership, Use, and Potential Changes due to Transportation Policies in Ho Chi Minh City, Vietnam","year":2019,"lang":"en","type":"article","venue":"Journal of Transportation Engineering Part A Systems","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Optech (Canada)","funders":"","keywords":"Ho chi minh; Megacity; TRIPS architecture; Car ownership; Business; Revenue; Consumption (sociology); Transport engineering; Public transport; Socioeconomics; Economics; Engineering; Finance; Economy; Low income","score_opus":0.01797874122814131,"score_gpt":0.2656392870693417,"score_spread":0.2476605458412004,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2976711075","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.980043,0.0001002775,0.018216254,0.00022933933,0.0009113928,0.00036849338,0.00006948012,0.000024386645,0.000037401795],"genre_scores_gemma":[0.9981164,0.000086700675,0.001502297,0.000015882297,0.00009835148,0.000011265913,0.000050746017,0.000016278502,0.00010212384],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99840665,0.000055122433,0.00068903895,0.0001358913,0.00051770045,0.00019558628],"domain_scores_gemma":[0.9991371,0.0000664178,0.00037519954,0.00007377791,0.00023391342,0.0001135622],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006493188,0.00012990834,0.00036502938,0.0004198261,0.000047365498,0.000058305402,0.000100374775,0.000106680905,0.000015430618],"category_scores_gemma":[0.000020802532,0.00013023552,0.000070256276,0.00039330954,0.000022310858,0.00044759043,5.466228e-7,0.00013736472,5.918823e-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":[0.000032470576,0.000044617376,0.2727127,0.00019331822,0.000038554568,0.000015769725,0.013470454,0.7095391,0.0020969862,0.0017621239,0.000029070996,0.00006482225],"study_design_scores_gemma":[0.0005792031,0.00011820154,0.99067,0.00046651147,0.000047009347,0.0000024919677,0.0023754712,0.0032098237,0.00011564058,0.0000027582105,0.0022620726,0.00015078868],"about_ca_topic_score_codex":0.00060080655,"about_ca_topic_score_gemma":0.0013510503,"teacher_disagreement_score":0.7179573,"about_ca_system_score_codex":0.00005592718,"about_ca_system_score_gemma":0.00009141274,"threshold_uncertainty_score":0.5310851},"labels":[],"label_agreement":null},{"id":"W2977640696","doi":"10.13140/rg.2.2.21494.80963","title":"Comparison Between Trip and Trip Chain Models: Evidence from Montreal Commuter Train Corridor","year":2017,"lang":"en","type":"article","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Transport engineering; Geography; Engineering","score_opus":0.057617188571439444,"score_gpt":0.3161587102405775,"score_spread":0.2585415216691381,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2977640696","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.45092553,0.00165396,0.5355612,0.0085553415,0.0001731633,0.00077403436,0.00023753858,0.000556893,0.0015623096],"genre_scores_gemma":[0.97174454,0.00060729164,0.025893059,0.00037456732,0.00027620036,0.00012087809,0.00010135834,0.000033556324,0.00084854563],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9977065,0.0003223554,0.0005028774,0.00044601,0.00047799895,0.0005442595],"domain_scores_gemma":[0.997863,0.0004823209,0.00045979014,0.0006929714,0.00011736341,0.00038455412],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0012869883,0.00026004488,0.00047351743,0.00017976573,0.0017813309,0.0006799339,0.00076967746,0.0003553973,0.000033834844],"category_scores_gemma":[0.00043733147,0.0002741367,0.00010682098,0.00014938616,0.00035676395,0.0011719884,0.0000617121,0.00038422053,0.000005556487],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021402058,0.00013492032,0.86996466,0.000019719373,0.000074345895,0.000028381246,0.031364013,0.010047006,0.00016206315,0.004891573,0.0033656375,0.079733685],"study_design_scores_gemma":[0.00081173546,0.00008164943,0.7817006,0.00018517973,0.00009419411,0.0000012350023,0.0013206691,0.2096495,0.0002559024,0.0043972414,0.0010836372,0.0004184472],"about_ca_topic_score_codex":0.27443,"about_ca_topic_score_gemma":0.11279032,"teacher_disagreement_score":0.520819,"about_ca_system_score_codex":0.00018233081,"about_ca_system_score_gemma":0.00021419788,"threshold_uncertainty_score":0.9999711},"labels":[],"label_agreement":null},{"id":"W2977642222","doi":"10.1061/9780784482292.381","title":"A Trajectory-Mining Approach to Derive Travel Time Skim Matrix in Dynamic Traffic Assignment","year":2019,"lang":"en","type":"article","venue":"CICTP 2019","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ministry of Education and Child Care","funders":"","keywords":"Computer science; Trajectory; Impedance parameters; Matrix (chemical analysis); Travel time; Path (computing); Mode (computer interface); Scale (ratio); Mathematical optimization; Algorithm; Electrical impedance; Transport engineering; Engineering; Mathematics","score_opus":0.009240174948928979,"score_gpt":0.26847013535146136,"score_spread":0.2592299604025324,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2977642222","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9687567,0.0000640204,0.0064067114,0.00019961264,0.00021130094,0.0005905205,0.0000193233,0.000092173876,0.023659632],"genre_scores_gemma":[0.98424715,0.000010313144,0.0040502436,0.00007637297,0.000029561288,0.000020704905,0.0000807029,0.00001614781,0.011468828],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99885607,0.000090258414,0.00020350778,0.00027286875,0.00028604362,0.00029124453],"domain_scores_gemma":[0.999641,0.0000641162,0.000059867383,0.00010448898,0.00002771176,0.000102834776],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003842045,0.00010690595,0.00017147833,0.00015428665,0.000097536955,0.000047483925,0.00015312342,0.00011092005,0.00019875454],"category_scores_gemma":[0.000017463408,0.000113719754,0.00004490718,0.0003557139,0.000030571595,0.00013509333,0.000004594676,0.00009573527,0.00047055908],"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":[0.0001098581,0.00043150684,0.015819881,0.000055990757,0.000042822783,0.000008212294,0.25783837,0.7105637,0.001892694,0.0016189936,0.0032946793,0.008323266],"study_design_scores_gemma":[0.004002849,0.00040416708,0.6911012,0.0003237529,0.000088218294,0.000004064921,0.06122439,0.23094912,0.0001219626,0.000066812245,0.009972995,0.0017405015],"about_ca_topic_score_codex":0.00014312609,"about_ca_topic_score_gemma":0.00017100337,"teacher_disagreement_score":0.6752813,"about_ca_system_score_codex":0.00013493131,"about_ca_system_score_gemma":0.00013219398,"threshold_uncertainty_score":0.6048242},"labels":[],"label_agreement":null},{"id":"W2978001016","doi":"","title":"Limited Stop Bus Service: An Evaluation of an Implementation Strategy","year":2010,"lang":"en","type":"article","venue":"eScholarship@McGill (McGill)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ministère des Transports; McGill University","funders":"","keywords":"Service (business); Transport engineering; Level of service; Operations research; Computer science; Operations management; Business; Engineering; Marketing","score_opus":0.05071161652866974,"score_gpt":0.3480010679317837,"score_spread":0.29728945140311397,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2978001016","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98719585,0.000008203611,0.0000022903223,0.00005543301,0.0004088036,0.0005347176,0.00035097418,0.00019435062,0.011249385],"genre_scores_gemma":[0.99681145,0.000012142285,0.0019197143,0.00014728429,0.000054279244,0.00004247281,0.0009167905,0.000032440592,0.00006342381],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99674207,0.0007686897,0.0005130199,0.00043490154,0.0011919296,0.00034939864],"domain_scores_gemma":[0.99748266,0.00008239937,0.00034982446,0.0003513898,0.0014575686,0.00027615466],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0035564997,0.00018917507,0.00019130365,0.00020644913,0.0011103163,0.000085706546,0.00033611958,0.0002656095,0.0009875925],"category_scores_gemma":[0.00020210104,0.00021559886,0.000056922483,0.0007666191,0.00007713698,0.0021698514,0.000011934482,0.00037090192,0.000026618267],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012723413,0.00056403584,0.0029214595,0.000046743557,0.00007275389,0.000005348003,0.0012274229,0.01677134,0.11506572,0.25413895,0.0000023770845,0.6090566],"study_design_scores_gemma":[0.013789264,0.0018816066,0.5681177,0.00017867218,0.0016409139,0.000018416275,0.06653611,0.028659822,0.17424837,0.10493554,0.03633168,0.0036619545],"about_ca_topic_score_codex":0.0057324194,"about_ca_topic_score_gemma":0.07531211,"teacher_disagreement_score":0.60539466,"about_ca_system_score_codex":0.0001421886,"about_ca_system_score_gemma":0.00017985702,"threshold_uncertainty_score":0.9999256},"labels":[],"label_agreement":null},{"id":"W2979160349","doi":"10.1080/21680566.2019.1670116","title":"Evolutionary game theoretical approach for equilibrium of cross-border traffic","year":2019,"lang":"en","type":"article","venue":"Transportmetrica B Transport Dynamics","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Replicator equation; Set (abstract data type); Stability (learning theory); Game theory; Quality (philosophy); Equilibrium point; Event (particle physics); Point (geometry); Mathematical economics; Mathematics; Machine learning","score_opus":0.010897622619555293,"score_gpt":0.32570630784414084,"score_spread":0.31480868522458555,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2979160349","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.692986,0.00019969416,0.28280813,0.00017061796,0.0004366374,0.001516349,0.0010069904,0.00031007995,0.020565504],"genre_scores_gemma":[0.9735959,0.00009628383,0.02069826,0.000043115095,0.00006790516,0.0000582274,0.0030779666,0.00006072609,0.0023015854],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9970746,0.00005814719,0.00085259153,0.00058333017,0.0008018587,0.000629465],"domain_scores_gemma":[0.9984854,0.0002415461,0.00023127529,0.00033498634,0.00048243644,0.00022439723],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00072711555,0.0002866833,0.00055522093,0.0004437492,0.00016306403,0.000025691903,0.00037994885,0.00040142835,0.00076283194],"category_scores_gemma":[0.0000405918,0.00030268606,0.000421477,0.0017453532,0.0008595564,0.0004153431,0.0000020013767,0.00023582281,0.000007187784],"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":[0.00068672805,0.0006622188,0.24413285,0.00038901035,0.000116650954,0.000005040757,0.0045038103,0.3319006,0.000023608563,0.41593254,0.000023171066,0.0016237779],"study_design_scores_gemma":[0.0081492625,0.0009134045,0.5603884,0.00014886336,0.0008476092,0.000008003619,0.0035810962,0.4045551,0.000081509854,0.0037015716,0.01541763,0.002207578],"about_ca_topic_score_codex":0.00011412004,"about_ca_topic_score_gemma":0.00008836025,"teacher_disagreement_score":0.41223097,"about_ca_system_score_codex":0.00012138501,"about_ca_system_score_gemma":0.00039908773,"threshold_uncertainty_score":0.99994254},"labels":[],"label_agreement":null},{"id":"W2981194977","doi":"10.5753/courb.2019.7466","title":"Realocação em Sistemas de Compartilhamento de Veículos em Uma Via","year":2019,"lang":"pt","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Humanities; Physics; Philosophy","score_opus":0.02864896660077033,"score_gpt":0.3063641567227487,"score_spread":0.2777151901219784,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2981194977","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.91047907,0.00020671943,0.049903177,0.0014041797,0.00071045663,0.00061447977,0.00002159399,0.0003023973,0.036357906],"genre_scores_gemma":[0.9402775,0.000042378357,0.0013220385,0.00060997694,0.00009536788,0.000010943956,0.00006232617,0.000019163297,0.057560287],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9980647,0.00023957659,0.00038624718,0.00032616258,0.00044040842,0.0005429114],"domain_scores_gemma":[0.9991132,0.0001114685,0.00017380841,0.00022154463,0.00011423709,0.00026575025],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0007262931,0.00017954073,0.00026028682,0.00007336816,0.00031823738,0.00017966593,0.0002335865,0.0002084175,0.002182114],"category_scores_gemma":[0.000025698124,0.00018363776,0.00011684225,0.00038455255,0.000050827857,0.00022643992,0.000010319635,0.00015732578,0.00090290676],"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":[0.00019506073,0.0005096136,0.46348107,0.00036338498,0.00020989103,0.00002968899,0.408072,0.08258598,0.0005379904,0.012989476,0.025622204,0.005403632],"study_design_scores_gemma":[0.0027747455,0.00051719276,0.41137996,0.000621743,0.000394737,0.0000087392955,0.26713234,0.28390548,0.0010490672,0.00084647833,0.029819654,0.0015498371],"about_ca_topic_score_codex":0.0016443549,"about_ca_topic_score_gemma":0.008457783,"teacher_disagreement_score":0.2013195,"about_ca_system_score_codex":0.00019629358,"about_ca_system_score_gemma":0.00024256016,"threshold_uncertainty_score":0.999875},"labels":[],"label_agreement":null},{"id":"W2981515357","doi":"10.1007/978-3-030-26676-9","title":"Fuzzy Transportation and Transshipment Problems","year":2019,"lang":"en","type":"book","venue":"Studies in fuzziness and soft computing","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":19,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Transshipment (information security); Fuzzy transportation; Fuzzy logic; Transportation theory; Computer science; Operations research; Mathematical optimization; Fuzzy set operations; Engineering; Fuzzy set; Mathematics; Artificial intelligence","score_opus":0.04746413163775181,"score_gpt":0.32040074133988306,"score_spread":0.27293660970213124,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2981515357","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15043338,0.09144977,0.008721737,0.0035626725,0.0075965817,0.0061817043,0.00024785634,0.001001723,0.73080456],"genre_scores_gemma":[0.89052176,0.013874837,0.0013705898,0.00016899047,0.00050618075,0.000028652023,0.00032406292,0.00006204233,0.093142904],"study_design_codex":"qualitative","study_design_gemma":"not_applicable","domain_scores_codex":[0.9987195,0.000053761716,0.00037865428,0.00035785037,0.00025361872,0.00023659588],"domain_scores_gemma":[0.99933326,0.00027279434,0.00016236673,0.00006485793,0.00011842439,0.000048294576],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054522546,0.00020221538,0.0004079543,0.00012541735,0.00039216073,0.000046387187,0.000071760514,0.0001817838,0.0000029713374],"category_scores_gemma":[0.000028814977,0.00020233812,0.000036072393,0.0001119063,0.00028559045,0.000097237695,0.0000071376357,0.0002145806,0.0000013022868],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006252504,0.00007957369,0.07292638,0.0049116905,0.00036182636,0.000047505375,0.7247918,0.03078351,0.0000025323575,0.09836064,0.0031639533,0.06450801],"study_design_scores_gemma":[0.010754482,0.00064873346,0.25879145,0.027664324,0.0012725625,0.000009048126,0.16487741,0.0046628728,0.0000033249132,0.10014026,0.42441046,0.00676506],"about_ca_topic_score_codex":0.00012306667,"about_ca_topic_score_gemma":0.0018040054,"teacher_disagreement_score":0.74008834,"about_ca_system_score_codex":0.000072579576,"about_ca_system_score_gemma":0.00013244012,"threshold_uncertainty_score":0.8251109},"labels":[],"label_agreement":null},{"id":"W2981614872","doi":"10.1002/cpe.5530","title":"The modeling of urban rail transit emergency delay propagation scope under network operation mode","year":2019,"lang":"en","type":"article","venue":"Concurrency and Computation Practice and Experience","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ministry of Education and Child Care","funders":"National Key Research and Development Program of China; Science and Technology Commission of Shanghai Municipality; National Natural Science Foundation of China","keywords":"Urban rail transit; Reliability (semiconductor); Scope (computer science); Computer science; Mode (computer interface); Transit (satellite); Transport engineering; Public transport; Simulation; Engineering; Power (physics)","score_opus":0.024750118871127637,"score_gpt":0.3350850981481921,"score_spread":0.31033497927706444,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2981614872","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3734072,0.0029917145,0.6187195,0.0008791289,0.0005043329,0.00035164144,0.0000019544761,0.000035981502,0.0031085196],"genre_scores_gemma":[0.99568486,0.0030815601,0.00097232615,0.000068415764,0.000052081494,0.000015953307,0.00001956197,0.000004316465,0.00010089923],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999026,0.00016354646,0.0002819333,0.00018035785,0.00021676396,0.00013140106],"domain_scores_gemma":[0.9993295,0.00017513074,0.00015680572,0.000057795478,0.00023391441,0.00004686615],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043482744,0.000074002855,0.000085536645,0.000022075255,0.0006995288,0.00008570438,0.00006222383,0.00005115086,0.000018858434],"category_scores_gemma":[0.00007833444,0.00006302443,0.000016185488,0.00018244518,0.00009123173,0.0012642527,0.0000055423375,0.00007943229,0.0000026147075],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007583478,0.000027797716,0.0022338023,0.000021455575,0.000013537183,3.2924675e-7,0.08509056,0.7923373,0.000104398634,0.10816849,0.000070095826,0.011856404],"study_design_scores_gemma":[0.00039659304,0.00008616116,0.0007955655,0.000057341225,0.000034077613,0.0000020991013,0.031602334,0.96196705,0.000036721016,0.0020892115,0.002750057,0.00018279965],"about_ca_topic_score_codex":0.00026326644,"about_ca_topic_score_gemma":0.00009680028,"teacher_disagreement_score":0.6222777,"about_ca_system_score_codex":0.000009449626,"about_ca_system_score_gemma":0.000090848174,"threshold_uncertainty_score":0.53802806},"labels":[],"label_agreement":null},{"id":"W2982175325","doi":"10.1007/978-3-030-33904-3_48","title":"A Spatiotemporal Analysis of Taxis Demand: A Case Study in the Manizales City","year":2019,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Taxis; Computer science; Population; Quarter (Canadian coin); Service (business); Operations research; Geography; Transport engineering; Economy; Economics; Demography; Mathematics; Engineering; Sociology","score_opus":0.03117885930993699,"score_gpt":0.30313934655096186,"score_spread":0.2719604872410249,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2982175325","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22898728,0.000104783576,0.76722103,0.00028062615,0.00031157452,0.0008232966,0.000021238515,0.000029530373,0.0022206674],"genre_scores_gemma":[0.994566,0.000011225778,0.0051128464,0.00013164175,0.000050971164,0.0000030933138,0.000013153821,0.000005690785,0.00010532887],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981376,0.00012141371,0.00036319997,0.00044809116,0.0007302031,0.00019947109],"domain_scores_gemma":[0.9987236,0.0004977948,0.00025079242,0.0003417156,0.00015116764,0.000034929708],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018261623,0.00015569842,0.00034729537,0.00086731697,0.0002128363,0.000105666106,0.0005956724,0.000139999,0.000037617985],"category_scores_gemma":[0.00007498957,0.00012077682,0.00008559569,0.0015129632,0.00040557628,0.00014931189,0.000033377102,0.00026711353,0.0000015470544],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011946802,0.00012281172,0.28232312,0.000018319195,0.00008148325,0.00059576577,0.15047064,0.54474026,6.494537e-7,0.0024398528,0.0000044809326,0.019190643],"study_design_scores_gemma":[0.0019417427,0.0010421551,0.3898294,0.00063698355,0.0015903913,0.00008605211,0.0020440072,0.5839224,0.000026132417,0.015699347,0.0009860568,0.002195338],"about_ca_topic_score_codex":0.006784222,"about_ca_topic_score_gemma":0.13285644,"teacher_disagreement_score":0.76557875,"about_ca_system_score_codex":0.0000792023,"about_ca_system_score_gemma":0.00038708613,"threshold_uncertainty_score":0.9998297},"labels":[],"label_agreement":null},{"id":"W2982529329","doi":"","title":"Transit network analysis: providing an optimal transit network strategy for mid-size transit systems","year":2019,"lang":"en","type":"dissertation","venue":"Mspace (University of Manitoba)","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":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Transit (satellite); Transport engineering; Transit time; Computer science; Public transport; Engineering","score_opus":0.022357965829084937,"score_gpt":0.24870667367351457,"score_spread":0.22634870784442962,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2982529329","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6731682,0.0017839752,0.3083235,0.00044288294,0.0027292853,0.004226344,0.00054278923,0.0005856835,0.008197342],"genre_scores_gemma":[0.987717,0.00030709838,0.0043644374,0.000016460186,0.00046864338,0.000005664009,0.0035199875,0.00005852258,0.0035422235],"study_design_codex":"simulation_or_modeling","study_design_gemma":"qualitative","domain_scores_codex":[0.9973048,0.00032636605,0.00034498377,0.0006944603,0.0006618896,0.000667527],"domain_scores_gemma":[0.99822384,0.00023484197,0.0005537267,0.00030357967,0.00046167066,0.00022234532],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00082940725,0.00038001122,0.0009116393,0.000265329,0.0012015079,0.00015308839,0.00058445585,0.00075560866,0.000036688398],"category_scores_gemma":[0.000011912959,0.0005220394,0.00056712463,0.0012598683,0.00011119253,0.0006628545,0.00000311981,0.00032625668,0.000006868218],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00091620744,0.000069578535,0.00328286,0.0004230035,0.00091639947,0.000012703359,0.017051242,0.9748807,0.000027241242,0.0013693424,0.0005077988,0.0005429357],"study_design_scores_gemma":[0.0028323885,0.00067286287,0.077506356,0.0008693539,0.011088839,0.0000010356521,0.80280846,0.08920509,0.000020669855,0.00006540073,0.013166514,0.001763044],"about_ca_topic_score_codex":0.0107916705,"about_ca_topic_score_gemma":0.65540934,"teacher_disagreement_score":0.8856756,"about_ca_system_score_codex":0.00014181013,"about_ca_system_score_gemma":0.0004948829,"threshold_uncertainty_score":0.99972314},"labels":[],"label_agreement":null},{"id":"W2982708850","doi":"10.1109/ictis.2019.8883855","title":"A Mechanism for Pricing and Allocation of Mobility Permits on Single-Bottleneck Roadways","year":2019,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Bottleneck; Computer science; Mathematical optimization; Operations research; Computer network; Engineering","score_opus":0.026428117502715454,"score_gpt":0.27963927629307034,"score_spread":0.2532111587903549,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2982708850","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9492044,0.000006570407,0.035763986,0.00032166793,0.00009684946,0.00040046993,0.000004198467,0.00004450784,0.014157341],"genre_scores_gemma":[0.99390846,0.0000069172715,0.004840181,0.00006959054,0.000013669587,0.000007538333,0.000010829093,0.0000033808321,0.0011394047],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9995742,0.00002086129,0.00010511252,0.00011280066,0.00011394616,0.000073068826],"domain_scores_gemma":[0.99965906,0.00009766499,0.00005305502,0.000053392007,0.000112185015,0.000024626419],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025062528,0.000035986497,0.00006554999,0.000031058476,0.00008051461,0.000011685468,0.000032492688,0.00004389216,0.000033498574],"category_scores_gemma":[0.00006751413,0.000033635355,0.000017168355,0.000069521346,0.000020499674,0.00008697611,0.0000014168618,0.00001763563,0.0000024186634],"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":[0.00006447082,0.00015270026,0.013236079,0.00008204234,0.000011126786,6.53062e-8,0.017896159,0.0048182327,0.00903193,0.9493237,0.00005745305,0.005326023],"study_design_scores_gemma":[0.010325484,0.0050259707,0.42201856,0.00096298486,0.0003029699,0.0000015625635,0.08526669,0.1639381,0.21201335,0.088434085,0.009386996,0.0023232594],"about_ca_topic_score_codex":0.0002497031,"about_ca_topic_score_gemma":0.00026214836,"teacher_disagreement_score":0.8608896,"about_ca_system_score_codex":0.000018919633,"about_ca_system_score_gemma":0.000030256397,"threshold_uncertainty_score":0.137161},"labels":[],"label_agreement":null},{"id":"W2983319294","doi":"10.4018/978-1-5225-9570-0.ch014","title":"A Hybrid Approach Based on SERVQUAL, SERVPERF, and IPA for Measuring Transit Service Quality","year":2019,"lang":"en","type":"book-chapter","venue":"Advances in logistics, operations, and management science book series","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Concordia University","funders":"","keywords":"SERVQUAL; Public transport; Service quality; Perception; Quality (philosophy); Service (business); Scope (computer science); Computer science; Business; Psychology; Marketing; Transport engineering; Engineering","score_opus":0.04906581467067337,"score_gpt":0.31164407883539724,"score_spread":0.26257826416472385,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2983319294","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0001375052,0.0037356245,0.3455725,0.003465867,0.00080124714,0.003718722,0.00032332688,0.00020199455,0.64204323],"genre_scores_gemma":[0.13858539,0.1272942,0.25459668,0.0143244285,0.0005484476,0.00076431717,0.0014218927,0.0001895159,0.46227512],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9979979,0.00004126527,0.00042128254,0.0006978268,0.00054023747,0.00030149554],"domain_scores_gemma":[0.9991434,0.000111141184,0.00012239005,0.0002455481,0.0002842691,0.000093232804],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0013450395,0.00025249788,0.00030622684,0.00026690908,0.0010376927,0.0003027603,0.0002676356,0.00008811525,0.000016864511],"category_scores_gemma":[0.00010132192,0.0002596462,0.000033025168,0.000104436614,0.0008626615,0.0016868181,0.000025566405,0.00013104062,0.0000027963067],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000065236796,0.00002376993,0.00012741533,0.0005595624,0.000007749688,0.000001657602,0.0012826339,0.225334,0.0000010210446,0.77124166,0.000036669695,0.0013186224],"study_design_scores_gemma":[0.002501279,0.0004434091,0.0019398715,0.0014118804,0.00025514487,0.0000022241825,0.008707178,0.08090439,0.000037282523,0.02142678,0.88024724,0.0021233428],"about_ca_topic_score_codex":0.00012210639,"about_ca_topic_score_gemma":0.0020884483,"teacher_disagreement_score":0.8802106,"about_ca_system_score_codex":0.000094979216,"about_ca_system_score_gemma":0.00020325696,"threshold_uncertainty_score":0.9999856},"labels":[],"label_agreement":null},{"id":"W2984140570","doi":"10.1016/j.jth.2019.100713","title":"Exploring the Relationship between Tour Complexity and Health in Halifax Transit and Auto Riders","year":2019,"lang":"en","type":"article","venue":"Journal of Transport & Health","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Transit (satellite); Transport engineering; Advertising; Psychology; Geography; Business; Engineering; Public transport","score_opus":0.27550193227159275,"score_gpt":0.3771779308387175,"score_spread":0.10167599856712473,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2984140570","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9395313,0.00048015744,0.002865807,0.05655798,0.00015819402,0.00024648814,0.000017984756,0.000015173198,0.00012690383],"genre_scores_gemma":[0.99676543,0.00087051984,0.0016634945,0.00057075685,0.00007930677,0.0000013480704,0.000013144487,0.000007546973,0.000028463566],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99856037,0.0001966878,0.0006077504,0.000114245704,0.00028766677,0.00023325307],"domain_scores_gemma":[0.99913424,0.00023104784,0.00031798432,0.000061929815,0.000046652593,0.00020813453],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029914074,0.00007970244,0.00029203983,0.00013670413,0.00038285917,0.000023346372,0.00008623867,0.00003760513,0.000011568769],"category_scores_gemma":[0.000018960194,0.00006515087,0.000042385756,0.00025286933,0.000100115685,0.0004368448,9.545166e-7,0.0003366284,6.7299453e-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":[0.000021759846,0.000013465152,0.94883955,0.000061777166,0.000006650121,0.0000017818712,0.044343866,0.0005681064,2.1766283e-7,0.0041350364,0.00005530092,0.0019524965],"study_design_scores_gemma":[0.0005092001,0.000100534475,0.9916248,0.00017848113,0.000008441776,0.0000035498158,0.004697254,0.000020811502,2.1558547e-7,0.0010006055,0.0017970408,0.000059084476],"about_ca_topic_score_codex":0.0059530754,"about_ca_topic_score_gemma":0.015519778,"teacher_disagreement_score":0.057234105,"about_ca_system_score_codex":0.00009379393,"about_ca_system_score_gemma":0.00051842566,"threshold_uncertainty_score":0.89993083},"labels":[],"label_agreement":null},{"id":"W2985000958","doi":"10.1007/978-3-030-19462-8_6","title":"Methods for Linearly Constrained Problems","year":2019,"lang":"en","type":"book-chapter","venue":"International series in management science/operations research/International series in operations research & management science","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University; University of New Brunswick","funders":"","keywords":"Quadratic programming; Nonlinear programming; Quadratically constrained quadratic program; Mathematical optimization; Second-order cone programming; Sequential quadratic programming; Mathematics; Linear programming; Quadratic equation; Differentiable function; Convex optimization; Linear-fractional programming; Optimization problem; Active set method; Regular polygon; Computer science; Nonlinear system","score_opus":0.10434789328691996,"score_gpt":0.4908927643642119,"score_spread":0.38654487107729196,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2985000958","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00046232616,0.00012659785,0.019071307,0.021550344,0.004105791,0.007783666,0.0002721039,0.00017312558,0.94645476],"genre_scores_gemma":[0.034685966,0.00629471,0.13881882,0.00020142423,0.00046205724,0.002185236,0.0008366149,0.00009861651,0.81641656],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9847648,0.0005655312,0.0016884163,0.0025262092,0.008457348,0.0019976627],"domain_scores_gemma":[0.9919686,0.0005547938,0.0001446255,0.0011513137,0.005771548,0.0004091188],"candidate_categories":["metaresearch","metaepi_narrow","bibliometrics","sts","scholarly_communication","open_science","insufficient_payload"],"consensus_categories":["sts"],"category_scores_codex":[0.033531696,0.0005700111,0.00052447873,0.0113084605,0.005004229,0.0052869166,0.0069044195,0.0003121693,0.002202793],"category_scores_gemma":[0.0023774803,0.0006247576,0.00017889988,0.0046529802,0.011681268,0.0076162065,0.0015263034,0.0014610615,0.00029472398],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001417751,0.00018306654,0.00014463822,0.000081022205,0.00008762758,0.00003610158,0.0030134101,0.16449144,0.00017211805,0.8246468,0.0008048297,0.0061971797],"study_design_scores_gemma":[0.0017820427,0.00029287825,0.0014723454,0.0011230351,0.000028811133,0.000009354641,0.013858341,0.08838544,0.00013813822,0.035882525,0.8558828,0.0011442575],"about_ca_topic_score_codex":0.0015685448,"about_ca_topic_score_gemma":0.015505902,"teacher_disagreement_score":0.855078,"about_ca_system_score_codex":0.004530435,"about_ca_system_score_gemma":0.0024705995,"threshold_uncertainty_score":0.99989754},"labels":[],"label_agreement":null},{"id":"W2986449385","doi":"","title":"Crowding and Congestion in Banff Alberta Canada","year":2019,"lang":"en","type":"article","venue":"Scholarworks (University of Massachusetts Amherst)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Geography","score_opus":0.007111628803809116,"score_gpt":0.199967643303779,"score_spread":0.1928560144999699,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2986449385","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98062414,0.00006397615,0.00023381277,0.002338663,0.00014091154,0.00013463186,0.0000060745483,0.000017291402,0.01644049],"genre_scores_gemma":[0.9961644,0.00005721545,0.00044175427,0.00005147928,0.000008362922,7.742773e-8,0.000017410965,0.0000040702694,0.0032552567],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99930996,0.00009488858,0.00007534526,0.00016091419,0.00020845175,0.00015042553],"domain_scores_gemma":[0.99959517,0.0001223914,0.000074651616,0.00007958869,0.000053942207,0.00007428841],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033180273,0.000058115762,0.00011076508,0.000088691864,0.00019671791,0.000024815683,0.00011768472,0.00010169177,0.00021544502],"category_scores_gemma":[0.000047722773,0.000079463876,0.000017217077,0.00025899528,0.00007523538,0.00046529097,0.000010008744,0.00016988548,0.0000048967463],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000054595974,0.000017397868,0.9725231,0.000020967862,0.000015914928,0.000012630709,0.011494276,0.006693155,0.00003871681,0.006864851,0.0008815631,0.0013828487],"study_design_scores_gemma":[0.001221762,0.000023915585,0.92542106,0.00015622406,0.000026266112,5.481279e-7,0.036794942,0.0013894079,0.000006089935,0.000108835055,0.034612305,0.00023865257],"about_ca_topic_score_codex":0.7429953,"about_ca_topic_score_gemma":0.9690643,"teacher_disagreement_score":0.22606899,"about_ca_system_score_codex":0.00012098228,"about_ca_system_score_gemma":0.00024452992,"threshold_uncertainty_score":0.3240443},"labels":[],"label_agreement":null},{"id":"W2989314738","doi":"10.1145/3357000.3366139","title":"Relative Reachability Analysis as a Tool for Urban Mobility Planning","year":2019,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Deutsche Forschungsgemeinschaft","keywords":"Reachability; Computer science; Theoretical computer science","score_opus":0.019522515717884485,"score_gpt":0.3214755868646429,"score_spread":0.3019530711467584,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2989314738","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.87233406,0.00002410102,0.07364566,0.0003865167,0.0000760701,0.00054572115,0.000017404956,0.0001494323,0.052821048],"genre_scores_gemma":[0.97996557,0.0000015698633,0.005874883,0.000062108455,0.00002694864,0.000025126432,0.00007649131,0.0000035360838,0.013963776],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99915594,0.00008633448,0.00017782686,0.00024346403,0.00018535051,0.00015107689],"domain_scores_gemma":[0.9992089,0.00038174103,0.000068616915,0.0001461617,0.0001470721,0.00004747921],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000936687,0.000058868507,0.00013880972,0.000072795265,0.00021340894,0.000033435004,0.000078836914,0.000080565835,0.00064417993],"category_scores_gemma":[0.000289896,0.000055005763,0.00012279273,0.00050952175,0.00005744264,0.00026525802,0.0000028570707,0.000055955992,0.00002911663],"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":[0.000051098024,0.00004899375,0.88010156,0.000007735745,0.00010891712,1.7597957e-7,0.043181878,0.014767227,0.000005151952,0.061214052,0.00028359846,0.00022963641],"study_design_scores_gemma":[0.0007742175,0.00015860882,0.9092067,0.000017859182,0.00049829966,6.4856046e-8,0.032161508,0.014371994,0.000044102835,0.0062567173,0.036092233,0.00041772335],"about_ca_topic_score_codex":0.0013977941,"about_ca_topic_score_gemma":0.00045921988,"teacher_disagreement_score":0.10763151,"about_ca_system_score_codex":0.000053918127,"about_ca_system_score_gemma":0.00008622253,"threshold_uncertainty_score":0.70533186},"labels":[],"label_agreement":null},{"id":"W2990515088","doi":"10.1016/j.cstp.2019.11.007","title":"Mode shift impacts of optimal time-dependent congestion pricing in large networks: A simulation-based case study in the greater toronto area","year":2019,"lang":"en","type":"article","venue":"Case Studies on Transport Policy","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; University of Toronto","keywords":"Toll; Congestion pricing; Mode (computer interface); Computer science; Component (thermodynamics); Mode choice; Value of time; Discrete choice; Toll road; Schedule; Traffic congestion; Operations research; Microsimulation; Maximization; Transport engineering; Duration (music); Econometric model; Range (aeronautics); Econometrics; Economics; Travel time; Engineering; Public transport; Microeconomics","score_opus":0.027818555539087332,"score_gpt":0.3525668235016147,"score_spread":0.32474826796252737,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2990515088","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9974874,0.00008866603,0.00063709746,0.00015584173,0.00006999261,0.0010925718,0.000039702947,0.000038794235,0.0003898973],"genre_scores_gemma":[0.99962306,0.00001895144,0.000030260495,0.00014601341,0.000060397433,0.000043797205,0.000028556458,0.000014099861,0.00003488938],"study_design_codex":"simulation_or_modeling","study_design_gemma":"qualitative","domain_scores_codex":[0.9983336,0.00024734568,0.00049083185,0.00026764144,0.00032466088,0.0003359303],"domain_scores_gemma":[0.99901885,0.00048055692,0.00015434314,0.00019306653,0.00009529911,0.000057899153],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010875898,0.00017595667,0.0003129857,0.00016701658,0.00021745394,0.000017318807,0.00009628384,0.0000869653,0.000031687596],"category_scores_gemma":[0.00008411172,0.00014046794,0.00006231114,0.00040962108,0.00006658504,0.00020861757,0.0000030318217,0.00013929332,0.0000028351371],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010121866,0.00019990704,0.19066103,0.000016028662,0.000022684093,0.0022514374,0.15516585,0.65144,4.9853423e-7,0.00009867317,0.0000013092581,0.000041357853],"study_design_scores_gemma":[0.0076676104,0.0010300708,0.33221945,0.00039663824,0.0002209171,0.00007417827,0.38980752,0.26780945,0.0000111005265,0.000024289498,0.000031381915,0.0007073704],"about_ca_topic_score_codex":0.039060306,"about_ca_topic_score_gemma":0.2505304,"teacher_disagreement_score":0.38363054,"about_ca_system_score_codex":0.00024447797,"about_ca_system_score_gemma":0.00015266094,"threshold_uncertainty_score":0.9673387},"labels":[],"label_agreement":null},{"id":"W2991397895","doi":"10.1007/s12469-019-00213-0","title":"Modeling passengers’ perceptions of intercity train service quality for regular and special days","year":2019,"lang":"en","type":"article","venue":"Public Transport","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Adaptive neuro fuzzy inference system; Transport engineering; Ranking (information retrieval); Service quality; Service (business); Level of service; Public transport; Quality (philosophy); Scale (ratio); Likert scale; Computer science; Statistics; Engineering; Mathematics; Fuzzy logic; Geography; Machine learning; Artificial intelligence; Fuzzy control system; Marketing; Business; Cartography","score_opus":0.04883045772986812,"score_gpt":0.3134970623279742,"score_spread":0.2646666045981061,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2991397895","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.91884875,0.000010943683,0.07485963,0.0024618257,0.00018851101,0.00034442803,0.0001280379,0.00006609542,0.0030918112],"genre_scores_gemma":[0.9963173,0.000026810045,0.0029154124,0.00010156204,0.00017979334,0.000015754225,0.00023042258,0.000009556686,0.00020337164],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9989757,0.00005843689,0.0003129262,0.00021594157,0.00024091594,0.00019610548],"domain_scores_gemma":[0.99942076,0.000056168396,0.00006647493,0.00011426463,0.00024427997,0.00009806713],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009221716,0.000082550585,0.00018327532,0.00007742193,0.00017512943,0.000025573927,0.000121468525,0.00010854193,0.00025848264],"category_scores_gemma":[0.000027911201,0.00008921987,0.00007330724,0.00022892776,0.000065021006,0.00036565893,0.0000017612839,0.000075621145,0.0000024814576],"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":[0.00017471799,0.00024485443,0.65798855,0.0004012522,0.00007894749,7.0795085e-7,0.17007288,0.019850718,0.0007292516,0.1445007,0.00013054245,0.005826906],"study_design_scores_gemma":[0.001806083,0.0000683277,0.9129043,0.0000747631,0.000090269474,6.0727234e-7,0.046834804,0.02643219,0.00002325245,0.0028264644,0.008459978,0.00047892742],"about_ca_topic_score_codex":0.0014533825,"about_ca_topic_score_gemma":0.00889755,"teacher_disagreement_score":0.2549158,"about_ca_system_score_codex":0.000023982722,"about_ca_system_score_gemma":0.00013755578,"threshold_uncertainty_score":0.4965042},"labels":[],"label_agreement":null},{"id":"W2992867506","doi":"","title":"A Remedy for Montreal’s Transportation Network","year":2018,"lang":"en","type":"article","venue":"Civil engineering","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Transport engineering; Business; Engineering; Civil engineering","score_opus":0.01156034461497639,"score_gpt":0.2450018468359422,"score_spread":0.23344150222096582,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2992867506","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023469977,0.00010584193,0.9663308,0.0002927886,0.00096932956,0.00031491637,0.000016405354,0.0004602094,0.00803969],"genre_scores_gemma":[0.9914945,0.00001765119,0.0071169953,0.000037518024,0.00078611047,0.000027776785,0.000058704616,0.000012338054,0.00044843132],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99950635,0.000006217735,0.00010657123,0.00009866659,0.00009536452,0.000186854],"domain_scores_gemma":[0.999741,0.00005052551,0.000026677,0.000050139126,0.00007906865,0.00005255788],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019406251,0.000051573974,0.00006092527,0.00003071081,0.00019164718,0.000020529786,0.000054380693,0.000050760464,0.000044957647],"category_scores_gemma":[0.00004515821,0.000059476148,0.00003016582,0.00017083724,0.00002412286,0.00010552108,3.7185393e-7,0.000029697329,0.0000048719917],"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":[0.00006230032,0.000031349,0.009512862,0.000064843654,0.000049345585,0.0000030934264,0.044835743,0.8687594,0.00023725216,0.054343052,0.015970072,0.0061307014],"study_design_scores_gemma":[0.001510103,0.00018342846,0.22664277,0.00025273854,0.00011720701,4.3231373e-7,0.0012433059,0.13126151,0.00042407494,0.0021628814,0.6354178,0.00078369316],"about_ca_topic_score_codex":0.00022121113,"about_ca_topic_score_gemma":0.014022776,"teacher_disagreement_score":0.9680245,"about_ca_system_score_codex":0.000019099109,"about_ca_system_score_gemma":0.000028465309,"threshold_uncertainty_score":0.7825038},"labels":[],"label_agreement":null},{"id":"W2993943972","doi":"","title":"Evaluating Longitudinal Impacts of Transit Investments on Transit Demand","year":2019,"lang":"en","type":"dissertation","venue":"TSpace","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Transit (satellite); Transport engineering; Engineering; Public transport","score_opus":0.07135806376668845,"score_gpt":0.43471350159199373,"score_spread":0.36335543782530527,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2993943972","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96133375,0.00028957453,0.00047965543,0.00018278779,0.00056918233,0.00050155056,0.000019025842,0.00006576299,0.036558695],"genre_scores_gemma":[0.98520535,0.00014073392,0.0005470243,0.000048329675,0.00006604027,0.0000101866435,0.0008011689,0.000029105262,0.013152077],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.9982767,0.00013066357,0.00029774298,0.00029042683,0.0007566899,0.00024775916],"domain_scores_gemma":[0.99912935,0.0001183851,0.00030834053,0.00015051753,0.00019026625,0.00010314618],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005574324,0.0001940866,0.0003119064,0.0001648044,0.00020952619,0.000037043694,0.00015034735,0.0002951672,0.0002832838],"category_scores_gemma":[0.000092499635,0.00020245032,0.00011499709,0.0002707943,0.00004489819,0.00011443625,7.8105825e-7,0.00020532103,0.000035406934],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001478477,0.00037651576,0.024774428,0.0013315202,0.00040329414,0.00001399135,0.8131945,0.1377767,0.0040411092,0.00701964,0.0010015428,0.008588276],"study_design_scores_gemma":[0.0052053654,0.0016259364,0.8594059,0.0048883413,0.0015714862,0.0000013753522,0.10530494,0.0028580746,0.014307096,0.000649386,0.002114179,0.0020679608],"about_ca_topic_score_codex":0.0019087845,"about_ca_topic_score_gemma":0.003149934,"teacher_disagreement_score":0.83463144,"about_ca_system_score_codex":0.00006503158,"about_ca_system_score_gemma":0.00041801765,"threshold_uncertainty_score":0.82556844},"labels":[],"label_agreement":null},{"id":"W2995569039","doi":"10.5555/1480-6800.22.1.78","title":"Bus Public Transport Improvements and Service Integration in Penang, Malaysia","year":2019,"lang":"en","type":"article","venue":"Arab world geographer","topic":"Transportation Planning and Optimization","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":"Public transport; Business; Service (business); Public service; Transport engineering; Marketing; Public administration; Political science; Engineering","score_opus":0.0113707809916368,"score_gpt":0.2487748530311754,"score_spread":0.23740407203953862,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2995569039","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97312593,0.00008737133,0.0001779599,0.00223981,0.00017614533,0.00031435775,0.0000047651974,0.00006880133,0.023804827],"genre_scores_gemma":[0.99572945,0.00006156592,0.00042075812,0.00068158493,0.000022083786,0.000019559388,0.00007739018,0.000008407083,0.0029791782],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9991418,0.00004746082,0.00018000424,0.0002113356,0.00020052955,0.00021888458],"domain_scores_gemma":[0.9996744,0.00002507731,0.000059699232,0.00009577465,0.00007748007,0.000067567795],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032236695,0.00008603836,0.000099694575,0.00023472427,0.0001033505,0.000052208157,0.00009368655,0.000053191674,0.00055380445],"category_scores_gemma":[0.000005821302,0.000084991,0.000029171762,0.0010968834,0.000038558894,0.0004139787,0.000003088422,0.00009974321,0.000032871743],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012061783,0.00003395166,0.9848467,0.00000914684,0.000006873721,0.0000014069099,0.004338911,0.00027316052,0.000052292748,0.005990604,0.000053509633,0.0043813586],"study_design_scores_gemma":[0.00054157595,0.00001529761,0.9864786,0.000043549226,0.0000102677295,1.18973226e-7,0.0019312595,0.0005147154,0.000014167503,0.00054313627,0.009762707,0.00014457431],"about_ca_topic_score_codex":0.0039364966,"about_ca_topic_score_gemma":0.060582444,"teacher_disagreement_score":0.05664595,"about_ca_system_score_codex":0.000014404938,"about_ca_system_score_gemma":0.000035282937,"threshold_uncertainty_score":0.9565595},"labels":[],"label_agreement":null},{"id":"W2995838261","doi":"10.1155/2019/5021026","title":"Tour-Based Truck Demand Modeling with Entropy Maximization Using GPS Data","year":2019,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Korea Railroad Research Institute","keywords":"Truck; Entropy maximization; Global Positioning System; Maximization; Bay; Entropy (arrow of time); Computer science; Environmental science; Transport engineering; Meteorology; Principle of maximum entropy; Operations research; Geography; Mathematics; Engineering; Civil engineering; Telecommunications; Artificial intelligence; Mathematical optimization; Automotive engineering","score_opus":0.029736317657286935,"score_gpt":0.2953570162467821,"score_spread":0.26562069858949516,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2995838261","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5017126,0.00005479168,0.4977616,0.00008807778,0.00018154513,0.0001229266,0.00001335085,0.000017929715,0.000047142792],"genre_scores_gemma":[0.88432825,0.00006737767,0.11518518,0.000046703994,0.000090324116,7.207657e-7,0.00024779222,0.000016480422,0.000017170725],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99856865,0.00005116214,0.00047719805,0.00017849519,0.0005543317,0.0001701668],"domain_scores_gemma":[0.99875736,0.00005164156,0.00051014544,0.00016046983,0.0004312632,0.00008909327],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042750087,0.00010778042,0.00019611379,0.00015845381,0.00017183978,0.00004573906,0.0002052569,0.00007127653,0.00007684371],"category_scores_gemma":[0.000026636191,0.000097532386,0.000044747318,0.00032684824,0.000032647084,0.0014805509,7.3424184e-7,0.00013549563,0.0000015890793],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034201358,0.000034809807,0.01788116,0.000021942791,0.000019785539,0.000008662788,0.0030613064,0.976727,0.0005742348,0.00085028115,0.0000045231786,0.00047429936],"study_design_scores_gemma":[0.008151129,0.0004906548,0.04591508,0.00080989004,0.000534517,0.000008033566,0.011496807,0.9285577,0.00058645976,0.0010307692,0.0017548108,0.00066417333],"about_ca_topic_score_codex":0.000067103865,"about_ca_topic_score_gemma":0.00043170538,"teacher_disagreement_score":0.38261563,"about_ca_system_score_codex":0.000060684917,"about_ca_system_score_gemma":0.0003382846,"threshold_uncertainty_score":0.39772552},"labels":[],"label_agreement":null},{"id":"W2997049217","doi":"10.1155/2020/1720949","title":"A Traffic Flow Evolution Process toward Mixed Equilibrium with Multicriteria of Route Choice Behaviour","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Traffic flow (computer networking); Disequilibrium; Computer science; Mathematical optimization; Mathematical economics; Mathematics","score_opus":0.01816404755128536,"score_gpt":0.28832605234437186,"score_spread":0.2701620047930865,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2997049217","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9643872,0.000095168754,0.03422544,0.0007529501,0.00022016898,0.00020143893,0.000044147666,0.000042646745,0.000030869676],"genre_scores_gemma":[0.98039365,0.000026887232,0.01930148,0.000035032557,0.00013195263,0.0000041851235,0.00008035798,0.000016004888,0.000010464571],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9984794,0.000058207283,0.0005988163,0.0001472166,0.00054978114,0.00016656342],"domain_scores_gemma":[0.9983944,0.0000526587,0.0006594343,0.00004874215,0.00068605755,0.00015869504],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019490556,0.00011830016,0.00025785796,0.00009656067,0.00008743402,0.00002054396,0.00013938584,0.0000783248,0.000023520246],"category_scores_gemma":[0.00007383949,0.00010914874,0.00008917107,0.00047646102,0.00008172735,0.0009770131,4.07169e-7,0.00015362496,6.5492344e-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":[0.0007083565,0.000090469584,0.028522106,0.0001058583,0.00003031237,0.000014628668,0.06485607,0.9020438,0.002065465,0.0000965264,0.000012986992,0.0014533802],"study_design_scores_gemma":[0.0053833807,0.0012103954,0.94764364,0.00047367276,0.0004793383,0.000004167423,0.03559079,0.005408385,0.0027245227,0.00008160576,0.0005691272,0.0004309564],"about_ca_topic_score_codex":0.000054092987,"about_ca_topic_score_gemma":0.0004300757,"teacher_disagreement_score":0.91912156,"about_ca_system_score_codex":0.000047780424,"about_ca_system_score_gemma":0.0002891512,"threshold_uncertainty_score":0.44509566},"labels":[],"label_agreement":null},{"id":"W2997275449","doi":"","title":"Mobility pricing in Metro Vancouver: Implementing a decongestion charge in the region","year":2019,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Charge (physics); Transport engineering; Business; Geography; Environmental planning; Engineering; Physics","score_opus":0.02126433062115989,"score_gpt":0.30056495769200914,"score_spread":0.27930062707084924,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2997275449","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97952664,0.000010520343,0.0012785068,0.00038537182,0.00011570618,0.00029490524,3.5179264e-7,0.000028803343,0.018359208],"genre_scores_gemma":[0.99914867,0.000011047583,0.00034208153,0.00012463705,0.000016891932,0.000011218871,0.0000061234587,0.000002149485,0.00033716144],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99927247,0.00013441849,0.00016048056,0.000118512486,0.00015019336,0.00016394984],"domain_scores_gemma":[0.99971634,0.00012703658,0.00005096993,0.00006992057,0.000023176752,0.000012554615],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012516861,0.000037266604,0.000058835456,0.00007434608,0.00008997997,0.000025037658,0.00007937923,0.00003468145,0.00010387088],"category_scores_gemma":[0.00006282102,0.00002910537,0.00001606212,0.00038119676,0.000012068512,0.00021260748,0.000002789796,0.000070076196,0.000008552244],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000048568636,0.000022912669,0.9794137,0.0000046912855,7.213799e-7,7.9970323e-7,0.013389553,0.0008505415,0.0000063927037,0.004736006,0.00012657218,0.0014432496],"study_design_scores_gemma":[0.00055454246,0.000022531769,0.9308653,0.00003559603,0.000004410183,5.6117592e-8,0.058374178,0.004425258,0.000035588884,0.0005377328,0.005027573,0.000117218784],"about_ca_topic_score_codex":0.004589533,"about_ca_topic_score_gemma":0.25830165,"teacher_disagreement_score":0.25371212,"about_ca_system_score_codex":0.000062503,"about_ca_system_score_gemma":0.00003568551,"threshold_uncertainty_score":0.7552324},"labels":[],"label_agreement":null},{"id":"W2998394500","doi":"10.1155/2020/5070347","title":"Sensitivity Analysis of Fleet Size for Dynamic Headway-Based Control Method Performance in terms of Passengers’ Experience","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China; Natural Science Foundation of Shanghai","keywords":"Headway; Control (management); Sensitivity (control systems); Engineering; Automotive engineering; Simulation; Computer science; Transport engineering; Electronic engineering","score_opus":0.013594470255704842,"score_gpt":0.32014979425750084,"score_spread":0.306555324001796,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2998394500","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6697362,0.000014308011,0.32960737,0.0003667705,0.000051270257,0.00014250523,0.00006227329,0.0000064873207,0.000012793621],"genre_scores_gemma":[0.952235,0.000041995667,0.0475839,0.00007494409,0.000014433662,0.000005240073,0.00003631642,0.000006176584,0.0000019902334],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9985869,0.000114355506,0.000716904,0.00012261324,0.0003359886,0.00012326121],"domain_scores_gemma":[0.998177,0.0005803634,0.0008111526,0.00005274434,0.00030763634,0.0000711156],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000581975,0.000083611936,0.00043094528,0.00019413859,0.000052359796,0.0000053618373,0.000074495474,0.00006116096,0.000010870483],"category_scores_gemma":[0.00018564754,0.00008335178,0.00019275228,0.00089980423,0.00006580863,0.00038779047,1.7985326e-7,0.00008876158,4.155324e-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":[0.00066997507,0.000047278056,0.1373432,0.000055861616,0.00006867234,0.0000033442655,0.022655131,0.8265189,0.011081717,0.00009010833,4.4111388e-7,0.0014653687],"study_design_scores_gemma":[0.0019868414,0.00022828778,0.92164254,0.00007760447,0.00040363843,1.1158205e-7,0.0049857474,0.066774845,0.0037108627,0.00003262226,0.000054182045,0.00010270431],"about_ca_topic_score_codex":0.000051143044,"about_ca_topic_score_gemma":0.0012805541,"teacher_disagreement_score":0.7842994,"about_ca_system_score_codex":0.000034171517,"about_ca_system_score_gemma":0.00009928706,"threshold_uncertainty_score":0.33989868},"labels":[],"label_agreement":null},{"id":"W2998636888","doi":"10.1155/2019/1524178","title":"Day-to-Day Evolution of Traffic Flow with Dynamic Rerouting in Degradable Transport Network","year":2019,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","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":"Government of Jiangsu Province; National Natural Science Foundation of China","keywords":"Cumulative prospect theory; Uniqueness; Heuristic; Traffic flow (computer networking); Computer science; Set (abstract data type); Mathematical optimization; Stability (learning theory); Expected utility hypothesis; Point (geometry); Operations research; Simulation; Mathematics; Statistics","score_opus":0.0049388631857537595,"score_gpt":0.24135658292646875,"score_spread":0.236417719740715,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2998636888","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9352525,0.00017056552,0.06350405,0.0001665848,0.00037497055,0.00032485553,0.000011438729,0.000025238673,0.00016981487],"genre_scores_gemma":[0.9718409,0.000100692116,0.027810836,0.000018379624,0.00005205164,0.000003854701,0.000051669984,0.00001891466,0.000102691054],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9980775,0.00009698109,0.00079765386,0.00017948721,0.0005646237,0.0002837539],"domain_scores_gemma":[0.99875766,0.00013766444,0.0005802182,0.00009372205,0.00032375063,0.000106977976],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001110959,0.00013271104,0.0003355657,0.000265282,0.00010608892,0.000009634653,0.00014261637,0.000097710494,0.000033568303],"category_scores_gemma":[0.000030272258,0.0001249607,0.00009037592,0.0010283048,0.000045978937,0.0006965812,3.2370716e-7,0.00020514923,0.0000020374985],"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":[0.0003263347,0.000053122,0.082114875,0.000035173463,0.000016854263,0.000011298208,0.01917194,0.8951725,0.00027009525,0.00030661153,0.0000040942255,0.0025171267],"study_design_scores_gemma":[0.0019072214,0.00040033594,0.9826763,0.0008812272,0.000080426,0.0000016922486,0.009553418,0.0035715518,0.000051734034,0.00015674571,0.00048591342,0.00023343033],"about_ca_topic_score_codex":0.00009787882,"about_ca_topic_score_gemma":0.016269483,"teacher_disagreement_score":0.90056145,"about_ca_system_score_codex":0.00017345494,"about_ca_system_score_gemma":0.00036784596,"threshold_uncertainty_score":0.9078753},"labels":[],"label_agreement":null},{"id":"W2999179865","doi":"10.1155/2020/4242964","title":"Influencing Factors in Congestion Pricing Acceptability: A Literature Review","year":2020,"lang":"en","type":"review","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":84,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Key Research and Development Program of China; Southeast University","keywords":"Congestion pricing; Singapore Area Licensing Scheme; Road pricing; Traffic congestion; Business; Politics; Guideline; Public opinion; Economics; Public economics; Transport engineering; Political science; Engineering","score_opus":0.026077008751304004,"score_gpt":0.34978027547113133,"score_spread":0.3237032667198273,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2999179865","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00036896617,0.9978962,0.00034550685,0.00016722214,0.00042359502,0.0006655121,0.0000350571,0.000033959426,0.00006398599],"genre_scores_gemma":[0.00073979475,0.9966344,0.002019202,0.000062253384,0.0001430271,0.000011841911,0.0003498193,0.000028039854,0.0000115937155],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.997015,0.00031386278,0.0016565871,0.00024450585,0.0005603785,0.0002096957],"domain_scores_gemma":[0.9972873,0.00025797077,0.0018135805,0.00009836606,0.00039256003,0.0001502324],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00075345597,0.00027864476,0.0013745348,0.00036575878,0.00010925827,0.000052222076,0.00022636334,0.00027814397,0.00003866705],"category_scores_gemma":[0.000442204,0.00024027594,0.00042730692,0.0015855588,0.000046272988,0.0010572067,0.0000010022999,0.0007873863,0.0000015750702],"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":[0.000029222498,0.000049843955,0.0010696976,0.03353245,0.000065634886,0.00016124165,0.037616916,0.00602243,7.744386e-7,0.00027379915,0.000030464016,0.9211475],"study_design_scores_gemma":[0.0002975848,0.00006344948,0.0059613334,0.14432688,0.00068589987,0.000004938953,0.0016274549,8.622915e-7,4.814201e-7,0.00004973297,0.8466895,0.0002918775],"about_ca_topic_score_codex":0.000025501393,"about_ca_topic_score_gemma":0.00037962483,"teacher_disagreement_score":0.92085564,"about_ca_system_score_codex":0.00027590152,"about_ca_system_score_gemma":0.00071261753,"threshold_uncertainty_score":0.97981685},"labels":[],"label_agreement":null},{"id":"W2999306202","doi":"10.1155/2020/7382569","title":"Using Clustering Methods in Multinomial Logit Model for Departure Time Choice","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":14,"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":"Iran University of Science and Technology","keywords":"Multinomial logistic regression; Cluster analysis; Computer science; Discretization; Set (abstract data type); Choice set; Data set; Utility maximization; Hierarchical clustering; Econometrics; Data mining; Statistics; Mathematics; Machine learning; Artificial intelligence","score_opus":0.08242723184148797,"score_gpt":0.4070317753956692,"score_spread":0.3246045435541812,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2999306202","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15224813,0.000052339114,0.8467705,0.0005446731,0.00013387713,0.00018111501,0.000014491897,0.000019072584,0.000035792527],"genre_scores_gemma":[0.45400184,0.00002235454,0.5456693,0.00012031638,0.00013413663,0.0000022901093,0.000020489259,0.000009764506,0.000019468725],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990188,0.00005960149,0.0004843743,0.0001130363,0.00017743427,0.00014675209],"domain_scores_gemma":[0.99915576,0.00016057289,0.00036332445,0.000028934153,0.00019448531,0.00009693348],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003979403,0.00008046538,0.00019377729,0.00008122397,0.000107380685,0.000018786726,0.00009189981,0.00008463421,0.000009827109],"category_scores_gemma":[0.00018316395,0.00008465442,0.00008866107,0.00021510637,0.000024956595,0.0005914143,4.7796374e-7,0.00013114678,3.2239382e-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":[0.00023717919,0.000015941538,0.0016670238,0.000021685371,0.000007553946,0.0000031185562,0.022992456,0.96565545,0.0064961324,0.00006439823,0.000008906898,0.002830166],"study_design_scores_gemma":[0.0022280142,0.00008526126,0.0152405845,0.00008479759,0.000074375916,6.101107e-7,0.0020442384,0.977838,0.0004596084,0.00027764562,0.0014878156,0.00017908416],"about_ca_topic_score_codex":0.000026196705,"about_ca_topic_score_gemma":0.0005313699,"teacher_disagreement_score":0.3017537,"about_ca_system_score_codex":0.000053305354,"about_ca_system_score_gemma":0.00013764421,"threshold_uncertainty_score":0.3452107},"labels":[],"label_agreement":null},{"id":"W2999324663","doi":"10.4271/2019-36-0286","title":"Stop &amp; amp; Start technology influence on Brazilian urban off-cycle - Simulated versus experimental data analysis","year":2020,"lang":"en","type":"article","venue":"SAE technical papers on CD-ROM/SAE technical paper series","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Chrysler (Canada)","funders":"","keywords":"Computer science; Environmental science","score_opus":0.03465054815660965,"score_gpt":0.32306270348333854,"score_spread":0.28841215532672887,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2999324663","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9628694,0.00038529816,0.000008332891,0.014827235,0.00023455055,0.0010076023,0.00038411835,0.0045286757,0.015754776],"genre_scores_gemma":[0.9929202,0.00021352134,0.0030608145,0.0023449524,0.00013447333,0.00006526169,0.0008686183,0.0000743522,0.0003177702],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9946761,0.00027554337,0.0010553506,0.0017032429,0.0013656659,0.0009240948],"domain_scores_gemma":[0.99644387,0.00056583516,0.00033920584,0.0018738207,0.0001773361,0.000599933],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00066456525,0.00061723485,0.0009398703,0.00044050778,0.0009921271,0.00019014868,0.0021223153,0.000991819,0.0011775286],"category_scores_gemma":[0.0021675264,0.0006102023,0.00034324225,0.0038242566,0.0017198261,0.0008538833,0.0003027289,0.0011495982,0.00032589832],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0042786505,0.0015586352,0.010387972,0.000046848,0.0009086796,0.00010635857,0.0033532886,0.03871905,0.89790106,0.025081966,0.014752342,0.0029051488],"study_design_scores_gemma":[0.0014062055,0.0009485023,0.72697717,0.00007928093,0.00044072987,0.0000017406359,0.0012602493,0.0000037172188,0.00006054713,0.00019777886,0.26770914,0.0009149469],"about_ca_topic_score_codex":0.00037440506,"about_ca_topic_score_gemma":0.17344216,"teacher_disagreement_score":0.8978405,"about_ca_system_score_codex":0.00029326626,"about_ca_system_score_gemma":0.00021381285,"threshold_uncertainty_score":0.99973553},"labels":[],"label_agreement":null},{"id":"W2999369501","doi":"10.1155/2020/5379218","title":"Analysis of a Flexible Transit Network in a Radial Street Pattern","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Transit (satellite); Traverse; Transit system; Computer science; Grid; Grid network; Service (business); Hinge; Public transport; Simulation; Mathematical optimization; Topology (electrical circuits); Transport engineering; Engineering; Mathematics; Structural engineering; Geography","score_opus":0.01691539084953963,"score_gpt":0.2830353540869504,"score_spread":0.2661199632374108,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2999369501","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9051126,0.00016744662,0.09378668,0.0005506411,0.00015472839,0.00009809146,0.000027288841,0.000014955524,0.000087535176],"genre_scores_gemma":[0.99663836,0.00015996613,0.0029023187,0.00011045941,0.00010960608,0.0000015446051,0.00006583123,0.000006395248,0.000005526938],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9986968,0.000065238375,0.0006418037,0.00010144796,0.00035290088,0.00014183282],"domain_scores_gemma":[0.9991701,0.00006882048,0.000446205,0.00003952166,0.00017057652,0.00010476178],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027858512,0.00007226789,0.0003086517,0.00021761995,0.000048926348,0.000008704618,0.00010099265,0.00006037366,0.00008498484],"category_scores_gemma":[0.000021094027,0.0000757822,0.0001711208,0.0016293593,0.00003924013,0.0003710223,2.1579308e-7,0.00012310252,3.14264e-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":[0.00017461022,0.000029886589,0.20126265,0.000009564969,0.00011450138,0.00001554839,0.049107064,0.74355984,0.000043236643,0.00026970266,0.000009416557,0.0054039555],"study_design_scores_gemma":[0.0011321062,0.00015035899,0.9849314,0.000058421352,0.0005582265,8.663586e-8,0.0113219675,0.0010752081,0.00005109142,0.00007765085,0.00053894735,0.000104495724],"about_ca_topic_score_codex":0.00025939714,"about_ca_topic_score_gemma":0.007466568,"teacher_disagreement_score":0.78366876,"about_ca_system_score_codex":0.00002273331,"about_ca_system_score_gemma":0.000102950245,"threshold_uncertainty_score":0.41665202},"labels":[],"label_agreement":null},{"id":"W2999457445","doi":"10.22201/fi.25940732e.2020.21n1.005","title":"Análisis del tiempo de ascenso de pasajeros en buses de la ciudad de Bogotá usando un diseño factorial","year":2020,"lang":"es","type":"article","venue":"Ingeniería Investigación y Tecnología","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ministère des Transports","funders":"","keywords":"Humanities; Art","score_opus":0.016629975263742873,"score_gpt":0.29145096059680725,"score_spread":0.27482098533306437,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2999457445","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.974327,0.0007940569,0.014130067,0.007026996,0.0005531994,0.00040263968,0.0002369313,0.00075861946,0.0017704729],"genre_scores_gemma":[0.97673887,0.0016073164,0.017002236,0.0028014805,0.0015005469,0.00005821986,0.000083922474,0.00008609711,0.000121282384],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99539745,0.0013117892,0.000670749,0.00073844823,0.0005970666,0.0012845102],"domain_scores_gemma":[0.997094,0.0009780057,0.00038271994,0.00032081985,0.00014919095,0.0010752914],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0013276349,0.0005149607,0.0005450864,0.0002007843,0.0008764046,0.0005184272,0.0008497091,0.0011634344,0.00017839503],"category_scores_gemma":[0.0035882804,0.00055945414,0.0002489064,0.00096745545,0.0011572264,0.00043382557,0.00007038882,0.0009329963,0.000058425114],"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":[0.00022149482,0.00017432796,0.87577045,0.00013237156,0.00025357824,0.00016236825,0.088525996,0.009810851,0.011646401,0.0051512406,0.0050460575,0.003104864],"study_design_scores_gemma":[0.0063272985,0.0008797573,0.6893226,0.0007091203,0.0016053034,0.000059140613,0.012371821,0.01549273,0.051006623,0.016571436,0.20238219,0.0032719902],"about_ca_topic_score_codex":0.0028621368,"about_ca_topic_score_gemma":0.00014059892,"teacher_disagreement_score":0.19733614,"about_ca_system_score_codex":0.0004972441,"about_ca_system_score_gemma":0.0019017037,"threshold_uncertainty_score":0.9996857},"labels":[],"label_agreement":null},{"id":"W2999804022","doi":"10.1155/2020/4190632","title":"Guidance Optimization of Travelers’ Travel Mode Choice Based on Fuel Tax Rate and Bus Departure Quantity in Two-Mode Transportation System","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","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":"Natural Science Foundation of Hunan Province; National Natural Science Foundation of China; Natural Science Foundation of Guangxi Zhuang Autonomous Region","keywords":"Walrasian auction; Fuel tax; Mode (computer interface); Automotive engineering; Transport engineering; Simulation; Tax rate; Service (business); Operations research; Computer science; Engineering; Economics; Microeconomics","score_opus":0.016409392769766537,"score_gpt":0.29717893628116326,"score_spread":0.2807695435113967,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2999804022","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6438821,0.00013984929,0.35419694,0.0008422557,0.00021522056,0.00037939753,0.00018422445,0.00003887176,0.00012113027],"genre_scores_gemma":[0.97383946,0.00020550417,0.02548861,0.00013859474,0.000087124055,0.000008148724,0.00020147009,0.00002366613,0.000007410059],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9977004,0.0001882541,0.0010662806,0.0002668436,0.0005649644,0.00021324081],"domain_scores_gemma":[0.9981185,0.00019836274,0.0009764338,0.00008383724,0.00045347805,0.00016935356],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049335114,0.00019796447,0.0004329926,0.00022924747,0.00011843866,0.000026471036,0.00014193126,0.00012471946,0.00001030752],"category_scores_gemma":[0.000113396905,0.00020771344,0.00011479682,0.0006672666,0.000084339175,0.0007978241,2.7283787e-7,0.00025697993,3.5119072e-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":[0.00077887665,0.00007688418,0.018566646,0.0002449467,0.000015589481,0.000019859694,0.01799788,0.95885986,0.001488446,0.0016295658,0.0000069420958,0.0003145317],"study_design_scores_gemma":[0.0069215866,0.00042538182,0.7403702,0.0009227073,0.00020459683,9.77444e-7,0.010386435,0.2384451,0.0016557,0.000101941114,0.00012718755,0.00043818157],"about_ca_topic_score_codex":0.0005938947,"about_ca_topic_score_gemma":0.007278497,"teacher_disagreement_score":0.72180355,"about_ca_system_score_codex":0.00008425864,"about_ca_system_score_gemma":0.00022003514,"threshold_uncertainty_score":0.8470308},"labels":[],"label_agreement":null},{"id":"W3000029929","doi":"10.1155/2020/7059584","title":"Transit Route Network Design for Low-Mobility Individuals Using a Hybrid Metaheuristic Approach","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Key Research and Development Program of China; China Railway","keywords":"Metaheuristic; Computer science; Ant colony optimization algorithms; Public transport; Genetic algorithm; Transit (satellite); Mathematical optimization; Operations research; Ant colony; Transport engineering; Artificial intelligence; Engineering; Machine learning; Mathematics","score_opus":0.050281986501134676,"score_gpt":0.3025273908685353,"score_spread":0.2522454043674006,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3000029929","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1364568,0.00018881762,0.8621355,0.00034390143,0.0002329451,0.00050918065,0.00007134715,0.000037968137,0.000023508526],"genre_scores_gemma":[0.69675195,0.000058335103,0.3026506,0.00013760533,0.00027917972,0.000008124173,0.00009336215,0.000015293172,0.000005533613],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9982089,0.00015281873,0.00074137445,0.00019679137,0.00044726007,0.00025288324],"domain_scores_gemma":[0.99851024,0.00021469146,0.00061320345,0.000060502247,0.0004009272,0.00020043449],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009686898,0.00013780834,0.0003382414,0.00006332038,0.00030584427,0.00004376867,0.00015951291,0.000070896545,0.00001525639],"category_scores_gemma":[0.0001313347,0.00013914489,0.00019381278,0.00037976142,0.00006761877,0.0006923116,3.955789e-7,0.00016544138,4.3493947e-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":[0.00039146977,0.00006449378,0.0015530429,0.00006689361,0.000060012302,0.0000060727143,0.014789448,0.98147285,0.00026577685,0.0005423788,0.000072011826,0.0007155757],"study_design_scores_gemma":[0.043837238,0.0055544144,0.46090984,0.0020302348,0.010823303,0.000046932604,0.064241454,0.30983227,0.008439487,0.036677383,0.05194937,0.0056580342],"about_ca_topic_score_codex":0.000015290956,"about_ca_topic_score_gemma":0.00001832712,"teacher_disagreement_score":0.6716405,"about_ca_system_score_codex":0.000052824926,"about_ca_system_score_gemma":0.00029599312,"threshold_uncertainty_score":0.56741637},"labels":[],"label_agreement":null},{"id":"W3000113396","doi":"10.1155/2020/2895320","title":"An Improved NSGA-II Algorithm for Transit Network Design and Frequency Setting Problem","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Genetic algorithm; Mathematical optimization; Computer science; Schedule; Network planning and design; Convergence (economics); Population; Flow network; Algorithm; Mathematics; Computer network","score_opus":0.01470952003020207,"score_gpt":0.27457037701071474,"score_spread":0.25986085698051264,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3000113396","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03416694,0.00020373156,0.9636748,0.0012694928,0.00014945555,0.00045229596,0.0000212353,0.00004863365,0.000013422861],"genre_scores_gemma":[0.3227183,0.00012195518,0.6766106,0.00017160842,0.00029470734,0.000008957869,0.000054501805,0.000014100847,0.0000052887167],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99882597,0.00008256896,0.0005053843,0.00016080598,0.00022034068,0.00020490384],"domain_scores_gemma":[0.9989111,0.00010655697,0.00040968563,0.000036572503,0.00033658696,0.00019950456],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006234393,0.000107741405,0.0001943542,0.000044365544,0.00043840232,0.000037898473,0.00009692597,0.00008388568,0.000008876226],"category_scores_gemma":[0.000025986814,0.00010837585,0.000068780464,0.00022910754,0.000044606597,0.00096982653,2.4919714e-7,0.00013354307,1.1946973e-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":[0.000227216,0.000041026302,0.00073897175,0.000037356007,0.000035458404,0.0000075163052,0.07178012,0.8226747,0.0028969941,0.00065357896,0.00003938548,0.100867696],"study_design_scores_gemma":[0.04198384,0.02486932,0.168616,0.0018356864,0.0032191423,0.000031112195,0.14832032,0.51012325,0.006179447,0.0628002,0.027289622,0.004732086],"about_ca_topic_score_codex":0.000022276146,"about_ca_topic_score_gemma":0.00013284806,"teacher_disagreement_score":0.31255144,"about_ca_system_score_codex":0.000020383046,"about_ca_system_score_gemma":0.00015165322,"threshold_uncertainty_score":0.44194388},"labels":[],"label_agreement":null},{"id":"W3000216886","doi":"10.48550/arxiv.2001.01838","title":"Network-Based Analysis of Public Transportation Systems in North American Cities","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"York University","funders":"","keywords":"Public transport; Scalability; Heuristic; Big data; Computer science; Domain (mathematical analysis); Transit system; Graph; Population; Graph theory; Information system; Data science; Data mining; Theoretical computer science; Transport engineering; Transit (satellite); Artificial intelligence; Engineering; Database; Mathematics","score_opus":0.08820228412770123,"score_gpt":0.21121943676259142,"score_spread":0.12301715263489019,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3000216886","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.90834606,0.000036175363,0.09034544,0.00014404216,0.00013941109,0.000213151,0.00017669235,0.00009845298,0.0005005886],"genre_scores_gemma":[0.9983755,0.00011621314,0.00014987946,0.000037869668,0.000037782167,0.0000015142167,0.0011945869,0.000009119602,0.00007755163],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9986469,0.00029191072,0.0002885514,0.00040785418,0.00014152586,0.0002232255],"domain_scores_gemma":[0.99892116,0.000119799915,0.0004719593,0.00018167072,0.00019070088,0.00011474017],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027840404,0.00013875686,0.0004387483,0.0005476193,0.00011375851,0.000041670326,0.00029383737,0.00011222433,0.000020982316],"category_scores_gemma":[0.000027540824,0.00018195162,0.00017270549,0.0047078654,0.00024231439,0.00012812186,0.000006895529,0.00021419546,0.000001423649],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015167231,0.000014409333,0.4596152,0.000023203183,0.00010541104,0.000011957211,0.0015937409,0.5300169,7.85145e-8,0.008568522,0.00001257174,0.000022865901],"study_design_scores_gemma":[0.00016743748,0.000017206063,0.72892714,0.000027388895,0.0006080193,3.403707e-9,0.0031425834,0.2666373,2.0503877e-7,0.00012816611,0.00016125404,0.000183344],"about_ca_topic_score_codex":0.0174576,"about_ca_topic_score_gemma":0.17307973,"teacher_disagreement_score":0.2693119,"about_ca_system_score_codex":0.00012303787,"about_ca_system_score_gemma":0.00035170716,"threshold_uncertainty_score":0.98908526},"labels":[],"label_agreement":null},{"id":"W3000410372","doi":"10.1061/9780784481523.260","title":"Individuals’ Activity-Travel Behavior in Travel Demand Models: A Review of Recent Progress","year":2018,"lang":"en","type":"review","venue":"CICTP 2018","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Saint Mary's University; Dalhousie University","funders":"","keywords":"Travel behavior; Transport engineering; Work (physics); Demand management; Investment (military); Transportation planning; Strengths and weaknesses; Computer science; Mode choice; Land use; Demand forecasting; Business; Operations research; Public transport; Economics; Marketing; Engineering","score_opus":0.1500571527723831,"score_gpt":0.40691669595656166,"score_spread":0.2568595431841786,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3000410372","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00001404859,0.99293685,0.0003464894,0.00006346329,0.00030652157,0.0017202056,0.00017248846,0.000033634824,0.0044062994],"genre_scores_gemma":[0.000033475997,0.9979502,0.00082259346,0.000035374112,0.00013532987,0.00032811594,0.00028317186,0.00003492491,0.00037681754],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99740654,0.00049567356,0.00077556004,0.0004190439,0.00055883825,0.0003443243],"domain_scores_gemma":[0.99853563,0.00010396257,0.0007475532,0.00029015713,0.00020124193,0.00012146478],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0014987628,0.00028774654,0.0012626526,0.00024537076,0.0001424261,0.000034211724,0.0004104937,0.0004305112,0.00025600765],"category_scores_gemma":[0.00010133805,0.00026043155,0.00023941399,0.00081177044,0.0003488153,0.00023103757,0.000016980637,0.0002734679,0.000030226916],"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":[0.0000066520493,0.00042184695,0.0003327709,0.054977216,0.00007812121,0.000009458319,0.0046208347,0.000002951186,5.095313e-8,0.00082436285,0.0037659737,0.93495977],"study_design_scores_gemma":[0.00017140423,0.000058741905,0.0006563632,0.08563435,0.0008205178,0.000002067035,0.00011170482,0.000007918535,8.103691e-7,0.0000536979,0.9120927,0.00038976016],"about_ca_topic_score_codex":0.00011832091,"about_ca_topic_score_gemma":0.00021759783,"teacher_disagreement_score":0.93457,"about_ca_system_score_codex":0.0001286378,"about_ca_system_score_gemma":0.0008408906,"threshold_uncertainty_score":0.9999848},"labels":[],"label_agreement":null},{"id":"W3000737438","doi":"10.1155/2020/8287648","title":"Method for Determining the Valid Travel Route of Railways Based on Generalised Cost under the Syncretic Railway Network","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Natural Science Foundation of Gansu Province; National Natural Science Foundation of China","keywords":"Train; Transport engineering; Tree traversal; Beijing; Schedule; Operations research; Computer science; Flow network; Path (computing); Travel time; Engineering; China; Mathematical optimization; Computer network; Mathematics; Algorithm","score_opus":0.04052340830806527,"score_gpt":0.3277325091990045,"score_spread":0.2872091008909392,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3000737438","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05580928,0.000056196892,0.9358699,0.0071523986,0.00032975478,0.0005689865,0.00005038729,0.000017236149,0.00014586777],"genre_scores_gemma":[0.9276712,0.000046429857,0.070298955,0.0015761999,0.0002941151,0.00001564232,0.000063771564,0.00001745413,0.000016233433],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9984329,0.00021147181,0.0005910531,0.00012447393,0.00045683104,0.00018326857],"domain_scores_gemma":[0.99779737,0.00093831227,0.00071742886,0.00007996137,0.00038066265,0.00008625501],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010149992,0.00011358457,0.00023484793,0.00004132793,0.0003635084,0.000029912135,0.00022160304,0.000066748806,0.000019612904],"category_scores_gemma":[0.000100932164,0.0000743498,0.00019501826,0.00034552458,0.00008276029,0.00020937539,4.3868837e-7,0.00017138537,3.022567e-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":[0.000431608,0.00001885389,0.0014052782,0.000016987724,0.000025919604,0.000001182221,0.010462908,0.980581,0.00031377867,0.0033100934,0.00012425719,0.0033081588],"study_design_scores_gemma":[0.013960816,0.0019140383,0.7880997,0.00061768503,0.001435865,0.0000016003706,0.03226113,0.13102245,0.003988383,0.0035587724,0.022330958,0.0008085997],"about_ca_topic_score_codex":0.000016116437,"about_ca_topic_score_gemma":0.0001291118,"teacher_disagreement_score":0.87186193,"about_ca_system_score_codex":0.000030608233,"about_ca_system_score_gemma":0.00018568648,"threshold_uncertainty_score":0.3031897},"labels":[],"label_agreement":null},{"id":"W3000797373","doi":"10.11575/prism/37489","title":"Dynamic Transit Passenger Origin/Destination Estimation: A Bilevel Variational Inequality Approach","year":2020,"lang":"en","type":"dissertation","venue":"Open MIND","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Bilevel optimization; Transit (satellite); Variational inequality; Estimation; Inequality; Computer science; Econometrics; Mathematical optimization; Transport engineering; Mathematics; Economics; Engineering; Public transport; Mathematical analysis; Optimization problem","score_opus":0.06566448058617301,"score_gpt":0.3712850186870034,"score_spread":0.3056205381008304,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3000797373","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.078522176,0.00018144403,0.47680798,0.0045520323,0.0027172405,0.0054385904,0.001033769,0.00013294496,0.43061382],"genre_scores_gemma":[0.5403633,0.000041836134,0.38797608,0.00008108286,0.00019323024,0.00019081763,0.05240136,0.000058540812,0.018693762],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99756,0.0002622408,0.0005610244,0.00060048467,0.0007644295,0.0002517824],"domain_scores_gemma":[0.99871373,0.00014690481,0.0004526819,0.00016696221,0.0003647609,0.00015495892],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00089261273,0.00025693417,0.0003386336,0.00014358162,0.000675935,0.00051174127,0.0004946827,0.0004177014,0.0019296502],"category_scores_gemma":[0.00026119297,0.00028787454,0.00008834725,0.0006480017,0.000050039107,0.0006948572,0.0000076224846,0.0003194894,0.00015713209],"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":[0.0008310227,0.0010903161,0.0019201828,0.000519903,0.00044870382,0.000038202732,0.38269067,0.16572343,0.0002407332,0.029002365,0.0016082055,0.41588625],"study_design_scores_gemma":[0.0041095912,0.00022405395,0.12396449,0.0007408789,0.0012469187,0.000005365706,0.033845264,0.66635317,0.00019558595,0.0052308855,0.16054644,0.0035373801],"about_ca_topic_score_codex":0.0003939021,"about_ca_topic_score_gemma":0.0017263002,"teacher_disagreement_score":0.5006297,"about_ca_system_score_codex":0.0001670151,"about_ca_system_score_gemma":0.0012875735,"threshold_uncertainty_score":0.9999573},"labels":[],"label_agreement":null},{"id":"W3001282412","doi":"10.1155/2020/4951953","title":"Macroscopic Fundamental Diagram Based Discrete Transportation Network Design","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Southeastern Transportation Research, Innovation, Development and Education Center; China Scholarship Council; University of Florida; U.S. Department of Transportation","keywords":"Network planning and design; Flow network; Mathematical optimization; Computer science; Network simulation; VisSim; Cluster analysis; Traffic generation model; Simulation; Mathematics; Engineering; Distributed computing; Transport engineering; Real-time computing; Microsimulation","score_opus":0.021994525813224055,"score_gpt":0.2927887789687638,"score_spread":0.2707942531555398,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3001282412","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1804562,0.00023389609,0.81572205,0.0023695054,0.0006171736,0.00038071017,0.000048115628,0.00009254114,0.000079827325],"genre_scores_gemma":[0.9235375,0.00017139161,0.075172275,0.0005130443,0.00034150004,0.000007841189,0.00021329659,0.00002224771,0.000020922507],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9980616,0.00013527025,0.000732249,0.00018562756,0.0006092584,0.00027600702],"domain_scores_gemma":[0.9986554,0.0001306005,0.0006218807,0.00006066928,0.00026315261,0.00026829078],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039432524,0.00015771326,0.00026924416,0.000072322786,0.00030281275,0.000048427395,0.0001617469,0.00010313128,0.00012746174],"category_scores_gemma":[0.000037115118,0.0001601201,0.00016184221,0.0005244809,0.00011385953,0.0008226725,2.2176707e-7,0.00021994194,0.000004410862],"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":[0.0005191079,0.000038005805,0.012026291,0.000028878161,0.000026380932,0.000033322107,0.024147874,0.959882,0.00042450672,0.00065181585,0.00022858432,0.0019932091],"study_design_scores_gemma":[0.011405805,0.0025291282,0.89987975,0.00071910257,0.0008667327,0.0000019492882,0.03064113,0.007989198,0.0029701665,0.0022843243,0.03933592,0.0013767971],"about_ca_topic_score_codex":0.000037901533,"about_ca_topic_score_gemma":0.00037338765,"teacher_disagreement_score":0.95189285,"about_ca_system_score_codex":0.000057634246,"about_ca_system_score_gemma":0.00026351062,"threshold_uncertainty_score":0.65295076},"labels":[],"label_agreement":null},{"id":"W3001727567","doi":"10.1080/23249935.2020.1722279","title":"Reduced gradient algorithm for user equilibrium traffic assignment problem","year":2020,"lang":"en","type":"article","venue":"Transportmetrica A Transport Science","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":40,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Path (computing); Convergence (economics); Algorithm; Mathematical optimization; Mathematics; Flow network; Flow (mathematics); Computer science","score_opus":0.03622401172152028,"score_gpt":0.29003762372177344,"score_spread":0.25381361200025315,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3001727567","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20395768,0.00024011637,0.7776036,0.0076893847,0.00073903234,0.0033155745,0.00044108,0.0011916215,0.004821884],"genre_scores_gemma":[0.92711097,0.00008343329,0.07133475,0.0005095355,0.0001851573,0.00015808307,0.00017834033,0.000040181774,0.0003995575],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99536085,0.000047780482,0.0007686256,0.0010773083,0.0016555507,0.0010898913],"domain_scores_gemma":[0.99804586,0.00012246711,0.00022054378,0.000217711,0.00039929972,0.0009941078],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0014244278,0.00032389548,0.00042775695,0.0004734691,0.0009984934,0.000108357315,0.00085224246,0.00015810804,0.0001673311],"category_scores_gemma":[0.000062316525,0.00032897355,0.00026995034,0.005727196,0.0009843986,0.00095440267,0.0000038257126,0.00022601917,0.000016608106],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011707421,0.0038726456,0.035232175,0.0007232868,0.00036748438,0.00033146827,0.2916509,0.23152232,0.015172693,0.036271617,0.0034206468,0.38026404],"study_design_scores_gemma":[0.015963357,0.0051981304,0.14491667,0.00045489226,0.0017676136,0.000012770467,0.016733402,0.115919024,0.016773174,0.00084610516,0.6741214,0.007293488],"about_ca_topic_score_codex":0.0001437519,"about_ca_topic_score_gemma":0.00008213759,"teacher_disagreement_score":0.7231533,"about_ca_system_score_codex":0.00016331753,"about_ca_system_score_gemma":0.0011192665,"threshold_uncertainty_score":0.99991626},"labels":[],"label_agreement":null},{"id":"W3003419814","doi":"10.1155/2020/6385359","title":"Study on the Selection Model of Staying Adjustment Bus Lines along Rail Transit","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":14,"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":"Huaqiao University; Natural Science Foundation of Fujian Province; National Natural Science Foundation of China","keywords":"Rail transit; Public transport; Transport engineering; Transit (satellite); Line (geometry); Selection (genetic algorithm); Urban rail transit; Bus rapid transit; Engineering; Computer science","score_opus":0.04552283402799784,"score_gpt":0.3043585674811881,"score_spread":0.2588357334531903,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3003419814","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9082748,0.00005908497,0.08908204,0.001999995,0.00015461068,0.0003127908,0.000011883285,0.00002509998,0.00007968729],"genre_scores_gemma":[0.996628,0.00011771632,0.0029308966,0.00015783467,0.0001235962,0.000005141794,0.000010320953,0.000010897249,0.00001562431],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9985348,0.00009202312,0.00057842757,0.00011234596,0.00056817615,0.000114232374],"domain_scores_gemma":[0.99889493,0.00010581152,0.00049217447,0.000044006552,0.0003862386,0.000076853335],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003940268,0.000095190626,0.00018803132,0.00007562082,0.00018798292,0.000013793059,0.00011450712,0.000044814125,0.000015206522],"category_scores_gemma":[0.00005863515,0.00007409697,0.00009617581,0.00037357892,0.0000374446,0.00037670045,2.949141e-7,0.0001754105,6.0104946e-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":[0.0003229789,0.00012259708,0.0048643416,0.00001138787,0.000036792255,0.0000030801802,0.09887236,0.89136225,0.0020944844,0.0011967873,0.0000201872,0.0010927271],"study_design_scores_gemma":[0.0066295355,0.003303163,0.6961161,0.00033905837,0.00079220696,0.0000015677366,0.2648394,0.018947428,0.006617917,0.0012293418,0.00061921513,0.00056503597],"about_ca_topic_score_codex":0.00003652985,"about_ca_topic_score_gemma":0.000407291,"teacher_disagreement_score":0.8724148,"about_ca_system_score_codex":0.00003222428,"about_ca_system_score_gemma":0.00014122995,"threshold_uncertainty_score":0.30215865},"labels":[],"label_agreement":null},{"id":"W3003481879","doi":"","title":"La tarification routière au Québec - Quelles leçons tirer de l’expérience des précurseurs ?","year":2019,"lang":"fr","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Politics; Political science; Welfare economics; Business; Economy; Economics; Law","score_opus":0.03587241741878299,"score_gpt":0.3356432397521178,"score_spread":0.2997708223333348,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3003481879","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7840957,0.00030262375,0.00018828937,0.0020758852,0.000541101,0.0006915176,0.00006489095,0.00006822405,0.21197179],"genre_scores_gemma":[0.9639148,0.010095353,0.0020780894,0.000048748254,0.00017964705,0.00015190986,0.00014323604,0.00004927179,0.02333896],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99595857,0.0011328892,0.0006506318,0.0008881399,0.0003814066,0.0009883476],"domain_scores_gemma":[0.99736315,0.0012519312,0.00029316082,0.00057940674,0.00021701555,0.00029533793],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0036319143,0.00029715511,0.00038766753,0.00028033037,0.0007857686,0.00020999662,0.0007868808,0.00075140106,0.00015788488],"category_scores_gemma":[0.00069369486,0.00035584226,0.00016076509,0.00035623083,0.0020201385,0.0003573736,0.00010063032,0.0013858064,0.000095137446],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":true,"study_design_scores_codex":[0.000062528656,0.00020568944,0.45602384,0.00015860308,0.00005282723,0.000015363345,0.092132844,0.3080998,0.00002905679,0.036124337,0.00006621892,0.10702887],"study_design_scores_gemma":[0.00056044967,0.000101625,0.6799171,0.0007272361,0.00003745952,0.000009437002,0.03822834,0.028792154,0.000054371198,0.0014464408,0.24934651,0.0007788483],"about_ca_topic_score_codex":0.08526818,"about_ca_topic_score_gemma":0.19340046,"teacher_disagreement_score":0.27930763,"about_ca_system_score_codex":0.0041088574,"about_ca_system_score_gemma":0.007329968,"threshold_uncertainty_score":0.9998894},"labels":[],"label_agreement":null},{"id":"W3003707463","doi":"10.1155/2020/4317402","title":"Reliability Optimization Model of Stop-Skipping Bus Operation with Capacity Constraints","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Division of Graduate Education; Humanities and Social Science Fund of Ministry of Education of China; National Natural Science Foundation of China","keywords":"Headway; Reliability (semiconductor); Genetic algorithm; Service (business); Reliability engineering; Interval (graph theory); Engineering; Computer science; Simulation","score_opus":0.025101113711866303,"score_gpt":0.26375992120512914,"score_spread":0.23865880749326285,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3003707463","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3887972,0.000014563786,0.61027515,0.00048739096,0.00006156577,0.00013762958,0.00002472094,0.000021709779,0.00018005997],"genre_scores_gemma":[0.8234897,0.000077145116,0.17624788,0.00007644196,0.000044255023,0.0000014875028,0.00004821166,0.000009146529,0.0000057276134],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985828,0.00006728713,0.00061510876,0.00013776244,0.00047785052,0.00011919503],"domain_scores_gemma":[0.99828744,0.000050438866,0.0006600293,0.000057879664,0.0008177687,0.00012641918],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003520359,0.000097874385,0.00022600952,0.00007255877,0.0001558534,0.000019465784,0.00009959784,0.000077580116,0.00004504823],"category_scores_gemma":[0.00011423047,0.00009064233,0.0000664061,0.00028500665,0.00018219273,0.0010452237,4.4084135e-7,0.0001532102,3.4512823e-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":[0.0002773915,0.000040939092,0.0019767098,0.000036899673,0.000015148218,0.0000021036371,0.03278657,0.9608701,0.0017687059,0.0014744342,0.0000054451243,0.0007455879],"study_design_scores_gemma":[0.011971922,0.0021437842,0.114998825,0.0009840381,0.0008175874,0.000008254753,0.054184597,0.8027061,0.00853771,0.0016542858,0.00069404976,0.0012988002],"about_ca_topic_score_codex":0.00003053472,"about_ca_topic_score_gemma":0.0000832875,"teacher_disagreement_score":0.4346925,"about_ca_system_score_codex":0.000047934933,"about_ca_system_score_gemma":0.00025850412,"threshold_uncertainty_score":0.3696287},"labels":[],"label_agreement":null},{"id":"W3003771105","doi":"10.1177/0361198120904380","title":"Perceived Quality of Bus Transit Services: A Route-Level Analysis","year":2020,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Service quality; Service (business); Quality (philosophy); Type of service; Transport engineering; Quality of service; Public transport; Computer science; Set (abstract data type); Level of service; Perception; Engineering; Telecommunications; Business; Marketing; Psychology","score_opus":0.22942015493928059,"score_gpt":0.44403662350603484,"score_spread":0.21461646856675426,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3003771105","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97677904,0.00015462744,0.011710997,0.009290827,0.00022645012,0.0008468151,0.00040189442,0.000050897575,0.0005384523],"genre_scores_gemma":[0.99476886,0.0009195141,0.003407535,0.00015252709,0.0001912819,0.000025604342,0.000105438034,0.00003853114,0.00039071697],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9876303,0.0030271683,0.0022409952,0.0004973092,0.005763189,0.0008410564],"domain_scores_gemma":[0.99044126,0.0012893981,0.00094251864,0.00039626533,0.0062192422,0.000711334],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0068260618,0.00025887004,0.00083637174,0.0011467895,0.0010130187,0.00014438029,0.0015087803,0.00029804738,0.0006974141],"category_scores_gemma":[0.00031174943,0.00021675734,0.0009230317,0.0074314433,0.0009886957,0.0008874492,0.000003922405,0.0016476688,0.000013365267],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0026472919,0.0003466989,0.81865746,0.00061543885,0.0010513203,0.00004759629,0.12513061,0.038018726,0.003239079,0.0073854355,0.0007428666,0.0021174874],"study_design_scores_gemma":[0.0013956185,0.0002821586,0.96812415,0.00017126749,0.0003429588,3.839096e-8,0.02241807,0.00039510272,0.00023970657,0.0005481259,0.0058705145,0.00021228865],"about_ca_topic_score_codex":0.070627026,"about_ca_topic_score_gemma":0.26120415,"teacher_disagreement_score":0.19057712,"about_ca_system_score_codex":0.00017866044,"about_ca_system_score_gemma":0.0011833906,"threshold_uncertainty_score":0.9355618},"labels":[],"label_agreement":null},{"id":"W3004541699","doi":"10.1049/iet-its.2019.0158","title":"Analysis of overlapping origin–destination pairs between bus stations to enhance the efficiency of bus operations","year":2020,"lang":"en","type":"article","venue":"IET Intelligent Transport Systems","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Ministry of Land, Infrastructure and Transport","keywords":"Computer science; Transport engineering; Computer network; Engineering","score_opus":0.04836147317387864,"score_gpt":0.3362075803102233,"score_spread":0.2878461071363447,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3004541699","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.43004408,0.000085170686,0.5674197,0.00077174435,0.00015366114,0.00052814133,0.00023280393,0.000060710143,0.00070399506],"genre_scores_gemma":[0.99879426,0.00005703235,0.00052327587,0.000048501643,0.0000741201,0.00003581474,0.00029539695,0.00000949571,0.00016210577],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.998018,0.00013900861,0.0008117588,0.0002482884,0.00059896894,0.0001839682],"domain_scores_gemma":[0.9987799,0.00023518682,0.00020966248,0.00016242136,0.00048396006,0.00012885203],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00068684015,0.00011626839,0.00033864743,0.00022322785,0.00030487438,0.000030055055,0.00027588356,0.00007074713,0.00006735854],"category_scores_gemma":[0.000120202734,0.00010147344,0.00014686995,0.0028357734,0.00010732122,0.00014751968,0.0000025403335,0.00008939602,0.0000078973035],"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":[0.000009891365,0.000038268394,0.08400187,0.0000499246,0.00020206509,6.8187154e-7,0.07338374,0.8371096,0.00027264588,0.0046129646,0.000031045754,0.0002872928],"study_design_scores_gemma":[0.00071067753,0.00092536636,0.52050966,0.0010752861,0.007847793,6.812809e-7,0.15872167,0.24865557,0.013380775,0.000070050344,0.046220142,0.001882344],"about_ca_topic_score_codex":0.00459548,"about_ca_topic_score_gemma":0.0024490717,"teacher_disagreement_score":0.58845407,"about_ca_system_score_codex":0.000045044802,"about_ca_system_score_gemma":0.0001846979,"threshold_uncertainty_score":0.69470215},"labels":[],"label_agreement":null},{"id":"W3004628365","doi":"10.1093/jeea/jvz082","title":"Can Tolling Help Everyone? Estimating the Aggregate and Distributional Consequences of Congestion Pricing","year":2020,"lang":"en","type":"article","venue":"Journal of the European Economic Association","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":50,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Toll; Congestion pricing; Value of time; Revenue; Road pricing; Economics; Schedule; Traffic congestion; Pareto principle; Microeconomics; Welfare; Aggregate (composite); Social Welfare; Relevance (law); Value (mathematics); Distribution (mathematics); Computer science; Transport engineering; Travel time; Operations management; Finance; Engineering","score_opus":0.01815921928573524,"score_gpt":0.2391823725979191,"score_spread":0.22102315331218386,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3004628365","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97344345,0.0001093678,0.00396682,0.020843403,0.0005218102,0.00008903268,0.00002904347,0.000010922143,0.0009861764],"genre_scores_gemma":[0.99887025,0.000048375252,0.00053422514,0.00013164387,0.00034689013,1.8397245e-7,0.0000036083336,0.000004186693,0.000060633316],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99891007,0.00044399366,0.00035471586,0.000057010096,0.00015560992,0.00007861181],"domain_scores_gemma":[0.99821055,0.0002628421,0.0013472101,0.000029802508,0.000111075286,0.000038541515],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018212465,0.00004541988,0.000101835736,0.000016239843,0.00032336765,0.00005819619,0.00013444532,0.000022870141,0.0000058241676],"category_scores_gemma":[0.0009289757,0.000032119435,0.000049785674,0.00006671169,0.000082796214,0.00016809368,0.000009456883,0.0001198191,0.0000014789003],"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":[0.000041134255,0.000015012236,0.5284062,0.000020512789,0.00014517555,0.0000028008155,0.03341698,0.4288661,0.00077668566,0.003697647,0.0014574332,0.003154335],"study_design_scores_gemma":[0.0011536484,0.00009533915,0.95282763,0.00035866987,0.0002662688,0.000011873047,0.008363808,0.0318516,0.00085164525,0.0015493608,0.0024187947,0.0002513346],"about_ca_topic_score_codex":0.00009351044,"about_ca_topic_score_gemma":0.00006198945,"teacher_disagreement_score":0.42442146,"about_ca_system_score_codex":0.00018029267,"about_ca_system_score_gemma":0.00014155169,"threshold_uncertainty_score":0.24871153},"labels":[],"label_agreement":null},{"id":"W3005439545","doi":"","title":"Effects of transit quality of service characteristics on daily bus ridership","year":2015,"lang":"en","type":"article","venue":"QUT ePrints (Queensland University of Technology)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"University of Toronto; University of South Florida; U.S. Department of Transportation","keywords":"Transport engineering; Service (business); Level of service; Service quality; Transit (satellite); Public transport; Travel time; Bus rapid transit; Quality (philosophy); Quality of service; Twin cities; Business; Computer science; Geography; Engineering; Telecommunications; Marketing","score_opus":0.029740105228457346,"score_gpt":0.2626634199709948,"score_spread":0.23292331474253744,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3005439545","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.992384,0.00001333684,0.0026067484,0.0015525634,0.000059618225,0.00012613836,0.000026884438,0.000072433366,0.0031582417],"genre_scores_gemma":[0.99844265,0.00004153766,0.0012815824,0.00001738636,0.0000056703298,1.685435e-7,0.000010217855,0.0000032065175,0.00019757444],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99931127,0.00007842439,0.00016209418,0.00013399613,0.00020577737,0.00010841604],"domain_scores_gemma":[0.99916536,0.00009240861,0.00024354749,0.00014690893,0.00030021978,0.000051532825],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003221945,0.00006527449,0.00022905938,0.00018000526,0.00006833791,0.0000018936339,0.00023954618,0.00019773164,0.000023377499],"category_scores_gemma":[0.0001590218,0.00007582081,0.000041358242,0.00037887247,0.0002587233,0.000060597344,0.0000116920355,0.00010408216,0.0000060445163],"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":[0.0016195221,0.00089906726,0.82779086,0.0010221858,0.0002482149,0.00003515625,0.051924773,0.0015870585,0.004162735,0.09661302,0.00061963865,0.013477803],"study_design_scores_gemma":[0.002241357,0.00022357547,0.968711,0.00022489086,0.00010142206,2.444421e-7,0.012364467,0.000069493304,0.009075264,0.001971026,0.004799029,0.00021818424],"about_ca_topic_score_codex":0.002635692,"about_ca_topic_score_gemma":0.00095002673,"teacher_disagreement_score":0.14092022,"about_ca_system_score_codex":0.000026694928,"about_ca_system_score_gemma":0.00010559868,"threshold_uncertainty_score":0.39843953},"labels":[],"label_agreement":null},{"id":"W3006008512","doi":"10.1155/2020/4169826","title":"Stochastic Electric Vehicle Network with Elastic Demand and Environmental Costs","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Scientific Research Foundation of the Graduate School of Southeast University; National Key Research and Development Program of China; Natural Science Research of Jiangsu Higher Education Institutions of China; Government of Jiangsu Province; Six Talent Peaks Project in Jiangsu Province; Nantong Science and Technology Bureau; Science and Technology Project of Nantong City; Southeast University; National Natural Science Foundation of China","keywords":"Electric vehicle; Transport engineering; Computer science; Flow network; Operations research; Range (aeronautics); Engineering; Mathematical optimization; Mathematics","score_opus":0.0057839661254091585,"score_gpt":0.22143049931046407,"score_spread":0.21564653318505492,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3006008512","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8958588,0.00042079572,0.1030257,0.00045441775,0.00006859949,0.00011354562,0.0000055069067,0.000017760674,0.0000348466],"genre_scores_gemma":[0.9964894,0.000212401,0.0029993625,0.00011982109,0.00013830172,0.0000014775208,0.000022398655,0.00000901436,0.000007800237],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9992182,0.000033874563,0.00024313062,0.0000934828,0.0002778099,0.00013349485],"domain_scores_gemma":[0.9994676,0.00007679061,0.00024134332,0.000021750013,0.000045104152,0.00014739014],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011756325,0.00007203847,0.00012633645,0.00003599231,0.00016378863,0.000018289873,0.000046864596,0.00003715192,0.00001597394],"category_scores_gemma":[0.000018586195,0.00006503902,0.00002522305,0.0002094516,0.000047790352,0.00036990372,2.8469046e-7,0.00011974803,8.723059e-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":[0.00044043156,0.000022050213,0.017905865,0.000008796503,0.000023537506,0.000020353871,0.0110095395,0.96637785,0.0010050186,0.00036930802,0.000021785458,0.002795486],"study_design_scores_gemma":[0.003923663,0.0013866784,0.97680235,0.00019151295,0.00033919027,0.000009119451,0.010074354,0.0052577895,0.00017274306,0.00036131268,0.0011140445,0.0003672731],"about_ca_topic_score_codex":0.000006415896,"about_ca_topic_score_gemma":0.00009071674,"teacher_disagreement_score":0.96112,"about_ca_system_score_codex":0.00003235565,"about_ca_system_score_gemma":0.00005777391,"threshold_uncertainty_score":0.26522145},"labels":[],"label_agreement":null},{"id":"W3006156720","doi":"10.1016/j.tra.2020.01.017","title":"Modeling and forecasting daily non-work/school activity patterns in an activity-based model using skeleton schedule constraints","year":2020,"lang":"en","type":"article","venue":"Transportation Research Part A Policy and Practice","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":19,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Replicate; Schedule; Scheduling (production processes); Computer science; Replication (statistics); Operations research; Simulation; Operations management; Statistics; Engineering; Mathematics","score_opus":0.2913410205762511,"score_gpt":0.4637811721339234,"score_spread":0.17244015155767228,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3006156720","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8697382,0.000012630261,0.12424069,0.005059547,0.000013922105,0.00037842407,0.000060384868,0.00004509576,0.0004510904],"genre_scores_gemma":[0.99334306,0.00009050504,0.005890377,0.00043452735,0.00012902648,0.000026814798,0.000050213985,0.000019000548,0.00001649873],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99755,0.000726157,0.00027071376,0.00041212753,0.0005847419,0.00045626066],"domain_scores_gemma":[0.9981912,0.0008649659,0.000115624876,0.00009560821,0.00028175657,0.00045087567],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022484248,0.00013947304,0.00019553192,0.00023608323,0.00066948007,0.0002034683,0.0000958868,0.0001529991,0.000024171344],"category_scores_gemma":[0.0013057686,0.00016272638,0.000027435546,0.0007489884,0.00022119215,0.0019161785,0.0000039019337,0.0005640415,0.000001393323],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010030561,0.00012728968,0.036987077,0.00012734885,0.000014619283,0.000022715594,0.026107224,0.92978364,0.0010389325,0.0011269853,0.0000062888853,0.003654827],"study_design_scores_gemma":[0.0008968708,0.00007549207,0.014983021,0.00009704051,0.00002155937,4.1450045e-7,0.0037100662,0.9797407,0.00007737107,0.00008607604,0.00011486778,0.00019655435],"about_ca_topic_score_codex":0.010270056,"about_ca_topic_score_gemma":0.0075884825,"teacher_disagreement_score":0.12360482,"about_ca_system_score_codex":0.000064478925,"about_ca_system_score_gemma":0.0009013692,"threshold_uncertainty_score":0.99632066},"labels":[],"label_agreement":null},{"id":"W3006185976","doi":"10.1109/tits.2020.2971686","title":"Novel Electric Bus Energy Consumption Model Based on Probabilistic Synthetic Speed Profile Integrated With HVAC","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":50,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Government of Ontario","keywords":"Unavailability; HVAC; Energy consumption; Simulation; Energy (signal processing); Real-time computing; Automotive engineering; Air conditioning; Computer science; Engineering; Probabilistic logic; Set (abstract data type); Reliability engineering; Electrical engineering; Artificial intelligence","score_opus":0.04115925662932911,"score_gpt":0.2617182840595342,"score_spread":0.22055902743020506,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3006185976","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012813254,0.000022005805,0.9835673,0.00042655264,0.0003727511,0.001010817,0.000359911,0.00054299255,0.00088442134],"genre_scores_gemma":[0.996999,0.00006432995,0.0014001771,0.00028778758,0.00005131796,0.00022925583,0.00029130085,0.00005977718,0.00061700906],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971879,0.00016919522,0.0007117886,0.0006338695,0.00090029987,0.0003969279],"domain_scores_gemma":[0.99856144,0.00022249931,0.0002570128,0.00021782082,0.00042306146,0.00031814372],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00027265117,0.00035265807,0.00035638106,0.00034155673,0.00051121385,0.00012466827,0.00021049807,0.0002278241,0.0002442534],"category_scores_gemma":[0.0000152446055,0.0003271154,0.0001415362,0.0010753592,0.0001309124,0.00023815193,6.9038684e-8,0.0003222296,0.00006894881],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00066346733,0.000354125,0.000075190226,0.00010700121,0.00005099262,0.000005965966,0.0048725656,0.9889176,0.00084224885,0.0029532588,0.00005019034,0.0011073855],"study_design_scores_gemma":[0.00088656205,0.00048250196,0.0001240324,0.00031637822,0.00019148784,0.0000012291454,0.0014968788,0.9890691,0.0062144976,0.000012704255,0.00072962913,0.00047500673],"about_ca_topic_score_codex":0.0009888809,"about_ca_topic_score_gemma":0.00089693174,"teacher_disagreement_score":0.9841858,"about_ca_system_score_codex":0.00021700827,"about_ca_system_score_gemma":0.00045606893,"threshold_uncertainty_score":0.9999181},"labels":[],"label_agreement":null},{"id":"W3006378814","doi":"10.1155/2020/3987591","title":"Bilevel Programming for Traffic Signal Coordinated Control considering Pedestrian Crossing","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Key Research and Development Program of China; Hong Kong Polytechnic University; Government of Jiangsu Province; Six Talent Peaks Project in Jiangsu Province; National Natural Science Foundation of China","keywords":"Pedestrian; VisSim; Pedestrian crossing; Computer science; Bilevel optimization; Coding (social sciences); Traffic flow (computer networking); Simulation; Transport engineering; Mathematical optimization; Engineering; Optimization problem; Intersection (aeronautics); Algorithm; Computer network; Mathematics; Statistics","score_opus":0.0292370356969027,"score_gpt":0.3015889884957246,"score_spread":0.27235195279882185,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3006378814","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5430875,0.00017376665,0.45362872,0.0022391153,0.00027147264,0.0004610339,0.000035577603,0.00008184638,0.0000209912],"genre_scores_gemma":[0.95441437,0.000023007371,0.045112778,0.00014874482,0.00022293733,0.000008457274,0.000042582502,0.000016217362,0.00001089064],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9987242,0.000047430767,0.0005995723,0.0001343865,0.00028224484,0.00021212785],"domain_scores_gemma":[0.99854696,0.00019589491,0.0005531693,0.00002880952,0.0004948004,0.00018035051],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036957875,0.000105948355,0.00023753662,0.000081274586,0.00045479543,0.000114825714,0.000091464906,0.0000777797,0.0000206334],"category_scores_gemma":[0.00014974082,0.00010879811,0.00013528849,0.00028830697,0.0001042928,0.0006402668,2.1323122e-7,0.00014197765,5.4182345e-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":[0.0006947638,0.00004373299,0.0030977903,0.00006719342,0.000045908157,0.000022088809,0.03850977,0.9118427,0.0017277304,0.0003398799,0.00003606263,0.043572374],"study_design_scores_gemma":[0.09718768,0.007510558,0.2849775,0.0025421933,0.0031189274,0.000037943937,0.19679621,0.051471993,0.007898172,0.0028966,0.34145102,0.004111221],"about_ca_topic_score_codex":0.000013694634,"about_ca_topic_score_gemma":0.00015664696,"teacher_disagreement_score":0.8603707,"about_ca_system_score_codex":0.00003755689,"about_ca_system_score_gemma":0.00035444114,"threshold_uncertainty_score":0.4436658},"labels":[],"label_agreement":null},{"id":"W3006532802","doi":"10.1061/jtepbs.0000332","title":"Transit Holding Control Model for Real-Time Connection Protection","year":2020,"lang":"en","type":"article","venue":"Journal of Transportation Engineering Part A Systems","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Transit (satellite); Scheduling (production processes); Transfer (computing); Transfer station; Public transport; Probabilistic logic; Sensitivity (control systems); Computer science; Rapid transit; Transit system; Control (management); Transport engineering; Operations research; Engineering; Operations management","score_opus":0.0297566373232986,"score_gpt":0.24442996679533918,"score_spread":0.2146733294720406,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3006532802","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04897935,0.000054749362,0.9488946,0.00077764917,0.0005321868,0.00053385476,0.00006143918,0.00012666402,0.000039503],"genre_scores_gemma":[0.996925,0.000045266053,0.0023227418,0.000025513002,0.0005092793,0.000035975987,0.000034802233,0.000022807712,0.000078598474],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99872434,0.000039749528,0.00062277884,0.00011952719,0.0003277362,0.00016584272],"domain_scores_gemma":[0.9990133,0.000069996975,0.00036083267,0.000038338956,0.00035899258,0.00015854761],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060302665,0.00010917408,0.0002634713,0.00011082445,0.00015368899,0.0000555001,0.00007942774,0.00010858952,0.0000080431755],"category_scores_gemma":[0.000068604146,0.000115903866,0.0001419115,0.00022193529,0.000014120019,0.00041981495,1.2824002e-7,0.00011566421,0.0000016644933],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013651869,0.0000131985225,0.0001727767,0.00011388172,0.000048461643,0.0000024053452,0.010301624,0.9827222,0.005140431,0.001091229,0.00019524852,0.00006200146],"study_design_scores_gemma":[0.0011857839,0.00012800182,0.00057656085,0.00013793702,0.00012227842,0.0000017191288,0.0007025758,0.9910086,0.00012158479,0.000006820159,0.005861718,0.00014644396],"about_ca_topic_score_codex":0.000054421023,"about_ca_topic_score_gemma":0.00001881936,"teacher_disagreement_score":0.94794565,"about_ca_system_score_codex":0.00005497988,"about_ca_system_score_gemma":0.00011047038,"threshold_uncertainty_score":0.47264224},"labels":[],"label_agreement":null},{"id":"W3006669229","doi":"","title":"Bus Bridging Decision-Support Toolkit: Optimization Framework and Policy Analysis","year":2019,"lang":"en","type":"dissertation","venue":"TSpace","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Ontario Centres of Excellence","keywords":"Bridging (networking); Computer science; Decision support system; Operations research; Management science; Engineering; Data mining; Computer security","score_opus":0.013469357405456043,"score_gpt":0.37517237043260565,"score_spread":0.36170301302714963,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3006669229","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09236716,0.00073252746,0.7984053,0.0020132742,0.0017576853,0.00088463264,0.000059298818,0.0005048255,0.103275314],"genre_scores_gemma":[0.7755108,0.0032684852,0.0947807,0.0004571091,0.0008331775,0.000037496215,0.006488295,0.0001054794,0.118518464],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99828,0.00008194696,0.00031468945,0.000439682,0.00058131065,0.0003023873],"domain_scores_gemma":[0.99875855,0.0002724786,0.00032944971,0.00024040784,0.00025175136,0.00014736861],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00040103463,0.00021867357,0.0003778582,0.0007528985,0.0004703983,0.00025318415,0.00018219881,0.00052196794,0.0007642063],"category_scores_gemma":[0.0005596782,0.00024590522,0.00013736002,0.0018345519,0.000048851725,0.00021715411,0.0000066015673,0.00026923238,0.00004045676],"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":[0.00012662793,0.000043489814,0.010860763,0.000080571874,0.00042704926,0.000009194203,0.15129988,0.80030215,0.0000030032336,0.011306541,0.0014089517,0.024131812],"study_design_scores_gemma":[0.0030727687,0.00038186938,0.31915492,0.002558869,0.011513527,0.0000055739424,0.29470855,0.263257,0.00010782265,0.0048349667,0.093812115,0.006591998],"about_ca_topic_score_codex":0.0035065194,"about_ca_topic_score_gemma":0.0020519532,"teacher_disagreement_score":0.7036246,"about_ca_system_score_codex":0.00010085048,"about_ca_system_score_gemma":0.0005443302,"threshold_uncertainty_score":0.99999934},"labels":[],"label_agreement":null},{"id":"W3007639774","doi":"","title":"Accord de partenariat pour le Transport en Commun entre la Province de l'Ontario et la Ville de Toronto","year":2008,"lang":"fr","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Political science; Geography; Humanities; Art","score_opus":0.01423792669178973,"score_gpt":0.26638532532533776,"score_spread":0.252147398633548,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3007639774","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6078147,0.00074351503,0.06292622,0.008740537,0.00023005887,0.00029880457,0.000035002104,0.0002320162,0.3189791],"genre_scores_gemma":[0.878326,0.0009699003,0.02424701,0.00071029883,0.00008648863,0.000015616599,0.00006340763,0.000020202426,0.09556105],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9981065,0.0006817097,0.00027698823,0.00021968203,0.00022700433,0.0004881204],"domain_scores_gemma":[0.99915576,0.00032315936,0.00009363236,0.00014772303,0.000058054102,0.0002216858],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000960443,0.00017638454,0.00019864578,0.00001980845,0.00047128138,0.000046520396,0.0002671109,0.0003214692,0.0012571745],"category_scores_gemma":[0.000026036174,0.00019012608,0.00010984217,0.0000970636,0.00020466086,0.00054179475,0.0000068191202,0.00027837782,0.000020184087],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007281118,0.00048534825,0.60582495,0.00003215657,0.000057767476,0.00018488927,0.31133983,0.027471062,0.00004906206,0.047414076,0.005245991,0.0018220634],"study_design_scores_gemma":[0.0012509784,0.00007577334,0.53813016,0.00011177159,0.00010795454,0.00007225186,0.028551731,0.0032805752,0.00011306532,0.000643523,0.4271988,0.000463399],"about_ca_topic_score_codex":0.7518353,"about_ca_topic_score_gemma":0.91793203,"teacher_disagreement_score":0.4219528,"about_ca_system_score_codex":0.00061214453,"about_ca_system_score_gemma":0.0051303334,"threshold_uncertainty_score":0.9996558},"labels":[],"label_agreement":null},{"id":"W3008591799","doi":"10.1155/2020/1902162","title":"Agent-Based Simulation to Improve Policy Sensitivity of Trip-Based Models","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":62,"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":"Seventh Framework Programme; Technische Universität München; Deutsche Forschungsgemeinschaft; European Commission","keywords":"Microsimulation; Nested logit; TRIPS architecture; Mode choice; Trip generation; Computer science; Aggregate (composite); Operations research; Travel behavior; Transport engineering; Mode (computer interface); Sensitivity (control systems); Trip distribution; Econometrics; Economics; Engineering; Public transport","score_opus":0.026154246689677196,"score_gpt":0.3159541246372538,"score_spread":0.2897998779475766,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3008591799","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.37028036,0.000015121922,0.6266139,0.0026336077,0.00012814453,0.0001978543,0.000051335042,0.000022662858,0.000057001365],"genre_scores_gemma":[0.972916,0.000008890892,0.026234537,0.00061987573,0.0001544183,0.000002042843,0.000044163273,0.00001263903,0.000007444101],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99847406,0.0001207832,0.0005931621,0.00013368875,0.0005294349,0.000148896],"domain_scores_gemma":[0.9982838,0.00020771945,0.0005880694,0.000060754268,0.00064885785,0.00021079075],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047436953,0.00010118749,0.00023555783,0.00020506483,0.000101806654,0.000015133539,0.00007516462,0.00007431575,0.000012135289],"category_scores_gemma":[0.00024576532,0.00010642338,0.00013659673,0.00064849394,0.000041613817,0.00041859996,3.445956e-7,0.00011129162,0.0000010388],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00060272345,0.000052518484,0.0008452679,0.00003551498,0.000011143583,0.0000084501835,0.010425908,0.9740288,0.008734653,0.00066183985,0.000007354744,0.004585816],"study_design_scores_gemma":[0.009584443,0.0017348455,0.13276273,0.00043473244,0.0003757185,3.8177072e-7,0.007735614,0.8123822,0.02854991,0.0014785108,0.004155959,0.00080493256],"about_ca_topic_score_codex":0.00014270777,"about_ca_topic_score_gemma":0.00025391174,"teacher_disagreement_score":0.6026356,"about_ca_system_score_codex":0.00006563996,"about_ca_system_score_gemma":0.0004987107,"threshold_uncertainty_score":0.43398196},"labels":[],"label_agreement":null},{"id":"W3010566518","doi":"10.1155/2020/6517248","title":"Optimal Integrated Model for Feeder Transit Route Design and Frequency-Setting Problem with Stop Selection","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Chengdu Science and Technology Bureau; Six Talent Peaks Project in Jiangsu Province; Nantong Science and Technology Bureau; Civil Aviation University of China","keywords":"Genetic algorithm; Dijkstra's algorithm; Selection (genetic algorithm); Service quality; Transport engineering; Transit (satellite); Service (business); Computer science; Simulation; Operations research; Engineering; Public transport; Shortest path problem","score_opus":0.02015331134014459,"score_gpt":0.266516457321649,"score_spread":0.24636314598150444,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3010566518","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11316761,0.000061458566,0.8843586,0.0019151497,0.00003450809,0.0003706178,0.000015917833,0.000043865464,0.00003228064],"genre_scores_gemma":[0.559993,0.00004639832,0.4397748,0.00007768309,0.000039435276,0.00000841015,0.000033583237,0.000011481887,0.000015158005],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989797,0.000047920967,0.00039763,0.00015175068,0.0002636134,0.00015940442],"domain_scores_gemma":[0.9989233,0.000064739244,0.00034814348,0.000021559172,0.0005133645,0.0001288668],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031972677,0.00011287976,0.00017896389,0.000073929434,0.0002857958,0.000043917833,0.000062127525,0.00007488412,0.0000056405274],"category_scores_gemma":[0.000027253296,0.0000999015,0.000050069124,0.00028403502,0.000047759342,0.00087293546,1.5858161e-7,0.00016651204,1.2784626e-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":[0.00071446254,0.000018494009,0.00092749886,0.000033664768,0.000030125573,0.0000033016743,0.046598963,0.9476483,0.0012440373,0.0008290179,0.000018655324,0.0019334607],"study_design_scores_gemma":[0.011650939,0.0033129205,0.023649437,0.000804442,0.0009974971,0.000020400106,0.04658485,0.9042389,0.003301887,0.0022604326,0.0020746947,0.0011035989],"about_ca_topic_score_codex":0.000028119506,"about_ca_topic_score_gemma":0.00026909276,"teacher_disagreement_score":0.44682544,"about_ca_system_score_codex":0.000041101815,"about_ca_system_score_gemma":0.00027594905,"threshold_uncertainty_score":0.4073865},"labels":[],"label_agreement":null},{"id":"W3011685449","doi":"10.1155/2020/3096260","title":"Lewis–Mogridge Points: A Nonarbitrary Method to Include Induced Traffic in Cost-Benefit Analyses","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","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":"Narodowe Centrum Badań i Rozwoju","keywords":"Dual (grammatical number); Investment (military); Computer science; Measure (data warehouse); Boundary (topology); Econometrics; Mathematical optimization; Economics; Operations research; Mathematics; Data mining","score_opus":0.06051805542408263,"score_gpt":0.38815221825350127,"score_spread":0.32763416282941865,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3011685449","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93312806,0.000116028044,0.0597048,0.0062351963,0.0002033952,0.000351552,0.000028112005,0.00004903153,0.00018379775],"genre_scores_gemma":[0.92174083,0.00008653965,0.07690404,0.0010303606,0.00014909654,0.000009129414,0.000052694122,0.000016935825,0.000010396139],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9981359,0.00012676477,0.00081557286,0.00020624699,0.00048199153,0.00023351044],"domain_scores_gemma":[0.99876,0.00015423752,0.00041599118,0.000060799393,0.00030795773,0.00030105075],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000584692,0.00014310556,0.00034016743,0.00032441184,0.00012850165,0.000034162804,0.00019847178,0.00010619715,0.000048056616],"category_scores_gemma":[0.000117968586,0.00014614986,0.00013344534,0.0012813056,0.000023579383,0.0008607611,0.0000010934448,0.00029567129,0.000004799768],"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":[0.0005082601,0.000089055866,0.0034256722,0.000022269536,0.000032086136,0.00006938456,0.066681765,0.9047439,0.006293975,0.0007218449,0.000046724643,0.017365064],"study_design_scores_gemma":[0.006402374,0.0011870317,0.91597813,0.00054277247,0.00032453027,0.0000062365857,0.051848914,0.0019671968,0.007014028,0.0011511313,0.012632749,0.0009449131],"about_ca_topic_score_codex":0.00012682057,"about_ca_topic_score_gemma":0.001983139,"teacher_disagreement_score":0.9125525,"about_ca_system_score_codex":0.00008639384,"about_ca_system_score_gemma":0.00021619008,"threshold_uncertainty_score":0.59598184},"labels":[],"label_agreement":null},{"id":"W3011842657","doi":"10.1155/2020/6326953","title":"Optimal Parameters of Service in a Public Transportation Market with Pricing","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","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":"Government of Shandong Province","keywords":"Schedule; Service (business); Competition (biology); Operations research; Public transport; Computer science; Microeconomics; Mathematical optimization; Transport engineering; Business; Economics; Engineering; Marketing; Mathematics","score_opus":0.01933552376129225,"score_gpt":0.2613152481582752,"score_spread":0.24197972439698298,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3011842657","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96626383,0.000054657,0.029844673,0.003330693,0.000077931196,0.00019888477,0.000019629944,0.000022732776,0.0001869841],"genre_scores_gemma":[0.9452801,0.000107585765,0.054291666,0.00021274923,0.000030702762,0.000003699972,0.000053592863,0.000013418029,0.000006519324],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.998366,0.00007064262,0.0007269248,0.00013918056,0.0005197343,0.00017751678],"domain_scores_gemma":[0.99850404,0.00009751873,0.0007148968,0.000048355756,0.0004933308,0.00014183698],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003562547,0.00010991948,0.00026819026,0.00018474787,0.00006684534,0.000018265502,0.0001343872,0.00007218167,0.00004217583],"category_scores_gemma":[0.00004811543,0.00010452452,0.00006728838,0.0010489711,0.000055190692,0.0010396427,2.0213872e-7,0.00018376761,3.8754607e-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":[0.0010462886,0.00007952665,0.05759259,0.0001048915,0.000035258377,0.00003801378,0.08798316,0.84967965,0.0006334503,0.0006988941,0.000016581604,0.002091709],"study_design_scores_gemma":[0.0037001816,0.0005794241,0.9533941,0.00037090122,0.00013515554,0.0000015375981,0.038377613,0.0010051626,0.0008039221,0.00010466448,0.0012447194,0.00028259022],"about_ca_topic_score_codex":0.00014909252,"about_ca_topic_score_gemma":0.0033299571,"teacher_disagreement_score":0.89580154,"about_ca_system_score_codex":0.000039159706,"about_ca_system_score_gemma":0.00025924298,"threshold_uncertainty_score":0.42623863},"labels":[],"label_agreement":null},{"id":"W3012526415","doi":"10.3390/en13061377","title":"Performance Evaluation of Bus Rapid Transit System: A Comparative Analysis of Alternative Approaches for Energy Efficient Eco-Friendly Public Transport System","year":2020,"lang":"en","type":"article","venue":"Energies","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Bus rapid transit; Public transport; Environmentally friendly; Transport engineering; Efficient energy use; Computer science; Hybrid system; Automotive engineering; Environmental economics; Engineering; Economics; Electrical engineering","score_opus":0.10003333265200653,"score_gpt":0.293566707958812,"score_spread":0.19353337530680548,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3012526415","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9390925,0.0002647031,0.054018904,0.00012578326,0.000110689936,0.00032206587,0.00019840035,0.00008382958,0.005783124],"genre_scores_gemma":[0.9986145,0.00002025041,0.0008319742,0.000005655292,0.000046199897,0.00010107546,0.00035809955,0.000006632707,0.000015620073],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981409,0.00024386002,0.00047779313,0.00024709414,0.0007243599,0.00016601892],"domain_scores_gemma":[0.99873674,0.00011324823,0.00036503127,0.00010031224,0.00061089813,0.0000737954],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009144304,0.000120855206,0.00044801645,0.00024898135,0.0001927438,0.000016216909,0.00017781823,0.000067414585,0.000015576881],"category_scores_gemma":[0.000021771313,0.000114699804,0.00018085896,0.0011490135,0.00014361486,0.00016194905,0.0000022888512,0.00003421808,2.8982114e-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":[0.000086896536,0.00003661974,0.0010504934,0.00014266153,0.00045867055,1.6540528e-7,0.05415259,0.9038669,0.000039123137,0.0393645,0.00000862781,0.00079276407],"study_design_scores_gemma":[0.0005326839,0.000108071195,0.004358344,0.00005106283,0.0012233171,6.979574e-8,0.051727153,0.93970364,0.0019238163,0.0000013939996,0.00025246432,0.00011797254],"about_ca_topic_score_codex":0.00053900597,"about_ca_topic_score_gemma":0.0004857113,"teacher_disagreement_score":0.05952199,"about_ca_system_score_codex":0.000088487745,"about_ca_system_score_gemma":0.00021185551,"threshold_uncertainty_score":0.46773225},"labels":[],"label_agreement":null},{"id":"W3013292417","doi":"10.31235/osf.io/c4yvx","title":"Accessibility Beyond the Schedule","year":2019,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Schedule; Computer science; Transit (satellite); Public transport; Service (business); Transport engineering; Operations research; Component (thermodynamics); Global Positioning System; Engineering; Telecommunications; Business","score_opus":0.015611647856736657,"score_gpt":0.3081693778603025,"score_spread":0.2925577300035659,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3013292417","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5805206,0.000013728861,0.0011049787,0.0027826768,0.0001983578,0.000102412836,8.2914653e-7,0.000065563334,0.41521087],"genre_scores_gemma":[0.98304844,0.000003863952,0.0007104909,0.00041529938,0.000029087381,0.0000017909924,0.000004462758,0.0000014061443,0.015785133],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9996481,0.00003769394,0.0000523769,0.00006874455,0.0001229419,0.000070169765],"domain_scores_gemma":[0.9997967,0.000051762738,0.000018160245,0.00007991276,0.000033531407,0.000019922925],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00033055648,0.000020332718,0.000025697636,0.000007984071,0.00018156176,0.00004262171,0.000101470476,0.000024209045,0.0019998609],"category_scores_gemma":[0.000023345132,0.000012694146,0.000014706511,0.00011405112,0.000042782074,0.00013842431,0.0000023566445,0.00003323615,0.00019196792],"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":[0.000006564823,0.000023691813,0.6990803,0.0000033818606,0.000005199365,1.737078e-7,0.01112633,0.0013050897,0.000037797417,0.28349835,0.0026828574,0.0022302521],"study_design_scores_gemma":[0.00032330072,0.000017700753,0.8501009,0.000005298492,0.000010386455,8.572469e-8,0.013878846,0.0013124691,0.00026916465,0.0073819445,0.12654151,0.00015842989],"about_ca_topic_score_codex":0.00028934173,"about_ca_topic_score_gemma":0.0005959939,"teacher_disagreement_score":0.4025279,"about_ca_system_score_codex":0.000008238973,"about_ca_system_score_gemma":0.000047453297,"threshold_uncertainty_score":0.99891245},"labels":[],"label_agreement":null},{"id":"W3013353295","doi":"10.1145/3355611","title":"Transit Signal Priority Along a Signalized Arterial","year":2020,"lang":"en","type":"article","venue":"ACM Transactions on Spatial Algorithms and Systems","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"VisSim; Bus priority; Schedule; Computer science; Transport engineering; Transit (satellite); Queueing theory; Travel time; Truncation (statistics); Operations research; Real-time computing; Simulation; Microsimulation; Public transport; Computer network; Engineering","score_opus":0.03378433976602814,"score_gpt":0.27466283176893935,"score_spread":0.24087849200291123,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3013353295","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02989119,0.00008360912,0.96565944,0.0024847784,0.000692262,0.00048503446,0.00012296214,0.00022508077,0.00035561802],"genre_scores_gemma":[0.9972986,0.00007712371,0.0017397274,0.00017396828,0.0004944966,0.00003652376,0.000027659824,0.000015462374,0.00013641217],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984771,0.00020561717,0.00034015288,0.00031762721,0.00042040693,0.00023909957],"domain_scores_gemma":[0.9993095,0.00012572003,0.00008210902,0.00011436727,0.00009527337,0.00027303406],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027622125,0.00015256857,0.0002547354,0.000059740072,0.00068162347,0.00018038269,0.00015394136,0.0001500702,0.00021191557],"category_scores_gemma":[0.000020863363,0.00015158133,0.00008337739,0.00023934046,0.00010545112,0.00021562676,0.0000010239594,0.00017480705,0.00001841138],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0035474894,0.0009330714,0.0036808054,0.00045269897,0.00070916966,0.00019251488,0.27633306,0.17543921,0.0047114687,0.0029967853,0.00070288044,0.53030086],"study_design_scores_gemma":[0.026583616,0.004276003,0.035195693,0.00082997273,0.0014241613,0.000048367634,0.060156725,0.619638,0.0025690931,0.0011386261,0.24288101,0.0052586677],"about_ca_topic_score_codex":0.009836233,"about_ca_topic_score_gemma":0.0012062513,"teacher_disagreement_score":0.96740746,"about_ca_system_score_codex":0.000026359483,"about_ca_system_score_gemma":0.00011248814,"threshold_uncertainty_score":0.9967573},"labels":[],"label_agreement":null},{"id":"W3013599416","doi":"10.24012/dumf.404832","title":"Yüksek Hızlı Demiryolları, Yolcu Ve Yük Taşımacılığı Karşılaştırmaları Bağlamında Küresel Ölçekli Bir Derleme Çalışması","year":2020,"lang":"tr","type":"article","venue":"DÜMF Mühendislik Dergisi","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Stantec (Canada)","funders":"","keywords":"Physics","score_opus":0.03360330740992459,"score_gpt":0.2849273723931262,"score_spread":0.2513240649832016,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3013599416","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.48256183,0.021919616,0.06399428,0.14840887,0.01731559,0.008555867,0.0062545636,0.0072428742,0.2437465],"genre_scores_gemma":[0.980004,0.0025345378,0.004356015,0.0043530734,0.0020580688,0.00010055215,0.0013139403,0.00023670608,0.0050431],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9915088,0.0006891441,0.0017461934,0.0018830952,0.0021616353,0.0020111639],"domain_scores_gemma":[0.9955968,0.0005817666,0.000912169,0.0006733711,0.00063357415,0.0016022829],"candidate_categories":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0011270259,0.0010902296,0.0011608978,0.0003866253,0.0020022518,0.00083173753,0.0015027169,0.0010179323,0.0039101266],"category_scores_gemma":[0.00077140465,0.001204314,0.0006576621,0.0029075916,0.00070145377,0.0013841726,0.0001539828,0.0013090519,0.002391109],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014980836,0.001496534,0.26432467,0.0012766048,0.0018535644,0.001301583,0.2764137,0.038844638,0.0014466044,0.06007041,0.33528107,0.016192513],"study_design_scores_gemma":[0.004758665,0.0006477398,0.038270965,0.00043191336,0.0010142263,0.000018673183,0.04600945,0.020855773,0.0006194909,0.0010854256,0.8830927,0.0031949999],"about_ca_topic_score_codex":0.0014775562,"about_ca_topic_score_gemma":0.0017166913,"teacher_disagreement_score":0.5478116,"about_ca_system_score_codex":0.00031036328,"about_ca_system_score_gemma":0.00092812354,"threshold_uncertainty_score":0.999297},"labels":[],"label_agreement":null},{"id":"W3013864932","doi":"10.1109/mnet.001.1900368","title":"Crowdsensing-Based Personalized Dynamic Route Planning for Smart Vehicles","year":2020,"lang":"en","type":"article","venue":"IEEE Network","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Planner; Route planning; Routing (electronic design automation); Policy-based routing; Intelligent transportation system; Latency (audio); Computer network; Static routing; Routing protocol; Transport engineering; Artificial intelligence; Engineering; Telecommunications","score_opus":0.03592766808868754,"score_gpt":0.3023967824747354,"score_spread":0.26646911438604787,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3013864932","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32492432,0.001275916,0.6150262,0.03674523,0.00835387,0.0016965553,0.00013055532,0.0018394316,0.010007909],"genre_scores_gemma":[0.98897094,0.0000062094136,0.0059353053,0.0029722408,0.0015181524,0.000012672054,0.00010493292,0.000019176483,0.00046039172],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991472,0.00006774369,0.00014927525,0.00018616185,0.00018148392,0.00026813665],"domain_scores_gemma":[0.9994716,0.00021303343,0.00007764306,0.000050890918,0.0000702382,0.00011658385],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023566147,0.00008468639,0.00012590567,0.000017600709,0.0005223874,0.00006821643,0.00008892095,0.000082966755,0.000015398828],"category_scores_gemma":[0.000055411492,0.00009304288,0.000072270086,0.0002378113,0.00008291011,0.000077745375,0.0000013319805,0.00007710281,0.000008972103],"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":[0.00025431966,0.000010869112,0.009971611,0.000019563236,0.00002097927,0.0000068242307,0.008835254,0.95475054,0.00006228,0.0005853103,0.024508346,0.00097410375],"study_design_scores_gemma":[0.0020914874,0.0001328727,0.010060677,0.00019066157,0.00010626689,3.3219246e-7,0.001508694,0.43498498,0.000073894225,0.0008500163,0.5494624,0.0005377386],"about_ca_topic_score_codex":0.000072236755,"about_ca_topic_score_gemma":0.00017898221,"teacher_disagreement_score":0.6640466,"about_ca_system_score_codex":0.00002992896,"about_ca_system_score_gemma":0.00012406384,"threshold_uncertainty_score":0.4017834},"labels":[],"label_agreement":null},{"id":"W3014857582","doi":"10.1016/j.cstp.2020.03.008","title":"A comprehensive welfare impact analysis for road expansion projects: A case study","year":2020,"lang":"en","type":"article","venue":"Case Studies on Transport Policy","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Mitacs","keywords":"Toll; Welfare; Revenue; Occupancy; Investment (military); Social Welfare; Economics; Toll road; Index (typography); Business; Economic surplus; Public economics; Finance; Engineering","score_opus":0.1008128243116542,"score_gpt":0.4126919477864269,"score_spread":0.31187912347477276,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3014857582","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9939839,0.00015208888,0.00076754595,0.0026163068,0.00007939273,0.0015620653,0.00034144224,0.00022196493,0.00027524235],"genre_scores_gemma":[0.9985472,0.00006277919,0.00020892323,0.00054207543,0.0002703903,0.00018856463,0.00010465865,0.000023117404,0.000052277424],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.9982198,0.00013725704,0.00046095878,0.000490177,0.00031396825,0.0003778299],"domain_scores_gemma":[0.998827,0.00012967523,0.00016723966,0.00018146569,0.0004252688,0.0002693494],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.00022991301,0.00026176655,0.00054834783,0.00034107064,0.0013956438,0.000031514912,0.00009824873,0.00008213454,0.000022382736],"category_scores_gemma":[0.0001454833,0.00022720009,0.000369615,0.002030666,0.00015367616,0.00015457727,0.000004510631,0.0001253195,0.0000035200799],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004930086,0.00044692098,0.045370884,0.00013627831,0.0033953935,0.032933135,0.85212404,0.06199626,0.000005556171,0.0006529594,0.00020440231,0.002241194],"study_design_scores_gemma":[0.00249021,0.0016694217,0.023475042,0.0000252696,0.0023413007,0.00023537612,0.96560365,0.0010800238,0.000011797214,0.000011480696,0.0024826282,0.0005738005],"about_ca_topic_score_codex":0.017748795,"about_ca_topic_score_gemma":0.01611658,"teacher_disagreement_score":0.113479644,"about_ca_system_score_codex":0.0001225111,"about_ca_system_score_gemma":0.00022445958,"threshold_uncertainty_score":0.9999044},"labels":[],"label_agreement":null},{"id":"W3016459259","doi":"10.3390/s20082276","title":"Determining the Optimal Restricted Driving Zone Using Genetic Algorithm in a Smart City","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University; Laurentian University","funders":"","keywords":"Metropolitan area; Traffic congestion; Genetic algorithm; Computer science; Control (management); Transport engineering; Engineering; Geography; Artificial intelligence; Machine learning","score_opus":0.038814252314847704,"score_gpt":0.2883093116506178,"score_spread":0.2494950593357701,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3016459259","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9780347,0.000010142432,0.020798197,0.00058243104,0.00007560548,0.00011081048,0.0000031434317,0.00006205112,0.0003229218],"genre_scores_gemma":[0.9569676,0.000012999269,0.042724766,0.00013339457,0.00009990451,0.0000014523833,0.0000053320255,0.0000073635715,0.00004719488],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99919844,0.00013141701,0.00015535658,0.00014199904,0.00019270257,0.00018009728],"domain_scores_gemma":[0.9996841,0.000096303615,0.000058410948,0.000052232022,0.000042768734,0.000066188986],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001276059,0.000059933693,0.000077980614,0.000039247716,0.00028260748,0.000049933664,0.000093930175,0.000052055664,0.000022029037],"category_scores_gemma":[0.00016887118,0.00005423589,0.000024321536,0.0004787798,0.00006819912,0.00006517589,0.000006660155,0.00011245137,0.0000036679994],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008173826,0.000017777205,0.41432294,0.0000035474839,0.000007960353,0.00003428069,0.046818946,0.524235,0.000045453715,0.000035479505,0.000037383288,0.0144330645],"study_design_scores_gemma":[0.00023645435,0.000016111773,0.47087935,0.000018280267,0.000012533194,4.8541676e-7,0.003272947,0.5244133,0.00001966424,0.0000048794564,0.0010186115,0.000107397485],"about_ca_topic_score_codex":0.00090098695,"about_ca_topic_score_gemma":0.00031522303,"teacher_disagreement_score":0.056556385,"about_ca_system_score_codex":0.00002914983,"about_ca_system_score_gemma":0.00006782346,"threshold_uncertainty_score":0.22116753},"labels":[],"label_agreement":null},{"id":"W3018511307","doi":"10.1016/j.energy.2020.117699","title":"Joint optimization of delay-recovery and energy-saving in a metro system: A case study from China","year":2020,"lang":"en","type":"article","venue":"Energy","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":22,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"National Key Research and Development Program of China Stem Cell and Translational Research; China Scholarship Council; National Natural Science Foundation of China","keywords":"Energy consumption; Headway; Genetic algorithm; Energy (signal processing); Sorting; Computer science; Efficient energy use; Simulation; Engineering; Mathematics","score_opus":0.018101006664416778,"score_gpt":0.24050713038173704,"score_spread":0.22240612371732027,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3018511307","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8893159,0.00018687066,0.10928913,0.000111895395,0.00008975652,0.000045330326,0.000013310341,0.00005638469,0.0008913917],"genre_scores_gemma":[0.99686205,0.000037349055,0.0029200893,0.000040991454,0.000059663726,0.000009607658,0.000028074237,0.000008139829,0.00003401325],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99914116,0.00017523541,0.00024639198,0.00017748565,0.00016342559,0.00009632338],"domain_scores_gemma":[0.99967283,0.000054933964,0.00011052159,0.000056404755,0.00003474706,0.00007054544],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016054857,0.000069200876,0.00015910961,0.00008339894,0.000108134685,0.000029990028,0.000046881356,0.00005149377,0.00002277531],"category_scores_gemma":[0.00005228228,0.00007240722,0.000022758928,0.00034049075,0.000023709119,0.00015793575,0.00000876864,0.00003599143,1.6690333e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031471376,0.00006581815,0.031793065,0.000010900138,0.000026663274,0.000334926,0.030364383,0.93354446,0.000022134709,0.0022325474,0.000022672075,0.0015509488],"study_design_scores_gemma":[0.0028748482,0.00050697266,0.031838626,0.0002130844,0.00018826361,0.00001401077,0.2359562,0.7268985,0.00020788288,0.00009524535,0.0005871825,0.00061920594],"about_ca_topic_score_codex":0.10665784,"about_ca_topic_score_gemma":0.031750638,"teacher_disagreement_score":0.206646,"about_ca_system_score_codex":0.000036001922,"about_ca_system_score_gemma":0.000049862083,"threshold_uncertainty_score":0.9859174},"labels":[],"label_agreement":null},{"id":"W3020897414","doi":"10.1177/0361198120917399","title":"Capacity-Constrained Bus Bridging Optimization Framework","year":2020,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Queue; Bridging (networking); Computer science; Service (business); Transit (satellite); Bus rapid transit; Transport engineering; Operations research; Public transport; Engineering; Computer network; Business","score_opus":0.12513309617497934,"score_gpt":0.39064706473614513,"score_spread":0.2655139685611658,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3020897414","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7081745,0.00021295395,0.23829556,0.0490336,0.0011028049,0.0015640787,0.00012495661,0.00017097956,0.0013205283],"genre_scores_gemma":[0.9703868,0.0009253687,0.027221309,0.0003394907,0.000726703,0.000036456262,0.00003747694,0.00006126611,0.00026511808],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9900358,0.0021720687,0.0014977165,0.000491993,0.0047634733,0.0010389705],"domain_scores_gemma":[0.99208194,0.001412657,0.0006346763,0.0003235058,0.0047251577,0.0008220563],"candidate_categories":["sts","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.005156551,0.00026561733,0.0004749031,0.0007153517,0.0019064639,0.0003157785,0.001245794,0.00035817278,0.0008387086],"category_scores_gemma":[0.0013361967,0.00023255109,0.00040225562,0.004089858,0.0013022653,0.0011234221,0.0000046466694,0.0027483162,0.000030403435],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002056454,0.00039853793,0.31186408,0.00039986288,0.00031984752,0.00020056686,0.118961446,0.48148593,0.00096297124,0.062335387,0.010978111,0.010036833],"study_design_scores_gemma":[0.005146895,0.0014784973,0.82866347,0.001828196,0.00031559766,0.0000011245108,0.041789055,0.01451866,0.0013369876,0.009989553,0.093655795,0.0012761834],"about_ca_topic_score_codex":0.009748266,"about_ca_topic_score_gemma":0.015540626,"teacher_disagreement_score":0.5167994,"about_ca_system_score_codex":0.00024890128,"about_ca_system_score_gemma":0.0012877485,"threshold_uncertainty_score":0.99955237},"labels":[],"label_agreement":null},{"id":"W3021134829","doi":"10.2139/ssrn.2358415","title":"A Generalized Random Regret Minimization Model","year":2013,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Transport Canada","funders":"Nederlandse Organisatie voor Wetenschappelijk Onderzoek","keywords":"Regret; Mathematics; Random variable; Minification; Maximization; Constant (computer programming); Function (biology); Value (mathematics); Mathematical optimization; Econometrics; Statistics; Computer science","score_opus":0.01860271378879293,"score_gpt":0.28353388288806775,"score_spread":0.26493116909927483,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3021134829","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.096495196,0.0043838224,0.88474673,0.0046397336,0.0012435184,0.0008386562,0.000023387183,0.00027842174,0.007350555],"genre_scores_gemma":[0.9229871,0.040000547,0.0096854465,0.00023540035,0.0010656505,0.000063764455,0.00036014963,0.00006393322,0.025537979],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99676603,0.00034024956,0.0004653713,0.00029574375,0.00056416466,0.001568431],"domain_scores_gemma":[0.9987822,0.000046526016,0.0004992683,0.00017229475,0.00036065644,0.00013901744],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019918985,0.00021833811,0.00030802703,0.00019238469,0.00064895506,0.00028077222,0.00039809,0.00042149072,0.00012137204],"category_scores_gemma":[0.00014723484,0.00021271325,0.00025097938,0.00016249905,0.00008467843,0.00025340894,0.00002286665,0.0022109908,0.000022943606],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000119081466,0.00004200156,0.00052834663,0.000012486016,0.0001970493,0.0000015092252,0.007985122,0.72339535,0.0000069347284,0.2626006,0.0019465185,0.003165008],"study_design_scores_gemma":[0.0027153355,0.000045127006,0.00009220702,0.00011679621,0.00024146613,0.000012690028,0.003297614,0.24934147,0.00000429265,0.74006414,0.003496318,0.0005725537],"about_ca_topic_score_codex":0.0010888289,"about_ca_topic_score_gemma":0.0032371548,"teacher_disagreement_score":0.8750613,"about_ca_system_score_codex":0.00087600196,"about_ca_system_score_gemma":0.0077371746,"threshold_uncertainty_score":0.997888},"labels":[],"label_agreement":null},{"id":"W3021500469","doi":"","title":"Have They Bunched Yet? An Exploratory Study of the Impacts of Bus Bunching on Dwell and Running Times","year":2016,"lang":"en","type":"article","venue":"eScholarship@McGill (McGill)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Dwell time; Transit (satellite); Computer science; Local bus; Public transport; Transport engineering; Real-time computing; Simulation; Engineering; Control bus; System bus; Computer hardware","score_opus":0.025091595799561547,"score_gpt":0.2680230679439568,"score_spread":0.24293147214439526,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3021500469","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9911841,0.000026742524,9.945552e-7,0.000043655087,0.00019377665,0.00036219726,0.00008766911,0.00007140125,0.008029443],"genre_scores_gemma":[0.99941033,0.000036461093,0.0001396254,0.00006027508,0.000017555903,0.00001080359,0.0000037240748,0.000027337663,0.00029390925],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99777967,0.00056718604,0.00042032026,0.00034115207,0.0006138667,0.00027780986],"domain_scores_gemma":[0.9985713,0.00033406078,0.00037938033,0.00034262097,0.00021319425,0.00015943864],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015106961,0.00018453984,0.00026010833,0.00012848804,0.0008989287,0.00003025672,0.00031773487,0.00013119841,0.000049638595],"category_scores_gemma":[0.00058748695,0.00012200178,0.0000655302,0.00022963919,0.00014618954,0.0007502325,0.00003637703,0.00020353179,0.0000065250074],"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":[0.0013657203,0.004588995,0.36076748,0.00023385829,0.00069109525,0.00004680387,0.063534975,0.0069399616,0.19533844,0.2435752,0.000023109496,0.12289436],"study_design_scores_gemma":[0.013891688,0.004631139,0.5741029,0.0030509182,0.00070848956,0.0000074344066,0.18445973,0.00015957127,0.1666911,0.04088612,0.00860494,0.0028060027],"about_ca_topic_score_codex":0.0012328315,"about_ca_topic_score_gemma":0.0065411315,"teacher_disagreement_score":0.21333538,"about_ca_system_score_codex":0.00009531222,"about_ca_system_score_gemma":0.000046599518,"threshold_uncertainty_score":0.69139236},"labels":[],"label_agreement":null},{"id":"W3021996150","doi":"10.1177/0361198120917384","title":"Evaluation of Subway Bottleneck Mitigation Strategies using Microscopic, Agent-Based Simulation","year":2020,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Bottleneck; Headway; Testbed; Dwell time; Computer science; Consolidation (business); Throughput; Transport engineering; Operations research; Simulation; Engineering; Computer network; Telecommunications; Business","score_opus":0.26104226919241624,"score_gpt":0.4767726235520289,"score_spread":0.21573035435961263,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3021996150","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9546227,0.00025775423,0.039903857,0.0029864293,0.00053725555,0.0013257634,0.00007820589,0.00003935693,0.0002487064],"genre_scores_gemma":[0.9949353,0.00016821745,0.004418844,0.00006244326,0.00023936672,0.000026815005,0.00005593889,0.00004222597,0.000050889666],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9854512,0.0038576035,0.0016139259,0.0003978886,0.008025392,0.00065399526],"domain_scores_gemma":[0.9852309,0.0008565647,0.0009438004,0.0002791631,0.012293141,0.00039639775],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011080746,0.00022379936,0.00042533313,0.00073371985,0.0010133691,0.00019433787,0.00076663087,0.00026789718,0.00042531564],"category_scores_gemma":[0.0008472183,0.00020094062,0.00034368198,0.0028993008,0.000885939,0.0012279368,0.0000029342593,0.001086932,0.000007689215],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00079908204,0.00020145073,0.09724727,0.0002503925,0.00012663884,0.000011578738,0.022641774,0.8625598,0.008967404,0.0037042631,0.00058144145,0.0029088887],"study_design_scores_gemma":[0.0055671963,0.0009215697,0.8042188,0.0010111064,0.00068514684,1.0809275e-7,0.035185844,0.1294509,0.00887455,0.0054776384,0.008054524,0.0005525664],"about_ca_topic_score_codex":0.0095660705,"about_ca_topic_score_gemma":0.026328249,"teacher_disagreement_score":0.7331089,"about_ca_system_score_codex":0.00040963336,"about_ca_system_score_gemma":0.0041245064,"threshold_uncertainty_score":0.9970293},"labels":[],"label_agreement":null},{"id":"W3022171003","doi":"10.1155/2020/6530530","title":"Impact of Carriage Crowding Level on Bus Dwell Time: Modelling and Analysis","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","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":"People's Government of Jilin Province; China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Dwell time; Crowding; Carriage; Schedule; Estimation; Statistics; Regression analysis; Computer science; Simulation; Transport engineering; Mathematics; Engineering; Medicine; Structural engineering; Psychology","score_opus":0.03551167388109531,"score_gpt":0.31280632259746516,"score_spread":0.27729464871636983,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3022171003","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8658696,0.000079238955,0.13361676,0.00015735946,0.000039957857,0.00006044268,0.000051045114,0.000011640167,0.00011396391],"genre_scores_gemma":[0.98864645,0.00020252024,0.010997506,0.000023698587,0.00005645624,4.0611246e-7,0.000045831785,0.000007378712,0.000019756393],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9989915,0.00004017577,0.00043648048,0.00010425503,0.00032374173,0.00010386761],"domain_scores_gemma":[0.9990234,0.00008416091,0.00047278867,0.000036993788,0.000255946,0.00012670523],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022378183,0.00008177117,0.00025166667,0.00018096808,0.00009954151,0.00001673214,0.00006573496,0.00005349794,0.000043096607],"category_scores_gemma":[0.000034540717,0.000075420794,0.00018510534,0.0005998813,0.000039745486,0.00039290337,2.8928696e-7,0.00011102055,6.31082e-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":[0.00022640276,0.000020695506,0.021860566,0.00001051149,0.0001518991,0.000006462378,0.02205378,0.953852,0.00092249265,0.000164333,0.0000074420336,0.0007234087],"study_design_scores_gemma":[0.0021067522,0.0008466461,0.9480861,0.00014708638,0.0013481003,7.2430277e-7,0.0045480733,0.04110536,0.000948107,0.00034780492,0.00019388518,0.00032137963],"about_ca_topic_score_codex":0.00009540228,"about_ca_topic_score_gemma":0.000050566763,"teacher_disagreement_score":0.92622554,"about_ca_system_score_codex":0.00003132757,"about_ca_system_score_gemma":0.000088515655,"threshold_uncertainty_score":0.30755708},"labels":[],"label_agreement":null},{"id":"W3022460042","doi":"10.1016/s0573-8555(04)66006-8","title":"Land-use, Transportation and Urban Development","year":2004,"lang":"en","type":"book-chapter","venue":"Contributions to economic analysis","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Subject matter; Traffic congestion; Economic geography; Land use; Quarter (Canadian coin); Location theory; Regional science; Space (punctuation); Urban planning; Geography; Environmental planning; Economics; Transport engineering; Computer science; Civil engineering; Economic growth; Engineering","score_opus":0.013469536010137007,"score_gpt":0.25699599743154233,"score_spread":0.24352646142140533,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3022460042","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03353353,0.0023307179,0.50620973,0.01040582,0.0013713434,0.003991734,0.019356433,0.0013943159,0.4214064],"genre_scores_gemma":[0.6775783,0.00063681306,0.0034696045,0.0002734266,0.00027243653,0.000055100612,0.009760941,0.000041422652,0.30791196],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99890184,0.000015857968,0.00042361763,0.00034773606,0.00011729046,0.00019364509],"domain_scores_gemma":[0.9992491,0.0000710314,0.00018810677,0.00013279886,0.00015458674,0.00020439367],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024926302,0.0001739696,0.0003701361,0.00047404846,0.00052109885,0.00012359196,0.00009439507,0.00024485757,0.0008366043],"category_scores_gemma":[0.000025133379,0.00020853114,0.00015284157,0.00011096294,0.00008400147,0.00017139867,0.000003106926,0.00010698962,0.0001378094],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002259038,0.0000143928055,0.030727152,0.000008461528,0.0017633241,0.000005704492,0.0066963816,0.08008676,3.5632047e-7,0.8783453,0.0011736305,0.0011559165],"study_design_scores_gemma":[0.00049905694,0.000017665401,0.055353507,0.000066482295,0.0028477788,1.4765016e-7,0.00014255613,0.00021297485,0.0000047956805,0.0029323767,0.9372558,0.00066688226],"about_ca_topic_score_codex":0.0014103218,"about_ca_topic_score_gemma":0.028867392,"teacher_disagreement_score":0.9360821,"about_ca_system_score_codex":0.0006262199,"about_ca_system_score_gemma":0.0005752473,"threshold_uncertainty_score":0.9888533},"labels":[],"label_agreement":null},{"id":"W3022557193","doi":"10.1155/2020/5197025","title":"Regional Logistics Network Design in Mitigating Truck Flow-Caused Congestion Problems","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","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":"China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Truck; Network planning and design; Flow network; Heuristics; Computer science; Node (physics); Traffic congestion; Transport engineering; Lagrangian relaxation; Routing (electronic design automation); Traffic flow (computer networking); Service level; Function (biology); Operations research; Mathematical optimization; Computer network; Engineering; Automotive engineering","score_opus":0.04955064507358572,"score_gpt":0.28822630340739824,"score_spread":0.23867565833381252,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3022557193","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13253646,0.00051735947,0.8607766,0.004855295,0.0005698896,0.0004917344,0.00001652478,0.000081722916,0.000154397],"genre_scores_gemma":[0.87945795,0.0003450452,0.11947568,0.00027907788,0.00031311577,0.000004386062,0.00009381894,0.0000132383475,0.000017701963],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9984568,0.00014650289,0.0006669187,0.00012956465,0.0004072318,0.000193019],"domain_scores_gemma":[0.9987931,0.00020063172,0.00052660395,0.000035081972,0.00030983274,0.00013473212],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00057329464,0.00009869887,0.0002016454,0.00006984872,0.00014569887,0.000026215916,0.000100582285,0.000093350776,0.000023995262],"category_scores_gemma":[0.0001768824,0.00010383927,0.00005668686,0.0005365785,0.00007051322,0.0005299967,3.654888e-7,0.00023268945,0.0000015242912],"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":[0.00016986694,0.000021911661,0.0073014232,0.000020748688,0.000010834166,0.000023903918,0.022120494,0.96753794,0.0001796593,0.000795116,0.0001992429,0.0016188408],"study_design_scores_gemma":[0.011496095,0.0015343007,0.8457589,0.002035449,0.00039302997,0.000007970198,0.02747795,0.07499422,0.00026005862,0.012787566,0.021985235,0.0012691806],"about_ca_topic_score_codex":0.00001896836,"about_ca_topic_score_gemma":0.00043244142,"teacher_disagreement_score":0.89254373,"about_ca_system_score_codex":0.000052991127,"about_ca_system_score_gemma":0.00021776366,"threshold_uncertainty_score":0.42344427},"labels":[],"label_agreement":null},{"id":"W3022903754","doi":"","title":"Large-Scale Application of MILATRAS: Case Study of the Toronto Transit Network","year":2009,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto; University of British Columbia","funders":"","keywords":"Transit (satellite); Computer science; Operationalization; Path (computing); Transport engineering; Sequence (biology); Operations research; Simulation; Public transport; Engineering; Computer network","score_opus":0.010429552520468864,"score_gpt":0.28449300220218754,"score_spread":0.2740634496817187,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3022903754","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.957073,0.000068621426,0.03335773,0.0001645096,0.000056216493,0.00040015165,0.0000036539423,0.000032286283,0.00884387],"genre_scores_gemma":[0.9990727,0.000009303694,0.00068887294,0.00004615777,0.000034061708,0.0000034149216,0.0000024220806,0.000001761146,0.00014134974],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.9994623,0.000057822333,0.00016298644,0.00007525766,0.0001600284,0.00008161305],"domain_scores_gemma":[0.99972314,0.000021794554,0.00007040206,0.00010270882,0.0000602473,0.000021704987],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024734452,0.000034302066,0.000070524875,0.000005711012,0.00016752801,0.0000043264017,0.00007866756,0.000034611847,0.000052888463],"category_scores_gemma":[0.0000034955108,0.000025716217,0.00002865224,0.00016178741,0.000021095124,0.00007592974,9.676951e-7,0.000022861888,3.0319168e-7],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000045141333,0.001308636,0.19515394,0.000011352309,0.000029063176,0.000006195476,0.6334047,0.14915322,0.00010716029,0.008620914,0.0010246112,0.011135118],"study_design_scores_gemma":[0.0012189387,0.00030321308,0.2817221,0.000020085372,0.0001304694,0.0000039449164,0.7069372,0.0056228093,0.0001252003,0.00041605555,0.0033102059,0.00018978312],"about_ca_topic_score_codex":0.011754397,"about_ca_topic_score_gemma":0.22892904,"teacher_disagreement_score":0.21717463,"about_ca_system_score_codex":0.000011711681,"about_ca_system_score_gemma":0.000020513426,"threshold_uncertainty_score":0.99482644},"labels":[],"label_agreement":null},{"id":"W3023128246","doi":"","title":"Variation in Bus Transit Service: Understanding the Impacts of Various Improvement Strategies on Transit Service Reliability","year":2013,"lang":"en","type":"article","venue":"eScholarship@McGill (McGill)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"McGill University","funders":"","keywords":"Service (business); Reliability (semiconductor); Transit (satellite); Schedule; Variation (astronomy); Smart card; Transport engineering; Public transport; Bus rapid transit; Level of service; Passenger information; Computer science; Operations research; Engineering; Computer security; Business","score_opus":0.02396311872171993,"score_gpt":0.2533162079788404,"score_spread":0.22935308925712047,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3023128246","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98350173,0.000016806442,0.0001944473,0.0016440618,0.00024228221,0.0010105338,0.00014075132,0.00013882815,0.013110581],"genre_scores_gemma":[0.998595,0.000041989784,0.00037030218,0.00081225066,0.000017572374,0.000052659543,0.00003957377,0.000028565639,0.000042066],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99723744,0.0004501165,0.00065220275,0.0004481798,0.00073296897,0.00047909046],"domain_scores_gemma":[0.99845433,0.0003710401,0.00027174052,0.00034438167,0.00039919934,0.00015927637],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017071712,0.00025541222,0.00028711278,0.00016509485,0.0009938824,0.00014214432,0.00038045002,0.00026803804,0.00019990212],"category_scores_gemma":[0.00013081759,0.00022237613,0.00009083775,0.0012340352,0.00007865172,0.0013710866,0.000011008129,0.00047545534,0.000031830445],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004361605,0.00085934513,0.0012541629,0.0005608893,0.00016595425,0.000012292516,0.014044726,0.29511675,0.049119443,0.6263611,0.000003930423,0.0120652495],"study_design_scores_gemma":[0.008238462,0.0012365034,0.46690887,0.0012095831,0.0004969336,0.0000054010197,0.08826171,0.010341401,0.010289552,0.40759698,0.0027929507,0.002621648],"about_ca_topic_score_codex":0.033972766,"about_ca_topic_score_gemma":0.05015363,"teacher_disagreement_score":0.4656547,"about_ca_system_score_codex":0.0006915549,"about_ca_system_score_gemma":0.00014040974,"threshold_uncertainty_score":0.9724601},"labels":[],"label_agreement":null},{"id":"W3025438315","doi":"10.1080/23248378.2020.1756475","title":"Modelling and solving an integrated freight train scheduling and trip planning problem with hazardous materials","year":2020,"lang":"en","type":"article","venue":"International Journal of Rail Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Concordia University","keywords":"Scheduling (production processes); Integer programming; Hazardous waste; Schedule; Mathematical optimization; Limiting; Linear programming; Computer science; Operations research; Job shop scheduling; Heuristic; Population; Engineering; Mathematics","score_opus":0.03328663891641936,"score_gpt":0.2804914759642945,"score_spread":0.24720483704787516,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3025438315","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.70845896,0.00011907987,0.28969547,0.0014033699,0.000107661755,0.00008694789,0.000027299897,0.00003085762,0.00007036848],"genre_scores_gemma":[0.9360084,0.0001515343,0.06336704,0.00014278905,0.00020120126,0.0000021493038,0.000101562044,0.000014112909,0.000011240901],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99871844,0.00007492666,0.00045952326,0.00015982379,0.000464365,0.00012292324],"domain_scores_gemma":[0.99892426,0.00005962871,0.0003684792,0.000024391997,0.0004630372,0.0001602022],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047029823,0.00011201471,0.00018051069,0.00013550016,0.00015538135,0.00019608911,0.00012698033,0.00007454635,0.000034972334],"category_scores_gemma":[0.00002984908,0.00009952869,0.000025475741,0.0001261403,0.00008128492,0.0009866443,0.0000010218822,0.00015313448,2.8683522e-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":[0.0009114076,0.00004371616,0.015386112,0.0000350313,0.00013916656,0.00013385419,0.11630867,0.8568307,0.0021565042,0.0035638793,0.000012828732,0.004478131],"study_design_scores_gemma":[0.0339115,0.00481643,0.13976748,0.0072584003,0.0017534741,0.0003577659,0.23572007,0.52659744,0.01517183,0.00850041,0.022006243,0.004138943],"about_ca_topic_score_codex":0.00023170892,"about_ca_topic_score_gemma":0.0001389625,"teacher_disagreement_score":0.33023325,"about_ca_system_score_codex":0.000026152082,"about_ca_system_score_gemma":0.0001474991,"threshold_uncertainty_score":0.40586624},"labels":[],"label_agreement":null},{"id":"W3025937784","doi":"10.5198/jtlu.2020.1454","title":"Testing microsimulation uncertainty of the parcel-based space development module of the Baltimore PECAS Demo Model","year":2020,"lang":"en","type":"article","venue":"Journal of Transport and Land Use","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"National Key Research and Development Program of China; Wuhan University of Technology; Wuhan University; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Microsimulation; Space (punctuation); Development (topology); Computer science; Simulation; Operations research; Variation (astronomy); Set (abstract data type); Industrial engineering; Mathematics; Transport engineering; Engineering","score_opus":0.05095546757415909,"score_gpt":0.2660073306965834,"score_spread":0.2150518631224243,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3025937784","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99151105,0.00003568854,0.007159794,0.0011027929,0.000040886633,0.000077854784,0.00001078474,0.0000040020896,0.000057149486],"genre_scores_gemma":[0.99494684,0.000008545181,0.0048973956,0.00008873494,0.000022532968,2.8017757e-7,0.000002951087,0.0000036352499,0.000029062945],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99929947,0.0000342774,0.00029460277,0.00005365114,0.0002478069,0.00007017876],"domain_scores_gemma":[0.99935144,0.00006897645,0.00030780968,0.000041036954,0.00018047713,0.000050235696],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023888393,0.00005213963,0.00011844533,0.000018448902,0.00016752775,0.000009350737,0.00010101386,0.000039746854,0.0000044508115],"category_scores_gemma":[0.000055685563,0.0000299761,0.000058900565,0.00017297045,0.000071238916,0.00009282437,0.0000017958405,0.0000873248,2.8421233e-8],"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":[0.00003768705,0.000011741341,0.5484691,0.000017299386,0.0000061942674,4.6051773e-7,0.0059854975,0.44509265,0.00023733272,0.000025282841,0.0000069404186,0.00010975486],"study_design_scores_gemma":[0.0005376902,0.000021401727,0.9398168,0.00012914088,0.00005931566,4.636502e-7,0.00031926928,0.0576726,0.0007885179,0.000034657336,0.0005662379,0.00005391451],"about_ca_topic_score_codex":0.00014381263,"about_ca_topic_score_gemma":0.00031129958,"teacher_disagreement_score":0.39134765,"about_ca_system_score_codex":0.000010285322,"about_ca_system_score_gemma":0.00027777004,"threshold_uncertainty_score":0.12885049},"labels":[],"label_agreement":null},{"id":"W3028934148","doi":"10.1155/2020/9191834","title":"Price Regulation Mechanism of Travelers’ Travel Mode Choice in the Driverless Transportation Network","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Natural Science Foundation of Hunan Province; Scientific Research Foundation of Hunan Provincial Education Department; National Natural Science Foundation of China","keywords":"Supply and demand; Mode (computer interface); Disequilibrium; Mode choice; Multimodal transport; Travel behavior; Uniqueness; Economics; Computer science; Microeconomics; Transport engineering; Public transport; Engineering","score_opus":0.018701413625481812,"score_gpt":0.27752131508368466,"score_spread":0.25881990145820283,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3028934148","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.789749,0.000108214655,0.20687944,0.0021722708,0.00029913572,0.0004370695,0.000035453588,0.000022385226,0.00029701975],"genre_scores_gemma":[0.9906327,0.00020618903,0.008578923,0.00020986273,0.00021935091,0.0000059759013,0.00011986948,0.000014364255,0.000012762854],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99781036,0.00016221238,0.00093086995,0.00015892535,0.00072897214,0.00020868388],"domain_scores_gemma":[0.99839187,0.0002031522,0.00089047226,0.00007422999,0.00034467428,0.00009562798],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00061458896,0.00013226471,0.00028791238,0.000109569366,0.00014945399,0.000019520314,0.00024190127,0.000118326345,0.000031676922],"category_scores_gemma":[0.00005613575,0.00011799544,0.00013602081,0.00087351433,0.000067154535,0.0007946739,2.2458191e-7,0.00024891575,5.974404e-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":[0.00027312044,0.00006826679,0.006045467,0.000047571946,0.000020924233,0.000009988738,0.14746268,0.8144522,0.001710766,0.028558554,0.000042904172,0.0013075704],"study_design_scores_gemma":[0.0018716623,0.0002279539,0.9685984,0.0001627977,0.0001303506,7.57507e-7,0.022144966,0.0014924301,0.00061621546,0.003714259,0.00084343506,0.00019673642],"about_ca_topic_score_codex":0.00017431204,"about_ca_topic_score_gemma":0.0024567673,"teacher_disagreement_score":0.96255296,"about_ca_system_score_codex":0.00004094272,"about_ca_system_score_gemma":0.00017075933,"threshold_uncertainty_score":0.48117146},"labels":[],"label_agreement":null},{"id":"W3030320748","doi":"10.1139/cjce-2019-0737","title":"Active group route guidance based on DODE: A novel modeling framework integrating dynamic driver behavior","year":2020,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Transportation Planning and Optimization","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":"National Natural Science Foundation of China","keywords":"Reliability (semiconductor); Computer science; Key (lock); Transport engineering; Work (physics); Guidance system; Simulation; Reliability engineering; Engineering; Computer security","score_opus":0.014697966632572345,"score_gpt":0.23996868387813416,"score_spread":0.22527071724556183,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3030320748","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04577981,0.000064169675,0.9523392,0.00080464757,0.0003520387,0.000075574324,0.000025443916,0.000029389734,0.0005297404],"genre_scores_gemma":[0.9820112,0.0000042402958,0.017551236,0.00025557092,0.00014430328,0.0000028652323,0.000006482914,0.000017598963,0.0000065163927],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99919987,0.000017970504,0.00023864294,0.00010356204,0.00021312822,0.0002268006],"domain_scores_gemma":[0.9991687,0.00007020941,0.00010720467,0.00005046617,0.00013587809,0.00046753342],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018722368,0.00009896984,0.00013642224,0.0001541464,0.0001755087,0.00007308782,0.00016852087,0.000089566296,0.00005707272],"category_scores_gemma":[0.00042807643,0.000107728556,0.00006978868,0.0002976679,0.000021416176,0.00023096577,0.0000012463465,0.00038252716,0.0000012423351],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006861906,0.00000454498,0.001277127,0.000006300629,0.000007905234,0.000030375197,0.0071591255,0.98970383,0.000096658165,0.0013150433,0.000016143564,0.00037608662],"study_design_scores_gemma":[0.00020853343,0.000042770804,0.0029879643,0.00035752912,0.000026649135,0.000001353856,0.0011001031,0.99440914,0.0000074510476,0.000018823068,0.00069815654,0.00014150121],"about_ca_topic_score_codex":0.0016044443,"about_ca_topic_score_gemma":0.084384784,"teacher_disagreement_score":0.9362314,"about_ca_system_score_codex":0.00024042992,"about_ca_system_score_gemma":0.00052705564,"threshold_uncertainty_score":0.9323228},"labels":[],"label_agreement":null},{"id":"W3030801938","doi":"10.1051/matecconf/202031401005","title":"Expediency of ATO in heavy rail: A survey for the Dutch Railways","year":2020,"lang":"en","type":"article","venue":"MATEC Web of Conferences","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"European Commission; Canadian Centre for Applied Research in Cancer Control","keywords":"Quality function deployment; Software deployment; Function (biology); Quality (philosophy); Transport engineering; Market segmentation; Computer science; Product (mathematics); Operations research; New product development; Business; Engineering; Marketing","score_opus":0.08254895886404641,"score_gpt":0.3186519086641134,"score_spread":0.23610294980006702,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3030801938","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9681448,0.00035289658,0.0066403816,0.0058287275,0.00025460165,0.00063491403,0.00015296516,0.000062196086,0.017928537],"genre_scores_gemma":[0.9995194,0.00012159486,0.00018806935,0.00004407728,0.000022285049,0.000016543132,0.000017080938,0.000002975906,0.00006795444],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9992732,0.000094386436,0.00024293677,0.00009785792,0.00018256217,0.00010904741],"domain_scores_gemma":[0.99918634,0.00046199936,0.00012261201,0.00005170896,0.0001451135,0.00003220335],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000544999,0.000050479408,0.00014491112,0.000033430275,0.00007769348,0.000018067933,0.00019621466,0.00004536922,0.00015340901],"category_scores_gemma":[0.0003011645,0.000037946607,0.000030805997,0.00026258372,0.0001573433,0.000069617614,0.000004796714,0.00003699496,0.0000014737308],"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":[0.0003975461,0.000082809536,0.7717914,0.0001374681,0.000030420815,9.59955e-7,0.11141994,0.0024010008,0.0002588334,0.1083043,0.0017265676,0.0034487715],"study_design_scores_gemma":[0.0018090318,0.0003313223,0.8735852,0.0001587407,0.000041581163,1.3901672e-7,0.071904376,0.01206118,0.0030506535,0.002235906,0.034455985,0.00036588905],"about_ca_topic_score_codex":0.0042717247,"about_ca_topic_score_gemma":0.009764333,"teacher_disagreement_score":0.106068395,"about_ca_system_score_codex":0.000003692539,"about_ca_system_score_gemma":0.00048579267,"threshold_uncertainty_score":0.6457599},"labels":[],"label_agreement":null},{"id":"W3032904850","doi":"","title":"Prévision et visualisation de l'affluence dans les transports en commun à l'aide de méthodes d'apprentissage automatique","year":2019,"lang":"fr","type":"dissertation","venue":"theses.fr (ABES)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Humanities; Political science; Georeference; Geography; Art","score_opus":0.0354521715139688,"score_gpt":0.34836081992490553,"score_spread":0.31290864841093674,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3032904850","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8440662,0.0004851888,0.13471267,0.000447167,0.00072340417,0.0008989191,0.00018407652,0.00045120087,0.018031158],"genre_scores_gemma":[0.9696602,0.0022335204,0.02160044,0.00016834076,0.00014225031,0.0001057995,0.0041698725,0.00012301741,0.0017965143],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.9939605,0.002563199,0.0010059611,0.0007087712,0.00094017666,0.0008214055],"domain_scores_gemma":[0.996813,0.001357212,0.00079034513,0.00050332444,0.0002591312,0.00027700933],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0033363535,0.0006496428,0.0006861171,0.0002683234,0.0009179148,0.00025403732,0.00079434185,0.0011529323,0.00056429463],"category_scores_gemma":[0.00035551688,0.0007152779,0.00031171626,0.0006647255,0.00029583828,0.0009701693,0.000016187762,0.0008771892,0.000095425305],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009896603,0.00034627464,0.12940176,0.00074255647,0.00010849651,0.000027850721,0.67910933,0.0853086,0.005893364,0.090696186,0.000047194262,0.008219447],"study_design_scores_gemma":[0.0011196734,0.0002394645,0.6102426,0.0045980234,0.00073792774,0.000018299612,0.31507772,0.04736176,0.0099968165,0.0028928115,0.0060155885,0.001699341],"about_ca_topic_score_codex":0.03245114,"about_ca_topic_score_gemma":0.024099296,"teacher_disagreement_score":0.4808408,"about_ca_system_score_codex":0.0005410892,"about_ca_system_score_gemma":0.00089162495,"threshold_uncertainty_score":0.99952984},"labels":[],"label_agreement":null},{"id":"W3034426105","doi":"10.1155/2020/5398298","title":"Resilient Schedule Coordination for a Bus Transit Corridor","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","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":"China Scholarship Council; Science and Technology Commission of Shanghai Municipality","keywords":"Schedule; Transit (satellite); Computer science; Intersection (aeronautics); Resilience (materials science); Transport engineering; Public transport; Operations research; Engineering","score_opus":0.019884207282025015,"score_gpt":0.29511304001821453,"score_spread":0.27522883273618953,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3034426105","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4368893,0.0002202057,0.5507997,0.010511684,0.0006498521,0.0005084917,0.000050106522,0.00006874004,0.00030191298],"genre_scores_gemma":[0.9671225,0.00008205368,0.032091226,0.000275556,0.00024349269,0.000008279749,0.00008898151,0.000013391753,0.00007452896],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99882406,0.00003938493,0.00049649214,0.00012391202,0.00036753865,0.00014858716],"domain_scores_gemma":[0.99869806,0.000092346825,0.00040890658,0.000037378566,0.0006062325,0.00015707474],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030190527,0.000086079745,0.00017995866,0.000082948834,0.00019916886,0.00002588458,0.00010615231,0.00007130829,0.000030126761],"category_scores_gemma":[0.00013952586,0.00008895081,0.00012714842,0.00031238198,0.000045385965,0.00062009424,1.9477686e-7,0.00011018397,0.0000017715038],"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":[0.0015920475,0.00011750414,0.003223094,0.00010126339,0.000052805386,0.000016049336,0.07675611,0.88573647,0.0062525314,0.010775106,0.0009515491,0.014425475],"study_design_scores_gemma":[0.027875366,0.0047322446,0.4118422,0.0008027609,0.0012994795,0.000008227781,0.10294142,0.008249059,0.016669039,0.0060820146,0.41772774,0.0017704486],"about_ca_topic_score_codex":0.000009762094,"about_ca_topic_score_gemma":0.00018424382,"teacher_disagreement_score":0.8774874,"about_ca_system_score_codex":0.00004139618,"about_ca_system_score_gemma":0.00017522798,"threshold_uncertainty_score":0.36273086},"labels":[],"label_agreement":null},{"id":"W3034602597","doi":"10.18564/jasss.4282","title":"A Software Architecture for Mechanism-Based Social Systems Modelling in Agent-Based Simulation Models","year":2020,"lang":"en","type":"article","venue":"Journal of Artificial Societies and Social Simulation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre for Addiction and Mental Health","funders":"National Institute on Alcohol Abuse and Alcoholism; Economic and Social Research Council; National Institutes of Health","keywords":"Computer science; Architecture; Mechanism (biology); Agent-based social simulation; Software architecture; Software; Artificial intelligence; Theoretical computer science; Multi-agent system; Programming language","score_opus":0.08701946918673273,"score_gpt":0.31249773050739205,"score_spread":0.22547826132065932,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3034602597","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10428562,0.000056024594,0.892041,0.0030281267,0.00016994352,0.0003390917,0.000026529984,0.00003901397,0.0000145950735],"genre_scores_gemma":[0.99313277,0.0000048517218,0.0055824188,0.00038809545,0.0008208163,0.00000643628,0.000037528498,0.000018998233,0.000008054931],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99834156,0.0001845103,0.0005932122,0.00015625806,0.0004996284,0.00022485285],"domain_scores_gemma":[0.9985967,0.00038199965,0.0004957643,0.000023771885,0.00042234684,0.00007941522],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00072561315,0.0001273129,0.00028849766,0.000095124895,0.00095568656,0.00016593901,0.0000825022,0.0002508544,0.000005913837],"category_scores_gemma":[0.00009962701,0.00013664413,0.0001878146,0.00024932396,0.00008399156,0.00029898837,0.0000035317362,0.00018862805,2.468796e-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":[0.0003200049,0.00003389899,0.0000865229,0.000065058106,0.000013132181,8.6437166e-7,0.04639482,0.9465792,0.000019841516,0.0048320694,0.00003053822,0.0016240412],"study_design_scores_gemma":[0.0006889422,0.000079974925,0.000050953517,0.000026883912,0.000046730158,2.3693135e-8,0.007949297,0.97956,0.000008811508,0.010819581,0.0006280679,0.00014073821],"about_ca_topic_score_codex":0.00008154588,"about_ca_topic_score_gemma":0.000048335445,"teacher_disagreement_score":0.8888472,"about_ca_system_score_codex":0.000112367474,"about_ca_system_score_gemma":0.00023760421,"threshold_uncertainty_score":0.7350465},"labels":[],"label_agreement":null},{"id":"W3035513085","doi":"10.18757/ejtir.2019.19.2.4379","title":"Multichannel queueing behaviour in urban bicycle traffic","year":2019,"lang":"en","type":"article","venue":"European journal of transport and infrastructure research","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Transport Canada","funders":"","keywords":"Queue; Queueing theory; FIFO (computing and electronics); Computer science; Simulation; Computer network; Transport engineering; Real-time computing; Engineering","score_opus":0.023205249104724712,"score_gpt":0.3158840639190998,"score_spread":0.2926788148143751,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3035513085","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99276996,0.00024194602,0.00019012354,0.000323324,0.00019810261,0.00012757779,0.0000036158526,0.0000123178415,0.0061330497],"genre_scores_gemma":[0.9983598,0.00016271879,0.00087299984,0.000019770989,0.00016418446,3.0244374e-7,0.0000058107094,0.000016383432,0.00039800612],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99812806,0.00047241815,0.00039305238,0.00013874928,0.00057236466,0.00029536372],"domain_scores_gemma":[0.99935645,0.00007227569,0.00010567788,0.00007348849,0.00022676901,0.00016533416],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031589412,0.00008252163,0.00016518631,0.00040020613,0.00017236339,0.000049002752,0.00021772248,0.000048132595,0.00017126903],"category_scores_gemma":[0.00004093065,0.00007289949,0.000051916893,0.00041543704,0.00014494467,0.000320384,0.0000034280315,0.00060130283,0.0000072328507],"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":[0.0001289358,0.000038572056,0.918773,0.000029268142,0.000013125085,0.0002326522,0.05459511,0.019361796,0.00023287385,0.00032176138,0.0003001491,0.005972784],"study_design_scores_gemma":[0.0008014019,0.00013525272,0.9848882,0.00013287063,0.000007706004,0.0000067574824,0.0071048746,0.00019233569,0.000016985445,0.000028017788,0.0065913736,0.00009423242],"about_ca_topic_score_codex":0.00007991341,"about_ca_topic_score_gemma":0.00015297184,"teacher_disagreement_score":0.066115215,"about_ca_system_score_codex":0.000035643658,"about_ca_system_score_gemma":0.00014838712,"threshold_uncertainty_score":0.2972755},"labels":[],"label_agreement":null},{"id":"W3036015049","doi":"10.1155/2020/8850565","title":"Identifying Crowding Impact on Departure Time Choice of Commuters in Urban Rail Transit","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Crowding; Econometrics; Choice set; Value of time; Revealed preference; Transport engineering; Willingness to pay; Mixed logit; Train; Mode choice; Economics; Public transport; Computer science; Statistics; Geography; Logistic regression; Travel time; Mathematics; Microeconomics; Engineering; Psychology","score_opus":0.02392412514458787,"score_gpt":0.32286576419000457,"score_spread":0.2989416390454167,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3036015049","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98834205,0.00012888268,0.010045489,0.0009818199,0.00013534889,0.00013236143,0.000023336856,0.000021477055,0.00018922429],"genre_scores_gemma":[0.9968072,0.00009300903,0.0028126014,0.00012436557,0.00009111433,9.937447e-7,0.000044086188,0.000011807992,0.000014822608],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99867547,0.000080052654,0.00058329286,0.00010502987,0.00040751643,0.00014865669],"domain_scores_gemma":[0.9990461,0.00015527954,0.0004734599,0.000046586236,0.00015829896,0.000120290155],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002870077,0.00010359863,0.00026058598,0.00015798472,0.00008571612,0.000019998173,0.00013666948,0.00007554012,0.000053901887],"category_scores_gemma":[0.00006539793,0.00009849559,0.00014852895,0.000473141,0.000042937074,0.00068424264,3.000162e-7,0.00023026427,0.0000016773014],"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":[0.0006540389,0.00007108716,0.050337646,0.00005186587,0.00003907242,0.000030972657,0.101126574,0.8381889,0.0077738776,0.0003061199,0.00012399504,0.0012958872],"study_design_scores_gemma":[0.0030100876,0.00048200577,0.984326,0.00053722464,0.00010449664,9.180091e-7,0.0065945834,0.00055385934,0.0024595156,0.0001974776,0.0014811271,0.00025268187],"about_ca_topic_score_codex":0.00007458226,"about_ca_topic_score_gemma":0.00032312475,"teacher_disagreement_score":0.9339884,"about_ca_system_score_codex":0.000057326044,"about_ca_system_score_gemma":0.00010122024,"threshold_uncertainty_score":0.40165335},"labels":[],"label_agreement":null},{"id":"W3036117810","doi":"10.18280/ijsdp.150406","title":"Structural Equation Modelling of Household Long-Distance Flexible Travel Behavior","year":2020,"lang":"en","type":"article","venue":"International Journal of Sustainable Development and Planning","topic":"Transportation Planning and Optimization","field":"Social Sciences","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":"Fundamental Research Funds for the Central Universities; Ministry of Education of the People's Republic of China","keywords":"Structural equation modeling; Travel behavior; Transport engineering; Econometrics; Computer science; Economics; Engineering; Mathematics; Statistics","score_opus":0.07912128329893617,"score_gpt":0.2958151129516054,"score_spread":0.21669382965266923,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3036117810","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7720547,0.00023169725,0.2268505,0.00027233717,0.00014991112,0.00006558378,0.0000032253029,0.000011442409,0.00036062725],"genre_scores_gemma":[0.9889874,0.000037737027,0.010503054,0.000060948358,0.00014058391,0.000001529973,0.000021537851,0.0000059695963,0.00024126042],"study_design_codex":"simulation_or_modeling","study_design_gemma":"qualitative","domain_scores_codex":[0.99889404,0.000025580483,0.00038945957,0.00008224431,0.000476301,0.00013240776],"domain_scores_gemma":[0.99888915,0.00005224574,0.00034286853,0.000017863807,0.0006126488,0.00008521593],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033697547,0.00006897202,0.00012161916,0.00014208896,0.00015734062,0.00007393857,0.00015539267,0.000045958655,0.000018592571],"category_scores_gemma":[0.00007214328,0.00006937902,0.000028529517,0.00013992787,0.00003954014,0.00047371275,0.000009122526,0.00010437428,2.194498e-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":[0.00034325384,0.000029596382,0.16563992,0.000060030954,0.00010077585,0.0005149886,0.17234848,0.63902515,0.00004461642,0.018381141,0.00013503346,0.0033769927],"study_design_scores_gemma":[0.004405268,0.0002575807,0.43634295,0.0008567715,0.00019366266,0.00004572523,0.46787527,0.06906118,0.005263905,0.001654965,0.0130210845,0.0010216292],"about_ca_topic_score_codex":0.00006465038,"about_ca_topic_score_gemma":0.0000034202653,"teacher_disagreement_score":0.569964,"about_ca_system_score_codex":0.0000664579,"about_ca_system_score_gemma":0.00028751703,"threshold_uncertainty_score":0.28291944},"labels":[],"label_agreement":null},{"id":"W3036888153","doi":"10.1155/2020/2626084","title":"A Two-Class Stochastic Network Equilibrium Model under Adverse Weather Conditions","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","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":"Science and Technology Commission of Shanghai Municipality; Shanghai Municipal Education Commission; National Natural Science Foundation of China","keywords":"Adverse weather; Class (philosophy); Variational inequality; Computer science; Bounded function; Mathematical optimization; Operations research; Simulation; Engineering; Mathematics; Meteorology; Geography; Artificial intelligence","score_opus":0.022058379846427674,"score_gpt":0.2961725067164845,"score_spread":0.2741141268700568,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3036888153","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4077572,0.00013796569,0.58555263,0.004592841,0.0006373316,0.00026205392,0.00005441698,0.00009763736,0.00090792554],"genre_scores_gemma":[0.9833853,0.00003114519,0.015383996,0.00063527434,0.00033997349,0.000004220361,0.00007793298,0.000018641485,0.00012350516],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986833,0.000051644358,0.0004967143,0.00013338153,0.00042283937,0.00021214626],"domain_scores_gemma":[0.9988924,0.000100797246,0.000426748,0.00005365323,0.00031066732,0.00021567798],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019651641,0.00011049107,0.0001957324,0.00006182159,0.00018717795,0.000019322177,0.000121325,0.00006720802,0.000095754556],"category_scores_gemma":[0.000049769315,0.00011402987,0.00012820869,0.00036291842,0.000081887374,0.00078041933,6.390402e-7,0.00019152474,0.0000054042252],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015470808,0.000026599711,0.0005066405,0.0000073728647,0.000027795455,0.000008718685,0.013506948,0.9750057,0.0006515298,0.009762156,0.00025894147,0.00008289625],"study_design_scores_gemma":[0.028725142,0.001857042,0.1966738,0.0013768392,0.0025819389,0.000018863857,0.119471304,0.54311657,0.00042030698,0.088825,0.013826425,0.0031067594],"about_ca_topic_score_codex":0.000014491868,"about_ca_topic_score_gemma":0.00021902633,"teacher_disagreement_score":0.5756281,"about_ca_system_score_codex":0.000046735862,"about_ca_system_score_gemma":0.00026323783,"threshold_uncertainty_score":0.4650003},"labels":[],"label_agreement":null},{"id":"W3037150108","doi":"10.1155/2020/7160681","title":"Stochastic Risk Assessment with a Lagrangian Solution for the Optimal Cost Allocation in High-Speed Rail Networks","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","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":"National Social Science Fund of China; National Natural Science Foundation of China","keywords":"Lagrangian relaxation; Component (thermodynamics); Mathematical optimization; Computer science; Process (computing); Key (lock); Function (biology); Genetic algorithm; Relaxation (psychology); Reliability engineering; Operations research; Engineering; Mathematics","score_opus":0.014782594896813776,"score_gpt":0.28589231950724714,"score_spread":0.27110972461043337,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3037150108","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12629943,0.00008663295,0.8684285,0.004380637,0.00019089063,0.0005656171,0.00001847771,0.000020450478,0.0000093390645],"genre_scores_gemma":[0.971114,0.00019491099,0.028224697,0.000120483535,0.00019485876,0.000020846905,0.000109868204,0.000013047103,0.000007301796],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99880695,0.00007721816,0.00044754895,0.00012918626,0.00036066675,0.00017845444],"domain_scores_gemma":[0.99862254,0.00026380055,0.0005853109,0.00004868334,0.00038682306,0.000092860755],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005717564,0.00009911666,0.000169598,0.00007074943,0.00028275547,0.000040833766,0.00011732691,0.00006658891,0.0000101773285],"category_scores_gemma":[0.000067032204,0.00007616309,0.0000659852,0.00041141774,0.00006497252,0.00057665707,4.0198196e-7,0.00023045465,3.0925204e-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":[0.0007103349,0.00003519,0.00346786,0.000008442255,0.00002930209,0.0000027222482,0.011649115,0.97578585,0.000060070775,0.0008221539,0.000025243566,0.007403707],"study_design_scores_gemma":[0.006754952,0.00076070585,0.7116754,0.00017838906,0.00041205794,0.0000012372361,0.016297907,0.2622175,0.000024121618,0.00020943851,0.0011801466,0.00028813662],"about_ca_topic_score_codex":0.0001790529,"about_ca_topic_score_gemma":0.0031192745,"teacher_disagreement_score":0.84481454,"about_ca_system_score_codex":0.000082834944,"about_ca_system_score_gemma":0.00019355048,"threshold_uncertainty_score":0.31058404},"labels":[],"label_agreement":null},{"id":"W3039867868","doi":"10.1155/2020/4294195","title":"A Matheuristic Iterative Approach for Profit-Oriented Line Planning Applied to the Chinese High-Speed Railway Network","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Key Research and Development Program of China; China Scholarship Council; National Natural Science Foundation of China","keywords":"Ticket; Computer science; Profit (economics); Revenue; Network planning and design; Mathematical optimization; Operations research; Integer programming; Iterative and incremental development; Net profit; Iterative method; Operator (biology); Linear programming; Algorithm; Engineering; Mathematics; Economics","score_opus":0.01664822144080411,"score_gpt":0.29112563726665025,"score_spread":0.2744774158258461,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3039867868","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2201054,0.0000871077,0.775907,0.002524508,0.00031357998,0.000794204,0.00004160079,0.00004605998,0.00018056876],"genre_scores_gemma":[0.8959638,0.000016269065,0.10235302,0.0005321237,0.0008097802,0.00002785237,0.00023664006,0.00001856474,0.000041934993],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99862796,0.000053527365,0.0005452193,0.00017352619,0.00038461766,0.0002151264],"domain_scores_gemma":[0.99876404,0.00016718931,0.00047315343,0.00006047947,0.00037361964,0.00016151993],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045151118,0.00013678288,0.00025582724,0.000058775582,0.00038589656,0.000043048807,0.00016586401,0.000061995466,0.000009445916],"category_scores_gemma":[0.00013325964,0.000099173114,0.00009270758,0.0007097534,0.000039508344,0.0002708035,9.408533e-7,0.00018179577,0.0000012644424],"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":[0.00095965236,0.000017359363,0.0024917026,0.000025854084,0.000033315246,0.0000033787883,0.07766773,0.9137854,0.00024629943,0.0038630706,0.00021256477,0.00069369824],"study_design_scores_gemma":[0.019384116,0.003273795,0.79070574,0.00071442506,0.0011046086,0.0000066830853,0.08872115,0.042225834,0.0007982488,0.005611352,0.04540972,0.0020443443],"about_ca_topic_score_codex":0.000006785744,"about_ca_topic_score_gemma":0.000029436416,"teacher_disagreement_score":0.87155956,"about_ca_system_score_codex":0.000029278332,"about_ca_system_score_gemma":0.00009421985,"threshold_uncertainty_score":0.40441623},"labels":[],"label_agreement":null},{"id":"W3039868372","doi":"10.1155/2020/8863262","title":"Characteristics Analysis of Bus Stop Failure Using Automatic Vehicle Location Data","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; Government of Jiangsu Province; China Postdoctoral Science Foundation; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Bottleneck; Automatic vehicle location; Queue; Failure rate; Duration (music); Cumulative distribution function; Computer science; Engineering; Real-time computing; Transport engineering; Simulation; Statistics; Reliability engineering; Probability density function; Mathematics; Computer network; Embedded system; Telecommunications","score_opus":0.037400505828429284,"score_gpt":0.31777243925428983,"score_spread":0.28037193342586053,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3039868372","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8802783,0.000060771672,0.11869936,0.00063237187,0.00011982937,0.00008861273,0.0000866487,0.000022263786,0.000011852872],"genre_scores_gemma":[0.97296876,0.000091651265,0.026340771,0.000063130436,0.00008225292,4.0783968e-7,0.00044160118,0.000007933474,0.0000034676007],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9985716,0.00006213858,0.0006956595,0.00012474599,0.00044352893,0.00010236307],"domain_scores_gemma":[0.99817073,0.00007086474,0.00097390765,0.00011651889,0.0005677588,0.00010022091],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032565958,0.00007593769,0.00028855345,0.0001893174,0.000105253865,0.000020291378,0.00021948089,0.000060142218,0.000045734647],"category_scores_gemma":[0.00015555117,0.000078864236,0.00007541861,0.0014121527,0.000051831932,0.0008832528,0.0000011718952,0.000105136176,5.813233e-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":[0.0001044229,0.000067546935,0.046330743,0.00008930823,0.00032481577,0.000010698667,0.044075295,0.89970696,0.0030850105,0.00050981867,0.000023162838,0.0056722458],"study_design_scores_gemma":[0.0007607667,0.000108188004,0.8720273,0.00012707914,0.0025586714,4.3351116e-7,0.010446955,0.11263977,0.00022800331,0.000056762678,0.000866551,0.00017953356],"about_ca_topic_score_codex":0.00005511398,"about_ca_topic_score_gemma":0.0002823373,"teacher_disagreement_score":0.8256965,"about_ca_system_score_codex":0.00003213682,"about_ca_system_score_gemma":0.00020495793,"threshold_uncertainty_score":0.32159904},"labels":[],"label_agreement":null},{"id":"W3041161889","doi":"10.1155/2020/8393724","title":"Experimental Study of Day-to-Day Route-Choice Behavior: Evaluating the Effect of ATIS Market Penetration","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"China Scholarship Council; National Natural Science Foundation of China","keywords":"Market penetration; Travel time; Penetration (warfare); Simulation; Flow network; Transport engineering; Computer science; Travel behavior; Penetration rate; Operations research; Engineering; Mathematics; Mathematical optimization","score_opus":0.02785901884087525,"score_gpt":0.36581500705159986,"score_spread":0.3379559882107246,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3041161889","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99523103,0.000070114205,0.0034290943,0.00018064739,0.0002391074,0.00069302163,0.000014435185,0.00001291356,0.00012965026],"genre_scores_gemma":[0.99808514,0.000014938104,0.0017142188,0.000025255045,0.00009415896,0.0000194303,0.000021274296,0.000011317783,0.000014238638],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9977395,0.00044846663,0.0007612225,0.00013041151,0.00080857,0.00011185231],"domain_scores_gemma":[0.99818355,0.0005288469,0.0008241487,0.0000752184,0.0002952922,0.00009296798],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012444726,0.00010683016,0.00025838055,0.00008028928,0.0001623621,0.00001674112,0.000171523,0.000046234036,0.00007058556],"category_scores_gemma":[0.00031680887,0.00008534193,0.00009287649,0.0004239603,0.00004316199,0.0004644765,0.0000011472433,0.00012707004,4.560457e-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":[0.000978155,0.00032056763,0.3094889,0.00004609983,0.00006524983,0.0000072954704,0.23809563,0.40446505,0.037843242,0.000055179215,0.000048199276,0.00858644],"study_design_scores_gemma":[0.002262553,0.006618723,0.9523149,0.00008312875,0.00028385036,3.2564668e-7,0.025927296,0.00025710487,0.012044799,0.0000053503145,0.0000866934,0.00011527998],"about_ca_topic_score_codex":0.00016174583,"about_ca_topic_score_gemma":0.0005966521,"teacher_disagreement_score":0.64282596,"about_ca_system_score_codex":0.000037599602,"about_ca_system_score_gemma":0.00009145604,"threshold_uncertainty_score":0.3480143},"labels":[],"label_agreement":null},{"id":"W3042006131","doi":"10.1155/2020/3236841","title":"Coordinated Headway-Based Control Method to Improve Public Transit Reliability considering Control Points Layout","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","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":"National Natural Science Foundation of China; Natural Science Foundation of Shanghai","keywords":"Headway; Reliability (semiconductor); Control (management); Dwell time; Public transport; Engineering; Point (geometry); Computer science; Simulation; Transport engineering; Mathematics","score_opus":0.017113394325381198,"score_gpt":0.2977299843464679,"score_spread":0.28061659002108674,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3042006131","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.114084385,0.00004638702,0.8529349,0.031799413,0.0003050397,0.0005349643,0.00011335756,0.000095754585,0.000085786],"genre_scores_gemma":[0.93101054,0.000007209113,0.06614513,0.002630562,0.00012902278,0.000012024589,0.000035049157,0.000021626936,0.000008839612],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9975776,0.0003221553,0.000937467,0.00027568714,0.0005690331,0.000318026],"domain_scores_gemma":[0.9974098,0.0003951646,0.0005547678,0.00009649542,0.0010412053,0.00050255435],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012509886,0.00017978925,0.00046670376,0.00015493162,0.0002474684,0.000065168075,0.00018182355,0.00012778876,0.000080604164],"category_scores_gemma":[0.00048188164,0.00018142707,0.00020472353,0.0005794843,0.00007166706,0.00077794486,3.1646454e-7,0.00029723535,0.0000046557534],"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":[0.0013404314,0.00007973162,0.009458378,0.000047048034,0.000056498637,0.000030998166,0.013181693,0.96522415,0.006265632,0.0007899336,0.000060902028,0.0034645821],"study_design_scores_gemma":[0.09981393,0.0067524374,0.64199513,0.0009697579,0.0024933394,0.000011900095,0.041020885,0.07131852,0.019922616,0.004107238,0.10791822,0.0036760026],"about_ca_topic_score_codex":0.00007101331,"about_ca_topic_score_gemma":0.00026820766,"teacher_disagreement_score":0.89390564,"about_ca_system_score_codex":0.00009268686,"about_ca_system_score_gemma":0.00044882597,"threshold_uncertainty_score":0.7398381},"labels":[],"label_agreement":null},{"id":"W3042155940","doi":"10.1155/2020/2530154","title":"Bilevel Optimization for the Hazmat Transportation Problem with Lane Reservation","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Bilevel optimization; Particle swarm optimization; Mathematical optimization; Reservation; Genetic algorithm; Computer science; Metaheuristic; Key (lock); Computation; Sensitivity (control systems); Optimization problem; Operations research; Engineering; Algorithm; Mathematics","score_opus":0.028091489900667913,"score_gpt":0.2821741575445032,"score_spread":0.2540826676438353,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3042155940","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05120561,0.00016547415,0.9352889,0.012057235,0.00021394342,0.0008417213,0.00007232864,0.00007055519,0.00008423164],"genre_scores_gemma":[0.78669345,0.0004268841,0.21161158,0.0004082074,0.0003014002,0.000040523948,0.00043419527,0.00003035494,0.00005337988],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99839556,0.00005246336,0.0006414928,0.00016412094,0.00056201784,0.00018434392],"domain_scores_gemma":[0.99790525,0.00023234267,0.00075216976,0.00006995702,0.00091964024,0.00012064387],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045947215,0.00013206921,0.00020142374,0.00008232636,0.00040419764,0.00005557346,0.00017701513,0.000085587824,0.000042806707],"category_scores_gemma":[0.0000775109,0.000096551084,0.000098822966,0.0005532727,0.00007858388,0.0010906414,2.3208283e-7,0.00015757063,7.5765763e-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":[0.0008847107,0.000032041404,0.0030034643,0.000057018802,0.000042221523,0.000003784418,0.027807208,0.9608412,0.00015448,0.0031732575,0.00020347547,0.0037971374],"study_design_scores_gemma":[0.024588153,0.005293739,0.63455176,0.00124543,0.0026062764,0.000008633758,0.08883059,0.05889033,0.002203464,0.0046072323,0.17511903,0.0020553668],"about_ca_topic_score_codex":0.00005483886,"about_ca_topic_score_gemma":0.00087850605,"teacher_disagreement_score":0.9019509,"about_ca_system_score_codex":0.000041121522,"about_ca_system_score_gemma":0.00022824066,"threshold_uncertainty_score":0.3937239},"labels":[],"label_agreement":null},{"id":"W3043653890","doi":"10.1155/2020/8844590","title":"Development of Fuzzy Level of Service Criteria for Bus Rapid Transit considering User Heterogeneities in China","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","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":"National Natural Science Foundation of China","keywords":"Bus rapid transit; Transport engineering; Fuzzy logic; Service (business); Public transport; Computer science; Market segmentation; Level of service; Cluster analysis; Metric (unit); China; Business; Engineering; Geography; Marketing; Artificial intelligence","score_opus":0.08271671871830584,"score_gpt":0.325966988420274,"score_spread":0.24325026970196817,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3043653890","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93752974,0.00012063502,0.061129566,0.0007865597,0.00013421303,0.00018516277,0.00007257931,0.000008752551,0.000032762873],"genre_scores_gemma":[0.86510456,0.0000686237,0.13466123,0.00007478009,0.000030507135,0.0000038949565,0.00004417767,0.00000887482,0.0000033508986],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.998651,0.000032384567,0.0008427307,0.000091569644,0.0002655691,0.000116726005],"domain_scores_gemma":[0.9989875,0.00005916,0.0005042353,0.00003206767,0.00035203138,0.00006501162],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029695994,0.000084507934,0.00026912516,0.00010932147,0.0000650097,0.0000071721656,0.000089795554,0.000056279587,0.000019750498],"category_scores_gemma":[0.00003218215,0.00008957224,0.00007429435,0.00026829878,0.00003515018,0.00041992438,3.9231796e-7,0.000069956855,1.10685e-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":[0.0011647361,0.00010647587,0.007085284,0.00064291933,0.000076404685,0.000007494557,0.47328943,0.45444018,0.055222962,0.0010278061,0.000013779979,0.0069225207],"study_design_scores_gemma":[0.006502092,0.00039259036,0.822254,0.0008640037,0.0001609429,0.0000016543794,0.05573976,0.0003317739,0.10738318,0.00066792045,0.0052704588,0.00043160078],"about_ca_topic_score_codex":0.000045035536,"about_ca_topic_score_gemma":0.0022544025,"teacher_disagreement_score":0.81516874,"about_ca_system_score_codex":0.000021808743,"about_ca_system_score_gemma":0.00022817834,"threshold_uncertainty_score":0.36526498},"labels":[],"label_agreement":null},{"id":"W3044181869","doi":"10.5038/2375-0901.22.1.2","title":"Valuing Public Transport Customer Amenities: International Transit Agency Practice","year":2020,"lang":"en","type":"article","venue":"Journal of Public Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Amenity; Public transport; Agency (philosophy); Marketing; Business; Regional science; Geography; Transport engineering; Sociology; Engineering; Finance; Social science","score_opus":0.06190217714009463,"score_gpt":0.31831364010156815,"score_spread":0.25641146296147355,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3044181869","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19342509,0.0005422808,0.57644683,0.18805802,0.0029250702,0.00057366013,0.00017347507,0.00037441702,0.03748114],"genre_scores_gemma":[0.9899153,0.0006529797,0.006835914,0.001542695,0.000672289,0.0000056240274,0.00019753576,0.000026385333,0.00015126103],"study_design_codex":"qualitative","study_design_gemma":"not_applicable","domain_scores_codex":[0.9968244,0.00024094016,0.0010187145,0.00021740707,0.0013645727,0.00033399698],"domain_scores_gemma":[0.9969473,0.00017115255,0.0007953733,0.00007815122,0.0015408561,0.0004671828],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015300572,0.00017158363,0.00027542317,0.00032840596,0.00031593136,0.00018052426,0.00043159578,0.00015751908,0.0008004718],"category_scores_gemma":[0.00043425173,0.00017839664,0.00024219594,0.0008121124,0.00013500097,0.0041613877,8.3653697e-7,0.00039144576,0.0000186816],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00090007304,0.0010552374,0.08578951,0.00018398884,0.0011581855,0.0006479602,0.6728109,0.023744449,0.0015795719,0.13278826,0.004954113,0.07438777],"study_design_scores_gemma":[0.0021490166,0.0001942577,0.056092996,0.00006387956,0.00031567403,0.000014155595,0.03938589,0.0003181625,0.00006636157,0.00027600682,0.9006972,0.00042645802],"about_ca_topic_score_codex":0.00011858755,"about_ca_topic_score_gemma":0.00032297726,"teacher_disagreement_score":0.895743,"about_ca_system_score_codex":0.00010483603,"about_ca_system_score_gemma":0.0007758689,"threshold_uncertainty_score":0.8764605},"labels":[],"label_agreement":null},{"id":"W3044272882","doi":"10.1007/s12469-020-00238-w","title":"Evaluation of bus bridging scenarios for railway service disruption management: a users’ delay modelling tool","year":2020,"lang":"en","type":"article","venue":"Public Transport","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan; University of Toronto","funders":"","keywords":"Bridging (networking); Public transport; Transport engineering; Queueing theory; Service (business); Quality of service; Computer science; Engineering; Transit (satellite); Computer network","score_opus":0.09160856715805758,"score_gpt":0.30652736036059564,"score_spread":0.21491879320253804,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3044272882","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.31151685,0.000053130774,0.68081594,0.004569274,0.00011878151,0.00089619256,0.000054455733,0.00015527579,0.0018201248],"genre_scores_gemma":[0.99081236,0.000049098642,0.008076164,0.00035089717,0.000102198595,0.00009999708,0.00043978437,0.000018557012,0.000050919552],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99807435,0.00008398265,0.00037309996,0.00028270236,0.00092495146,0.0002608834],"domain_scores_gemma":[0.99882364,0.000034728033,0.0001527327,0.00010433182,0.00076975115,0.00011481186],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018372283,0.00011050303,0.00015441369,0.00009994081,0.0002835884,0.00004614935,0.00017717964,0.00008583369,0.000092615795],"category_scores_gemma":[0.000038999475,0.00012622694,0.00008617002,0.00065724034,0.000035923684,0.0006313972,0.0000023625232,0.00006961904,0.0000044086464],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006087524,0.000058837864,0.00883607,0.00016497771,0.00008106439,0.0000013502369,0.029894859,0.9382697,0.000025188829,0.009350662,0.00006396539,0.013192465],"study_design_scores_gemma":[0.00169592,0.000029443469,0.0114360675,0.00006254097,0.0004946764,2.58682e-7,0.0030645507,0.97021574,0.000025470537,0.00034365026,0.01236894,0.00026272537],"about_ca_topic_score_codex":0.00041114094,"about_ca_topic_score_gemma":0.00077288976,"teacher_disagreement_score":0.67929554,"about_ca_system_score_codex":0.000061505765,"about_ca_system_score_gemma":0.00021884937,"threshold_uncertainty_score":0.51473856},"labels":[],"label_agreement":null},{"id":"W3046283084","doi":"10.1155/2020/3753601","title":"Application of Modified NSGA-II to the Transit Network Design Problem","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Sorting; Mathematical optimization; Genetic algorithm; Heuristic; Set (abstract data type); Computation; Multi-objective optimization; Computer science; Network planning and design; Solution set; Routing (electronic design automation); Pareto principle; Algorithm; Mathematics","score_opus":0.022954833325178475,"score_gpt":0.2740857975846605,"score_spread":0.251130964259482,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3046283084","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.043980327,0.00008094646,0.94888526,0.006330496,0.0001180863,0.0004578899,0.000008840885,0.000025511325,0.00011261197],"genre_scores_gemma":[0.915969,0.00009493659,0.08334901,0.00031570366,0.0002175032,0.000011215113,0.000020484815,0.000010075554,0.000012059322],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9986404,0.00009662411,0.00057824084,0.00011244535,0.00042780762,0.00014446407],"domain_scores_gemma":[0.99886155,0.00009538629,0.00047893784,0.00006293342,0.000375077,0.0001261395],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005858182,0.000083796695,0.00018199784,0.000046441448,0.0002562586,0.0000124725575,0.00019283635,0.000057173605,0.000013871102],"category_scores_gemma":[0.000035997342,0.00006805511,0.00008612436,0.0006287309,0.000040651765,0.00031922973,5.413801e-7,0.00012825307,0.0000014374981],"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":[0.00033804527,0.000024297467,0.00037803632,0.000013790096,0.000017672768,0.0000012843014,0.05970967,0.9275741,0.0009514131,0.0029083074,0.00013426681,0.007949094],"study_design_scores_gemma":[0.017394332,0.008927793,0.4726408,0.0012541383,0.0024551156,0.000010275417,0.10100036,0.05435523,0.01581495,0.026460068,0.29677668,0.0029102364],"about_ca_topic_score_codex":0.000039871054,"about_ca_topic_score_gemma":0.00026833723,"teacher_disagreement_score":0.8732189,"about_ca_system_score_codex":0.000020322414,"about_ca_system_score_gemma":0.00012102609,"threshold_uncertainty_score":0.2775207},"labels":[],"label_agreement":null},{"id":"W3046797660","doi":"10.1155/2020/4271871","title":"Estimating Wait Time and Passenger Load in a Saturated Metro Network: A Data-Driven Approach","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Ministry of Science and Technology of the People's Republic of China","keywords":"Transport engineering; Schedule; Public transport; Revenue; Service (business); Service quality; Transit (satellite); Data collection; Travel time; Quality of service; Computer science; Operations research; Engineering; Business; Telecommunications; Marketing","score_opus":0.02853431828910097,"score_gpt":0.289346224897348,"score_spread":0.26081190660824705,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3046797660","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93093854,0.00047763935,0.06568102,0.0019381434,0.00021552724,0.00029347852,0.00004266307,0.000062511186,0.0003505036],"genre_scores_gemma":[0.8183211,0.000072666495,0.18110041,0.00010517667,0.00018849241,0.0000013628467,0.00019010092,0.000009514792,0.000011151364],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99871606,0.00008003746,0.00049133727,0.0001728755,0.00037147463,0.00016819211],"domain_scores_gemma":[0.99915075,0.00008766964,0.0003855351,0.000062614265,0.00019080777,0.00012262305],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004992773,0.00009128876,0.0002234991,0.000059209717,0.000112347225,0.00003787323,0.00014604078,0.00006771396,0.000017395882],"category_scores_gemma":[0.00013100535,0.00008879471,0.000030621075,0.000499946,0.00004406904,0.0009622991,0.000001584326,0.00020858733,0.000001206867],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015023067,0.000024860848,0.007644499,0.000021106598,0.000019525149,0.000020597217,0.018671872,0.9702375,0.00019247344,0.00012645687,0.00010157419,0.0027892964],"study_design_scores_gemma":[0.0043354356,0.00027213278,0.45092267,0.000347269,0.00025599735,0.0000044861454,0.010133869,0.5298638,0.000024621339,0.00043959133,0.002882491,0.0005177023],"about_ca_topic_score_codex":0.000051472845,"about_ca_topic_score_gemma":0.00045604332,"teacher_disagreement_score":0.44327816,"about_ca_system_score_codex":0.00003972281,"about_ca_system_score_gemma":0.00016608626,"threshold_uncertainty_score":0.36209434},"labels":[],"label_agreement":null},{"id":"W3048093630","doi":"","title":"Transit demand determinants: an empirical analysis of long-run and asymmteric effects","year":2020,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Transport engineering; Economics; Econometrics; Engineering","score_opus":0.09766031148021703,"score_gpt":0.4329034583490703,"score_spread":0.33524314686885326,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3048093630","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9762293,0.0002485826,0.019311056,0.003172367,0.0001653288,0.00070173654,0.00006698098,0.000032323474,0.00007232583],"genre_scores_gemma":[0.99679255,0.0011552572,0.0016318532,0.00012620895,0.00012426123,0.000022277518,0.000035875742,0.000032315347,0.00007942445],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9918406,0.0024006881,0.0013427066,0.000450902,0.00329395,0.0006711427],"domain_scores_gemma":[0.99460953,0.0013088525,0.00050745934,0.00027262152,0.002544735,0.00075679447],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004235321,0.00021880893,0.00069827575,0.0011700734,0.00082199555,0.0001374223,0.0007917543,0.0002482597,0.00017607787],"category_scores_gemma":[0.0003281875,0.0001785412,0.000405193,0.005325687,0.0010697925,0.0009262108,0.0000033118304,0.0011497258,0.000002970612],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011145801,0.00020325393,0.93547213,0.000276843,0.0004302981,0.00011993125,0.044282585,0.00982614,0.0006704738,0.0005790986,0.00028569988,0.00673897],"study_design_scores_gemma":[0.0010696648,0.00076883845,0.9886786,0.00015462509,0.0005780038,1.4677431e-7,0.004544201,0.0018966971,0.0005790694,0.0002674006,0.0012840661,0.00017874168],"about_ca_topic_score_codex":0.0057153576,"about_ca_topic_score_gemma":0.07876874,"teacher_disagreement_score":0.073053375,"about_ca_system_score_codex":0.00008440078,"about_ca_system_score_gemma":0.0006273711,"threshold_uncertainty_score":0.9380413},"labels":[],"label_agreement":null},{"id":"W3048161349","doi":"10.1155/2020/1269415","title":"A Proportional-Switch Adjustment Model towards Mixed Equilibrium with Multiroute Choice Behaviour Criterion","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","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":"Science and Technology Bureau, Changsha; Natural Science Foundation of Hunan Province; Changsha University of Science and Technology; National Natural Science Foundation of China","keywords":"Variational inequality; Uniqueness; Stability (learning theory); Traffic flow (computer networking); Residual; Mathematical optimization; Lyapunov function; Computer science; Mathematics; Mathematical economics","score_opus":0.026624127056527967,"score_gpt":0.30072859191551604,"score_spread":0.2741044648589881,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3048161349","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8958723,0.00011483415,0.09921698,0.0040066275,0.0002723518,0.00030863087,0.000041341522,0.000069841226,0.00009710889],"genre_scores_gemma":[0.95056516,0.00010764029,0.048572272,0.0002910998,0.00021725977,0.0000105594245,0.00013316708,0.0000211565,0.000081694816],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9981337,0.000048153128,0.0006169317,0.00019425589,0.00079840986,0.00020850147],"domain_scores_gemma":[0.99841577,0.00002968819,0.0006068787,0.000063566134,0.0006391744,0.00024494127],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002168841,0.00014865711,0.00024634236,0.000088974346,0.00014038713,0.000035379722,0.00014175478,0.00008386476,0.000034450757],"category_scores_gemma":[0.000051493545,0.00013118528,0.00010504756,0.00032572963,0.000067677625,0.0010868353,9.258205e-7,0.00020251767,0.0000016592376],"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":[0.0009467091,0.00013320034,0.02049846,0.000050231643,0.000039179256,0.000033209515,0.033159927,0.9390645,0.0022308885,0.00048753913,0.00012355081,0.0032325815],"study_design_scores_gemma":[0.0041136374,0.0009224479,0.9740888,0.00023344163,0.0003569194,0.0000045035295,0.0071662655,0.008937864,0.0012684366,0.00019065749,0.0022768695,0.00044017824],"about_ca_topic_score_codex":0.00007831405,"about_ca_topic_score_gemma":0.0004097473,"teacher_disagreement_score":0.95359033,"about_ca_system_score_codex":0.00007610606,"about_ca_system_score_gemma":0.0004195802,"threshold_uncertainty_score":0.534958},"labels":[],"label_agreement":null},{"id":"W3048815065","doi":"","title":"Étude de l’impact de l’implantation d’une voie dynamique réservée aux véhicules à occupation multiple : modélisation analytique et numérique du cas d’étude lyonnais","year":2019,"lang":"fr","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Humanities; Political science; Art","score_opus":0.01902172748011281,"score_gpt":0.31400878267772586,"score_spread":0.29498705519761304,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3048815065","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.69260514,0.00015161102,0.29664263,0.0063334317,0.00042001894,0.0006164527,0.0001426551,0.0002430662,0.002845005],"genre_scores_gemma":[0.98925227,0.0014184975,0.005604653,0.00081175787,0.000259906,0.00005710291,0.0015441262,0.00005939552,0.0009922786],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99649364,0.0008835587,0.0007172475,0.00058966514,0.00056546705,0.0007504471],"domain_scores_gemma":[0.9977729,0.0005882379,0.0004892979,0.00029250284,0.0005081585,0.00034894422],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0016923087,0.0004306162,0.000412355,0.00028559854,0.00055801915,0.00028644677,0.00029710974,0.00068924966,0.0007127706],"category_scores_gemma":[0.0003236784,0.00045147876,0.0002551405,0.0010717975,0.0001553882,0.0017153217,0.000018160123,0.00043149613,0.00015461653],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000099683704,0.00015742349,0.4627723,0.00007575386,0.00007461227,0.000012030359,0.046008296,0.47713313,0.0003797484,0.012296689,0.00032973592,0.0006605791],"study_design_scores_gemma":[0.0007446972,0.00011123422,0.5376909,0.00012692432,0.000054210876,0.000016233407,0.0058808327,0.4533327,0.00029699958,0.0010854414,0.00030259308,0.00035728313],"about_ca_topic_score_codex":0.12206771,"about_ca_topic_score_gemma":0.2620338,"teacher_disagreement_score":0.29664716,"about_ca_system_score_codex":0.00096763886,"about_ca_system_score_gemma":0.00088130025,"threshold_uncertainty_score":0.9997937},"labels":[],"label_agreement":null},{"id":"W3049033324","doi":"","title":"Supporting transit-oriented development along the Southwest Rapid Transit Corridor in Winnipeg: recommendations for station area planning","year":2011,"lang":"en","type":"dissertation","venue":"Mspace (University of Manitoba)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Transit (satellite); Transit-oriented development; Transport engineering; Geography; Environmental planning; Engineering; Public transport","score_opus":0.03973408143842472,"score_gpt":0.2702703561613919,"score_spread":0.23053627472296717,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3049033324","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8697189,0.00021220748,0.11900085,0.0018404021,0.0011800681,0.002056304,0.0002985213,0.00019711736,0.005495589],"genre_scores_gemma":[0.97022134,0.00016188681,0.014511378,0.000051705178,0.0000646417,0.000013601542,0.011243835,0.00004531249,0.003686323],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.99858904,0.00013310874,0.00031707506,0.0003132503,0.00032129526,0.00032625961],"domain_scores_gemma":[0.99879456,0.00018045255,0.00053088146,0.000121543664,0.000298707,0.00007384473],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007093734,0.00019033895,0.00027412706,0.00031759118,0.0011097618,0.000031079726,0.00027174794,0.00024614178,0.000055996614],"category_scores_gemma":[0.000047139925,0.00022699648,0.000110269575,0.00041401814,0.00008563123,0.00032941718,0.000003875867,0.00023534709,0.0000044388325],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011069741,0.00027993825,0.12722468,0.00043760185,0.00024237385,0.000033457243,0.8392091,0.008649292,0.00008442171,0.0019350152,0.0042675124,0.016529635],"study_design_scores_gemma":[0.0010484932,0.000056460245,0.30697376,0.0004891108,0.00018120673,3.84365e-7,0.65096444,0.0005772729,0.00009059747,0.00004601285,0.03920428,0.00036797678],"about_ca_topic_score_codex":0.010615002,"about_ca_topic_score_gemma":0.8201594,"teacher_disagreement_score":0.8095444,"about_ca_system_score_codex":0.00012862629,"about_ca_system_score_gemma":0.00047498496,"threshold_uncertainty_score":0.9959734},"labels":[],"label_agreement":null},{"id":"W3059645049","doi":"10.1177/0361198120937310","title":"Existing Problems of Transit Signal Priority on Streetcar Routes","year":2020,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Intersection (aeronautics); Transit (satellite); Public transport; Transport engineering; Bus priority; Computer science; Arrival time; SIGNAL (programming language); Travel time; Operations research; Engineering","score_opus":0.17592691916654593,"score_gpt":0.40845055137041664,"score_spread":0.2325236322038707,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3059645049","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9766375,0.00019799985,0.0073364982,0.012401412,0.00033871716,0.0013013725,0.00015032038,0.00006715065,0.0015690605],"genre_scores_gemma":[0.99632925,0.00064580847,0.0022527184,0.00009779467,0.00031721982,0.000026630738,0.000030391384,0.00004445267,0.0002557468],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9900643,0.0018953823,0.0016362654,0.00043779105,0.0051430706,0.0008231456],"domain_scores_gemma":[0.99281925,0.0014526286,0.0007180028,0.00026873275,0.004150398,0.00059099775],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0052407756,0.00024375123,0.00053048664,0.0006229869,0.0010730766,0.00012291812,0.0011331247,0.0002517142,0.00039685066],"category_scores_gemma":[0.00044042285,0.00020165143,0.00043352085,0.0029099896,0.0010074442,0.000680163,0.0000030906874,0.0020373582,0.000013284842],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0049215686,0.0009629796,0.61020094,0.0012634839,0.0004305026,0.00017084421,0.15948398,0.14375088,0.0073582316,0.046564247,0.006015202,0.018877156],"study_design_scores_gemma":[0.0033666103,0.0023061023,0.9193349,0.001395187,0.00015329884,1.9198363e-7,0.023549208,0.00091543666,0.0038950231,0.003269326,0.04131371,0.0005010206],"about_ca_topic_score_codex":0.01310276,"about_ca_topic_score_gemma":0.04066211,"teacher_disagreement_score":0.30913395,"about_ca_system_score_codex":0.0001582588,"about_ca_system_score_gemma":0.0011979752,"threshold_uncertainty_score":0.99346906},"labels":[],"label_agreement":null},{"id":"W3080416813","doi":"10.1139/cjce-2020-0222","title":"Research on optimized design of road space in mixed sections of historical district: a case study of Xi’an, China","year":2020,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Transportation Planning and Optimization","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":"","keywords":"Plan (archaeology); Division (mathematics); Space (punctuation); Section (typography); China; Transport engineering; Research Object; Computer science; Operations research; Engineering; Geography; Mathematics; Regional science","score_opus":0.06906915581630357,"score_gpt":0.30380225162580216,"score_spread":0.23473309580949858,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3080416813","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96634376,0.000113981776,0.032740857,0.0002540331,0.00017090974,0.00015911639,0.0000075572707,0.0000053370713,0.00020442145],"genre_scores_gemma":[0.9989098,0.000009748103,0.0010136921,0.0000015867572,0.000042428255,0.000001612637,7.0601544e-7,0.0000073547685,0.00001309659],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989594,0.0001787623,0.00036003036,0.00007092167,0.00028159944,0.0001492409],"domain_scores_gemma":[0.99912786,0.00014086207,0.00013691734,0.000058339097,0.00021271997,0.0003233062],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008493297,0.000053914187,0.00020730923,0.00048586004,0.00007177936,0.0000091159645,0.000121875644,0.0000488847,0.000023451066],"category_scores_gemma":[0.00046906335,0.000058086047,0.00003531117,0.00090826646,0.000029148687,0.00010496981,0.0000015930485,0.00024218607,8.330203e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003217633,0.000052552918,0.0017214568,0.000015233758,0.000014042796,0.00028776866,0.04019639,0.9573608,0.000058295325,0.00008802958,0.00010883842,0.000064386215],"study_design_scores_gemma":[0.011765459,0.009825467,0.24013552,0.0012347394,0.00029393574,0.00022260711,0.2888994,0.44390893,0.0008106768,0.00007281546,0.001796442,0.0010340208],"about_ca_topic_score_codex":0.06744667,"about_ca_topic_score_gemma":0.22988322,"teacher_disagreement_score":0.5134519,"about_ca_system_score_codex":0.00026378443,"about_ca_system_score_gemma":0.0006545215,"threshold_uncertainty_score":0.93876326},"labels":[],"label_agreement":null},{"id":"W3081191273","doi":"10.1080/01441647.2020.1806942","title":"Waiting time and headway modelling for urban transit systems – a critical review and proposed approach","year":2020,"lang":"en","type":"review","venue":"Transport Reviews","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":65,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Headway; Public transport; Transit (satellite); Schedule; Transport engineering; Computer science; Service (business); Operations research; TRIPS architecture; Plan (archaeology); Travel time; Business; Engineering; Geography; Marketing","score_opus":0.1399593117712731,"score_gpt":0.3623950257524949,"score_spread":0.22243571398122178,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3081191273","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[1.3562065e-7,0.95376784,0.038019568,0.00027716838,0.000069334244,0.0066115353,0.00021651748,0.00013027435,0.0009076542],"genre_scores_gemma":[0.0000070573956,0.9910685,0.0068091857,0.00012691075,0.00022469806,0.00036797783,0.00092389,0.00006819462,0.00040360758],"study_design_codex":"systematic_review","study_design_gemma":"not_applicable","domain_scores_codex":[0.99660784,0.00045867878,0.0014637219,0.0007676633,0.00028673274,0.0004153438],"domain_scores_gemma":[0.9987502,0.0002463736,0.0003672815,0.00018985601,0.00010716309,0.00033909848],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0019753363,0.00049760705,0.0033110392,0.00007951195,0.00044620835,0.0000852402,0.00020720852,0.0003300643,0.000021321257],"category_scores_gemma":[0.00009264988,0.00040766623,0.00045562253,0.00040158464,0.00020425448,0.0002033494,0.0000027815377,0.00034592242,0.000010607222],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008748641,0.000058164867,0.000003536072,0.6423707,0.00011576746,0.000011158971,0.003892547,0.00014954314,6.9777066e-8,0.008292391,0.00094277674,0.3441546],"study_design_scores_gemma":[0.000104798186,0.000028885133,1.2593843e-7,0.04859956,0.0037018857,0.000008964729,0.00003664915,0.002708229,7.65885e-9,0.0000064467167,0.944383,0.00042143074],"about_ca_topic_score_codex":0.000081128266,"about_ca_topic_score_gemma":0.000010126392,"teacher_disagreement_score":0.94344026,"about_ca_system_score_codex":0.000040978826,"about_ca_system_score_gemma":0.00029474992,"threshold_uncertainty_score":0.9998375},"labels":[],"label_agreement":null},{"id":"W3081255775","doi":"10.1007/s12469-020-00246-w","title":"The impact of centroid connectors on transit assignment outcomes","year":2020,"lang":"en","type":"article","venue":"Public Transport","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Association Nationale de la Recherche et de la Technologie","keywords":"Transit (satellite); Centroid; Reliability (semiconductor); Transport engineering; Computer science; Rail transit; Transit system; Public transport; Engineering; Simulation; Geography; Artificial intelligence","score_opus":0.04007582065022759,"score_gpt":0.30729668416030426,"score_spread":0.2672208635100767,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3081255775","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92364824,0.000083542815,0.012206843,0.045391534,0.0002859145,0.0005805491,0.00017756884,0.00028387347,0.01734192],"genre_scores_gemma":[0.99941415,0.00006376,0.0000539423,0.00020741789,0.000044726505,0.0000085976135,0.00007151275,0.000010040988,0.00012583825],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9987914,0.00007135573,0.00026986684,0.00015908001,0.00043856827,0.00026973418],"domain_scores_gemma":[0.99936205,0.00015217566,0.00009479679,0.00010112101,0.000077573626,0.00021228674],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032222658,0.00010623869,0.0001665391,0.0000338426,0.00032746585,0.000034477205,0.00021846964,0.00005942507,0.00020559314],"category_scores_gemma":[0.00006200902,0.00006994816,0.0001996712,0.00029981148,0.0001356724,0.00014305346,7.6021996e-7,0.000102245576,0.0000100800335],"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":[0.00010019473,0.000114736904,0.9292816,0.000008184227,0.00013248883,0.0000041595586,0.022215543,0.0151110245,0.0000504924,0.030165987,0.00076758565,0.002048022],"study_design_scores_gemma":[0.0005533546,0.00013688077,0.97796106,0.000006515275,0.000024526204,6.9211374e-8,0.0014159032,0.00015827159,0.000038332742,0.000064419386,0.019512368,0.0001283242],"about_ca_topic_score_codex":0.0006957015,"about_ca_topic_score_gemma":0.00047494544,"teacher_disagreement_score":0.07576591,"about_ca_system_score_codex":0.000044480905,"about_ca_system_score_gemma":0.00023712084,"threshold_uncertainty_score":0.28524032},"labels":[],"label_agreement":null},{"id":"W3081649496","doi":"10.1155/2020/8848741","title":"Psychological Influences on Bus Travel Mode Choice: A Comparative Analysis between Two Chinese Cities","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Social Science Fund of China","keywords":"Theory of planned behavior; Mode (computer interface); Sample (material); Travel behavior; Mode choice; Transport engineering; Psychology; Control (management); Econometrics; Computer science; Public transport; Mathematics; Engineering; Artificial intelligence","score_opus":0.04687470281114951,"score_gpt":0.3997318687301793,"score_spread":0.35285716591902977,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3081649496","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9771768,0.00005969715,0.020629048,0.0011696257,0.00011694245,0.00011746601,0.00006142305,0.00003519426,0.0006338138],"genre_scores_gemma":[0.9945861,0.00008475914,0.0046320963,0.00026337375,0.0002936112,0.000003446465,0.0001120438,0.000005598026,0.000018985374],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9983282,0.00011142937,0.0006259419,0.00018408742,0.000584536,0.00016579428],"domain_scores_gemma":[0.9986392,0.00024823434,0.00056036236,0.00005571123,0.00030199214,0.00019446645],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020576236,0.00014138203,0.00043237553,0.00020971938,0.00021625591,0.000039611514,0.00017815485,0.00007503663,0.000059990816],"category_scores_gemma":[0.00007486814,0.000116743984,0.00021409827,0.0011762627,0.00011799503,0.0005869351,4.1541733e-7,0.00025359524,0.0000030774165],"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":[0.00021998375,0.00004611129,0.24338244,0.0000060320635,0.00016898995,0.0000085837155,0.05626064,0.69823915,0.00016975887,0.00068799965,0.000016612814,0.0007937236],"study_design_scores_gemma":[0.0009988955,0.00028913407,0.9915615,0.000024303834,0.00032728043,1.5369622e-7,0.0056625316,0.00024268401,0.00006072446,0.0004437922,0.00025144155,0.00013758428],"about_ca_topic_score_codex":0.000097544806,"about_ca_topic_score_gemma":0.0007429337,"teacher_disagreement_score":0.748179,"about_ca_system_score_codex":0.000030222296,"about_ca_system_score_gemma":0.00005643514,"threshold_uncertainty_score":0.47606817},"labels":[],"label_agreement":null},{"id":"W3081691901","doi":"10.1061/9780784483176.001","title":"A Critical Review of Transit Level of Service Measures and an Overview of a Proposed New Approach","year":2020,"lang":"en","type":"review","venue":"International Conference on Transportation and Development 2020","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Measure (data warehouse); Computer science; Quality of service; Service (business); Service level; Transport engineering; Transit (satellite); Quality (philosophy); Service quality; Public transport; Level of service; Operator (biology); Operations research; Risk analysis (engineering); Telecommunications; Engineering; Business; Data mining; Marketing","score_opus":0.4568508718963828,"score_gpt":0.44734235435045433,"score_spread":0.009508517545928463,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3081691901","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000010609903,0.9836768,0.011202407,0.0013913106,0.00010495675,0.0007751861,0.00052265456,0.00002846119,0.0022876023],"genre_scores_gemma":[0.001167512,0.9868444,0.01050224,0.00019813784,0.000024830782,0.000016718384,0.0011653324,0.000013191047,0.00006760111],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99780387,0.00014036155,0.00095851393,0.00032467608,0.0006750158,0.000097593926],"domain_scores_gemma":[0.99860257,0.000092283306,0.0004407821,0.00007732684,0.0006305637,0.00015649281],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035972762,0.00020720523,0.00084996206,0.00011614862,0.00005160747,0.000019874056,0.00021662345,0.00014277366,0.00024762776],"category_scores_gemma":[0.000086160544,0.00019147637,0.000084697436,0.00029362162,0.00011881254,0.00017216717,0.000002064897,0.0001380027,0.0000015966494],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010061088,0.00023627795,0.00009988453,0.109623365,0.00029924972,0.0000058721703,0.019466141,0.000014717414,0.000006418309,0.14139727,0.00018037386,0.7285698],"study_design_scores_gemma":[0.0011601789,0.0001994584,0.0028062821,0.14545478,0.0010620485,0.000004139189,0.002164135,0.00012534628,0.000051976785,0.0005943405,0.84547544,0.0009018621],"about_ca_topic_score_codex":0.00024466423,"about_ca_topic_score_gemma":0.00042716725,"teacher_disagreement_score":0.8452951,"about_ca_system_score_codex":0.000019707963,"about_ca_system_score_gemma":0.0016304214,"threshold_uncertainty_score":0.780818},"labels":[],"label_agreement":null},{"id":"W3082187706","doi":"10.1155/2020/9272845","title":"The Influence of Jam Density and Merging Cyclists on the Queue Discharge Rate","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","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":"European Research Council","keywords":"Queue; Merge (version control); Computer science; Simulation; Transport engineering; Computer network; Engineering","score_opus":0.011178939519153094,"score_gpt":0.2691530125903285,"score_spread":0.25797407307117537,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3082187706","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9901768,0.000082696926,0.0020020693,0.0074915,0.00008443753,0.00009646976,0.0000055635237,0.000008742866,0.00005174541],"genre_scores_gemma":[0.99863875,0.0006625034,0.00037617775,0.00025327574,0.000047859234,0.0000012579433,0.0000040699515,0.000004515109,0.000011585069],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99915063,0.00009527691,0.000329965,0.0000700411,0.00026475845,0.00008930027],"domain_scores_gemma":[0.99891746,0.00032753154,0.00042573732,0.000041617197,0.00022428186,0.00006337504],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051384105,0.000058403923,0.00010443831,0.000021806845,0.00034810265,0.00002328427,0.00010300117,0.000028227947,0.0000037999437],"category_scores_gemma":[0.0002227374,0.000035911413,0.00004410761,0.000186713,0.00011799029,0.00031777227,5.259288e-7,0.00014072473,4.649029e-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":[0.00060755876,0.000030995736,0.03367177,0.0000322446,0.000046281908,0.000010243435,0.09000722,0.81142247,0.008019444,0.053479362,0.00006645008,0.0026059863],"study_design_scores_gemma":[0.0003976172,0.000082854,0.987285,0.00008987321,0.00004017651,3.8193662e-7,0.007608163,0.00013971188,0.0012893485,0.0008572527,0.0021377846,0.00007184746],"about_ca_topic_score_codex":0.000028952601,"about_ca_topic_score_gemma":0.00028796372,"teacher_disagreement_score":0.9536132,"about_ca_system_score_codex":0.000010279772,"about_ca_system_score_gemma":0.000058690846,"threshold_uncertainty_score":0.26773593},"labels":[],"label_agreement":null},{"id":"W3082775988","doi":"10.1287/ijoc.2020.0967","title":"A Learning-Based Matheuristic for Stochastic Multicommodity Network Design","year":2020,"lang":"en","type":"article","venue":"INFORMS journal on computing","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal; Université de Montréal","funders":"","keywords":"Mathematical optimization; Heuristic; Network planning and design; Computer science; Quality (philosophy); Stochastic programming; Mathematics","score_opus":0.05454592099144406,"score_gpt":0.30608663531899005,"score_spread":0.251540714327546,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3082775988","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008165923,0.000010710248,0.98996747,0.0010553509,0.00028685998,0.00020332876,0.0000013845342,0.00011976889,0.00018919347],"genre_scores_gemma":[0.9549967,0.0000015040852,0.04322376,0.0010114429,0.0007267782,0.0000018174651,0.000007673741,0.00001076305,0.000019582994],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99898916,0.000084061125,0.0002947742,0.00009062592,0.00026252127,0.0002788435],"domain_scores_gemma":[0.9984333,0.00091713056,0.00027684285,0.00003229244,0.00015031885,0.00019012908],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009517378,0.00008816117,0.0001327558,0.00004023297,0.0012233597,0.00017944115,0.00014449157,0.000056712917,0.000018701267],"category_scores_gemma":[0.0009919237,0.00007843355,0.000074421434,0.00018284103,0.00003921955,0.000117369775,0.0000040130467,0.00031547042,0.000014168404],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010204446,0.000010480887,0.00037991055,0.00000703646,0.000008335662,0.000002963842,0.005596723,0.98721844,5.429627e-7,0.0005488922,0.00051388436,0.005610733],"study_design_scores_gemma":[0.0006341059,0.00027880282,0.0007124528,0.00008254414,0.000014558475,0.0000011769647,0.0004898083,0.99367684,0.0000017884597,0.0002567345,0.0037369172,0.0001142634],"about_ca_topic_score_codex":0.000006273054,"about_ca_topic_score_gemma":0.0000023172229,"teacher_disagreement_score":0.94683075,"about_ca_system_score_codex":0.000045667664,"about_ca_system_score_gemma":0.00018770019,"threshold_uncertainty_score":0.9409218},"labels":[],"label_agreement":null},{"id":"W3083443405","doi":"10.1155/2020/8872748","title":"Optimal Design of Bus Stop Locations Integrating Continuum Approximation and Discrete Models","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","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":"Southwest Jiaotong University; National Natural Science Foundation of China","keywords":"Mathematical optimization; Intersection (aeronautics); Minification; Constraint (computer-aided design); Computer science; Optimal design; Nonlinear system; Point location; Point (geometry); Mathematics; Engineering; Transport engineering","score_opus":0.02703651678371497,"score_gpt":0.2828070050198663,"score_spread":0.2557704882361513,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3083443405","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19907643,0.0001395668,0.799594,0.00082289154,0.00007483735,0.00017293889,0.0000108184295,0.000016094576,0.00009242585],"genre_scores_gemma":[0.8296245,0.00021008178,0.17002533,0.0000369823,0.000053642645,0.0000030796873,0.00002741925,0.000007822686,0.000011194552],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988439,0.00007793852,0.0005675487,0.000104072664,0.00030421471,0.0001023208],"domain_scores_gemma":[0.9986899,0.000110194414,0.0006246499,0.000036638514,0.00043554822,0.00010306018],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003438721,0.00008333054,0.00019421679,0.00008207628,0.0001326938,0.00002333835,0.00008014693,0.000058119567,0.000008557561],"category_scores_gemma":[0.000098821765,0.000080570455,0.000050690494,0.00025551612,0.000092479706,0.0008274386,5.625624e-7,0.00012658999,2.2249498e-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":[0.00016331927,0.000019179544,0.0005439247,0.000029947723,0.000017465718,0.0000021618941,0.07047167,0.9179145,0.0027940716,0.005831555,0.000014286709,0.0021978936],"study_design_scores_gemma":[0.013347775,0.0033239373,0.12956399,0.0018936853,0.001390294,0.000013730825,0.37057203,0.43835685,0.014463669,0.022895845,0.0023815832,0.0017966075],"about_ca_topic_score_codex":0.000025947818,"about_ca_topic_score_gemma":0.00006338136,"teacher_disagreement_score":0.630548,"about_ca_system_score_codex":0.000019693221,"about_ca_system_score_gemma":0.00012590083,"threshold_uncertainty_score":0.32855678},"labels":[],"label_agreement":null},{"id":"W3083864203","doi":"10.1155/2020/8836511","title":"Heterogenous Trip Distance-Based Route Choice Behavior Analysis Using Real-World Large-Scale Taxi Trajectory Data","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Key Research and Development Program of China; Shaanxi Provincial Science and Technology Department","keywords":"Multinomial logistic regression; Trajectory; Revealed preference; Akaike information criterion; Computer science; Scale (ratio); Travel behavior; Statistic; Econometrics; TRIPS architecture; Statistics; Preference; Variable (mathematics); Operations research; Transport engineering; Mathematics; Geography; Engineering","score_opus":0.05060259645794654,"score_gpt":0.3386272164008085,"score_spread":0.288024619942862,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3083864203","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7302676,0.00014861643,0.2681629,0.0003165213,0.00025803255,0.00021287824,0.0005139589,0.000059271708,0.0000602249],"genre_scores_gemma":[0.9638455,0.000124589,0.034560405,0.00016524624,0.00023030391,0.0000036313709,0.0010201548,0.00002087986,0.000029302495],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9978425,0.00012078875,0.00080579135,0.00028913285,0.0006764877,0.0002652932],"domain_scores_gemma":[0.9982756,0.00009927707,0.0008470905,0.00019913325,0.00032575626,0.00025315105],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044582487,0.0001555729,0.00039577912,0.00027271087,0.00033407702,0.000055109937,0.00036018918,0.00007950618,0.000076363525],"category_scores_gemma":[0.000047235437,0.00016288974,0.00022341315,0.0016220344,0.00006383637,0.0011022673,0.0000013280476,0.00022559369,9.034688e-7],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002408643,0.0001410842,0.24333036,0.000024767673,0.00013075823,0.000045007448,0.013683443,0.7408498,0.00060017843,0.000049017566,0.000019885147,0.00088484166],"study_design_scores_gemma":[0.0027506063,0.00015678968,0.9619671,0.00008094645,0.0034247374,4.989594e-7,0.0057239146,0.013269617,0.00031649848,0.000014418709,0.011882633,0.0004122348],"about_ca_topic_score_codex":0.0004063469,"about_ca_topic_score_gemma":0.029722614,"teacher_disagreement_score":0.7275802,"about_ca_system_score_codex":0.000115435345,"about_ca_system_score_gemma":0.0003250566,"threshold_uncertainty_score":0.9879824},"labels":[],"label_agreement":null},{"id":"W3087279774","doi":"10.1155/2020/2010875","title":"Dynamic Combined-Mode Traffic Network Model considering Transfer Behaviors","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","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":"Fundamental Research Funds for the Central Universities; Xi'an Municipal Bureau of Science and Technology; Natural Science Foundation of Shaanxi Province; National Natural Science Foundation of China","keywords":"Queue; Mode (computer interface); Heuristic; Mode choice; Computer science; Transfer (computing); Point (geometry); Variational inequality; Simulation; Mathematical optimization; Operations research; Public transport; Transport engineering; Engineering; Artificial intelligence; Computer network; Mathematics","score_opus":0.015701518975172895,"score_gpt":0.2876051384431972,"score_spread":0.27190361946802427,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3087279774","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.84836787,0.00011167524,0.14942113,0.0014840678,0.0002797255,0.0001636379,0.000020202726,0.00007269452,0.00007897467],"genre_scores_gemma":[0.96864194,0.0003213282,0.030587116,0.00025915346,0.000080594116,0.0000033168085,0.0000628055,0.000021172717,0.000022598468],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984969,0.00004909573,0.0006345985,0.00014792291,0.0004414951,0.00023001072],"domain_scores_gemma":[0.9992181,0.00005733388,0.00021708454,0.000050232135,0.0002412362,0.00021600013],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021482293,0.0001280887,0.00025418145,0.00006729816,0.00023040599,0.000029694547,0.0001322319,0.00009600758,0.000033844484],"category_scores_gemma":[0.00002360312,0.0001367815,0.00014632645,0.000337599,0.000073480645,0.0006771124,3.4247714e-7,0.00024458062,0.000001702176],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002349025,0.000034720586,0.0011554653,0.000016302763,0.000018189909,0.000021557986,0.034951087,0.96023864,0.0006669411,0.0010576707,0.000043729648,0.0015608105],"study_design_scores_gemma":[0.024343988,0.0029481258,0.21011154,0.0013401732,0.0024317924,0.000018525217,0.06628814,0.67080647,0.0011493335,0.006259003,0.011022839,0.0032800536],"about_ca_topic_score_codex":0.000009002827,"about_ca_topic_score_gemma":0.0005476965,"teacher_disagreement_score":0.28943214,"about_ca_system_score_codex":0.000044383836,"about_ca_system_score_gemma":0.00021093724,"threshold_uncertainty_score":0.5577788},"labels":[],"label_agreement":null},{"id":"W3091129065","doi":"10.24012/dumf.667374","title":"Gelişmekte Olan ve Gelişmiş Ülkelerde Yüksek Hızlı Demiryolu Yatırım Kriterlerinin İncelenmesi","year":2020,"lang":"tr","type":"article","venue":"DÜMF Mühendislik Dergisi","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Stantec (Canada)","funders":"","keywords":"Physics","score_opus":0.03202586407707694,"score_gpt":0.2793766431226636,"score_spread":0.24735077904558664,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3091129065","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.72961634,0.010832951,0.02963283,0.05318108,0.008739831,0.0038511602,0.0021830613,0.003930463,0.15803225],"genre_scores_gemma":[0.98342717,0.0019605625,0.0038099014,0.003628642,0.0018268444,0.00006186128,0.0007220381,0.0001557325,0.0044072615],"study_design_codex":"qualitative","study_design_gemma":"not_applicable","domain_scores_codex":[0.99374163,0.00045958586,0.0013393915,0.0014918006,0.0014742757,0.0014933108],"domain_scores_gemma":[0.99696654,0.0003466434,0.00069896237,0.0004891422,0.0003148335,0.0011838521],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00073650136,0.0008131686,0.0009035253,0.00023585695,0.0012529842,0.00060134707,0.0011262888,0.00072729704,0.0027455958],"category_scores_gemma":[0.00029884448,0.0009163558,0.00045917497,0.0016162016,0.0004666349,0.00095267384,0.000092589864,0.0008763901,0.0014590038],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00138924,0.0011921377,0.19220725,0.0011003783,0.0012989864,0.0010798447,0.4032536,0.02597635,0.0013337411,0.027067805,0.30925155,0.034849126],"study_design_scores_gemma":[0.0043283394,0.00075942674,0.080099076,0.000508532,0.0007573372,0.000019051658,0.029259637,0.019917317,0.0005447385,0.00082170404,0.85985446,0.0031303843],"about_ca_topic_score_codex":0.0010691024,"about_ca_topic_score_gemma":0.0008830876,"teacher_disagreement_score":0.5506029,"about_ca_system_score_codex":0.00019021747,"about_ca_system_score_gemma":0.0004945774,"threshold_uncertainty_score":0.99932873},"labels":[],"label_agreement":null},{"id":"W3092076984","doi":"10.1155/2020/8860115","title":"An Optimization Method of Multiclass Price Railway Passenger Transport Ticket Allocation under High Passenger Demand","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","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":"China Academy of Railway Sciences; South China University of Technology; National Natural Science Foundation of China; China Railway","keywords":"Ticket; Computer science; Particle swarm optimization; Operations research; Aviation; Transport engineering; Mathematical optimization; Engineering; Computer network; Algorithm","score_opus":0.01766407162787777,"score_gpt":0.3072866513331738,"score_spread":0.28962257970529603,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3092076984","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2307,0.0000615379,0.76669466,0.0018873877,0.00024314555,0.0002580407,0.000026746817,0.000052053932,0.00007641388],"genre_scores_gemma":[0.764107,0.00025380703,0.2350358,0.00018363676,0.00016046662,0.0000049471105,0.00021140027,0.00002547733,0.000017447674],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9973678,0.0002593224,0.0011094606,0.00025750665,0.000775218,0.0002307099],"domain_scores_gemma":[0.9973016,0.00020074852,0.0010761537,0.00011917164,0.0010228181,0.00027953036],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008487642,0.00018846762,0.0003979628,0.00017721349,0.00018011779,0.000026669439,0.00021000058,0.00018509933,0.00013684161],"category_scores_gemma":[0.000089080204,0.0001899627,0.00014321532,0.00067077804,0.000088402034,0.0015945998,5.6715976e-7,0.00023454615,0.0000013442985],"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":[0.00039233247,0.00014715095,0.0031568874,0.000057149737,0.000059581373,0.000009262713,0.028226066,0.95669025,0.0073176026,0.0020531816,0.000017073648,0.0018734607],"study_design_scores_gemma":[0.007936585,0.001602749,0.8731552,0.0003860999,0.001262221,0.000007638719,0.04158575,0.052524883,0.015555246,0.0012417425,0.0035047329,0.0012371741],"about_ca_topic_score_codex":0.00007507921,"about_ca_topic_score_gemma":0.00022440049,"teacher_disagreement_score":0.9041654,"about_ca_system_score_codex":0.000061561805,"about_ca_system_score_gemma":0.00026910965,"threshold_uncertainty_score":0.7746454},"labels":[],"label_agreement":null},{"id":"W3096971240","doi":"10.1155/2020/8816020","title":"Origin-Destination-Based Travel Time Reliability under Different Rainfall Intensities: An Investigation Using Open-Source Data","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","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":"China Scholarship Council; Chang'an University","keywords":"Reliability (semiconductor); Consistency (knowledge bases); Meteorology; Statistics; Travel time; Range (aeronautics); Environmental science; Log-normal distribution; Computer science; Mathematics; Transport engineering; Geography; Engineering","score_opus":0.07669797682512933,"score_gpt":0.3364626780941337,"score_spread":0.2597647012690044,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3096971240","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7719757,0.000018246303,0.2244286,0.0029813375,0.00017456998,0.00027472264,0.00005545763,0.000049759048,0.000041631934],"genre_scores_gemma":[0.95595086,0.000017647752,0.04209421,0.00082268956,0.00017468669,0.000002059547,0.0008808799,0.000023963928,0.00003302774],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99795,0.00022355177,0.0007598178,0.00030327786,0.0005835558,0.00017975707],"domain_scores_gemma":[0.997832,0.00019911295,0.0007444942,0.00020173246,0.0007356266,0.0002870463],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00079587795,0.00015618277,0.00029465315,0.000105125764,0.00031276647,0.00012904925,0.0005721525,0.00009719351,0.00006939503],"category_scores_gemma":[0.00024864398,0.000152884,0.000058309855,0.00040601543,0.00014490317,0.0025248153,0.000004235174,0.00022584778,0.0000019082888],"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":[0.00031224897,0.00008576081,0.010939266,0.000032744556,0.000019094234,0.0000057537654,0.026042087,0.9576868,0.0032879608,0.0005897529,0.00005730386,0.00094126083],"study_design_scores_gemma":[0.006420984,0.00090282894,0.6933779,0.00055917,0.0005483041,0.0000036556862,0.04319386,0.24624556,0.0015389059,0.0034061386,0.0028192403,0.0009834475],"about_ca_topic_score_codex":0.000272639,"about_ca_topic_score_gemma":0.0004041932,"teacher_disagreement_score":0.7114412,"about_ca_system_score_codex":0.00012311399,"about_ca_system_score_gemma":0.0005915016,"threshold_uncertainty_score":0.6234428},"labels":[],"label_agreement":null},{"id":"W3096978635","doi":"10.1155/2020/9020574","title":"Effects of Link Capacity Reductions on the Reliability of an Urban Rail Transit Network","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":14,"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":"China Scholarship Council","keywords":"Train; Reliability (semiconductor); Crowding; Transport engineering; Computer science; Engineering; Geography; Power (physics)","score_opus":0.01466938648796928,"score_gpt":0.2560021771709568,"score_spread":0.24133279068298752,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3096978635","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98024493,0.00006527962,0.016137118,0.0028841437,0.0003050163,0.00023915192,0.00001626656,0.000018892724,0.00008918947],"genre_scores_gemma":[0.9922367,0.00011939874,0.0072339554,0.00010055058,0.00027625146,0.000002798653,0.000015428643,0.000008292072,0.0000066208772],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9985658,0.00019060133,0.0005940113,0.000111728674,0.00042145315,0.000116422496],"domain_scores_gemma":[0.9984761,0.00030047254,0.00062378106,0.000079556,0.0004136685,0.00010644988],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051216874,0.00008479352,0.00022895548,0.000041584808,0.00016435652,0.0000071597888,0.00014215187,0.000074896,0.000019362518],"category_scores_gemma":[0.00018748306,0.00006733805,0.00013649878,0.00044540738,0.00016096019,0.00040686844,2.438478e-7,0.00022055372,3.0995926e-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":[0.00049242977,0.000109248496,0.0021147714,0.00008726242,0.000028260123,0.0000029293644,0.0669591,0.92024446,0.0036120345,0.00502417,0.00006649572,0.0012588575],"study_design_scores_gemma":[0.004032961,0.002980243,0.9120673,0.0007949334,0.0007380355,0.0000014937402,0.021702511,0.00086905743,0.04240133,0.007827589,0.0061055226,0.00047902344],"about_ca_topic_score_codex":0.00005050703,"about_ca_topic_score_gemma":0.00012882847,"teacher_disagreement_score":0.91937536,"about_ca_system_score_codex":0.000020925843,"about_ca_system_score_gemma":0.00011203546,"threshold_uncertainty_score":0.2745966},"labels":[],"label_agreement":null},{"id":"W3097980905","doi":"10.1155/2020/8879664","title":"Developing an Extenics-Based Model for Evaluating Bus Transit System","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","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":"Natural Science Basic Research Program of Shaanxi Province","keywords":"Analytic hierarchy process; Service quality; Transit (satellite); Transport engineering; Transit system; Computer science; Entropy (arrow of time); Public transport; Service (business); Engineering; Reliability engineering; Operations research","score_opus":0.08366517583121429,"score_gpt":0.3699511737475119,"score_spread":0.2862859979162976,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3097980905","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1960413,0.00005368669,0.8021894,0.0012062925,0.00015953775,0.00024249358,0.000023694784,0.000060256883,0.000023345894],"genre_scores_gemma":[0.73540545,0.0000145919075,0.26419106,0.00018719597,0.00010170295,0.000007881061,0.00007127476,0.000014458012,0.0000063595694],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99852437,0.00006243531,0.00062401994,0.00015185347,0.00046528163,0.00017205642],"domain_scores_gemma":[0.99847394,0.00008968445,0.00048986345,0.00004659869,0.0007362191,0.0001636931],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006203893,0.000103283994,0.00021300545,0.00007844824,0.00029244076,0.000034485758,0.00013452164,0.00007681314,0.0000044086355],"category_scores_gemma":[0.00007166675,0.00010743831,0.00011573516,0.0002495369,0.000028882801,0.0007602282,1.5640778e-7,0.00010852464,4.2783515e-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":[0.00047021802,0.000016003034,0.00020788006,0.00009924003,0.000012282032,0.0000039473944,0.031063208,0.95821965,0.00093567715,0.005423171,0.0000044555563,0.003544273],"study_design_scores_gemma":[0.003692734,0.0006478516,0.006386185,0.00035025366,0.0002478032,9.158231e-7,0.022234619,0.9641192,0.0007125865,0.0007002449,0.0005548503,0.00035276948],"about_ca_topic_score_codex":0.000009838595,"about_ca_topic_score_gemma":0.0002949354,"teacher_disagreement_score":0.53936416,"about_ca_system_score_codex":0.00009235295,"about_ca_system_score_gemma":0.00061507936,"threshold_uncertainty_score":0.43812072},"labels":[],"label_agreement":null},{"id":"W3104443311","doi":"10.1155/2020/5830593","title":"Optimal Operation Scheme with Short-Turn, Express, and Local Services in an Urban Rail Transit Line","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Key Research and Development Program of China","keywords":"Mathematical optimization; Linearization; Scheme (mathematics); Operator (biology); Heuristic; Urban rail transit; Computer science; Process (computing); Integer programming; Transit (satellite); Linear programming; Local search (optimization); Nonlinear system; Engineering; Transport engineering; Public transport; Mathematics","score_opus":0.01339282415138526,"score_gpt":0.2700810452366635,"score_spread":0.2566882210852782,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3104443311","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.86197424,0.00020074323,0.13665365,0.00091117737,0.000049137172,0.00014471755,0.0000117079,0.000026400792,0.000028222612],"genre_scores_gemma":[0.9521777,0.00020596905,0.047197968,0.00016253177,0.00012564842,0.0000033132267,0.00010842038,0.000013203819,0.0000052563187],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.998813,0.00005760544,0.00045095835,0.0001693408,0.00036500557,0.00014408921],"domain_scores_gemma":[0.9993488,0.000024653335,0.00017342057,0.00004230514,0.00022736205,0.00018346128],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020478781,0.00011483053,0.00020317295,0.00008511904,0.00012397277,0.000053566586,0.00009626886,0.00008208503,0.000020364183],"category_scores_gemma":[0.0000055989035,0.000104796614,0.00003150161,0.00025778988,0.00008021867,0.001805041,3.9204417e-7,0.00019288334,3.4067543e-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":[0.00085680635,0.000058523015,0.032726195,0.000046061472,0.000012752478,0.000041639738,0.085680366,0.87450874,0.0039697993,0.00036777928,0.0000032814921,0.0017280817],"study_design_scores_gemma":[0.011764005,0.004005222,0.7768432,0.00092709,0.0003363308,0.000013394259,0.15373419,0.036792368,0.0067010857,0.0001798701,0.007488013,0.001215255],"about_ca_topic_score_codex":0.000042986114,"about_ca_topic_score_gemma":0.0023753343,"teacher_disagreement_score":0.83771634,"about_ca_system_score_codex":0.000023253922,"about_ca_system_score_gemma":0.00009885949,"threshold_uncertainty_score":0.4273482},"labels":[],"label_agreement":null},{"id":"W3104538589","doi":"10.1177/0361198120966326","title":"Impact of Weather, Activities, and Service Disruptions on Transportation Demand","year":2020,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Polytechnique Montréal","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Transport engineering; Service (business); Demand forecasting; Mode (computer interface); Supply and demand; Level of service; Variable (mathematics); Public transport; Demand characteristics; Environmental science; Computer science; Operations research; Engineering; Business; Statistics; Mathematics; Economics","score_opus":0.11078468177006379,"score_gpt":0.4231690980818986,"score_spread":0.31238441631183483,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3104538589","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9865013,0.00015536143,0.0025295557,0.00911475,0.00019431645,0.00089689856,0.00031430039,0.000043107862,0.00025045624],"genre_scores_gemma":[0.9971306,0.0014052421,0.00080816116,0.00012829121,0.00018148939,0.00003403635,0.00006689082,0.000048552865,0.00019675127],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99299026,0.0013723527,0.0012731542,0.0004190587,0.0032450708,0.00070010056],"domain_scores_gemma":[0.99455076,0.0012033817,0.0006343891,0.00026804183,0.00271215,0.0006312801],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027885872,0.00026603474,0.0005109806,0.0006800742,0.0010077478,0.00012076162,0.00069819466,0.000254508,0.00033821756],"category_scores_gemma":[0.00022518741,0.00021148275,0.00041168614,0.0028771134,0.0007762768,0.00089181395,0.000001756481,0.0014962389,0.000007760623],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0048384187,0.000641751,0.80042034,0.0004920003,0.00045833638,0.000072816365,0.12741454,0.03556498,0.005572216,0.015070557,0.004042092,0.0054119565],"study_design_scores_gemma":[0.0016383174,0.0011078335,0.9814299,0.00033418596,0.000096799966,1.5199782e-7,0.010149678,0.00025819827,0.00038320446,0.00082619896,0.0035567512,0.00021882556],"about_ca_topic_score_codex":0.020105446,"about_ca_topic_score_gemma":0.061050184,"teacher_disagreement_score":0.18100952,"about_ca_system_score_codex":0.00019047878,"about_ca_system_score_gemma":0.0011176764,"threshold_uncertainty_score":0.98641974},"labels":[],"label_agreement":null},{"id":"W3105791543","doi":"10.19272/201806702006","title":"Causal Linkage between Road Infrastructure and Manufacturing Agglomeration in Northeast China","year":2018,"lang":"en","type":"article","venue":"Data Archiving and Networked Services (DANS)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institute on Governance","funders":"","keywords":"Linkage (software); Economies of agglomeration; Economic geography; China; Business; Transport engineering; Industrial organization; Economics; Geography; Engineering; Microeconomics; Chemistry","score_opus":0.01265473365714031,"score_gpt":0.27023038960101187,"score_spread":0.25757565594387155,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3105791543","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99556774,0.00007953578,0.0024481155,0.00017037804,0.00011783627,0.00012392997,0.00014316184,0.000068532114,0.001280743],"genre_scores_gemma":[0.9960741,0.00026080033,0.0013494891,0.000059936254,0.0006287912,0.000002410149,0.0015668023,0.000011313429,0.0000463671],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9988517,0.00014641377,0.00021333428,0.00035120803,0.0001730532,0.0002642881],"domain_scores_gemma":[0.99948454,0.000075679236,0.000090699265,0.000223862,0.000013923635,0.0001112736],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004668336,0.0001285386,0.00014831986,0.000087433604,0.00063438446,0.00015852296,0.00029588243,0.00008011859,0.000012722529],"category_scores_gemma":[0.000008056109,0.00012205634,0.000009463774,0.00014704814,0.00015854031,0.0005282644,0.000078651916,0.00017496405,0.0000020293237],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025896987,0.000008780507,0.88353944,0.00005822069,0.000017476013,0.0000041192884,0.05940886,0.0017562355,0.000019465078,0.00009642198,0.000037652848,0.055027466],"study_design_scores_gemma":[0.0002010632,0.000020128824,0.9693978,0.00014146761,0.000018986519,0.000001043892,0.001585355,0.02634356,0.000008092495,0.00025876646,0.0018827432,0.00014101647],"about_ca_topic_score_codex":0.0050676274,"about_ca_topic_score_gemma":0.035393234,"teacher_disagreement_score":0.08585837,"about_ca_system_score_codex":0.000012648822,"about_ca_system_score_gemma":0.000028005808,"threshold_uncertainty_score":0.9822083},"labels":[],"label_agreement":null},{"id":"W3106696364","doi":"10.1155/2020/8895927","title":"Design, Validation, and Comparative Analysis of a Private Bus Location Tracking Information System","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Service (business); TRIPS architecture; Architecture; Tracking system; Automatic vehicle location; Systems design; Public transport; Track (disk drive); Transport engineering; Engineering; Global Positioning System; Operating system; Software engineering","score_opus":0.040196328600570555,"score_gpt":0.29748128565765597,"score_spread":0.25728495705708543,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3106696364","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.40695173,0.00007780134,0.5924568,0.00018868812,0.00006564077,0.00016064082,0.000011160364,0.00002254505,0.00006494769],"genre_scores_gemma":[0.97937465,0.00006997337,0.02038302,0.000044539298,0.00002821086,0.0000024607853,0.000093224684,0.0000026516325,0.0000012761827],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99860847,0.000103055194,0.0007482042,0.00007586449,0.0003821383,0.00008228352],"domain_scores_gemma":[0.99772424,0.00009909841,0.0010900041,0.000037737736,0.0009612531,0.0000876866],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043029405,0.000077278164,0.00027884258,0.00028455802,0.00012375657,0.00003440585,0.00007050528,0.000053707296,0.000008423447],"category_scores_gemma":[0.00006806826,0.00007778189,0.00006255702,0.0013131015,0.000050023857,0.0016853597,4.1016222e-7,0.000079563855,5.4296805e-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":[0.00019629515,0.000010851509,0.0049028755,0.00006215935,0.00015360441,8.178502e-7,0.10648218,0.88169336,0.00052570074,0.0050866795,0.0000035831893,0.0008818838],"study_design_scores_gemma":[0.0019882608,0.00035111865,0.9002944,0.00027174756,0.002225451,0.0000010939851,0.07140258,0.018316273,0.004064292,0.00012386242,0.000679884,0.00028103145],"about_ca_topic_score_codex":0.000021569214,"about_ca_topic_score_gemma":0.000024476594,"teacher_disagreement_score":0.8953915,"about_ca_system_score_codex":0.00004050132,"about_ca_system_score_gemma":0.000098886674,"threshold_uncertainty_score":0.31718534},"labels":[],"label_agreement":null},{"id":"W3106806699","doi":"10.1155/2020/7981791","title":"Assessing the Impacts of Autonomous Bus-on-Demand Based on Agent-Based Simulation: A Case Study of Fuyang, Zhejiang, China","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China; Science and Technology Commission of Shanghai Municipality; Natural Science Foundation of Shanghai","keywords":"Schedule; Computer science; Transport engineering; Control (management); Local bus; Simulation; Control bus; Engineering; System bus","score_opus":0.03103631291174817,"score_gpt":0.3506762157778662,"score_spread":0.319639902866118,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3106806699","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96996087,0.00003631012,0.028682211,0.0007095684,0.00015373637,0.00033669005,0.000016252896,0.00001995071,0.000084426494],"genre_scores_gemma":[0.9977903,0.0000068321874,0.0019142423,0.00016938159,0.0000806011,0.0000027156586,0.000019329967,0.000013679235,0.000002923066],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9981506,0.00019148682,0.0007416067,0.00013564224,0.0006514206,0.0001292651],"domain_scores_gemma":[0.9979529,0.0003738779,0.001118071,0.00010088168,0.00033852545,0.000115737355],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005273199,0.00012498944,0.0002651763,0.00014435081,0.00023802657,0.00003372413,0.00012167231,0.00005839836,0.000036587684],"category_scores_gemma":[0.00013816627,0.00009810296,0.00012409165,0.00049706415,0.00006404927,0.00044744785,4.077347e-7,0.0001850233,3.6006787e-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":[0.00032617297,0.00031677086,0.014494418,0.00003870894,0.000026467455,0.00017176989,0.04441574,0.9386441,0.00017731916,0.000030192321,0.000006186321,0.0013521593],"study_design_scores_gemma":[0.009048324,0.004464426,0.77834857,0.0005329455,0.0005282377,0.0000053302447,0.09253445,0.112415016,0.0014034805,0.000061999424,0.00028801154,0.00036923782],"about_ca_topic_score_codex":0.00015081331,"about_ca_topic_score_gemma":0.0003186767,"teacher_disagreement_score":0.8262291,"about_ca_system_score_codex":0.00004242015,"about_ca_system_score_gemma":0.00027470558,"threshold_uncertainty_score":0.40005225},"labels":[],"label_agreement":null},{"id":"W3107532707","doi":"10.1155/2020/6678158","title":"Multimode Traffic Travel Behavior Characteristics Analysis and Congestion Governance Research","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Traffic congestion; Transport engineering; Demand management; Public transport; Travel behavior; Sustainable development; Business; Sustainable transport; Mode choice; Environmental economics; Economics; Engineering; Sustainability","score_opus":0.04011197853442226,"score_gpt":0.34884751010462156,"score_spread":0.3087355315701993,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3107532707","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98438185,0.00014771454,0.013782663,0.0013082221,0.00012065472,0.00015290316,0.00005501501,0.000019325844,0.00003166821],"genre_scores_gemma":[0.9933481,0.0010906246,0.005292139,0.00004858008,0.0001062585,0.0000047558883,0.00007704992,0.0000088034385,0.00002364924],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9985287,0.00010817103,0.00047133717,0.00014549347,0.0005877634,0.00015851676],"domain_scores_gemma":[0.9987039,0.00012018615,0.00038534598,0.000042846452,0.0005654598,0.00018228641],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00053046044,0.000079300684,0.00022843762,0.00013492392,0.00022404658,0.00004071489,0.00009241002,0.000079323145,0.000117299795],"category_scores_gemma":[0.00013974233,0.000082042716,0.00008468438,0.0008243573,0.000116396644,0.0005123444,4.915804e-7,0.00025276537,7.981655e-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":[0.0011127985,0.00029418108,0.16338815,0.00009132583,0.00029518796,0.0001561902,0.18877119,0.53153783,0.0044343784,0.0034527327,0.0000778804,0.10638816],"study_design_scores_gemma":[0.0005708243,0.000120804565,0.9929582,0.000024733394,0.00034579303,5.2302374e-7,0.004481306,0.0006278565,0.00007907093,0.000016339842,0.000683951,0.00009063129],"about_ca_topic_score_codex":0.00007772589,"about_ca_topic_score_gemma":0.001096121,"teacher_disagreement_score":0.82957,"about_ca_system_score_codex":0.000040099076,"about_ca_system_score_gemma":0.00010977845,"threshold_uncertainty_score":0.33456048},"labels":[],"label_agreement":null},{"id":"W3108666724","doi":"10.1155/2020/6675042","title":"Passenger Volume Prediction by a Combined Input-Output and Distributed Lag Model and Data Analytics of Industrial Investment","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","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":"Investment (military); Volume (thermodynamics); Lag; Distributed lag; Consumption (sociology); Gross output; Production (economics); Gross domestic product; Order (exchange); Transport engineering; Economics; Econometrics; Economy; Engineering; Computer science; Macroeconomics; Finance","score_opus":0.0541917265702422,"score_gpt":0.28517838597634054,"score_spread":0.23098665940609833,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3108666724","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.89496094,0.00019635023,0.1004851,0.0027526778,0.000120021316,0.00019912641,0.0012368658,0.000023832014,0.000025078802],"genre_scores_gemma":[0.99360704,0.0004877384,0.004639381,0.00010102395,0.000061154766,9.932218e-7,0.001081643,0.0000070974866,0.000013895078],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99887437,0.00004687998,0.0005086179,0.00014170707,0.00033541888,0.00009301163],"domain_scores_gemma":[0.99906415,0.000047528054,0.00046722003,0.00006623851,0.00019997585,0.00015491885],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002856977,0.00008134592,0.0001985767,0.00005411835,0.00009072167,0.00002200794,0.00009764831,0.00009088141,0.0000041650583],"category_scores_gemma":[0.00011651781,0.000081547565,0.000024248937,0.00022838061,0.00009226779,0.0007054558,0.0000021671538,0.00014539983,7.498183e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00099191,0.00016338691,0.11301147,0.000073712945,0.00014538852,0.0000091304,0.028159628,0.84593683,0.0016844227,0.0011964462,0.004404791,0.00422285],"study_design_scores_gemma":[0.021513492,0.0025895399,0.4624291,0.00060206134,0.0019214519,0.000003905295,0.025194105,0.45632726,0.0006485781,0.0031450868,0.024697384,0.00092804106],"about_ca_topic_score_codex":0.000028330938,"about_ca_topic_score_gemma":0.00006980198,"teacher_disagreement_score":0.3896096,"about_ca_system_score_codex":0.000019840629,"about_ca_system_score_gemma":0.00014156866,"threshold_uncertainty_score":0.33254132},"labels":[],"label_agreement":null},{"id":"W3108961689","doi":"10.1155/2020/1801320","title":"Multiperspective Bus Route Planning in a Stackelberg Game Framework","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Stackelberg competition; Bilevel optimization; Service provider; Computer science; Service (business); Operations research; Game theory; Subsidy; Government (linguistics); Business; Microeconomics; Economics; Optimization problem; Marketing; Engineering","score_opus":0.020580059719885153,"score_gpt":0.31519310035120573,"score_spread":0.2946130406313206,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3108961689","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.90334934,0.0003257273,0.09322411,0.0022069346,0.00032589457,0.000169038,0.000015261994,0.000043395987,0.0003403045],"genre_scores_gemma":[0.97449297,0.00017538856,0.024876583,0.00021399898,0.00019181026,0.000002587259,0.0000202088,0.000012092294,0.000014349909],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9987272,0.00006543789,0.0004997473,0.00013718175,0.0003947683,0.00017565736],"domain_scores_gemma":[0.9990341,0.00014435344,0.0003890105,0.00003974457,0.00024729015,0.00014547668],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023546035,0.00009587152,0.00020788568,0.00012473398,0.0000839345,0.000025352596,0.00011587013,0.000096587355,0.00003637169],"category_scores_gemma":[0.00023153299,0.00010050818,0.00007934044,0.0005423106,0.000048294045,0.00072457304,4.388191e-7,0.0003263973,0.0000022976274],"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":[0.00037292438,0.000038067767,0.04614225,0.0000120594295,0.000016069216,0.00009761348,0.24150424,0.7073433,0.00021111069,0.002942345,0.000015102865,0.0013049362],"study_design_scores_gemma":[0.0026823913,0.00031304284,0.8845623,0.0004851135,0.000068867506,0.0000016533453,0.10107495,0.00059805973,0.00028038438,0.0031612595,0.006439577,0.00033240663],"about_ca_topic_score_codex":0.00007569194,"about_ca_topic_score_gemma":0.0003254101,"teacher_disagreement_score":0.83842003,"about_ca_system_score_codex":0.00008747433,"about_ca_system_score_gemma":0.00015620913,"threshold_uncertainty_score":0.40986046},"labels":[],"label_agreement":null},{"id":"W3109409596","doi":"10.3390/su12229734","title":"Maintenance Decision-Making of an Urban Rail Transit System in a Regionalized Network-Wide Perspective","year":2020,"lang":"en","type":"article","venue":"Sustainability","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Bijzonder Onderzoeksfonds UGent; National Natural Science Foundation of China","keywords":"Node (physics); Optimal maintenance; Reliability (semiconductor); Computer science; Reliability engineering; Network planning and design; Operations research; Integer programming; Mathematical optimization; Engineering; Computer network; Mathematics","score_opus":0.011596326510728713,"score_gpt":0.29720912488744644,"score_spread":0.2856127983767177,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3109409596","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.79534453,0.00026211687,0.18866687,0.009742682,0.00015260548,0.0012724786,0.000014914581,0.0002727798,0.0042710593],"genre_scores_gemma":[0.9968721,0.000003566083,0.0028568169,0.00015114358,0.00006320486,0.000013950941,0.0000047350673,0.000007259471,0.000027218977],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","domain_scores_codex":[0.99849,0.00034265692,0.00031602292,0.0003010378,0.0002905081,0.0002598067],"domain_scores_gemma":[0.99851954,0.0003370169,0.00011511346,0.00013249237,0.0008110351,0.00008482702],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009682095,0.000084175306,0.0002151142,0.000041970943,0.00017030672,0.000021493115,0.00016636297,0.00008240043,0.000021785667],"category_scores_gemma":[0.0019536617,0.000085952095,0.00006814377,0.0006931073,0.0001897663,0.00022822786,0.000005734579,0.0001142542,5.6952484e-7],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014317814,0.00010589361,0.20435666,0.00021848119,0.000015321062,0.000065301385,0.2964481,0.1602972,0.0000010244196,0.33479056,0.000613923,0.001655734],"study_design_scores_gemma":[0.0021546716,0.00025315527,0.2594185,0.0004826958,0.000045841152,0.0000010689148,0.62341744,0.030492133,0.0000036935069,0.077236146,0.0060072,0.0004874582],"about_ca_topic_score_codex":0.0014551514,"about_ca_topic_score_gemma":0.0018422282,"teacher_disagreement_score":0.32696933,"about_ca_system_score_codex":0.0007202032,"about_ca_system_score_gemma":0.00063486054,"threshold_uncertainty_score":0.3505025},"labels":[],"label_agreement":null},{"id":"W3109761444","doi":"10.1016/j.inffus.2020.11.010","title":"Online-review analysis based large-scale group decision-making for determining passenger demands and evaluating passenger satisfaction: Case study of high-speed rail system in China","year":2020,"lang":"en","type":"article","venue":"Information Fusion","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":218,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Research Grants Council, University Grants Committee; National Natural Science Foundation of China; City University of Hong Kong","keywords":"Microblogging; Computer science; Scale (ratio); Process (computing); Transport engineering; Cluster analysis; China; Social media; Engineering; World Wide Web; Artificial intelligence","score_opus":0.026484105322068015,"score_gpt":0.3382999057933979,"score_spread":0.3118158004713299,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3109761444","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9208334,0.000080676095,0.07787747,0.0001616573,0.00008011495,0.00081954116,0.000043200493,0.00006399238,0.000039965038],"genre_scores_gemma":[0.98951477,0.00004358019,0.010078143,0.00015308106,0.00003435609,0.000030461471,0.0001387174,0.000005995432,8.941627e-7],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981984,0.0001829429,0.00083776447,0.00016282657,0.00046728158,0.00015079831],"domain_scores_gemma":[0.99867636,0.0004127886,0.00050784135,0.000103336424,0.00022623401,0.00007341381],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013697849,0.00011550273,0.00033157648,0.00027837252,0.00047514215,0.000079284415,0.00006401143,0.00008158972,0.000038693597],"category_scores_gemma":[0.00039037812,0.00011014606,0.00006943684,0.0010782233,0.00001826108,0.0007640415,0.000013521599,0.000089915484,9.906297e-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":[0.0002463579,0.00023296624,0.31853065,0.0018108715,0.00012754949,0.000038873317,0.19433671,0.2855885,0.000025776319,0.00013548034,0.00009578818,0.1988305],"study_design_scores_gemma":[0.0017795191,0.00019141824,0.1992185,0.0008268457,0.00038481675,0.0000037122722,0.05620912,0.7410713,0.0000018175085,0.000004310433,0.00013795309,0.00017070818],"about_ca_topic_score_codex":0.00081411924,"about_ca_topic_score_gemma":0.006945047,"teacher_disagreement_score":0.45548278,"about_ca_system_score_codex":0.00005491089,"about_ca_system_score_gemma":0.00006432925,"threshold_uncertainty_score":0.4491626},"labels":[],"label_agreement":null},{"id":"W3110560189","doi":"10.18280/jesa.530503","title":"Efficient Original-Destination Bandwidth: A Novel Model for Arterial Traffic Signal Coordination","year":2020,"lang":"en","type":"article","venue":"Journal Européen des Systèmes Automatisés","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":"Bandwidth (computing); Computer science; Interval (graph theory); Real-time computing; Sequence (biology); Time lag; Lag; Algorithm; SIGNAL (programming language); Telecommunications; Simulation; Electronic engineering; Engineering; Mathematics; Computer network","score_opus":0.03926863311228103,"score_gpt":0.28796397161763787,"score_spread":0.24869533850535686,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3110560189","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32007465,0.000039768176,0.67677546,0.0017330891,0.00029372424,0.0003551566,0.00004162973,0.00024733876,0.00043919776],"genre_scores_gemma":[0.9550696,0.000011598583,0.043665603,0.00016345638,0.0005796485,0.000019938416,0.000038226746,0.000030209532,0.00042172056],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99817413,0.00014554764,0.000525829,0.00023791801,0.0005840107,0.0003325372],"domain_scores_gemma":[0.9985884,0.00016307703,0.0003997061,0.000059562666,0.00051668595,0.00027258164],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007565154,0.00016689635,0.0002267623,0.00013422474,0.0010650113,0.00036040365,0.00020515735,0.000100173645,0.000087074404],"category_scores_gemma":[0.00040826702,0.00016378316,0.00012681464,0.00040876333,0.00011215591,0.00028905136,0.000006007121,0.00016086135,0.000019859723],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000102811435,0.00009099006,0.00010310365,0.00005868643,0.000025511015,0.000008029178,0.018195238,0.9514536,0.0009852406,0.003992604,0.0013266532,0.023657486],"study_design_scores_gemma":[0.0012592879,0.000121528006,0.006768309,0.000090054855,0.00006216426,0.000021234573,0.0005518519,0.989223,0.000017925324,0.0002835824,0.0014035341,0.00019751074],"about_ca_topic_score_codex":0.000013546857,"about_ca_topic_score_gemma":0.000024118617,"teacher_disagreement_score":0.6349949,"about_ca_system_score_codex":0.00019110055,"about_ca_system_score_gemma":0.00038462435,"threshold_uncertainty_score":0.8191313},"labels":[],"label_agreement":null},{"id":"W3110742867","doi":"10.1155/2020/6659931","title":"A Network-Based Model of Passenger Transfer Flow between Bus and Metro: An Application to the Public Transport System of Beijing","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Beijing; Public transport; Transfer (computing); Transfer station; Computer science; Flow network; Multimodal transport; Bus network; Transport network; Transport engineering; Smart card; Network model; Simulation; Operations research; Mathematical optimization; Data mining; Engineering; Computer network; Mathematics","score_opus":0.025363712379546998,"score_gpt":0.2678637611071476,"score_spread":0.24250004872760061,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3110742867","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.45832196,0.00006211345,0.53970116,0.0015456746,0.00005197892,0.000229676,0.000056409277,0.00001843165,0.000012571228],"genre_scores_gemma":[0.9737622,0.00003110213,0.025804998,0.000103221704,0.00018006573,0.000010022028,0.00009099836,0.000016144302,0.000001224197],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9981913,0.00009275862,0.00083211955,0.00016181704,0.00055113423,0.00017090152],"domain_scores_gemma":[0.99870026,0.000098350436,0.00038443145,0.00009227564,0.00051798765,0.00020669517],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00071946805,0.0001175659,0.000341405,0.000109039844,0.00015484981,0.0000133481435,0.00018291584,0.000088438304,0.0000031697475],"category_scores_gemma":[0.000021085603,0.00009971719,0.00010246945,0.000615686,0.00006800593,0.00053785514,3.7622092e-7,0.00014628445,1.690881e-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":[0.00019515994,0.000027622616,0.02595382,0.00010177881,0.00003071983,0.0000011725155,0.014872207,0.9516915,0.0012519951,0.0025153717,0.000003941028,0.0033546854],"study_design_scores_gemma":[0.0069286367,0.0019182381,0.714686,0.0008747242,0.0018759578,0.0000015517668,0.036774006,0.22830908,0.0037110262,0.0005041133,0.0034038164,0.0010128757],"about_ca_topic_score_codex":0.000037543814,"about_ca_topic_score_gemma":0.00092758395,"teacher_disagreement_score":0.7233825,"about_ca_system_score_codex":0.00003654614,"about_ca_system_score_gemma":0.00019250381,"threshold_uncertainty_score":0.4066349},"labels":[],"label_agreement":null},{"id":"W3110868338","doi":"10.1155/2020/8853872","title":"Route Design Model of Multiple Feeder Bus Service Based on Existing Bus Lines","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"China Scholarship Council; Jiangsu University; Government of Jiangsu Province; Nanjing Forestry University","keywords":"Tabu search; Bus network; Partition (number theory); Computer science; Service (business); Cluster analysis; Mathematical optimization; Engineering; Transport engineering; Control bus; Algorithm; Mathematics; System bus","score_opus":0.07363407726042216,"score_gpt":0.3065695308509022,"score_spread":0.23293545359048007,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3110868338","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27827236,0.000048903974,0.7188826,0.0021847542,0.00017064871,0.00018623614,0.000036222806,0.000043460583,0.00017481108],"genre_scores_gemma":[0.8665513,0.000043820353,0.13278149,0.00045618296,0.0000944049,0.0000022491754,0.00004209598,0.000013580284,0.000014892361],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99859345,0.00006484522,0.00058546354,0.00012652726,0.0004927263,0.00013695954],"domain_scores_gemma":[0.9982662,0.00021313601,0.00064776867,0.000053131465,0.00069429196,0.0001254355],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003094766,0.00010717886,0.0002156835,0.00009292877,0.00014432882,0.000016731898,0.00012874612,0.0000746163,0.000013002153],"category_scores_gemma":[0.00017830332,0.00010567671,0.00008645409,0.00041313723,0.000031017058,0.00043694128,3.4530393e-7,0.00014605976,0.00000104902],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00058354746,0.000049807844,0.0030706348,0.000044453343,0.000011593013,0.0000058130104,0.01736727,0.97420925,0.0032420692,0.00021225057,0.000015139525,0.0011881505],"study_design_scores_gemma":[0.0045478726,0.0005279298,0.0524698,0.0004907062,0.00020589693,4.7522286e-7,0.008609203,0.924826,0.0066690524,0.0005130699,0.0007415825,0.000398376],"about_ca_topic_score_codex":0.00006537279,"about_ca_topic_score_gemma":0.00026669982,"teacher_disagreement_score":0.58827895,"about_ca_system_score_codex":0.000029561432,"about_ca_system_score_gemma":0.00024649338,"threshold_uncertainty_score":0.4309371},"labels":[],"label_agreement":null},{"id":"W3111416000","doi":"10.1155/2020/8846715","title":"Dynamic Origin-Destination Matrix Estimation Based on Urban Rail Transit AFC Data: Deep Optimization Framework with Forward Passing and Backpropagation Techniques","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Beijing Municipal Natural Science Foundation; State Key Laboratory of Rail Traffic Control and Safety; China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Computer science; Urban rail transit; Scheduling (production processes); Backpropagation; Public transport; Flow network; Estimation; Real-time computing; Artificial neural network; Data mining; Transport engineering; Mathematical optimization; Engineering; Artificial intelligence","score_opus":0.013962009675158317,"score_gpt":0.30215123873852723,"score_spread":0.2881892290633689,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3111416000","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028697673,0.00007631288,0.96718556,0.0032806452,0.00011930187,0.0004086737,0.000031584816,0.00013095717,0.000069320355],"genre_scores_gemma":[0.5812897,0.00011185787,0.41788074,0.00013751052,0.00006763326,0.0000054279503,0.000480808,0.00002124191,0.000005069435],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99804765,0.00011567368,0.00062683685,0.00031060536,0.0007144331,0.0001848238],"domain_scores_gemma":[0.99824744,0.00019572752,0.00076778646,0.00013906238,0.00048165297,0.00016830549],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00050865166,0.00019185588,0.00026125068,0.00022052525,0.00034219635,0.00011784346,0.00017713185,0.00015286765,0.00002395303],"category_scores_gemma":[0.00020503548,0.00018190872,0.000047347257,0.0006447149,0.000098953744,0.001994734,0.0000010457422,0.00028480063,7.8064863e-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":[0.00063725974,0.000049046343,0.0013493514,0.000073421215,0.000016511642,0.000010619309,0.008534809,0.97426707,0.00014833183,0.0010469,0.000013211477,0.0138534475],"study_design_scores_gemma":[0.0023502638,0.001042833,0.022665039,0.0008756039,0.00037481944,0.0000038210474,0.003786186,0.96621597,0.00041541475,0.0008033904,0.00097343774,0.00049323455],"about_ca_topic_score_codex":0.000012555637,"about_ca_topic_score_gemma":0.00010616014,"teacher_disagreement_score":0.55259204,"about_ca_system_score_codex":0.00010210463,"about_ca_system_score_gemma":0.0002187439,"threshold_uncertainty_score":0.7418023},"labels":[],"label_agreement":null},{"id":"W3112082390","doi":"10.1155/2020/8880335","title":"Restriction Analysis of Transport Policy for Bridges Using the Trajectory Data","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","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":"Natural Science Foundation of Ningbo; Department of Education of Zhejiang Province; Natural Science Foundation of Zhejiang Province; National Natural Science Foundation of China","keywords":"Premise; Trajectory; Transport engineering; Computer science; Control (management); Transport policy; Traffic congestion; Engineering; Public transport","score_opus":0.06891414264386538,"score_gpt":0.36341028233627976,"score_spread":0.2944961396924144,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3112082390","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.63332886,0.00015980168,0.36373833,0.0019904163,0.00017166915,0.0002130139,0.00035073745,0.000018213088,0.000028951079],"genre_scores_gemma":[0.9856684,0.0004490487,0.013113699,0.000108604676,0.00025351762,0.000001500967,0.00038934406,0.000009535155,0.0000063314624],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9986325,0.00005799788,0.00064469007,0.00013787136,0.0004040233,0.00012291508],"domain_scores_gemma":[0.998518,0.00015176096,0.00072937296,0.00012019531,0.0003969591,0.00008374583],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004950291,0.00007993617,0.00025994616,0.00025365077,0.00018988532,0.000011191472,0.00028146902,0.00005857105,0.0000097517295],"category_scores_gemma":[0.00013093534,0.000067223846,0.00018506315,0.0014373855,0.00007946061,0.00065662473,5.246243e-7,0.00010081299,5.7594786e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029101357,0.00003106123,0.017316237,0.000025236877,0.00025966545,0.000001992127,0.021631531,0.9565918,0.0012486376,0.0013689735,0.000024820618,0.0012090617],"study_design_scores_gemma":[0.0014242465,0.00023177727,0.95104915,0.000062802406,0.004213152,5.182329e-7,0.020763732,0.014212292,0.0004697762,0.00018825366,0.0071745054,0.00020981423],"about_ca_topic_score_codex":0.00037611334,"about_ca_topic_score_gemma":0.0011681467,"teacher_disagreement_score":0.9423795,"about_ca_system_score_codex":0.00003709927,"about_ca_system_score_gemma":0.00037358853,"threshold_uncertainty_score":0.27413088},"labels":[],"label_agreement":null},{"id":"W3112968276","doi":"10.1155/2020/8860277","title":"Examining the Impact of Adverse Weather on Travel Time Reliability of Urban Corridors in Shanghai","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; National Key Research and Development Program of China","keywords":"Adverse weather; Reliability (semiconductor); Environmental science; Travel time; Log-normal distribution; Transport engineering; Meteorology; Statistics; Engineering; Geography; Mathematics","score_opus":0.020162983902346138,"score_gpt":0.2850322075068309,"score_spread":0.2648692236044847,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3112968276","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.997772,0.00002997828,0.0013261026,0.00024293907,0.000066273686,0.00014760654,0.000028508637,0.0000067385286,0.00037989076],"genre_scores_gemma":[0.9987722,0.000047771184,0.0010575871,0.000028565792,0.000043987486,0.0000011808294,0.00001578343,0.0000079478405,0.000024960495],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9987501,0.00009437186,0.00060871936,0.0000942254,0.00034803152,0.00010455321],"domain_scores_gemma":[0.99883044,0.00017777215,0.00064536405,0.000062366584,0.0002178352,0.000066205306],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004983248,0.000081123406,0.00023229039,0.000085331085,0.000046794106,0.0000026533025,0.000124488,0.000056505778,0.00007963018],"category_scores_gemma":[0.00014249893,0.000059268677,0.0001317027,0.00041058147,0.000089739246,0.00030349923,3.691123e-7,0.00015519693,9.1889063e-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":[0.0005738905,0.00007622259,0.13032569,0.00001535452,0.000015173129,0.0000043622626,0.07623213,0.78982335,0.0022427589,0.00009910153,0.000029894783,0.0005620722],"study_design_scores_gemma":[0.0008756164,0.0005000132,0.98798454,0.000114674396,0.0000309229,1.6976009e-7,0.009264645,0.00052933523,0.0005016012,0.00009760612,0.000036388712,0.00006447519],"about_ca_topic_score_codex":0.0001497598,"about_ca_topic_score_gemma":0.000087855326,"teacher_disagreement_score":0.85765886,"about_ca_system_score_codex":0.00004760925,"about_ca_system_score_gemma":0.00015724293,"threshold_uncertainty_score":0.24169065},"labels":[],"label_agreement":null},{"id":"W3113165276","doi":"10.1155/2020/5947802","title":"A Short-Turn Dispatching Strategy to Improve the Reliability of Bus Operation","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","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":"Natural Science Foundation of Hebei Province; Beijing University of Technology; National Natural Science Foundation of China; University of Wisconsin-Milwaukee","keywords":"Reliability (semiconductor); Dwell time; Process (computing); State (computer science); Point (geometry); Computer science; Engineering; Simulation; Reliability engineering; Transport engineering; Automotive engineering","score_opus":0.016264455969990627,"score_gpt":0.2979748524176226,"score_spread":0.28171039644763196,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3113165276","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92551255,0.00005072977,0.07033954,0.003444163,0.00024290991,0.0002609693,0.00002469809,0.000017409026,0.00010703229],"genre_scores_gemma":[0.9938773,0.00007894116,0.005686222,0.00016429956,0.00014825347,0.0000038514577,0.000021489574,0.000007855094,0.000011788698],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99866337,0.00007968518,0.0005942119,0.0001183592,0.00042893377,0.00011547126],"domain_scores_gemma":[0.9989831,0.0000915551,0.00029962885,0.00006675295,0.0004347709,0.00012421711],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048013084,0.000079261576,0.00016818276,0.000042451196,0.00016710859,0.000027915772,0.00015091572,0.00005123746,0.000018189741],"category_scores_gemma":[0.00014921019,0.00006023596,0.00008808613,0.00030428622,0.00004994511,0.0006133606,6.9698143e-7,0.0001659732,8.891629e-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":[0.00024294724,0.00003117005,0.006702584,0.000022278247,0.000011022642,0.0000030981043,0.051819496,0.9224551,0.0052392036,0.0009752315,0.000023666216,0.012474233],"study_design_scores_gemma":[0.0013704229,0.0012251848,0.94073486,0.00015483265,0.00020156977,9.821392e-7,0.043976195,0.0015513407,0.0054909466,0.0010804619,0.0038769927,0.0003362047],"about_ca_topic_score_codex":0.00009019654,"about_ca_topic_score_gemma":0.0005093939,"teacher_disagreement_score":0.93403226,"about_ca_system_score_codex":0.000030736515,"about_ca_system_score_gemma":0.00014324421,"threshold_uncertainty_score":0.24563512},"labels":[],"label_agreement":null},{"id":"W3113230120","doi":"10.1155/2020/2470579","title":"Zone Fare System Design in a Rail Transit Line","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","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":"Fok Ying Tong Education Foundation; National Natural Science Foundation of China","keywords":"TRIPS architecture; Computer science; Line (geometry); Standard deviation; Public transport; Metric (unit); Mathematical optimization; Transport engineering; Boundary line; Boundary (topology); Operations research; Engineering; Mathematics; Statistics; Operations management","score_opus":0.0254110385963295,"score_gpt":0.27683426862731064,"score_spread":0.25142323003098116,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3113230120","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18887657,0.00029353413,0.8073427,0.0027099631,0.00027020162,0.00026085414,0.00001495462,0.00006555568,0.00016564257],"genre_scores_gemma":[0.96953654,0.0001639998,0.029976148,0.00012180498,0.00013963635,0.0000033962713,0.000026315292,0.000011902198,0.000020233383],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99860966,0.00010532307,0.0006203279,0.00011810396,0.00039404273,0.0001525657],"domain_scores_gemma":[0.999166,0.000071351235,0.00034056537,0.000037036865,0.00024180594,0.00014321912],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042249463,0.000092554634,0.00023081608,0.000116664865,0.00009704901,0.000019996614,0.00011348741,0.00007928518,0.000024795894],"category_scores_gemma":[0.000043713877,0.00009349194,0.0000817028,0.0005205075,0.000032396125,0.000574565,1.8625087e-7,0.00018120212,0.0000026534997],"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":[0.00048407365,0.000033580098,0.0013578687,0.000056612254,0.000012992193,0.000096984855,0.06397805,0.92833906,0.00075522834,0.0016272271,0.000023050063,0.0032352905],"study_design_scores_gemma":[0.030099241,0.0035226657,0.5302546,0.0033022175,0.00081406656,0.000026787335,0.36317894,0.01604601,0.006330955,0.0010631623,0.04324248,0.0021188313],"about_ca_topic_score_codex":0.000043616972,"about_ca_topic_score_gemma":0.00042615677,"teacher_disagreement_score":0.912293,"about_ca_system_score_codex":0.00006346432,"about_ca_system_score_gemma":0.00016526281,"threshold_uncertainty_score":0.38124904},"labels":[],"label_agreement":null},{"id":"W3116323082","doi":"10.1155/2020/8859913","title":"Between Pricing and Investment, What Mobility Policies Would Be Advantageous for Île-de-France?","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","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":"VINCI; Conseil Régional, Île-de-France","keywords":"Subsidy; Public transport; Investment (military); Revenue; Transport engineering; Environmental economics; Service (business); Mode (computer interface); Business; Economics; Finance; Computer science; Engineering; Marketing","score_opus":0.026372895264719174,"score_gpt":0.31720139331919983,"score_spread":0.29082849805448063,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3116323082","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9349521,0.00060827803,0.05813968,0.005722127,0.00015449765,0.0003056265,0.00003851211,0.000037769783,0.00004139826],"genre_scores_gemma":[0.9788138,0.0009156644,0.018645242,0.0013241555,0.00019376825,0.000007409132,0.000053871518,0.000013679001,0.000032359185],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.99891824,0.000043423453,0.00045573123,0.00013468873,0.00026008923,0.0001878347],"domain_scores_gemma":[0.99893266,0.00020337626,0.00039876756,0.000042109048,0.00022699097,0.00019607256],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003579215,0.00009942304,0.00022147232,0.00006786284,0.0002226172,0.00006513535,0.000088516616,0.000110098044,0.000008158462],"category_scores_gemma":[0.00013765135,0.000100070734,0.00007849407,0.00021293291,0.000090248766,0.0013603515,6.5512364e-7,0.00018593435,1.7787701e-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":[0.0007492284,0.00013421747,0.21224177,0.00042663596,0.0001514157,0.000022758,0.40222856,0.33418164,0.0033037153,0.013227324,0.00020943099,0.03312329],"study_design_scores_gemma":[0.005638793,0.0012095728,0.8294932,0.00047207504,0.00050183584,0.0000033635981,0.08508462,0.00041915293,0.002280662,0.015955957,0.058298808,0.00064192165],"about_ca_topic_score_codex":0.00004775705,"about_ca_topic_score_gemma":0.00010613092,"teacher_disagreement_score":0.61725146,"about_ca_system_score_codex":0.000041624084,"about_ca_system_score_gemma":0.00012746511,"threshold_uncertainty_score":0.4080766},"labels":[],"label_agreement":null},{"id":"W3116365506","doi":"10.18757/ejtir.2021.21.1.4939","title":"Estimating impacts of covid19 on transport capacity in railway networks","year":2021,"lang":"en","type":"preprint","venue":"European journal of transport and infrastructure research","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Transport Canada","funders":"","keywords":"Train; Distancing; Transport engineering; Public transport; Computer science; Transport network; Scheduling (production processes); Passenger transport; Coronavirus disease 2019 (COVID-19); Operations research; Engineering; Operations management; Geography","score_opus":0.0611958357413426,"score_gpt":0.33959442003127777,"score_spread":0.27839858428993514,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3116365506","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98108673,0.0005833079,0.013198225,0.00028685166,0.0005998584,0.00023973614,0.0000315095,0.000014411803,0.003959383],"genre_scores_gemma":[0.99049896,0.0013274867,0.0075771264,0.0000315645,0.00042509055,9.612577e-7,0.00007819732,0.000034610264,0.000026012633],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9955681,0.0012509724,0.0011586483,0.000321031,0.0012413928,0.0004598665],"domain_scores_gemma":[0.99788827,0.00022567972,0.0005837219,0.00020597067,0.0007827068,0.00031364177],"candidate_categories":["research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0072396295,0.00023655397,0.0006129021,0.00060517737,0.00023936959,0.000071693765,0.0004715718,0.00022801307,0.0001206483],"category_scores_gemma":[0.00021391206,0.00021595487,0.00019651977,0.0005677263,0.000494249,0.00023149289,0.000016988872,0.002735017,3.204788e-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":[0.00035085902,0.00009049429,0.29168177,0.00041589118,0.00009675168,0.0007544648,0.0439237,0.65716296,0.00008992536,0.00025594275,0.00015566939,0.0050215675],"study_design_scores_gemma":[0.0009651359,0.0002493512,0.99081963,0.0026316252,0.00005353592,0.000013134051,0.002644822,0.0013144804,0.00004958458,0.0001969696,0.00080530427,0.00025645003],"about_ca_topic_score_codex":0.00048370147,"about_ca_topic_score_gemma":0.00061895803,"teacher_disagreement_score":0.69913787,"about_ca_system_score_codex":0.00009764569,"about_ca_system_score_gemma":0.00081605813,"threshold_uncertainty_score":0.9995657},"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":"W3117543102","doi":"10.1155/2020/6665161","title":"Optimization Scheme of Tradable Credits and Bus Departure Quantity for Travelers’ Travel Mode Choice Guidance","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","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":"Natural Science Foundation of Hunan Province; Scientific Research Foundation of Hunan Provincial Education Department; National Natural Science Foundation of China; Guangxi Provincial Key Laboratory of Precision Navigation Technology and Application, Guilin University of Technology; Natural Science Foundation of Guangxi Zhuang Autonomous Region","keywords":"Mode (computer interface); Scheme (mathematics); Mode choice; Transport engineering; Travel time; Business; Computer science; Public transport; Engineering; Mathematics","score_opus":0.027541386788100603,"score_gpt":0.3064478743694006,"score_spread":0.2789064875813,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3117543102","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3350129,0.00053305196,0.6623324,0.0014554689,0.00019016724,0.00028936335,0.00009975236,0.000022024697,0.00006481036],"genre_scores_gemma":[0.8126619,0.0005515033,0.1864652,0.00007040876,0.00015150053,0.0000044671474,0.000065724766,0.000012906039,0.000016387408],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9987261,0.000038687118,0.00059577177,0.00014883521,0.00034348492,0.00014709354],"domain_scores_gemma":[0.9985012,0.00013523236,0.0006282066,0.00004327335,0.000548725,0.00014338002],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002561255,0.0001074249,0.0002665837,0.00007171622,0.00015004037,0.000018990899,0.00010227133,0.0001036178,0.000013408563],"category_scores_gemma":[0.00019599209,0.000110920206,0.00009117292,0.00029186747,0.0000707022,0.0007745799,3.708882e-7,0.0001149058,9.2571426e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000534435,0.0000542866,0.005849552,0.00012294676,0.00003513916,0.000002388493,0.021709725,0.9642531,0.003277564,0.002708507,0.000092882256,0.0013594412],"study_design_scores_gemma":[0.019452034,0.0026745482,0.7637833,0.0010528908,0.0010114112,0.0000072217176,0.036261298,0.13920264,0.020570958,0.0021174387,0.012359224,0.0015070798],"about_ca_topic_score_codex":0.00005966556,"about_ca_topic_score_gemma":0.0005190602,"teacher_disagreement_score":0.8250505,"about_ca_system_score_codex":0.000020962661,"about_ca_system_score_gemma":0.00015084943,"threshold_uncertainty_score":0.45231947},"labels":[],"label_agreement":null},{"id":"W3118577422","doi":"10.3390/su13010337","title":"Modeling the Modal Shift towards a More Sustainable Transport by Stated Preference in Riyadh, Saudi Arabia","year":2021,"lang":"en","type":"article","venue":"Sustainability","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":40,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"King Fahd University of Petroleum and Minerals","keywords":"Transport engineering; Public transport; Modal shift; Mode choice; Mode (computer interface); Business; Engineering; Computer science","score_opus":0.018583778842686,"score_gpt":0.29192888149868007,"score_spread":0.2733451026559941,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3118577422","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9647883,0.00023270056,0.021164034,0.011039173,0.00008050766,0.00066246756,0.000036309208,0.00014212879,0.0018543769],"genre_scores_gemma":[0.9967426,0.000060600887,0.00020503973,0.00012543594,0.00003251302,0.00007664359,0.0001596092,0.00001285569,0.0025847203],"study_design_codex":"simulation_or_modeling","study_design_gemma":"qualitative","domain_scores_codex":[0.99760854,0.00038177142,0.00039929402,0.00045616293,0.00049334025,0.00066090585],"domain_scores_gemma":[0.99825245,0.000087522414,0.000060224644,0.0003137364,0.0011644593,0.000121582074],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015128768,0.00015901153,0.00021371654,0.000058793004,0.0005985118,0.000089908084,0.0002672076,0.00015475819,0.000107976935],"category_scores_gemma":[0.0008338449,0.00014126657,0.000078059646,0.0010069126,0.00026763233,0.00043029847,0.000016921242,0.000320154,0.0000010085025],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018078982,0.00037767427,0.18326269,0.00030562095,0.000023489783,0.00014622363,0.1663625,0.6168496,0.000005740456,0.030565923,0.000321893,0.0015978827],"study_design_scores_gemma":[0.0017902597,0.00008049522,0.398831,0.000069922986,0.000084395055,0.0000013760766,0.4208388,0.070418194,0.00012925496,0.09234255,0.014472363,0.00094137137],"about_ca_topic_score_codex":0.031078633,"about_ca_topic_score_gemma":0.010121474,"teacher_disagreement_score":0.54643136,"about_ca_system_score_codex":0.0007651894,"about_ca_system_score_gemma":0.0030663917,"threshold_uncertainty_score":0.9753735},"labels":[],"label_agreement":null},{"id":"W3118737277","doi":"10.2139/ssrn.3580956","title":"Comparison of British Columbia and Washington State City Powers","year":2020,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"State (computer science); Political science; Geography; Economic history; History; Computer science","score_opus":0.015367170316483608,"score_gpt":0.2822651702192605,"score_spread":0.2668979999027769,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3118737277","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9922977,0.00071449485,0.005284031,0.0008279529,0.000057485708,0.000053963995,0.000008574236,0.000026616,0.00072914263],"genre_scores_gemma":[0.9973094,0.0017363247,0.0001577259,0.00006357047,0.000045720426,3.378965e-7,0.0000044524295,0.000004986533,0.00067747204],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9989362,0.00006841638,0.00018509706,0.00008643859,0.00021148016,0.0005124072],"domain_scores_gemma":[0.9996513,0.000026212876,0.00013728892,0.000020527837,0.00007116449,0.00009349147],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063230423,0.00003191915,0.00011344206,0.000012505886,0.0002483751,0.000116347495,0.00008510076,0.000035002926,0.00004114811],"category_scores_gemma":[0.000061679755,0.000051362458,0.000027677397,0.00013746636,0.000077104385,0.000158227,0.0000033246458,0.00045118926,7.8468196e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000036087924,0.000047388505,0.94160736,0.000010413656,0.00006361494,0.0000028525592,0.025087474,0.0012619391,0.00006478686,0.0025071024,0.0017318934,0.027579075],"study_design_scores_gemma":[0.005012612,0.0022729908,0.5858043,0.0002494994,0.00025852933,0.00006864783,0.26602834,0.0041602273,0.00011700466,0.09256197,0.04225665,0.0012092537],"about_ca_topic_score_codex":0.010594998,"about_ca_topic_score_gemma":0.25288495,"teacher_disagreement_score":0.3558031,"about_ca_system_score_codex":0.000078238525,"about_ca_system_score_gemma":0.00065069104,"threshold_uncertainty_score":0.99599355},"labels":[],"label_agreement":null},{"id":"W3119505565","doi":"10.1287/trsc.2022.1147","title":"Departure Time Choice Models in Urban Transportation Systems Based on Mean Field Games","year":2022,"lang":"en","type":"article","venue":"Transportation Science","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"European Commission","keywords":"Queue; Mathematical optimization; Computer science; Game theory; Point (geometry); Field (mathematics); Mathematical economics; Mathematics","score_opus":0.02056232498680118,"score_gpt":0.280011843733553,"score_spread":0.2594495187467518,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3119505565","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9530171,0.00012252439,0.028510435,0.0024345121,0.0010289602,0.0013818288,0.00037174762,0.00051607325,0.012616784],"genre_scores_gemma":[0.997814,0.000008667368,0.0005031402,0.00054886134,0.000043980894,0.00013322374,0.00034280712,0.000015040162,0.00059026416],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969124,0.00015380979,0.00045693113,0.0005164651,0.0015556818,0.00040471627],"domain_scores_gemma":[0.9990316,0.00026488092,0.00018321665,0.00020208365,0.00016184333,0.00015639944],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013919262,0.00015499578,0.00018638554,0.0004904604,0.0010552685,0.000100171375,0.00043965902,0.00007299815,0.00027973394],"category_scores_gemma":[0.000045911645,0.00017582903,0.000064504624,0.002152181,0.00019092581,0.0008302885,9.1667425e-7,0.00025987695,0.000009909403],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000067447734,0.00009151913,0.024757152,0.000011743223,0.0000020342125,0.000011113864,0.021303557,0.942016,0.00011820064,0.010935464,0.00033459743,0.000351185],"study_design_scores_gemma":[0.0023125487,0.0004339971,0.37676823,0.00014605215,0.00006419369,4.911673e-7,0.011938725,0.58475304,0.00035521472,0.00070659997,0.021523966,0.0009969649],"about_ca_topic_score_codex":0.0035948192,"about_ca_topic_score_gemma":0.0070379404,"teacher_disagreement_score":0.35726297,"about_ca_system_score_codex":0.00018069528,"about_ca_system_score_gemma":0.0005556355,"threshold_uncertainty_score":0.8116378},"labels":[],"label_agreement":null},{"id":"W3120357042","doi":"10.1177/0361198120986170","title":"Evaluating the Role and Evolution of Factors Influencing Rapid Transit Planning in Ecuador","year":2021,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Process (computing); Transportation planning; Component (thermodynamics); Analytic hierarchy process; Risk analysis (engineering); Operations research; Economics; Business; Management science; Process management; Computer science; Engineering; Transport engineering","score_opus":0.12438528617420909,"score_gpt":0.4335332880961075,"score_spread":0.30914800192189845,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3120357042","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99522656,0.0014516502,0.00065323553,0.0015530199,0.00023723362,0.00056761055,0.000030160754,0.000016563841,0.00026395908],"genre_scores_gemma":[0.99765617,0.00075733237,0.0012603237,0.000017827619,0.00008566952,0.000022023925,0.000016424758,0.000025469564,0.00015875052],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99164915,0.0027135625,0.0013234958,0.00033257162,0.003324655,0.000656545],"domain_scores_gemma":[0.9938868,0.0019572563,0.0004879236,0.00026886692,0.0031829202,0.00021618415],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008394494,0.00017473867,0.00037641023,0.0007519512,0.0010798676,0.00011868414,0.00057852623,0.00019829265,0.00013207368],"category_scores_gemma":[0.0006649573,0.00012952955,0.00021410808,0.0029201705,0.00081725663,0.0007148395,0.000004145648,0.0015570729,0.0000012547133],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000525076,0.000110894376,0.89077747,0.000113307375,0.00006530127,0.000043258806,0.06722811,0.02797388,0.004455339,0.002555456,0.000052332813,0.006099598],"study_design_scores_gemma":[0.0008903621,0.00022824036,0.91687703,0.00049286196,0.000045771667,2.77686e-7,0.07584177,0.00038544662,0.0019202121,0.0025547107,0.00064260425,0.00012074176],"about_ca_topic_score_codex":0.02392102,"about_ca_topic_score_gemma":0.07684956,"teacher_disagreement_score":0.052928533,"about_ca_system_score_codex":0.00027439292,"about_ca_system_score_gemma":0.0017222414,"threshold_uncertainty_score":0.98257875},"labels":[],"label_agreement":null},{"id":"W3121150490","doi":"","title":"Step by Step: Revisiting Step Tolling in the Bottleneck Model","year":2010,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Toll; Bottleneck; Interval (graph theory); Limiting; Road pricing; Limit (mathematics); Congestion pricing; Computer science; Economics; Mathematical economics; Operations research; Mathematical optimization; Traffic congestion; Transport engineering; Mathematics; Engineering; Operations management","score_opus":0.0403715436364062,"score_gpt":0.34527228992365433,"score_spread":0.3049007462872481,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3121150490","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.70817345,0.000609968,0.00310882,0.0057908692,0.0008639376,0.0027757552,0.00034924125,0.00016043111,0.27816755],"genre_scores_gemma":[0.96782213,0.0112596005,0.016361931,0.00021354124,0.00044297116,0.00021436875,0.00030973935,0.00006040265,0.0033152879],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968959,0.0006179987,0.0006398113,0.00065389904,0.00049568515,0.0006967156],"domain_scores_gemma":[0.99816424,0.0008353453,0.00022620193,0.000527452,0.0001265447,0.00012020785],"candidate_categories":["research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0064104237,0.00020519005,0.00034067594,0.00027878056,0.00048615728,0.0003686886,0.0009293392,0.00058571645,0.00004458322],"category_scores_gemma":[0.0005681869,0.00020265582,0.00010360496,0.00021607689,0.00026686376,0.00017567514,0.000107707085,0.0024151437,0.0000052062796],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007159277,0.00011903764,0.017706499,0.00016875344,0.00003450265,0.00002206187,0.017213423,0.7999323,0.000037716418,0.005313106,0.0009132416,0.15846777],"study_design_scores_gemma":[0.00068565144,0.000028242051,0.002629133,0.0005720782,0.000015010519,0.000001011914,0.020888312,0.8959616,0.00001211859,0.0006467455,0.077911586,0.00064855994],"about_ca_topic_score_codex":0.0018262757,"about_ca_topic_score_gemma":0.010071006,"teacher_disagreement_score":0.27485228,"about_ca_system_score_codex":0.0004021325,"about_ca_system_score_gemma":0.00077400106,"threshold_uncertainty_score":0.99988633},"labels":[],"label_agreement":null},{"id":"W3121641146","doi":"","title":"A Quarter Century of Data Envelopment Analysis Applied to the Transport Sector: A Bibliometric Analysis","year":2016,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Data envelopment analysis; Scope (computer science); Quarter (Canadian coin); Descriptive statistics; Field (mathematics); Regional science; Bibliometrics; Operations research; Data science; Computer science; Statistics; Geography; Engineering; Library science; Mathematics","score_opus":0.058622429966590436,"score_gpt":0.35572033128045316,"score_spread":0.2970979013138627,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3121641146","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.79177964,0.00048129016,0.010046686,0.005466773,0.0007315581,0.004125389,0.00468774,0.00017908406,0.18250185],"genre_scores_gemma":[0.9912053,0.0058927108,0.0012065079,0.000040143055,0.00009418564,0.00011340563,0.0010387115,0.000016190714,0.00039286935],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9967668,0.00030676226,0.0007586818,0.0009071235,0.0007320829,0.00052854174],"domain_scores_gemma":[0.99738574,0.0005643701,0.00027356652,0.0013884843,0.00018460002,0.00020324004],"candidate_categories":["bibliometrics"],"consensus_categories":["bibliometrics"],"category_scores_codex":[0.005071676,0.00018646527,0.0006204383,0.043279868,0.0002498107,0.00009970604,0.0016420067,0.0002722642,0.0002934032],"category_scores_gemma":[0.00013601463,0.00014877066,0.00026562568,0.05012683,0.00021451358,0.00011786368,0.00017617448,0.00044894795,0.0000061455853],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001916767,0.00023300262,0.38806897,0.00007563921,0.006477548,0.0000071456984,0.027245296,0.39374283,0.000011991479,0.0007242154,0.0002236695,0.18299802],"study_design_scores_gemma":[0.00044740582,0.000035464218,0.91773045,0.00006384447,0.0014038032,9.900611e-8,0.0074708737,0.0056173033,0.00001250697,0.00015017825,0.06651533,0.0005527652],"about_ca_topic_score_codex":0.0013208614,"about_ca_topic_score_gemma":0.019119179,"teacher_disagreement_score":0.5296615,"about_ca_system_score_codex":0.0003591653,"about_ca_system_score_gemma":0.00074525154,"threshold_uncertainty_score":0.99877936},"labels":[],"label_agreement":null},{"id":"W3121647810","doi":"","title":"Cost Recovery from Congestion Tolls with Long-run Uncertainty","year":2013,"lang":"en","type":"article","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Economics","score_opus":0.015143055836394751,"score_gpt":0.236455144644045,"score_spread":0.22131208880765024,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3121647810","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.58447754,0.0002514133,0.2920378,0.01867509,0.00023927672,0.0009036991,0.00007242159,0.00048545768,0.10285729],"genre_scores_gemma":[0.9643581,0.00024090917,0.022814583,0.0001727552,0.000027037819,0.00006755328,0.000657883,0.000018892055,0.011642323],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9967481,0.0020536436,0.00024310617,0.0003327292,0.00036932144,0.0002531003],"domain_scores_gemma":[0.996504,0.0012181936,0.00020560762,0.00044868473,0.0014521533,0.00017135989],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001675454,0.00013159761,0.00014802953,0.000077174736,0.0005770519,0.0003524132,0.00033779014,0.0001213701,0.0010034116],"category_scores_gemma":[0.0006128852,0.00012788577,0.000050480092,0.0003981612,0.00024965327,0.0004716768,0.000021840695,0.00015319517,0.0001331208],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015914287,0.001269519,0.22789966,0.00005908363,0.00028774363,0.000016714761,0.1993282,0.02027361,0.0013313072,0.15587075,0.014698459,0.3788058],"study_design_scores_gemma":[0.0045701563,0.0000057166712,0.7917561,0.0028957585,0.00024328753,0.0000064145324,0.013098634,0.02830277,0.010813345,0.010057375,0.13613811,0.0021123104],"about_ca_topic_score_codex":0.025924155,"about_ca_topic_score_gemma":0.034496844,"teacher_disagreement_score":0.5638565,"about_ca_system_score_codex":0.00008841421,"about_ca_system_score_gemma":0.00019876946,"threshold_uncertainty_score":0.9999098},"labels":[],"label_agreement":null},{"id":"W3121672237","doi":"10.1155/2021/8862893","title":"Discrete Event Simulation of Bus Terminals: A Modular Approach with a High Spatial Resolution","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","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":"K2","keywords":"Modular design; Terminal (telecommunication); Queue; Ping (video games); Event (particle physics); Discrete event simulation; Computer science; Public transport; Simulation; Engineering; Transport engineering; Computer network; Operating system","score_opus":0.012089188304550658,"score_gpt":0.2841205393674467,"score_spread":0.27203135106289605,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3121672237","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.51213294,0.000117421,0.48727113,0.00013940505,0.00011834485,0.00010497101,0.000016460137,0.000012312145,0.000087039],"genre_scores_gemma":[0.9573446,0.00011782423,0.04216969,0.000013742287,0.00010428513,0.0000034224288,0.00017595044,0.000011553686,0.000058921498],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99838614,0.000110009,0.00059244875,0.00014179024,0.00063069054,0.00013891036],"domain_scores_gemma":[0.9981852,0.0000706158,0.0007388743,0.00008555513,0.0008425491,0.00007719054],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003439168,0.000097875905,0.0002311194,0.00012126515,0.00013628777,0.00001765469,0.0000737414,0.00007750851,0.000022294003],"category_scores_gemma":[0.00006203187,0.00008932256,0.00009296269,0.00039028024,0.00007228623,0.0006067694,5.5482246e-7,0.00012501504,2.643661e-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":[0.00042157588,0.00010249244,0.0027717592,0.000041599258,0.000032842716,0.000025595722,0.010518072,0.98169696,0.0009710396,0.0008632656,0.0000019961424,0.002552781],"study_design_scores_gemma":[0.0056739114,0.000865403,0.9289626,0.00081479555,0.00061726087,0.000015122243,0.020474205,0.033132073,0.0047590085,0.0020210398,0.002130915,0.0005336853],"about_ca_topic_score_codex":0.00016130951,"about_ca_topic_score_gemma":0.00062715536,"teacher_disagreement_score":0.9485649,"about_ca_system_score_codex":0.000056151384,"about_ca_system_score_gemma":0.0002658429,"threshold_uncertainty_score":0.36424682},"labels":[],"label_agreement":null},{"id":"W3121779396","doi":"","title":"Introducing behavioral change in transportation into energy/economy/environment models","year":2012,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Key (lock); Climate change; Quarter (Canadian coin); Economic model; Scale (ratio); Energy (signal processing); Environmental economics; Economics; Business; Computer science; Microeconomics; Geography; Ecology","score_opus":0.06367157414281119,"score_gpt":0.3409389487557887,"score_spread":0.27726737461297746,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3121779396","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9459832,0.0008546776,0.00097654754,0.002617338,0.0012323658,0.0020752593,0.00013237639,0.00015384758,0.0459744],"genre_scores_gemma":[0.9874763,0.0086451145,0.0018352225,0.00005247089,0.00040415104,0.0005705188,0.00063667435,0.000041726584,0.0003378211],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973581,0.0003294479,0.00064224895,0.00070875045,0.00026290724,0.00069858733],"domain_scores_gemma":[0.99905014,0.00011739097,0.00019138791,0.00038814204,0.000051134954,0.00020178627],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0020200848,0.00022529143,0.00035697856,0.00057051075,0.00022742622,0.00010272958,0.00037699006,0.0005091516,0.00015828181],"category_scores_gemma":[0.000020470268,0.00029274504,0.00009626739,0.00013668611,0.00026474165,0.00054982526,0.00004389677,0.0008247305,0.0000049478476],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007022442,0.00032687854,0.06319755,0.00008165238,0.000021312506,0.000014173488,0.08801767,0.67699265,0.0000073117203,0.009330776,0.000010640518,0.16192918],"study_design_scores_gemma":[0.0062933713,0.0004218026,0.33550653,0.002123168,0.00020773873,0.0000023635325,0.06594668,0.34160298,0.00022815024,0.05832245,0.18286002,0.0064847437],"about_ca_topic_score_codex":0.01666744,"about_ca_topic_score_gemma":0.041026235,"teacher_disagreement_score":0.33538964,"about_ca_system_score_codex":0.0014489592,"about_ca_system_score_gemma":0.0003384489,"threshold_uncertainty_score":0.9999525},"labels":[],"label_agreement":null},{"id":"W3122017571","doi":"","title":"Pareto Improvements from Lexus Lanes: The effects of pricing a portion of the lanes on congested highways","year":2015,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Toll; Revenue; Pareto principle; Transport engineering; Externality; Rush hour; Travel time; Traffic congestion; Congestion pricing; Economics; Computer science; Microeconomics; Engineering; Operations management; Finance","score_opus":0.03261020857813309,"score_gpt":0.3226165499436814,"score_spread":0.2900063413655483,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3122017571","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98677194,0.00006823341,0.0000027212116,0.00035160835,0.0005521147,0.000828045,0.00006605175,0.000026291513,0.011332965],"genre_scores_gemma":[0.99821603,0.0010873479,0.00004562271,0.000029481436,0.00008458916,0.000057612608,0.00010206588,0.000013598312,0.0003636459],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9981626,0.00048701317,0.0004158169,0.00028908075,0.00040630478,0.00023917477],"domain_scores_gemma":[0.99791723,0.00101449,0.00040759335,0.00042153732,0.00018383087,0.00005528439],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015815332,0.00011739849,0.00025879254,0.00011232108,0.0001641068,0.00004049791,0.000488312,0.00022561724,0.000009215917],"category_scores_gemma":[0.0008928617,0.00008331449,0.00007311305,0.00013140867,0.00030066422,0.000046774378,0.00008635706,0.0004907496,0.0000011674587],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006032048,0.0005809896,0.5711307,0.0010561844,0.00048575757,0.000018610503,0.095099285,0.23637415,0.0015055785,0.0045114895,0.00057247665,0.08806158],"study_design_scores_gemma":[0.0031763585,0.00056871754,0.9250848,0.003816591,0.0001511328,4.775178e-7,0.030013049,0.014337166,0.0052825315,0.006572355,0.0101174265,0.0008793744],"about_ca_topic_score_codex":0.006973157,"about_ca_topic_score_gemma":0.0058479737,"teacher_disagreement_score":0.35395414,"about_ca_system_score_codex":0.000250899,"about_ca_system_score_gemma":0.0005642945,"threshold_uncertainty_score":0.9996395},"labels":[],"label_agreement":null},{"id":"W3122396129","doi":"10.1016/j.trc.2021.103050","title":"ResLogit: A residual neural network logit model for data-driven choice modelling","year":2021,"lang":"en","type":"preprint","venue":"Transportation Research Part C Emerging Technologies","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Interpretability; Computer science; Multinomial logistic regression; Residual; Logit; Artificial neural network; Artificial intelligence; Discrete choice; Econometrics; Machine learning; Econometric model; Mixed logit; Logistic regression; Algorithm; Mathematics","score_opus":0.3303453341144098,"score_gpt":0.4484011390891473,"score_spread":0.1180558049747375,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3122396129","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08552036,0.0025935802,0.8847208,0.016389284,0.0009902624,0.0030861353,0.0016062054,0.004636777,0.00045658715],"genre_scores_gemma":[0.81651753,0.0050579193,0.16131426,0.000050108927,0.00048016745,0.0009496544,0.014616434,0.00009725293,0.00091664697],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.994301,0.00034623704,0.0009467013,0.0015355048,0.0015469827,0.0013235274],"domain_scores_gemma":[0.9957392,0.0009170721,0.00038298624,0.0014420515,0.0013882652,0.00013042415],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.003133165,0.0003958413,0.0005767282,0.00051022373,0.0018737984,0.0004600616,0.002217666,0.0010931442,0.00003231039],"category_scores_gemma":[0.0010326226,0.00044677805,0.00017314845,0.0010930834,0.0007871725,0.00065404776,0.00016680345,0.0019063321,0.0000032398364],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006336538,0.000054566557,0.002341901,0.0002144162,0.000082169965,0.000018141705,0.006039964,0.972317,0.000004004708,0.0061206003,0.011097097,0.0016467631],"study_design_scores_gemma":[0.00031736956,0.00003244893,0.00024155503,0.000402418,0.000093233044,9.715024e-8,0.008760378,0.97015697,0.000016759413,0.009578681,0.009930126,0.00046996752],"about_ca_topic_score_codex":0.0029532074,"about_ca_topic_score_gemma":0.023688806,"teacher_disagreement_score":0.7309972,"about_ca_system_score_codex":0.00014566146,"about_ca_system_score_gemma":0.0011777087,"threshold_uncertainty_score":0.9997984},"labels":[],"label_agreement":null},{"id":"W3122576198","doi":"10.2139/ssrn.2897428","title":"Connectivity of Intercity Passenger Transportation in China: A Multi-Modal and Network Approach","year":2017,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Modal; Transport engineering; Passenger transport; China; Business; Computer science; Geography; Engineering","score_opus":0.01443357149360479,"score_gpt":0.2839043091121747,"score_spread":0.2694707376185699,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3122576198","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93773043,0.00024561386,0.060917012,0.00030491632,0.00009042738,0.000103992264,0.0000028230443,0.000011902512,0.00059290347],"genre_scores_gemma":[0.9977681,0.0009967868,0.0010234495,0.0000058729333,0.000082198174,0.0000028407433,0.0000062441986,0.000005792133,0.00010873531],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9988296,0.000108343535,0.00019139571,0.00011894394,0.00014943408,0.0006022571],"domain_scores_gemma":[0.99956733,0.000028630588,0.00023703293,0.00007315756,0.000049370927,0.0000445065],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001896951,0.0000672521,0.00014344262,0.00004845837,0.00053039397,0.000053084026,0.0001343324,0.00007781242,0.0000043288333],"category_scores_gemma":[0.000077919416,0.00006558939,0.000038414415,0.00006971464,0.00012764466,0.0003554212,0.0000021522478,0.0005574117,2.1336395e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010338266,0.0001269156,0.86755145,0.000011130055,0.000041025396,0.0000019820084,0.013965297,0.007181591,0.000027095564,0.10356895,0.0000070776964,0.007414117],"study_design_scores_gemma":[0.00075397355,0.00004664986,0.98134905,0.000025866575,0.000016707665,0.0000028426675,0.0031185583,0.0017198366,0.000005153351,0.01282924,0.000046343728,0.000085768705],"about_ca_topic_score_codex":0.002907144,"about_ca_topic_score_gemma":0.0703516,"teacher_disagreement_score":0.11379762,"about_ca_system_score_codex":0.0001179868,"about_ca_system_score_gemma":0.00048114843,"threshold_uncertainty_score":0.94661206},"labels":[],"label_agreement":null},{"id":"W3123200614","doi":"10.1155/2021/6659384","title":"Profit Maximization Model with Fare Structures and Subsidy Constraints for Urban Rail Transit","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","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":"Division of Graduate Education; China Scholarship Council; National Natural Science Foundation of China","keywords":"Subsidy; Headway; Profit maximization; Price elasticity of demand; Public transport; Maximization; Social Welfare; Urban rail transit; Profit (economics); Microeconomics; Economics; Transport engineering; Transit system; Welfare; Computer science; Engineering; Transit (satellite); Market economy","score_opus":0.014030236027787375,"score_gpt":0.2702764187128798,"score_spread":0.2562461826850924,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3123200614","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4295609,0.00022880649,0.56895113,0.0006885149,0.000117055184,0.0002419423,0.000070704584,0.000024690473,0.00011624266],"genre_scores_gemma":[0.9052157,0.00020972629,0.09414241,0.000065505046,0.000055979363,0.0000062239037,0.0001849073,0.000013943567,0.000105614796],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9989963,0.000032980286,0.00037367493,0.00014620482,0.00030627003,0.00014455948],"domain_scores_gemma":[0.9987033,0.00005602295,0.00031514865,0.00004801095,0.0007812275,0.00009627247],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017659433,0.000102387974,0.00018532413,0.00008154565,0.00022837993,0.000033551612,0.000052176147,0.00008001355,0.000021370319],"category_scores_gemma":[0.00003577975,0.000094375115,0.000056574914,0.0001976076,0.00011704595,0.00058590656,2.17499e-7,0.00010011896,7.185326e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004963558,0.000028873304,0.012360323,0.00007987385,0.000052665084,0.000022983877,0.0331959,0.9234466,0.0012745935,0.024578935,0.000034759123,0.004428122],"study_design_scores_gemma":[0.026130224,0.0013350601,0.78407604,0.0011205893,0.0017138716,0.00009078671,0.1114715,0.013076788,0.014830978,0.038449503,0.0058528734,0.0018518113],"about_ca_topic_score_codex":0.000005264381,"about_ca_topic_score_gemma":0.00054010254,"teacher_disagreement_score":0.9103698,"about_ca_system_score_codex":0.00002673457,"about_ca_system_score_gemma":0.0003341564,"threshold_uncertainty_score":0.38485056},"labels":[],"label_agreement":null},{"id":"W3123324160","doi":"","title":"Equilibrium in a dynamic model of congestion with large and small users","year":2018,"lang":"en","type":"article","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Economics; Mathematical economics","score_opus":0.014007638608552192,"score_gpt":0.2397991669080389,"score_spread":0.2257915282994867,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3123324160","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.85596323,0.000059802012,0.12438614,0.0021846246,0.000020539152,0.00013707003,0.000014869127,0.00005770118,0.017176002],"genre_scores_gemma":[0.97255874,0.00007228907,0.025597814,0.000028200298,0.0000029790453,0.0000062605613,0.000047800284,0.00000822626,0.001677696],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99854785,0.00078123616,0.00016808652,0.00018776681,0.00015686799,0.00015817396],"domain_scores_gemma":[0.99867445,0.00025257506,0.00012255702,0.00021304187,0.0006746941,0.00006269221],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00212403,0.00006753543,0.00009839158,0.00009579584,0.0001408451,0.000047924954,0.00015258224,0.00006596904,0.00002011121],"category_scores_gemma":[0.0002819241,0.0000693025,0.000016348475,0.000308869,0.0003105193,0.00015330724,0.000019876561,0.00007210936,0.000001797566],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013424299,0.0006268236,0.22033192,0.00009328358,0.00003662402,0.0000026300295,0.28186798,0.0059284423,0.0056117685,0.47334963,0.000086322376,0.011930357],"study_design_scores_gemma":[0.00227814,0.0000035030457,0.11195711,0.0011867306,0.000043922875,0.0000017535323,0.00419467,0.8697789,0.0051167915,0.003611418,0.0013775346,0.00044957653],"about_ca_topic_score_codex":0.0013231484,"about_ca_topic_score_gemma":0.052133825,"teacher_disagreement_score":0.8638504,"about_ca_system_score_codex":0.00002853854,"about_ca_system_score_gemma":0.00013174194,"threshold_uncertainty_score":0.9651623},"labels":[],"label_agreement":null},{"id":"W3123501822","doi":"","title":"Step Tolling with Bottleneck Queuing Congestion","year":2012,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Toll; Bottleneck; Queueing theory; Interval (graph theory); Limiting; Queue; Limit (mathematics); Computer science; Congestion pricing; Mathematical optimization; Traffic congestion; Mathematics; Transport engineering; Engineering; Computer network","score_opus":0.010686777424999318,"score_gpt":0.2642367029258171,"score_spread":0.2535499255008178,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3123501822","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7487906,0.00275364,0.23795596,0.0012985545,0.00046881277,0.00013774536,0.0000011730094,0.00012798115,0.008465509],"genre_scores_gemma":[0.9957548,0.0012300806,0.0013170091,0.00003936782,0.0004247249,0.0000018754184,0.000005365433,0.000009700568,0.0012171176],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","domain_scores_codex":[0.9982325,0.00008963472,0.0001230217,0.000068483576,0.00026033318,0.0012260323],"domain_scores_gemma":[0.9996367,0.000039332084,0.00010380283,0.00003922258,0.00008315427,0.000097774915],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001693114,0.00006383813,0.000070911214,0.00005759156,0.0005418659,0.000056525663,0.00007730912,0.00005454187,0.000030404459],"category_scores_gemma":[0.0000401281,0.000055073335,0.000025717925,0.00016211925,0.000040278424,0.00048929214,0.0000013634999,0.00053060206,0.000012203376],"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":[0.000091457536,0.000067091445,0.2768323,0.0000045964825,0.00009238566,0.0000019820352,0.013391799,0.008970903,0.00004276054,0.6777012,0.000085953674,0.022717595],"study_design_scores_gemma":[0.009444432,0.002074151,0.2597889,0.0009059312,0.0010313009,0.0009121253,0.4322944,0.0036795887,0.00040935198,0.07475341,0.21108016,0.0036262397],"about_ca_topic_score_codex":0.00030926,"about_ca_topic_score_gemma":0.0036388452,"teacher_disagreement_score":0.6029478,"about_ca_system_score_codex":0.00034996745,"about_ca_system_score_gemma":0.0008970276,"threshold_uncertainty_score":0.41676491},"labels":[],"label_agreement":null},{"id":"W3123820998","doi":"","title":"Les composantes de la modélisation d’un projet de transport urbain : la spécificité de la Grande Région de Montréal","year":2012,"lang":"en","type":"article","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Humanities; Political science; Art","score_opus":0.013846401415440204,"score_gpt":0.2529442626796422,"score_spread":0.23909786126420202,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3123820998","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6002877,0.0004912056,0.3447798,0.002035931,0.000038526996,0.00021205103,0.000023837272,0.00026670593,0.051864263],"genre_scores_gemma":[0.9556809,0.00049255614,0.042373203,0.00006104594,0.000038500268,0.000040070314,0.0001982877,0.00002669932,0.001088748],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9900946,0.008580705,0.00029909445,0.00023641551,0.00032221596,0.00046695772],"domain_scores_gemma":[0.9968425,0.002094649,0.00019264457,0.00030275038,0.00032462954,0.00024284983],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0120904595,0.00016343301,0.00016585125,0.00012119298,0.00081282866,0.00018619883,0.00036431025,0.00027671794,0.00006306361],"category_scores_gemma":[0.0006697446,0.00018021205,0.00010380127,0.0003571257,0.00038818922,0.000310696,0.00001617687,0.00028987782,0.000004909105],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000044042168,0.0006074866,0.5420669,0.000038537084,0.000033544406,0.0000074167347,0.21221511,0.0032049962,0.0030719312,0.22340341,0.00025567345,0.015050985],"study_design_scores_gemma":[0.0016113722,0.0000018722545,0.87726015,0.0005902698,0.00014659476,0.000041260337,0.006816793,0.041944504,0.016548557,0.0079084225,0.046475474,0.0006547495],"about_ca_topic_score_codex":0.0155956345,"about_ca_topic_score_gemma":0.004982109,"teacher_disagreement_score":0.3553932,"about_ca_system_score_codex":0.0002049652,"about_ca_system_score_gemma":0.00030007394,"threshold_uncertainty_score":0.9909596},"labels":[],"label_agreement":null},{"id":"W3124076328","doi":"10.2139/ssrn.1089097","title":"Intertechnology Effects in Intelligent Transportation Systems","year":2008,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Ministry of Education, India; Ministry of Earth Sciences; University of Minnesota; California Department of Transportation","keywords":"Intelligent transportation system; Transport engineering; Computer science; Business; Engineering","score_opus":0.009652880346839691,"score_gpt":0.25563027332487986,"score_spread":0.24597739297804017,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3124076328","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.84390765,0.0022637136,0.1514555,0.000573392,0.00057441776,0.00022571172,0.0000010045925,0.0001047819,0.0008938475],"genre_scores_gemma":[0.9932847,0.0058208816,0.00008996834,0.00002245614,0.000076476455,0.000008844721,0.0000092023465,0.000008193988,0.0006792652],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99844056,0.00011112756,0.00024818908,0.000111097645,0.00019879178,0.0008902023],"domain_scores_gemma":[0.9997004,0.000049842485,0.000097317185,0.000048544935,0.000058965714,0.000044930086],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00081199117,0.00007172071,0.000117086995,0.00020405321,0.00026505286,0.000016548604,0.00013830187,0.0001118468,0.0000074095333],"category_scores_gemma":[0.000047615602,0.000071533104,0.00004102707,0.00030387368,0.00007749179,0.00016816158,7.623786e-7,0.00074588973,0.000012206074],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006626098,0.00010896045,0.11764831,0.000015285548,0.00005351919,0.000069934715,0.02497051,0.01444487,0.00005049198,0.8327265,0.00007778498,0.009767576],"study_design_scores_gemma":[0.00928152,0.0027878592,0.2955825,0.0010631569,0.00025508984,0.001120729,0.2628622,0.006725527,0.00067407195,0.36332554,0.053679995,0.0026418052],"about_ca_topic_score_codex":0.0012626176,"about_ca_topic_score_gemma":0.017661953,"teacher_disagreement_score":0.46940097,"about_ca_system_score_codex":0.000499931,"about_ca_system_score_gemma":0.00091249583,"threshold_uncertainty_score":0.9855785},"labels":[],"label_agreement":null},{"id":"W3124120203","doi":"10.1680/jmuen.20.00038","title":"A cost-effective methodology to compare travel time and speed: a tale of 11 cities","year":2021,"lang":"en","type":"article","venue":"Proceedings of the Institution of Civil Engineers - Municipal Engineer","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"TRIPS architecture; Downtown; Destinations; Transport engineering; Central business district; Geography; Travel time; Regional science; Service (business); Computer science; Business; Tourism; Marketing; Engineering","score_opus":0.04312665433636012,"score_gpt":0.300306885036137,"score_spread":0.2571802306997769,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3124120203","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97722673,0.00032632824,0.010985054,0.0005578012,0.00031447853,0.0008343879,0.00009294263,0.000060337345,0.009601947],"genre_scores_gemma":[0.99191827,0.000036331643,0.0076325648,0.000026021848,0.000029318526,0.000018241462,0.000008389606,0.0000086462815,0.00032220472],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991303,0.000022607588,0.00028480883,0.00013580658,0.00026787407,0.00015857705],"domain_scores_gemma":[0.999074,0.00017034562,0.0001357076,0.00006949545,0.00047969236,0.00007077455],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00052071275,0.00011019095,0.0003060309,0.00015288046,0.000083273415,0.0000101804535,0.00017432979,0.00007209142,0.000031775668],"category_scores_gemma":[0.0007367791,0.000101628604,0.00007708827,0.0005632468,0.00028566047,0.00016529494,0.000026280968,0.000102346494,5.7435716e-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":[0.00010082998,0.00012433135,0.0043443847,0.0004695619,0.00014677893,0.0000011510773,0.114655346,0.6579786,0.032350205,0.18890478,0.0004195326,0.0005045064],"study_design_scores_gemma":[0.006796044,0.0006162603,0.16009536,0.004044275,0.0013165302,0.000053588094,0.13599418,0.17985193,0.46908092,0.0020421147,0.03793166,0.002177127],"about_ca_topic_score_codex":0.0004801896,"about_ca_topic_score_gemma":0.00040619227,"teacher_disagreement_score":0.47812665,"about_ca_system_score_codex":0.00003705748,"about_ca_system_score_gemma":0.00008336017,"threshold_uncertainty_score":0.41442943},"labels":[],"label_agreement":null},{"id":"W3124598808","doi":"","title":"DOES THE OVERALL QUALITY IN THE SUPPLY OF AN URBAN BUS SERVICE AFFECT HOUSE PRICES? - A NORTH-AMERICAN CASE STUDY","year":2006,"lang":"en","type":"article","venue":"Book of Abstracts: 13th Annual European Real Estate Society Conference","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Affect (linguistics); Quality (philosophy); Business; Service (business); Service quality; House price; Operations management; Marketing; Economics; Monetary economics; Psychology","score_opus":0.01927404544845737,"score_gpt":0.29595308289199357,"score_spread":0.2766790374435362,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3124598808","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9903049,0.0000080136015,0.000028742566,0.00037956372,0.00003857497,0.0006333265,0.0002265305,0.000084679836,0.008295703],"genre_scores_gemma":[0.9989354,0.00027860823,0.000296035,0.00022538332,0.00006504352,0.000007687307,0.0000619073,0.000022382457,0.000107573884],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.99691665,0.0011348625,0.0007024576,0.00032075064,0.0006008569,0.00032439933],"domain_scores_gemma":[0.99788946,0.0004648751,0.0007671746,0.0003522938,0.0004402874,0.00008593956],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023228535,0.0001944141,0.00027210085,0.00003122811,0.00034721746,0.00010624676,0.0005468732,0.00003658025,0.000009008418],"category_scores_gemma":[0.00005828826,0.00011405732,0.00010137845,0.00045973336,0.0005099629,0.0004973718,0.000022209502,0.00028185762,0.000002800308],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013039129,0.0011387523,0.2526365,0.00008767706,0.000051075807,0.00037000602,0.72678167,0.01369641,0.00003188777,0.00041469335,0.00045915347,0.0042017996],"study_design_scores_gemma":[0.00041326974,0.00020556584,0.8181755,0.000017820537,0.00003360937,0.000004365367,0.17994198,0.000091120644,0.000007159765,0.0000073450947,0.0009557289,0.00014653576],"about_ca_topic_score_codex":0.27609196,"about_ca_topic_score_gemma":0.18506369,"teacher_disagreement_score":0.565539,"about_ca_system_score_codex":0.000033756987,"about_ca_system_score_gemma":0.0003016926,"threshold_uncertainty_score":0.82980675},"labels":[],"label_agreement":null},{"id":"W3124780846","doi":"","title":"Characteristics of Bus Transit Vehicles in the United States: How They Have Changed Over a Quarter Century","year":2012,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Transit (satellite); Revenue; Spare part; Quarter (Canadian coin); Transport engineering; Business; Public transport; Finance; Engineering; Operations management; Geography","score_opus":0.040902566608744695,"score_gpt":0.31743281458372546,"score_spread":0.27653024797498077,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3124780846","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9929234,0.00006906281,0.000016880935,0.0023881665,0.00022149994,0.00069468917,0.00024908368,0.000021102329,0.003416165],"genre_scores_gemma":[0.98660713,0.012174212,0.00010067856,0.00008995048,0.00014191869,0.00007568253,0.0006640358,0.000020142099,0.00012626266],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.99768084,0.0007782907,0.0003877879,0.00030130846,0.00034677953,0.0005049968],"domain_scores_gemma":[0.99869305,0.0005557724,0.00020562136,0.00033459495,0.0001242621,0.0000866958],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027512868,0.00015750661,0.00030224942,0.0004367795,0.0001543959,0.00014432911,0.00046788654,0.00033114813,0.000038723592],"category_scores_gemma":[0.00014700144,0.00014120272,0.000082694954,0.0001439692,0.00027149456,0.00011470506,0.000031816722,0.00087610853,0.000001192723],"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":[0.00035569532,0.00054200966,0.25232825,0.00038606537,0.00011125814,0.000028979777,0.6850337,0.025098354,0.000033161432,0.0022022557,0.000112571346,0.033767745],"study_design_scores_gemma":[0.0012557601,0.000094160314,0.7920028,0.0006228339,0.00004187757,9.376038e-7,0.13055204,0.01332831,0.000016578231,0.00075760036,0.060647372,0.00067973754],"about_ca_topic_score_codex":0.0026462271,"about_ca_topic_score_gemma":0.008338163,"teacher_disagreement_score":0.5544816,"about_ca_system_score_codex":0.00023229413,"about_ca_system_score_gemma":0.00024851022,"threshold_uncertainty_score":0.575808},"labels":[],"label_agreement":null},{"id":"W3125157142","doi":"10.1139/cjce-2020-0666","title":"Information entropy evaluation model of bus-line reliability considering the combination of bus stops and bus travel time","year":2021,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Reliability (semiconductor); Local bus; Line (geometry); Reliability engineering; Computer science; Transport engineering; Simulation; Engineering; Control bus; Real-time computing; System bus; Mathematics","score_opus":0.014089204449812024,"score_gpt":0.22365991130773996,"score_spread":0.20957070685792795,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3125157142","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.87251437,0.00049419026,0.12421707,0.0011934871,0.00028601568,0.00018497171,0.000035898294,0.000008864049,0.0010651146],"genre_scores_gemma":[0.9988887,0.00005335887,0.0009816288,0.000016235786,0.000021122403,0.0000010348646,0.000015573769,0.0000037231473,0.000018629386],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991743,0.000049129903,0.00036718778,0.000037832066,0.00027121828,0.000100361875],"domain_scores_gemma":[0.9985424,0.00009556384,0.0002234459,0.000059352515,0.0009712792,0.00010791213],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010372777,0.000052189556,0.00012474191,0.00013216138,0.00009028122,0.00002841238,0.000059836013,0.000049599614,0.00004703221],"category_scores_gemma":[0.00087614136,0.00005044527,0.00003055408,0.00022603953,0.000053783988,0.00039024398,0.000001721655,0.000096090334,2.3066475e-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":[0.000003923299,0.000004372845,0.00081467396,0.000028557206,0.000011859387,6.909271e-7,0.009944705,0.9853898,0.0006473832,0.0025960237,0.00003736214,0.0005206228],"study_design_scores_gemma":[0.0005984503,0.000031685326,0.016182065,0.00014933862,0.00007505079,0.000006777157,0.0019844533,0.9786744,0.0011695984,0.0006751587,0.0003616059,0.000091431015],"about_ca_topic_score_codex":0.00091041916,"about_ca_topic_score_gemma":0.012092388,"teacher_disagreement_score":0.1263743,"about_ca_system_score_codex":0.000087959066,"about_ca_system_score_gemma":0.0011187595,"threshold_uncertainty_score":0.67478365},"labels":[],"label_agreement":null},{"id":"W3125613930","doi":"10.1108/s0573-8555(2005)0000266007.html?utm_source=repec&utm_medium=feed&utm_campaign=repec","title":"Land-use, Transportation and Urban Development","year":2004,"lang":"en","type":"preprint","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Subject matter; Traffic congestion; Land use; Economic geography; Location theory; Regional science; Urban planning; Quarter (Canadian coin); Environmental planning; Geography; Transport engineering; Economics; Civil engineering; Engineering; Economic growth","score_opus":0.028744830847492193,"score_gpt":0.2758680020166147,"score_spread":0.24712317116912252,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3125613930","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8781569,0.0003499016,0.09897094,0.0013791049,0.0009004139,0.000880224,0.00007747654,0.00077422854,0.018510789],"genre_scores_gemma":[0.9757538,0.00021432218,0.019954415,0.00010847043,0.00006837777,0.00002197079,0.0008196037,0.000011032597,0.0030480078],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9991628,0.000028037233,0.00020710436,0.00023251142,0.00023432972,0.00013524786],"domain_scores_gemma":[0.9996573,0.000029917657,0.00008318956,0.00006561682,0.00007867919,0.000085317675],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001750971,0.0001079139,0.00011713137,0.00007056331,0.00024304212,0.0001249116,0.000069282396,0.00021530622,0.00008132713],"category_scores_gemma":[0.000014831162,0.000110041765,0.000024811432,0.0000722764,0.00005791413,0.00012951938,0.0000034875202,0.00014517883,0.000006253649],"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":[0.00002847634,0.000075911244,0.71810377,0.00016901475,0.000071244714,0.000013037846,0.1759652,0.05719286,0.0000025313752,0.043832816,0.0010419603,0.0035032036],"study_design_scores_gemma":[0.0004136595,0.000009247533,0.9206481,0.00021344416,0.000048944483,1.3306003e-7,0.001270371,0.000078171885,0.000046430807,0.0014909834,0.0753319,0.00044864175],"about_ca_topic_score_codex":0.003421563,"about_ca_topic_score_gemma":0.014647349,"teacher_disagreement_score":0.20254433,"about_ca_system_score_codex":0.000062198415,"about_ca_system_score_gemma":0.00050899445,"threshold_uncertainty_score":0.8173564},"labels":[],"label_agreement":null},{"id":"W3125624299","doi":"10.1061/9780784482292.413","title":"A Multiple Path Gradient Projection Method for Solving the Logit-Based Stochastic User Equilibrium Problem","year":2019,"lang":"en","type":"article","venue":"CICTP 2019","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Path (computing); Gradient descent; Variational inequality; Mathematical optimization; Logit; Computer science; Stochastic gradient descent; Projection (relational algebra); Set (abstract data type); Algorithm; Mathematics; Artificial neural network; Artificial intelligence; Machine learning","score_opus":0.023643728050438944,"score_gpt":0.3018332930497648,"score_spread":0.27818956499932584,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3125624299","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.088195615,0.00006415558,0.9052392,0.001615766,0.00091823714,0.0026122944,0.000048299687,0.00023741699,0.00106899],"genre_scores_gemma":[0.9641507,0.000001827172,0.032082222,0.00019449429,0.0001272172,0.00014827064,0.00010532955,0.000019793888,0.0031701208],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990678,0.000090073954,0.00016195781,0.00021760595,0.00020900212,0.000253554],"domain_scores_gemma":[0.99911344,0.0004545614,0.00011814534,0.00013870472,0.00013104173,0.00004410117],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00079988403,0.00008795715,0.0001049975,0.000053944153,0.0002808279,0.00007471616,0.00013927254,0.000078515135,0.000033759454],"category_scores_gemma":[0.00012395107,0.00006659358,0.00007052987,0.0001937542,0.000039412378,0.00015730251,0.000005350529,0.000081191974,0.000032342035],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036905403,0.00024384732,0.041804545,0.00019067424,0.00007415056,9.978714e-7,0.044601247,0.8667638,0.004320597,0.02519482,0.011171066,0.005265219],"study_design_scores_gemma":[0.0038134053,0.00051500316,0.014064673,0.0002679188,0.00017536042,0.0000012670408,0.009417894,0.9142835,0.0008726601,0.0020727303,0.053687375,0.000828193],"about_ca_topic_score_codex":0.00060176087,"about_ca_topic_score_gemma":0.00030388864,"teacher_disagreement_score":0.8759551,"about_ca_system_score_codex":0.000053181288,"about_ca_system_score_gemma":0.00017468799,"threshold_uncertainty_score":0.27156073},"labels":[],"label_agreement":null},{"id":"W3125692962","doi":"10.1016/j.ejor.2012.02.017","title":"Highway toll pricing","year":2012,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"National Social Science Fund of China; Social Sciences and Humanities Research Council of Canada; Rice University; National Science Foundation","keywords":"Toll; Road pricing; Business; Computer science; Operations research; Transport engineering; Mathematics; Traffic congestion; Engineering","score_opus":0.12980381512609354,"score_gpt":0.4124041660984312,"score_spread":0.2826003509723377,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3125692962","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4884726,0.0009700823,0.018655153,0.009002679,0.0011749886,0.00020288742,0.0000059466374,0.00003688755,0.48147875],"genre_scores_gemma":[0.9907942,0.000081896236,0.0046871062,0.00008265862,0.0012159687,3.742819e-7,0.0000049127657,0.000008540291,0.003124366],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9971541,0.0012969003,0.00025429597,0.00005041346,0.0010033742,0.0002408805],"domain_scores_gemma":[0.9983988,0.0002349007,0.000071099224,0.000046156278,0.0010574564,0.00019161568],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010207753,0.00003737281,0.00006129754,0.00017692182,0.00058933016,0.00011541982,0.00018240347,0.00001451133,0.0004242226],"category_scores_gemma":[0.0009308275,0.000031790685,0.000029293178,0.0003325284,0.0001007783,0.0005988255,0.000008841018,0.00026101808,0.00013713431],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003117938,0.0006964781,0.16646747,0.000029381763,0.00012927862,0.0002383873,0.17497928,0.026469052,0.0045821285,0.4476791,0.15071803,0.027699592],"study_design_scores_gemma":[0.0005826954,0.00016264168,0.23351105,0.00008028309,0.000008768211,0.00001951522,0.0054227645,0.00011626385,0.00031444285,0.000087717235,0.75955755,0.00013632166],"about_ca_topic_score_codex":0.000020539928,"about_ca_topic_score_gemma":0.0000069245284,"teacher_disagreement_score":0.6088395,"about_ca_system_score_codex":0.00006300836,"about_ca_system_score_gemma":0.00029886147,"threshold_uncertainty_score":0.46449402},"labels":[],"label_agreement":null},{"id":"W3125887624","doi":"","title":"Valuing Different Road Options for Stonehenge","year":2002,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Business","score_opus":0.07596074098560483,"score_gpt":0.37142475239262585,"score_spread":0.295464011407021,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3125887624","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7515984,0.0006950605,0.00079153426,0.003940303,0.0024041364,0.0041164877,0.0005503457,0.0003374975,0.2355662],"genre_scores_gemma":[0.96765834,0.016291298,0.0028479816,0.000034627006,0.00045141848,0.0006033079,0.00029575123,0.00004762871,0.011769637],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99789894,0.00027844612,0.00041674628,0.000543659,0.0002774552,0.00058473414],"domain_scores_gemma":[0.998733,0.00047515286,0.00013410028,0.0003365488,0.00015557163,0.00016566808],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015053764,0.00016787916,0.00029069604,0.00035983144,0.0005828235,0.00019061982,0.00040919636,0.00038968807,0.00018179855],"category_scores_gemma":[0.00039668498,0.00019348647,0.00015122328,0.00009981192,0.00023646935,0.00009851051,0.00007464349,0.00073063053,0.0000058351784],"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":[0.00010413541,0.0004926604,0.015050988,0.00033161716,0.00015784497,0.000010488017,0.033540107,0.49640608,0.000025579697,0.0212302,0.0004471075,0.4322032],"study_design_scores_gemma":[0.004035418,0.00033461212,0.15932004,0.0014929158,0.00011770696,0.0000020709117,0.031247007,0.31133345,0.00007350174,0.015855612,0.4732793,0.0029083483],"about_ca_topic_score_codex":0.000395292,"about_ca_topic_score_gemma":0.0024381464,"teacher_disagreement_score":0.4728322,"about_ca_system_score_codex":0.0006932138,"about_ca_system_score_gemma":0.00029055594,"threshold_uncertainty_score":0.78901494},"labels":[],"label_agreement":null},{"id":"W3125985778","doi":"","title":"The economics of crowding in rail transit","year":2017,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Train; Revenue; Marginal cost; Transit (satellite); Crowding; Economics; Microeconomics; Welfare; Transit system; Transport engineering; Public transport; Engineering; Finance","score_opus":0.052101317539531525,"score_gpt":0.3602148730384982,"score_spread":0.3081135554989667,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3125985778","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.735906,0.00025703132,0.000022254713,0.0027131706,0.00081266835,0.00082187884,0.000067596295,0.000030328123,0.25936908],"genre_scores_gemma":[0.96750164,0.030144738,0.00029215484,0.00001227758,0.00011135741,0.000054935037,0.000032644777,0.000021509852,0.0018287555],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9980836,0.00029541663,0.00057244534,0.0004039153,0.00015959807,0.00048504985],"domain_scores_gemma":[0.9982432,0.0007520335,0.0002866397,0.00053508393,0.00009544163,0.00008758046],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0043243007,0.00013330134,0.00031341898,0.00026718204,0.0006032476,0.00023853789,0.0008853419,0.00035646316,0.000023401295],"category_scores_gemma":[0.000544785,0.00013672786,0.0001082294,0.000066831424,0.0006939107,0.00014210188,0.00008063158,0.00085595204,0.0000019889856],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021981017,0.00010815751,0.0641963,0.00014047667,0.00007804829,0.000014258167,0.030458877,0.63054216,0.0000128837455,0.028283413,0.00005385874,0.24589176],"study_design_scores_gemma":[0.0056381584,0.0002533502,0.3291152,0.0027727042,0.00006481847,0.000003142929,0.06703199,0.0967943,0.00039785664,0.066454664,0.4286307,0.0028430952],"about_ca_topic_score_codex":0.0017228782,"about_ca_topic_score_gemma":0.03737026,"teacher_disagreement_score":0.53374785,"about_ca_system_score_codex":0.00046647704,"about_ca_system_score_gemma":0.0011564735,"threshold_uncertainty_score":0.9801952},"labels":[],"label_agreement":null},{"id":"W3127937113","doi":"10.1177/0361198121990698","title":"Predictive Analytics of Streetcar Bunching Occurrence Time for Real-Time Applications","year":2021,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto; University of Saskatchewan","funders":"","keywords":"Global Positioning System; Computer science; Transit (satellite); Real-time data; Kalman filter; Real-time computing; Transport engineering; TRIPS architecture; Transit time; Automatic vehicle location; Benchmark (surveying); Simulation; Operations research; Public transport; Engineering; Telecommunications; Artificial intelligence","score_opus":0.07689704388562399,"score_gpt":0.4096362699360902,"score_spread":0.33273922605046624,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3127937113","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.932983,0.00034225223,0.054353476,0.00461778,0.00050094345,0.0030554268,0.001979787,0.00009485136,0.0020725068],"genre_scores_gemma":[0.97951156,0.0025116806,0.013757184,0.000029369861,0.00036292055,0.00019707897,0.0004388332,0.00005312227,0.0031382493],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9927492,0.0013303828,0.0014541798,0.00041639144,0.0033291385,0.0007207177],"domain_scores_gemma":[0.98609245,0.0024493341,0.0007251548,0.0004056756,0.009965645,0.00036172458],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005480367,0.00019539359,0.00048730563,0.0006995283,0.0011813518,0.00011765975,0.00087408634,0.0002335459,0.00039991163],"category_scores_gemma":[0.00054599915,0.0001755013,0.0004436973,0.003011582,0.00089533074,0.0006285601,0.0000043514624,0.0009863102,0.000014606057],"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":[0.0049878177,0.0026951374,0.62262785,0.001771495,0.0015614319,0.00015605867,0.08354865,0.12409048,0.032467924,0.058641735,0.044661425,0.02278999],"study_design_scores_gemma":[0.0044708676,0.0013244132,0.8638338,0.001379942,0.0005858808,7.490483e-7,0.025141263,0.0030706364,0.006989039,0.012161955,0.080278814,0.00076267903],"about_ca_topic_score_codex":0.004251751,"about_ca_topic_score_gemma":0.015588535,"teacher_disagreement_score":0.2412059,"about_ca_system_score_codex":0.00022333064,"about_ca_system_score_gemma":0.0025183856,"threshold_uncertainty_score":0.90861225},"labels":[],"label_agreement":null},{"id":"W3129690166","doi":"10.1155/2021/5597130","title":"Calibrating Path Choices and Train Capacities for Urban Rail Transit Simulation Models Using Smart Card and Train Movement Data","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Massachusetts Institute of Technology","keywords":"Computer science; Transit (satellite); Sensitivity (control systems); Randomness; Path (computing); Data collection; Urban rail transit; Simulation; Calibration; Transport engineering; Public transport; Engineering","score_opus":0.06614983674856525,"score_gpt":0.32385224926706985,"score_spread":0.25770241251850456,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3129690166","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5381836,0.0007499366,0.46020344,0.00025534316,0.0001634361,0.00018194168,0.00022418919,0.000015615771,0.000022470515],"genre_scores_gemma":[0.9407096,0.0003468338,0.05828841,0.00011450788,0.00015083473,0.0000031053496,0.00033825918,0.000015060962,0.000033387267],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986957,0.00008523234,0.0005239811,0.00019002357,0.00032467998,0.0001803917],"domain_scores_gemma":[0.9989583,0.00024136514,0.0003440768,0.00007833505,0.0002657976,0.00011213189],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006541531,0.000116207855,0.00022892775,0.00008418071,0.00032419237,0.00006901653,0.00007048106,0.00007845061,0.000004464952],"category_scores_gemma":[0.00007218347,0.00012262794,0.00005125278,0.0001531312,0.000087269866,0.0021982908,9.688204e-7,0.00010058041,1.2059714e-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":[0.00009229326,0.000030339443,0.0030444444,0.000093132054,0.0000409584,0.000005319623,0.07888392,0.9113473,0.0017361774,0.0021782382,0.0000074061636,0.0025404259],"study_design_scores_gemma":[0.0076056444,0.000350577,0.076508954,0.0010334443,0.00092545693,0.000007433111,0.18951662,0.7063026,0.001512548,0.010822033,0.0044578672,0.0009568323],"about_ca_topic_score_codex":0.00009816072,"about_ca_topic_score_gemma":0.0008591296,"teacher_disagreement_score":0.40252596,"about_ca_system_score_codex":0.0000354858,"about_ca_system_score_gemma":0.00018222896,"threshold_uncertainty_score":0.5000622},"labels":[],"label_agreement":null},{"id":"W3129964354","doi":"10.1155/2021/3932627","title":"Optimum Equilibrium Passenger Flow Control Strategies with Delay Penalty Functions under Oversaturated Condition on Urban Rail Transit","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Urban rail transit; Simulated annealing; Flow (mathematics); Flow control (data); Line (geometry); Computer science; Control (management); Constant (computer programming); Control theory (sociology); Beijing; Function (biology); Sensitivity (control systems); Simulation; Transport engineering; Mathematical optimization; Engineering; Mathematics; Algorithm; Telecommunications","score_opus":0.00998433937724205,"score_gpt":0.2629790754663867,"score_spread":0.2529947360891447,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3129964354","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.60683113,0.00021428836,0.3883717,0.0022936475,0.0007402638,0.00025537767,0.0001470246,0.00009147983,0.0010550491],"genre_scores_gemma":[0.99348444,0.0000935609,0.0051214476,0.00023315892,0.00019057521,0.000006669198,0.0005471895,0.00002268661,0.0003003023],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99818313,0.00015156974,0.00054210844,0.00021606719,0.0006588301,0.00024826606],"domain_scores_gemma":[0.99809176,0.0001688377,0.0004471582,0.000100286896,0.0010334591,0.00015850017],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002653054,0.00017812604,0.0002819228,0.00014889354,0.0003164941,0.00011187107,0.000082122686,0.00013266156,0.00024326151],"category_scores_gemma":[0.000024185509,0.00016031487,0.0001402466,0.0005066416,0.00010789716,0.0015234302,2.489355e-7,0.0003179126,0.000004497896],"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":[0.001019068,0.00012956561,0.00062239234,0.000018017372,0.00012639463,0.00012574071,0.008563205,0.98066306,0.0038043319,0.004433161,0.00022051613,0.00027452438],"study_design_scores_gemma":[0.032153454,0.0033351262,0.70555186,0.0013520437,0.0023401496,0.0001014764,0.2207602,0.0045920853,0.0054967576,0.0051783,0.017129779,0.0020087487],"about_ca_topic_score_codex":0.000020728454,"about_ca_topic_score_gemma":0.00092101004,"teacher_disagreement_score":0.976071,"about_ca_system_score_codex":0.00009747583,"about_ca_system_score_gemma":0.0006942557,"threshold_uncertainty_score":0.6537451},"labels":[],"label_agreement":null},{"id":"W3130388201","doi":"10.1287/trsc.2020.1033","title":"A Strategic Markovian Traffic Equilibrium Model for Capacitated Networks","year":2021,"lang":"en","type":"article","venue":"Transportation Science","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Arc (geometry); Markov process; Flow network; Mathematical optimization; Computer science; Work (physics); Markov chain; Traffic flow (computer networking); Mathematical economics; Mathematics; Engineering; Computer network","score_opus":0.057840495474320845,"score_gpt":0.3191556694347269,"score_spread":0.26131517396040604,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3130388201","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.64254934,0.00007430999,0.35149923,0.00093928614,0.0003975676,0.00038126146,0.000089098685,0.00026498624,0.0038049207],"genre_scores_gemma":[0.98541933,0.000035002224,0.012593968,0.00018675796,0.000050886847,0.00003506183,0.00029409426,0.000012205195,0.0013727079],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982379,0.0000388657,0.00031697663,0.00041955177,0.0005427053,0.00044399398],"domain_scores_gemma":[0.9987737,0.000087116074,0.00010565236,0.00014470768,0.00068381045,0.00020499087],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00084244594,0.00011215676,0.00013389777,0.00011539023,0.0006937745,0.0001775124,0.00023387728,0.00008864461,0.00007689896],"category_scores_gemma":[0.00005592341,0.00012568115,0.00007402585,0.0014321221,0.00048762825,0.0007031112,8.7521335e-7,0.00009015744,0.0000035004734],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023509223,0.000034111345,0.00041981615,0.000010542655,0.0000040663826,0.000004443414,0.0129649555,0.9539849,0.0007140697,0.03127882,0.000070102964,0.00049062434],"study_design_scores_gemma":[0.00049539114,0.00001987456,0.0043399087,0.000022660204,0.000031482414,4.4653845e-7,0.0049379286,0.988104,0.00019389823,0.0013156601,0.0003086563,0.0002301093],"about_ca_topic_score_codex":0.000060877115,"about_ca_topic_score_gemma":0.0028271286,"teacher_disagreement_score":0.34287,"about_ca_system_score_codex":0.000055028886,"about_ca_system_score_gemma":0.0013153914,"threshold_uncertainty_score":0.53360224},"labels":[],"label_agreement":null},{"id":"W3131441060","doi":"10.1287/trsc.2020.1028","title":"A Data-Driven Method for Reconstructing a Distribution from a Truncated Sample with an Application to Inferring Car-Sharing Demand","year":2021,"lang":"en","type":"article","venue":"Transportation Science","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Truncation (statistics); Sample (material); Computer science; Trip distribution; Mathematical optimization; Process (computing); Sampling (signal processing); Maximum likelihood; Algorithm; Data mining; Statistics; Mathematics; Machine learning","score_opus":0.06315858534908499,"score_gpt":0.3767507232281914,"score_spread":0.31359213787910645,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3131441060","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.35517344,0.000004509007,0.6430251,0.00018745681,0.000054931676,0.0002823484,0.0011415316,0.00009483038,0.000035846828],"genre_scores_gemma":[0.6612817,0.0000033587646,0.33207512,0.000059253525,0.000043643653,0.00005485024,0.0064650015,0.000007749579,0.000009331903],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981635,0.000048954953,0.0003092469,0.0007412646,0.0004564183,0.0002806113],"domain_scores_gemma":[0.99845916,0.0002252149,0.00015393201,0.0003579221,0.0005988074,0.00020496392],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010816994,0.00010363672,0.0001458776,0.00008182506,0.0009501492,0.0002155781,0.00041141032,0.000054966033,0.000022998798],"category_scores_gemma":[0.0002970935,0.00010985984,0.000021141423,0.001376085,0.0001290196,0.0013579672,0.0000054790303,0.00007025523,0.0000017146882],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032738337,0.00015226167,0.36924893,0.00005540515,0.00005335565,0.000007636371,0.08659639,0.43653435,0.019975938,0.04202133,0.000028096563,0.044998925],"study_design_scores_gemma":[0.0017027623,0.0001355607,0.37161753,0.00018505554,0.00024037842,0.0000023187158,0.024778392,0.5828296,0.0071032154,0.0037094255,0.0068657757,0.00082996394],"about_ca_topic_score_codex":0.00470933,"about_ca_topic_score_gemma":0.038815632,"teacher_disagreement_score":0.31094998,"about_ca_system_score_codex":0.000084626365,"about_ca_system_score_gemma":0.00057553034,"threshold_uncertainty_score":0.97872347},"labels":[],"label_agreement":null},{"id":"W3131447838","doi":"10.1287/trsc.2021.1062","title":"The Single-Line Design Problem for Demand-Adaptive Transit Systems: A Modeling Framework and Decomposition Approach for the Stationary-Demand Case","year":2021,"lang":"en","type":"article","venue":"Transportation Science","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Flexibility (engineering); Adaptability; Public transport; Decomposition; Computer science; Process (computing); Variety (cybernetics); Transit (satellite); Operations research; Industrial engineering; Transport engineering; Engineering; Economics; Artificial intelligence","score_opus":0.08429365334086981,"score_gpt":0.33679149007728915,"score_spread":0.25249783673641935,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3131447838","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00919374,0.00081567914,0.98712337,0.0009059428,0.00014493425,0.0016025485,0.00009238008,0.00006048431,0.00006089739],"genre_scores_gemma":[0.7930019,0.0001422691,0.20624362,0.0000615283,0.000057097892,0.0003476191,0.00007920849,0.000010335029,0.000056430694],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99834883,0.000119976285,0.000386742,0.000387057,0.0004501903,0.0003072037],"domain_scores_gemma":[0.9973684,0.0014491155,0.00014490639,0.00012553971,0.0008136817,0.00009836901],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0024855554,0.000121124176,0.00013091366,0.000058687252,0.003917256,0.000376649,0.00016571958,0.00008074081,0.000001800423],"category_scores_gemma":[0.00012207453,0.000088104964,0.00005507982,0.00063525914,0.00042351824,0.00055277743,0.0000011978852,0.0000910213,1.7924373e-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":[0.000116356394,0.0000343434,0.000039989027,0.000033374567,0.00001544818,0.000004494679,0.02113437,0.90983796,0.00015688327,0.0670826,0.000011348191,0.0015328529],"study_design_scores_gemma":[0.00047322066,0.000082744096,0.00018490959,0.000053121035,0.00011733377,0.000013678237,0.026274117,0.9674389,0.00016971838,0.0048290044,0.00020584952,0.00015739875],"about_ca_topic_score_codex":0.00017272544,"about_ca_topic_score_gemma":0.0005011207,"teacher_disagreement_score":0.7838082,"about_ca_system_score_codex":0.00005105082,"about_ca_system_score_gemma":0.00041977238,"threshold_uncertainty_score":0.9973795},"labels":[],"label_agreement":null},{"id":"W3131486053","doi":"10.1155/2021/8871876","title":"Resilience-Based Optimization of Postdisaster Restoration Strategy for Road Networks","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; Ministry of Education of the People's Republic of China; Natural Science Foundation of Zhejiang Province; National Natural Science Foundation of China","keywords":"Resilience (materials science); Computer science; Schedule; Integer programming; Operations research; Duration (music); Genetic algorithm; Disequilibrium; Sensitivity (control systems); Perspective (graphical); Transport engineering; Mathematical optimization; Engineering; Mathematics; Artificial intelligence; Machine learning; Algorithm","score_opus":0.018408007315992295,"score_gpt":0.3004825658742637,"score_spread":0.2820745585582714,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3131486053","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.111647554,0.00027042738,0.88678217,0.00038152162,0.0004496893,0.0002004202,0.000023086108,0.000017383107,0.0002277284],"genre_scores_gemma":[0.9260357,0.00018269762,0.07321727,0.00004846158,0.00012269015,0.0000052887885,0.0003062116,0.000011614985,0.00007006731],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.998604,0.00008972896,0.0006768404,0.00012391701,0.00036550255,0.00013998649],"domain_scores_gemma":[0.9974988,0.000120351244,0.00074791163,0.00007581511,0.001484704,0.00007240679],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042368576,0.00008591436,0.00019369155,0.00011849663,0.00015383301,0.000028263345,0.00008236039,0.0001045765,0.00003891524],"category_scores_gemma":[0.0001219676,0.00009081948,0.00011769164,0.00040912043,0.00006390717,0.00069237634,2.8355478e-7,0.00009107385,1.4198801e-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":[0.00034276195,0.00006692701,0.0018432352,0.000026910322,0.000013113861,0.0000049192845,0.0028409644,0.9875305,0.00039631894,0.0011630472,0.000029886824,0.005741395],"study_design_scores_gemma":[0.016622776,0.0027385624,0.62495846,0.0016527332,0.0009944483,0.000008546652,0.056269046,0.27414984,0.012810294,0.0026550253,0.0057715783,0.0013687235],"about_ca_topic_score_codex":0.000021866721,"about_ca_topic_score_gemma":0.00048015665,"teacher_disagreement_score":0.81438816,"about_ca_system_score_codex":0.000046178047,"about_ca_system_score_gemma":0.0004872263,"threshold_uncertainty_score":0.37035108},"labels":[],"label_agreement":null},{"id":"W3131778921","doi":"10.1016/j.jtrangeo.2021.102992","title":"Studying border crossing choice behavior of trucks moving between Ontario, Canada and the United States","year":2021,"lang":"en","type":"article","venue":"Journal of Transport Geography","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Border crossing; Destinations; TRIPS architecture; Truck; Multinomial logistic regression; Transport engineering; Geography; Windsor; Taxis; Transit (satellite); Engineering; Tourism; Public transport; Immigration; Statistics; Mathematics","score_opus":0.014603761000954523,"score_gpt":0.27592140644511864,"score_spread":0.26131764544416414,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3131778921","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9981837,0.00033931131,0.00067990925,0.00040480506,0.0001549058,0.000077788434,0.000022884178,0.000006286543,0.00013037346],"genre_scores_gemma":[0.9987412,0.00013028896,0.00087565754,0.00008850421,0.00005233598,0.0000012502486,0.000043475335,0.0000065108084,0.00006075244],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99865586,0.000096169184,0.00050719484,0.00008993035,0.0004910367,0.00015979024],"domain_scores_gemma":[0.9987656,0.00024945746,0.0003716787,0.00006615858,0.0004527297,0.000094400355],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00066158944,0.00008432351,0.00025135736,0.00014543264,0.00052249973,0.000065176675,0.00012011445,0.00004869214,0.000056636167],"category_scores_gemma":[0.000029578297,0.000066178814,0.0001143426,0.000567023,0.00029038693,0.0001765773,0.0000022152126,0.0002313611,1.2208571e-8],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002774197,0.000022207812,0.9766088,0.00001064482,0.000085374944,0.000029247596,0.01914797,0.003479706,0.0000061004603,0.000036346053,0.000013536026,0.000532328],"study_design_scores_gemma":[0.0009111201,0.000016562524,0.98597413,0.00007006572,0.00030259136,0.0000023230743,0.005773243,0.0000058278033,0.00003482559,0.000044426393,0.0067892824,0.000075602424],"about_ca_topic_score_codex":0.9433716,"about_ca_topic_score_gemma":0.9773211,"teacher_disagreement_score":0.033949483,"about_ca_system_score_codex":0.0000346354,"about_ca_system_score_gemma":0.001057179,"threshold_uncertainty_score":0.4018698},"labels":[],"label_agreement":null},{"id":"W3131946126","doi":"10.1155/2021/8815653","title":"Computational Methods for Calculating Multimodal Multiclass Traffic Network Equilibrium: Simulation Benchmark on a Large-Scale Test Case","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"H2020 European Research Council; Horizon 2020 Framework Programme; European Commission","keywords":"Heuristic; Benchmark (surveying); Computer science; Metaheuristic; Mathematical optimization; Computation; Multimodal transport; Path (computing); Algorithm; Artificial intelligence; Mathematics; Engineering","score_opus":0.021175427099807166,"score_gpt":0.37171914145663043,"score_spread":0.35054371435682324,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3131946126","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.42003015,0.000093363415,0.5786033,0.00025266557,0.0006150412,0.00025617296,0.00006078197,0.000038607766,0.000049909442],"genre_scores_gemma":[0.5848464,0.000009543968,0.41453785,0.00007989946,0.00022800738,0.0000064768333,0.00023812939,0.000015314623,0.00003839351],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99803346,0.00020796747,0.0008221693,0.0002349712,0.0003913208,0.00031011962],"domain_scores_gemma":[0.99567306,0.0023758844,0.0006236943,0.0000797341,0.0010944771,0.0001531771],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001090209,0.00015393403,0.00028820577,0.00011015959,0.00048461993,0.00005732944,0.00007554879,0.00013320667,0.000040968578],"category_scores_gemma":[0.0005086522,0.00016550846,0.00021199687,0.00047674685,0.000048705882,0.0005741466,9.5218917e-7,0.00020064785,7.0481843e-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":[0.00018247015,0.00018253276,0.0022735358,0.000029737033,0.00003064448,0.00011610772,0.014320382,0.97030336,0.00019022197,0.00037810227,0.000024520255,0.011968391],"study_design_scores_gemma":[0.0036462233,0.00026043857,0.023630792,0.00019993146,0.0001788788,0.000031450705,0.008694353,0.95835793,0.00012420576,0.00091629097,0.003661343,0.0002981516],"about_ca_topic_score_codex":0.000008796599,"about_ca_topic_score_gemma":0.0006310377,"teacher_disagreement_score":0.16481622,"about_ca_system_score_codex":0.00010722048,"about_ca_system_score_gemma":0.0002720277,"threshold_uncertainty_score":0.6749239},"labels":[],"label_agreement":null},{"id":"W3133043970","doi":"10.1155/2021/6674037","title":"Simulation-Based Optimization for the Operation of Toll Plaza at Car Park Exit with Mixed Types of Tollbooths and Waiting-Time-Dependent Service","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Toll; Queueing theory; Computer science; Metamodeling; Service (business); Mathematical optimization; Integer programming; Time horizon; Operations research; Representation (politics); Transport engineering; Simulation; Engineering; Computer network; Algorithm; Mathematics","score_opus":0.012602262370474999,"score_gpt":0.2688167847465429,"score_spread":0.2562145223760679,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3133043970","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6394664,0.00029968942,0.3589227,0.00069463206,0.00014629512,0.00035377924,0.00005941431,0.000014735629,0.000042329208],"genre_scores_gemma":[0.97477794,0.00013474931,0.024760002,0.00004741992,0.000040524446,0.0000066635093,0.00016465686,0.000012469654,0.000055547516],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988256,0.00006483715,0.00052118633,0.00011533942,0.0003774972,0.00009555918],"domain_scores_gemma":[0.99715763,0.0007520479,0.00066998653,0.00007102638,0.001303851,0.00004543545],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003412507,0.00008847686,0.00020704037,0.000093333525,0.00021378235,0.000018365068,0.000066741224,0.000065007116,0.000043150776],"category_scores_gemma":[0.0001194425,0.00007078272,0.000054584514,0.00031364744,0.000055528544,0.00033360874,7.165433e-7,0.00005698593,1.16629614e-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":[0.00050793175,0.000040586525,0.0027543989,0.00007354136,0.000042167645,0.0000018341561,0.0054314947,0.9891432,0.0011684828,0.00022285305,0.0000040342934,0.00060946605],"study_design_scores_gemma":[0.0054835165,0.0005546184,0.03401821,0.00047279437,0.0006896432,0.0000019176643,0.010118546,0.9247748,0.022742037,0.00011679837,0.0007269642,0.00030010226],"about_ca_topic_score_codex":0.00004292627,"about_ca_topic_score_gemma":0.0015219146,"teacher_disagreement_score":0.33531153,"about_ca_system_score_codex":0.000030000001,"about_ca_system_score_gemma":0.00022707036,"threshold_uncertainty_score":0.28864357},"labels":[],"label_agreement":null},{"id":"W3133223995","doi":"10.5198/jtlu.2021.1863","title":"An agent-based transportation impact sketch planning (TISP) model system","year":2021,"lang":"en","type":"article","venue":"Journal of Transport and Land Use","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto","funders":"University of Toronto","keywords":"Microsimulation; Transport engineering; Redevelopment; Land use; Trip generation; Transportation planning; Population; Software deployment; Land-use planning; Pedestrian; Geographic information system; Urban planning; Computer science; Operations research; Engineering; Geography; Civil engineering; TRIPS architecture","score_opus":0.0336474126371582,"score_gpt":0.31710459206962094,"score_spread":0.28345717943246274,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3133223995","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93614453,0.00021061415,0.06314087,0.000102458325,0.00013741327,0.00005463922,0.000066720255,0.000034567074,0.00010818327],"genre_scores_gemma":[0.9954327,0.00009071181,0.0040757013,0.000054927554,0.0000942415,9.605928e-7,0.00016876412,0.000012061005,0.00006994914],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9988998,0.000050148916,0.00040246375,0.00012484717,0.00035150538,0.00017124027],"domain_scores_gemma":[0.9991575,0.00004613248,0.00021056873,0.00007253772,0.00028535482,0.00022791601],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040554738,0.00010661336,0.00022409677,0.00010545549,0.00022961551,0.000072684496,0.000074904456,0.00010570443,0.000030666397],"category_scores_gemma":[0.000009072353,0.000090440495,0.00012464463,0.00017428784,0.000037085512,0.00063448097,2.0577482e-7,0.00014414397,3.033979e-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":[0.00015639586,0.00006368639,0.60516614,0.000038344344,0.000033457334,0.00029623683,0.008783486,0.38479432,0.00010648281,0.00031865435,0.00003857808,0.00020421963],"study_design_scores_gemma":[0.0019366415,0.00016671543,0.9553378,0.00038129682,0.00031595345,0.000022963208,0.004206457,0.03597424,0.00015599262,0.00006500814,0.0011570806,0.0002798157],"about_ca_topic_score_codex":0.00028838447,"about_ca_topic_score_gemma":0.0010395456,"teacher_disagreement_score":0.3501717,"about_ca_system_score_codex":0.0000369618,"about_ca_system_score_gemma":0.0003853986,"threshold_uncertainty_score":0.36880565},"labels":[],"label_agreement":null},{"id":"W3133862002","doi":"10.1155/2021/6624129","title":"An Intelligent Framework for Analyzing the Feasible Modes of Transportation in Metropolitan Cities: A Hybrid Multicriteria Approach","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","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":"","keywords":"Metropolitan area; Ranking (information retrieval); TOPSIS; Analytic hierarchy process; Consistency (knowledge bases); Public transport; Ideal solution; Computer science; Process (computing); Mode (computer interface); Fuzzy logic; Mode choice; Transport engineering; Operations research; Sensitivity (control systems); Engineering; Artificial intelligence; Geography","score_opus":0.030567993147894938,"score_gpt":0.34042190963708846,"score_spread":0.3098539164891935,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3133862002","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5619068,0.00033386503,0.43716893,0.00015343116,0.00014584925,0.00018034324,0.00007054565,0.000008740935,0.000031483007],"genre_scores_gemma":[0.82752293,0.00042709764,0.17170107,0.00002562211,0.000074084855,0.000012883956,0.00021289707,0.000013381716,0.000010023833],"study_design_codex":"simulation_or_modeling","study_design_gemma":"qualitative","domain_scores_codex":[0.99832654,0.00012751285,0.0008378843,0.00016255594,0.00035277152,0.00019270768],"domain_scores_gemma":[0.9983089,0.00025831896,0.0005515045,0.000112474976,0.0006891907,0.00007963416],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00065012247,0.000113476344,0.00028376593,0.00019723087,0.00016384601,0.000036035966,0.000160319,0.000076024364,0.000017155407],"category_scores_gemma":[0.00011610645,0.00010060174,0.00016317383,0.0005361926,0.00009716287,0.0005637748,2.3795835e-7,0.00017582446,7.5596795e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00052788446,0.00032872646,0.018671608,0.00014990571,0.00006580938,0.000012659067,0.079293065,0.8129604,0.0047156233,0.079898536,0.000006284357,0.0033695328],"study_design_scores_gemma":[0.0042754314,0.00068531645,0.2915685,0.0010707217,0.0006514523,0.000005028525,0.5893463,0.007538857,0.041372996,0.061584186,0.0011762306,0.00072501],"about_ca_topic_score_codex":0.0001474333,"about_ca_topic_score_gemma":0.0013118368,"teacher_disagreement_score":0.80542153,"about_ca_system_score_codex":0.00010693661,"about_ca_system_score_gemma":0.00023889658,"threshold_uncertainty_score":0.410242},"labels":[],"label_agreement":null},{"id":"W3140244825","doi":"","title":"Congestion Relief: Assessing the Case for Road Tolls in Canada","year":2007,"lang":"en","type":"article","venue":"C.D. Howe Institute Commentary","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Road pricing; Singapore Area Licensing Scheme; Traffic congestion; Congestion pricing; Transport engineering; Business; Regional science; Geography; Engineering","score_opus":0.031190517850434682,"score_gpt":0.32789771106761534,"score_spread":0.29670719321718064,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3140244825","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95840055,0.00015446359,0.010440468,0.025536502,0.0018208914,0.00050086225,0.000031296633,0.000041612813,0.0030733836],"genre_scores_gemma":[0.99392176,0.000032597236,0.0016308244,0.0040784436,0.00014397364,0.00001348935,0.00011957129,0.000005404818,0.000053935306],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992451,0.00005136478,0.00021246,0.0001200544,0.0001414702,0.00022955328],"domain_scores_gemma":[0.9995073,0.00021144254,0.00007351474,0.00009140597,0.000051518564,0.00006480177],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00081697217,0.00006892462,0.00007959204,0.0000467607,0.00070469483,0.00005649749,0.000112892514,0.00004050408,0.000013505458],"category_scores_gemma":[0.000071708026,0.000061389845,0.000020639654,0.00020385747,0.00008683209,0.00036952656,0.000005573318,0.00010612438,5.7525e-7],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017772964,0.0002590459,0.40925884,0.00008248221,0.00008735231,0.0043044332,0.03626564,0.071960144,0.00006375902,0.017675387,0.1455543,0.31431088],"study_design_scores_gemma":[0.0018599224,0.000040511586,0.20348214,0.0001854918,0.00006244269,0.000060074737,0.058853623,0.0023038215,0.00015760677,0.00036284982,0.73214847,0.00048307623],"about_ca_topic_score_codex":0.93346566,"about_ca_topic_score_gemma":0.99552435,"teacher_disagreement_score":0.5865941,"about_ca_system_score_codex":0.0005275358,"about_ca_system_score_gemma":0.0005578094,"threshold_uncertainty_score":0.5420014},"labels":[],"label_agreement":null},{"id":"W3142789469","doi":"","title":"Mind the Funding Gap: Transit Financing in Los Angeles County and Metro Vancouver","year":2019,"lang":"en","type":"article","venue":"TSpace (University of Toronto)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"University of Toronto; Fulbright Canada; Northwestern University","keywords":"Transit (satellite); Finance; Metropolitan area; Transit system; Public transport; Business; Political science; Public administration; Engineering; Transport engineering; Geography","score_opus":0.017159916623659132,"score_gpt":0.24457741487192883,"score_spread":0.2274174982482697,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3142789469","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96385455,0.0002735413,0.001554476,0.0002558586,0.00013690861,0.0001210678,0.000018573053,0.000012034376,0.03377301],"genre_scores_gemma":[0.99678975,0.00008610151,0.00081496034,0.000012309393,0.000012402406,5.1333025e-8,0.0000029508956,0.0000022478873,0.0022791945],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99956995,0.000044755343,0.000040067836,0.000099717196,0.00013540708,0.00011008308],"domain_scores_gemma":[0.9997669,0.000066987806,0.000051452025,0.00005698765,0.00003001609,0.00002769764],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00025192005,0.000041985197,0.00008617421,0.00002164048,0.00017484011,0.000008884474,0.000086486936,0.000047433557,0.0015137128],"category_scores_gemma":[0.000007779446,0.000043680666,0.000022663125,0.00007099101,0.00007685159,0.00034978706,0.0000048313573,0.000043682503,0.0000012537857],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019861458,0.0000682648,0.57486904,0.00008164106,0.000045365152,0.000015334084,0.3768046,0.00665609,0.00036048674,0.004904079,0.0037570463,0.03223942],"study_design_scores_gemma":[0.00045423562,0.000021686912,0.78898436,0.000045055654,0.000023110888,5.908579e-8,0.2010806,0.00087952917,0.00000995524,0.0000117678,0.008396867,0.00009278147],"about_ca_topic_score_codex":0.12156394,"about_ca_topic_score_gemma":0.8543426,"teacher_disagreement_score":0.7327786,"about_ca_system_score_codex":0.0000956584,"about_ca_system_score_gemma":0.00004404138,"threshold_uncertainty_score":0.99939907},"labels":[],"label_agreement":null},{"id":"W3143718980","doi":"10.1155/2021/6624130","title":"An Overlapping Phase Approach to Optimize Bus Signal Priority Control under Two-Way Signal Coordination on Urban Arterials","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Hefei University; National Natural Science Foundation of China","keywords":"SIGNAL (programming language); Transport engineering; Control (management); Phase (matter); Signal timing; Field (mathematics); Computer science; Bus priority; Simulation; Engineering; Public transport; Artificial intelligence; Mathematics","score_opus":0.018305374106610085,"score_gpt":0.3129163883367881,"score_spread":0.294611014230178,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3143718980","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.47836095,0.00005960842,0.51980954,0.00046655355,0.00053111947,0.0002703397,0.000056163375,0.000045007455,0.00040074726],"genre_scores_gemma":[0.965878,0.000025250729,0.032807223,0.00034236946,0.00060762477,0.000011117252,0.00024311755,0.000020598216,0.00006471541],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9976147,0.0002876516,0.0007747668,0.0002842084,0.0007686136,0.00027009542],"domain_scores_gemma":[0.9979381,0.00015975126,0.0005882238,0.00011296085,0.00091774785,0.00028324156],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008470631,0.00017881127,0.00036693492,0.00020544753,0.00035251322,0.00011507969,0.00014911589,0.00011261706,0.00010834555],"category_scores_gemma":[0.000055853354,0.00018799653,0.00014026868,0.000493042,0.00005313631,0.001108914,5.2412867e-7,0.00022342651,0.0000026255445],"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":[0.0008821981,0.00076132786,0.0005452739,0.000016140337,0.000052705258,0.000029015062,0.01513085,0.9453075,0.027730074,0.0054242844,0.000069271155,0.00405134],"study_design_scores_gemma":[0.12699875,0.009774504,0.60920024,0.0019396323,0.002953844,0.00006715076,0.14293268,0.030593975,0.047427926,0.007980506,0.015299761,0.0048310175],"about_ca_topic_score_codex":0.00003341577,"about_ca_topic_score_gemma":0.00011558232,"teacher_disagreement_score":0.91471356,"about_ca_system_score_codex":0.00014548287,"about_ca_system_score_gemma":0.000341679,"threshold_uncertainty_score":0.7666276},"labels":[],"label_agreement":null},{"id":"W3144840300","doi":"10.1787/04525828-en","title":"High-occupancy Toll Lanes","year":2020,"lang":"en","type":"paratext","venue":"Discussion papers","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Toll; Occupancy; Context (archaeology); Transport engineering; Metering mode; Computer science; Traffic congestion; Geography; Engineering; Civil engineering","score_opus":0.01586391875677931,"score_gpt":0.2902604794946327,"score_spread":0.27439656073785335,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3144840300","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0002634582,0.00044013385,0.00026320945,0.016724056,0.005935284,0.00039049264,0.00037390666,0.00025962442,0.97534984],"genre_scores_gemma":[0.07130047,0.0018242141,0.00069373683,0.001078147,0.0013236157,0.000037461705,0.004868645,0.000053908458,0.9188198],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9984482,0.00015460239,0.00026369057,0.00037337223,0.00048747758,0.0002726425],"domain_scores_gemma":[0.9993739,0.00004706415,0.00017529845,0.0001446736,0.000046543766,0.00021253117],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00011737304,0.0002105586,0.00029408466,0.00007803473,0.000503666,0.00009744631,0.00025746037,0.000357052,0.015093805],"category_scores_gemma":[0.0000641516,0.00013183664,0.00012428431,0.00027404158,0.0001015049,0.00010472981,0.000011144656,0.0002682897,0.0051321746],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000042340936,0.000031977757,0.00037195237,0.00006890642,0.000037312868,0.0000142184135,0.013712787,0.0016332036,0.000038516697,0.0026263047,0.97494483,0.006477675],"study_design_scores_gemma":[0.00018216879,0.000017449913,0.0006854355,0.000117968026,0.00003349286,9.986268e-8,0.0031844492,0.000006350095,0.00001100531,0.00003318591,0.9954636,0.00026479285],"about_ca_topic_score_codex":0.00064742984,"about_ca_topic_score_gemma":0.0003424873,"teacher_disagreement_score":0.07103701,"about_ca_system_score_codex":0.000048265094,"about_ca_system_score_gemma":0.00028848273,"threshold_uncertainty_score":0.9956424},"labels":[],"label_agreement":null},{"id":"W3145986153","doi":"","title":"SOME MEASURES OF INCREASING RAIL TRANSIT RIDERSHIPS: CASE STUDIES","year":2000,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Public transport; Transport engineering; Revenue; Business; Transportation planning; Government (linguistics); Transit (satellite); Service quality; Transit system; Service (business); Finance; Engineering; Marketing","score_opus":0.09765606073529648,"score_gpt":0.3357843764534309,"score_spread":0.23812831571813442,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3145986153","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98263943,0.0010195207,0.0005209069,0.00092245516,0.00007284121,0.00009974387,0.000005295032,0.00011790326,0.014601874],"genre_scores_gemma":[0.9965567,0.00049304985,0.0014020598,0.000102151826,0.000064137574,0.000001936638,0.0000028298039,0.0000037702453,0.0013733445],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.99932015,0.00013973966,0.00015372522,0.0000877483,0.00018932091,0.0001093314],"domain_scores_gemma":[0.99965245,0.00014469434,0.000029506708,0.000047735044,0.00008354276,0.0000420666],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005773473,0.000048389364,0.00010350725,0.00003955576,0.00026173776,0.000014095298,0.000042274398,0.000038670714,0.00030933737],"category_scores_gemma":[0.00008488301,0.000043774722,0.00003383779,0.00014609027,0.00014254003,0.00021534067,5.428963e-7,0.000035454457,0.000007314247],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00042642525,0.00025767676,0.026785787,0.00014555108,0.00039158974,0.0007437408,0.7508754,0.055030786,0.00030755883,0.08459928,0.0026341344,0.077802055],"study_design_scores_gemma":[0.0060482346,0.0003877179,0.031786695,0.0006833178,0.00087806134,0.00037897663,0.86250484,0.0016231794,0.005308593,0.028434532,0.05972222,0.0022436262],"about_ca_topic_score_codex":0.0034792805,"about_ca_topic_score_gemma":0.003981587,"teacher_disagreement_score":0.11162943,"about_ca_system_score_codex":0.0000139833055,"about_ca_system_score_gemma":0.000046219044,"threshold_uncertainty_score":0.52596545},"labels":[],"label_agreement":null},{"id":"W3149566804","doi":"10.1155/2021/6675605","title":"A Data-Driven Urban Metro Management Approach for Crowd Density Control","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"McGill University","funders":"National Natural Science Foundation of China","keywords":"Crowds; Pedestrian; Crowding; Transport engineering; Constraint (computer-aided design); Computer science; Range (aeronautics); Public transport; Computer security; Engineering","score_opus":0.025112879417693742,"score_gpt":0.300200873280179,"score_spread":0.2750879938624853,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3149566804","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09048431,0.00023705351,0.9075262,0.00041667942,0.00038021445,0.00029495405,0.00017262764,0.000029910687,0.0004580756],"genre_scores_gemma":[0.77549165,0.0001508884,0.22331534,0.00009629884,0.00014176499,0.0000062951885,0.00066430954,0.000007907767,0.00012553978],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9987732,0.00006206033,0.00044160403,0.0001814202,0.00038209048,0.00015959436],"domain_scores_gemma":[0.9987525,0.0000846093,0.0003723321,0.00014186057,0.0005581659,0.00009053008],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004803606,0.00008379073,0.00020485176,0.00008916494,0.00021702712,0.00003865184,0.00018665526,0.000056952333,0.000014526178],"category_scores_gemma":[0.00005510822,0.00008600689,0.00009810577,0.00025779541,0.000038840928,0.00066933065,9.709318e-7,0.00009610606,4.5764793e-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":[0.000743752,0.00035992602,0.034486692,0.00016949866,0.0003119487,0.00010994204,0.012067782,0.9257695,0.0004316599,0.015203389,0.0012741378,0.0090717785],"study_design_scores_gemma":[0.016980434,0.0003613788,0.7989126,0.00026746775,0.0022918726,0.0000094479265,0.054824226,0.008687631,0.000795982,0.0024685594,0.113533475,0.00086691143],"about_ca_topic_score_codex":0.000010225205,"about_ca_topic_score_gemma":0.00043360106,"teacher_disagreement_score":0.91708183,"about_ca_system_score_codex":0.000046638153,"about_ca_system_score_gemma":0.00013235418,"threshold_uncertainty_score":0.35072592},"labels":[],"label_agreement":null},{"id":"W3150080804","doi":"10.1155/2021/8840516","title":"Dynamic Automated Search of Shunting Routes within Mesoscopic Rail-Traffic Simulators","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","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":"European Social Fund; European Regional Development Fund; Univerzita Pardubice","keywords":"Train; Computer science; Track (disk drive); Set (abstract data type); Interlocking; Process (computing); Digraph; Software; Simulation; Algorithm; Engineering; Reliability engineering","score_opus":0.010369527566223924,"score_gpt":0.3088859736029897,"score_spread":0.29851644603676575,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3150080804","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99476695,0.00027010855,0.0040232926,0.0002232419,0.00044016528,0.00009022564,0.00001263178,0.00008248996,0.0000908987],"genre_scores_gemma":[0.9755396,0.00016068244,0.024064088,0.000020463416,0.000031941927,8.630057e-7,0.000075459844,0.000013918864,0.00009297977],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99824846,0.00011010055,0.0007668125,0.00012937984,0.00057363167,0.00017159374],"domain_scores_gemma":[0.9982494,0.00014845737,0.0005788105,0.0000735334,0.000859546,0.00009022151],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005388412,0.000096472904,0.00025222907,0.00016772652,0.00016466917,0.000024717743,0.000108094384,0.00009051631,0.000048656762],"category_scores_gemma":[0.00012668848,0.00009984716,0.000110840716,0.0006356163,0.00007801286,0.000592883,6.888199e-7,0.00017762478,0.0000011272502],"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":[0.000055662706,0.000058447942,0.010147708,0.000049291524,0.000036652345,0.000030161862,0.029708529,0.95507413,0.0029733516,0.00051837694,0.0000044639432,0.0013432353],"study_design_scores_gemma":[0.0044032624,0.0003379032,0.8586963,0.0011942723,0.00035792703,0.000008876514,0.07661945,0.04408937,0.012640854,0.00045418116,0.0006524492,0.000545157],"about_ca_topic_score_codex":0.000029871531,"about_ca_topic_score_gemma":0.000771315,"teacher_disagreement_score":0.91098475,"about_ca_system_score_codex":0.00005605723,"about_ca_system_score_gemma":0.0004716623,"threshold_uncertainty_score":0.4071649},"labels":[],"label_agreement":null},{"id":"W3150577558","doi":"10.3390/su13073780","title":"Mode Choice Change under Environmental Constraints in the Combined Modal Split and Traffic Assignment Model","year":2021,"lang":"en","type":"article","venue":"Sustainability","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Modal; Constraint (computer-aided design); Subsidy; Mode (computer interface); Environmental economics; Environmental pollution; Computer science; Environmental science; Transport engineering; Engineering; Environmental protection; Economics","score_opus":0.02632165068302281,"score_gpt":0.3060986222000353,"score_spread":0.2797769715170125,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3150577558","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.990058,0.000054533004,0.0026142828,0.006333838,0.000024408686,0.0004022571,0.000022789212,0.00002382716,0.00046606894],"genre_scores_gemma":[0.99923337,0.000024119194,0.00017321022,0.00028882443,0.00002013597,0.00004670248,0.000037448266,0.0000039156284,0.0001722574],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9989566,0.0002439951,0.0001363881,0.00021395824,0.00024188399,0.00020713719],"domain_scores_gemma":[0.99965876,0.00012593996,0.000030896434,0.00009020024,0.000040877756,0.000053293512],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005042513,0.000072454764,0.000087035616,0.000021795144,0.00024003883,0.000045244135,0.000071463815,0.00006759503,0.000049596616],"category_scores_gemma":[0.00009672527,0.000064860076,0.000025610647,0.000117528114,0.00036271653,0.00015241673,0.00000916246,0.000106661806,5.794517e-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":[0.000074328236,0.0010307153,0.1845828,0.00009750054,0.000019685029,0.000042125925,0.20074931,0.5095095,0.000034749497,0.08652982,0.000072652096,0.01725679],"study_design_scores_gemma":[0.0011296183,0.0000406676,0.7139466,0.00001094431,0.000026605872,0.00000115776,0.124711774,0.13936166,0.000009369496,0.020128572,0.00038593938,0.00024711472],"about_ca_topic_score_codex":0.0003510433,"about_ca_topic_score_gemma":0.0012347171,"teacher_disagreement_score":0.5293638,"about_ca_system_score_codex":0.00032840075,"about_ca_system_score_gemma":0.0002545656,"threshold_uncertainty_score":0.2644917},"labels":[],"label_agreement":null},{"id":"W3153132274","doi":"10.1155/2021/6691299","title":"Research on Optimization of Customized Bus Routes Based on Uncertainty Theory","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Postdoctoral Science Foundation of Jiangsu Province; Education Department of Jiangxi Province; Southeast University; National Natural Science Foundation of China","keywords":"Genetic algorithm; Process (computing); Computer science; Mathematical optimization; Route planning; Optimization problem; Scheme (mathematics); Optimization algorithm; Algorithm; Mathematics","score_opus":0.028672331648861647,"score_gpt":0.3531338350964949,"score_spread":0.32446150344763325,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3153132274","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.65032095,0.0003347863,0.33552113,0.0030489005,0.0014625397,0.00059396506,0.00009874566,0.00008853033,0.008530481],"genre_scores_gemma":[0.977942,0.00025219945,0.021262197,0.00010982952,0.0000979742,0.0000046525124,0.00013558676,0.00001506119,0.00018050785],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9974217,0.0005686035,0.0006064346,0.00016412216,0.001045747,0.00019339455],"domain_scores_gemma":[0.99652344,0.0009776383,0.00048248074,0.00012416167,0.0017936269,0.000098673474],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019761655,0.000100640966,0.00024802302,0.00036017364,0.00024557573,0.00002650352,0.00013150762,0.00011061711,0.00017794324],"category_scores_gemma":[0.00044457897,0.00009506008,0.00013401594,0.0008651741,0.00012602346,0.00031752168,5.101034e-7,0.00029499512,0.0000018742727],"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":[0.0016256083,0.00019403937,0.00055358745,0.00002112184,0.000019642375,0.000025246469,0.004886294,0.9756273,0.0002680369,0.014440175,0.000044755598,0.0022941618],"study_design_scores_gemma":[0.06520097,0.008391208,0.40357107,0.008895913,0.0013021523,0.000011343886,0.28192148,0.10815179,0.056260608,0.03876276,0.024546858,0.0029838614],"about_ca_topic_score_codex":0.00002874669,"about_ca_topic_score_gemma":0.00015239594,"teacher_disagreement_score":0.8674755,"about_ca_system_score_codex":0.00010776359,"about_ca_system_score_gemma":0.00057218224,"threshold_uncertainty_score":0.38764375},"labels":[],"label_agreement":null},{"id":"W3153149517","doi":"10.1155/2021/5548956","title":"Urban Rail Transit System Network Reliability Analysis Based on a Coupled Map Lattice Model","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Urban rail transit; Reliability (semiconductor); Flow network; Computer science; Network model; Transport engineering; Engineering; Data mining; Mathematical optimization; Mathematics","score_opus":0.010616982116214434,"score_gpt":0.2632791117011864,"score_spread":0.252662129584972,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3153149517","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18177836,0.00018046344,0.8151348,0.0015415592,0.00052573584,0.00018819621,0.000044627795,0.00008730266,0.0005189381],"genre_scores_gemma":[0.95100653,0.000045799643,0.048243202,0.00015409714,0.00014695186,0.0000048709676,0.00019375575,0.0000141516775,0.00019065902],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997833,0.00017997163,0.00076676265,0.00022934898,0.00075625104,0.0002346286],"domain_scores_gemma":[0.9980415,0.00018690446,0.0005329149,0.00016194423,0.0009139007,0.00016281729],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00092622347,0.00013932037,0.00039000012,0.00017077132,0.0003047562,0.00004302299,0.000120686214,0.000123131,0.000046574576],"category_scores_gemma":[0.0000559469,0.00014147913,0.0003452299,0.0012307616,0.00005300455,0.0004232755,2.849999e-7,0.00022498172,0.0000018190875],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00042102355,0.00009244852,0.0055224197,0.000056438414,0.00010447546,0.00004656677,0.005761591,0.9845453,0.000052675023,0.0031864056,0.00010136699,0.00010932817],"study_design_scores_gemma":[0.0036261615,0.00024958196,0.15364921,0.0005016819,0.0031289177,0.0000012653823,0.008960229,0.8254115,0.0001524958,0.00072150223,0.0030812754,0.00051618234],"about_ca_topic_score_codex":0.00003742538,"about_ca_topic_score_gemma":0.0008130134,"teacher_disagreement_score":0.76922816,"about_ca_system_score_codex":0.00014765088,"about_ca_system_score_gemma":0.00044919358,"threshold_uncertainty_score":0.5769352},"labels":[],"label_agreement":null},{"id":"W3153535872","doi":"","title":"Modelling Transportation System Impacts of Housing Supply Dynamics","year":2020,"lang":"en","type":"dissertation","venue":"TSpace","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"System dynamics; Dynamics (music); Transport engineering; Engineering; Business; Environmental planning; Environmental science; Computer science; Sociology","score_opus":0.01985555008149617,"score_gpt":0.3107835482946206,"score_spread":0.29092799821312443,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3153535872","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8515345,0.0006116627,0.1045204,0.0004611596,0.0014554599,0.0008476539,0.0002444193,0.0006210138,0.039703716],"genre_scores_gemma":[0.9870852,0.00021122661,0.0038725475,0.000008781939,0.00009889662,0.0000074708496,0.007215909,0.000043169566,0.0014567843],"study_design_codex":"simulation_or_modeling","study_design_gemma":"qualitative","domain_scores_codex":[0.9985935,0.00006760397,0.00038374873,0.00024370963,0.0005000831,0.000211375],"domain_scores_gemma":[0.9990137,0.00005445356,0.0004682841,0.000099132136,0.00024475093,0.000119664175],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019991367,0.0001756847,0.0003117695,0.00013253652,0.00023731553,0.00004595281,0.0001401653,0.00031424875,0.000038714905],"category_scores_gemma":[0.000022324715,0.00020741145,0.00011066524,0.00039717028,0.00003362345,0.00014661254,5.338671e-7,0.00019557748,0.00000656568],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024093936,0.00003791809,0.0026367602,0.001717184,0.000083589344,0.000018758872,0.43681285,0.5107425,0.00010718417,0.046569332,0.00017935906,0.00085361954],"study_design_scores_gemma":[0.00068649073,0.00008799,0.0028651608,0.0024505437,0.00058644824,3.0222992e-7,0.5430865,0.44641998,0.00084473233,0.0001275813,0.0019954983,0.00084872113],"about_ca_topic_score_codex":0.011339386,"about_ca_topic_score_gemma":0.019420965,"teacher_disagreement_score":0.13555071,"about_ca_system_score_codex":0.00016112925,"about_ca_system_score_gemma":0.00040459927,"threshold_uncertainty_score":0.99847203},"labels":[],"label_agreement":null},{"id":"W3153923212","doi":"10.1049/ntw2.12025","title":"Game theoretic approach for public multi‐mode transportation in smart cities","year":2021,"lang":"en","type":"article","venue":"IET Networks","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Public transport; Mode (computer interface); Computer science; Game theory; Smart city; Transport engineering; Business; Computer security; Engineering; Human–computer interaction; Economics; Mathematical economics; Internet of Things","score_opus":0.0317398769099584,"score_gpt":0.2828196749411528,"score_spread":0.2510797980311944,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3153923212","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04270247,0.00033746552,0.95140123,0.00056785793,0.00030802158,0.00032103725,0.000032013242,0.0001169527,0.0042129564],"genre_scores_gemma":[0.98431444,0.00018675944,0.013063477,0.00016114074,0.00012104153,0.00008201415,0.0012988482,0.000012695146,0.0007595931],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990835,0.00010493783,0.0002035264,0.00019877794,0.00014818944,0.00026107125],"domain_scores_gemma":[0.999563,0.000116919604,0.00005567229,0.00008151372,0.0001225987,0.000060314647],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003649426,0.00007724188,0.00011786446,0.000051325744,0.00015988262,0.00007939239,0.00008575907,0.00013620389,0.000044641383],"category_scores_gemma":[0.00004360565,0.00008472362,0.000057099922,0.00035176013,0.000097440825,0.00018300524,0.0000010216633,0.00010323635,8.501515e-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":[0.000041114326,0.00015434611,0.107421,0.000045894292,0.000019810088,0.0000064649507,0.033502597,0.72661847,0.0000041547073,0.12857527,0.0005468544,0.0030640236],"study_design_scores_gemma":[0.0022591033,0.000038800437,0.11483763,0.00009612839,0.000059634454,7.419475e-7,0.021311786,0.840092,0.000029454417,0.0048680888,0.015851099,0.00055553764],"about_ca_topic_score_codex":0.00020425714,"about_ca_topic_score_gemma":0.005570315,"teacher_disagreement_score":0.94161195,"about_ca_system_score_codex":0.00003107394,"about_ca_system_score_gemma":0.00012247308,"threshold_uncertainty_score":0.3454929},"labels":[],"label_agreement":null},{"id":"W3154408997","doi":"10.1016/j.ecotra.2021.100208","title":"Optimization of the cost of urban traffic through an online bidding platform for commuters","year":2021,"lang":"en","type":"article","venue":"Economics of Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Bidding; Computer science; Traffic congestion; Value of time; Nash equilibrium; Value (mathematics); Transport engineering; Operations research; Simulation; Microeconomics; Economics; Travel time; Engineering","score_opus":0.05050351424562764,"score_gpt":0.2882913342728664,"score_spread":0.23778782002723875,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3154408997","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9530362,0.00003501704,0.045242738,0.00022804916,0.00021963513,0.0002982957,0.00072164705,0.000015396246,0.00020300661],"genre_scores_gemma":[0.9661817,0.00021447921,0.031730235,0.000029125891,0.000026845071,0.0000066213934,0.0017737814,0.00001026342,0.000026974974],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992246,0.0000256,0.0004640875,0.000115608906,0.00008080561,0.000089287205],"domain_scores_gemma":[0.9991311,0.00009346459,0.0004124074,0.00012526069,0.00021346126,0.000024317449],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019192867,0.00006345225,0.00016752063,0.000037832426,0.00011363638,0.000008025233,0.00012905557,0.00007690379,0.000025572705],"category_scores_gemma":[0.00002242111,0.00006586991,0.00009687794,0.00016004666,0.00010660303,0.00046425927,7.9322007e-7,0.000039365055,5.1119677e-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":[0.000040501305,0.00009298648,0.004774517,0.00004473921,0.000020756228,5.2693625e-8,0.01822919,0.9606804,0.000078264384,0.014978489,0.000014666712,0.0010454269],"study_design_scores_gemma":[0.008123908,0.00051691954,0.2999032,0.00070375495,0.0008467737,8.528764e-7,0.08019879,0.5525289,0.043515787,0.0023728453,0.0101957945,0.0010924642],"about_ca_topic_score_codex":0.00018470686,"about_ca_topic_score_gemma":0.0056774714,"teacher_disagreement_score":0.4081515,"about_ca_system_score_codex":0.000025916546,"about_ca_system_score_gemma":0.00018205006,"threshold_uncertainty_score":0.31681624},"labels":[],"label_agreement":null},{"id":"W3155229376","doi":"","title":"Express Access: Assessing the Equity Implications of Bus Express Routes on Montreal, Canada","year":2021,"lang":"en","type":"article","venue":"Transportation Research Board 100th Annual MeetingTransportation Research BoardTransportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Equity (law); Business; Telecommunications; Computer science; Political science","score_opus":0.13226738040987934,"score_gpt":0.46791364655425066,"score_spread":0.3356462661443713,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3155229376","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9240901,0.0012337514,0.0048495843,0.028732298,0.0009032177,0.005753265,0.003960341,0.0005661666,0.02991127],"genre_scores_gemma":[0.9862967,0.0026099104,0.0019222315,0.00035403974,0.00049471954,0.0014880863,0.0034726684,0.00019401746,0.0031675857],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.96703947,0.008035059,0.0030081337,0.0023530335,0.015666524,0.003897765],"domain_scores_gemma":[0.9600702,0.010318086,0.00074517995,0.0018544818,0.02550571,0.0015063692],"candidate_categories":["metaepi_narrow","sts","scholarly_communication","research_integrity","insufficient_payload"],"consensus_categories":["sts"],"category_scores_codex":[0.02223388,0.0007836663,0.0010979805,0.002122845,0.0075286906,0.0013586628,0.0032648807,0.0007687488,0.001219114],"category_scores_gemma":[0.0032952381,0.00075933017,0.00045472046,0.009896023,0.0038526277,0.0027478854,0.000060084007,0.0045677237,0.00005199195],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0028785046,0.003749614,0.41036004,0.0024685771,0.0009970512,0.0013165317,0.1293518,0.1011161,0.0119399335,0.18396844,0.12959594,0.022257486],"study_design_scores_gemma":[0.0019855981,0.00032946342,0.8016893,0.000853876,0.00010581302,8.168341e-7,0.08469664,0.00017551518,0.005221381,0.0053170044,0.098723225,0.000901316],"about_ca_topic_score_codex":0.77681875,"about_ca_topic_score_gemma":0.9377868,"teacher_disagreement_score":0.39132932,"about_ca_system_score_codex":0.0011713504,"about_ca_system_score_gemma":0.014595738,"threshold_uncertainty_score":0.99969393},"labels":[],"label_agreement":null},{"id":"W3155354292","doi":"10.1155/2021/1986851","title":"A Two-Phase Gradient Projection Algorithm for Solving the Combined Modal Split and Traffic Assignment Problem with Nested Logit Function","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","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":"Ministry of Science and ICT, South Korea; Hong Kong Polytechnic University; National Research Foundation of Korea; Chiang Mai University; National Research Foundation","keywords":"Algorithm; Modal; Nested logit; Computer science; Function (biology); Mode (computer interface); Mathematical optimization; Projection (relational algebra); Scale (ratio); Mathematics","score_opus":0.01422872428437941,"score_gpt":0.2829403795104499,"score_spread":0.2687116552260705,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3155354292","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.54022694,0.00013588861,0.4580447,0.00073414843,0.00027105308,0.000511857,0.000014513982,0.00003129969,0.000029617011],"genre_scores_gemma":[0.93977475,0.00012107202,0.059681088,0.000050218827,0.000113803355,0.000039923954,0.00012565925,0.000013446835,0.0000800488],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99885416,0.00005924797,0.00040550003,0.00015847289,0.0003587837,0.0001638302],"domain_scores_gemma":[0.99878603,0.00010399029,0.00042905725,0.000047695783,0.00055885885,0.000074349235],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000442397,0.00010605392,0.00016249261,0.00007765978,0.0004388985,0.00006867946,0.0000450147,0.00005040989,0.000007172429],"category_scores_gemma":[0.000018375003,0.00007927946,0.000064680506,0.00031305227,0.00006778395,0.00054897805,4.4362727e-7,0.00013014796,1.2292372e-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":[0.0016810681,0.00049672136,0.0014967105,0.000073465664,0.0001516576,0.000035555826,0.031062247,0.70523703,0.0014234955,0.0036939138,0.00003591672,0.2546122],"study_design_scores_gemma":[0.101904154,0.018989557,0.4318252,0.0022981241,0.0045881746,0.00019627778,0.259887,0.14468499,0.004859992,0.00969492,0.018566359,0.0025052428],"about_ca_topic_score_codex":0.000021875121,"about_ca_topic_score_gemma":0.0007682933,"teacher_disagreement_score":0.56055206,"about_ca_system_score_codex":0.000068955225,"about_ca_system_score_gemma":0.00023049788,"threshold_uncertainty_score":0.33756968},"labels":[],"label_agreement":null},{"id":"W3155453126","doi":"10.1155/2021/5530814","title":"Exploring Route Choice Behaviours Accommodating Stochastic Choice Set Generations","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","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":"Ministry of Science and ICT, South Korea; National Research Foundation of Korea; Ministry of Science, ICT and Future Planning; National Research Foundation","keywords":"Choice set; Set (abstract data type); Computer science; Operations research; Economics; Engineering; Econometrics","score_opus":0.08730264364760955,"score_gpt":0.3384915668030003,"score_spread":0.25118892315539076,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3155453126","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9307321,0.00018280763,0.06706942,0.0007279533,0.0009593203,0.00010069057,0.00004702949,0.000048160095,0.00013255744],"genre_scores_gemma":[0.98029447,0.00016160177,0.01854747,0.0000752964,0.00043235326,0.000010394133,0.00032913868,0.000018585053,0.00013068593],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9984138,0.00009257895,0.00061011943,0.000158238,0.0005139108,0.00021134225],"domain_scores_gemma":[0.99827385,0.0002418114,0.0004922088,0.00009620675,0.00075312186,0.00014279164],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003129906,0.00011539156,0.00019809615,0.00013574945,0.00046772847,0.000077076096,0.00012353543,0.00006136322,0.000059976304],"category_scores_gemma":[0.0002766373,0.00012998325,0.00011503898,0.0005060826,0.000041303738,0.0020227921,9.947003e-7,0.00024118344,0.000001720371],"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":[0.000021000606,0.000062877785,0.020918434,0.000010587334,0.000029835657,0.000028212382,0.021839907,0.94924897,0.0012805465,0.0014134335,0.00003391376,0.005112287],"study_design_scores_gemma":[0.0016882312,0.00008398304,0.97088665,0.00028103165,0.0002930989,0.000007606522,0.018967152,0.00031793644,0.0012327217,0.00024244933,0.005632516,0.00036661475],"about_ca_topic_score_codex":0.00023031683,"about_ca_topic_score_gemma":0.0060639577,"teacher_disagreement_score":0.9499682,"about_ca_system_score_codex":0.000090888,"about_ca_system_score_gemma":0.00033890284,"threshold_uncertainty_score":0.5300563},"labels":[],"label_agreement":null},{"id":"W3155939352","doi":"10.1155/2021/8813461","title":"Metro Train Operation Plan Analysis Based on Station Travel Time Reliability","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","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":"Ministry of Science and Technology of the People's Republic of China","keywords":"Plan (archaeology); Reliability (semiconductor); Transport engineering; Service (business); Beijing; Preprocessor; Computer science; Operations research; Engineering; Cluster analysis; Artificial intelligence","score_opus":0.011815080337000584,"score_gpt":0.28373536904085955,"score_spread":0.271920288703859,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3155939352","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.70025617,0.000044530516,0.29661193,0.0016192205,0.00030750103,0.00018184342,0.00012746776,0.0000420908,0.00080927],"genre_scores_gemma":[0.97167975,0.00004728403,0.026819944,0.00017421442,0.00008379688,0.000003938919,0.0010117838,0.000011004292,0.00016830162],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9979699,0.00024850582,0.00068456784,0.00020382795,0.0007301263,0.00016304932],"domain_scores_gemma":[0.9983156,0.00023618464,0.00046333007,0.000115552786,0.0007465133,0.00012283071],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00089459855,0.00011846119,0.00029235063,0.00034654015,0.00024080723,0.000053964122,0.0000896819,0.00010082467,0.00033261173],"category_scores_gemma":[0.00018824576,0.00012025258,0.00022908855,0.0011157119,0.00004766991,0.0006371466,2.2824665e-7,0.0001740706,0.0000047514404],"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":[0.0002361865,0.00014405092,0.006898535,0.000010605983,0.00007056749,0.000022641325,0.007895207,0.9811876,0.0011185808,0.0005739431,0.000033308792,0.0018087678],"study_design_scores_gemma":[0.0022641206,0.00031267293,0.9627187,0.00007558014,0.0009498327,5.979412e-7,0.009221936,0.01953703,0.0027527486,0.0005604379,0.0013001142,0.00030621805],"about_ca_topic_score_codex":0.000046692403,"about_ca_topic_score_gemma":0.0011695868,"teacher_disagreement_score":0.96165055,"about_ca_system_score_codex":0.00012554828,"about_ca_system_score_gemma":0.0004018244,"threshold_uncertainty_score":0.49037582},"labels":[],"label_agreement":null},{"id":"W3157159542","doi":"10.82308/12567","title":"Construction of a detailed deterministic user-equilibrium traffic assignment model for the Greater Montreal area using Geographic Information Systems","year":2005,"lang":"en","type":"article","venue":"eScholarship@McGill (McGill)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Ministère des Transports; McGill University","keywords":"Computer science; Geographic information system; Transport engineering; Operations research; Geography; Engineering; Remote sensing","score_opus":0.02853541995021014,"score_gpt":0.24629771166733297,"score_spread":0.21776229171712283,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3157159542","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99382675,0.000029526129,0.0027461532,0.00004785251,0.00026610133,0.00093834277,0.00050963706,0.00013817115,0.0014974627],"genre_scores_gemma":[0.9963036,0.000025782143,0.0032998698,0.0000542475,0.000028431634,0.00009855311,0.000060207793,0.000019889245,0.000109382185],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981191,0.00016379135,0.00064551923,0.00022591357,0.0004950556,0.0003506063],"domain_scores_gemma":[0.99865544,0.00025892816,0.00044033173,0.00018828824,0.00033575835,0.000121274665],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000898996,0.00019796632,0.00024281238,0.00020164937,0.0010468616,0.000114773015,0.00020551239,0.00019472606,0.000020034253],"category_scores_gemma":[0.00018505105,0.00017032535,0.00014639203,0.00030652017,0.00016798303,0.0013466212,0.000012346589,0.0001599036,0.0000051433467],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012090521,0.00004778923,0.0008011689,0.000063469546,0.00008101962,7.7244647e-7,0.00029883714,0.95053905,0.0007837499,0.02941624,0.0000016696256,0.017845303],"study_design_scores_gemma":[0.0008587834,0.000054743166,0.0012676716,0.0001106907,0.00022365553,0.0000056244407,0.0013935263,0.99307054,0.0004601761,0.00041977956,0.0018333887,0.00030140014],"about_ca_topic_score_codex":0.00039506453,"about_ca_topic_score_gemma":0.0008760124,"teacher_disagreement_score":0.042531487,"about_ca_system_score_codex":0.00021681002,"about_ca_system_score_gemma":0.0000674772,"threshold_uncertainty_score":0.8051719},"labels":[],"label_agreement":null},{"id":"W3157911836","doi":"10.1093/sleep/zsab072.293","title":"294 Impacts of travel and time zone differences in the National Hockey League (NHL)","year":2021,"lang":"en","type":"article","venue":"SLEEP","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; Canadian Sleep & Circadian Network","funders":"Naval Medical Research Center; U.S. Navy","keywords":"League; Overtime; Evening; Demography; Weekend effect; Ice hockey; Morning; Geography; Psychology; Demographic economics; Medicine; Economics; Physical medicine and rehabilitation","score_opus":0.022731751649372047,"score_gpt":0.27600585398205146,"score_spread":0.25327410233267944,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3157911836","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98821425,0.00015653764,0.00037104546,0.0016271966,0.00004501293,0.000054644526,0.00001685253,0.000008243073,0.009506212],"genre_scores_gemma":[0.99919695,0.000052722113,0.00023049231,0.00010580842,0.000040204657,0.0000019432055,0.000023137758,0.0000014779791,0.00034729083],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9994512,0.00008372557,0.00009214044,0.00006861522,0.0002331552,0.00007113316],"domain_scores_gemma":[0.9997513,0.000102850456,0.000035144647,0.000024327865,0.00006620407,0.000020148134],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028345117,0.00003062233,0.000059954284,0.000028727254,0.00008537821,0.000022308546,0.000051541978,0.000032344764,0.00007616254],"category_scores_gemma":[0.0000981733,0.00002377617,0.000013328034,0.00018346304,0.00006653677,0.00007046461,0.0000019750494,0.00004078139,0.0000032805897],"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":[0.0000462818,0.00033688263,0.6649477,0.000042599124,0.000042894877,0.000018618028,0.26201865,0.001383053,0.0021794916,0.061312877,0.0016586299,0.006012324],"study_design_scores_gemma":[0.00023987303,0.000015876381,0.9911474,0.000019363311,0.0000075604976,7.455245e-7,0.0063184495,0.0007490663,0.00016520506,0.0009641337,0.00031692983,0.00005540827],"about_ca_topic_score_codex":0.00040475433,"about_ca_topic_score_gemma":0.0012568227,"teacher_disagreement_score":0.3261997,"about_ca_system_score_codex":0.00000818451,"about_ca_system_score_gemma":0.000067435416,"threshold_uncertainty_score":0.09695641},"labels":[],"label_agreement":null},{"id":"W3158286708","doi":"10.1155/2021/6667335","title":"Comparing Dynamic User Equilibrium and Noniterative Stochastic Route Choice in a Simulation-Based Dynamic Traffic Assignment Model: Practical Considerations for Large-Scale Networks","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Scale (ratio); Flow network; Mathematical optimization; Traffic simulation; Dynamic network analysis; Operations research; Simulation; Transport engineering; Engineering; Microsimulation; Mathematics","score_opus":0.025672925292830252,"score_gpt":0.341455416089927,"score_spread":0.31578249079709675,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3158286708","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.43006882,0.00005702788,0.5691461,0.00036841905,0.00010344747,0.0002134452,0.000024784857,0.0000139260355,0.000004060948],"genre_scores_gemma":[0.9467833,0.0000143294765,0.052829772,0.00007900693,0.00002840409,0.000019478184,0.0001998611,0.000015997266,0.00002987264],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985636,0.00010448624,0.0006208971,0.00019630557,0.00029265246,0.00022208791],"domain_scores_gemma":[0.99764764,0.0013013757,0.00039274382,0.00005495861,0.0004947293,0.000108565786],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003903632,0.00012433392,0.0002610945,0.0001346794,0.00020464738,0.000083863284,0.000029633222,0.00010081071,0.000010903671],"category_scores_gemma":[0.00024446618,0.00013766593,0.00007677569,0.00023931173,0.00005370822,0.0008557313,8.150106e-7,0.00021732134,1.0782922e-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":[0.00014005364,0.00013340014,0.004183222,0.000017887916,0.00001915172,0.000014040289,0.009811649,0.98519886,0.00007873321,0.00030376072,0.0000018737908,0.00009734709],"study_design_scores_gemma":[0.0021622437,0.000056251956,0.059399534,0.00013163191,0.00008793406,9.786548e-7,0.002097577,0.9356446,0.0000058865867,0.00026217324,0.000020201567,0.00013097111],"about_ca_topic_score_codex":0.000009098388,"about_ca_topic_score_gemma":0.013154987,"teacher_disagreement_score":0.51671445,"about_ca_system_score_codex":0.0001453421,"about_ca_system_score_gemma":0.00045623782,"threshold_uncertainty_score":0.7340792},"labels":[],"label_agreement":null},{"id":"W3158493169","doi":"10.1177/03611981211007836","title":"Assessing the Bus Bridging Effectiveness on the Operational Resilience of the Subway Service in Toronto","year":2021,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Bridging (networking); Transport engineering; Queueing theory; Operations research; Resilience (materials science); Computer science; Service (business); Engineering; Computer network; Business","score_opus":0.08829569436246223,"score_gpt":0.435447050918589,"score_spread":0.3471513565561267,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3158493169","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97463167,0.00025012158,0.000987338,0.021487005,0.0005567544,0.00093447644,0.00003196638,0.000012721865,0.0011079275],"genre_scores_gemma":[0.9983981,0.0005178376,0.00026575214,0.00023266228,0.00014993419,0.000062521874,0.0000148829195,0.00002508881,0.00033320297],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.98742867,0.0061395857,0.001083625,0.0003475214,0.004373692,0.00062689296],"domain_scores_gemma":[0.98973536,0.004670853,0.0004559125,0.00051070034,0.0044863857,0.00014080781],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.012806939,0.00018228764,0.0003047112,0.00020644956,0.0021503086,0.00032361603,0.0014624618,0.00015512257,0.0002898253],"category_scores_gemma":[0.00089668785,0.00009975444,0.00026911,0.0033486541,0.000889745,0.0009985127,0.0000084007925,0.0016897452,0.0000042193615],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009302489,0.00049416086,0.77010804,0.00031733676,0.00014308868,0.00007353122,0.041724738,0.10156489,0.0059994296,0.07326566,0.0011842734,0.004194591],"study_design_scores_gemma":[0.00064139743,0.000070129645,0.9687821,0.0009021006,0.00002719888,3.7455789e-7,0.023287274,0.000420045,0.0027825544,0.0010394182,0.001936598,0.00011080444],"about_ca_topic_score_codex":0.057940178,"about_ca_topic_score_gemma":0.4504972,"teacher_disagreement_score":0.39255705,"about_ca_system_score_codex":0.0005438846,"about_ca_system_score_gemma":0.0024378062,"threshold_uncertainty_score":0.9991487},"labels":[],"label_agreement":null},{"id":"W3159033197","doi":"10.1016/j.eswa.2021.115083","title":"A socially motivating and environmentally friendly tour recommendation framework for tourist groups","year":2021,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":26,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Tourism; Maximization; Computer science; Consumption (sociology); Cover (algebra); Set (abstract data type); Environmental economics; Minification; Operations research; Microeconomics; World Wide Web; Economics; Sociology; Mathematics; Political science; Engineering","score_opus":0.01665075919318615,"score_gpt":0.2948714532704566,"score_spread":0.27822069407727046,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3159033197","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0031419096,0.0002520571,0.98970664,0.0027710742,0.00009588709,0.0008284021,0.000048049897,0.00010573136,0.0030502575],"genre_scores_gemma":[0.80991715,0.0001427668,0.18547228,0.00027209122,0.0006100805,0.0020587388,0.0005308726,0.000027601589,0.00096840755],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991428,0.000068149755,0.00020656952,0.00026144527,0.00015580091,0.00016523735],"domain_scores_gemma":[0.9992863,0.00028731936,0.00014274851,0.00011077729,0.00008938749,0.00008350124],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018911119,0.0000873714,0.00011616675,0.000028408302,0.0009807128,0.00013239644,0.00006510571,0.000095775846,0.0000525553],"category_scores_gemma":[0.00006404363,0.000089753805,0.000023849218,0.00017032148,0.000069573995,0.00015948659,0.000006075658,0.000066397915,0.0000040542445],"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":[0.00004263944,0.00025272308,0.012188003,0.000086574284,0.00010413303,0.0000024036826,0.057866756,0.0013952713,0.0008512692,0.9046265,0.004983978,0.017599767],"study_design_scores_gemma":[0.0010871562,0.00010824975,0.0073698154,0.00021908857,0.00005623128,0.000008277764,0.12176586,0.0014184247,0.00027126897,0.005204466,0.8618378,0.00065337104],"about_ca_topic_score_codex":0.00026655718,"about_ca_topic_score_gemma":0.00033942945,"teacher_disagreement_score":0.899422,"about_ca_system_score_codex":0.00007199795,"about_ca_system_score_gemma":0.00010783787,"threshold_uncertainty_score":0.75429493},"labels":[],"label_agreement":null},{"id":"W3159487126","doi":"10.82308/54788","title":"An economic analysis of public transportation in Montréal","year":2013,"lang":"en","type":"article","venue":"eScholarship@McGill (McGill)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; McGill University","keywords":"Economic analysis; Business; Economics; Environmental planning; Environmental science; Agricultural economics","score_opus":0.01887267185040067,"score_gpt":0.25206589214038394,"score_spread":0.23319322028998327,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3159487126","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.990788,0.00003018966,0.000012154735,0.00008612591,0.000110484085,0.00024569273,0.00034570685,0.0001025649,0.008279093],"genre_scores_gemma":[0.9982899,0.00008820833,0.0009456356,0.00006565277,0.000010045158,0.000040755815,0.00039709933,0.000018221492,0.00014447198],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99811274,0.00030672076,0.00057927903,0.00035694725,0.00030977558,0.00033455496],"domain_scores_gemma":[0.9990358,0.000108419146,0.00024077807,0.00022755908,0.00017032702,0.00021716276],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000903636,0.00014676711,0.00031212188,0.0006611373,0.00040473868,0.00006144353,0.00026412154,0.0001857078,0.0007604731],"category_scores_gemma":[0.000081453974,0.00016793435,0.00014242569,0.0011466399,0.00008699005,0.0018848509,0.0000027668475,0.0001805371,0.000054399665],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000045115798,0.0004690368,0.19844016,0.000027659442,0.00049597153,0.000010702135,0.0009410343,0.1814695,0.0037906182,0.5159885,0.000004478464,0.0983172],"study_design_scores_gemma":[0.0008078595,0.00008131444,0.9756656,0.000022480106,0.00034988648,1.855254e-7,0.0031735417,0.0064187814,0.0009503474,0.006131603,0.005901333,0.00049704546],"about_ca_topic_score_codex":0.0505631,"about_ca_topic_score_gemma":0.18795788,"teacher_disagreement_score":0.77722543,"about_ca_system_score_codex":0.0002474525,"about_ca_system_score_gemma":0.000047559814,"threshold_uncertainty_score":0.9557593},"labels":[],"label_agreement":null},{"id":"W3159517316","doi":"10.1155/2021/7497094","title":"An Affinity Propagation-Based Clustering Method for the Temporal Dynamics Management of High-Speed Railway Passenger Demand","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China; China Railway","keywords":"Cluster analysis; Interval (graph theory); Occupancy; Computer science; Passenger train; Schedule; Flow (mathematics); Real-time computing; Simulation; Transport engineering; Engineering; Mathematics; Automotive engineering; Artificial intelligence","score_opus":0.016082832828315116,"score_gpt":0.3154837527113243,"score_spread":0.29940091988300915,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3159517316","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12454841,0.00006806929,0.87373996,0.0009517756,0.00033705556,0.00026764706,0.0000376544,0.000016237953,0.000033207558],"genre_scores_gemma":[0.7408555,0.00008263059,0.25872064,0.000033061457,0.000053265725,0.000006148501,0.00019818122,0.000009611131,0.00004097302],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9987058,0.00011920399,0.00054388825,0.0001231393,0.00038291118,0.00012505495],"domain_scores_gemma":[0.9982767,0.0002556722,0.00059341604,0.00010025619,0.00071393495,0.00006003884],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00088710035,0.00008730198,0.00018368391,0.00007018307,0.00024760823,0.000031172935,0.00012240662,0.000059444294,0.000022092947],"category_scores_gemma":[0.000030895557,0.000073035,0.00010652711,0.00029925015,0.00004498009,0.0004052972,5.2018584e-7,0.00009627984,1.1738871e-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":[0.00027148373,0.00009476117,0.0022655942,0.000093542614,0.00006985946,0.000011117243,0.003910083,0.97877795,0.00046094204,0.0042266524,0.0000073162323,0.009810699],"study_design_scores_gemma":[0.0091986265,0.000654132,0.6950491,0.0007424918,0.0015177908,0.0000038885605,0.060636275,0.21544892,0.009575862,0.004157137,0.0023806351,0.0006351388],"about_ca_topic_score_codex":0.00007106803,"about_ca_topic_score_gemma":0.0038305456,"teacher_disagreement_score":0.763329,"about_ca_system_score_codex":0.000055894052,"about_ca_system_score_gemma":0.00017802686,"threshold_uncertainty_score":0.2978281},"labels":[],"label_agreement":null},{"id":"W3159535311","doi":"10.5281/zenodo.6363385","title":"Transit Equity Planning in the Greater Toronto and Hamilton Area","year":2016,"lang":"en","type":"dissertation","venue":"York University Digital Library (York University)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"York University","funders":"University of Waterloo; York University","keywords":"Equity (law); Transit (satellite); Transport engineering; Business; Geography; Political science; Engineering; Public transport","score_opus":0.02155673562603948,"score_gpt":0.22144007997065304,"score_spread":0.19988334434461355,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3159535311","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19049452,0.00019729818,0.00019857049,0.00028349154,0.00019924773,0.00035171155,0.00049900234,0.00025122173,0.8075249],"genre_scores_gemma":[0.8549851,0.00037808114,0.00008219203,0.00006580453,0.000072142124,1.777879e-7,0.0017478823,0.000022842238,0.14264573],"study_design_codex":"qualitative","study_design_gemma":"not_applicable","domain_scores_codex":[0.9985696,0.00015452776,0.0001413548,0.00042878254,0.00034919978,0.00035655152],"domain_scores_gemma":[0.99933016,0.00015964109,0.00014901899,0.00017703437,0.00003178587,0.00015238556],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000082950784,0.00025221554,0.00024710037,0.00030214127,0.0007414506,0.00032940554,0.0006834917,0.00037628363,0.00018631582],"category_scores_gemma":[0.000008013934,0.00024165386,0.0001184409,0.00056651415,0.00018736642,0.003253679,0.000027252707,0.0002289879,0.000010988979],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0054137087,0.00051235966,0.28229958,0.00042359755,0.00048874697,0.0033779775,0.5249542,0.0007222227,0.000021102496,0.13055807,0.028796984,0.02243143],"study_design_scores_gemma":[0.0012848806,0.000092366194,0.050046016,0.0006158761,0.0001487093,0.0000021137782,0.25742924,0.000013717679,0.000008560829,0.00030141242,0.6892947,0.0007624472],"about_ca_topic_score_codex":0.00068888493,"about_ca_topic_score_gemma":0.0030890512,"teacher_disagreement_score":0.6648792,"about_ca_system_score_codex":0.00017723757,"about_ca_system_score_gemma":0.0002717622,"threshold_uncertainty_score":0.98543584},"labels":[],"label_agreement":null},{"id":"W3160213952","doi":"","title":"Pricing It Right for Climate: Using Mobility Pricing to Drive Down Transport Emissions in Metro Vancouver and Montreal","year":2020,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Environmental science; Transport engineering; Business; Meteorology; Economics; Geography; Engineering","score_opus":0.02480540573730853,"score_gpt":0.30468296833290365,"score_spread":0.2798775625955951,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3160213952","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.81991947,0.000015596046,0.17171448,0.0014404168,0.00011008322,0.00075864914,0.000027914453,0.00011301526,0.0059003835],"genre_scores_gemma":[0.97260714,0.000020480365,0.026747085,0.00047890318,0.000042784453,0.000013195123,0.000012801387,0.0000076002534,0.00007000763],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9990369,0.00003375206,0.00025554976,0.00026529262,0.00016442125,0.0002440773],"domain_scores_gemma":[0.9995103,0.00014814491,0.00004979655,0.000050687082,0.00005200126,0.000189104],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000357069,0.00008582553,0.00015816325,0.0000637294,0.00033596472,0.000023674736,0.00006403467,0.00006844266,0.00008561289],"category_scores_gemma":[0.000107809654,0.00007748368,0.000034864428,0.00040570402,0.000030936306,0.00021070884,0.0000043122745,0.00006586843,6.581151e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023818409,0.00012525156,0.65865564,0.00014551215,0.000023426399,0.000015941941,0.16912758,0.1667748,0.0005865015,0.0010673449,0.0009525378,0.0022873008],"study_design_scores_gemma":[0.0041751796,0.00034704665,0.5480534,0.00037469403,0.00028445406,3.7885084e-7,0.16157801,0.25692484,0.0020587232,0.0007580311,0.023996405,0.0014488458],"about_ca_topic_score_codex":0.0039045613,"about_ca_topic_score_gemma":0.06529053,"teacher_disagreement_score":0.15268768,"about_ca_system_score_codex":0.000061018174,"about_ca_system_score_gemma":0.000081537386,"threshold_uncertainty_score":0.9517655},"labels":[],"label_agreement":null},{"id":"W3160485983","doi":"10.1177/03611981211012421","title":"Transit Users’ Mode Choice Behavior During Light Rail Transit Short-Term Planned Service Disruption","year":2021,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Multinomial logistic regression; Light rail transit; Service (business); Transit (satellite); Mode choice; Term (time); Payment; Revealed preference; Transport engineering; Preference; Travel behavior; Mode (computer interface); Business; Public transport; Econometrics; Computer science; Statistics; Marketing; Economics; Engineering; Finance; Mathematics","score_opus":0.09388115095856991,"score_gpt":0.4088208392152588,"score_spread":0.3149396882566889,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3160485983","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9828514,0.00026632706,0.0027640776,0.011268228,0.0008758589,0.0012094331,0.0002104892,0.000109422086,0.00044475062],"genre_scores_gemma":[0.9944458,0.0015948936,0.0012488449,0.00009670941,0.00047123196,0.00013238384,0.00021567021,0.000095251715,0.0016992361],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9880491,0.0023686194,0.0018634022,0.0007440372,0.0055642463,0.0014105969],"domain_scores_gemma":[0.99069583,0.00086544163,0.00042184198,0.00059957,0.006624829,0.0007924788],"candidate_categories":["metaepi_narrow","sts","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0035406037,0.00040201412,0.00065334316,0.0009940967,0.0025106885,0.00040452695,0.001252754,0.0004884829,0.00075213244],"category_scores_gemma":[0.00020754761,0.00036716645,0.0006025106,0.0042265016,0.0005458794,0.00171528,0.000005325992,0.0025900227,0.000028871005],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0033959816,0.0013899374,0.8251803,0.0008025463,0.00043907628,0.0012184851,0.08855109,0.024641208,0.044359736,0.0042788703,0.0019595316,0.003783246],"study_design_scores_gemma":[0.002227288,0.00022804376,0.96756595,0.0005814816,0.00023267813,0.000002280509,0.011705235,0.00011046032,0.005085633,0.00037470154,0.011459489,0.00042674947],"about_ca_topic_score_codex":0.012808548,"about_ca_topic_score_gemma":0.38641217,"teacher_disagreement_score":0.37360364,"about_ca_system_score_codex":0.0004486243,"about_ca_system_score_gemma":0.0016508817,"threshold_uncertainty_score":0.99987805},"labels":[],"label_agreement":null},{"id":"W3163441265","doi":"10.1061/jtepbs.0000537","title":"Social Impact Analysis of Various Road Capacity Expansion Options: A Case of Managed Highway Lanes","year":2021,"lang":"en","type":"article","venue":"Journal of Transportation Engineering Part A Systems","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Toll; Transport engineering; Occupancy; Toll road; Value of time; Fuel efficiency; Business; Vehicle miles of travel; Consumption (sociology); Traffic volume; Travel time; Environmental economics; Economics; Engineering; Civil engineering","score_opus":0.023591095601293913,"score_gpt":0.29258417590550934,"score_spread":0.2689930803042154,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3163441265","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96314156,0.00038141597,0.035764605,0.000042375777,0.0003762497,0.00008812926,0.00013635789,0.00002690923,0.000042376174],"genre_scores_gemma":[0.99859476,0.00007935013,0.0010579932,0.0000020121622,0.000099850535,0.0000031831669,0.000105849336,0.000009678019,0.000047320802],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9983211,0.00012700599,0.0008972588,0.0001020752,0.00041708065,0.00013547609],"domain_scores_gemma":[0.99837106,0.00009551302,0.00069088634,0.00008257337,0.00067533855,0.000084606116],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00074306177,0.00010573727,0.00048777641,0.00045510178,0.00011298596,0.000025554076,0.00007665221,0.00010997012,0.00003973768],"category_scores_gemma":[0.000055142045,0.000103522136,0.00034565837,0.0012164239,0.000035561683,0.0002443358,6.0721106e-7,0.00012208235,2.7322952e-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":[0.000026707732,0.000077355915,0.009755651,0.00014057351,0.0008430642,0.0002383963,0.022552202,0.96322155,0.0009762035,0.002040782,0.000055166965,0.00007234695],"study_design_scores_gemma":[0.0026641667,0.0002718141,0.869624,0.0009628959,0.007840656,0.00020531891,0.027148103,0.08640935,0.0012727466,0.000034181474,0.002773753,0.00079300575],"about_ca_topic_score_codex":0.0018210799,"about_ca_topic_score_gemma":0.0013475013,"teacher_disagreement_score":0.8768122,"about_ca_system_score_codex":0.000058263235,"about_ca_system_score_gemma":0.00015297494,"threshold_uncertainty_score":0.422151},"labels":[],"label_agreement":null},{"id":"W3164547741","doi":"10.82308/21404","title":"The use of Bluetooth and smartphone GPS data to investigate factors associated to variations of travel times and delays","year":2017,"lang":"en","type":"article","venue":"eScholarship@McGill (McGill)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Global Positioning System; Bluetooth; Computer science; Internet privacy; Telecommunications; Wireless","score_opus":0.12343836122616679,"score_gpt":0.29827923265084355,"score_spread":0.17484087142467675,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3164547741","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99562883,0.000017787237,0.00002985871,0.00033849184,0.00014166311,0.0002990547,0.0021680177,0.00003440558,0.0013418974],"genre_scores_gemma":[0.9968274,0.00007881189,0.0024702516,0.00010137903,0.0000071023264,0.000006260937,0.00009571132,0.00001659906,0.00039643265],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99854845,0.00018635798,0.0003571198,0.0003178003,0.00035241965,0.00023785171],"domain_scores_gemma":[0.99807084,0.000557067,0.00034163793,0.0005230232,0.00024489744,0.0002625486],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0010901486,0.00013581138,0.00022282505,0.00009196782,0.0024757802,0.00017202491,0.0004907956,0.00012419031,0.000012777505],"category_scores_gemma":[0.005003698,0.00011783893,0.000027732753,0.00021985418,0.00026831584,0.000947007,0.00011279839,0.00014601876,0.0000024766036],"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":[0.00029599064,0.00039857594,0.26004386,0.0001097493,0.0008489259,0.000010131103,0.0060517285,0.004835602,0.041558668,0.62934965,0.00029120725,0.056205932],"study_design_scores_gemma":[0.0006687687,0.0001140676,0.9697107,0.00014190748,0.00015877422,3.8480025e-7,0.00082737993,0.0005113173,0.0049766363,0.0040816846,0.018420702,0.00038764224],"about_ca_topic_score_codex":0.0050670705,"about_ca_topic_score_gemma":0.0125539685,"teacher_disagreement_score":0.7096669,"about_ca_system_score_codex":0.000043433796,"about_ca_system_score_gemma":0.000048650778,"threshold_uncertainty_score":0.99882287},"labels":[],"label_agreement":null},{"id":"W3165972090","doi":"10.1287/ijoc.2022.1198","title":"A Catalog of Formulations for the Network Pricing Problem","year":2022,"lang":"en","type":"article","venue":"INFORMS journal on computing","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Mathematical optimization; Computer science; Robustness (evolution); Enumeration; Shortest path problem; Curse of dimensionality; Preprocessor; Bilevel optimization; Set (abstract data type); Path (computing); Optimization problem; Mathematics; Theoretical computer science; Artificial intelligence","score_opus":0.025776670609482494,"score_gpt":0.2993647196029922,"score_spread":0.27358804899350975,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3165972090","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.119204424,0.00013802615,0.86646485,0.0024069352,0.0012780956,0.0007570627,0.000014920415,0.000077587116,0.009658095],"genre_scores_gemma":[0.98975784,0.000006016389,0.009658186,0.00021212666,0.00024034888,0.0000046306245,0.000010711487,0.000004211721,0.00010590447],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99924046,0.000025319445,0.00027491618,0.000039555016,0.00026084456,0.00015889696],"domain_scores_gemma":[0.99904484,0.00046358706,0.00031761857,0.000040789215,0.00010389002,0.000029301213],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0013805958,0.00003793454,0.000064498636,0.000037733746,0.0029228772,0.000051031642,0.00015050147,0.000014754008,0.000018061002],"category_scores_gemma":[0.00006252177,0.000027313643,0.000057467743,0.00027301014,0.000024685414,0.000122028214,0.000011189357,0.00016972875,4.072097e-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":[0.000014607236,0.000007274364,0.0038376055,0.0000028805757,0.000011346182,2.7991624e-7,0.012733701,0.9438925,5.485513e-7,0.025762308,0.00056744314,0.013169537],"study_design_scores_gemma":[0.0019237483,0.0006478007,0.042358015,0.00022071706,0.00009307771,0.000037928563,0.024087874,0.5424582,0.00001505456,0.017294243,0.3704618,0.00040155993],"about_ca_topic_score_codex":0.00006051852,"about_ca_topic_score_gemma":0.000036882844,"teacher_disagreement_score":0.87055343,"about_ca_system_score_codex":0.000054772518,"about_ca_system_score_gemma":0.0001374203,"threshold_uncertainty_score":0.9983752},"labels":[],"label_agreement":null},{"id":"W3167267223","doi":"","title":"A smart solution to smart cities: An advanced traveler general information system","year":2021,"lang":"en","type":"article","venue":"Advances in Automobile Engineering","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Metropolitan area; Travel behavior; Environmental economics; Greenhouse gas; Energy consumption; Business; Fuel efficiency; Consumption (sociology); Social cost; Transport engineering; Information system; Engineering; Economics; Geography","score_opus":0.006010554231483096,"score_gpt":0.24851693861469543,"score_spread":0.24250638438321234,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3167267223","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.53380656,0.00048719972,0.4561844,0.00013042602,0.0019856137,0.0004634942,0.000030073894,0.00083860324,0.00607363],"genre_scores_gemma":[0.972367,0.00010153268,0.026953127,0.00006384277,0.00008857708,0.00011661214,0.00011599908,0.000012242018,0.00018105138],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99896467,0.000043907152,0.00029522844,0.00016846471,0.00024449136,0.00028326354],"domain_scores_gemma":[0.9995687,0.000039642713,0.000052311014,0.00012353332,0.00010922855,0.000106611995],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026805434,0.00010764811,0.00013780493,0.00016089698,0.00013997294,0.000067837515,0.000098833014,0.00007213315,0.00002560881],"category_scores_gemma":[0.00009447068,0.00013157951,0.000029815876,0.0005764458,0.00001691476,0.001981886,0.0000069125003,0.000099027886,0.000014345179],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007795739,0.000010180701,0.0017720562,0.000053515676,0.0000028058562,0.0000052990235,0.008554759,0.9729087,0.00016066001,0.0068437476,0.000010023663,0.009670466],"study_design_scores_gemma":[0.0016878493,0.00015791008,0.056698848,0.00085053366,0.000026788925,0.000012267653,0.033335198,0.5502511,0.0024624318,0.0000729306,0.35325634,0.0011877472],"about_ca_topic_score_codex":0.00014522768,"about_ca_topic_score_gemma":0.0006918663,"teacher_disagreement_score":0.43856046,"about_ca_system_score_codex":0.00021546066,"about_ca_system_score_gemma":0.000098316814,"threshold_uncertainty_score":0.5365657},"labels":[],"label_agreement":null},{"id":"W3169861247","doi":"10.1155/2021/6658319","title":"A Cooperative Intermodal Transportation Network Flow Control Method Based on Model Predictive Control","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Model predictive control; Computer science; Container (type theory); Digitization; Transport network; Train; Flow network; Routing (electronic design automation); Control (management); Distributed computing; Simulation; Engineering; Computer network; Mathematical optimization","score_opus":0.009675901619705227,"score_gpt":0.2897411243857809,"score_spread":0.2800652227660757,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3169861247","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022052463,0.00010428277,0.9750267,0.0011748185,0.0005648946,0.00034042614,0.000300517,0.00005574841,0.00038012484],"genre_scores_gemma":[0.90418917,0.00007673881,0.09427733,0.0007837099,0.000247637,0.000021285545,0.00030606595,0.000025314373,0.00007276347],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99742556,0.00035817406,0.00087750296,0.00027428946,0.000760346,0.00030414117],"domain_scores_gemma":[0.9970606,0.00044501474,0.00067219604,0.000113122274,0.0015142899,0.00019476296],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00081051944,0.00021175187,0.00045098033,0.00014201556,0.00034260002,0.000050225266,0.0001335401,0.00016765115,0.00007871493],"category_scores_gemma":[0.00013046012,0.0002110732,0.00025339087,0.0004906391,0.00008074463,0.00074718223,1.7759234e-7,0.00036799166,0.0000015639043],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0020618748,0.00014759522,0.0021912328,0.000015590087,0.00007872505,0.00007483991,0.011004972,0.97954714,0.00029977396,0.0025760974,0.0001048372,0.0018973126],"study_design_scores_gemma":[0.010632851,0.00067513704,0.08189622,0.00040209998,0.0005602377,0.0000021208405,0.0045690825,0.89787394,0.00040312004,0.0016783784,0.00091480196,0.0003919913],"about_ca_topic_score_codex":0.00001534704,"about_ca_topic_score_gemma":0.00076822523,"teacher_disagreement_score":0.8821367,"about_ca_system_score_codex":0.00012388916,"about_ca_system_score_gemma":0.00080407487,"threshold_uncertainty_score":0.86073154},"labels":[],"label_agreement":null},{"id":"W3171984561","doi":"10.1016/j.commtr.2022.100052","title":"Quantifying out-of-station waiting time in oversaturated urban metro systems","year":2022,"lang":"en","type":"article","venue":"Communications in Transportation Research","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Beijing; Transfer station; Transfer (computing); Queueing theory; Metro station; Computer science; Queue; Transport engineering; Scheduling (production processes); Megacity; Operations research; Metropolitan area; Traffic congestion; Service (business); Computer network; Operations management; Business; Engineering; Geography","score_opus":0.24492803835110663,"score_gpt":0.45524966029978825,"score_spread":0.21032162194868162,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3171984561","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9899645,0.0009523701,0.0005903554,0.0015363682,0.00021404089,0.0010173918,0.00017793436,0.0000974479,0.005449604],"genre_scores_gemma":[0.9967625,0.00023887104,0.0011662532,0.00001046431,0.000011844435,0.00023742445,0.0012943074,0.000012846146,0.00026548008],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9960739,0.0018703735,0.00065971917,0.00021721612,0.0008694635,0.00030932648],"domain_scores_gemma":[0.99788535,0.0010680961,0.0001828776,0.00043976065,0.00037222283,0.00005169212],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004565312,0.00007943238,0.00017645553,0.0009814887,0.00089552184,0.000050637227,0.0006885128,0.00007117671,0.00013166996],"category_scores_gemma":[0.00023278798,0.000103613595,0.000038809103,0.0027815944,0.00024442212,0.00030941956,0.0000157031,0.0006459268,0.000008442295],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012431685,0.0004641747,0.4662991,0.0000820931,0.00002271944,0.000010867336,0.20141973,0.22195828,0.00070966495,0.10720436,0.00043920788,0.001265492],"study_design_scores_gemma":[0.002439122,0.00014665422,0.40478885,0.00035814164,0.000030318713,2.039561e-7,0.29451287,0.2535596,0.000102365564,0.0007096854,0.04278346,0.0005687418],"about_ca_topic_score_codex":0.013226664,"about_ca_topic_score_gemma":0.03757694,"teacher_disagreement_score":0.10649467,"about_ca_system_score_codex":0.0003374751,"about_ca_system_score_gemma":0.00031494308,"threshold_uncertainty_score":0.99334437},"labels":[],"label_agreement":null},{"id":"W3173619715","doi":"10.32920/ryerson.14656557.v1","title":"The Impact of Lane Closures on Congestion and Reliability: A Case Study of Toronto's Gardiner Expressway","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"McMaster University; Toronto Metropolitan University","funders":"","keywords":"Closure (psychology); Reliability (semiconductor); Travel time; Transport engineering; Traffic congestion; Psychological intervention; Intervention (counseling); Computer science; Business; Economics; Engineering; Psychology","score_opus":0.025057391009236634,"score_gpt":0.35039776597809114,"score_spread":0.3253403749688545,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3173619715","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99439824,0.00025100945,0.00010010709,0.000056362314,0.00012571044,0.00039904276,0.000021171774,0.000024434938,0.004623943],"genre_scores_gemma":[0.9991075,0.0003460813,0.00022316385,0.0000024430428,0.000030881947,0.000015524267,0.000021988251,0.0000051460515,0.00024726984],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99884915,0.00033889065,0.00026776746,0.00019887631,0.00025917965,0.00008613314],"domain_scores_gemma":[0.9989035,0.00036242467,0.00019424375,0.00023234043,0.0002644906,0.000042959116],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00070939085,0.00009470927,0.00020611168,0.00001551485,0.00015824132,0.000053335043,0.000079102014,0.000122030935,0.00008784175],"category_scores_gemma":[0.00022331778,0.000060827027,0.00006604549,0.000038275462,0.000103913386,0.00005724411,0.000022484266,0.00011634479,7.2663354e-8],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031460435,0.0010902326,0.37809417,0.000111895366,0.0002788654,0.0001300419,0.23582444,0.3772052,0.000028992587,0.00096005015,0.0008231767,0.005138357],"study_design_scores_gemma":[0.0012542641,0.0011016836,0.667315,0.00026105877,0.00025637608,0.000011201565,0.32735306,0.0016169928,0.000060458933,0.00021449063,0.00017631757,0.00037909276],"about_ca_topic_score_codex":0.31485638,"about_ca_topic_score_gemma":0.08782023,"teacher_disagreement_score":0.37558818,"about_ca_system_score_codex":0.000047813217,"about_ca_system_score_gemma":0.0001518874,"threshold_uncertainty_score":0.92882466},"labels":[],"label_agreement":null},{"id":"W3173741689","doi":"10.1155/2022/6198451","title":"Optimization of Bus Scheduling and Bus-Berth Matching at Curbside Stops under Connected Vehicle Environment","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Punctuality; Scheduling (production processes); Queue; Bus priority; Queueing theory; Local bus; Computer science; Integer programming; Service (business); Public transport; Engineering; Real-time computing; Transport engineering; Computer network; Control bus; System bus; Operations management","score_opus":0.010386422609956578,"score_gpt":0.2494568735438077,"score_spread":0.2390704509338511,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3173741689","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9041842,0.0003408818,0.09469685,0.00036559693,0.00018857772,0.00013064286,0.000022364264,0.000017865174,0.000053019405],"genre_scores_gemma":[0.96588904,0.0005169744,0.03333478,0.000051211446,0.00003285386,0.0000045175457,0.00009159144,0.000013418396,0.0000655888],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99860656,0.00010772144,0.00051697635,0.00012678267,0.0005101989,0.00013172976],"domain_scores_gemma":[0.9989759,0.000112259804,0.0006597767,0.00005479387,0.000120359015,0.00007694978],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042223686,0.0000872009,0.00017864435,0.00013247422,0.00045896936,0.000013064606,0.00007988655,0.000040854135,0.00018990549],"category_scores_gemma":[0.000016491209,0.000096870936,0.000056382996,0.00021578839,0.00006372493,0.00044013542,0.0000026045104,0.00015801542,3.1146038e-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":[0.00019823045,0.000050800743,0.005055305,0.00001475348,0.000022743263,0.0000063197385,0.020453038,0.96994543,0.0025595766,0.0010797916,0.0000030554704,0.0006109608],"study_design_scores_gemma":[0.012302307,0.0013134201,0.7565804,0.00040462855,0.00074112555,0.000032898282,0.18293871,0.029525023,0.0041119633,0.0056667137,0.0051560584,0.0012267475],"about_ca_topic_score_codex":0.00009953749,"about_ca_topic_score_gemma":0.000189485,"teacher_disagreement_score":0.9404204,"about_ca_system_score_codex":0.00013285638,"about_ca_system_score_gemma":0.00008222593,"threshold_uncertainty_score":0.39502823},"labels":[],"label_agreement":null},{"id":"W3174096199","doi":"10.1016/j.cstp.2021.06.013","title":"Integrated framework of departure time choice, mode choice, and route assignment for large-scale networks","year":2021,"lang":"en","type":"article","venue":"Case Studies on Transport Policy","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; University of Toronto","keywords":"Mode choice; Transit (satellite); Mode (computer interface); Computer science; Transport engineering; Scale (ratio); Operations research; Traffic simulation; Traffic congestion; Travel time; Public transport; Microsimulation; Engineering; Geography","score_opus":0.02021671576372931,"score_gpt":0.3418237060016461,"score_spread":0.3216069902379168,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3174096199","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8654326,0.0033033828,0.11714113,0.0044431323,0.00071871374,0.0013468916,0.0019336562,0.00033127933,0.0053491713],"genre_scores_gemma":[0.9930437,0.0007167407,0.0033097235,0.000572147,0.00037461353,0.000060063034,0.00031891617,0.00002119971,0.0015828491],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9987638,0.000065063105,0.00034628005,0.00029914477,0.00019678749,0.00032892026],"domain_scores_gemma":[0.9988754,0.00044314278,0.00012899534,0.00014592096,0.00027570507,0.00013084759],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024157233,0.0001704782,0.00034451458,0.000080749356,0.0004946655,0.000017164726,0.00006570541,0.00018561847,0.00002350204],"category_scores_gemma":[0.00026087594,0.00016049005,0.000103213664,0.00047616326,0.00014640288,0.00009308446,0.000004177413,0.00016633613,0.0000012693907],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00056665234,0.0015961785,0.22861077,0.00083139376,0.0023793834,0.0012418664,0.27738848,0.3526182,0.00014933104,0.11394634,0.010013103,0.010658323],"study_design_scores_gemma":[0.013344765,0.0015285661,0.18835603,0.0034268904,0.0030651637,0.00013902738,0.11173102,0.057092335,0.0017608664,0.007467653,0.6077451,0.004342552],"about_ca_topic_score_codex":0.0005014597,"about_ca_topic_score_gemma":0.009483414,"teacher_disagreement_score":0.597732,"about_ca_system_score_codex":0.00006632721,"about_ca_system_score_gemma":0.00019326672,"threshold_uncertainty_score":0.6544594},"labels":[],"label_agreement":null},{"id":"W3174585579","doi":"10.1609/aaai.v36i4.20284","title":"The Perils of Learning Before Optimizing","year":2022,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Machine Intelligence Institute; Compute Canada; Defense Advanced Research Projects Agency; Canadian Institute for Advanced Research; Nvidia","keywords":"A priori and a posteriori; Computer science; Range (aeronautics); Function (biology); Task (project management); Mathematical optimization; Optimization problem; Machine learning; Artificial intelligence; Algorithm; Mathematics","score_opus":0.05691481360200981,"score_gpt":0.31127061990211113,"score_spread":0.25435580630010135,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3174585579","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9054127,0.00008943012,0.002101881,0.010748479,0.0010308508,0.0007941841,0.000017244558,0.00014409228,0.07966114],"genre_scores_gemma":[0.9982531,0.000053434047,0.00037685767,0.000032164065,0.00003870515,0.00002467974,0.0000011964709,0.000007566062,0.001212274],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","domain_scores_codex":[0.9986154,0.000049927996,0.000358164,0.00016455616,0.0006005429,0.0002114336],"domain_scores_gemma":[0.9989437,0.00012432752,0.0004041702,0.00007647802,0.0004128781,0.00003847411],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0011459383,0.00008377044,0.0001214578,0.000058012312,0.0018367801,0.00008264647,0.0007002086,0.000034720942,0.00020192127],"category_scores_gemma":[0.00049375946,0.000060749757,0.000080311176,0.00054347137,0.0004349295,0.00011962973,0.00006211825,0.00029972402,0.0000039500674],"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":[0.000089743655,0.00004775951,0.0024869477,0.000011598212,0.000011533439,1.1614412e-7,0.04036379,0.015721582,0.0022984655,0.9175592,0.0000711464,0.02133813],"study_design_scores_gemma":[0.00013480497,0.0011927478,0.0026465312,0.00039674874,0.00011364978,0.000003093629,0.6150132,0.07351031,0.12765397,0.15730384,0.02130077,0.0007303378],"about_ca_topic_score_codex":0.00021205946,"about_ca_topic_score_gemma":0.00008768214,"teacher_disagreement_score":0.76025534,"about_ca_system_score_codex":0.000042328553,"about_ca_system_score_gemma":0.00013839574,"threshold_uncertainty_score":0.99946266},"labels":[],"label_agreement":null},{"id":"W3174693724","doi":"10.1155/2021/2763304","title":"How Do Different Treatments of Catchment Area Affect the Station Level Demand Modeling of Urban Rail Transit?","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Catchment area; Drainage basin; Regression analysis; Linear regression; Negative binomial distribution; Environmental science; Residential area; Variables; Statistics; Distance decay; Hydrology (agriculture); Geography; Mathematics; Engineering; Civil engineering; Cartography","score_opus":0.0322978635199391,"score_gpt":0.2866556337791757,"score_spread":0.25435777025923656,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3174693724","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7843792,0.0005487955,0.21410584,0.00052509905,0.00018249592,0.00015189785,0.000062430336,0.0000066219395,0.000037650614],"genre_scores_gemma":[0.99499416,0.0008811334,0.003845467,0.000011505995,0.00003932336,0.000004469827,0.00012326983,0.000009508257,0.000091175],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99849284,0.00012024139,0.00055683724,0.000116477866,0.0005850147,0.00012860667],"domain_scores_gemma":[0.998489,0.00011743507,0.0006285618,0.00008637382,0.0006159538,0.00006266501],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002869646,0.000113197,0.00027498114,0.00009006864,0.00014009309,0.000025142306,0.00009135877,0.000060932616,0.000012557775],"category_scores_gemma":[0.000036212048,0.00008486381,0.00016540135,0.00025266703,0.000057369125,0.000436312,4.3641637e-7,0.00010184984,6.380615e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026885906,0.00020818842,0.014536218,0.00006740447,0.00014417776,0.000010880053,0.068576895,0.90450984,0.0072480515,0.0014222453,0.000012155228,0.00299509],"study_design_scores_gemma":[0.0103203105,0.0009932779,0.7824235,0.0014097226,0.0019005535,0.000007085416,0.1287152,0.0067929765,0.061759215,0.0046650544,0.00040104115,0.00061209034],"about_ca_topic_score_codex":0.00005392138,"about_ca_topic_score_gemma":0.00080827123,"teacher_disagreement_score":0.8977169,"about_ca_system_score_codex":0.00005655557,"about_ca_system_score_gemma":0.00016575334,"threshold_uncertainty_score":0.34606457},"labels":[],"label_agreement":null},{"id":"W3176509696","doi":"10.1155/2021/8830561","title":"Research on Hub-and-Spoke Transportation Network of China Railway Express","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Ministry of Education of the People's Republic of China; China Railway","keywords":"Port (circuit theory); Spoke-hub distribution paradigm; Node (physics); Lagrangian relaxation; Mode (computer interface); Construct (python library); Transport engineering; Heuristic; Key (lock); Operations research; Flow network; Point (geometry); Dual (grammatical number); Network model; Computer science; Engineering; Mathematical optimization; Computer network; Mathematics; Data mining","score_opus":0.02962058897847384,"score_gpt":0.350190186468833,"score_spread":0.3205695974903591,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3176509696","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9866079,0.00068533566,0.010008677,0.0008240619,0.0005485019,0.00017404939,0.00003678988,0.000024284398,0.0010903649],"genre_scores_gemma":[0.98716134,0.0015256676,0.010674964,0.000037477093,0.00021413108,0.000004736154,0.00011951572,0.000016762731,0.00024541293],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99762076,0.00024019871,0.00074623217,0.00019148095,0.0009405157,0.00026078758],"domain_scores_gemma":[0.99784815,0.00028773802,0.0004910293,0.00010613278,0.0011289652,0.00013796479],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012345081,0.000113355665,0.00028587328,0.0001633536,0.00030839007,0.000028303146,0.00012210791,0.00012683928,0.00007245639],"category_scores_gemma":[0.00008755912,0.00011531019,0.00011637269,0.00078320806,0.00015528555,0.0005630082,4.8943764e-7,0.00035035016,9.4708486e-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":[0.00071024045,0.00020092561,0.014131189,0.000099332734,0.000062098494,0.00011478621,0.045408066,0.8906804,0.0024389722,0.037348256,0.0003265813,0.008479206],"study_design_scores_gemma":[0.0019679347,0.00038763366,0.9570631,0.0006298627,0.00010429595,0.0000029485234,0.017543828,0.000040508563,0.003686846,0.004978854,0.013366831,0.00022739038],"about_ca_topic_score_codex":0.00007136528,"about_ca_topic_score_gemma":0.00093148334,"teacher_disagreement_score":0.9429319,"about_ca_system_score_codex":0.000045855257,"about_ca_system_score_gemma":0.00030421166,"threshold_uncertainty_score":0.4702213},"labels":[],"label_agreement":null},{"id":"W3180931089","doi":"10.1155/2021/5597694","title":"The Effects of Multitasking and Tools Carried by Travelers Onboard on the Perceived Trip Time","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":14,"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":"Respondent; Human multitasking; Transport engineering; Multivariate analysis of variance; Perception; Exploratory factor analysis; Travel behavior; Beijing; Travel survey; Sample (material); Applied psychology; Computer science; Engineering; Psychology; China; Geography; Statistics; Mathematics; Structural equation modeling","score_opus":0.00854564777010679,"score_gpt":0.25544991962758873,"score_spread":0.24690427185748193,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3180931089","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9969232,0.00044358833,0.0006315393,0.0014063128,0.0002369801,0.00015146677,0.000013471639,0.000008647091,0.00018478841],"genre_scores_gemma":[0.99765515,0.0010204165,0.0009842999,0.00007883096,0.00003735154,0.000003094066,0.000022664806,0.0000072718162,0.00019092666],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99897933,0.0001340881,0.00033716016,0.000083074025,0.00035460852,0.00011176885],"domain_scores_gemma":[0.9981896,0.001103814,0.0003339178,0.000059736263,0.00026103153,0.000051873954],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003695847,0.00007219331,0.00014193609,0.000030147843,0.0003410404,0.000048534523,0.0000837196,0.00004858334,0.000012328758],"category_scores_gemma":[0.0003380608,0.000047555008,0.00007517814,0.00017350526,0.00010767322,0.00029632886,3.4398258e-7,0.00013856367,4.259143e-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":[0.0017343407,0.0003426686,0.007431556,0.00022658892,0.00048219674,0.00012020006,0.3142025,0.117497675,0.4337597,0.015202923,0.0014242254,0.10757546],"study_design_scores_gemma":[0.003894093,0.000527365,0.9052167,0.00069180806,0.00032115847,0.0000033989036,0.049860276,0.00018810312,0.029730728,0.0010845762,0.00816411,0.00031769465],"about_ca_topic_score_codex":0.00001588098,"about_ca_topic_score_gemma":0.00012155848,"teacher_disagreement_score":0.8977851,"about_ca_system_score_codex":0.000024377927,"about_ca_system_score_gemma":0.00010338396,"threshold_uncertainty_score":0.26230416},"labels":[],"label_agreement":null},{"id":"W3181710663","doi":"10.1061/jtepbs.0000549","title":"Average Travel Time, Planning Time Index, and Buffer Time Index Thresholds for Freeway Weaving Sections, Merging Areas, and Diverging Areas","year":2021,"lang":"en","type":"article","venue":"Journal of Transportation Engineering Part A Systems","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"SNC-Lavalin (Canada)","funders":"","keywords":"Weaving; Speed limit; Index (typography); Travel time; Transport engineering; Limit (mathematics); Traffic volume; Statistics; Computer science; Simulation; Engineering; Mathematics","score_opus":0.009946408643185486,"score_gpt":0.23222322224870975,"score_spread":0.22227681360552426,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3181710663","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7125874,0.0020772994,0.28350362,0.00020075515,0.00083547214,0.00034449025,0.00012379287,0.000119045966,0.00020811106],"genre_scores_gemma":[0.99774885,0.00009234882,0.0009538697,0.000015855392,0.00034202228,0.00001237533,0.00011823026,0.00003220995,0.00068421906],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984033,0.00005839978,0.0006433846,0.0002132887,0.0004175118,0.00026409654],"domain_scores_gemma":[0.9988698,0.00023285655,0.0003346754,0.00007555426,0.00029716818,0.00018995864],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007419345,0.00018505579,0.0003815128,0.000250651,0.00036964603,0.00017488326,0.00008153012,0.00015660476,0.0000311573],"category_scores_gemma":[0.00007462967,0.00020033783,0.000095644325,0.0002794268,0.00003453493,0.0005534591,0.0000021989088,0.00020975324,0.0000015242225],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005562841,0.000030794152,0.05542088,0.00018013964,0.00016943878,0.000060990187,0.01241766,0.9287919,0.0018234472,0.0006312405,0.000296077,0.0001218541],"study_design_scores_gemma":[0.004406598,0.00018478553,0.3606654,0.004056648,0.0005412155,0.00021249497,0.0072854753,0.5958411,0.0006604426,0.00008298986,0.024775911,0.001286932],"about_ca_topic_score_codex":0.00009161022,"about_ca_topic_score_gemma":0.000028846782,"teacher_disagreement_score":0.33295077,"about_ca_system_score_codex":0.0000944871,"about_ca_system_score_gemma":0.00012631233,"threshold_uncertainty_score":0.81695396},"labels":[],"label_agreement":null},{"id":"W3184153405","doi":"10.1016/j.trb.2021.06.016","title":"Routing policy choice prediction in a stochastic network: Recursive model and solution algorithm","year":2021,"lang":"en","type":"article","venue":"Transportation Research Part B Methodological","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":17,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science; Mathematical optimization; Routing (electronic design automation); Node (physics); State space; Function (biology); Set (abstract data type); State (computer science); Algorithm; Mathematics","score_opus":0.36865282557395385,"score_gpt":0.49547261157538164,"score_spread":0.1268197860014278,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3184153405","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09554874,0.00019120678,0.89925194,0.0031751227,0.00014808732,0.00045729202,0.00007376769,0.00013338323,0.0010204422],"genre_scores_gemma":[0.80965006,0.0005872647,0.18670717,0.00018809404,0.00068692997,0.00015750682,0.00050679094,0.000020753516,0.0014954252],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99544156,0.002382795,0.0004157482,0.0004490598,0.0006884567,0.00062236056],"domain_scores_gemma":[0.99716705,0.0019941696,0.000080997735,0.00010460366,0.00045835512,0.00019483415],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0062621925,0.0001113873,0.00022096196,0.000195401,0.0006922168,0.00006683659,0.000094178904,0.00025255678,0.000088305416],"category_scores_gemma":[0.002580936,0.000117443036,0.00004753995,0.0013392142,0.00030693124,0.00028355228,0.000005422341,0.00051203766,0.000004487939],"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":[0.00010198476,0.00012584923,0.02281588,0.000019740093,0.000020594085,0.000039080493,0.016240701,0.8504942,0.00014100192,0.09246998,0.0005317238,0.01699927],"study_design_scores_gemma":[0.0015855232,0.00015297584,0.5426244,0.0002480747,0.000045226898,0.0000023795985,0.006346173,0.40012428,0.000052410724,0.04570624,0.0027158114,0.00039652517],"about_ca_topic_score_codex":0.0018266855,"about_ca_topic_score_gemma":0.0070584416,"teacher_disagreement_score":0.7141013,"about_ca_system_score_codex":0.0001262103,"about_ca_system_score_gemma":0.00055496953,"threshold_uncertainty_score":0.5324042},"labels":[],"label_agreement":null},{"id":"W3184986741","doi":"10.1155/2021/6672961","title":"Revisiting the Benefits of Combining Data of a Different Nature: Strategic Forecasting of New Mode Alternatives","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Instituto de Sistemas Complejos de Ingeniería; Agencia Nacional de Investigación y Desarrollo; Volvo Research and Educational Foundations","keywords":"Mode (computer interface); Computer science; Operations research; Preference; Survey data collection; Service (business); Data science; Business; Economics; Engineering; Marketing; Microeconomics; Mathematics","score_opus":0.07355998309565771,"score_gpt":0.34375440489241943,"score_spread":0.27019442179676173,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3184986741","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9925297,0.0011343495,0.0056017945,0.00025959188,0.00018521555,0.00007031622,0.00006349791,0.000004416764,0.00015110681],"genre_scores_gemma":[0.98944134,0.00068586273,0.0096133845,0.000008927578,0.000103981816,3.3012793e-7,0.00012284055,0.000007019595,0.000016320693],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9984854,0.00007483566,0.0007608766,0.000105449944,0.0004765134,0.00009694511],"domain_scores_gemma":[0.99754775,0.00025704465,0.0014368508,0.00012570307,0.0005884484,0.000044199078],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039017628,0.000075847056,0.00026282677,0.0000673648,0.000081909806,0.0000106387,0.00023858249,0.00005813753,0.000012733504],"category_scores_gemma":[0.00014991911,0.000060102935,0.00007871709,0.00029391894,0.000059979135,0.00044838508,0.0000024182218,0.00019490492,2.1402487e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020807397,0.00006824847,0.016802212,0.00016342402,0.000109235516,0.000007665959,0.04066904,0.8973532,0.0043324423,0.02156126,0.0000040329523,0.018721167],"study_design_scores_gemma":[0.0052784956,0.00042042887,0.7807089,0.006308968,0.00087102904,0.000012044828,0.13042171,0.010618937,0.048743553,0.015919417,0.0002133656,0.00048315723],"about_ca_topic_score_codex":0.000075580494,"about_ca_topic_score_gemma":0.000419921,"teacher_disagreement_score":0.88673425,"about_ca_system_score_codex":0.000012284173,"about_ca_system_score_gemma":0.00022670468,"threshold_uncertainty_score":0.24509266},"labels":[],"label_agreement":null},{"id":"W3186552409","doi":"10.82308/36602","title":"Urban public transportation systems: understanding the impacts of service improvement strategies on service reliability and passenger's perception","year":2015,"lang":"en","type":"article","venue":"eScholarship@McGill (McGill)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Reliability (semiconductor); Service (business); Public transport; Perception; Business; Transport engineering; Public service; Computer science; Marketing; Public relations; Engineering; Psychology; Political science","score_opus":0.053519248942922655,"score_gpt":0.2716981773789192,"score_spread":0.21817892843599657,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3186552409","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9909976,0.00004374028,0.00004299359,0.0010992702,0.00032008375,0.00060526596,0.0002667389,0.00016112177,0.0064631603],"genre_scores_gemma":[0.9991988,0.00004856539,0.00013559988,0.0003616848,0.000032339332,0.000032456057,0.0001242338,0.000024515244,0.000041832882],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","domain_scores_codex":[0.9975066,0.00045299728,0.0005093811,0.00038954042,0.0007914073,0.00035005593],"domain_scores_gemma":[0.99820423,0.00022963078,0.00033759483,0.0002731972,0.000679981,0.0002753792],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023198659,0.00021419112,0.00023211133,0.00012494133,0.0010551836,0.00019207265,0.00023304058,0.00020441749,0.000018682722],"category_scores_gemma":[0.00019674885,0.00018121638,0.000053296084,0.0007474742,0.000105144405,0.0014573717,0.000008414658,0.0002961207,0.0000086123055],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033199321,0.00032353794,0.0064082416,0.0006203497,0.00012480267,0.000006189822,0.008936582,0.026292602,0.0063174027,0.94723237,0.00003209165,0.0033738653],"study_design_scores_gemma":[0.0057434198,0.0014638861,0.14375919,0.0010800209,0.0005784621,0.0000061548235,0.7589973,0.005371685,0.0013495262,0.05745888,0.0220712,0.0021202473],"about_ca_topic_score_codex":0.008637381,"about_ca_topic_score_gemma":0.020440998,"teacher_disagreement_score":0.8897735,"about_ca_system_score_codex":0.00064694136,"about_ca_system_score_gemma":0.0001537754,"threshold_uncertainty_score":0.9979642},"labels":[],"label_agreement":null},{"id":"W3187548265","doi":"10.1155/2021/3529984","title":"A MultiObjective Optimization Approach for Integrated Timetabling and Vehicle Scheduling with Uncertainty","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Mathematical optimization; Robustness (evolution); Multi-objective optimization; Computer science; Scheduling (production processes); Schedule; Job shop scheduling; Pareto principle; Operations research; Engineering; Mathematics","score_opus":0.013300857486460371,"score_gpt":0.2745514512470643,"score_spread":0.26125059376060394,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3187548265","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19473295,0.00020408945,0.80459505,0.000121668556,0.00007329578,0.00016231969,0.000014051318,0.000020692663,0.00007586191],"genre_scores_gemma":[0.499181,0.00013454774,0.5004166,0.000022716617,0.000041244537,0.000005940655,0.00015133608,0.000008285168,0.000038331855],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99913085,0.000055305925,0.00031661728,0.00014129949,0.00022969887,0.00012621556],"domain_scores_gemma":[0.9982729,0.00010901106,0.0003218462,0.0000389763,0.0011832569,0.00007402612],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003252823,0.000084791915,0.00017200602,0.00009866281,0.00024901287,0.000047516423,0.00004102177,0.000064251835,0.000008451666],"category_scores_gemma":[0.00011211038,0.00007619792,0.00004847134,0.00039469078,0.000056632252,0.00063393306,3.4294368e-7,0.00011619192,4.2634554e-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":[0.00034626044,0.000054809643,0.0017272732,0.00002374238,0.000038432292,0.0000050960925,0.01109376,0.9812107,0.00054517546,0.0007891024,0.0000012146119,0.0041644415],"study_design_scores_gemma":[0.01518829,0.0008609008,0.06486944,0.00073068537,0.00092153315,0.00003115201,0.19725883,0.71312684,0.0036326086,0.0005468969,0.0019147835,0.00091802067],"about_ca_topic_score_codex":0.00003626389,"about_ca_topic_score_gemma":0.00021680283,"teacher_disagreement_score":0.30444807,"about_ca_system_score_codex":0.000047022222,"about_ca_system_score_gemma":0.00027833236,"threshold_uncertainty_score":0.31072608},"labels":[],"label_agreement":null},{"id":"W3188442502","doi":"10.1016/j.ejor.2021.07.052","title":"An iterative two-stage heuristic algorithm for a bilevel service network design and pricing model","year":2021,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Computer Research Institute of Montréal","funders":"Belgian Federal Science Policy Office","keywords":"Computer science; Mathematical optimization; Solver; Bilevel optimization; Heuristic; Network planning and design; Neighbourhood (mathematics); Integer programming; Operations research; Service (business); Algorithm; Optimization problem; Mathematics; Economics; Artificial intelligence","score_opus":0.16148043410837365,"score_gpt":0.4214360052813088,"score_spread":0.25995557117293516,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3188442502","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0076829563,0.00016730421,0.98968166,0.0012085178,0.000068711706,0.00017109413,0.00001963019,0.000007843254,0.0009922602],"genre_scores_gemma":[0.29800552,0.00010120632,0.699598,0.00033089166,0.0006039175,0.000004101531,0.000041788113,0.000018982402,0.0012955886],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969603,0.0017694412,0.0002924469,0.0001493949,0.0006101994,0.00021821656],"domain_scores_gemma":[0.9963079,0.00058644515,0.00008426932,0.000061344675,0.0027903968,0.00016960752],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0066504907,0.0000631421,0.00010379624,0.00009234179,0.00093955523,0.00036980343,0.00014705404,0.000018200886,0.000044895318],"category_scores_gemma":[0.0004271299,0.00005866123,0.000024918721,0.00036015475,0.000070984686,0.00046643725,0.000011097694,0.00022202017,0.0000030387598],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000050061266,0.00004203264,0.00009629775,0.0000062637246,0.000017002767,0.00006703198,0.009574198,0.9769752,0.0003486593,0.0063375044,0.00068253384,0.0058032027],"study_design_scores_gemma":[0.0007014429,0.00017155659,0.0009425328,0.00007364322,0.000009406764,0.000010292806,0.0016168779,0.9930944,0.000074970165,0.00062753726,0.0025891773,0.00008820109],"about_ca_topic_score_codex":0.000014061767,"about_ca_topic_score_gemma":0.00003386345,"teacher_disagreement_score":0.29032254,"about_ca_system_score_codex":0.000043985794,"about_ca_system_score_gemma":0.0009417322,"threshold_uncertainty_score":0.72263944},"labels":[],"label_agreement":null},{"id":"W3188450084","doi":"","title":"Le Benchmarking : expérimentations appliquées à l'étude du transport urbain à Montréal","year":2006,"lang":"fr","type":"article","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Benchmarking; Humanities; Political science; Art; Business","score_opus":0.0072257432713386995,"score_gpt":0.2243874401315957,"score_spread":0.217161696860257,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3188450084","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11122927,0.01394508,0.83075255,0.028112127,0.00085562695,0.0012606758,0.0002793785,0.0011113093,0.012453975],"genre_scores_gemma":[0.9106486,0.0012043159,0.076812014,0.0011020612,0.0010384776,0.00046227587,0.0006172829,0.000100061014,0.008014943],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9960697,0.00031048345,0.001004743,0.0007181466,0.00076026685,0.001136655],"domain_scores_gemma":[0.99827695,0.00019596922,0.00045840928,0.00046334876,0.00021532869,0.00039000224],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0009883458,0.0005224526,0.00050400733,0.00040490896,0.0015244999,0.00022057192,0.00050355855,0.00059705327,0.00021428781],"category_scores_gemma":[0.000035931807,0.00066399726,0.00034642618,0.0013048844,0.00046795106,0.00094254967,0.000028625176,0.0005048988,0.000041150513],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008133696,0.000876377,0.115468495,0.00006499801,0.00009013495,0.00013521756,0.0070044845,0.24006869,0.0007040263,0.6179175,0.010431846,0.0071569337],"study_design_scores_gemma":[0.0030567558,0.00036448173,0.62740743,0.00048711282,0.00060091633,0.00009098895,0.0067458707,0.13530073,0.0031699953,0.015210635,0.20491892,0.0026461487],"about_ca_topic_score_codex":0.4593983,"about_ca_topic_score_gemma":0.29016218,"teacher_disagreement_score":0.7994193,"about_ca_system_score_codex":0.0005139587,"about_ca_system_score_gemma":0.0008713893,"threshold_uncertainty_score":0.99977535},"labels":[],"label_agreement":null},{"id":"W3188682062","doi":"10.5267/j.dsl.2021.5.002","title":"School bus routing problem considering affinity among children","year":2021,"lang":"en","type":"article","venue":"Decision Science Letters","topic":"Transportation Planning and Optimization","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":"Universidad del Valle","keywords":"Routing (electronic design automation); Vehicle routing problem; Point (geometry); School bus; Computer science; TRIPS architecture; Process (computing); Mathematical optimization; Operations research; Transport engineering; Computer network; Engineering; Mathematics","score_opus":0.019211245371935384,"score_gpt":0.29454337947408116,"score_spread":0.2753321341021458,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3188682062","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96544343,0.000017001836,0.028089358,0.0024782373,0.000304606,0.00012752674,0.0000022743577,0.00012427529,0.0034133173],"genre_scores_gemma":[0.96365464,0.000013522928,0.034614947,0.0015488657,0.00008229483,0.000004147039,0.000005515332,0.0000061785004,0.00006986999],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99785286,0.000079147256,0.00026538852,0.00042395154,0.0009922334,0.0003864186],"domain_scores_gemma":[0.9990136,0.00023550357,0.00011195467,0.00020202003,0.00020693264,0.00023001205],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0016542728,0.000087847155,0.000112085974,0.0001568761,0.0014244155,0.0005261127,0.00033255815,0.00005007828,0.00017689398],"category_scores_gemma":[0.0016189833,0.00008943371,0.000046801848,0.001739544,0.0004727001,0.0009281511,0.000036506124,0.00015959256,0.000057012337],"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":[0.00000512607,0.000020998397,0.9611911,0.0000013698084,0.0000038706335,0.00003419069,0.005554233,0.01929789,0.0045557735,0.0017190246,0.0012964206,0.0063199936],"study_design_scores_gemma":[0.0002843946,0.0000052737555,0.9938246,0.00008541102,0.000008528642,0.0000051626753,0.0018753585,0.0005306544,0.0017529133,0.00039681312,0.00100852,0.00022238982],"about_ca_topic_score_codex":0.0003737072,"about_ca_topic_score_gemma":0.0003794518,"teacher_disagreement_score":0.032633472,"about_ca_system_score_codex":0.000093398274,"about_ca_system_score_gemma":0.00040959643,"threshold_uncertainty_score":0.9998756},"labels":[],"label_agreement":null},{"id":"W3190512476","doi":"10.1177/03611981211029641","title":"Development of an Integrated Transportation and Land Use Microsimulation Model on a Flexible Modeling Platform","year":2021,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Microsimulation; Software deployment; Transport engineering; Population; Land use; Transportation planning; Travel survey; Computer science; Mode choice; Baseline (sea); Travel behavior; Engineering; Public transport; Civil engineering","score_opus":0.20276885453710647,"score_gpt":0.42015291495450474,"score_spread":0.21738406041739827,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3190512476","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96339273,0.00007841461,0.034945723,0.00055127195,0.00019678599,0.0005906748,0.00012976535,0.00004206123,0.00007258611],"genre_scores_gemma":[0.9595181,0.0005438046,0.039069645,0.000038948336,0.00006367091,0.000027322962,0.0002757355,0.000045345394,0.0004174777],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99337006,0.00076018734,0.0016122205,0.00045907393,0.0031546205,0.0006438179],"domain_scores_gemma":[0.9929641,0.0006201669,0.00048515626,0.00031217493,0.0052179242,0.0004004786],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0039293636,0.00024488984,0.0004405375,0.0008636414,0.0012378794,0.0002165165,0.00045876091,0.0002911327,0.00008007139],"category_scores_gemma":[0.00022429475,0.00021127169,0.0001948737,0.0019094684,0.00043950137,0.0015911073,0.0000022408033,0.0012279111,0.00000251489],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0025990226,0.00063714996,0.08598367,0.00025669712,0.00016002814,0.00005874817,0.07190313,0.8113377,0.00535427,0.009608729,0.00013706302,0.011963771],"study_design_scores_gemma":[0.005018354,0.0006665561,0.82764024,0.0016930931,0.0001930885,6.77739e-7,0.030996064,0.110195994,0.00949783,0.0045388523,0.008813442,0.0007458087],"about_ca_topic_score_codex":0.0074496977,"about_ca_topic_score_gemma":0.16004501,"teacher_disagreement_score":0.7416566,"about_ca_system_score_codex":0.00022632553,"about_ca_system_score_gemma":0.0022366159,"threshold_uncertainty_score":0.99915975},"labels":[],"label_agreement":null},{"id":"W3192790252","doi":"10.3390/su13168706","title":"Optimal Urban Transit Investment Model and Its Application","year":2021,"lang":"en","type":"article","venue":"Sustainability","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"University of Toronto","keywords":"Toll; Investment (military); Transport engineering; Transit (satellite); Urban transit; Network planning and design; Service (business); Transportation planning; Operations research; Public transport; Computer science; Business; Economics; Engineering; Economy; Telecommunications","score_opus":0.01219122009637764,"score_gpt":0.28538639169654706,"score_spread":0.27319517160016943,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3192790252","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9323038,0.00030881932,0.0565992,0.0063352427,0.000032598615,0.00043393057,0.000011859867,0.00012173165,0.0038528228],"genre_scores_gemma":[0.99687636,0.000026038711,0.0015690095,0.00017169298,0.000023564411,0.000032871805,0.000035807334,0.0000040452824,0.0012606087],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99929565,0.00008301007,0.00011461355,0.00020940512,0.00015019484,0.0001471267],"domain_scores_gemma":[0.9992708,0.000028324186,0.00002665105,0.00009454343,0.00049133954,0.0000883219],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003184215,0.000052906842,0.00006939901,0.000019465358,0.00027482014,0.00002935626,0.000042977885,0.000061321414,0.000017086519],"category_scores_gemma":[0.0001798795,0.000059404647,0.000022534525,0.0001771869,0.0000770618,0.00015341915,0.000004685347,0.000055499655,0.0000010972013],"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":[0.000033001295,0.00016265218,0.02436276,0.00013013322,0.000012282596,0.000007082946,0.0647558,0.3867446,0.000104688115,0.5211436,0.00025686872,0.0022865022],"study_design_scores_gemma":[0.0017880789,0.0000941229,0.16719696,0.000027125285,0.0001565284,0.0000024160663,0.060295872,0.602545,0.0014194354,0.10682211,0.0586613,0.0009910149],"about_ca_topic_score_codex":0.00012487001,"about_ca_topic_score_gemma":0.00024326236,"teacher_disagreement_score":0.4143215,"about_ca_system_score_codex":0.00017360249,"about_ca_system_score_gemma":0.00061510823,"threshold_uncertainty_score":0.24224512},"labels":[],"label_agreement":null},{"id":"W3193113430","doi":"10.1007/978-3-030-64018-7_2","title":"Fixed-Charge Network Design Problems","year":2020,"lang":"en","type":"book-chapter","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Computer Research Institute of Montréal; Université de Montréal; Polytechnique Montréal; Université du Québec à Montréal","funders":"","keywords":"Fixed charge; Variable (mathematics); Network planning and design; Mathematical optimization; Fixed cost; Set (abstract data type); Flow network; Arc (geometry); Function (biology); Computer science; Path (computing); Binary number; Variable cost; Flow (mathematics); Mathematics; Economics; Microeconomics; Computer network","score_opus":0.05116181939406793,"score_gpt":0.25681896497976975,"score_spread":0.2056571455857018,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3193113430","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[6.55594e-8,0.00021362978,0.10669618,0.0011182867,0.00029829761,0.00033940995,0.000010046105,0.00030957817,0.8910145],"genre_scores_gemma":[0.0004127387,0.000468772,0.014486089,0.00065238867,0.0007661875,0.000009357697,0.00018044133,0.000035658744,0.98298836],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99899626,0.000035342196,0.00021808383,0.00024546447,0.00031696007,0.00018791971],"domain_scores_gemma":[0.9994792,0.000090891044,0.00013595694,0.00008653135,0.000079594174,0.00012784854],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003026396,0.00014894606,0.00018156154,0.000031555453,0.0003192685,0.00006303888,0.00014476164,0.00028770935,0.004350418],"category_scores_gemma":[0.000018950952,0.00014880918,0.0000728327,0.00004833185,0.000071247894,0.00008315068,0.0000050077847,0.000190614,0.0006947509],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000072697176,0.0000029316554,0.000029844088,0.000011531986,0.00002578521,0.0000067017963,0.0018673987,0.02095281,2.6535227e-7,0.86452615,0.11176069,0.00080862606],"study_design_scores_gemma":[0.00007649231,0.000025351515,0.000008116139,0.00007770346,0.000036344434,1.3188433e-7,0.000031707557,0.00039584583,7.44076e-7,0.019866163,0.9792484,0.0002329911],"about_ca_topic_score_codex":0.00008853732,"about_ca_topic_score_gemma":0.0002466391,"teacher_disagreement_score":0.8674877,"about_ca_system_score_codex":0.000029648434,"about_ca_system_score_gemma":0.00018792693,"threshold_uncertainty_score":0.99655974},"labels":[],"label_agreement":null},{"id":"W3193287672","doi":"10.1007/s10696-021-09428-5","title":"Anticipatory approach for dynamic and stochastic shipment matching in hinterland synchromodal transportation","year":2021,"lang":"en","type":"article","venue":"Flexible Services and Manufacturing Journal","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"China Scholarship Council; Natural Sciences and Engineering Research Council of Canada; Nederlandse Organisatie voor Wetenschappelijk Onderzoek","keywords":"Computer science; Matching (statistics); Operations research; Time horizon; Sample (material); Mathematical optimization; Mathematics","score_opus":0.01527701281062132,"score_gpt":0.2805591928716274,"score_spread":0.26528218006100607,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3193287672","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9167491,0.0005953119,0.08210683,0.00018260458,0.00013036885,0.00011379346,0.000012625845,0.00002357846,0.0000857629],"genre_scores_gemma":[0.99307054,0.00033187066,0.006262247,0.00007659025,0.000056483135,0.0000065841264,0.000078999976,0.000008980936,0.00010768029],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99925864,0.000040323674,0.00020191654,0.0001638921,0.0001428908,0.00019231488],"domain_scores_gemma":[0.9997078,0.000042794363,0.00008533969,0.00003539064,0.00003460763,0.00009407121],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032171182,0.00008506309,0.00012549013,0.00008834252,0.00037783393,0.00018175262,0.000050118375,0.000059696267,0.0000116307165],"category_scores_gemma":[0.0000022575416,0.00008423684,0.000026462678,0.000036440564,0.000026129002,0.00028411412,0.0000032166404,0.00012529475,1.5792365e-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":[0.0004048558,0.0003041994,0.023635445,0.002450967,0.00017356944,0.00013666779,0.2926305,0.64404124,0.0005878689,0.0022585816,0.000013431869,0.033362675],"study_design_scores_gemma":[0.006142257,0.00020546535,0.75743276,0.0017671017,0.0002922243,0.00011044046,0.13177149,0.08544879,0.0012765834,0.012594051,0.0018122019,0.0011466442],"about_ca_topic_score_codex":0.00026897245,"about_ca_topic_score_gemma":0.0025679262,"teacher_disagreement_score":0.7337973,"about_ca_system_score_codex":0.00003311224,"about_ca_system_score_gemma":0.000053865122,"threshold_uncertainty_score":0.34350786},"labels":[],"label_agreement":null},{"id":"W3194009386","doi":"10.11159/icert21.301","title":"Electric Travelling Planner as a support for promoting of environmental friendly mobility - remarks based on international research project","year":2021,"lang":"en","type":"article","venue":"Proceedings of the World Congress on New Technologies","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Transport Canada","funders":"Narodowe Centrum Badań i Rozwoju","keywords":"Planner; Environmentally friendly; Computer science; Operations research; Engineering; Artificial intelligence; Ecology","score_opus":0.028142521640736908,"score_gpt":0.33077710373082425,"score_spread":0.30263458209008737,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3194009386","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9432923,0.00010515107,0.00014289255,0.013288754,0.0006086811,0.0022736958,0.00009930014,0.00049033697,0.03969885],"genre_scores_gemma":[0.9937186,0.000033176966,0.0025303115,0.00002168984,0.000024343959,0.00006108828,0.000009915114,0.000009876901,0.0035909475],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9985559,0.000019411405,0.0002687844,0.0002885867,0.0006321302,0.00023517228],"domain_scores_gemma":[0.9991402,0.0003000271,0.0002393971,0.00010705076,0.00019332652,0.000019982384],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00087026926,0.00009444253,0.00014845296,0.00033693688,0.00025933314,0.00004849123,0.00051351206,0.000106743,0.000025847747],"category_scores_gemma":[0.0014123713,0.00007718951,0.00007792555,0.00074658706,0.00026017634,0.000113629125,0.000032072458,0.00026612627,6.7590895e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0053226906,0.004680574,0.23021947,0.001165972,0.0005148652,0.000017190894,0.026988205,0.004936574,0.17678605,0.33595377,0.036183733,0.17723092],"study_design_scores_gemma":[0.0011450653,0.0006905095,0.004357375,0.0007299911,0.000040059113,0.0000014040372,0.017646205,0.005143081,0.9383462,0.008398402,0.023214914,0.00028676106],"about_ca_topic_score_codex":0.000052820043,"about_ca_topic_score_gemma":0.00005891373,"teacher_disagreement_score":0.7615602,"about_ca_system_score_codex":0.00009456841,"about_ca_system_score_gemma":0.0002882306,"threshold_uncertainty_score":0.3147697},"labels":[],"label_agreement":null},{"id":"W3195132113","doi":"10.1155/2021/8836324","title":"A Time-Varying Coupling Analysis of Expressway Traffic Volume and Manufacturing PMI","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","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":"Truck; Traffic volume; Transport engineering; Autoregressive model; Engineering; Automotive engineering; Econometrics; Mathematics","score_opus":0.009704425125992256,"score_gpt":0.26496816254356104,"score_spread":0.2552637374175688,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3195132113","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99124163,0.00060200633,0.0077655762,0.0001556793,0.00010479775,0.00004778396,0.000012524145,0.00001555931,0.000054426],"genre_scores_gemma":[0.9899639,0.000634184,0.009166724,0.000013954732,0.000038323524,8.9170817e-7,0.000054577242,0.0000072864277,0.00012016766],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9988667,0.000035128898,0.0005126108,0.00011612232,0.0003550612,0.00011441088],"domain_scores_gemma":[0.998938,0.00013346708,0.00047947187,0.000055869517,0.00031597994,0.00007719662],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034338262,0.00007391187,0.00027498702,0.00024936566,0.00013399175,0.00002332362,0.000057837948,0.000058979644,0.00010066108],"category_scores_gemma":[0.000049320424,0.00007869135,0.00013295402,0.00044539096,0.000059215792,0.00059103954,5.463177e-7,0.00010470758,3.886447e-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":[0.000070327056,0.000028607574,0.0045176707,0.00002401097,0.00018302862,0.00001770207,0.01823927,0.9705815,0.004182064,0.000039445877,0.000004132981,0.0021122757],"study_design_scores_gemma":[0.0019988439,0.00015341562,0.93075186,0.0006557051,0.0036996284,0.000004163584,0.020997392,0.023682347,0.0156233,0.000098652905,0.0019075448,0.00042712715],"about_ca_topic_score_codex":0.000015022473,"about_ca_topic_score_gemma":0.00018874939,"teacher_disagreement_score":0.9468991,"about_ca_system_score_codex":0.000023963026,"about_ca_system_score_gemma":0.00010655884,"threshold_uncertainty_score":0.320894},"labels":[],"label_agreement":null},{"id":"W3195146957","doi":"10.1155/2021/8871288","title":"The Use of Road Microsimulation Software within BIM Environments: A Preliminary Assessment","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Interoperability; Flexibility (engineering); Building information modeling; Context (archaeology); Interface (matter); Software; Systems engineering; Process (computing); Computer science; Plan (archaeology); Microsimulation; Transport engineering; Engineering; Risk analysis (engineering); Construction engineering","score_opus":0.021891912985581016,"score_gpt":0.299649704473003,"score_spread":0.27775779148742197,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3195146957","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8481572,0.00033786384,0.15034842,0.00041249394,0.00053458835,0.00015493507,0.000023836761,0.000013851831,0.000016766711],"genre_scores_gemma":[0.91689116,0.0007221015,0.08201888,0.000039583014,0.000045889046,0.0000029688358,0.00009158331,0.000009893182,0.0001779397],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99852407,0.00012131459,0.0006486519,0.00009843625,0.00049426686,0.00011327616],"domain_scores_gemma":[0.9985046,0.00024038508,0.0007945142,0.00008960497,0.00031377428,0.00005714559],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035825928,0.00007687489,0.00014015507,0.00005535194,0.00029184684,0.00004041937,0.000082433005,0.00006248841,0.000016028249],"category_scores_gemma":[0.00013174495,0.000064848,0.000095168565,0.00022708182,0.00009028661,0.0008055559,8.246103e-7,0.00013450655,4.2460138e-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":[0.00019430745,0.00009233547,0.01947308,0.000013435042,0.000036968828,0.000016479786,0.009701208,0.95519704,0.0025907618,0.0012345372,0.000022470427,0.011427384],"study_design_scores_gemma":[0.0010361219,0.00023682367,0.97728664,0.00017433231,0.00013081913,0.0000025556355,0.0069319387,0.0004443471,0.0021849363,0.000574378,0.010866378,0.00013075549],"about_ca_topic_score_codex":0.000032537722,"about_ca_topic_score_gemma":0.00033249764,"teacher_disagreement_score":0.95781356,"about_ca_system_score_codex":0.00006314085,"about_ca_system_score_gemma":0.00025240422,"threshold_uncertainty_score":0.26444244},"labels":[],"label_agreement":null},{"id":"W3196392993","doi":"10.22059/jieng.2021.326142.1784","title":"Reliable Urban Transportation Network Design Problem Considering Recurrent Traffic Congestions","year":2021,"lang":"en","type":"article","venue":"Advances in Industrial Engineering","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Network traffic control; Reliability (semiconductor); Traffic congestion; Computer science; Heuristic; Transport engineering; Network congestion; Flow network; Traffic congestion reconstruction with Kerner's three-phase theory; Computer network; Engineering; Mathematical optimization; Artificial intelligence; Network packet","score_opus":0.03513347809083115,"score_gpt":0.2686750878672445,"score_spread":0.23354160977641333,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3196392993","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15195276,0.027125226,0.78631246,0.0028013708,0.016387511,0.0034759725,0.00009974681,0.0025905317,0.0092544425],"genre_scores_gemma":[0.91790724,0.0021674412,0.078480735,0.000031201453,0.0008345101,0.00011054662,0.00017320781,0.00003501926,0.0002601078],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99884576,0.00006758062,0.00034133304,0.00022367392,0.00020125989,0.00032041842],"domain_scores_gemma":[0.9994282,0.0002580979,0.000074553565,0.00007887346,0.000077887285,0.00008238944],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004313745,0.00011329978,0.0001630464,0.00007356309,0.00018357352,0.000048841182,0.00007358133,0.00014916735,0.000048105765],"category_scores_gemma":[0.00021256614,0.00014246585,0.00003468688,0.0007611506,0.000036014353,0.0004925135,0.0000017735179,0.00027949671,0.0000025404763],"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":[0.000015597989,0.000017511908,0.0018030475,0.000009872223,0.000005699296,0.000017077307,0.0018496813,0.9852676,0.000014010234,0.0062959623,0.00024775398,0.0044562104],"study_design_scores_gemma":[0.0046818526,0.00017813312,0.0026053248,0.0025033138,0.00014124646,0.0000058782593,0.0047030766,0.10586507,0.0008589264,0.0011419386,0.8757672,0.0015480483],"about_ca_topic_score_codex":0.000042320324,"about_ca_topic_score_gemma":0.00071262504,"teacher_disagreement_score":0.8794025,"about_ca_system_score_codex":0.000095470474,"about_ca_system_score_gemma":0.00024423728,"threshold_uncertainty_score":0.5809589},"labels":[],"label_agreement":null},{"id":"W3196725569","doi":"10.1155/2021/9454873","title":"Exploring Influencing Factors of Intercity Mode Choice from View of Entire Travel Chain","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Education Department of Shaanxi Province; Natural Science Foundation of Shaanxi Province; National Natural Science Foundation of China","keywords":"Punctuality; Multinomial logistic regression; Mode choice; Mode (computer interface); Ticket; Odds; Travel behavior; Econometrics; Multinomial probit; Purchasing; Logit; Mixed logit; Computer science; Bayesian probability; Discrete choice; Transport engineering; Logistic regression; Operations research; Statistics; Economics; Business; Marketing; Engineering; Mathematics; Public transport","score_opus":0.0583845260259736,"score_gpt":0.31097282805418525,"score_spread":0.25258830202821164,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3196725569","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9775219,0.00025794504,0.021632316,0.000059753023,0.00035248132,0.000059437243,0.000052849868,0.000009085599,0.000054227177],"genre_scores_gemma":[0.9907656,0.0008744086,0.008197772,0.000010465154,0.00005444631,0.0000014337137,0.00007441849,0.000008714845,0.000012786499],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99853307,0.000070396105,0.00075838395,0.0001064247,0.00042176232,0.00010997373],"domain_scores_gemma":[0.99838287,0.00016929701,0.00074190187,0.000068791036,0.00056647265,0.00007065305],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020073136,0.00008743096,0.0002979948,0.00010914655,0.00007209963,0.000008913351,0.000101132966,0.000053654854,0.00004176686],"category_scores_gemma":[0.00011892007,0.00008895562,0.00014001045,0.00033386977,0.000050725997,0.00097335177,6.785412e-7,0.0001354677,1.4840346e-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":[0.0001648512,0.00019609905,0.21748747,0.00018500586,0.00012892325,0.000019551473,0.24309795,0.4427308,0.08044315,0.0013291779,0.0000030818353,0.014213906],"study_design_scores_gemma":[0.00063558854,0.00005204107,0.92146033,0.00057085353,0.000092763126,2.4815245e-7,0.03299882,0.0000467384,0.043504026,0.0002989498,0.00023653591,0.00010307973],"about_ca_topic_score_codex":0.0007975548,"about_ca_topic_score_gemma":0.0021915138,"teacher_disagreement_score":0.7039729,"about_ca_system_score_codex":0.000040779865,"about_ca_system_score_gemma":0.00017616733,"threshold_uncertainty_score":0.36275047},"labels":[],"label_agreement":null},{"id":"W3196796150","doi":"10.1155/2021/6937228","title":"Analysis of Spatial-Temporal Characteristics of Operations in Public Transport Networks Based on Multisource Data","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Headway; Stability (learning theory); Granularity; Public transport; Computer science; Bus network; Operational efficiency; Real-time computing; Measure (data warehouse); Transport engineering; Simulation; Computer network; Data mining; Engineering; System bus; Control bus","score_opus":0.028233420487387268,"score_gpt":0.30373837216977273,"score_spread":0.2755049516823855,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3196796150","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7001156,0.00005249011,0.29884955,0.0003368799,0.00018888952,0.000101236015,0.00028898392,0.000008552179,0.000057802008],"genre_scores_gemma":[0.98657024,0.00020949381,0.009710907,0.000041574025,0.00004223372,0.0000017179998,0.0033990107,0.000009703408,0.000015113936],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99789864,0.00013111312,0.0011344702,0.00017634445,0.000518377,0.00014106695],"domain_scores_gemma":[0.99814147,0.00015103158,0.00066531147,0.00022123088,0.00073796086,0.00008297756],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006837274,0.00009985965,0.00044676437,0.0004908118,0.00007690561,0.000013366953,0.0002155536,0.0000980451,0.00008617866],"category_scores_gemma":[0.00013931244,0.00010627946,0.00014381185,0.0014808452,0.00007505168,0.0005251144,8.173032e-7,0.00017095936,1.01736646e-7],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009914293,0.00019419333,0.31913543,0.000013949742,0.00007736013,0.000018471537,0.0027514326,0.6747888,0.000105751926,0.00019384034,0.0000010593043,0.0026206102],"study_design_scores_gemma":[0.00082076684,0.00006574267,0.9267285,0.0001047656,0.0004066539,1.6043121e-7,0.0014842591,0.069920436,0.0000791598,0.000006433593,0.00028976452,0.00009339645],"about_ca_topic_score_codex":0.0005300942,"about_ca_topic_score_gemma":0.030421276,"teacher_disagreement_score":0.607593,"about_ca_system_score_codex":0.000034289766,"about_ca_system_score_gemma":0.00043179296,"threshold_uncertainty_score":0.987271},"labels":[],"label_agreement":null},{"id":"W3197370333","doi":"","title":"Densification ou prolongement des réseaux de transport structurants ? Une recension des écrits sur les coûts et les bénéfices attendus","year":2021,"lang":"fr","type":"article","venue":"CIRANO Project Reports","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Political science; Humanities; Prioritization; Business; Art","score_opus":0.08582304792964818,"score_gpt":0.3386974182257121,"score_spread":0.2528743702960639,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3197370333","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9679148,0.016877318,0.0061180755,0.0039988183,0.0012762097,0.0008448188,0.00008138554,0.00027167777,0.0026168723],"genre_scores_gemma":[0.972998,0.010840788,0.010442915,0.00013145176,0.00018310233,0.00006672796,0.0007720233,0.00005898456,0.0045059896],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99582875,0.0006961395,0.0010308255,0.0009121212,0.00080065185,0.00073152606],"domain_scores_gemma":[0.9972366,0.00016004984,0.0006285255,0.00039739194,0.0013760179,0.00020142256],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0016141449,0.00039657595,0.00044387457,0.00016488657,0.0012763689,0.00016892717,0.00015232993,0.00050678605,0.00019029638],"category_scores_gemma":[0.00044212877,0.00042824986,0.00019223105,0.0013398678,0.0008257354,0.00075459055,0.00001425788,0.00039285532,0.0000051989396],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015524856,0.00055379525,0.73377043,0.0020693105,0.00023862992,0.0032117288,0.0841679,0.0054782857,0.0075728307,0.0036826965,0.0010463764,0.15805276],"study_design_scores_gemma":[0.0006860668,0.00013721347,0.9475623,0.0022787014,0.0005282211,0.0007212097,0.011349612,0.00041930805,0.0058890874,0.0029857766,0.02660558,0.000836943],"about_ca_topic_score_codex":0.018459732,"about_ca_topic_score_gemma":0.037527002,"teacher_disagreement_score":0.21379185,"about_ca_system_score_codex":0.00025416468,"about_ca_system_score_gemma":0.001447575,"threshold_uncertainty_score":0.99981695},"labels":[],"label_agreement":null},{"id":"W3197440965","doi":"10.1155/2021/8373703","title":"Can Monetary Incentive Regulate Boarding Passenger Distribution on the Metro Station Platform?","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","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":"Southeast University","keywords":"Incentive; Transport engineering; Distribution (mathematics); Occupancy; Payment; Willingness to pay; Logistic regression; Business; Engineering; Computer science; Finance; Economics; Microeconomics","score_opus":0.015061649258376577,"score_gpt":0.26833204775746083,"score_spread":0.2532703984990843,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3197440965","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9756741,0.00013427436,0.018634746,0.004491649,0.0005709151,0.00014434391,0.00010994534,0.000028391456,0.00021163616],"genre_scores_gemma":[0.99658173,0.0003073918,0.0020738028,0.00014132044,0.00013220412,0.0000039313672,0.0006531808,0.000010122403,0.00009630167],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9984889,0.00012728455,0.0004750093,0.00013160075,0.0006006988,0.00017648523],"domain_scores_gemma":[0.99846816,0.00022626697,0.00051454984,0.00008360343,0.0006272249,0.00008016719],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051890826,0.00010302203,0.00016052423,0.000070699134,0.0004152169,0.00005064359,0.00008839035,0.000071386254,0.000049665847],"category_scores_gemma":[0.0001461888,0.000084213236,0.00011519961,0.0004705854,0.000060806546,0.00061830203,6.1186813e-7,0.00022358636,0.0000016925225],"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":[0.00064218126,0.00019256347,0.019445905,0.00003713176,0.00017632848,0.00014290224,0.049318757,0.85705423,0.0030332387,0.054106034,0.00070774864,0.015142954],"study_design_scores_gemma":[0.0015541585,0.00019010165,0.92560494,0.00025744113,0.0002395184,0.0000024916471,0.04775852,0.000317443,0.007718818,0.007637262,0.008409482,0.00030984447],"about_ca_topic_score_codex":0.00006666388,"about_ca_topic_score_gemma":0.0013150076,"teacher_disagreement_score":0.90615904,"about_ca_system_score_codex":0.00015167215,"about_ca_system_score_gemma":0.00020457915,"threshold_uncertainty_score":0.3434116},"labels":[],"label_agreement":null},{"id":"W3197630606","doi":"10.1155/2021/4738177","title":"Expression and Validation of Online Bus Headways considering Passenger Crowding","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","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":"Ministry of Education, India; Chang'an University; Ministry of Education of the People's Republic of China; Natural Science Foundation of Zhejiang Province; National Natural Science Foundation of China","keywords":"Crowding; Transport engineering; Headway; Schedule; Engineering; Index (typography); Automotive engineering; Simulation; Computer science","score_opus":0.022552311389896558,"score_gpt":0.3063999595674229,"score_spread":0.28384764817752633,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3197630606","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98263526,0.0003949429,0.016149344,0.00041034466,0.0002472238,0.000052737974,0.000014091894,0.000014086704,0.00008195873],"genre_scores_gemma":[0.95654887,0.0007081548,0.042549606,0.0000218344,0.00007001365,6.391263e-7,0.00006614073,0.000007187678,0.000027583392],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9989795,0.000061297425,0.00047582513,0.00009202208,0.00029890795,0.00009241811],"domain_scores_gemma":[0.998787,0.00012407669,0.00044785222,0.000044589517,0.0005308084,0.000065673994],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025380653,0.00006386972,0.00016623415,0.00009288181,0.000121809084,0.00001776946,0.000035844816,0.000059452308,0.000030420637],"category_scores_gemma":[0.00011761398,0.00006577524,0.000050713264,0.00020280611,0.00004874063,0.00059371325,6.282397e-7,0.00010102227,1.278251e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024876822,0.00022642726,0.0635045,0.0001715188,0.000052798343,0.00009621902,0.06256265,0.47235325,0.3878675,0.0030118562,0.00005169895,0.009852801],"study_design_scores_gemma":[0.003429875,0.0001934907,0.5917726,0.0013327573,0.00021701961,0.00001903511,0.052407324,0.0001325468,0.3429395,0.0023753934,0.0048017083,0.000378718],"about_ca_topic_score_codex":0.00001574169,"about_ca_topic_score_gemma":0.00018119489,"teacher_disagreement_score":0.52826816,"about_ca_system_score_codex":0.000020282005,"about_ca_system_score_gemma":0.00015228463,"threshold_uncertainty_score":0.26822364},"labels":[],"label_agreement":null},{"id":"W3199693625","doi":"10.1287/ijoc.2021.1091","title":"Predicting Tactical Solutions to Operational Planning Problems under Imperfect Information","year":2022,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Université de Montréal","funders":"","keywords":"Computer science; Intersection (aeronautics); Stochastic programming; Context (archaeology); Mathematical optimization; Set (abstract data type); Probabilistic logic; Artificial intelligence; Machine learning; Mathematics","score_opus":0.0696493471260677,"score_gpt":0.21875092252318984,"score_spread":0.14910157539712215,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3199693625","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6913661,0.0000069062794,0.2950138,0.00094203954,0.00028283423,0.0003031098,0.000039974784,0.00022350687,0.011821751],"genre_scores_gemma":[0.9986598,0.0000032487505,0.00023757666,0.00031223753,0.00003999696,0.0000034303803,0.0000972563,0.0000040543537,0.0006424367],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99927646,0.00012147996,0.00011222612,0.00014668705,0.00013720487,0.00020591922],"domain_scores_gemma":[0.9996029,0.00007969886,0.00005366354,0.00007079135,0.00008692839,0.00010602828],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.00039373964,0.000058798025,0.000056734403,0.00014430113,0.0019701503,0.000059711194,0.00013558027,0.000035879224,0.0003044154],"category_scores_gemma":[0.000060449638,0.00007634527,0.00003200759,0.0006469697,0.00004161085,0.0008057338,0.00003290132,0.00014376253,0.00003084045],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015744212,0.000016806498,0.032328676,0.0000017450187,0.000007864308,0.0000024967865,0.0060535125,0.83067656,0.000006663365,0.13052177,0.00033975256,0.000028410703],"study_design_scores_gemma":[0.0015423173,0.00030823937,0.094409764,0.000041601317,0.00011114458,0.000006531582,0.07377203,0.75787765,0.00001171774,0.0044232043,0.06677728,0.0007185365],"about_ca_topic_score_codex":0.0005986359,"about_ca_topic_score_gemma":0.0001722338,"teacher_disagreement_score":0.30729368,"about_ca_system_score_codex":0.00021222961,"about_ca_system_score_gemma":0.00019446103,"threshold_uncertainty_score":0.99932915},"labels":[],"label_agreement":null},{"id":"W3200358703","doi":"10.1155/2021/9219176","title":"Modeling Intercity Travel Mode Choice with Data Balance Changes: A Comparative Analysis of Bayesian Logit Model and Artificial Neural Networks","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Education Department of Shaanxi Province; Natural Science Foundation of Shaanxi Province; National Natural Science Foundation of China","keywords":"Multinomial logistic regression; Artificial neural network; Computer science; Mode choice; Multilayer perceptron; Bayesian probability; Regression analysis; Heuristic; Logistic regression; Machine learning; Artificial intelligence; Engineering; Transport engineering; Public transport","score_opus":0.06421963231824378,"score_gpt":0.34400550098923477,"score_spread":0.279785868670991,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3200358703","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4550075,0.00011490191,0.5445227,0.00016794191,0.000040537343,0.000049880986,0.00007077789,0.0000062700965,0.000019504942],"genre_scores_gemma":[0.97858536,0.0002456397,0.020627618,0.00004183113,0.00005162455,0.0000015400419,0.00043245105,0.000005969163,0.000007955377],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987701,0.00006836189,0.0004740137,0.00020372652,0.0003413538,0.00014239934],"domain_scores_gemma":[0.9987651,0.000083685656,0.00040378328,0.00012750526,0.0005318313,0.000088101646],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025568178,0.00010512696,0.00038670775,0.00016277713,0.00012955278,0.00002953557,0.00014264438,0.00006430691,0.000007171795],"category_scores_gemma":[0.000026472386,0.00009781553,0.000056435878,0.0007172279,0.00007945811,0.00070691004,0.0000017378121,0.000169101,1.8682636e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023736808,0.000051492192,0.005356882,0.000012080058,0.00018232035,0.000007811483,0.018636864,0.97407943,0.00023686653,0.00039930103,0.000001522291,0.00079807534],"study_design_scores_gemma":[0.0003721961,0.000050752144,0.02308693,0.00006157824,0.00066217483,7.212851e-7,0.007698026,0.9677999,0.000048550934,0.00011616125,0.000003617797,0.000099374745],"about_ca_topic_score_codex":0.000181362,"about_ca_topic_score_gemma":0.036449995,"teacher_disagreement_score":0.5238951,"about_ca_system_score_codex":0.000022776972,"about_ca_system_score_gemma":0.000115990486,"threshold_uncertainty_score":0.98113227},"labels":[],"label_agreement":null},{"id":"W3201646768","doi":"10.18280/jesa.540413","title":"Real-Time Traffic Controlling System Using Multi-Agent Technology","year":2021,"lang":"en","type":"article","venue":"Journal Européen des Systèmes Automatisés","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"NetLogo; Computer science; Multi-agent system; Fuzzy logic; Scheduling (production processes); Distributed computing; Real-time computing; The Internet; Artificial intelligence; Engineering; Operating system; Operations management","score_opus":0.027504446087147246,"score_gpt":0.2899545432317101,"score_spread":0.26245009714456285,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3201646768","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9475359,0.0010602754,0.04632104,0.00033084766,0.000835626,0.00022033494,0.000014535686,0.0009749488,0.0027065],"genre_scores_gemma":[0.93056154,0.00031617537,0.06733396,0.000020249927,0.00023252657,0.0000039097044,0.0000085294205,0.000036600177,0.001486531],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99770314,0.00054666406,0.0006282064,0.00023263667,0.0004639657,0.00042540618],"domain_scores_gemma":[0.9985854,0.00011016057,0.00042809188,0.00015056576,0.0005359003,0.00018985549],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0008687933,0.00016327524,0.00035442773,0.0002681548,0.0013712615,0.00033716366,0.00021171532,0.00017221297,0.00011978615],"category_scores_gemma":[0.000268789,0.00016133874,0.00013068588,0.0007727366,0.00015984391,0.00030558035,0.000013041766,0.00023980276,0.000078204415],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006890361,0.00041775862,0.016011897,0.0005163511,0.0006236403,0.0043720137,0.031418975,0.78900015,0.030176187,0.0156647,0.0006391668,0.11109026],"study_design_scores_gemma":[0.003269394,0.00011576642,0.07857893,0.0024693746,0.00043959497,0.0019697489,0.02891768,0.87918746,0.00047603107,0.0002287271,0.0034381847,0.000909133],"about_ca_topic_score_codex":0.00009669169,"about_ca_topic_score_gemma":0.00008416056,"teacher_disagreement_score":0.11018113,"about_ca_system_score_codex":0.00044455912,"about_ca_system_score_gemma":0.0005335509,"threshold_uncertainty_score":0.99992883},"labels":[],"label_agreement":null},{"id":"W3201802353","doi":"10.1061/jtepbs.0000606","title":"Driver Behavior on Freeway Entrance Ramp Terminals","year":2021,"lang":"en","type":"article","venue":"Journal of Transportation Engineering Part A Systems","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Merge (version control); Acceleration; Simulation; Operating speed; Computer science; Engineering; Physics","score_opus":0.016205065780371845,"score_gpt":0.2620923129973888,"score_spread":0.24588724721701694,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3201802353","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94917697,0.00077425543,0.044179205,0.0003374311,0.0044835885,0.00024818405,0.00006901787,0.00010400029,0.0006273395],"genre_scores_gemma":[0.9978014,0.00015389429,0.0009950363,0.000025389712,0.00037724597,0.000010956997,0.00004415463,0.000015430202,0.0005764693],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99854434,0.000058775277,0.00057007954,0.00011575106,0.000532244,0.00017879109],"domain_scores_gemma":[0.9990471,0.00008673996,0.00028059317,0.000086088374,0.00036307098,0.00013639202],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040304722,0.00010664928,0.0002257457,0.00011884505,0.00010817624,0.00006651486,0.00010384495,0.00009418656,0.00006449143],"category_scores_gemma":[0.000046193094,0.000109699184,0.00012393983,0.00026214207,0.000021801208,0.00026648727,3.0766722e-7,0.00016254037,0.000006024497],"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":[0.000039750114,0.00018392933,0.04473267,0.00011097977,0.000082376704,0.000555902,0.014996403,0.9290718,0.0017769439,0.006828917,0.0011614716,0.00045887486],"study_design_scores_gemma":[0.0026543564,0.00027927096,0.5192994,0.0021789765,0.00049777643,0.000071279625,0.005953034,0.0025660587,0.0041844593,0.000014307389,0.4614443,0.0008567669],"about_ca_topic_score_codex":0.00003324881,"about_ca_topic_score_gemma":0.000062678686,"teacher_disagreement_score":0.92650574,"about_ca_system_score_codex":0.000052772048,"about_ca_system_score_gemma":0.00015502883,"threshold_uncertainty_score":0.44734028},"labels":[],"label_agreement":null},{"id":"W3203938112","doi":"10.1155/2021/8599256","title":"Improving the Service Quality of Public Transit with Exclusive Bus Lanes: A Perspective from Passenger Satisfaction","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Public transport; Transport engineering; Service quality; Business; Service (business); Quality (philosophy); China; Transit (satellite); Bus rapid transit; Marketing; Engineering; Geography","score_opus":0.024213820864703573,"score_gpt":0.3017596393500049,"score_spread":0.2775458184853013,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3203938112","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9350894,0.00031597097,0.061049692,0.0029544241,0.00019978757,0.00012649101,0.000046652734,0.000019398516,0.00019819268],"genre_scores_gemma":[0.9911098,0.00018073246,0.008421319,0.00008637298,0.000102825026,0.0000032736325,0.00006308201,0.000010018181,0.00002259109],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.998443,0.00023227338,0.0005039293,0.00014259349,0.0005472064,0.0001309877],"domain_scores_gemma":[0.99716187,0.0002796667,0.000778607,0.00009471016,0.0016158845,0.00006927161],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041439454,0.000094814306,0.00021885129,0.00006622611,0.00022441873,0.000035604033,0.00008995732,0.000070815375,0.00007896334],"category_scores_gemma":[0.00008964841,0.00007235054,0.00008541902,0.0005395108,0.00006940585,0.00078570575,5.0400325e-7,0.00019476665,4.5827687e-7],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012691508,0.00026324714,0.14752764,0.00012822806,0.00044069753,0.00007759548,0.42706442,0.3324357,0.051755507,0.025092596,0.0000104622695,0.01393478],"study_design_scores_gemma":[0.0011029186,0.00006161532,0.8191554,0.00007640892,0.00012585225,0.000001869852,0.17683794,0.00003636232,0.0014595629,0.00081502926,0.00021685656,0.00011021211],"about_ca_topic_score_codex":0.002196458,"about_ca_topic_score_gemma":0.028984684,"teacher_disagreement_score":0.67162776,"about_ca_system_score_codex":0.00006826058,"about_ca_system_score_gemma":0.0004298656,"threshold_uncertainty_score":0.9887338},"labels":[],"label_agreement":null},{"id":"W3204424884","doi":"10.1155/2021/6684795","title":"An Exact Solution Approach for the Bus Line Planning Problem with Integrated Passenger Routing","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Vlaamse regering; Fonds Wetenschappelijk Onderzoek","keywords":"Benchmark (surveying); Mathematical optimization; Routing (electronic design automation); Computer science; Representation (politics); Network planning and design; Vehicle routing problem; Line (geometry); Mathematics; Computer network","score_opus":0.022891723648656777,"score_gpt":0.3005134954476564,"score_spread":0.2776217717989996,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3204424884","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13531741,0.00022450242,0.86342794,0.00046976318,0.00015090014,0.00023617501,0.0000139094245,0.000037042515,0.00012233776],"genre_scores_gemma":[0.82677966,0.00007556049,0.17258225,0.000041996307,0.00016364246,0.0000118770895,0.0002786526,0.000013380554,0.000052991018],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9988398,0.000078826175,0.00041701065,0.00014084253,0.00034365695,0.00017984638],"domain_scores_gemma":[0.9983307,0.00015614979,0.00046813328,0.00007286458,0.00089915324,0.00007299514],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006307399,0.000099543046,0.00015776805,0.00006345079,0.00045736769,0.00006625861,0.00010212715,0.00007077478,0.000008847545],"category_scores_gemma":[0.000050031616,0.000070232876,0.00006863947,0.00038577983,0.000052229403,0.0007731348,2.7032374e-7,0.00019399362,1.0789831e-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":[0.00032007997,0.000085831074,0.004555973,0.000019264355,0.000044233628,0.000011141333,0.02411461,0.9624727,0.0015438448,0.0009564064,0.000016818121,0.005859114],"study_design_scores_gemma":[0.012561836,0.0024841956,0.49574003,0.0014637671,0.0016836396,0.000062275416,0.36405316,0.08898645,0.0081086885,0.0015086109,0.021891693,0.00145568],"about_ca_topic_score_codex":0.000047675072,"about_ca_topic_score_gemma":0.00045560324,"teacher_disagreement_score":0.8734862,"about_ca_system_score_codex":0.000049999737,"about_ca_system_score_gemma":0.00031234848,"threshold_uncertainty_score":0.35177487},"labels":[],"label_agreement":null},{"id":"W3205049172","doi":"10.1155/2021/1422381","title":"Modeling Tourists’ Departure Time considering the Influence of Multisource Traffic Information","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","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":"Natural Science Foundation of Jiangsu Province; Government of Jiangsu Province; Ministry of Education","keywords":"Tourism; Multinomial logistic regression; Travel behavior; Traffic congestion; Transport engineering; Travel time; Business; Mixed logit; Computer science; Marketing; Geography; Logistic regression; Engineering","score_opus":0.008067473245959285,"score_gpt":0.2526288561534946,"score_spread":0.24456138290753535,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3205049172","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9759514,0.00015499098,0.023114825,0.0004833248,0.00011489233,0.000084680076,0.000010302731,0.000018708188,0.00006689274],"genre_scores_gemma":[0.99008393,0.00022187334,0.009505316,0.000077060555,0.00004024066,0.000001525064,0.00004128257,0.000005187833,0.000023554889],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9987506,0.00006459037,0.0006101375,0.00005876429,0.00040493865,0.00011094543],"domain_scores_gemma":[0.99847955,0.000111209476,0.0004404156,0.000066653,0.000849402,0.000052800984],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034950898,0.00007103018,0.00015052932,0.00007384809,0.00018632233,0.000028978127,0.00009422955,0.00006473431,0.000019774114],"category_scores_gemma":[0.00017284456,0.000060890266,0.00008382453,0.00029366414,0.000055344455,0.0012045417,6.1155515e-7,0.00015326467,0.0000015667572],"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":[0.000046430916,0.0000152233915,0.0005698877,0.000018354804,0.000014922807,0.000006467187,0.040014047,0.9560883,0.0007670232,0.00020965957,0.000008238536,0.00224144],"study_design_scores_gemma":[0.0104446625,0.0005252448,0.4763547,0.0026170765,0.0010493079,0.00009475343,0.23652828,0.23380591,0.0072762584,0.0019499934,0.027780581,0.001573248],"about_ca_topic_score_codex":0.00002692305,"about_ca_topic_score_gemma":0.00024285191,"teacher_disagreement_score":0.7222824,"about_ca_system_score_codex":0.000027005886,"about_ca_system_score_gemma":0.00023146028,"threshold_uncertainty_score":0.2483033},"labels":[],"label_agreement":null},{"id":"W3205200888","doi":"10.1155/2021/5120401","title":"Determining the Level of Service Scale of Public Transport System considering the Distribution of Service Quality","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","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":"Beijing; Service (business); Public transport; Crowding; Sample (material); Service quality; Scale (ratio); Transport engineering; Level of service; Computer science; Operations research; Quality (philosophy); Distribution (mathematics); Statistics; Mathematics; Engineering; Geography; Business; China; Marketing","score_opus":0.08715744166075669,"score_gpt":0.33178654204283775,"score_spread":0.24462910038208108,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3205200888","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96552724,0.0001158908,0.031953122,0.0017142172,0.00023017431,0.00014009315,0.0002271857,0.000011116285,0.00008095042],"genre_scores_gemma":[0.9972731,0.000067065,0.00237814,0.00006186475,0.000036532605,0.0000026780024,0.00016367578,0.000008217785,0.00000873245],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9976602,0.0002345765,0.0011974031,0.000106973865,0.0006587975,0.00014202952],"domain_scores_gemma":[0.995267,0.00033161088,0.0015184128,0.00014198638,0.0026855338,0.00005549621],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013919388,0.00009662864,0.00032307245,0.000048782018,0.00019730636,0.000010987528,0.00020337965,0.000077918165,0.000010745064],"category_scores_gemma":[0.00009507802,0.00007176096,0.0001420822,0.00079124345,0.00011225736,0.0004908578,0.0000011820695,0.00015078824,1.5081378e-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":[0.00031844585,0.0003025663,0.40352026,0.0016204034,0.0002158967,0.000010899858,0.13482587,0.42670643,0.011778171,0.015409662,0.000009689427,0.005281698],"study_design_scores_gemma":[0.00079067185,0.0000329541,0.93992335,0.00035745333,0.00015068219,0.0000023690375,0.05180484,0.000103373844,0.0064264075,0.000111324625,0.0002158556,0.00008071915],"about_ca_topic_score_codex":0.00044897266,"about_ca_topic_score_gemma":0.010062681,"teacher_disagreement_score":0.5364031,"about_ca_system_score_codex":0.0000437271,"about_ca_system_score_gemma":0.0004346755,"threshold_uncertainty_score":0.5615212},"labels":[],"label_agreement":null},{"id":"W3205353387","doi":"10.22004/ag.econ.314690","title":"Demand Model Developments to Assess High Speed Passenger Train Markets on Windsor to Quebec City Corridor - An Update","year":2021,"lang":"en","type":"article","venue":"AgEcon Search (University of Minnesota, USA)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Windsor; Transport engineering; Passenger transport; Aeronautics; Business; Computer science; Operations research; Engineering; Environmental science","score_opus":0.06402305284490961,"score_gpt":0.29742078551658807,"score_spread":0.23339773267167846,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3205353387","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97972614,0.0000024846731,0.012819732,0.0035489423,0.00010021511,0.00023703752,0.000108872635,0.000037652742,0.0034189497],"genre_scores_gemma":[0.9630947,0.000020145686,0.017957041,0.000428108,0.000026029626,3.1699426e-7,0.00016450958,0.000010007226,0.018299125],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.9982761,0.0002851336,0.00013743153,0.00039723926,0.0005598556,0.00034425117],"domain_scores_gemma":[0.9988817,0.00011086489,0.000059576596,0.00018783234,0.00034938147,0.00041063252],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007711412,0.00011947861,0.00021033204,0.00020868186,0.0004476829,0.000060427148,0.0003134762,0.000114486975,0.0011020108],"category_scores_gemma":[0.00007437844,0.00015858522,0.00005032606,0.00055659213,0.000087923094,0.0004866435,0.000036834237,0.0001309242,0.00012014424],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0028216974,0.002093062,0.16186601,0.00020562563,0.0005113855,0.0014453132,0.35535467,0.31397718,0.011750926,0.022546906,0.054843012,0.07258422],"study_design_scores_gemma":[0.001231555,0.000110662295,0.95295334,0.00009044801,0.00004194678,0.0000010483107,0.024418328,0.006418585,0.0013607903,0.00008882533,0.012791624,0.0004928709],"about_ca_topic_score_codex":0.0064072376,"about_ca_topic_score_gemma":0.086717874,"teacher_disagreement_score":0.7910873,"about_ca_system_score_codex":0.00014042418,"about_ca_system_score_gemma":0.0005878669,"threshold_uncertainty_score":0.9998111},"labels":[],"label_agreement":null},{"id":"W3205584321","doi":"10.32920/ryerson.14644281.v1","title":"HOV access location analysis at Erin Mills Parkway and Highway 403","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Federal Highway Administration; Medical Research Council; U.S. Department of Transportation","keywords":"VisSim; Transport engineering; Position (finance); Occupancy; Reliability (semiconductor); Travel time; Computer science; Microsimulation; Engineering; Civil engineering; Business","score_opus":0.03099872622457393,"score_gpt":0.3303647201369816,"score_spread":0.29936599391240765,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3205584321","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8388448,0.0013493674,0.12877128,0.002662303,0.0008888863,0.0005072348,0.00006841837,0.00041749445,0.026490256],"genre_scores_gemma":[0.9881195,0.0013057031,0.0023854487,0.00013947059,0.000080092315,0.000026710946,0.0013343456,0.000009181758,0.006599581],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99860907,0.00015703039,0.00026373425,0.00045280874,0.00034784694,0.00016951836],"domain_scores_gemma":[0.99912345,0.000090896625,0.00017520139,0.00022052498,0.0002771867,0.00011272485],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00038263545,0.00013909268,0.0002645843,0.00020137764,0.00034830012,0.00044125246,0.00020798839,0.00027339472,0.00091796514],"category_scores_gemma":[0.00006977653,0.00014142718,0.000095865405,0.0008120668,0.00009713307,0.00020669262,0.00008770518,0.00014133271,0.000006319851],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002216714,0.000076459524,0.4010984,0.00015793442,0.000840314,0.000013105615,0.029192809,0.5604103,0.00001926184,0.0023921353,0.002408834,0.0033682957],"study_design_scores_gemma":[0.00033193623,0.000011449689,0.9599629,0.00013358127,0.0015659864,3.6345998e-7,0.004960879,0.018781131,0.00026656268,0.00033310498,0.012816743,0.00083535776],"about_ca_topic_score_codex":0.007692733,"about_ca_topic_score_gemma":0.03893556,"teacher_disagreement_score":0.55886453,"about_ca_system_score_codex":0.00009623342,"about_ca_system_score_gemma":0.0002044159,"threshold_uncertainty_score":0.99999535},"labels":[],"label_agreement":null},{"id":"W3206367836","doi":"10.22215/etd/2018-12910","title":"A Model for Calculating Negative Externalities Experienced as a Result of Marginal Increases In Public Transit and Ridesharing Services on Arterial and Collector Road Segments","year":2018,"lang":"en","type":"dissertation","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Transit (satellite); VisSim; Transport engineering; Queueing theory; Public transport; Service (business); Externality; Level of service; Marginal cost; Computer science; Throughput; Traffic congestion; Engineering; Business; Intersection (aeronautics); Economics; Microeconomics; Computer network; Telecommunications","score_opus":0.0374102835157106,"score_gpt":0.3280232998704745,"score_spread":0.2906130163547639,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3206367836","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9965288,0.000028720264,0.0004248004,0.000024341392,0.00006776753,0.00047012258,0.000101708916,0.000025048397,0.002328704],"genre_scores_gemma":[0.9943103,0.000038061993,0.0032104456,0.00003145838,0.00005220213,0.00010618876,0.00034352564,0.000012487037,0.001895284],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.99886566,0.00005754084,0.00033262587,0.00028815883,0.0002676245,0.00018840945],"domain_scores_gemma":[0.9993541,0.00011772379,0.00021999328,0.00005166444,0.00018477139,0.000071709306],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024516438,0.00014896499,0.0002428911,0.00018779696,0.00029353338,0.00014360655,0.00008031597,0.00015285943,0.00003668302],"category_scores_gemma":[0.000105158404,0.00014913155,0.000030327163,0.00016252344,0.0000842523,0.0003108311,0.0000030642811,0.00005822668,1.6103786e-7],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.005490084,0.00018253061,0.007955977,0.00085683464,0.00010130545,0.0000037581067,0.97298586,0.0023564443,0.0017626408,0.0055848598,0.000030149155,0.0026895814],"study_design_scores_gemma":[0.007798069,0.00058485137,0.13070178,0.003451464,0.00015655522,0.0000013176319,0.48186556,0.3680447,0.0024385005,0.0037333267,0.00007498783,0.0011488751],"about_ca_topic_score_codex":0.017665511,"about_ca_topic_score_gemma":0.05176814,"teacher_disagreement_score":0.49112028,"about_ca_system_score_codex":0.000036039433,"about_ca_system_score_gemma":0.00018672006,"threshold_uncertainty_score":0.9888759},"labels":[],"label_agreement":null},{"id":"W3207235478","doi":"10.1155/2021/5590780","title":"A Hybrid Machine Learning and Optimization Model to Minimize the Total Cost of BRT Brake Components","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","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":"Amirkabir University of Technology","keywords":"Reliability (semiconductor); Brake; Bus rapid transit; Component (thermodynamics); Computer science; Transport engineering; Reliability engineering; Public transport; Service quality; Service (business); Automotive engineering; Engineering; Business","score_opus":0.016791109576065024,"score_gpt":0.28178667496816295,"score_spread":0.26499556539209795,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3207235478","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.35256925,0.0002255654,0.64589024,0.00086387485,0.00015953816,0.00013851194,0.000037624668,0.000012951056,0.00010245883],"genre_scores_gemma":[0.9031221,0.0004900778,0.09595222,0.00005958216,0.000040557632,0.0000027887602,0.00012285628,0.000009847115,0.00019995237],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988843,0.00008197716,0.00044851616,0.00010429167,0.0003690152,0.00011190576],"domain_scores_gemma":[0.9989114,0.00010425105,0.00037356277,0.00004534293,0.0004757286,0.000089727306],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003153758,0.00007774099,0.00017437864,0.0000790394,0.00020828437,0.000025714404,0.00006264726,0.000034739292,0.000026417583],"category_scores_gemma":[0.00010443791,0.00006829307,0.000061527164,0.00021985521,0.000052239182,0.00033572945,0.000001205116,0.00013946442,2.6839797e-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":[0.00015168254,0.000037953905,0.0033518872,0.000012124379,0.00001818043,0.000008577615,0.009647968,0.98344666,0.001402513,0.00031685308,0.000009038905,0.0015965587],"study_design_scores_gemma":[0.007202413,0.00035469423,0.3220369,0.00071039394,0.0005389325,0.00004645394,0.023279855,0.6365471,0.0042727157,0.0008639344,0.0034317253,0.0007148878],"about_ca_topic_score_codex":0.00003592778,"about_ca_topic_score_gemma":0.0000945962,"teacher_disagreement_score":0.55055285,"about_ca_system_score_codex":0.000024066592,"about_ca_system_score_gemma":0.00011970019,"threshold_uncertainty_score":0.27849105},"labels":[],"label_agreement":null},{"id":"W3207375018","doi":"10.1155/2021/6934998","title":"Identifying the Service Areas and Travel Demand of the Commuter Customized Bus Based on Mobile Phone Signaling Data","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Public transport; Transport engineering; Service (business); Mobile phone; Computer science; Traffic congestion; Point of interest; Operations research; Business; Telecommunications; Engineering; Marketing","score_opus":0.028743712421611337,"score_gpt":0.30361694492473834,"score_spread":0.274873232503127,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3207375018","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9617959,0.00044391275,0.035660673,0.0014051887,0.00035875768,0.00018797754,0.00005984652,0.000008613887,0.000079128244],"genre_scores_gemma":[0.99362844,0.00020685846,0.00575183,0.00023560376,0.00005086759,0.000002501946,0.00009272457,0.000007718086,0.0000234576],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9987731,0.00017175374,0.00041934705,0.00011035872,0.00043100482,0.00009446427],"domain_scores_gemma":[0.99860686,0.00030219037,0.00047588823,0.00018541924,0.0003878423,0.000041777566],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00070527196,0.00007380291,0.00016350533,0.000043836233,0.00030152718,0.000035562658,0.00024187335,0.000046215602,0.00002578731],"category_scores_gemma":[0.00005991627,0.000049834394,0.00005703617,0.0003629632,0.00006645676,0.00037916497,0.0000021111032,0.00016816158,3.1176762e-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":[0.00037884945,0.00010783596,0.00839547,0.00007600278,0.000050938565,0.000013720044,0.036796555,0.9420359,0.009956728,0.00033245023,0.00004089343,0.0018146198],"study_design_scores_gemma":[0.010304656,0.00013985005,0.8545531,0.0017990114,0.0009011842,0.000011123708,0.09049194,0.010405114,0.026578553,0.0013392512,0.0030267865,0.00044941812],"about_ca_topic_score_codex":0.00005331495,"about_ca_topic_score_gemma":0.0009975903,"teacher_disagreement_score":0.93163085,"about_ca_system_score_codex":0.000016248461,"about_ca_system_score_gemma":0.00017525563,"threshold_uncertainty_score":0.23191339},"labels":[],"label_agreement":null},{"id":"W3208872071","doi":"10.32920/ryerson.14665590.v1","title":"Development of an urban travel demand model of the Town of Oakville","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"University of Toronto","keywords":"Transport engineering; Trip generation; TRIPS architecture; Transportation planning; Geographic information system; Trip distribution; Demand forecasting; Software; Demand management; Travel behavior; Mode (computer interface); Microcomputer; Computer science; Operations research; Engineering; Geography; Telecommunications; Economics","score_opus":0.039593460889615247,"score_gpt":0.284864200710571,"score_spread":0.24527073982095576,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3208872071","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9028385,0.000048439466,0.07326832,0.00007803762,0.00012796863,0.00018634462,0.000027260056,0.000012554611,0.02341253],"genre_scores_gemma":[0.9227435,0.000020072366,0.07596527,0.000010408902,0.000008455746,0.0000040893297,0.000049302864,0.0000039793513,0.0011949054],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990474,0.000051889783,0.00036470062,0.00013333764,0.00032990534,0.00007274638],"domain_scores_gemma":[0.999324,0.000019415473,0.0002610852,0.00015528475,0.00020976948,0.000030407436],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035267073,0.00006672806,0.00018491468,0.00003498633,0.00007936704,0.000008409629,0.00022155678,0.00013381353,0.00010053397],"category_scores_gemma":[0.000024505649,0.000051460058,0.000064132226,0.00010337204,0.00009342576,0.00004204969,0.000026565336,0.00007729429,5.963914e-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":[0.000014451194,0.00018256874,0.007741075,0.0001760438,0.00006656375,1.6693166e-7,0.28573126,0.6948014,0.0015283127,0.008465583,0.0000923798,0.0012002201],"study_design_scores_gemma":[0.0016358311,0.000066682915,0.27549753,0.0019022238,0.0004236069,3.1387827e-7,0.19029121,0.29260018,0.23220544,0.0031886708,0.0008641861,0.0013241487],"about_ca_topic_score_codex":0.00056279736,"about_ca_topic_score_gemma":0.0024271458,"teacher_disagreement_score":0.4022012,"about_ca_system_score_codex":0.000016670088,"about_ca_system_score_gemma":0.0009501706,"threshold_uncertainty_score":0.20984803},"labels":[],"label_agreement":null},{"id":"W3208986819","doi":"10.5281/zenodo.1133858","title":"A Collection Of Public Transport Network Data Sets For 27 Cities","year":2017,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Public transport; Data collection; Transport engineering; Business; Computer science; Geography; Engineering; Mathematics; Statistics","score_opus":0.12218677708134175,"score_gpt":0.33277350078371615,"score_spread":0.2105867237023744,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3208986819","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000375969,0.0001064999,0.0050914357,0.000634088,0.00054910145,0.0008247105,0.9834718,0.0003027994,0.008981936],"genre_scores_gemma":[0.0018330995,0.0006737041,0.00042268293,0.000036671645,0.0003754831,1.555879e-7,0.9947746,0.00041995922,0.0014636684],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9977251,0.0003894582,0.0003945352,0.0004952298,0.00057012064,0.00042554858],"domain_scores_gemma":[0.9973781,0.0000739987,0.00048829283,0.00092256215,0.0009732122,0.00016384058],"candidate_categories":["sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0023185539,0.00017044709,0.00027633857,0.0002535056,0.0056126355,0.00063986366,0.0023726432,0.0002450039,0.0038598333],"category_scores_gemma":[0.0013004129,0.00019891579,0.000071044386,0.0004361949,0.00041055353,0.0005355014,0.00020358988,0.0002541742,0.0002976461],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005274362,0.00006186565,0.000008214994,0.0001516051,0.00006558412,0.0000024529436,0.0010281682,0.00021808285,9.965185e-7,0.00050786807,0.9944634,0.0034390525],"study_design_scores_gemma":[0.00036102615,0.000100204285,0.00021680776,0.000086101616,0.00007817497,0.000003254742,0.00045019973,0.0001294496,0.0000010120527,0.00012146965,0.9982574,0.00019492555],"about_ca_topic_score_codex":0.00052546104,"about_ca_topic_score_gemma":0.0003686645,"teacher_disagreement_score":0.011302743,"about_ca_system_score_codex":0.000103079845,"about_ca_system_score_gemma":0.00006399279,"threshold_uncertainty_score":0.99705076},"labels":[],"label_agreement":null},{"id":"W3209669863","doi":"10.3390/info12110447","title":"Data-Driven Multi-Agent Vehicle Routing in a Congested City","year":2021,"lang":"en","type":"article","venue":"Information","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Athabasca University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Traffic congestion; Routing (electronic design automation); Task (project management); Path (computing); Real-time data; Real-time computing; Floating car data; Computer network; Travel time; Transport engineering; Engineering; World Wide Web","score_opus":0.07407957448572013,"score_gpt":0.3320050505975773,"score_spread":0.2579254761118572,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3209669863","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92152643,0.000025043355,0.05524067,0.0015909373,0.00045823678,0.00029091377,0.00014677002,0.00022452544,0.02049648],"genre_scores_gemma":[0.9952291,0.000016907123,0.0033499787,0.00019441063,0.000019321535,0.000003261533,0.0010716066,0.0000015172346,0.00011388142],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99942195,0.00005334007,0.00020230595,0.00006142459,0.00015873984,0.00010224276],"domain_scores_gemma":[0.99964935,0.00003425416,0.00007560734,0.00010434019,0.000105342195,0.000031104573],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032688715,0.00003359961,0.000049211103,0.000043772616,0.0001398191,0.00008233829,0.00009227242,0.000047945927,0.00005774962],"category_scores_gemma":[0.00024010544,0.0000383839,0.00000919589,0.00026043988,0.000019134352,0.0015250668,0.000012195087,0.000060187594,0.000038857834],"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":[0.000032177843,0.00015515627,0.5515302,0.000050160605,0.000025676838,0.000019759664,0.24848934,0.13136956,0.00009790641,0.017518131,0.0013999634,0.04931197],"study_design_scores_gemma":[0.001039524,0.0000065866166,0.5674903,0.00005790553,0.000009996602,6.907912e-7,0.01673389,0.3737782,0.000092431925,0.000037008576,0.040587723,0.00016576525],"about_ca_topic_score_codex":0.0006968777,"about_ca_topic_score_gemma":0.005146906,"teacher_disagreement_score":0.24240865,"about_ca_system_score_codex":0.000046410176,"about_ca_system_score_gemma":0.00013284273,"threshold_uncertainty_score":0.28720942},"labels":[],"label_agreement":null},{"id":"W3209986075","doi":"10.1109/access.2021.3122087","title":"Bus Trajectory Optimization With Holding, Speed and Traffic Signal Actuation in Controlled Transit Systems","year":2021,"lang":"en","type":"article","venue":"IEEE Access","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Université Laval","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; Natural Sciences and Engineering Research Council of Canada; Ministério da Educação; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Trajectory; Computer science; SIGNAL (programming language); Transit (satellite); Control theory (sociology); Trajectory optimization; Real-time computing; Engineering; Transport engineering; Control (management); Physics; Public transport; Artificial intelligence","score_opus":0.025600839303385192,"score_gpt":0.2913129340896164,"score_spread":0.2657120947862312,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3209986075","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.90610224,0.00027153568,0.091211416,0.00023922817,0.00024395232,0.0004542363,0.0000066199173,0.000090277375,0.0013805046],"genre_scores_gemma":[0.9989924,0.0001479688,0.0004320682,0.000046252353,0.00008439673,0.000018553585,0.000050431423,0.000011138148,0.00021681559],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989388,0.00015687376,0.00023256186,0.00022100827,0.00028432196,0.00016644884],"domain_scores_gemma":[0.9994897,0.00012330935,0.00010345152,0.0000606181,0.00015747467,0.000065408254],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034742185,0.000095669035,0.0002113459,0.0001207577,0.00018940419,0.00028330708,0.000085789725,0.00009582884,0.00006237795],"category_scores_gemma":[0.000028115574,0.000090436355,0.000023957822,0.00045938065,0.000060339913,0.0006020807,0.0000012349392,0.00008481385,8.1365636e-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":[0.00015223149,0.000040804614,0.0037892128,0.00001784529,0.000015799329,0.000019324745,0.0056576915,0.9895541,0.00011934809,0.00011566389,0.00001925946,0.00049871096],"study_design_scores_gemma":[0.019127108,0.00014719264,0.048605688,0.00046151012,0.00024935772,0.000012287114,0.012202703,0.9171038,0.0006989955,0.000028787996,0.0005292319,0.0008333024],"about_ca_topic_score_codex":0.00042643188,"about_ca_topic_score_gemma":0.00294435,"teacher_disagreement_score":0.09289014,"about_ca_system_score_codex":0.0000460223,"about_ca_system_score_gemma":0.00022022433,"threshold_uncertainty_score":0.36878875},"labels":[],"label_agreement":null},{"id":"W3210862020","doi":"10.5281/zenodo.3663643","title":"Comparison of physical connectivity particle tracking models in the Flemish Cap region","year":2019,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Bedford Institute of Oceanography","funders":"European Commission","keywords":"Flemish; Particle (ecology); Tracking (education); Computer science; Geography; Geology; Psychology","score_opus":0.08621566631811137,"score_gpt":0.32452183573445914,"score_spread":0.23830616941634777,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3210862020","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9420746,0.000020488815,0.004060851,0.00059375545,0.00003380495,0.00031419698,0.0000121925905,0.00015060024,0.052739486],"genre_scores_gemma":[0.9995945,0.0000085527045,0.000032710377,0.00003058624,0.00003528171,2.8335911e-8,0.00009731162,0.00009880443,0.000102188555],"study_design_codex":"qualitative","study_design_gemma":"not_applicable","domain_scores_codex":[0.99875796,0.0004557531,0.00014892567,0.0001564857,0.00031194696,0.00016894653],"domain_scores_gemma":[0.99944293,0.00007798499,0.000084016974,0.00015153349,0.00020414045,0.000039395967],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00072043703,0.000050204635,0.0001015392,0.000051403167,0.0007007453,0.00016451263,0.00035942165,0.000033714263,0.0003456344],"category_scores_gemma":[0.0001974955,0.00004624638,0.000028854467,0.00043460683,0.000106300715,0.00030190882,0.00003254322,0.00013686315,0.00017183225],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020279731,0.0011497678,0.008125384,0.00011103102,0.000031983578,0.000008016218,0.5123218,0.15274711,0.003956782,0.25063047,0.01268919,0.058025643],"study_design_scores_gemma":[0.003267965,0.0009769555,0.08792597,0.0002738708,0.000067952555,0.0000152883,0.10504741,0.29055375,0.0032276362,0.00604759,0.5017863,0.0008093194],"about_ca_topic_score_codex":0.00017589105,"about_ca_topic_score_gemma":0.000012489112,"teacher_disagreement_score":0.4890971,"about_ca_system_score_codex":0.000041658703,"about_ca_system_score_gemma":0.000004633686,"threshold_uncertainty_score":0.53896374},"labels":[],"label_agreement":null},{"id":"W3211202452","doi":"10.32920/ryerson.14665935.v1","title":"The environmental impact of urban land use and transportation strategies in the City of Toronto","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Timeline; Environmental planning; Plan (archaeology); Land use; Process (computing); Business; Sustainable development; Urban planning; Sustainable transport; Transport engineering; Environmental resource management; Sustainability; Engineering; Geography; Civil engineering; Political science; Computer science; Economics","score_opus":0.022338029312564085,"score_gpt":0.2904413392876138,"score_spread":0.2681033099750497,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3211202452","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9981788,0.0005387815,0.0003112528,0.000050435436,0.000036520825,0.0001700176,0.000079562546,0.0000056532836,0.0006289853],"genre_scores_gemma":[0.99789166,0.0016699913,0.00017747068,0.000004255147,0.000012302646,0.0000055296837,0.00018845075,0.000002622281,0.000047704263],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9992794,0.00012930181,0.00020859505,0.00010291861,0.00020600097,0.00007382776],"domain_scores_gemma":[0.99955696,0.00017454558,0.00013166764,0.00009936604,0.000020905938,0.00001654568],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035319477,0.00006719811,0.00010939777,0.000009379698,0.00009053114,0.000091263115,0.00010177471,0.00008962723,0.00007307031],"category_scores_gemma":[0.000013411122,0.000039971288,0.000058617632,0.000029544683,0.00015219078,0.0002232286,0.0000030336066,0.00008493407,2.5150346e-8],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017921007,0.0000316126,0.9089448,0.000011911881,0.000020910586,6.9646126e-7,0.08404965,0.005474822,0.00002524045,0.0012623618,0.000015819985,0.00014428979],"study_design_scores_gemma":[0.00008471928,0.000015812659,0.97160584,0.000020453643,0.000017539,4.1333976e-8,0.02799391,0.000080853846,0.000010639488,0.00009132286,0.00003570133,0.000043147014],"about_ca_topic_score_codex":0.13698669,"about_ca_topic_score_gemma":0.30032638,"teacher_disagreement_score":0.1633397,"about_ca_system_score_codex":0.0000351349,"about_ca_system_score_gemma":0.0001424232,"threshold_uncertainty_score":0.8687602},"labels":[],"label_agreement":null},{"id":"W3211487905","doi":"10.4018/978-1-7998-7176-7.ch010","title":"Bilevel Optimization of Taxing Strategies for Carbon Emissions Using Fuzzy Random Matrix Generators","year":2021,"lang":"en","type":"book-chapter","venue":"Advances in human services and public health (AHSPH) book series","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Multiple Sclerosis Society of Canada","funders":"","keywords":"Stackelberg competition; Bilevel optimization; Mathematical optimization; Fuzzy logic; Computer science; Cuckoo search; Carbon tax; Optimization problem; Economics; Mathematics; Mathematical economics; Greenhouse gas; Artificial intelligence","score_opus":0.03959494092338465,"score_gpt":0.34225457249520247,"score_spread":0.3026596315718178,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3211487905","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024924418,0.46682245,0.05581067,0.0073573086,0.004613914,0.009717243,0.0020929885,0.0010287509,0.42763227],"genre_scores_gemma":[0.15373817,0.56854814,0.16922213,0.0023680057,0.0028837693,0.00045194922,0.008333587,0.00061307556,0.093841136],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99775255,0.000117676376,0.00089295534,0.00045045663,0.0003520569,0.0004343368],"domain_scores_gemma":[0.99821794,0.0001487009,0.000853235,0.00018147759,0.00038849676,0.00021012907],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00067523564,0.0003036681,0.00064803154,0.0002835017,0.0009254856,0.00021058052,0.00018245963,0.0003180431,0.00011592072],"category_scores_gemma":[0.000022100166,0.00032110853,0.0000835703,0.00015543254,0.00023461625,0.002550963,0.000023825805,0.0002003862,8.696361e-8],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009318894,0.00004244571,0.0009767724,0.003490155,0.000062501524,0.000005252931,0.031180736,0.26837918,0.000015486497,0.69372255,0.000017013994,0.0020147616],"study_design_scores_gemma":[0.003346779,0.0003991739,0.00009973441,0.0044912165,0.00014672345,0.000007071918,0.06446977,0.0088945655,0.000021040427,0.026180418,0.8902615,0.001682014],"about_ca_topic_score_codex":0.001728311,"about_ca_topic_score_gemma":0.010784253,"teacher_disagreement_score":0.8902445,"about_ca_system_score_codex":0.00011821929,"about_ca_system_score_gemma":0.0012965057,"threshold_uncertainty_score":0.9999241},"labels":[],"label_agreement":null},{"id":"W3211833052","doi":"10.1007/s12469-021-00288-8","title":"Advanced systems in public transport, with a touch of data","year":2021,"lang":"en","type":"article","venue":"Public Transport","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Université de Montréal","funders":"","keywords":"Public transport; Human–computer interaction; Computer science; Transport engineering; Engineering","score_opus":0.057998536733413146,"score_gpt":0.29614958812334913,"score_spread":0.23815105138993597,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3211833052","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.89510167,0.0014388667,0.0411348,0.008825667,0.00061265903,0.0009755268,0.00095064385,0.00046342926,0.05049677],"genre_scores_gemma":[0.9939649,0.0002528488,0.002687126,0.000055859146,0.000045922723,0.00003222461,0.0022603455,0.000021718555,0.0006790273],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9977373,0.00010529824,0.00054698956,0.00049439055,0.00067396794,0.00044208177],"domain_scores_gemma":[0.99869543,0.0000671455,0.00015677005,0.0005295616,0.00035511455,0.00019595993],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00070480217,0.00015048456,0.00033451975,0.00020010349,0.0001476762,0.000046770063,0.00052548596,0.00013445641,0.00020026426],"category_scores_gemma":[0.000042272277,0.00014837962,0.000046421355,0.0015774993,0.00017426687,0.0015543739,0.000004154118,0.00017313537,0.0000024136712],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007245313,0.0004318364,0.93026435,0.0001699988,0.00007201101,0.00019402246,0.010136562,0.004324686,0.000062859064,0.04921815,0.00008509848,0.004967951],"study_design_scores_gemma":[0.002879698,0.00007356695,0.68394893,0.00026597045,0.00009041177,0.000010865002,0.015539368,0.00053835567,0.000059923,0.00009144536,0.29588354,0.00061793294],"about_ca_topic_score_codex":0.0024847884,"about_ca_topic_score_gemma":0.05070967,"teacher_disagreement_score":0.29579845,"about_ca_system_score_codex":0.00004472372,"about_ca_system_score_gemma":0.0013679817,"threshold_uncertainty_score":0.9666124},"labels":[],"label_agreement":null},{"id":"W3211839321","doi":"10.3390/su132212501","title":"Simulating the Impacts of Hybrid Campus and Autonomous Electric Vehicles as GHG Mitigation Strategies: A Case Study for a Mid-Size Canadian Post-Secondary School","year":2021,"lang":"en","type":"article","venue":"Sustainability","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Greenhouse gas; Penetration rate; Market penetration; Carbon footprint; University campus; Environmental economics; Government (linguistics); Penetration (warfare); Business; Environmental science; Computer science; Simulation; Engineering; Operations research; Marketing; Civil engineering; Economics","score_opus":0.006835833045854214,"score_gpt":0.29507336095321257,"score_spread":0.28823752790735835,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3211839321","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9970248,0.00017378425,0.000115895,0.0013057808,0.000039108356,0.001067148,0.00004030594,0.000031876323,0.00020132576],"genre_scores_gemma":[0.99961805,0.00000481599,0.000119577075,0.00008893846,0.000030050072,0.00003683156,0.000020202007,0.000007438854,0.000074096046],"study_design_codex":"observational","study_design_gemma":"qualitative","domain_scores_codex":[0.99877644,0.00029346484,0.00026063164,0.0002313253,0.00014739663,0.0002907632],"domain_scores_gemma":[0.9974101,0.00075181713,0.000114792405,0.00015289072,0.0013805028,0.00018986492],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009674783,0.0000900837,0.00013571688,0.000060204988,0.000844118,0.00016330581,0.000069441514,0.000046751746,0.000034377495],"category_scores_gemma":[0.0041430932,0.000082198785,0.000044843917,0.00034350363,0.000112576825,0.00030913737,0.000004905709,0.00012247126,2.0082464e-7],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022453947,0.0004729906,0.62291175,0.00077879627,0.00017622054,0.0018716226,0.315208,0.028883453,0.00033779023,0.005598185,0.00008599415,0.023450658],"study_design_scores_gemma":[0.0009209479,0.0003050106,0.26883307,0.000013473666,0.00008680034,0.00005673934,0.713796,0.0019672397,0.00026417203,0.013247248,0.0003041827,0.00020506003],"about_ca_topic_score_codex":0.48764,"about_ca_topic_score_gemma":0.5222179,"teacher_disagreement_score":0.39858806,"about_ca_system_score_codex":0.00043849656,"about_ca_system_score_gemma":0.011398444,"threshold_uncertainty_score":0.994206},"labels":[],"label_agreement":null},{"id":"W3213686003","doi":"10.5267/j.ijdns.2021.8.005","title":"The effects of different factors influencing commuterline in Indonesia","year":2021,"lang":"en","type":"article","venue":"International Journal of Data and Network Science","topic":"Transportation Planning and Optimization","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":"Likert scale; Marketing; Scale (ratio); Product (mathematics); Business; Public transport; Empirical research; Psychology; Transport engineering; Engineering; Geography","score_opus":0.019849403127702343,"score_gpt":0.3225552115991387,"score_spread":0.30270580847143636,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3213686003","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.996922,0.0004349451,0.0013024673,0.0005105609,0.000737309,0.000022265638,0.0000039961305,0.0000017556684,0.00006469694],"genre_scores_gemma":[0.9981187,0.0012664074,0.00045643788,0.000041659467,0.0000996424,1.5040428e-7,0.0000075599382,0.0000011149228,0.000008316108],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9990616,0.000067416884,0.00022780815,0.000071049035,0.0004797189,0.00009243203],"domain_scores_gemma":[0.99886173,0.0005347957,0.00019213115,0.00008453403,0.00028502976,0.00004180719],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000824745,0.000032669064,0.000072872266,0.0000548905,0.000176011,0.000096454474,0.0006661366,0.00001578304,0.0000021410228],"category_scores_gemma":[0.00034713367,0.000021850587,0.000012379648,0.00025895855,0.00025238757,0.0005260721,0.00006112597,0.00008462277,5.1411046e-8],"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":[0.00002360066,0.000037814756,0.97419655,0.00000485736,0.000018961038,0.0000236932,0.006193152,0.0014077789,0.000499215,0.004026895,0.00012285741,0.0134446025],"study_design_scores_gemma":[0.0003442986,0.00002837161,0.9935017,0.00024453856,0.00000959423,0.000005634693,0.0019440779,0.001290271,0.0005443719,0.0006642125,0.0013726961,0.000050239265],"about_ca_topic_score_codex":0.000055120872,"about_ca_topic_score_gemma":0.0005984186,"teacher_disagreement_score":0.019305121,"about_ca_system_score_codex":0.000024042087,"about_ca_system_score_gemma":0.00020170042,"threshold_uncertainty_score":0.13537522},"labels":[],"label_agreement":null},{"id":"W3214287578","doi":"10.22060/ajce.2020.17644.5638","title":"The Assessment of the Change in the Share of Public Transportation by Applying Demand Management Policies: Evidence from Extending Traffic Restriction to the Air Pollution Control Area in Tehran","year":2021,"lang":"en","type":"article","venue":"AUT Journal of Civil Engineering","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Toll; Public transport; Transport engineering; Traffic congestion; Payment; Toll road; Control (management); Business; Plan (archaeology); Environmental economics; Engineering; Geography; Economics; Finance","score_opus":0.030493559076592253,"score_gpt":0.291815244536552,"score_spread":0.26132168545995976,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3214287578","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8942632,0.0023681687,0.08750952,0.014613406,0.00039921145,0.0006953026,0.000038543036,0.00001324483,0.000099365425],"genre_scores_gemma":[0.9986626,0.0010353095,0.00012848437,0.00007454474,0.000051254698,0.000033574415,0.0000050798103,0.000004896882,0.000004242477],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9986817,0.00019307826,0.00042012997,0.00007447079,0.0004866091,0.00014397678],"domain_scores_gemma":[0.9991281,0.0003994384,0.00025578757,0.00009865526,0.000092336944,0.000025664496],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016493723,0.000066649016,0.00011815384,0.00010523518,0.00017187781,0.00004392806,0.00026280468,0.000035315894,0.0000054078323],"category_scores_gemma":[0.00016141342,0.000038706745,0.00006163308,0.0007825554,0.000023452223,0.00027159203,0.0000032092155,0.00016576752,4.506968e-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":[0.000009272179,0.00002870681,0.01848742,0.00002272889,0.000027891097,0.000003202237,0.02093825,0.95612574,0.00039124183,0.0016895126,0.00006478669,0.0022112667],"study_design_scores_gemma":[0.00034811505,0.00002184977,0.9643743,0.000730602,0.000044564582,7.932259e-7,0.010975114,0.022118682,0.00004610439,0.000028117784,0.0012539905,0.000057754216],"about_ca_topic_score_codex":0.00036775577,"about_ca_topic_score_gemma":0.008513106,"teacher_disagreement_score":0.9458869,"about_ca_system_score_codex":0.00009470421,"about_ca_system_score_gemma":0.000065357286,"threshold_uncertainty_score":0.47505128},"labels":[],"label_agreement":null},{"id":"W3216584402","doi":"10.1155/2021/5398316","title":"Modeling and Quantifying Interaction of Information and Capacity in Public Transport Disruptions","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","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":"Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Science Foundation","keywords":"Microsimulation; Computer science; Public transport; Perspective (graphical); Service (business); Transport engineering; Transportation planning; Operations research; Engineering; Business; Artificial intelligence","score_opus":0.05835315601025873,"score_gpt":0.32084973458657046,"score_spread":0.2624965785763117,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3216584402","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9334352,0.00013568171,0.06575542,0.00036959597,0.00014329008,0.00006128853,0.000011094247,0.000008126421,0.00008031556],"genre_scores_gemma":[0.98707575,0.0013298835,0.011496756,0.000016694314,0.000017213206,0.0000015257025,0.000055825876,0.000003577662,0.000002761954],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99896866,0.000047069443,0.00060067064,0.00006750421,0.00022404829,0.000092019225],"domain_scores_gemma":[0.9991597,0.000049209822,0.00028361284,0.00003298414,0.00041265367,0.00006183333],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039502382,0.00005946012,0.00015219324,0.0002223101,0.000098254684,0.000023394268,0.00003002449,0.000059631147,0.000007854364],"category_scores_gemma":[0.00007732163,0.00006452211,0.000036715126,0.00030731608,0.000047132242,0.0025064582,3.9987904e-7,0.00013716136,8.58654e-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":[0.00013313773,0.00008336961,0.15121788,0.00014053525,0.00002481434,0.000009938687,0.090693004,0.72802895,0.0016205375,0.014710522,7.165803e-7,0.013336567],"study_design_scores_gemma":[0.0019244312,0.000082430466,0.9177246,0.0004479044,0.000085901025,0.000013364941,0.06771678,0.008875622,0.0004252196,0.0017137557,0.00078467384,0.00020532624],"about_ca_topic_score_codex":0.00018194094,"about_ca_topic_score_gemma":0.005085276,"teacher_disagreement_score":0.76650673,"about_ca_system_score_codex":0.000030159856,"about_ca_system_score_gemma":0.00012178383,"threshold_uncertainty_score":0.28377032},"labels":[],"label_agreement":null},{"id":"W3216969914","doi":"10.18757/ejtir.2009.9.4.3321","title":"Book review: Harry Richardson and Chang-Hee Christine Bae (2008) Road Congestion Pricing in Europe: Implications for the United States","year":2009,"lang":"en","type":"article","venue":"European journal of transport and infrastructure research","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Road pricing; Congestion pricing; Traffic congestion; Singapore Area Licensing Scheme; Economics; Transport engineering; Engineering","score_opus":0.03428057499335417,"score_gpt":0.34650918583910023,"score_spread":0.31222861084574605,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3216969914","genre_codex":"review","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27611327,0.47045654,0.036650833,0.20915823,0.0004353409,0.0034486984,0.00017625558,0.00011714116,0.0034436737],"genre_scores_gemma":[0.6398205,0.35539904,0.000843874,0.0029088398,0.00035117427,0.0000048419984,0.00014214849,0.000024501851,0.0005050634],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.99851537,0.00038253222,0.0004358373,0.00013603055,0.00029149983,0.00023871427],"domain_scores_gemma":[0.99868023,0.00016982267,0.00017801297,0.00008462149,0.00076340744,0.00012391327],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003689484,0.0000911952,0.00016370183,0.00025379477,0.00047323218,0.000055293305,0.00018257937,0.000029145624,0.00003326549],"category_scores_gemma":[0.00027592614,0.000064779,0.000029072939,0.00076776627,0.00018040855,0.00022168049,0.00000372279,0.00038976638,4.083563e-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":[0.0011796863,0.00020554314,0.10907467,0.0010081696,0.00017303895,0.0001828876,0.07211119,0.025717136,0.001252944,0.0031771942,0.4019216,0.38399595],"study_design_scores_gemma":[0.00035575568,0.00013715071,0.66354954,0.00031810944,0.00002417475,0.000008273957,0.0003137163,0.00013892449,0.0000047479252,0.000051635954,0.33503938,0.000058562084],"about_ca_topic_score_codex":0.000049348437,"about_ca_topic_score_gemma":0.000020329167,"teacher_disagreement_score":0.5544749,"about_ca_system_score_codex":0.00002385363,"about_ca_system_score_gemma":0.00011189781,"threshold_uncertainty_score":0.36397672},"labels":[],"label_agreement":null},{"id":"W3217094571","doi":"10.14288/1.0343076","title":"UBC Chancellor Boulevard &amp; East Mall Intersection Redesign","year":2017,"lang":"en","type":"article","venue":"Open Collections","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Boulevard; Intersection (aeronautics); Computer science; Political science; Geography; Cartography; Archaeology","score_opus":0.07616231364335617,"score_gpt":0.3442155523913704,"score_spread":0.26805323874801423,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3217094571","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012336639,0.0000059328754,0.026303286,0.002351629,0.0016598756,0.00052359764,0.000022967632,0.00010979506,0.96778923],"genre_scores_gemma":[0.26432675,0.00003394725,0.0037280212,0.000095340874,0.00021596978,0.000095815834,0.000034944296,0.000011316209,0.7314579],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99923986,0.00008303386,0.00013828679,0.00020442056,0.00015779305,0.00017662425],"domain_scores_gemma":[0.99935085,0.000023599916,0.00014728107,0.00025375182,0.00013815842,0.000086378306],"candidate_categories":["sts","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000399337,0.000071759634,0.00009784715,0.000049734583,0.013372028,0.002923835,0.00043132596,0.0000806087,0.0020496685],"category_scores_gemma":[0.0001416937,0.00008039714,0.0000432436,0.00025880363,0.00012137853,0.00087467296,0.000026134383,0.00009896495,0.00006587858],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017238043,0.00018879888,0.0022500285,0.00000655903,0.00006757786,0.000004688902,0.014342314,0.0024848592,0.00015491397,0.0059343134,0.97207713,0.002316409],"study_design_scores_gemma":[0.00054432824,0.000045956367,0.0039571794,0.00003916082,0.00003298954,0.000001867026,0.0037225278,0.00031910374,0.000053496038,0.0011183514,0.98995584,0.00020919969],"about_ca_topic_score_codex":0.033605315,"about_ca_topic_score_gemma":0.08616742,"teacher_disagreement_score":0.26309308,"about_ca_system_score_codex":0.00010162535,"about_ca_system_score_gemma":0.00019430196,"threshold_uncertainty_score":0.99886256},"labels":[],"label_agreement":null},{"id":"W323394445","doi":"","title":"Hierarchical Clustering of Multiobjective Optimization Results to Inform Land-Use Decision Making","year":2009,"lang":"en","type":"article","venue":"Journal of the Urban and Regional Information Systems Association","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Plan (archaeology); Multi-objective optimization; Set (abstract data type); Variety (cybernetics); Computer science; Operations research; Management science; Work (physics); Land-use planning; Selection (genetic algorithm); Land use; Mathematical optimization; Engineering; Mathematics; Artificial intelligence; Machine learning","score_opus":0.02038495662632563,"score_gpt":0.2823591285506908,"score_spread":0.2619741719243652,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W323394445","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32832578,0.00022636294,0.6391901,0.016560486,0.0040258854,0.0019700336,0.00015847795,0.000114621886,0.009428265],"genre_scores_gemma":[0.99393606,0.000049127397,0.0053174305,0.00029948287,0.00015372063,0.0000012149432,0.000014439077,0.000002664255,0.00022587088],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9981413,0.0001216139,0.00081348355,0.000048404047,0.0007630828,0.00011212367],"domain_scores_gemma":[0.99715406,0.00032527602,0.0015094639,0.00006120499,0.0008858249,0.00006416631],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014534254,0.00007049945,0.0001626257,0.00023898158,0.00033569252,0.00016408335,0.00011165355,0.00012413907,0.0000013233303],"category_scores_gemma":[0.0011754307,0.00005183975,0.00007246839,0.00040597003,0.00002153077,0.0015734623,0.000008189751,0.00013153689,0.0000011493654],"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":[0.0003331442,0.000017288965,0.011188,0.000010193565,0.000024008881,1.812643e-7,0.024580766,0.9563949,0.0000034902398,0.001889513,0.003689295,0.0018692352],"study_design_scores_gemma":[0.003693939,0.0004075671,0.6965622,0.0020335189,0.00010759302,0.000019517014,0.0083227465,0.21466888,0.000014956279,0.00032446705,0.07344988,0.00039478048],"about_ca_topic_score_codex":0.000086019434,"about_ca_topic_score_gemma":0.0000357185,"teacher_disagreement_score":0.741726,"about_ca_system_score_codex":0.00027899534,"about_ca_system_score_gemma":0.00010970885,"threshold_uncertainty_score":0.25819093},"labels":[],"label_agreement":null},{"id":"W326376069","doi":"10.1177/0042098013505883","title":"Traffic Congestion’s Economic Impacts: Evidence from US Metropolitan Regions","year":2013,"lang":"en","type":"article","venue":"Urban Studies","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Metropolitan area; Traffic congestion; Productivity; Gridlock; Per capita; Economics; Panel data; Geography; Economic growth; Transport engineering; Population; Econometrics; Demography; Politics","score_opus":0.059795483815416724,"score_gpt":0.33131481492869014,"score_spread":0.2715193311132734,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W326376069","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96906763,0.016050497,0.00010460748,0.011108756,0.00052473915,0.00023938865,0.000027421442,0.0002316067,0.0026453247],"genre_scores_gemma":[0.99475855,0.002436815,0.0006148801,0.00012810674,0.00022940605,0.00003384983,0.000011187441,0.000007023062,0.0017801971],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99926656,0.00008141604,0.00017917012,0.00017531154,0.00010559714,0.00019194036],"domain_scores_gemma":[0.9991434,0.00046156358,0.00008626202,0.00010684114,0.00011393833,0.00008798916],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014644844,0.000087336135,0.00015055834,0.00005518587,0.0004975417,0.00006948759,0.00010774917,0.0000461572,0.00020534401],"category_scores_gemma":[0.0003520341,0.000080241705,0.000043932996,0.000094710726,0.00026095702,0.000433744,0.0000063439406,0.00005736941,0.00023444231],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015493431,0.000038102644,0.5202268,0.000013866954,0.00032350325,0.000002935537,0.09884283,0.008779805,0.00002168393,0.014016006,0.35634458,0.0013744115],"study_design_scores_gemma":[0.00040135733,0.00008458516,0.88014513,0.0002522016,0.00016478477,3.7754225e-7,0.08270284,0.0003814923,0.00001588754,0.002217057,0.03318459,0.00044968692],"about_ca_topic_score_codex":0.05047488,"about_ca_topic_score_gemma":0.048923057,"teacher_disagreement_score":0.35991836,"about_ca_system_score_codex":0.00042102506,"about_ca_system_score_gemma":0.0001309861,"threshold_uncertainty_score":0.9684316},"labels":[],"label_agreement":null},{"id":"W3325141","doi":"10.1007/978-1-4757-6871-8_5","title":"Estimation of Travel Demand Using Traffic Counts and Other Data Sources","year":2002,"lang":"en","type":"book-chapter","venue":"Applied optimization","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Estimator; Inference; Aggregate (composite); Computer science; A priori and a posteriori; Econometrics; Bayesian inference; Bayesian probability; Mathematical optimization; Mathematics; Statistics","score_opus":0.05217747640329066,"score_gpt":0.27775683441690224,"score_spread":0.2255793580136116,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3325141","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00015095901,0.0003388991,0.42825246,0.000027534605,0.0001120685,0.00038105153,0.0001648755,0.00007613031,0.570496],"genre_scores_gemma":[0.42360547,0.003945959,0.34518293,0.00041317308,0.00092012185,0.000020506179,0.007028705,0.00038805036,0.21849509],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992559,0.000010658456,0.00021855437,0.00020813194,0.00022516181,0.00008158518],"domain_scores_gemma":[0.9995051,0.000036719808,0.00023571547,0.00014937687,0.000038934548,0.00003416886],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017934554,0.00011018416,0.0001532413,0.00009475278,0.0001445821,0.000034912187,0.00010809804,0.00021547203,0.00053019455],"category_scores_gemma":[0.00001029257,0.00012325028,0.000014699253,0.000046107303,0.00012287126,0.000092866314,0.00000772411,0.000066139415,0.000008804651],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000052667106,0.0000067373276,0.000008874804,0.000023482458,0.000017141741,1.9943369e-7,0.0014792773,0.96194977,9.1268754e-7,0.032245103,0.00021097666,0.004052239],"study_design_scores_gemma":[0.00024233648,0.000007847207,0.000015368187,0.00009809357,0.00016556926,6.3123986e-7,0.0001486297,0.98575026,0.000004442632,0.00041370135,0.012914679,0.0002384735],"about_ca_topic_score_codex":0.000016509177,"about_ca_topic_score_gemma":0.000010215744,"teacher_disagreement_score":0.4234545,"about_ca_system_score_codex":0.000019285742,"about_ca_system_score_gemma":0.000046169338,"threshold_uncertainty_score":0.5805259},"labels":[],"label_agreement":null},{"id":"W337442820","doi":"","title":"Role of GIS in Improving the Reliability of Estimating Urban Travel Factors","year":2007,"lang":"en","type":"article","venue":"11th World Conference on Transport ResearchWorld Conference on Transport Research Society","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Reliability (semiconductor); Geographic information system; Estimation; Transport engineering; Travel time; Computer science; Transportation planning; GIS applications; Econometrics; Operations research; Geography; Engineering; Cartography; Mathematics","score_opus":0.09864116721639571,"score_gpt":0.37247700795255534,"score_spread":0.27383584073615963,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W337442820","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93382657,0.0000948893,0.0048609185,0.0030304983,0.00017568823,0.0023409529,0.0001837818,0.00014600636,0.055340696],"genre_scores_gemma":[0.9951594,0.00035058614,0.0020247626,0.000039556387,0.00010276206,0.00008356465,0.00017115533,0.00005107899,0.0020171239],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9899652,0.00076012075,0.0017394364,0.0011286442,0.0044294684,0.0019771177],"domain_scores_gemma":[0.9939936,0.0020226487,0.00044104044,0.000997783,0.0019742537,0.0005707167],"candidate_categories":["metaepi_narrow","sts","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.015618482,0.00050455774,0.0008979932,0.0009050136,0.0010524872,0.00008026274,0.0017517551,0.00036726828,0.0007914288],"category_scores_gemma":[0.00035802677,0.00041459533,0.00053259876,0.0039881268,0.0031948953,0.0004801073,0.000022821616,0.0027912525,0.0000072795],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013954636,0.0015092137,0.52707666,0.0006312562,0.00011521771,0.000040481093,0.12648252,0.0029538425,0.008576969,0.3200148,0.00012949182,0.0110740755],"study_design_scores_gemma":[0.0014281024,0.00057588716,0.91677505,0.0012618463,0.000047662754,3.132555e-7,0.054599497,0.0058417055,0.011321975,0.0067870226,0.0006472545,0.00071367144],"about_ca_topic_score_codex":0.021736354,"about_ca_topic_score_gemma":0.049721595,"teacher_disagreement_score":0.38969842,"about_ca_system_score_codex":0.00035982457,"about_ca_system_score_gemma":0.002693272,"threshold_uncertainty_score":0.9998306},"labels":[],"label_agreement":null},{"id":"W38009008","doi":"10.1287/trsc.2015.0597","title":"A Dynamic Formulation for Car Ownership Modeling","year":2015,"lang":"en","type":"article","venue":"Transportation Science","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Context (archaeology); Computer science; Quality (philosophy); Discrete choice; Consumer choice; Set (abstract data type); Frame (networking); Product (mathematics); Consumer behaviour; Operations research; Mathematical optimization; Microeconomics; Economics; Marketing; Business; Mathematics; Machine learning","score_opus":0.08848845493086999,"score_gpt":0.36053596990500264,"score_spread":0.27204751497413265,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W38009008","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3860602,0.00002254516,0.61085916,0.0005019638,0.00028518136,0.00034750326,0.000021303496,0.00015945327,0.001742669],"genre_scores_gemma":[0.97491264,0.00000745304,0.024488606,0.000073996474,0.000030786945,0.000032233766,0.00012982533,0.000007977888,0.00031648774],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99856436,0.000019947229,0.00023842692,0.0002559297,0.00064290204,0.00027841376],"domain_scores_gemma":[0.99891394,0.000042608604,0.00008284796,0.000096079944,0.00065791095,0.00020663315],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014666136,0.00007504824,0.000085427295,0.00017309524,0.00058190845,0.000084693325,0.00019731809,0.000059528174,0.000009469912],"category_scores_gemma":[0.00015256455,0.000076365715,0.000039933566,0.00078258436,0.00012490143,0.0010620006,5.86487e-7,0.000049311384,0.00000679517],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000069196896,0.000029156754,0.007931963,0.00001456906,0.0000038212947,8.7419954e-7,0.08489602,0.78508234,0.00043719358,0.119359784,0.000044241704,0.002130861],"study_design_scores_gemma":[0.0011035743,0.0000653727,0.012076385,0.00003226619,0.000039546354,2.2113755e-7,0.016860047,0.95634145,0.00013370812,0.009490435,0.0035278392,0.00032918178],"about_ca_topic_score_codex":0.00043559365,"about_ca_topic_score_gemma":0.0023662797,"teacher_disagreement_score":0.5888524,"about_ca_system_score_codex":0.000120303404,"about_ca_system_score_gemma":0.0006806031,"threshold_uncertainty_score":0.44756278},"labels":[],"label_agreement":null},{"id":"W39524364","doi":"10.1007/978-3-642-12788-5_9","title":"Household Behaviour in the Oregon2 Model","year":2010,"lang":"en","type":"book-chapter","venue":"Advances in spatial science","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Microsimulation; Grid; Representation (politics); Space (punctuation); Set (abstract data type); Development (topology); State space; Operations research; Computer science; Grid cell; Transport engineering; Geography; Engineering; Mathematics; Political science; Statistics","score_opus":0.022351028070218455,"score_gpt":0.3044452994790909,"score_spread":0.28209427140887244,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W39524364","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0041566407,0.00057710614,0.008879262,0.00068853155,0.001074331,0.00064614235,0.0000552429,0.00009377028,0.98382896],"genre_scores_gemma":[0.97120756,0.0015393397,0.0035149974,0.00027429033,0.00017253798,0.000025664513,0.00002140159,0.000019088555,0.02322515],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99802065,0.00002466121,0.0002809133,0.00039990703,0.0009409463,0.00033295053],"domain_scores_gemma":[0.9993569,0.00009572053,0.00016997082,0.00023790408,0.000073241004,0.00006626659],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012916569,0.0001509867,0.0001568921,0.00022714678,0.0004486349,0.00009833228,0.00091793446,0.00020454418,0.000050216273],"category_scores_gemma":[0.0000976023,0.00012333738,0.000037365964,0.00027858405,0.0013916839,0.00072929286,0.000018623645,0.0005548848,0.000007530883],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027272465,0.00007374699,0.020288685,0.000016053064,0.0000010254162,0.000051815532,0.031761814,0.16590546,0.00003889703,0.747676,0.000047135472,0.034112077],"study_design_scores_gemma":[0.0018778736,0.00024357796,0.054376796,0.0011118585,0.00010104992,0.000008073039,0.0042224685,0.019388177,0.00014007695,0.40166485,0.51338565,0.0034795608],"about_ca_topic_score_codex":0.0019926513,"about_ca_topic_score_gemma":0.11344014,"teacher_disagreement_score":0.9670509,"about_ca_system_score_codex":0.00009090296,"about_ca_system_score_gemma":0.0004954585,"threshold_uncertainty_score":0.90273726},"labels":[],"label_agreement":null},{"id":"W402747145","doi":"","title":"Strategies To Relieve Subway Crowding: Case Study From The Toronto Context","year":2015,"lang":"en","type":"article","venue":"Transportation Research Board 94th Annual MeetingTransportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Downtown; Train; Context (archaeology); Transport engineering; Public transport; Service (business); Business; Transit (satellite); Telecommunications; Computer science; Engineering; Geography; Marketing","score_opus":0.13686142909031765,"score_gpt":0.4425722116684648,"score_spread":0.3057107825781471,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W402747145","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97833526,0.0005041247,0.0022673965,0.0061729653,0.00056072493,0.004622419,0.00062944717,0.00050574966,0.006401926],"genre_scores_gemma":[0.99418604,0.00016766661,0.0016437914,0.00029658247,0.00062089314,0.00086547615,0.00049510685,0.000109356224,0.001615113],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.9835294,0.0043080985,0.0015165776,0.0014542534,0.007116451,0.0020752375],"domain_scores_gemma":[0.985629,0.0031671077,0.0002553449,0.00089273293,0.008264761,0.0017910117],"candidate_categories":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.016398849,0.00050796726,0.0006016605,0.0005708511,0.0037123628,0.0010625154,0.0013306757,0.000407065,0.00054535724],"category_scores_gemma":[0.0013294899,0.00045731713,0.00020887662,0.0030922352,0.0011425593,0.0023135012,0.000018981797,0.0016370033,0.0002339275],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015290179,0.0007256729,0.18537752,0.000041179188,0.00018005283,0.0023013172,0.7498288,0.007893762,0.00007726331,0.016247436,0.032801688,0.0029962542],"study_design_scores_gemma":[0.0017377506,0.00086314394,0.11722956,0.000104468614,0.000061880695,0.0000013819019,0.80706495,0.00007639774,0.000054540476,0.000806293,0.07152417,0.00047544748],"about_ca_topic_score_codex":0.7075098,"about_ca_topic_score_gemma":0.89533937,"teacher_disagreement_score":0.18782954,"about_ca_system_score_codex":0.0007426519,"about_ca_system_score_gemma":0.002850615,"threshold_uncertainty_score":0.9999745},"labels":[],"label_agreement":null},{"id":"W40489854","doi":"","title":"Park & Ride User Path Problem: Findings from On-Board Surveys","year":2007,"lang":"en","type":"article","venue":"11th World Conference on Transport ResearchWorld Conference on Transport Research Society","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Park and ride; Transport engineering; Path (computing); Transit (satellite); On board; Computer science; Public transport; Operations research; Geography; Engineering; Computer network","score_opus":0.1413763903675238,"score_gpt":0.38665589777702464,"score_spread":0.24527950740950083,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W40489854","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5654774,0.00014768721,0.017728446,0.029847018,0.00090146833,0.0060771983,0.0014020813,0.0017226875,0.37669605],"genre_scores_gemma":[0.9575806,0.002298818,0.0021526015,0.0006493413,0.00042786842,0.00032045942,0.0016008862,0.00015486502,0.034814563],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.981186,0.0018953689,0.0017346668,0.0026623565,0.008291362,0.0042302487],"domain_scores_gemma":[0.9909747,0.0025714983,0.00030467162,0.0015084875,0.0025486734,0.002091944],"candidate_categories":["metaepi_narrow","sts","research_integrity","insufficient_payload"],"consensus_categories":["sts"],"category_scores_codex":[0.021434559,0.0010844965,0.0012234148,0.0013259943,0.003252094,0.00053077476,0.0026015542,0.0007643663,0.0063918983],"category_scores_gemma":[0.00018926672,0.001057866,0.0008580815,0.004578922,0.0027822745,0.00084225007,0.000033051812,0.0052269464,0.0005692714],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0033350755,0.0027179217,0.32995865,0.00029983214,0.0006019569,0.00094483997,0.040596228,0.0009356728,0.0013618192,0.59138846,0.010584543,0.017275013],"study_design_scores_gemma":[0.004710337,0.0016124885,0.8770311,0.0029980855,0.00013242669,0.0000010200587,0.012685658,0.00063326856,0.0034781934,0.017054042,0.07676551,0.0028978703],"about_ca_topic_score_codex":0.02438422,"about_ca_topic_score_gemma":0.12105805,"teacher_disagreement_score":0.5743344,"about_ca_system_score_codex":0.00081373827,"about_ca_system_score_gemma":0.0036530632,"threshold_uncertainty_score":0.9999316},"labels":[],"label_agreement":null},{"id":"W4200140070","doi":"10.1155/2021/4219396","title":"Travel Choice Behavior Model Based on Mental Accounting of Travel Time and Cost","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"China Scholarship Council","keywords":"Function (biology); Econometrics; Travel time; Travel behavior; Sensitivity (control systems); Nonlinear system; Perspective (graphical); Linear model; Computer science; Simulation; Statistics; Operations research; Economics; Mathematics; Engineering; Microeconomics; Transport engineering; Artificial intelligence","score_opus":0.015093147407405472,"score_gpt":0.2943768904109049,"score_spread":0.27928374300349945,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200140070","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97658455,0.00006852815,0.022170391,0.00030423427,0.00015623735,0.00016344973,0.00008969929,0.000009959425,0.00045292557],"genre_scores_gemma":[0.98649985,0.00009665736,0.013035688,0.000077901095,0.00004110759,0.0000031559064,0.000117755684,0.000010766406,0.000117133044],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9988595,0.000036111942,0.000442183,0.00011297422,0.00043141792,0.000117816046],"domain_scores_gemma":[0.9990283,0.00009511545,0.00040138554,0.000048303933,0.00035020942,0.0000766434],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002561623,0.00008513978,0.00018917883,0.00010385089,0.00013815542,0.000019005809,0.000058382026,0.00006726167,0.000039432798],"category_scores_gemma":[0.000044600998,0.00008947789,0.00007573399,0.00020056266,0.00005668025,0.00042865658,3.003573e-7,0.00012157635,4.260866e-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":[0.0002258706,0.0002938733,0.019576687,0.00003616968,0.000020872503,0.00002526018,0.015949396,0.91855127,0.035934836,0.0005578666,0.000025775253,0.0088021215],"study_design_scores_gemma":[0.0024663256,0.00012777389,0.9682667,0.0002314889,0.00017282217,0.0000021991502,0.004562878,0.016172055,0.0076128333,0.00007493151,0.0001284118,0.00018153971],"about_ca_topic_score_codex":0.000033999026,"about_ca_topic_score_gemma":0.00025224398,"teacher_disagreement_score":0.94869006,"about_ca_system_score_codex":0.000035479145,"about_ca_system_score_gemma":0.000216408,"threshold_uncertainty_score":0.36488026},"labels":[],"label_agreement":null},{"id":"W4200169880","doi":"10.1155/2021/4310403","title":"A Multiroute Signal Control Model considering Coordination Rate of Green Bandwidth","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","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":"Natural Science Foundation of Shanghai","keywords":"Synchro; Bandwidth (computing); Simulation software; Computer science; Real-time computing; Simulation; Control theory (sociology); Software; Engineering; Telecommunications; Control (management); Electrical engineering","score_opus":0.015298569629487618,"score_gpt":0.27664426965273375,"score_spread":0.26134570002324614,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200169880","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6656539,0.00018620779,0.33269075,0.00092378113,0.00020562173,0.00011979502,0.000040560186,0.000020929956,0.00015848372],"genre_scores_gemma":[0.97594607,0.00012657991,0.023527332,0.00008646111,0.000057040088,0.0000021688231,0.000044194432,0.000009799687,0.00020037885],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99872637,0.0001014779,0.0006212966,0.00010695641,0.00031306327,0.00013082517],"domain_scores_gemma":[0.9980121,0.0001571298,0.00064378517,0.00005270265,0.0010524662,0.00008181043],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000480658,0.000085543645,0.00023995084,0.000112677655,0.00012670262,0.000016309763,0.000067212735,0.00007135422,0.000038554033],"category_scores_gemma":[0.00008122549,0.0000902229,0.000115288145,0.00026102332,0.0000644801,0.0006495411,3.817444e-7,0.00012280639,4.559342e-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":[0.00020169784,0.000057528116,0.0062776287,0.000029860816,0.00004304062,0.00003058954,0.015601623,0.9568079,0.015788715,0.0026327441,0.00001639519,0.0025122461],"study_design_scores_gemma":[0.035278242,0.0008200505,0.6832286,0.0016575813,0.0016485148,0.000027902746,0.053068582,0.13729158,0.05391628,0.02634002,0.005174098,0.0015485187],"about_ca_topic_score_codex":0.00008724285,"about_ca_topic_score_gemma":0.00090056995,"teacher_disagreement_score":0.81951636,"about_ca_system_score_codex":0.000039559833,"about_ca_system_score_gemma":0.00036322104,"threshold_uncertainty_score":0.3679183},"labels":[],"label_agreement":null},{"id":"W4200253760","doi":"10.3311/pptr.16383","title":"Land Use as a Criterion for the Selection of the Trip Starting Locations of Park and Ride Mode Travelers","year":2021,"lang":"en","type":"article","venue":"Periodica Polytechnica Transportation Engineering","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Transport Canada","funders":"Budapesti Műszaki és Gazdaságtudományi Egyetem","keywords":"TRIPS architecture; Selection (genetic algorithm); Transport engineering; Mode (computer interface); Land use; Computer science; Destinations; Order (exchange); Operations research; Geography; Mathematics; Engineering; Civil engineering; Business; Tourism; Machine learning","score_opus":0.015569473460426793,"score_gpt":0.2665349662986161,"score_spread":0.2509654928381893,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200253760","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.79854053,0.00020119124,0.19933555,0.0012192185,0.00011073339,0.00038896737,0.00010440223,0.000071491,0.000027928147],"genre_scores_gemma":[0.9940225,0.00017268388,0.0055859974,0.00003155831,0.00002134196,0.000047177095,0.000042433257,0.00001115956,0.000065186054],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9991918,0.00002775639,0.00032055666,0.00013039797,0.00020161626,0.00012785895],"domain_scores_gemma":[0.9993062,0.00024992216,0.00013325144,0.0001014071,0.00017211477,0.000037121306],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022296197,0.000077326644,0.00012008818,0.00005333971,0.00031484923,0.000033211898,0.00008153076,0.00007010763,0.000017790402],"category_scores_gemma":[0.0002081543,0.0000640326,0.00007385941,0.00038605643,0.00010205107,0.00016416387,0.0000013079687,0.00008721837,7.9895166e-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":[0.00011133207,0.00009147522,0.081781514,0.00026851156,0.00011393781,8.9028225e-7,0.04105691,0.8017169,0.056757048,0.015524718,0.000072955256,0.0025037895],"study_design_scores_gemma":[0.0010188547,0.000067762965,0.8500737,0.00029925158,0.0003588382,0.0000022690322,0.004431385,0.10429931,0.031096244,0.00014634209,0.007883285,0.00032274617],"about_ca_topic_score_codex":0.0008821413,"about_ca_topic_score_gemma":0.0020028134,"teacher_disagreement_score":0.7682922,"about_ca_system_score_codex":0.000018255625,"about_ca_system_score_gemma":0.000171902,"threshold_uncertainty_score":0.26111737},"labels":[],"label_agreement":null},{"id":"W4200396793","doi":"10.1155/2021/3207025","title":"Optimizing Customized Transit Service considering Stochastic Bus Arrival Time","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","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":"Fundamental Research Funds for the Central Universities; Young Scientists Fund; Education Department of Shaanxi Province; Natural Science Foundation of Shaanxi Province","keywords":"Probabilistic logic; Service (business); Arrival time; Bus rapid transit; Genetic algorithm; Computer science; Heuristic; Routing (electronic design automation); Operations research; Vehicle routing problem; Mathematical optimization; Transport engineering; Public transport; Engineering; Computer network; Mathematics","score_opus":0.013849917820211034,"score_gpt":0.271349197949878,"score_spread":0.25749928012966694,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200396793","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7397229,0.0005660348,0.25533098,0.0024581996,0.0009129912,0.000211621,0.0000276412,0.00010562435,0.0006640119],"genre_scores_gemma":[0.93506396,0.00013736938,0.06413118,0.00021803532,0.00017403804,0.0000028962834,0.000083176616,0.000022007973,0.00016732838],"study_design_codex":"simulation_or_modeling","study_design_gemma":"qualitative","domain_scores_codex":[0.9983615,0.00010552181,0.00064306473,0.00016034822,0.0005059124,0.00022362875],"domain_scores_gemma":[0.9982012,0.00018693096,0.00043471114,0.00008142556,0.0009378865,0.0001578815],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000400042,0.00012852855,0.00030613007,0.00012899308,0.00026787302,0.000051671588,0.00010760529,0.00009696612,0.00021790032],"category_scores_gemma":[0.000101424106,0.00014133842,0.00013727973,0.00050977286,0.000048398084,0.00078170723,6.082721e-7,0.00021801193,0.000009060256],"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":[0.0002462424,0.000046372457,0.00012363271,0.000023353414,0.00004734684,0.000106631895,0.034305237,0.9557248,0.007576316,0.00044229854,0.00002066576,0.0013371434],"study_design_scores_gemma":[0.11346798,0.0016934949,0.24917912,0.0096129915,0.0076823705,0.0006151583,0.41906753,0.038850963,0.053012766,0.017352981,0.0808216,0.0086430535],"about_ca_topic_score_codex":0.000030910473,"about_ca_topic_score_gemma":0.00056915474,"teacher_disagreement_score":0.9168738,"about_ca_system_score_codex":0.00006626767,"about_ca_system_score_gemma":0.00046144213,"threshold_uncertainty_score":0.5763613},"labels":[],"label_agreement":null},{"id":"W4200421755","doi":"10.1155/2021/5814879","title":"Mining Taxi Pick-Up Hotspots Based on Grid Information Entropy Clustering Algorithm","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Cluster analysis; Beijing; Data mining; Computer science; Grid; Entropy (arrow of time); Algorithm; Geography; Artificial intelligence","score_opus":0.010016969256957947,"score_gpt":0.26618622163719013,"score_spread":0.2561692523802322,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200421755","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28348187,0.00008580917,0.7104502,0.0013344706,0.0035442559,0.00019668697,0.000059971277,0.00008141388,0.00076531747],"genre_scores_gemma":[0.8866825,0.00014737486,0.11196932,0.0004240099,0.00031305745,0.000004807253,0.00033639165,0.000013572769,0.0001089483],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99835426,0.00007741946,0.00062861806,0.00010565947,0.00064414454,0.00018989002],"domain_scores_gemma":[0.99845546,0.00011253489,0.0005749502,0.000079135934,0.00065879175,0.000119133496],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036269132,0.00011392982,0.00018617592,0.00021223493,0.00025361683,0.0000718971,0.000094455354,0.000088353314,0.00009323277],"category_scores_gemma":[0.00010842703,0.00012052989,0.0001151519,0.00042037247,0.000036071844,0.001461287,5.522832e-7,0.0001720495,0.000004365963],"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":[0.0001902413,0.000052413132,0.0023619167,0.000027871984,0.000018959676,0.00004704175,0.020029139,0.91637254,0.0003173739,0.0003084762,0.000122019446,0.060152013],"study_design_scores_gemma":[0.013079649,0.0009607202,0.74834806,0.001628047,0.00040145067,0.000021744196,0.076214895,0.03266598,0.00729769,0.0005575135,0.117582545,0.0012417294],"about_ca_topic_score_codex":0.000023535851,"about_ca_topic_score_gemma":0.0002336768,"teacher_disagreement_score":0.88370657,"about_ca_system_score_codex":0.00009171059,"about_ca_system_score_gemma":0.00032531953,"threshold_uncertainty_score":0.49150664},"labels":[],"label_agreement":null},{"id":"W4200481023","doi":"10.1287/opre.2021.2189","title":"Technical Note—A Monge Sequence-Based Approach to Characterize the Competitive Newsvendor Problem","year":2021,"lang":"en","type":"article","venue":"Operations Research","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Newsvendor model; Replicate; Computer science; Cash flow; Spillover effect; Task (project management); Game theory; Sequence (biology); Replication (statistics); Cash; Microeconomics; Mathematical optimization; Operations research; Economics; Supply chain; Business; Mathematics; Finance","score_opus":0.14891773689538426,"score_gpt":0.41043164990857406,"score_spread":0.2615139130131898,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200481023","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022388022,0.00009818433,0.3804647,0.124634534,0.00025219517,0.0037638112,0.00035388547,0.00037539925,0.46766928],"genre_scores_gemma":[0.9204732,0.000034219596,0.07050855,0.00048878434,0.00015840742,0.0004894397,0.0004979943,0.00001301366,0.0073363627],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99786955,0.000684802,0.00016979717,0.0002654029,0.0007018268,0.00030861705],"domain_scores_gemma":[0.9983235,0.00023084218,0.000010884377,0.00021970108,0.0010846835,0.0001304078],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0013613489,0.00006458457,0.00008659643,0.00009769546,0.0017650042,0.00038997256,0.00026805108,0.00007932541,0.00022234113],"category_scores_gemma":[0.0005545368,0.000051754774,0.00003177005,0.0015776115,0.00027390796,0.00016633373,0.000021723954,0.0003272258,0.000094368195],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006375166,0.0007402916,0.001188398,0.00003128842,0.000016029484,0.000033321965,0.03735214,0.220333,0.027547058,0.7003545,0.005080018,0.0072601778],"study_design_scores_gemma":[0.0017103397,0.00031115962,0.032512795,0.0002751737,0.00004500533,0.000012819983,0.04125328,0.104208216,0.007899976,0.0005769379,0.81020373,0.0009905834],"about_ca_topic_score_codex":0.0013316724,"about_ca_topic_score_gemma":0.0058834096,"teacher_disagreement_score":0.89808524,"about_ca_system_score_codex":0.00012386774,"about_ca_system_score_gemma":0.0013678811,"threshold_uncertainty_score":0.99953455},"labels":[],"label_agreement":null},{"id":"W4200522767","doi":"10.1155/2021/5316574","title":"Optimization Problem of Pricing and Seat Allocation Based on Bilevel Multifollower Programming in High-Speed Railway","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Bilevel optimization; Train; Computer science; Mathematical optimization; Revenue; Operations research; Programming paradigm; Optimization problem; Engineering; Economics; Mathematics","score_opus":0.01142471449055754,"score_gpt":0.26978139704499315,"score_spread":0.2583566825544356,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200522767","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8174588,0.00011976428,0.18091768,0.0007601613,0.00021732907,0.0003608569,0.000009712072,0.00002617142,0.00012950542],"genre_scores_gemma":[0.7994878,0.00013566876,0.20017953,0.000032660013,0.00002679411,0.0000033904482,0.00009640328,0.000010436003,0.000027291286],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9985373,0.000099173274,0.00063495117,0.00015410557,0.0004343868,0.00014005469],"domain_scores_gemma":[0.9985978,0.00013598622,0.00053554215,0.000059925926,0.00060433306,0.00006638714],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005234463,0.00009845226,0.00021600208,0.0002540351,0.0000988611,0.000025546575,0.00005477908,0.000088193716,0.000018766517],"category_scores_gemma":[0.0000955937,0.00010049673,0.00005395163,0.00061264925,0.000048187747,0.0005405033,5.1536153e-7,0.00013175432,1.555137e-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":[0.00017405268,0.00014216935,0.018231247,0.000053246702,0.000009788686,0.000014333728,0.008761903,0.959835,0.0012575446,0.0005750141,0.0000015906406,0.010944134],"study_design_scores_gemma":[0.01247358,0.000783318,0.89670634,0.0025532488,0.00025702693,0.00000405594,0.016121615,0.05914958,0.009358423,0.00065238785,0.0012430806,0.00069733366],"about_ca_topic_score_codex":0.00007851675,"about_ca_topic_score_gemma":0.00059267547,"teacher_disagreement_score":0.90068537,"about_ca_system_score_codex":0.000068747024,"about_ca_system_score_gemma":0.00025002216,"threshold_uncertainty_score":0.40981376},"labels":[],"label_agreement":null},{"id":"W4200621161","doi":"10.1109/coniiti53815.2021.9619636","title":"Evaluation of the Level of Service of “Vía Expresa Paseo de la República” (Peru): Canada - Javier Prado Section","year":2021,"lang":"es","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Section (typography); Humanities; Computer science; Art; Operating system","score_opus":0.06412508858089029,"score_gpt":0.33306712106949365,"score_spread":0.2689420324886034,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200621161","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9727428,0.00016051246,0.0011051961,0.0025069118,0.00036729284,0.00024152531,0.00018033972,0.000009445399,0.022686003],"genre_scores_gemma":[0.99822193,0.00007076127,0.0007742984,0.0001318004,0.000044302473,0.0000060622765,0.000034663903,0.000006305481,0.00070987345],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9971685,0.0010231466,0.0003979,0.00015252804,0.0011469414,0.00011101455],"domain_scores_gemma":[0.99743414,0.00023556812,0.0003890405,0.0002050396,0.0016977346,0.000038478716],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001997852,0.00007670247,0.00015300403,0.000032725195,0.00012322201,0.000015653899,0.00013041522,0.00012515737,0.0003906884],"category_scores_gemma":[0.000492272,0.0000650501,0.00005707941,0.00081638567,0.00009087278,0.000110805835,0.000010855547,0.00008883319,2.3016489e-7],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013203562,0.0009408552,0.30787474,0.0011034193,0.00050823705,0.0000033518756,0.087372,0.4974229,0.047125455,0.026777325,0.01230692,0.018432751],"study_design_scores_gemma":[0.0010313934,0.000035423316,0.8872313,0.00039252048,0.0006028363,0.000002170826,0.021081058,0.017949998,0.06316191,0.00045109462,0.007866619,0.000193679],"about_ca_topic_score_codex":0.61839354,"about_ca_topic_score_gemma":0.75368744,"teacher_disagreement_score":0.57935655,"about_ca_system_score_codex":0.00012294984,"about_ca_system_score_gemma":0.004248834,"threshold_uncertainty_score":0.75372493},"labels":[],"label_agreement":null},{"id":"W4205156196","doi":"10.1016/b978-0-08-102671-7.10373-2","title":"Route Choice and Network Modeling","year":2021,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Utility maximization; Path (computing); Computer science; Perspective (graphical); Maximization; Interpretation (philosophy); Flow network; Operations research; Network planning and design; Mathematical optimization; Artificial intelligence; Engineering; Economics; Computer network; Mathematics; Mathematical economics","score_opus":0.02528507411027635,"score_gpt":0.26941503474731676,"score_spread":0.2441299606370404,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205156196","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000050566323,0.0022845678,0.00032815634,0.00011713417,0.00034293212,0.0001582103,0.0000111643885,0.00008427454,0.996623],"genre_scores_gemma":[0.0017335863,0.00068244006,0.0015802084,0.00026103933,0.0008110685,0.0000061782507,0.00008780016,0.00003159238,0.9948061],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9990232,0.000030842748,0.00022034386,0.00027150888,0.0002551635,0.00019893138],"domain_scores_gemma":[0.999494,0.000062842024,0.00010154851,0.00012795415,0.00010900244,0.00010462998],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024841973,0.00015488235,0.00022949552,0.000037444563,0.00040409333,0.000096183794,0.0000897229,0.00026401397,0.0001276463],"category_scores_gemma":[0.000019364064,0.00017231805,0.00006846261,0.0000143080515,0.00007998298,0.000050906772,0.000012969154,0.00022832176,0.000010716055],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000035755886,0.0000022352353,0.00022248687,0.000034563018,0.00006489145,0.000015449774,0.0041159047,0.012003703,2.2676134e-7,0.1036882,0.00015310728,0.87969565],"study_design_scores_gemma":[0.00008560094,0.000004895795,0.000027332582,0.00029568697,0.00007101957,5.6530644e-7,0.00006282459,0.001021867,7.629562e-8,0.0043083597,0.99390656,0.00021521047],"about_ca_topic_score_codex":0.00001961543,"about_ca_topic_score_gemma":0.0014532816,"teacher_disagreement_score":0.99375343,"about_ca_system_score_codex":0.000034895114,"about_ca_system_score_gemma":0.00017926845,"threshold_uncertainty_score":0.70269257},"labels":[],"label_agreement":null},{"id":"W4205318884","doi":"10.1155/2022/1829104","title":"Differential Congestion Pricing Strategies for Heterogeneous Users in the Mixed Traffic Condition","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","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":"China Scholarship Council","keywords":"Toll; Congestion pricing; Singapore Area Licensing Scheme; Computer science; Traffic congestion; Revenue; Road pricing; Value of time; Equity (law); Scheme (mathematics); Operations research; Transport engineering; Mathematical optimization; Business; Travel time; Engineering; Mathematics","score_opus":0.016459138260937714,"score_gpt":0.2911595157118985,"score_spread":0.2747003774509608,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205318884","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9788039,0.0000550249,0.019688686,0.00046395473,0.00059243746,0.00032316966,0.000033444947,0.000016968364,0.000022445218],"genre_scores_gemma":[0.9984392,0.00004756959,0.0011035035,0.00005531609,0.00008038159,0.000040125877,0.00021570038,0.000008065521,0.000010146119],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99878,0.00016392827,0.0004128755,0.000091863214,0.00041491582,0.00013641424],"domain_scores_gemma":[0.999237,0.00017148186,0.00038845805,0.000040634946,0.00013324608,0.000029156135],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004735967,0.00007183948,0.00012543787,0.00014535245,0.00043266875,0.000043202337,0.00012651023,0.00003426911,0.000048177073],"category_scores_gemma":[0.000021255068,0.00006474453,0.00008726757,0.00024911962,0.000040924493,0.00046642995,3.4905594e-7,0.00016283378,1.2572731e-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":[0.0002131824,0.000067873356,0.0006485528,0.000012051417,0.000010339963,0.000010288393,0.042052273,0.9513826,0.00039043516,0.0027590871,0.000022860528,0.0024304553],"study_design_scores_gemma":[0.008545005,0.0015560733,0.5195235,0.0001464239,0.00036501192,0.00002633625,0.4478269,0.0035686928,0.00034984265,0.0066162096,0.010897214,0.00057878805],"about_ca_topic_score_codex":0.00002625925,"about_ca_topic_score_gemma":0.0010366162,"teacher_disagreement_score":0.9478139,"about_ca_system_score_codex":0.000080160506,"about_ca_system_score_gemma":0.00014892925,"threshold_uncertainty_score":0.3327782},"labels":[],"label_agreement":null},{"id":"W4205357925","doi":"10.1155/2022/7064061","title":"Dynamic Optimization of Bus Line Schedule in Commuter Corridor Based on Bus IC Card Data","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Social Science Fund of China","keywords":"Schedule; Interval (graph theory); Genetic algorithm; Computer science; Line (geometry); Dimension (graph theory); Operating cost; Weighting; Operations research; Engineering; Mathematical optimization; Mathematics","score_opus":0.020915739033343168,"score_gpt":0.3064770719409453,"score_spread":0.28556133290760216,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205357925","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7268516,0.0002592133,0.26895937,0.0014743693,0.001205586,0.00046747495,0.00049479335,0.00004388103,0.00024376433],"genre_scores_gemma":[0.95571256,0.00009675433,0.042739965,0.000104189865,0.000028199389,0.000006235369,0.00126252,0.000015156231,0.00003443215],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981672,0.00016644923,0.00069352513,0.00016113558,0.0006647437,0.00014691857],"domain_scores_gemma":[0.9986714,0.00013444065,0.000687141,0.00019757061,0.00024905236,0.000060412603],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006994019,0.00009921648,0.00023557563,0.0003454673,0.00019715077,0.000013042125,0.0003324658,0.000051306957,0.000115741874],"category_scores_gemma":[0.0000704865,0.0001086848,0.000062996194,0.00064726727,0.000054496704,0.0005454356,0.0000025861737,0.00028257968,3.7778761e-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":[0.00046037958,0.00019113974,0.005354355,0.000019654452,0.000010170204,0.000018468272,0.0038382467,0.9885464,0.00017798704,0.00011064103,0.000024072531,0.0012485407],"study_design_scores_gemma":[0.010812178,0.0017639566,0.28849167,0.00053206034,0.00029281148,0.0000051335705,0.021297468,0.67127144,0.00043640487,0.00038090855,0.0040309276,0.0006850407],"about_ca_topic_score_codex":0.00010282035,"about_ca_topic_score_gemma":0.0008896203,"teacher_disagreement_score":0.3172749,"about_ca_system_score_codex":0.00012921859,"about_ca_system_score_gemma":0.0002889126,"threshold_uncertainty_score":0.44320375},"labels":[],"label_agreement":null},{"id":"W4205658123","doi":"10.1155/2022/5117488","title":"Last-Mile Shuttle Planning for Improving Bus Commuters’ Travel Time Reliability: A Case Study of Beijing","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities","keywords":"Beijing; Mile; Transport engineering; Reliability (semiconductor); Bus rapid transit; Service (business); Transit (satellite); Computer science; Operations research; Level of service; Public transport; Engineering; Geography; Business","score_opus":0.017786560931480982,"score_gpt":0.2998200386425657,"score_spread":0.28203347771108467,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205658123","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98016715,0.00006559577,0.01873797,0.00006877327,0.00038243277,0.00046613356,0.000055401262,0.000021015992,0.00003551049],"genre_scores_gemma":[0.98953015,0.000004303041,0.010237481,0.000020945814,0.00005912221,0.000026812031,0.000047995367,0.000015967084,0.00005720129],"study_design_codex":"simulation_or_modeling","study_design_gemma":"qualitative","domain_scores_codex":[0.99840474,0.00012732802,0.00070489186,0.00013895934,0.000460447,0.0001636453],"domain_scores_gemma":[0.9984582,0.0002762226,0.00077150477,0.00008713737,0.0003374902,0.000069426926],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001024455,0.00009444383,0.00025468727,0.0001803897,0.00062856544,0.000016975826,0.00012534985,0.00003628096,0.000033354096],"category_scores_gemma":[0.00007025756,0.000103710656,0.000104520856,0.00031588683,0.000038215017,0.0004559751,0.000001786721,0.00020220912,1.3007455e-7],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003194257,0.0002568192,0.010649295,0.00003811635,0.000028644834,0.00016822548,0.17606874,0.80886406,0.00096756284,0.00004859545,0.000020656707,0.002569844],"study_design_scores_gemma":[0.0073703076,0.0033570784,0.06810442,0.00010693645,0.00047678495,0.00009240882,0.91444194,0.0035735825,0.00026651734,0.00029328951,0.0015124349,0.00040427607],"about_ca_topic_score_codex":0.00032261354,"about_ca_topic_score_gemma":0.0005259121,"teacher_disagreement_score":0.8052905,"about_ca_system_score_codex":0.00011305257,"about_ca_system_score_gemma":0.00018846255,"threshold_uncertainty_score":0.48344806},"labels":[],"label_agreement":null},{"id":"W4206005877","doi":"10.7202/1083293ar","title":"MUST-B : un modèle LUTI multidisciplinaire au service de la complexité du phénomène urbain","year":2021,"lang":"fr","type":"article","venue":"Canadian Journal of Regional Science","topic":"Transportation Planning and Optimization","field":"Social Sciences","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":"Humanities; Political science; Philosophy","score_opus":0.05702080161487234,"score_gpt":0.2941350463290974,"score_spread":0.2371142447142251,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206005877","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7786934,0.005688236,0.009516716,0.19170804,0.002431225,0.00011156559,0.0000728565,0.000015892378,0.0117621],"genre_scores_gemma":[0.9843837,0.00017462834,0.01150808,0.001354979,0.000559352,9.3171946e-7,0.000008205439,0.000010766601,0.0019993465],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99778247,0.00032820742,0.00032861624,0.00024722944,0.00064961636,0.0006638558],"domain_scores_gemma":[0.9966344,0.00023391319,0.00030212096,0.00012726543,0.001144812,0.0015574354],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0021543612,0.00014038385,0.0001957325,0.0002237679,0.0017548752,0.0002212294,0.0006148194,0.00014077085,0.00029243206],"category_scores_gemma":[0.0003424926,0.00015265634,0.00010896639,0.0021781777,0.0024022264,0.0007314456,0.000013139818,0.00033049213,0.000016938746],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003968967,0.00020683427,0.17978978,0.00009973706,0.00008968432,0.0034271253,0.15578929,0.26529226,0.00069703266,0.3555922,0.019461254,0.01951513],"study_design_scores_gemma":[0.0011030576,0.000094709685,0.5434091,0.000819608,0.00012217698,0.0016306922,0.023381324,0.029026749,0.0002390519,0.009881342,0.38969028,0.0006018924],"about_ca_topic_score_codex":0.04784241,"about_ca_topic_score_gemma":0.2331807,"teacher_disagreement_score":0.37022904,"about_ca_system_score_codex":0.0010663916,"about_ca_system_score_gemma":0.026741516,"threshold_uncertainty_score":0.9995447},"labels":[],"label_agreement":null},{"id":"W4206292006","doi":"10.1155/2021/8793101","title":"Behavioral Intentions of Urban Rail Transit Passengers during the COVID-19 Pandemic in Tianjin, China: A Model Integrating the Theory of Planned Behavior and Customer Satisfaction Theory","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":76,"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":"Humanities and Social Science Fund of Ministry of Education of China; Ministry of Education of the People's Republic of China; National Natural Science Foundation of China","keywords":"Structural equation modeling; Theory of planned behavior; Public transport; Psychology; Travel behavior; Psychological intervention; Pandemic; Control (management); Customer satisfaction; Applied psychology; Business; Coronavirus disease 2019 (COVID-19); Transport engineering; Marketing; Social psychology; Engineering; Economics; Computer science; Medicine","score_opus":0.023298862338532282,"score_gpt":0.31181249367434455,"score_spread":0.28851363133581226,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206292006","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9731561,0.00027156636,0.025822613,0.00031941498,0.00012887003,0.00020058444,0.00005369106,0.000012660825,0.00003449743],"genre_scores_gemma":[0.9982979,0.00031638079,0.0012423154,0.00003002948,0.000022520097,0.000012003502,0.00003222545,0.000010364454,0.00003623214],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99836206,0.00033303013,0.0006789284,0.00011841568,0.0003786742,0.00012886825],"domain_scores_gemma":[0.9987467,0.00026361877,0.00058410916,0.000084521795,0.00025531003,0.00006576206],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012073581,0.0001024725,0.00021598933,0.000138087,0.0002555542,0.000016879148,0.00009762599,0.0000836276,0.000024340625],"category_scores_gemma":[0.00013833352,0.0000733681,0.00011709798,0.00036445228,0.00022356136,0.00042459415,0.0000011089395,0.00030253988,7.136011e-8],"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":[0.0009130684,0.00014479704,0.4547587,0.000073464,0.000048736754,0.00002124764,0.1886952,0.32512638,0.018030543,0.00791101,0.0000042899665,0.0042725545],"study_design_scores_gemma":[0.0012018476,0.000060606046,0.9072768,0.00011568846,0.00024238681,0.000011552385,0.08778315,0.00013252502,0.0004322603,0.002624141,0.000021928186,0.00009707659],"about_ca_topic_score_codex":0.00015141793,"about_ca_topic_score_gemma":0.008545374,"teacher_disagreement_score":0.45251814,"about_ca_system_score_codex":0.00007408582,"about_ca_system_score_gemma":0.0002968412,"threshold_uncertainty_score":0.4768519},"labels":[],"label_agreement":null},{"id":"W4206786481","doi":"10.1155/2022/5023518","title":"Resilient Transportation Network Design under Uncertain Link Capacity Using a Trilevel Optimization Model","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Flow network; Network planning and design; Computer science; Mathematical optimization; Operations research; Engineering; Mathematics; Computer network","score_opus":0.06363027501635704,"score_gpt":0.30665284667340825,"score_spread":0.2430225716570512,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206786481","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.094824836,0.0001518688,0.9033444,0.00049278385,0.00063284003,0.0003909586,0.00006583523,0.0000566321,0.000039840827],"genre_scores_gemma":[0.60435987,0.00015326017,0.3948371,0.00014830427,0.0001900142,0.000016810045,0.0001925589,0.000028775903,0.00007329139],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99697244,0.00035192468,0.0010150824,0.00024777997,0.0010649553,0.00034782634],"domain_scores_gemma":[0.9979446,0.00015181658,0.0010793784,0.000113397626,0.0005487814,0.00016203747],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014299438,0.00018989564,0.00031397445,0.00025902694,0.0011125578,0.00004063594,0.00021558566,0.00010966454,0.000118629185],"category_scores_gemma":[0.00003932337,0.0002159919,0.00017784584,0.0009109184,0.00008446991,0.0009523755,9.392527e-7,0.00039662377,3.799676e-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":[0.0005710572,0.000081788356,0.00052473694,0.000013595928,0.000036471363,0.000013608434,0.018830998,0.97592366,0.00021749917,0.003230983,0.00007793663,0.00047767418],"study_design_scores_gemma":[0.0035159113,0.000445283,0.012889019,0.0001438028,0.00044439186,0.000007276685,0.014497353,0.9574774,0.000100262674,0.008539368,0.0013055512,0.0006343785],"about_ca_topic_score_codex":0.00014614181,"about_ca_topic_score_gemma":0.00032205536,"teacher_disagreement_score":0.509535,"about_ca_system_score_codex":0.00039175994,"about_ca_system_score_gemma":0.0006772307,"threshold_uncertainty_score":0.8807894},"labels":[],"label_agreement":null},{"id":"W4210272766","doi":"10.3390/futuretransp2010009","title":"A New Stochastic Model for Bus Rapid Transit Scheduling with Uncertainty","year":2022,"lang":"en","type":"article","venue":"Future Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Bus rapid transit; Scheduling (production processes); Computer science; Mathematical optimization; Stochastic programming; Integer programming; Nonlinear system; Stochastic modelling; Operations research; Engineering; Public transport; Mathematics; Transport engineering; Algorithm","score_opus":0.016141538859761228,"score_gpt":0.2572736762002172,"score_spread":0.24113213734045597,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210272766","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021447137,0.00012381127,0.9742647,0.0025393597,0.0003230951,0.0006608865,0.00024435538,0.00020104874,0.0001956414],"genre_scores_gemma":[0.94801366,0.0000140716165,0.048852254,0.0001748378,0.0003141149,0.00016623865,0.0017884442,0.000026045951,0.0006503302],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99875194,0.00004171113,0.00022370453,0.00027007036,0.0004600505,0.00025252425],"domain_scores_gemma":[0.99951273,0.000051085583,0.000109499124,0.00008865514,0.00011653507,0.000121465164],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023482987,0.00012910547,0.00014447184,0.000096154625,0.0009810545,0.000031515585,0.00013203753,0.000066164,0.00020186233],"category_scores_gemma":[0.0000057777343,0.0001322975,0.000076763194,0.00040025194,0.000032824446,0.00021321002,4.115463e-7,0.00015990504,9.335951e-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":[0.0003906048,0.000025423706,0.00012398747,0.000014053283,0.000019222784,0.0000027496276,0.05235048,0.93061185,0.000018568338,0.012468069,0.00026009168,0.0037149142],"study_design_scores_gemma":[0.0065049776,0.0005810326,0.006070508,0.00006707587,0.0005907962,0.0000034507132,0.0582309,0.89047635,0.000024228184,0.0036336929,0.0327071,0.0011098763],"about_ca_topic_score_codex":0.00041815804,"about_ca_topic_score_gemma":0.0071707875,"teacher_disagreement_score":0.92656654,"about_ca_system_score_codex":0.00007153587,"about_ca_system_score_gemma":0.00053911126,"threshold_uncertainty_score":0.7545577},"labels":[],"label_agreement":null},{"id":"W4210759112","doi":"10.32920/19027391.v1","title":"Road pricing schemes: policy lessons from the Bay Bridge in San Francisco","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Trent University; Toronto Metropolitan University","funders":"","keywords":"Bridge (graph theory); Bay; Singapore Area Licensing Scheme; Road pricing; Congestion pricing; Revenue; Business; Traffic congestion; Key (lock); Transport engineering; Computer science; Engineering; Finance; Civil engineering","score_opus":0.04709190121791764,"score_gpt":0.3595842444156362,"score_spread":0.31249234319771857,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210759112","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.75432944,0.0010112923,0.026874134,0.04724031,0.0017984995,0.0013009908,0.0005459265,0.00058812316,0.16631126],"genre_scores_gemma":[0.9944384,0.00025509775,0.0016635571,0.00088042015,0.00041354838,0.000058197656,0.0004861764,0.000016252503,0.0017883651],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99839103,0.00026315483,0.00028840548,0.0003570572,0.00044785292,0.0002525173],"domain_scores_gemma":[0.99926853,0.0002107137,0.00014830218,0.0002735134,0.00004126506,0.000057659367],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00067340734,0.00013841242,0.00018796747,0.00012265198,0.00053803547,0.00013752587,0.0005059181,0.00017221918,0.001591845],"category_scores_gemma":[0.00022490627,0.00011708544,0.00006948484,0.0004898755,0.000086811066,0.00008697882,0.00008938828,0.0006265159,0.000012917249],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029770532,0.00013997784,0.43862557,0.00003233133,0.0000854694,0.00001826896,0.28434148,0.1810746,0.000015327354,0.07068851,0.0064723426,0.018476317],"study_design_scores_gemma":[0.00024237267,0.0000065012505,0.926329,0.00009785112,0.000021501832,6.8475224e-8,0.012319869,0.0014561986,0.0000068703453,0.0025153533,0.056704,0.00030038346],"about_ca_topic_score_codex":0.20664957,"about_ca_topic_score_gemma":0.07563447,"teacher_disagreement_score":0.48770344,"about_ca_system_score_codex":0.00018481626,"about_ca_system_score_gemma":0.00084164314,"threshold_uncertainty_score":0.99932086},"labels":[],"label_agreement":null},{"id":"W4210767194","doi":"10.32920/19027391","title":"Road pricing schemes: policy lessons from the Bay Bridge in San Francisco","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Trent University; Toronto Metropolitan University","funders":"","keywords":"Bridge (graph theory); Bay; Singapore Area Licensing Scheme; Road pricing; Congestion pricing; Revenue; Traffic congestion; Business; Key (lock); Transport engineering; Computer science; Engineering; Finance; Civil engineering","score_opus":0.04709190121791764,"score_gpt":0.3595842444156362,"score_spread":0.31249234319771857,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210767194","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.75432944,0.0010112923,0.026874134,0.04724031,0.0017984995,0.0013009908,0.0005459265,0.00058812316,0.16631126],"genre_scores_gemma":[0.9944384,0.00025509775,0.0016635571,0.00088042015,0.00041354838,0.000058197656,0.0004861764,0.000016252503,0.0017883651],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99839103,0.00026315483,0.00028840548,0.0003570572,0.00044785292,0.0002525173],"domain_scores_gemma":[0.99926853,0.0002107137,0.00014830218,0.0002735134,0.00004126506,0.000057659367],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00067340734,0.00013841242,0.00018796747,0.00012265198,0.00053803547,0.00013752587,0.0005059181,0.00017221918,0.001591845],"category_scores_gemma":[0.00022490627,0.00011708544,0.00006948484,0.0004898755,0.000086811066,0.00008697882,0.00008938828,0.0006265159,0.000012917249],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029770532,0.00013997784,0.43862557,0.00003233133,0.0000854694,0.00001826896,0.28434148,0.1810746,0.000015327354,0.07068851,0.0064723426,0.018476317],"study_design_scores_gemma":[0.00024237267,0.0000065012505,0.926329,0.00009785112,0.000021501832,6.8475224e-8,0.012319869,0.0014561986,0.0000068703453,0.0025153533,0.056704,0.00030038346],"about_ca_topic_score_codex":0.20664957,"about_ca_topic_score_gemma":0.07563447,"teacher_disagreement_score":0.48770344,"about_ca_system_score_codex":0.00018481626,"about_ca_system_score_gemma":0.00084164314,"threshold_uncertainty_score":0.99932086},"labels":[],"label_agreement":null},{"id":"W4210956102","doi":"10.1016/bs.atpp.2021.10.002","title":"Advances in integrated land use transport modeling","year":2022,"lang":"en","type":"book-chapter","venue":"Advances in transport policy and planning","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Land use; Order (exchange); Work (physics); Data science; Integrated business planning; Distribution (mathematics); Space (punctuation); Management science; Engineering; Business; Civil engineering","score_opus":0.03065591854315086,"score_gpt":0.3155655181510003,"score_spread":0.28490959960784945,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210956102","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026622588,0.05349748,0.009940204,0.0004768132,0.0010712453,0.0013827347,0.0013163106,0.0004974142,0.90519524],"genre_scores_gemma":[0.8010527,0.1675202,0.001740064,0.0003627085,0.00043799935,0.00007728635,0.0030047789,0.00012923045,0.025675071],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9975091,0.00005990441,0.0007877932,0.000619731,0.0004934715,0.0005299978],"domain_scores_gemma":[0.99916685,0.00024308078,0.00022140105,0.00015913263,0.00006071961,0.00014884844],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00054434163,0.00041808872,0.00059544214,0.000836489,0.00037506997,0.000031402564,0.00023591051,0.0003644285,0.00022677255],"category_scores_gemma":[0.000048053123,0.0004692452,0.0001000491,0.0003548727,0.0002035103,0.0018869565,0.0000049638493,0.0009090456,0.0000016022966],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023979114,0.000027948186,0.16073623,0.00013763951,0.000013936652,0.00034549995,0.020086529,0.72223675,3.0464315e-7,0.08906125,0.000003112712,0.007111018],"study_design_scores_gemma":[0.0010624285,0.00006585771,0.004217801,0.0015288852,0.00006509633,0.0000056876097,0.0018313177,0.002769505,5.977468e-7,0.011419079,0.97600484,0.0010288982],"about_ca_topic_score_codex":0.005496127,"about_ca_topic_score_gemma":0.029962428,"teacher_disagreement_score":0.97600174,"about_ca_system_score_codex":0.00016324242,"about_ca_system_score_gemma":0.00038500197,"threshold_uncertainty_score":0.99977595},"labels":[],"label_agreement":null},{"id":"W4212883438","doi":"10.1155/2022/6650310","title":"Traveller Recurrence and Inter- versus Intratraveller Speed Variability: Analysis with Bluetooth Data","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","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":"Morning; Evening; Statistics; Names of the days of the week; Variance (accounting); Bluetooth; Reliability (semiconductor); Time of day; Variance components; Econometrics; Traverse; Estimation; Computer science; Geography; Mathematics; Cartography; Telecommunications; Medicine; Engineering; Wireless","score_opus":0.028066330714608744,"score_gpt":0.30226617688993324,"score_spread":0.2741998461753245,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4212883438","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9225556,0.00011039173,0.07495428,0.0008327105,0.0006293958,0.00022022746,0.00018079109,0.000029467592,0.00048709105],"genre_scores_gemma":[0.9877143,0.00020508876,0.011591401,0.000031199943,0.00005698645,0.0000020816276,0.00033259133,0.000008252612,0.000058105245],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9984857,0.00016328518,0.00043845212,0.00022878984,0.00053849956,0.00014529105],"domain_scores_gemma":[0.9988926,0.00018844122,0.00043590285,0.0001674428,0.00021324464,0.000102335885],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010761463,0.0000984543,0.0002227163,0.00022704047,0.0003394864,0.00003696535,0.0002573949,0.000034921643,0.000232612],"category_scores_gemma":[0.000056558525,0.000091741116,0.00005284644,0.00095933146,0.000107329084,0.00068096514,0.0000033036977,0.00024845006,2.4622224e-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":[0.0041878168,0.0002674936,0.06824744,0.00003298948,0.00086824415,0.000065878536,0.0865498,0.8103817,0.000143443,0.0027231085,0.00013773116,0.02639436],"study_design_scores_gemma":[0.008851785,0.0018829164,0.8250307,0.00009668275,0.004358673,0.0000103800185,0.114993766,0.0039544217,0.00007620245,0.00076382374,0.039122954,0.000857701],"about_ca_topic_score_codex":0.00009466375,"about_ca_topic_score_gemma":0.0011865881,"teacher_disagreement_score":0.8064273,"about_ca_system_score_codex":0.00005677389,"about_ca_system_score_gemma":0.00021123335,"threshold_uncertainty_score":0.37410942},"labels":[],"label_agreement":null},{"id":"W4213006231","doi":"10.1155/2022/5027133","title":"Estimation Markov Decision Process of Multimodal Trip Chain between Integrated Transportation Hubs in Urban Agglomeration Based on Generalized Cost","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; National Key Research and Development Program of China; Natural Science Foundation of Shaanxi Province","keywords":"Beijing; Markov chain; Transport engineering; Multimodal transport; Computer science; Transfer (computing); Urban agglomeration; Process (computing); Mode choice; Operations research; Economics; Mathematics; Engineering; Public transport; Geography","score_opus":0.012731518960688241,"score_gpt":0.3056616732198615,"score_spread":0.29293015425917324,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4213006231","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8673631,0.000057252913,0.13090467,0.00035949046,0.00038461733,0.00065351644,0.0001990322,0.000035499623,0.0000428184],"genre_scores_gemma":[0.9739274,0.000057089343,0.024002932,0.00008080366,0.00006884938,0.000056005105,0.0017653924,0.000025419191,0.000016115468],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9965813,0.00030734012,0.0014074267,0.00024878487,0.0012238304,0.00023131772],"domain_scores_gemma":[0.99767476,0.00031373466,0.0012944647,0.00010895986,0.0004968074,0.00011124431],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013585472,0.00019404858,0.00042298416,0.0007984553,0.00032120306,0.000024607582,0.00020545843,0.000117824384,0.000117032396],"category_scores_gemma":[0.00014471573,0.000207204,0.00015332995,0.0013901737,0.000066971574,0.0007673096,4.7207607e-7,0.0003896911,4.6991303e-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":[0.001759868,0.00022537411,0.026107807,0.00003113397,0.000014639241,0.000012938483,0.02102247,0.9167818,0.00037956203,0.0002492513,0.000027413013,0.033387758],"study_design_scores_gemma":[0.011548072,0.0011138917,0.8975917,0.00054098514,0.00016619219,8.337029e-7,0.013791593,0.07029258,0.00197036,0.0010026922,0.0014470624,0.0005340133],"about_ca_topic_score_codex":0.00020779185,"about_ca_topic_score_gemma":0.0010136027,"teacher_disagreement_score":0.8714839,"about_ca_system_score_codex":0.0002823706,"about_ca_system_score_gemma":0.00045687016,"threshold_uncertainty_score":0.84495336},"labels":[],"label_agreement":null},{"id":"W4214647915","doi":"10.1155/2022/1252534","title":"An Overview of Agent-Based Models for Transport Simulation and Analysis","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":80,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Computer science; Adaptability; Agent-based model; Simulation modeling; Management science; Systems engineering; Data science; Industrial engineering; Artificial intelligence; Engineering","score_opus":0.05413851380138455,"score_gpt":0.36521401454270835,"score_spread":0.3110755007413238,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4214647915","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.556834,0.00034254335,0.44227514,0.0000926023,0.00009765979,0.00018394535,0.00014703617,0.000013270698,0.000013839164],"genre_scores_gemma":[0.9781893,0.000132738,0.021172564,0.000045423265,0.000022800587,0.000010891183,0.0004057755,0.000009179165,0.000011341277],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9986875,0.00008128266,0.0005596533,0.00011803364,0.0004503287,0.000103204766],"domain_scores_gemma":[0.99878234,0.000120664576,0.00060658547,0.000065063374,0.00034902897,0.00007633166],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006540251,0.00007405315,0.00024425148,0.0003180991,0.00026267572,0.0000075992903,0.0000909059,0.000035776688,0.000043324904],"category_scores_gemma":[0.000010661976,0.000081490914,0.00017396067,0.00062972284,0.000039655202,0.00061540963,2.1096406e-7,0.00007727211,1.6285318e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003523099,0.00009275174,0.013299484,0.00003574325,0.00007284858,0.000001940336,0.011733741,0.96985966,0.00012777171,0.0024758494,0.0000010709538,0.0019468357],"study_design_scores_gemma":[0.0038650988,0.0010206413,0.5405997,0.00005930041,0.0024998083,4.2740507e-7,0.011782837,0.43248975,0.00014173656,0.004618461,0.0025731598,0.000349084],"about_ca_topic_score_codex":0.000069984504,"about_ca_topic_score_gemma":0.0004513948,"teacher_disagreement_score":0.5373699,"about_ca_system_score_codex":0.00003877841,"about_ca_system_score_gemma":0.00013633192,"threshold_uncertainty_score":0.3323103},"labels":[],"label_agreement":null},{"id":"W4214776129","doi":"10.1155/2022/6614242","title":"Demonstration of Smart Railway Level Crossing Design and Validation Using Data from Metro Rail, South Africa","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"European Social Fund; European Regional Development Fund; Univerzita Pardubice","keywords":"Level crossing; Transport engineering; Closing (real estate); Computer science; Engineering","score_opus":0.11597822291495485,"score_gpt":0.3291695329522468,"score_spread":0.21319131003729197,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4214776129","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.58616686,0.00020943605,0.4130194,0.00006526211,0.00021138036,0.00011982708,0.00017857038,0.00001121197,0.000018060839],"genre_scores_gemma":[0.8485247,0.000029734763,0.15108135,0.000009506358,0.000050068196,0.0000017546654,0.0002756831,0.000010171629,0.000017068736],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99821097,0.00024200426,0.0006398975,0.00017435137,0.00060411624,0.00012866255],"domain_scores_gemma":[0.99839395,0.00020253392,0.00096564245,0.00011451283,0.00025614517,0.00006724042],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011955755,0.00009195692,0.0002018225,0.00018641216,0.000556063,0.00006030505,0.00018283496,0.000048013702,0.00004522473],"category_scores_gemma":[0.00007883144,0.00010165951,0.000045225792,0.0003899715,0.00010668529,0.0012698488,0.0000032187231,0.00015992054,1.5495915e-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":[0.000476218,0.00007051636,0.022804573,0.000013599969,0.0000501803,0.000007771729,0.063059,0.89821476,0.012390656,0.00024654326,0.000024602383,0.0026415866],"study_design_scores_gemma":[0.009507266,0.0010768494,0.7640738,0.00044326944,0.0016777293,0.000010003302,0.16211829,0.036885444,0.012628529,0.005428265,0.0049804063,0.0011701534],"about_ca_topic_score_codex":0.00014192583,"about_ca_topic_score_gemma":0.0001672071,"teacher_disagreement_score":0.8613293,"about_ca_system_score_codex":0.00006458306,"about_ca_system_score_gemma":0.00036451998,"threshold_uncertainty_score":0.42768437},"labels":[],"label_agreement":null},{"id":"W421709739","doi":"","title":"Development and Deployment of GIS Tools to Facilitate Transit Network Design and Operational Evaluation","year":2010,"lang":"en","type":"article","venue":"Journal of the Urban and Regional Information Systems Association","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Geographic information system; Computer science; Merge (version control); GIS applications; Transport engineering; Flow network; Software deployment; Geocoding; Population; Transit (satellite); Public transport; Geography; Engineering","score_opus":0.06551601233546304,"score_gpt":0.26731551095555156,"score_spread":0.2017994986200885,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W421709739","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9561925,0.00027575076,0.03712798,0.004160999,0.00090439635,0.00089497585,0.000013769,0.000012874195,0.00041678816],"genre_scores_gemma":[0.9919546,0.000050746523,0.0074664177,0.00016910033,0.00012832294,0.000010389323,0.000013853596,0.0000027062083,0.00020381549],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99844813,0.00018437975,0.00052836654,0.000046976536,0.0007048717,0.00008724435],"domain_scores_gemma":[0.99842685,0.00017528527,0.0006206981,0.000033273347,0.00067145354,0.00007245827],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00364126,0.00006083258,0.0001246141,0.00008131803,0.00033816596,0.00013707997,0.00005459601,0.00008611743,0.000003462855],"category_scores_gemma":[0.0002197064,0.000045300705,0.000024502899,0.00013417623,0.000033156044,0.0008027785,0.0000050445774,0.00009886838,7.1663817e-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":[0.00028541053,0.00006855998,0.09090382,0.00013510548,0.00034607132,3.6717873e-7,0.34237075,0.47677797,0.0007095931,0.03851841,0.027826155,0.022057796],"study_design_scores_gemma":[0.0026341234,0.00016142517,0.52444434,0.00046233262,0.00020245455,0.000020728447,0.017027214,0.030177869,0.0001811992,0.00036931338,0.4239236,0.00039538898],"about_ca_topic_score_codex":0.0000647144,"about_ca_topic_score_gemma":0.00007866277,"teacher_disagreement_score":0.44660008,"about_ca_system_score_codex":0.000099539175,"about_ca_system_score_gemma":0.00023787192,"threshold_uncertainty_score":0.26009336},"labels":[],"label_agreement":null},{"id":"W4220706800","doi":"10.1155/2022/5773362","title":"Optimization of Bus Rerouting to Alleviate the Impact of Rail Transit Construction","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","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":"Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China; Henan Provincial Department of Transportation; Government of Jiangsu Province; U.S. Department of Transportation","keywords":"Transport engineering; Urban rail transit; Rail transit; Scheme (mathematics); Service (business); Bus rapid transit; Transit (satellite); Engineering; Service level; Local bus; Public transport; Duration (music); Line (geometry); Computer science; Control bus; System bus","score_opus":0.012345987561498406,"score_gpt":0.2954182776381713,"score_spread":0.2830722900766729,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220706800","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.806984,0.00010071261,0.19157325,0.00051671395,0.00033025813,0.00025188117,0.000056287125,0.000014346289,0.00017253072],"genre_scores_gemma":[0.9779768,0.00011642236,0.021768246,0.000018492074,0.00004324066,0.00000459011,0.000043503405,0.000009876093,0.00001882176],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99834853,0.0001522475,0.0007392177,0.000091019065,0.00054421625,0.00012474667],"domain_scores_gemma":[0.9982611,0.000104214305,0.0009915984,0.00007377797,0.0005084696,0.00006086854],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007961727,0.0000821717,0.00021627366,0.00018694456,0.00030067496,0.000009168635,0.00015439105,0.000034687284,0.00013955495],"category_scores_gemma":[0.000063231186,0.000069125024,0.00018145522,0.00069670804,0.00007710204,0.00034923194,9.709535e-7,0.00015565562,1.3339233e-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":[0.00037837305,0.000045589233,0.0046985755,0.000010430902,0.000041811927,0.0000018835369,0.038597777,0.9516421,0.0012649434,0.0009169763,0.000018631248,0.0023829099],"study_design_scores_gemma":[0.0071168523,0.003430605,0.80150455,0.00044540098,0.0006896261,0.000031701118,0.16952766,0.008605944,0.0035217847,0.0024652446,0.0019091598,0.0007514595],"about_ca_topic_score_codex":0.00026482568,"about_ca_topic_score_gemma":0.00011862607,"teacher_disagreement_score":0.94303614,"about_ca_system_score_codex":0.00008940717,"about_ca_system_score_gemma":0.00024354462,"threshold_uncertainty_score":0.28188366},"labels":[],"label_agreement":null},{"id":"W4220709442","doi":"10.1155/2022/3215435","title":"How Snowfalls Affect the Operation of Taxi Fleets? A Case Study from Harbin, China","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","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":"National Key Research and Development Program of China","keywords":"Snow; Lag; China; Transport engineering; Autoregressive model; Distributed lag; Global Positioning System; Computer science; Environmental science; Snow removal; Meteorology; Econometrics; Geography; Engineering; Mathematics; Telecommunications","score_opus":0.014074845471791369,"score_gpt":0.28846517612562295,"score_spread":0.27439033065383156,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220709442","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.989482,0.0001838809,0.008186819,0.0010652383,0.0005354202,0.0004200494,0.000066841545,0.000017583221,0.000042161417],"genre_scores_gemma":[0.9974216,0.000049224815,0.0022182977,0.000034887864,0.0001094779,0.000019493751,0.000063265325,0.000011475668,0.00007226597],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99825794,0.00032880562,0.0004697981,0.00012766823,0.0006964179,0.000119385484],"domain_scores_gemma":[0.99875873,0.00014344182,0.00071332883,0.00010370978,0.00022427182,0.000056486002],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00081053155,0.00009699445,0.00020122068,0.00011423214,0.00067193405,0.00005222472,0.00017685017,0.000031420794,0.0000861681],"category_scores_gemma":[0.00005121892,0.0000794488,0.000107545435,0.00037042878,0.000055311,0.0006887432,0.000001539359,0.00024833545,2.3984242e-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":[0.00024828757,0.00037562492,0.033362623,0.000009184372,0.00009610786,0.00050376984,0.22130868,0.7387377,0.0013871922,0.00057145243,0.00006549923,0.0033338764],"study_design_scores_gemma":[0.003530046,0.001300903,0.55266047,0.000044010245,0.00040534307,0.000049969258,0.43818623,0.0003612747,0.0004859107,0.0006181988,0.0020883833,0.0002692247],"about_ca_topic_score_codex":0.0014073215,"about_ca_topic_score_gemma":0.0054897233,"teacher_disagreement_score":0.73837644,"about_ca_system_score_codex":0.00006476716,"about_ca_system_score_gemma":0.00015863166,"threshold_uncertainty_score":0.51680416},"labels":[],"label_agreement":null},{"id":"W4220742330","doi":"10.1155/2022/8895935","title":"Research on Coordinated Passenger Inflow Control for the Urban Rail Transit Network Based on the Station-to-Line Spatial-Temporal Relationship","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","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":"Fundamental Research Funds for the Central Universities; Beijing Municipal Natural Science Foundation; Beijing Jiaotong University; National Natural Science Foundation of China","keywords":"Inflow; Beijing; Urban rail transit; Volume (thermodynamics); Transport engineering; Control (management); Control volume; Flow (mathematics); Level of service; Line (geometry); Computer science; Engineering; Meteorology","score_opus":0.046380362146332836,"score_gpt":0.34254871429200245,"score_spread":0.2961683521456696,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220742330","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17763478,0.00012071995,0.76584274,0.051989526,0.0013258568,0.0024987608,0.00033293437,0.000065560685,0.00018912686],"genre_scores_gemma":[0.9964407,0.000012053766,0.0015794217,0.0010457389,0.00034270147,0.00018563335,0.00020759321,0.000024718256,0.00016140657],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9969506,0.0007365435,0.0006297669,0.00018168763,0.0011923985,0.00030895154],"domain_scores_gemma":[0.9936163,0.004870781,0.00042297688,0.00015365936,0.0008289871,0.00010730704],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0044942927,0.00012753156,0.00019008598,0.00023618435,0.0024104458,0.00006197835,0.00028673557,0.000061739636,0.00012374268],"category_scores_gemma":[0.00035329975,0.000091728565,0.00014791965,0.0011221744,0.000095128526,0.00020452721,7.119991e-7,0.0006530476,0.000002233177],"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":[0.002281395,0.000087672684,0.00551392,0.0000065287245,0.000029243934,0.0000051289067,0.009287498,0.96836585,0.00002720363,0.011231389,0.0020386449,0.0011255244],"study_design_scores_gemma":[0.011342826,0.004721478,0.727091,0.00030036183,0.0003819921,0.0000012316874,0.054463357,0.06674203,0.00012649021,0.007508934,0.12667148,0.0006487983],"about_ca_topic_score_codex":0.00019045599,"about_ca_topic_score_gemma":0.0022911686,"teacher_disagreement_score":0.90162385,"about_ca_system_score_codex":0.00015084997,"about_ca_system_score_gemma":0.00038189077,"threshold_uncertainty_score":0.99888825},"labels":[],"label_agreement":null},{"id":"W4220770854","doi":"10.1155/2022/4062132","title":"Defining Equity Criteria for Determining Fare Zones in Integrated Passenger Transport","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Public transport; Passenger transport; Equity (law); Disadvantage; Disadvantaged; Transport engineering; Private transport; Business; Sustainable transport; Service (business); Transport system; Sustainability; Economics; Computer science; Marketing; Engineering; Economic growth","score_opus":0.028424642923696068,"score_gpt":0.3499627151366627,"score_spread":0.32153807221296665,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220770854","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.943253,0.00033263516,0.054231104,0.00047480848,0.0009667048,0.00032598455,0.00015856345,0.000046589543,0.00021064027],"genre_scores_gemma":[0.96555215,0.00009372207,0.033813108,0.00007820467,0.00005734111,0.000041912546,0.0002982666,0.000018406257,0.000046915346],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99826014,0.000107648724,0.0007725311,0.0001562619,0.00045995362,0.00024347844],"domain_scores_gemma":[0.99891144,0.00017601687,0.00049702736,0.00005589458,0.00028192106,0.00007768398],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011416936,0.00011411613,0.00025550893,0.00029035346,0.000415196,0.000022111424,0.00017053545,0.000058846716,0.00012606937],"category_scores_gemma":[0.000077232944,0.00012561887,0.0001278236,0.0004961852,0.000040937313,0.0006345757,0.0000011107094,0.00026033388,2.6603163e-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":[0.0013031897,0.0002292358,0.06374327,0.00008624795,0.000041587118,0.00012333541,0.11936195,0.7595847,0.0009033284,0.00837373,0.00008629775,0.046163104],"study_design_scores_gemma":[0.006138174,0.0011367743,0.76359314,0.00041637538,0.0002507061,0.000014543748,0.16658214,0.001143098,0.0005250425,0.0055760206,0.053800073,0.0008239224],"about_ca_topic_score_codex":0.00009108587,"about_ca_topic_score_gemma":0.0020226077,"teacher_disagreement_score":0.7584416,"about_ca_system_score_codex":0.00014853248,"about_ca_system_score_gemma":0.0003060606,"threshold_uncertainty_score":0.5122589},"labels":[],"label_agreement":null},{"id":"W4220806945","doi":"10.1155/2022/5120401","title":"Determining the Level of Service Scale of Public Transport System considering the Distribution of Service Quality","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Beijing; Service (business); Public transport; Sample (material); Service quality; Crowding; Scale (ratio); Level of service; Transport engineering; Computer science; Quality (philosophy); Operations research; Statistics; Mathematics; Geography; Engineering; Business; China; Marketing","score_opus":0.08365730018073378,"score_gpt":0.3251705306719567,"score_spread":0.24151323049122295,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220806945","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9804487,0.00007334814,0.017008858,0.0015121677,0.0002627416,0.00021543595,0.0004063557,0.0000127248495,0.000059658545],"genre_scores_gemma":[0.9986025,0.000025026073,0.0010910119,0.000064513886,0.000028094715,0.00000776401,0.00016510481,0.000008822742,0.0000071466525],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99742603,0.00030354597,0.0011764029,0.00010105565,0.0008494482,0.00014353146],"domain_scores_gemma":[0.9964207,0.00030299043,0.0018221375,0.00013029827,0.0012757127,0.000048187125],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020839144,0.00009311847,0.00029601465,0.00007179448,0.00039715436,0.0000075805665,0.00029787084,0.00004528,0.00001853679],"category_scores_gemma":[0.000048633447,0.000071336624,0.0001339385,0.0008260788,0.00010534872,0.00041745644,0.0000020411262,0.00020015544,8.629019e-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":[0.00028774285,0.00016356616,0.18279314,0.0005068826,0.00008829159,0.0000026417197,0.095978454,0.70863223,0.0024434985,0.0070072045,0.000007375541,0.0020889835],"study_design_scores_gemma":[0.0008751714,0.00007726916,0.9118738,0.00013037006,0.00013456146,0.0000021027224,0.08498497,0.0001962525,0.0011077585,0.00011201835,0.00041930715,0.00008637205],"about_ca_topic_score_codex":0.0007771664,"about_ca_topic_score_gemma":0.004841199,"teacher_disagreement_score":0.7290807,"about_ca_system_score_codex":0.00007265122,"about_ca_system_score_gemma":0.00031686126,"threshold_uncertainty_score":0.30546305},"labels":[],"label_agreement":null},{"id":"W4220816875","doi":"10.1155/2022/6743552","title":"Coordination and Optimization of Long-Distance Passenger Departure Timetable Connected to High-Speed Railway Station: Considering the Heterogeneity of Transfer Passengers Demand","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","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":"Education Department of Jiangxi Province; National Natural Science Foundation of China; U.S. Department of Transportation","keywords":"Interval (graph theory); Transfer (computing); Transfer station; Transport engineering; Attractiveness; Arrival time; Computer science; Flow (mathematics); Matching (statistics); Passenger transport; Travel time; Simulation; Engineering; Mathematics; Statistics","score_opus":0.011553494118428593,"score_gpt":0.2591975774785846,"score_spread":0.24764408336015598,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220816875","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7818286,0.00026863563,0.21634425,0.0008886925,0.00021664635,0.00032178243,0.00009994905,0.000014476045,0.000016967235],"genre_scores_gemma":[0.9881374,0.00016057891,0.011432713,0.000052636,0.000029898247,0.000009424002,0.00014063624,0.000013683323,0.000023028613],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99818414,0.00023226404,0.0007121827,0.0001464831,0.0005841537,0.00014076893],"domain_scores_gemma":[0.99839985,0.00026396845,0.0005226508,0.000083626466,0.00065131695,0.000078581],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007687189,0.00011207908,0.00027808442,0.00016934183,0.00035127334,0.000019573734,0.00011120904,0.000051098457,0.00007649892],"category_scores_gemma":[0.00008333475,0.00010358614,0.00007362444,0.0005999192,0.0000977459,0.00049212016,0.0000016030068,0.00015533557,7.1756126e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037426228,0.00004776283,0.006426981,0.000039918486,0.00005499706,0.0000069124353,0.015570844,0.9723985,0.0030187613,0.0014844388,0.000028811297,0.0005477924],"study_design_scores_gemma":[0.013603155,0.0021646172,0.8367436,0.0005351166,0.0015067249,0.00003936183,0.065388754,0.019521564,0.050430823,0.0010675591,0.007642809,0.0013559005],"about_ca_topic_score_codex":0.000097521974,"about_ca_topic_score_gemma":0.0011672884,"teacher_disagreement_score":0.9528769,"about_ca_system_score_codex":0.000055050772,"about_ca_system_score_gemma":0.00013414078,"threshold_uncertainty_score":0.42241204},"labels":[],"label_agreement":null},{"id":"W4220934016","doi":"10.1155/2022/2499282","title":"Sentiment Analysis Models with Bayesian Approach: A Bike Preference Application in Metropolitan Cities","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Università degli Studi Mediterranea di Reggio Calabria","keywords":"Metropolitan area; Computer science; Posterior probability; Discrete choice; Revealed preference; Bayesian probability; Preference; Conditional probability; Econometrics; Bayesian inference; Machine learning; Artificial intelligence; Economics; Statistics; Geography; Mathematics","score_opus":0.019883856004934777,"score_gpt":0.26689340420346575,"score_spread":0.24700954819853096,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220934016","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4162249,0.00010526177,0.582205,0.00018849193,0.000053825886,0.00023619886,0.000029980592,0.000024140545,0.000932188],"genre_scores_gemma":[0.97070485,0.000059609345,0.02889553,0.000029229766,0.000022537073,0.00004536142,0.00015491579,0.000008906994,0.000079082914],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9983983,0.000118843695,0.0004736572,0.00015835841,0.00069068756,0.00016012239],"domain_scores_gemma":[0.9991399,0.000037991296,0.00048747996,0.000082931576,0.00017964473,0.000072039285],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047720375,0.00009113741,0.00022111852,0.0005863907,0.00026317837,0.000021520165,0.00014223855,0.000030746232,0.000032841097],"category_scores_gemma":[0.0000047465933,0.00008875524,0.00008986136,0.0016228837,0.000052574935,0.0006075257,9.147304e-7,0.00018433487,1.232618e-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":[0.00020718218,0.00010908242,0.02985383,0.000009006319,0.00008281939,0.000005135996,0.027684955,0.9277812,0.00003161244,0.01363128,0.000003338995,0.000600554],"study_design_scores_gemma":[0.0045709927,0.00070481346,0.5427013,0.000065874396,0.0016073687,0.0000053556964,0.38428223,0.05188015,0.00013292492,0.011527949,0.0017857328,0.00073531823],"about_ca_topic_score_codex":0.0005713255,"about_ca_topic_score_gemma":0.0029326996,"teacher_disagreement_score":0.87590104,"about_ca_system_score_codex":0.00028719803,"about_ca_system_score_gemma":0.00015196945,"threshold_uncertainty_score":0.36193335},"labels":[],"label_agreement":null},{"id":"W4220955104","doi":"10.32866/001c.33180","title":"The Equity Implications of Highway Development and Expansion: Four Indicators","year":2022,"lang":"en","type":"article","venue":"Findings","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Université du Québec à Montréal","funders":"","keywords":"Equity (law); Traffic congestion; Business; Transport engineering; Economics; Engineering","score_opus":0.037265356571708125,"score_gpt":0.30646765285177613,"score_spread":0.269202296280068,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220955104","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99031246,0.000057925834,0.00047171794,0.0026731067,0.00011322565,0.000110702844,0.000011980771,0.00003890603,0.006210006],"genre_scores_gemma":[0.9984734,0.000018679215,0.00067582953,0.0000526545,0.000009695619,0.0000325347,0.000014608181,0.000002543308,0.0007200554],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9994916,0.000035993584,0.00011178527,0.00007406749,0.00019375634,0.00009284533],"domain_scores_gemma":[0.9997455,0.00008847367,0.00006301898,0.000050050698,0.00002021182,0.000032735268],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.00051458855,0.000027679764,0.000037623064,0.00004600018,0.0019133479,0.000019830732,0.000118336764,0.000015940033,0.00006384025],"category_scores_gemma":[0.000033895154,0.000023809378,0.000010527341,0.00026933098,0.00007898554,0.000041298463,0.00002945267,0.000053264834,0.0000011754457],"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":[0.000024548297,0.000062827465,0.35783195,0.000014171978,0.000032571297,0.0000013954184,0.18877497,0.00053153204,0.0002302359,0.36265582,0.008418447,0.08142154],"study_design_scores_gemma":[0.00011081637,0.000014052068,0.59698826,0.000004139768,0.0000063032794,4.8230896e-7,0.0061235535,0.000008311014,0.00022145493,0.0008967856,0.3955584,0.00006747298],"about_ca_topic_score_codex":0.000057414483,"about_ca_topic_score_gemma":0.00009962313,"teacher_disagreement_score":0.38713995,"about_ca_system_score_codex":0.000038757524,"about_ca_system_score_gemma":0.0001399904,"threshold_uncertainty_score":0.999386},"labels":[],"label_agreement":null},{"id":"W4220957943","doi":"10.1155/2022/1245639","title":"Evaluation of Public Welfare Level of Urban Rail Transit considering Operation Management","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","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":"National Natural Science Foundation of China","keywords":"Public transport; Analytic hierarchy process; Beijing; TOPSIS; Transport engineering; Welfare; Index (typography); Environmental economics; Closeness; Subsidy; Social Welfare; Business; Economics; Geography; Computer science; Engineering; Operations research; China; Mathematics","score_opus":0.09473990057721321,"score_gpt":0.3303530734204558,"score_spread":0.23561317284324257,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220957943","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92695427,0.00046318935,0.06913804,0.0012333828,0.0006084703,0.0005163769,0.000109660075,0.00002056371,0.00095603947],"genre_scores_gemma":[0.9912258,0.00011735484,0.008437403,0.000015816073,0.000029322533,0.000014985906,0.000110335866,0.000010184407,0.000038830065],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9969831,0.00032051682,0.000795256,0.00011281292,0.0016634936,0.00012480498],"domain_scores_gemma":[0.99792737,0.00004076273,0.0008002709,0.00007646337,0.0010981641,0.000056989164],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028810042,0.00008225045,0.00020483964,0.00029477954,0.00031189006,0.000012512601,0.00013111465,0.00003633141,0.00032598],"category_scores_gemma":[0.000040656458,0.000092626826,0.00010667737,0.00042862562,0.00006062849,0.0006491937,0.0000011257101,0.00012467858,1.6902969e-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":[0.000102721366,0.00012891644,0.0030110576,0.000047909863,0.00008977821,0.0000046729406,0.032710657,0.9272614,0.0014410589,0.016028218,0.000025748377,0.019147823],"study_design_scores_gemma":[0.010870321,0.00080218725,0.7512468,0.00023358755,0.0017094428,0.0000073078063,0.20794307,0.0028874106,0.004610155,0.0036899305,0.015447683,0.0005520918],"about_ca_topic_score_codex":0.000054659893,"about_ca_topic_score_gemma":0.00046875307,"teacher_disagreement_score":0.92437404,"about_ca_system_score_codex":0.00013842095,"about_ca_system_score_gemma":0.00026767066,"threshold_uncertainty_score":0.37772125},"labels":[],"label_agreement":null},{"id":"W4220987146","doi":"10.1016/j.trc.2022.103619","title":"Predicting the probability distribution of bus travel time to measure the reliability of public transport services","year":2022,"lang":"en","type":"article","venue":"Transportation Research Part C Emerging Technologies","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":32,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Université de Montréal","funders":"","keywords":"Probabilistic logic; Reliability (semiconductor); Conditional probability distribution; Measure (data warehouse); Conditional probability; Computer science; Probability density function; Statistics; Statistical model; Benchmark (surveying); Public transport; Probability distribution; Mathematics; Econometrics; Data mining; Geography; Engineering; Transport engineering","score_opus":0.06654139327013724,"score_gpt":0.3339483393467028,"score_spread":0.26740694607656557,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220987146","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9741785,0.00013149089,0.0020315095,0.020778682,0.000075912874,0.0014264914,0.0005633767,0.000393921,0.00042010867],"genre_scores_gemma":[0.99873644,0.000059309277,0.00025643356,0.000012365652,0.000014226906,0.0004266251,0.00034210488,0.000010674819,0.00014179465],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.996054,0.0007270683,0.0006584814,0.0003468067,0.0017697376,0.00044392108],"domain_scores_gemma":[0.9977789,0.00051261275,0.00023982943,0.00048411073,0.0009337274,0.000050817962],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.008627466,0.000120706594,0.00021430393,0.00016368991,0.0018800986,0.000022704011,0.000985036,0.000102382386,0.0001859535],"category_scores_gemma":[0.00058538734,0.000088068045,0.00010547025,0.0026281334,0.000961538,0.00022114122,0.000018259823,0.0005547009,0.0000027490003],"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":[0.00035737408,0.00060105557,0.7018069,0.00046706304,0.00011039712,0.000003795991,0.10377451,0.14048094,0.0010950168,0.039155345,0.0011779491,0.010969676],"study_design_scores_gemma":[0.0005733841,0.00037267603,0.7416809,0.00014820421,0.00009390282,3.9238836e-7,0.20165892,0.002161098,0.002301623,0.007590377,0.04307163,0.00034688125],"about_ca_topic_score_codex":0.0030185012,"about_ca_topic_score_gemma":0.0029186436,"teacher_disagreement_score":0.13831984,"about_ca_system_score_codex":0.00010773025,"about_ca_system_score_gemma":0.00032138475,"threshold_uncertainty_score":0.99941933},"labels":[],"label_agreement":null},{"id":"W4220995668","doi":"10.1155/2022/2304097","title":"Hyperpath Searching Algorithm considering Delay at Intersection and Its Application in CVIS for Vehicle Navigation","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","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":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Intersection (aeronautics); SIGNAL (programming language); Computer science; Set (abstract data type); Signal timing; Real-time computing; Algorithm; Representation (politics); Travel time; Phase (matter); Simulation; Transport engineering; Engineering; Traffic signal","score_opus":0.014262276039666778,"score_gpt":0.29551460889396797,"score_spread":0.2812523328543012,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220995668","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93442076,0.00016341203,0.06457606,0.000247031,0.00021468541,0.00031480094,0.000027246655,0.000018291696,0.00001771433],"genre_scores_gemma":[0.9901886,0.000085021034,0.0094305845,0.00003060042,0.000053938315,0.00004439476,0.00012342296,0.000010423412,0.000033015476],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99901557,0.000074240525,0.00037643526,0.00012321776,0.00028732137,0.00012324269],"domain_scores_gemma":[0.99932534,0.00011211416,0.00031794934,0.000030116142,0.00016161695,0.000052863932],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006713093,0.00006242187,0.00011496144,0.00016890008,0.00044802757,0.000014732381,0.000052738957,0.000033307737,0.000008695448],"category_scores_gemma":[0.000028155153,0.000074130796,0.000041960557,0.00022841772,0.000018303976,0.0005224071,0.000001584208,0.00015514954,2.1581678e-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":[0.0004264204,0.00007678321,0.016408684,0.000049092163,0.000018637764,0.000015272142,0.066850916,0.83718055,0.01733716,0.00234912,0.0000073865017,0.059279956],"study_design_scores_gemma":[0.017961122,0.0020527495,0.5988001,0.00061540824,0.00034263488,0.000111130386,0.16543941,0.1493554,0.012572444,0.011018031,0.04031439,0.0014171823],"about_ca_topic_score_codex":0.00008993552,"about_ca_topic_score_gemma":0.00060810725,"teacher_disagreement_score":0.6878252,"about_ca_system_score_codex":0.00022489611,"about_ca_system_score_gemma":0.0000703066,"threshold_uncertainty_score":0.3445911},"labels":[],"label_agreement":null},{"id":"W4220997794","doi":"10.1155/2022/1785199","title":"Pricing Method of the Flexible Bus Service Based on Cumulative Prospect Theory","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Profit maximization; Maximization; Probabilistic logic; Profit (economics); Social Welfare; Computer science; Service (business); Operations research; Transport engineering; Microeconomics; Business; Economics; Engineering; Marketing","score_opus":0.016915026384977983,"score_gpt":0.3192727428614333,"score_spread":0.3023577164764553,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220997794","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.614172,0.00012974713,0.37540942,0.0029888775,0.0015819832,0.0008929248,0.000085472566,0.00007698756,0.004662596],"genre_scores_gemma":[0.9789359,0.000012989045,0.02054591,0.00030635923,0.000040631327,0.0000074616396,0.000016086102,0.000010460506,0.00012422814],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9983393,0.00032880297,0.00042019895,0.00009605344,0.00070611713,0.00010951752],"domain_scores_gemma":[0.99848056,0.0003719853,0.00075408985,0.00008402641,0.00027174293,0.000037588212],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012852015,0.00007364994,0.00014983812,0.000119385346,0.00043401905,0.000008345335,0.00018106432,0.000028308987,0.000109385546],"category_scores_gemma":[0.00008168525,0.00005924829,0.00010691881,0.0007846602,0.00003392636,0.00023374695,9.725609e-7,0.00022714355,2.061679e-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":[0.00039275046,0.00007023459,0.0026801652,0.00001585207,0.000013861253,0.0000023490115,0.027794972,0.9568288,0.0004364069,0.009400558,0.000006839847,0.0023572447],"study_design_scores_gemma":[0.0055493917,0.0011100908,0.85691434,0.00045821044,0.00044221,0.00000462305,0.072228126,0.010311557,0.011814068,0.024412995,0.016220834,0.0005335318],"about_ca_topic_score_codex":0.00006081514,"about_ca_topic_score_gemma":0.00014394954,"teacher_disagreement_score":0.9465172,"about_ca_system_score_codex":0.000090262474,"about_ca_system_score_gemma":0.0002613225,"threshold_uncertainty_score":0.33381677},"labels":[],"label_agreement":null},{"id":"W4221110942","doi":"10.2139/ssrn.4055703","title":"Planning Bike Lanes with Data: Ridership, Congestion, and Path Selection","year":2022,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Transport engineering; Selection (genetic algorithm); Traffic congestion; Computer science; Business; Engineering; Artificial intelligence","score_opus":0.023103215042638094,"score_gpt":0.28005479893719587,"score_spread":0.2569515838945578,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4221110942","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94922465,0.004146321,0.038087733,0.0050057936,0.0004041839,0.00027621165,0.000028565682,0.00023153101,0.0025949825],"genre_scores_gemma":[0.99728054,0.00083383446,0.00021587145,0.000052061034,0.00015244696,0.0000042944466,0.00007500724,0.000009122829,0.0013768419],"study_design_codex":"observational","study_design_gemma":"qualitative","domain_scores_codex":[0.99862075,0.00017548999,0.00011170151,0.0001523371,0.00030577273,0.0006339402],"domain_scores_gemma":[0.99967515,0.000049809754,0.0001096713,0.00005742158,0.000054955868,0.00005296844],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0017854996,0.00006261734,0.00007075555,0.00008160325,0.0016045872,0.00007890115,0.00014698868,0.000025935455,0.00005399773],"category_scores_gemma":[0.00003743735,0.000058786914,0.00000943684,0.00024895297,0.00004996805,0.0003253093,0.000010141799,0.0007553285,9.095805e-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":[0.0004387103,0.000110242814,0.6394142,0.000012268382,0.00024280617,0.00002080278,0.028195415,0.10042815,0.000033473905,0.21631663,0.0020898983,0.012697425],"study_design_scores_gemma":[0.0055817654,0.0034687347,0.12082377,0.00014001445,0.00050840975,0.0016436783,0.48778957,0.009416279,0.000013132359,0.08316307,0.2858688,0.0015827803],"about_ca_topic_score_codex":0.0004925649,"about_ca_topic_score_gemma":0.003421906,"teacher_disagreement_score":0.5185904,"about_ca_system_score_codex":0.00022583114,"about_ca_system_score_gemma":0.0013417054,"threshold_uncertainty_score":0.9996952},"labels":[],"label_agreement":null},{"id":"W4221126452","doi":"10.18280/isi.270112","title":"Smart-Approach Based Internet of Things and Skyline Query for Multicriteria Decisions for Travel Services","year":2022,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Transportation Planning and Optimization","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":"Skyline; Computer science; Cloud computing; The Internet; Tourism; Operator (biology); Services computing; Resource (disambiguation); World Wide Web; Data science; Database; Web service; Data mining","score_opus":0.022946295936549246,"score_gpt":0.2684360661197509,"score_spread":0.24548977018320167,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4221126452","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.165248,0.000050770155,0.83133346,0.000121324454,0.00023825251,0.0009135215,0.00030114866,0.00007131744,0.0017221844],"genre_scores_gemma":[0.9169982,0.000006186195,0.08117685,0.00024832704,0.000017994556,0.00025814364,0.0012143003,0.000006997216,0.00007300568],"study_design_codex":"qualitative","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99909127,0.000050463233,0.00042265325,0.00009089205,0.0001986137,0.00014611588],"domain_scores_gemma":[0.99904704,0.00026255538,0.0002644053,0.00006814926,0.0003122019,0.000045652196],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007920902,0.00007924173,0.00013998397,0.00016840691,0.00055561156,0.00009202353,0.00012608983,0.000054319542,0.00002169319],"category_scores_gemma":[0.00023078275,0.00008383333,0.000052109903,0.00018351935,0.000080415935,0.001130666,0.000011901995,0.000047727866,3.4002667e-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":[0.0013207002,0.0002222967,0.010681904,0.0025293473,0.000110136905,3.9651076e-7,0.78856987,0.046749827,0.000452269,0.05832973,0.0014501379,0.0895834],"study_design_scores_gemma":[0.0022588614,0.0002848157,0.009598509,0.0001669263,0.000075027994,0.000002051615,0.08986213,0.87644845,0.00051574735,0.0034473168,0.017019063,0.00032109622],"about_ca_topic_score_codex":0.0006285384,"about_ca_topic_score_gemma":0.000059031172,"teacher_disagreement_score":0.8296986,"about_ca_system_score_codex":0.00006657867,"about_ca_system_score_gemma":0.0000796748,"threshold_uncertainty_score":0.4273371},"labels":[],"label_agreement":null},{"id":"W4223654711","doi":"10.1155/2022/7690309","title":"Examine the Prediction Error of Ride-Hailing Travel Demands with Various Ignored Sparse Demand Effects","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Computer science; Service (business); On demand; Travel time; Space (punctuation); Order (exchange); Demand patterns; Artificial intelligence; Machine learning; Operations research; Data mining; Transport engineering; Demand management; Engineering; Business; Economics; Marketing","score_opus":0.010361756006167141,"score_gpt":0.25025721417698943,"score_spread":0.23989545817082228,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4223654711","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9393222,0.00022185064,0.059345588,0.00024931153,0.00040585204,0.00028676787,0.00003257609,0.000020922995,0.00011493384],"genre_scores_gemma":[0.9958895,0.00011478722,0.003723162,0.000037017446,0.00008007589,0.000016094633,0.00006856892,0.000013489152,0.00005729919],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99834,0.00015449882,0.0005361927,0.00011757646,0.00070305256,0.00014867773],"domain_scores_gemma":[0.9986436,0.00021450127,0.00073638925,0.0000786151,0.0002620048,0.00006491493],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007265936,0.00009940475,0.00020886963,0.00013882061,0.0005056555,0.000013598521,0.00014354351,0.000039541093,0.000030048901],"category_scores_gemma":[0.000042819946,0.00007642382,0.000079769066,0.00046063756,0.000093436174,0.00039507245,8.758384e-7,0.00023328973,1.4837227e-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":[0.0006037563,0.00010070721,0.015364293,0.000034237022,0.00006518597,0.000028532144,0.037619602,0.94154483,0.0019622296,0.00060790067,0.000021929147,0.002046784],"study_design_scores_gemma":[0.004046375,0.0014091172,0.947924,0.00016141863,0.00052236725,0.000019349236,0.040297974,0.0011327092,0.0024354865,0.00074936124,0.0010891662,0.00021264235],"about_ca_topic_score_codex":0.00012424623,"about_ca_topic_score_gemma":0.00096725504,"teacher_disagreement_score":0.9404121,"about_ca_system_score_codex":0.000059329417,"about_ca_system_score_gemma":0.00017679836,"threshold_uncertainty_score":0.38891444},"labels":[],"label_agreement":null},{"id":"W4223972541","doi":"10.1155/2022/5048678","title":"Factors and Mechanism Affecting the Attractiveness of Public Transport: Macroscopic and Microscopic Perspectives","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Ministry of Education of the People's Republic of China; National Natural Science Foundation of China","keywords":"Attractiveness; Public transport; Service (business); Business; Transport engineering; Structural equation modeling; Mechanism (biology); Key (lock); Public service; Economic geography; Environmental economics; Geography; Marketing; Computer science; Economics; Engineering; Political science; Psychology; Physics; Public relations","score_opus":0.02315694591151095,"score_gpt":0.29789700709014766,"score_spread":0.2747400611786367,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4223972541","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.993069,0.0008703907,0.0051587783,0.00043420028,0.00023226073,0.00016804255,0.000029513429,0.000012000509,0.00002580369],"genre_scores_gemma":[0.9985124,0.00040224468,0.0010000579,0.000012414406,0.000025615598,0.0000043658742,0.0000138238875,0.000009263008,0.00001983534],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.9989148,0.00015183048,0.00033684252,0.000118902295,0.00034753417,0.00013008816],"domain_scores_gemma":[0.999028,0.00020041017,0.00049036095,0.00004619074,0.00017376215,0.000061235485],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006517452,0.00009070446,0.00018932158,0.00011866067,0.0006435697,0.000019531077,0.00009427285,0.0000339252,0.00003320359],"category_scores_gemma":[0.000037918213,0.00007207547,0.000058027752,0.00026000067,0.00014465713,0.00047326536,0.0000012619529,0.00020844121,2.0148457e-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":[0.00015046731,0.00015559528,0.46570438,0.00009054719,0.000101444646,0.000011679288,0.4753298,0.022848386,0.013108073,0.019376537,0.0000021712274,0.0031209372],"study_design_scores_gemma":[0.00055197056,0.00014855327,0.801308,0.000028168406,0.00006174569,0.0000020161945,0.19573818,0.000006231101,0.0009812751,0.00086295325,0.00023501077,0.00007591946],"about_ca_topic_score_codex":0.000095252915,"about_ca_topic_score_gemma":0.000502668,"teacher_disagreement_score":0.3356036,"about_ca_system_score_codex":0.00004470569,"about_ca_system_score_gemma":0.000116932126,"threshold_uncertainty_score":0.4949883},"labels":[],"label_agreement":null},{"id":"W4224227735","doi":"","title":"SECOND QUARTER TRANSPORTATION REPORT FISCAL YEAR 2024","year":2022,"lang":"en","type":"article","venue":"OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Quarter (Canadian coin); Fiscal year; Business; Economics; Finance; Geography; Archaeology","score_opus":0.010330742880479884,"score_gpt":0.24052072123573331,"score_spread":0.23018997835525343,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4224227735","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.63567156,0.000092923416,0.012322448,0.001161065,0.0015641757,0.00045914715,0.0012940788,0.00019014452,0.34724444],"genre_scores_gemma":[0.9924742,0.000008866868,0.0014366289,0.00005099816,0.000014723044,0.000030766845,0.0043907124,0.0000036458891,0.0015894669],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99848515,0.000041447303,0.0005476347,0.00014976598,0.00062991196,0.00014608278],"domain_scores_gemma":[0.9992434,0.000063271254,0.00032463062,0.00013799619,0.00015245318,0.00007829733],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00070283696,0.00007861857,0.00013878185,0.00018398417,0.00050033734,0.00005081107,0.00013556385,0.000057753386,0.0008892654],"category_scores_gemma":[0.000038580994,0.00008251216,0.000062379055,0.00047399913,0.00031240372,0.0006100577,0.000011579273,0.000084053805,0.0000038339285],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006132267,0.0010699225,0.062496673,0.0002607395,0.0001500658,0.00003415856,0.0020025726,0.016636556,0.0007713902,0.75247705,0.16029632,0.0031913104],"study_design_scores_gemma":[0.00066592084,0.0002046436,0.2823096,0.000025967136,0.00006235293,0.000010538606,0.00072940317,0.00004271081,0.0003883089,0.0008839589,0.71441257,0.00026401933],"about_ca_topic_score_codex":0.000077852594,"about_ca_topic_score_gemma":0.00023833511,"teacher_disagreement_score":0.7515931,"about_ca_system_score_codex":0.00003249785,"about_ca_system_score_gemma":0.00013002854,"threshold_uncertainty_score":0.97368324},"labels":[],"label_agreement":null},{"id":"W4224299090","doi":"10.1155/2022/2507107","title":"A Quantitative Method to Delineate the Influence Area of Work Zone Considering Route Choice Inertia and Elastic Demand","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","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":"People's Government of Jilin Province","keywords":"Subnetwork; Node (physics); Computer science; Work (physics); Inertia; Traffic flow (computer networking); Flow network; Flow (mathematics); Mathematical optimization; Computer network; Mathematics; Engineering; Structural engineering","score_opus":0.022970785451264526,"score_gpt":0.3246117825244002,"score_spread":0.3016409970731357,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4224299090","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7495945,0.00021444012,0.24925874,0.00067588966,0.0001008804,0.000120985525,0.000014909845,0.0000081580365,0.000011485862],"genre_scores_gemma":[0.9039485,0.00006225255,0.09580786,0.00011426377,0.000017388256,0.000008544383,0.00000844768,0.000006650353,0.000026109008],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99875844,0.00016299759,0.0004895673,0.00010159965,0.00037387214,0.00011353378],"domain_scores_gemma":[0.99846506,0.0005836933,0.0004658464,0.000048888258,0.00036553555,0.000070968614],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00081133057,0.000074498384,0.00017496342,0.00011959243,0.00032684102,0.000016559618,0.0001008206,0.000022207716,0.000016126689],"category_scores_gemma":[0.00020926392,0.00006302753,0.00004463615,0.0005516505,0.000059797654,0.00032740322,0.0000019582274,0.00016702566,1.5699155e-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":[0.00028634182,0.000017688275,0.024462659,0.000009993063,0.000025483261,0.000006150107,0.03477727,0.93644935,0.0016468716,0.00072036596,0.000007746158,0.0015900577],"study_design_scores_gemma":[0.0011452619,0.0003791104,0.96731365,0.00018625092,0.00018993177,0.0000053798235,0.02500023,0.00079035794,0.00033721456,0.00056114973,0.0039097737,0.00018167912],"about_ca_topic_score_codex":0.00016556092,"about_ca_topic_score_gemma":0.0006767061,"teacher_disagreement_score":0.942851,"about_ca_system_score_codex":0.00003303788,"about_ca_system_score_gemma":0.00010711533,"threshold_uncertainty_score":0.25701883},"labels":[],"label_agreement":null},{"id":"W4224310523","doi":"10.1155/2022/1604303","title":"Vehicle Routing Simulation for Prediction of Commuter’s Behaviour","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"Toronto Metropolitan University","funders":"Ontario Centres of Excellence","keywords":"Computer science; Queueing theory; Discrete event simulation; Routing (electronic design automation); Event (particle physics); Set (abstract data type); Traffic flow (computer networking); Traffic simulation; Perspective (graphical); Simulation modeling; Operations research; Simulation; Distributed computing; Artificial intelligence; Microsimulation; Transport engineering; Computer security; Computer network; Engineering","score_opus":0.02409787698149604,"score_gpt":0.31225150576498667,"score_spread":0.2881536287834906,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4224310523","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8948121,0.00005369045,0.10378384,0.00019973551,0.00057296164,0.0002850638,0.0002100969,0.000024343757,0.000058155514],"genre_scores_gemma":[0.9919343,0.000020313644,0.007663647,0.000021867872,0.0000780796,0.000012739746,0.00021966732,0.0000094387,0.000039942952],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9987725,0.00007487731,0.0005712953,0.00007237168,0.00040784583,0.00010112999],"domain_scores_gemma":[0.99862766,0.00015777173,0.0007440929,0.00004654702,0.0003832478,0.000040694744],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006862147,0.000054237295,0.00013157967,0.00012217692,0.0003968435,0.000008679569,0.00009204575,0.000032907,0.000036378435],"category_scores_gemma":[0.00004970017,0.00006250835,0.0001031117,0.00022812962,0.000028662771,0.00044661266,6.908713e-7,0.00011522302,7.486829e-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":[0.0003175089,0.000069072295,0.036162466,0.000011454743,0.000012381187,0.0000011850275,0.020709176,0.93870395,0.00075629936,0.0010885188,0.000019715102,0.0021482916],"study_design_scores_gemma":[0.00550279,0.00088063726,0.93887085,0.00007908563,0.00023779615,0.0000013456605,0.034037516,0.012260677,0.00071793026,0.0012230503,0.0060104975,0.0001778501],"about_ca_topic_score_codex":0.000036801957,"about_ca_topic_score_gemma":0.000077608864,"teacher_disagreement_score":0.9264433,"about_ca_system_score_codex":0.000074698684,"about_ca_system_score_gemma":0.00010322653,"threshold_uncertainty_score":0.30522397},"labels":[],"label_agreement":null},{"id":"W4224919632","doi":"10.1016/j.trc.2022.103696","title":"Real-time dispatching of operating buses during unplanned disruptions to urban rail transit system","year":2022,"lang":"en","type":"article","venue":"Transportation Research Part C Emerging Technologies","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":35,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"National Key Research and Development Program of China; Shanghai Rising-Star Program; National Natural Science Foundation of China","keywords":"Spare part; Local bus; Transport engineering; Engineering; Public transport; Real-time computing; Computer science; Control bus; System bus; Operations management; Operating system","score_opus":0.04130260779184691,"score_gpt":0.35494325589782433,"score_spread":0.3136406481059774,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4224919632","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99087393,0.00006959899,0.0018654305,0.0028593466,0.00016488651,0.0007517569,0.00027532058,0.0018684928,0.0012712087],"genre_scores_gemma":[0.9960647,0.00011666853,0.0024232878,0.000004984365,0.000041286985,0.00039718498,0.0002324629,0.000029366942,0.00069006387],"study_design_codex":"simulation_or_modeling","study_design_gemma":"qualitative","domain_scores_codex":[0.996799,0.0003606235,0.0005886687,0.00042482498,0.0012339597,0.0005929446],"domain_scores_gemma":[0.99892575,0.00024193402,0.00013940455,0.00029087567,0.00030571842,0.00009631925],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0016795781,0.00015481084,0.00026881442,0.0007439663,0.0030515767,0.00006302029,0.00053779176,0.00009851003,0.00013643896],"category_scores_gemma":[0.00017046818,0.00017498551,0.00008687462,0.0021301773,0.00025442318,0.00029574096,0.000019535157,0.00044043618,0.000011132614],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004830079,0.00032269018,0.06728369,0.0005882096,0.00015439879,0.00011607491,0.20725906,0.54777384,0.047750425,0.1241363,0.0027532089,0.001379124],"study_design_scores_gemma":[0.0019593956,0.00067359547,0.09135639,0.0010736706,0.00013063097,0.0000029881387,0.8632335,0.0037132287,0.013143919,0.00090734515,0.022446143,0.0013591744],"about_ca_topic_score_codex":0.0031478489,"about_ca_topic_score_gemma":0.00145193,"teacher_disagreement_score":0.65597445,"about_ca_system_score_codex":0.00020108912,"about_ca_system_score_gemma":0.00018307338,"threshold_uncertainty_score":0.9982463},"labels":[],"label_agreement":null},{"id":"W4224981402","doi":"10.1155/2022/3664105","title":"Mathematical Model and Algorithm of Multi-Index Transportation Problem in the Background of Artificial Intelligence","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Transportation theory; Mathematical model; Computer science; Index (typography); Diversification (marketing strategy); Transportation planning; Water transport; Flow network; Intelligent transportation system; Operations research; Transport engineering; Mathematical optimization; Engineering; Mathematics; Business","score_opus":0.037920231560784605,"score_gpt":0.31956377829577576,"score_spread":0.28164354673499115,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4224981402","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5910839,0.00008944049,0.40830758,0.00016793574,0.000051700703,0.00020903113,0.00004756511,0.000005136986,0.000037733447],"genre_scores_gemma":[0.9133453,0.00010569222,0.08645859,0.000015918573,0.000013918572,0.000010306613,0.00003430463,0.0000068734653,0.000009120698],"study_design_codex":"simulation_or_modeling","study_design_gemma":"qualitative","domain_scores_codex":[0.9981745,0.00010678166,0.0009027085,0.00010000194,0.0006019447,0.00011409666],"domain_scores_gemma":[0.9989184,0.00013682159,0.0006468926,0.000056645968,0.00020522154,0.000036024896],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010111677,0.00007939659,0.00021262412,0.00017168064,0.00013469515,0.000008993082,0.00014778173,0.000043306096,0.000026834907],"category_scores_gemma":[0.000011018032,0.00007008422,0.00007290059,0.00043373043,0.00011878477,0.00036045682,5.340215e-7,0.00020202414,7.938613e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013096676,0.00026995252,0.0011354877,0.00004949687,0.0000109198945,0.0000067045166,0.07035819,0.8910216,0.000343822,0.024264172,0.0000014743396,0.0124071855],"study_design_scores_gemma":[0.0020975652,0.0008347714,0.2500755,0.00031503878,0.0002867261,0.000009369228,0.46239838,0.18721618,0.00086036214,0.09531447,0.00013482447,0.00045680493],"about_ca_topic_score_codex":0.000075859156,"about_ca_topic_score_gemma":0.0007228401,"teacher_disagreement_score":0.70380545,"about_ca_system_score_codex":0.000029834191,"about_ca_system_score_gemma":0.00011951906,"threshold_uncertainty_score":0.28579515},"labels":[],"label_agreement":null},{"id":"W4225133192","doi":"10.1155/2022/5280109","title":"Bus Predictive-Control Method considering the Impact of Traffic Lights","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","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":"National Natural Science Foundation of China","keywords":"Headway; Intersection (aeronautics); Reliability (semiconductor); Control (management); Schedule; Computer science; Probabilistic logic; Stability (learning theory); Engineering; Simulation; Automotive engineering; Transport engineering","score_opus":0.011822190647354897,"score_gpt":0.3134448697114283,"score_spread":0.3016226790640734,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4225133192","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9283765,0.00027415017,0.070148945,0.00041405993,0.00034801837,0.00021944886,0.0000641996,0.000022264678,0.0001324325],"genre_scores_gemma":[0.9936372,0.000067407775,0.0061439914,0.00002122498,0.0000702479,0.000007635201,0.000016352373,0.000008800234,0.000027154421],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9985441,0.00023901394,0.00050798897,0.00008397057,0.000493282,0.00013162581],"domain_scores_gemma":[0.99860924,0.00028935718,0.0007035188,0.000062089646,0.0002759018,0.000059908143],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00091198605,0.00007837699,0.00019650243,0.00012585864,0.0004055069,0.000011825561,0.0001446224,0.000030057152,0.00010991097],"category_scores_gemma":[0.000049678525,0.000058900474,0.00020270252,0.00035398063,0.000061052495,0.00035053055,5.24195e-7,0.00021920925,1.8195084e-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":[0.00038342306,0.00005265156,0.002745206,0.000004604141,0.00007120435,0.000009627175,0.03744828,0.95622665,0.0005028075,0.00034621794,0.000037276513,0.0021720545],"study_design_scores_gemma":[0.0066685923,0.0019771054,0.91175944,0.00011416136,0.00056005846,0.00003015504,0.059918467,0.011518775,0.0005080766,0.0021479488,0.00440523,0.000391988],"about_ca_topic_score_codex":0.00008718565,"about_ca_topic_score_gemma":0.00013167223,"teacher_disagreement_score":0.9447079,"about_ca_system_score_codex":0.00009008727,"about_ca_system_score_gemma":0.00030442016,"threshold_uncertainty_score":0.3118872},"labels":[],"label_agreement":null},{"id":"W4225277755","doi":"10.1080/23249935.2022.2056655","title":"Transfer time optimisation in public transit networks: assessment of alternative models","year":2022,"lang":"en","type":"article","venue":"Transportmetrica A Transport Science","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Ontario Centres of Excellence","keywords":"Headway; Public transport; Transfer (computing); Computer science; Node (physics); Transfer function; Transit (satellite); Function (biology); Operations research; Transport engineering; Simulation; Engineering","score_opus":0.04010991390471239,"score_gpt":0.3008145584188826,"score_spread":0.2607046445141702,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4225277755","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.626569,0.00014691976,0.35743394,0.0011183623,0.0003539417,0.0012196815,0.00029440664,0.00022085271,0.012642925],"genre_scores_gemma":[0.9959104,0.00019891947,0.0031735534,0.00011924052,0.00003260386,0.00013396742,0.0002776197,0.000018091965,0.00013561794],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9949919,0.00021872747,0.0009259888,0.0007211247,0.002365456,0.00077682454],"domain_scores_gemma":[0.99883074,0.00017274353,0.0001654482,0.00024430733,0.00029457264,0.00029221916],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.003724391,0.00024563662,0.00045393987,0.0013939707,0.0010191858,0.00003837267,0.0009980983,0.00009089233,0.0010409926],"category_scores_gemma":[0.000014440267,0.00025716858,0.00018839126,0.009971591,0.00072383875,0.0018305898,0.0000054014076,0.00044558296,9.3767187e-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":[0.00005871224,0.00041394742,0.026243169,0.000011658468,0.000016343614,0.000027693211,0.0104497075,0.9304505,0.000462301,0.02994202,0.0000034165455,0.0019205194],"study_design_scores_gemma":[0.0036523687,0.000568182,0.34164014,0.00007622181,0.0001883454,0.0000056748067,0.006261328,0.64036137,0.00033829582,0.0016688834,0.0038885444,0.0013506142],"about_ca_topic_score_codex":0.0017289196,"about_ca_topic_score_gemma":0.0008177259,"teacher_disagreement_score":0.36934143,"about_ca_system_score_codex":0.00042718224,"about_ca_system_score_gemma":0.0012514961,"threshold_uncertainty_score":0.9999881},"labels":[],"label_agreement":null},{"id":"W4229066385","doi":"10.1155/2022/4100704","title":"Understanding the Shortest Route Selection Behavior for Private Cars Using Trajectory Data and Navigation Information","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Major Science and Technology Projects in Anhui Province; Fundamental Research Funds for the Central Universities; Chinese University of Hong Kong; University of Toronto; National Natural Science Foundation of China","keywords":"TRIPS architecture; Shortest path problem; Selection (genetic algorithm); Trajectory; Transport engineering; Global Positioning System; Computer science; Preference; Travel behavior; Standard deviation; Travel time; Operations research; Geography; Statistics; Mathematics; Engineering; Artificial intelligence","score_opus":0.08074246274490716,"score_gpt":0.33078249389437586,"score_spread":0.2500400311494687,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4229066385","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.68593687,0.000047347203,0.31292623,0.00015978538,0.00039745783,0.00036317302,0.00013927107,0.000018869923,0.000010992211],"genre_scores_gemma":[0.98844385,0.000051223105,0.010708821,0.00003841159,0.00007339526,0.000016240687,0.00065425725,0.000008224027,0.0000056043723],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9988761,0.00007516153,0.00043284404,0.000091845184,0.00040499703,0.00011905658],"domain_scores_gemma":[0.99909264,0.000081732396,0.0005501159,0.0000678253,0.00016455518,0.000043143595],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00088869274,0.000070702576,0.00010198174,0.00012133397,0.0010897652,0.0000573487,0.00014784145,0.000034610417,0.000008416087],"category_scores_gemma":[0.00003297637,0.00006689102,0.0000382996,0.00030480826,0.000050437244,0.0020251188,0.000002102339,0.00016511335,4.684725e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017366816,0.000031617943,0.028534137,0.000017525339,0.000025813584,0.0000017671387,0.033050515,0.9308868,0.0007491522,0.0025886088,0.00002686847,0.0039134943],"study_design_scores_gemma":[0.0049239895,0.00075858785,0.71077526,0.00018129515,0.0011503181,0.000057655336,0.20691992,0.04709051,0.00024464037,0.0035095077,0.023736497,0.0006518107],"about_ca_topic_score_codex":0.00007090107,"about_ca_topic_score_gemma":0.0002613339,"teacher_disagreement_score":0.88379633,"about_ca_system_score_codex":0.00023899986,"about_ca_system_score_gemma":0.00017753725,"threshold_uncertainty_score":0.83817035},"labels":[],"label_agreement":null},{"id":"W4229796309","doi":"10.32920/ryerson.14656557","title":"The Impact of Lane Closures on Congestion and Reliability: A Case Study of Toronto's Gardiner Expressway","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"McMaster University; Toronto Metropolitan University","funders":"","keywords":"Reliability (semiconductor); Closure (psychology); Travel time; Transport engineering; Psychological intervention; Traffic congestion; Intervention (counseling); Computer science; Business; Economics; Engineering; Psychology","score_opus":0.025057391009236634,"score_gpt":0.35039776597809114,"score_spread":0.3253403749688545,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4229796309","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99439824,0.00025100945,0.00010010709,0.000056362314,0.00012571044,0.00039904276,0.000021171774,0.000024434938,0.004623943],"genre_scores_gemma":[0.9991075,0.0003460813,0.00022316385,0.0000024430428,0.000030881947,0.000015524267,0.000021988251,0.0000051460515,0.00024726984],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99884915,0.00033889065,0.00026776746,0.00019887631,0.00025917965,0.00008613314],"domain_scores_gemma":[0.9989035,0.00036242467,0.00019424375,0.00023234043,0.0002644906,0.000042959116],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00070939085,0.00009470927,0.00020611168,0.00001551485,0.00015824132,0.000053335043,0.000079102014,0.000122030935,0.00008784175],"category_scores_gemma":[0.00022331778,0.000060827027,0.00006604549,0.000038275462,0.000103913386,0.00005724411,0.000022484266,0.00011634479,7.2663354e-8],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031460435,0.0010902326,0.37809417,0.000111895366,0.0002788654,0.0001300419,0.23582444,0.3772052,0.000028992587,0.00096005015,0.0008231767,0.005138357],"study_design_scores_gemma":[0.0012542641,0.0011016836,0.667315,0.00026105877,0.00025637608,0.000011201565,0.32735306,0.0016169928,0.000060458933,0.00021449063,0.00017631757,0.00037909276],"about_ca_topic_score_codex":0.31485638,"about_ca_topic_score_gemma":0.08782023,"teacher_disagreement_score":0.37558818,"about_ca_system_score_codex":0.000047813217,"about_ca_system_score_gemma":0.0001518874,"threshold_uncertainty_score":0.92882466},"labels":[],"label_agreement":null},{"id":"W4229904387","doi":"10.1177/0361198106197100106","title":"Empirical Analysis of Transit Network Evolution","year":2006,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Socioeconomic status; Transit (satellite); Regression analysis; Supply and demand; Population; Variables; Econometrics; Geography; Variable (mathematics); Transit time; Transport engineering; Computer science; Statistics; Public transport; Demography; Mathematics; Economics; Engineering; Sociology","score_opus":0.08979849123395654,"score_gpt":0.42006304300252734,"score_spread":0.3302645517685708,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4229904387","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.964553,0.00034330488,0.029727159,0.0029950878,0.00050794677,0.0007041189,0.00010106151,0.00004450902,0.0010237902],"genre_scores_gemma":[0.9956835,0.00037585612,0.0026280908,0.00002850034,0.0003627691,0.000026322086,0.00008528564,0.000032703774,0.00077698333],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9893524,0.0024027994,0.0018989,0.00038988906,0.0049878256,0.0009681681],"domain_scores_gemma":[0.99187624,0.0014725786,0.0007525469,0.00038370315,0.005209707,0.00030521533],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008066568,0.00021541974,0.0006487218,0.001950654,0.0012454115,0.000107302185,0.00095336407,0.0003127924,0.00044505295],"category_scores_gemma":[0.00017617294,0.00017986658,0.00079718546,0.010457676,0.0012366,0.0006624523,0.000002505913,0.0013878485,0.0000071265945],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006272875,0.0002408317,0.75139517,0.000054681957,0.0003899562,0.000023335666,0.0060331183,0.21741278,0.0002568525,0.017679429,0.005283681,0.00060285954],"study_design_scores_gemma":[0.0007829416,0.00019680138,0.9755125,0.00014110847,0.00052421726,6.3766144e-8,0.004414216,0.00056450453,0.000101046106,0.0042446535,0.013348982,0.00016896692],"about_ca_topic_score_codex":0.0730492,"about_ca_topic_score_gemma":0.40601954,"teacher_disagreement_score":0.33297035,"about_ca_system_score_codex":0.00033979487,"about_ca_system_score_gemma":0.0012056046,"threshold_uncertainty_score":0.95788234},"labels":[],"label_agreement":null},{"id":"W4229925830","doi":"10.1002/cjs.5550350209","title":"Forthcoming papers/articles à paraître","year":2007,"lang":"fr","type":"article","venue":"Canadian Journal of Statistics","topic":"Transportation Planning and Optimization","field":"Social Sciences","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":"Citation; Library science; Computer science; Information retrieval; World Wide Web","score_opus":0.024885227768324088,"score_gpt":0.2614164526529894,"score_spread":0.23653122488466533,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4229925830","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1950172,0.015186381,0.7254383,0.011559041,0.011649285,0.0002425403,0.0013546281,0.000021800704,0.039530795],"genre_scores_gemma":[0.9455403,0.00024630042,0.048365258,0.00032176552,0.00046605608,1.2657175e-7,0.0000148505005,0.000013841888,0.005031505],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99873143,0.00005524832,0.00048053233,0.00007150586,0.00023852629,0.00042277706],"domain_scores_gemma":[0.99812704,0.00026425807,0.0003031477,0.000052195304,0.0003398032,0.0009135432],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000869201,0.00009041816,0.00014823386,0.00016818465,0.0003714115,0.00007277886,0.00012858462,0.0001078175,0.00083077344],"category_scores_gemma":[0.00027165955,0.00010185499,0.00004844128,0.00031674613,0.00033127714,0.00018667281,9.589623e-7,0.00020677448,0.000019333864],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003617219,0.000038099995,0.16153277,0.00007161801,0.00012273873,0.0026571238,0.061209384,0.009112292,0.00001667521,0.37300453,0.04242338,0.34977522],"study_design_scores_gemma":[0.00063310197,0.00020060774,0.14224474,0.0003350822,0.00023824182,0.000085978325,0.020730283,0.00056025205,0.00004557088,0.006694127,0.8278905,0.00034150112],"about_ca_topic_score_codex":0.017436672,"about_ca_topic_score_gemma":0.43762517,"teacher_disagreement_score":0.78546715,"about_ca_system_score_codex":0.00019377003,"about_ca_system_score_gemma":0.0015340314,"threshold_uncertainty_score":0.9891063},"labels":[],"label_agreement":null},{"id":"W4230201446","doi":"10.1109/wsc.2016.7822208","title":"A combined discrete-continuous simulation model for analyzing train-pedestrian interactions","year":2016,"lang":"en","type":"article","venue":"2016 Winter Simulation Conference (WSC)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Discrete event simulation; Continuous simulation; Computer science; Network traffic simulation; Stochastic simulation; Simulation modeling; Traffic simulation; Simulation language; Modeling and simulation; Dynamic simulation; Systems simulation; Simulation; Industrial engineering; Engineering; Microsimulation; Transport engineering; Network packet","score_opus":0.06520817074805944,"score_gpt":0.3576871380166796,"score_spread":0.29247896726862016,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4230201446","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.032086767,0.0000061188352,0.9626948,0.0018740696,0.000387726,0.00057748385,0.00010009071,0.00022976864,0.0020431322],"genre_scores_gemma":[0.9869442,0.0000105495055,0.0028761001,0.00009562694,0.00019023845,0.000048498998,0.00011839138,0.000023835093,0.009692582],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984327,0.00012014979,0.00048001064,0.0003724329,0.00025993452,0.00033479652],"domain_scores_gemma":[0.997876,0.0009184022,0.0002756634,0.00019992534,0.0005827168,0.00014729868],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039660864,0.00018348137,0.00022739438,0.00019870693,0.00048656296,0.00017828486,0.00019421971,0.00012502096,0.0003885255],"category_scores_gemma":[0.0005050819,0.000150069,0.00014137408,0.00018510844,0.00011800235,0.0010545794,0.0000097513785,0.00008305789,0.000040939605],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002540243,0.00005620284,0.0060342187,0.00001073653,0.00004070536,5.255789e-7,0.010986957,0.9577982,0.00026449334,0.005905566,0.00026899046,0.018379396],"study_design_scores_gemma":[0.0013944922,0.000055321703,0.0020978008,0.00015965874,0.00004992575,7.017712e-8,0.00056517124,0.98754394,0.000031347954,0.0030085319,0.004835464,0.00025827828],"about_ca_topic_score_codex":0.00011726095,"about_ca_topic_score_gemma":0.0008943711,"teacher_disagreement_score":0.9598187,"about_ca_system_score_codex":0.000115159564,"about_ca_system_score_gemma":0.00023350015,"threshold_uncertainty_score":0.61196357},"labels":[],"label_agreement":null},{"id":"W4230241181","doi":"10.32920/ryerson.14661327.v1","title":"Active transportation demand modeling and infrastructure performance assessment","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Transport engineering; TRIPS architecture; Service (business); Level of service; Pedestrian; Quality (philosophy); Supply and demand; Business; Computer science; Environmental economics; Engineering; Marketing; Economics","score_opus":0.015801382962547454,"score_gpt":0.2991570956066479,"score_spread":0.2833557126441004,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4230241181","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.83997047,0.00007456103,0.1539767,0.00023109517,0.00025110226,0.000195554,0.000022358015,0.00008788093,0.0051902873],"genre_scores_gemma":[0.977655,0.0013415364,0.019911375,0.00006116226,0.00007098077,0.000019191404,0.0007417088,0.000009760529,0.0001892681],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989937,0.00006247542,0.000206014,0.000300054,0.0002946295,0.0001431539],"domain_scores_gemma":[0.99953306,0.000023333007,0.00009772916,0.00008875048,0.00018224213,0.000074873504],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019709385,0.0001298267,0.00016461324,0.000061938474,0.00032767773,0.00014870356,0.00007805172,0.0002645883,0.00014396229],"category_scores_gemma":[0.000008946551,0.00013430843,0.000038280716,0.00009461927,0.00005070854,0.0002623305,0.0000072387875,0.0003332489,2.9583452e-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":[0.000008806871,0.000010176136,0.02108941,0.00008008388,0.00002851202,0.0000020240705,0.029892378,0.94329435,0.000007096322,0.0011914944,0.000020278949,0.0043753902],"study_design_scores_gemma":[0.00034774432,0.000017813018,0.36385232,0.00027116653,0.0001296056,3.6618096e-7,0.023295682,0.61044216,0.000050180162,0.0008089662,0.00033921856,0.00044477452],"about_ca_topic_score_codex":0.00079436257,"about_ca_topic_score_gemma":0.0019169073,"teacher_disagreement_score":0.34276292,"about_ca_system_score_codex":0.00006343003,"about_ca_system_score_gemma":0.00043628164,"threshold_uncertainty_score":0.5476939},"labels":[],"label_agreement":null},{"id":"W4230363499","doi":"10.1177/0361198105192300105","title":"Commodity-Based Truck Origin–Destination Matrix Estimation Using Input–Output Data and Genetic Algorithms","year":2005,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto","funders":"Ministère des Transports","keywords":"Commodity; Matrix (chemical analysis); Column (typography); Population; Truck; Algorithm; Production (economics); Value (mathematics); Computer science; Genetic algorithm; Liberian dollar; Mathematical optimization; Mathematics; Economics; Statistics; Engineering; Geometry; Microeconomics; Finance; Chemistry","score_opus":0.16864670251949745,"score_gpt":0.44896656613759334,"score_spread":0.2803198636180959,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4230363499","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.89701706,0.0002976762,0.094343685,0.006520538,0.00042781988,0.0010232844,0.00021912056,0.00006643839,0.00008439176],"genre_scores_gemma":[0.901784,0.0005301148,0.09668573,0.00006380971,0.00039273992,0.000022093049,0.00018340515,0.000049505437,0.00028861998],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99124575,0.0018911225,0.0014319657,0.00054289325,0.004071089,0.00081715384],"domain_scores_gemma":[0.9938061,0.0012465048,0.00066420756,0.0006041681,0.0032167346,0.00046228015],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.006361005,0.00025810732,0.00040083108,0.0011460965,0.0018662971,0.00035465526,0.0013539179,0.00027453335,0.00015075678],"category_scores_gemma":[0.00045662132,0.00022841661,0.0001551034,0.0022378522,0.0010203975,0.0016683287,0.000009214279,0.00148233,0.000012428595],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011819059,0.0005983164,0.51419353,0.00040826245,0.00017733355,0.0001220797,0.018576967,0.3624786,0.0005870489,0.005114201,0.005218115,0.091343604],"study_design_scores_gemma":[0.0024975385,0.0002931437,0.77403724,0.00049031235,0.00016735833,0.0000014473115,0.002832484,0.18175383,0.00019561865,0.0016515619,0.03568065,0.00039878802],"about_ca_topic_score_codex":0.012810336,"about_ca_topic_score_gemma":0.04850528,"teacher_disagreement_score":0.2598437,"about_ca_system_score_codex":0.00039122795,"about_ca_system_score_gemma":0.0016610002,"threshold_uncertainty_score":0.99943316},"labels":[],"label_agreement":null},{"id":"W4230832589","doi":"10.32920/ryerson.14649405.v1","title":"Quantifying the impacts of the modifiable areal unit problem in travel demand models","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Toronto Metropolitan University; Polytechnique Montréal","funders":"","keywords":"Scrutiny; Scale (ratio); Computer science; Unit (ring theory); Geography; Operations research; Regional science; Transport engineering; Cartography; Engineering; Mathematics","score_opus":0.09965879940876775,"score_gpt":0.32875087245078505,"score_spread":0.22909207304201729,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4230832589","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8603895,0.00044495662,0.054364704,0.0026125675,0.0004133303,0.0011555868,0.00004157068,0.00006307217,0.080514714],"genre_scores_gemma":[0.99686074,0.00024443987,0.0020328024,0.000050610524,0.000021535796,0.000018240693,0.000038175756,0.000007940441,0.0007254859],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987338,0.00024501877,0.0002976827,0.00018911964,0.00034755922,0.00018681651],"domain_scores_gemma":[0.99931765,0.000113615075,0.00018267808,0.00021488772,0.00013550397,0.000035690675],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008668362,0.00010270696,0.00017571858,0.000050270704,0.00024432724,0.00010689634,0.00033946702,0.00018540071,0.000045115776],"category_scores_gemma":[0.000043381777,0.00006199222,0.00007981108,0.00033542988,0.000109870256,0.00012317723,0.000040800172,0.00031935947,3.6320972e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000045756983,0.000024827605,0.014790488,0.000053116684,0.000012431373,6.257812e-7,0.043227423,0.9247863,0.000016679931,0.016989948,0.00003664681,0.000056927718],"study_design_scores_gemma":[0.0009196413,0.000021024107,0.18640603,0.0021650626,0.00016681635,0.0000011119186,0.12639526,0.65182567,0.000957075,0.030240702,0.00018885628,0.0007127445],"about_ca_topic_score_codex":0.039592538,"about_ca_topic_score_gemma":0.038965933,"teacher_disagreement_score":0.27296063,"about_ca_system_score_codex":0.000033219065,"about_ca_system_score_gemma":0.00065691967,"threshold_uncertainty_score":0.97857046},"labels":[],"label_agreement":null},{"id":"W4231514922","doi":"10.1109/itsc.2002.1041258","title":"Application of a simulation-based dynamic traffic assignment model","year":2003,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Inro Consultants (Canada)","funders":"","keywords":"Computer science; Queueing theory; Computation; Mathematical optimization; Traffic generation model; Path (computing); Network traffic simulation; Set (abstract data type); Traffic flow (computer networking); Simulation; Flow network; Network topology; Intelligent transportation system; Line (geometry); Traffic simulation; Real-time computing; Distributed computing; Algorithm; Computer network; Mathematics; Network traffic control; Transport engineering; Engineering","score_opus":0.015326201620238357,"score_gpt":0.30073329579839275,"score_spread":0.2854070941781544,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4231514922","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026179548,0.0000070021247,0.9670569,0.00009972288,0.00001726962,0.00014283608,0.0000028419568,0.0000590291,0.0064348816],"genre_scores_gemma":[0.9863496,0.0000018083118,0.01311538,0.000045292276,0.0000023692085,0.000010977082,0.000023159206,0.0000036238612,0.00044782626],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994962,0.00003837771,0.00012769505,0.000085274965,0.00018003043,0.00007240661],"domain_scores_gemma":[0.99969786,0.00009587308,0.000058644895,0.000051951287,0.00006438097,0.000031295167],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020071286,0.000035613237,0.000049208003,0.00003999569,0.00010064489,0.000007579026,0.00003501946,0.000040911895,0.000057533813],"category_scores_gemma":[0.000041355597,0.00003611067,0.00002266543,0.00015710377,0.000034732035,0.00005380575,2.7408706e-7,0.000020067238,0.0000041692233],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000027395847,0.000034376062,0.00059830875,0.000002694705,0.0000015731026,2.3491458e-8,0.0010822058,0.98414,0.00003400345,0.012794074,0.000005350611,0.0013047009],"study_design_scores_gemma":[0.00014107552,0.0000058100454,0.00038807353,0.0000023987693,0.000006091821,2.985465e-9,0.0002496133,0.99837625,0.000044936183,0.00023689083,0.0005055307,0.0000433278],"about_ca_topic_score_codex":0.000049723178,"about_ca_topic_score_gemma":0.00023453623,"teacher_disagreement_score":0.96017003,"about_ca_system_score_codex":0.000032924352,"about_ca_system_score_gemma":0.0001182995,"threshold_uncertainty_score":0.14725503},"labels":[],"label_agreement":null},{"id":"W4231850171","doi":"10.32920/ryerson.14654565","title":"Multi-Period Transportation Equilibrium Approach For Transportation Planning","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Lag; Time lag; Term (time); Period (music); Travel time; Distributed lag; Transportation planning; Computer science; Transport engineering; Econometrics; Economics; Engineering; Physics","score_opus":0.06023664899919299,"score_gpt":0.33014901541345154,"score_spread":0.26991236641425853,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4231850171","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07739908,0.00038626126,0.9165147,0.00035941336,0.00084967393,0.0013964821,0.0005911457,0.0005528355,0.0019504247],"genre_scores_gemma":[0.6518645,0.00004747968,0.31465277,0.000097362055,0.0002324083,0.0003660583,0.031210152,0.00005135089,0.0014778965],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99748135,0.00011196687,0.0006719579,0.0007831907,0.0005139469,0.00043757475],"domain_scores_gemma":[0.99871624,0.000080206926,0.00033377617,0.00024194372,0.00045415692,0.00017366625],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000549119,0.0003268854,0.0004329229,0.00016616855,0.0004181222,0.00028619208,0.00028580872,0.000686727,0.00012886728],"category_scores_gemma":[0.000054288004,0.00037764254,0.00032247894,0.00027480035,0.00011604761,0.0003819265,0.000002894205,0.00037131953,0.0000013977245],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000112122965,0.00029127064,0.014241732,0.0006960162,0.00014353312,0.000015499127,0.16775268,0.8126652,0.00032522777,0.0022682592,0.00072212756,0.00076631695],"study_design_scores_gemma":[0.009147557,0.00025471684,0.32322827,0.0016706932,0.0022357833,0.0000019307279,0.27305943,0.35642725,0.0019461198,0.00085818704,0.024948763,0.0062213205],"about_ca_topic_score_codex":0.0011008065,"about_ca_topic_score_gemma":0.002027293,"teacher_disagreement_score":0.6018619,"about_ca_system_score_codex":0.00008982914,"about_ca_system_score_gemma":0.00065833714,"threshold_uncertainty_score":0.99986756},"labels":[],"label_agreement":null},{"id":"W423205263","doi":"","title":"TraClus-DL: A Desire Line Clustering Framework to Identify Demand Corridors","year":2015,"lang":"en","type":"article","venue":"Transportation Research Board 94th Annual MeetingTransportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Identification (biology); Computer science; Supply and demand; Cluster analysis; Set (abstract data type); Line (geometry); Metric (unit); Operations research; Adaptability; Supply chain; Transport engineering; Operations management; Business; Engineering; Economics; Artificial intelligence; Mathematics; Marketing","score_opus":0.1445161291310216,"score_gpt":0.46309821754020014,"score_spread":0.31858208840917857,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W423205263","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8904234,0.00040192832,0.08852706,0.007827387,0.0010704872,0.0039410074,0.0005631372,0.0009850534,0.0062605464],"genre_scores_gemma":[0.9744133,0.0003934953,0.019806592,0.00034739362,0.00075069314,0.00063139107,0.0007308339,0.00016150718,0.0027648048],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.982869,0.0028103124,0.00174798,0.0015905718,0.0081806,0.0028015138],"domain_scores_gemma":[0.9856698,0.0020977678,0.00029904692,0.0007408441,0.008269766,0.0029227606],"candidate_categories":["metaepi_narrow","sts","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.018081816,0.00056600256,0.00073480274,0.00230917,0.0027905703,0.00068527623,0.001323768,0.0007709562,0.00043504208],"category_scores_gemma":[0.003365449,0.0006354115,0.00028098028,0.006103358,0.0011803976,0.001643096,0.00002148281,0.002425993,0.0005103813],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.005688868,0.0011713271,0.19105186,0.00072302145,0.00029115207,0.00090620114,0.45829898,0.25894207,0.00079094083,0.042566232,0.030208915,0.00936043],"study_design_scores_gemma":[0.0049566077,0.0021932616,0.6259737,0.0018369041,0.00017749867,0.0000017331113,0.2094809,0.0028160743,0.0010990596,0.015843023,0.1332983,0.0023229537],"about_ca_topic_score_codex":0.024552595,"about_ca_topic_score_gemma":0.069425195,"teacher_disagreement_score":0.43492183,"about_ca_system_score_codex":0.0006103987,"about_ca_system_score_gemma":0.0021266537,"threshold_uncertainty_score":0.9998754},"labels":[],"label_agreement":null},{"id":"W4233485808","doi":"10.22215/etd/2009-09089","title":"A comparison of neural networks and statistical methods in tour-based travel demand modeling","year":2009,"lang":"en","type":"dissertation","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Humanities; Art","score_opus":0.0560125572480073,"score_gpt":0.4378273653685518,"score_spread":0.3818148081205445,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4233485808","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04844229,0.00033822266,0.9466477,0.00003798792,0.00012184429,0.00018282287,0.000006629878,0.000026118656,0.004196354],"genre_scores_gemma":[0.91164696,0.000030781757,0.08735171,0.000026761943,0.000024658608,0.000004869322,0.00069252687,0.0000084834255,0.0002132639],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99878675,0.00023826254,0.00042558825,0.000190137,0.00019709821,0.00016219402],"domain_scores_gemma":[0.99939734,0.00027236523,0.00013347137,0.000049267972,0.00008300725,0.00006457011],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054876186,0.00011366253,0.00032912282,0.00013276229,0.00010366511,0.000032800508,0.000065381966,0.00025437385,0.000049984355],"category_scores_gemma":[0.00008330223,0.00011616209,0.000029219536,0.00018592391,0.000035631776,0.000058189013,6.997533e-7,0.00020039629,9.3165156e-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":[0.000085268905,0.000048979622,0.0059225494,0.00003390633,0.0000049197424,0.000001163803,0.0062108124,0.96954674,0.0000078008625,0.0027619477,0.000035072473,0.0153408395],"study_design_scores_gemma":[0.00023105329,0.000032979766,0.027599983,0.000058704987,0.000035221005,2.8574725e-8,0.0060163727,0.96562994,0.00001906044,0.00025159132,0.0000063699617,0.00011871931],"about_ca_topic_score_codex":0.001607946,"about_ca_topic_score_gemma":0.015716838,"teacher_disagreement_score":0.86320466,"about_ca_system_score_codex":0.000017374434,"about_ca_system_score_gemma":0.00010054757,"threshold_uncertainty_score":0.87703645},"labels":[],"label_agreement":null},{"id":"W4234601636","doi":"10.1002/atr.5670400202","title":"Special issue: Behavior in networks (II)","year":2006,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","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":"Library science; Engineering; Computer science","score_opus":0.008143577272085601,"score_gpt":0.276626178747153,"score_spread":0.2684826014750674,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4234601636","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98576826,0.00015133884,0.0063848193,0.0004784234,0.0021952118,0.00021311436,0.000011096876,0.000030317766,0.0047674035],"genre_scores_gemma":[0.98981845,0.00018324437,0.005107464,0.00003164164,0.00423744,0.000004298843,0.0000768013,0.0000098017035,0.0005308646],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99884945,0.00003842457,0.00053744164,0.00008648192,0.00032650394,0.0001616969],"domain_scores_gemma":[0.9993582,0.000036023408,0.00033501405,0.000038840426,0.00018149284,0.000050436585],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029492792,0.00007399254,0.00014800119,0.00016582712,0.00016145161,0.000018168319,0.000085995285,0.0000833696,0.00017584102],"category_scores_gemma":[0.000013339148,0.000078029225,0.00007234114,0.00036444984,0.000047561694,0.00057310803,2.7981676e-7,0.00015949916,0.0000014551778],"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":[0.0001485857,0.00015510763,0.084057406,0.0000040948325,0.0000037389814,0.00006919548,0.0072419816,0.89547575,0.00007527982,0.0023679351,0.0012725103,0.009128391],"study_design_scores_gemma":[0.00090818165,0.00008511229,0.9271846,0.000041028194,0.000034806933,0.0000010531862,0.0018624137,0.000035310695,0.000032395812,0.00031151992,0.06939688,0.00010672201],"about_ca_topic_score_codex":0.00017717881,"about_ca_topic_score_gemma":0.006221498,"teacher_disagreement_score":0.89544046,"about_ca_system_score_codex":0.00006111658,"about_ca_system_score_gemma":0.00008009714,"threshold_uncertainty_score":0.3471742},"labels":[],"label_agreement":null},{"id":"W4235121693","doi":"10.32920/ryerson.14646210","title":"North Toronto rapid transit line feasibility study","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Transport engineering; Public transport; Transit (satellite); Traffic congestion; Rush hour; Service (business); Travel time; Business; Computer science; Engineering","score_opus":0.05947842652069528,"score_gpt":0.34620921870706983,"score_spread":0.28673079218637454,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4235121693","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93930036,0.0005129626,0.019458415,0.00058759114,0.0011374353,0.0013455256,0.00004919846,0.00043018238,0.03717832],"genre_scores_gemma":[0.9945532,0.00025295946,0.0021358982,0.00008648778,0.00018743251,0.000029970974,0.00046944787,0.000011327003,0.0022732818],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99818105,0.00031370472,0.00033254744,0.00049939204,0.000480694,0.00019263064],"domain_scores_gemma":[0.9991172,0.00005087265,0.000095369454,0.00032742036,0.00028598818,0.0001231748],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006110485,0.0001577149,0.00026782183,0.000022596874,0.00028570925,0.0001672638,0.00023344536,0.00019123229,0.0026428648],"category_scores_gemma":[0.000060601207,0.00015922551,0.00012064805,0.00012233904,0.000054048018,0.00017016713,0.0000226266,0.00022860058,0.000004890182],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007575807,0.002030411,0.5963196,0.00009586844,0.00018795006,0.00003904426,0.33434847,0.0591973,0.0000016201166,0.00034142451,0.00060826336,0.006754285],"study_design_scores_gemma":[0.0007056794,0.00013098204,0.89590085,0.000044996108,0.00018124061,1.3133322e-7,0.09973092,0.0005468892,0.000008376083,0.000058958427,0.0021539733,0.0005370036],"about_ca_topic_score_codex":0.043258887,"about_ca_topic_score_gemma":0.77585894,"teacher_disagreement_score":0.7326001,"about_ca_system_score_codex":0.00017124349,"about_ca_system_score_gemma":0.00048417528,"threshold_uncertainty_score":0.99826884},"labels":[],"label_agreement":null},{"id":"W4235644395","doi":"10.1177/0361198106197100115","title":"Implementation of Nationwide Public Transport Smart Card in the Netherlands","year":2006,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ministry of Transportation of Ontario","funders":"","keywords":"Public transport; Ministry of Transport; Smart card; Business; Work (physics); Government (linguistics); Competition (biology); Transport engineering; Environmental economics; Marketing; Economics; Engineering; Computer science; Computer security","score_opus":0.09111717021444378,"score_gpt":0.41975419830962035,"score_spread":0.3286370280951766,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4235644395","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9844202,0.0001993835,0.0020621757,0.010161882,0.0004061803,0.0012406679,0.00008717826,0.000026453738,0.0013958691],"genre_scores_gemma":[0.99768066,0.0005879019,0.00082634727,0.000056083227,0.0002455702,0.0000876904,0.00010480345,0.000034976765,0.00037593947],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9887156,0.0027755247,0.0020008774,0.00035065602,0.0052498286,0.00090754824],"domain_scores_gemma":[0.99365306,0.001366682,0.00073215517,0.0003811954,0.0036872167,0.00017968616],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012364694,0.00022202948,0.00042238177,0.0013743493,0.0009979386,0.00013590613,0.001335765,0.00023156854,0.0002849951],"category_scores_gemma":[0.00013467675,0.00016222257,0.00037967664,0.004363405,0.00095028605,0.0009733254,0.0000022416166,0.0014657604,0.0000055662713],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025117194,0.00023793544,0.9475388,0.000080137055,0.000045685003,0.000035619432,0.020106768,0.002774389,0.00026174143,0.024266962,0.002516959,0.0018838523],"study_design_scores_gemma":[0.0014327967,0.00020396768,0.94264734,0.00012140969,0.00004333324,2.5316564e-7,0.026260348,0.00002592273,0.0003001003,0.0036143186,0.025199145,0.00015104008],"about_ca_topic_score_codex":0.1361988,"about_ca_topic_score_gemma":0.70078313,"teacher_disagreement_score":0.5645843,"about_ca_system_score_codex":0.00021667521,"about_ca_system_score_gemma":0.0015216329,"threshold_uncertainty_score":0.8695533},"labels":[],"label_agreement":null},{"id":"W4236319121","doi":"10.1111/itor.12655","title":"Special issue on “Transportation and Logistics with Autonomous Technologies”","year":2019,"lang":"en","type":"article","venue":"International Transactions in Operational Research","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Norwegian; Citation; Schools of economic thought; Operations research; Sociology; Library science; Computer science; Philosophy; Linguistics; Economics; Engineering; Neoclassical economics","score_opus":0.0507990038847326,"score_gpt":0.3886130158472323,"score_spread":0.3378140119624997,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4236319121","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18393303,0.00007079718,0.05813017,0.060833443,0.0040457454,0.0028116757,0.0004612456,0.00057151617,0.68914235],"genre_scores_gemma":[0.9848497,0.00021912238,0.0025420196,0.000080808335,0.0006288334,0.000066220426,0.00012258838,0.00001177795,0.011478922],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9982713,0.000091474954,0.00020957159,0.0002877298,0.0009232964,0.00021661345],"domain_scores_gemma":[0.9990684,0.0003480235,0.000029257015,0.00009407658,0.0004162144,0.000044072836],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00064584706,0.00008654992,0.00009023984,0.0004788969,0.0003304628,0.00013649945,0.00018934105,0.00012186918,0.0024131532],"category_scores_gemma":[0.000084382846,0.000084052284,0.000018753864,0.0004551751,0.0002785424,0.00034245208,0.0000012423418,0.00039941916,0.0001260602],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007383734,0.00052592566,0.046718094,0.000025180529,0.00007671399,0.000048058842,0.012017409,0.5146628,0.00010911229,0.36664584,0.0018879491,0.056544524],"study_design_scores_gemma":[0.0021101967,0.0004287184,0.062203176,0.00018726771,0.000011236908,0.0000052451487,0.016226197,0.009731119,0.0003904302,0.0024375594,0.9058246,0.0004442642],"about_ca_topic_score_codex":0.00064499374,"about_ca_topic_score_gemma":0.0043718405,"teacher_disagreement_score":0.9039366,"about_ca_system_score_codex":0.00022184149,"about_ca_system_score_gemma":0.0003162304,"threshold_uncertainty_score":0.9984988},"labels":[],"label_agreement":null},{"id":"W4236336641","doi":"10.1002/cjs.11504","title":"Issue Information","year":2020,"lang":"en","type":"paratext","venue":"Canadian Journal of Statistics","topic":"Transportation Planning and Optimization","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":true,"ca_institutions":"","funders":"","keywords":"Computer science; Citation; Information retrieval; Library science; World Wide Web","score_opus":0.017166976188306076,"score_gpt":0.2630599861127621,"score_spread":0.24589300992445604,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4236336641","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000028510936,0.0012321217,0.5127365,0.0070602708,0.022307606,0.0002918127,0.01161405,0.0000141534165,0.44471496],"genre_scores_gemma":[0.0628739,0.012135025,0.30385333,0.01716381,0.021337008,0.0000120026125,0.02274309,0.0002290918,0.55965275],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9990496,0.000058126534,0.00041018473,0.000042005548,0.00026509148,0.00017501517],"domain_scores_gemma":[0.9981344,0.00006553411,0.0005069345,0.000040557028,0.0006237291,0.00062885083],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00018491475,0.00008569346,0.00018153411,0.0002417192,0.0002600799,0.00017929616,0.00018654631,0.0001640356,0.0058989124],"category_scores_gemma":[0.00037090774,0.000094592346,0.000034429242,0.00018196621,0.00010114436,0.0002879571,8.668742e-7,0.0003007968,0.0022272782],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000003060479,8.0829994e-7,0.000047327045,0.000029359808,0.000017089467,0.000023299406,0.012593689,0.0022825103,3.0897525e-8,0.0031047198,0.97838074,0.003517359],"study_design_scores_gemma":[0.000093952825,0.000031148033,0.0001179487,0.00006567137,0.000034361008,0.0000017821657,0.0015842499,0.000041161125,4.7800637e-7,0.00012085592,0.9978119,0.000096477284],"about_ca_topic_score_codex":0.014886539,"about_ca_topic_score_gemma":0.042666767,"teacher_disagreement_score":0.20888317,"about_ca_system_score_codex":0.00016074783,"about_ca_system_score_gemma":0.0068382807,"threshold_uncertainty_score":0.99879205},"labels":[],"label_agreement":null},{"id":"W4236562045","doi":"10.32920/ryerson.14644281","title":"HOV access location analysis at Erin Mills Parkway and Highway 403","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Federal Highway Administration; Medical Research Council; U.S. Department of Transportation","keywords":"VisSim; Transport engineering; Position (finance); Reliability (semiconductor); Occupancy; Traffic volume; Travel time; Computer science; Microsimulation; Engineering; Civil engineering; Business","score_opus":0.03099872622457393,"score_gpt":0.3303647201369816,"score_spread":0.29936599391240765,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4236562045","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8388448,0.0013493674,0.12877128,0.002662303,0.0008888863,0.0005072348,0.00006841837,0.00041749445,0.026490256],"genre_scores_gemma":[0.9881195,0.0013057031,0.0023854487,0.00013947059,0.000080092315,0.000026710946,0.0013343456,0.000009181758,0.006599581],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99860907,0.00015703039,0.00026373425,0.00045280874,0.00034784694,0.00016951836],"domain_scores_gemma":[0.99912345,0.000090896625,0.00017520139,0.00022052498,0.0002771867,0.00011272485],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00038263545,0.00013909268,0.0002645843,0.00020137764,0.00034830012,0.00044125246,0.00020798839,0.00027339472,0.00091796514],"category_scores_gemma":[0.00006977653,0.00014142718,0.000095865405,0.0008120668,0.00009713307,0.00020669262,0.00008770518,0.00014133271,0.000006319851],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002216714,0.000076459524,0.4010984,0.00015793442,0.000840314,0.000013105615,0.029192809,0.5604103,0.00001926184,0.0023921353,0.002408834,0.0033682957],"study_design_scores_gemma":[0.00033193623,0.000011449689,0.9599629,0.00013358127,0.0015659864,3.6345998e-7,0.004960879,0.018781131,0.00026656268,0.00033310498,0.012816743,0.00083535776],"about_ca_topic_score_codex":0.007692733,"about_ca_topic_score_gemma":0.03893556,"teacher_disagreement_score":0.55886453,"about_ca_system_score_codex":0.00009623342,"about_ca_system_score_gemma":0.0002044159,"threshold_uncertainty_score":0.99999535},"labels":[],"label_agreement":null},{"id":"W4236665038","doi":"10.2139/ssrn.2350233","title":"A New Class of Dynamic Term Structure Models","year":2013,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Bank of Canada","funders":"","keywords":"Term (time); Class (philosophy); Computer science; Artificial intelligence; Physics","score_opus":0.005859627048769904,"score_gpt":0.24835045734833178,"score_spread":0.24249083029956187,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4236665038","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.74814284,0.0011319759,0.24277607,0.0023113454,0.00028525048,0.00022462664,0.0000056555486,0.00006155371,0.00506068],"genre_scores_gemma":[0.99468684,0.0008102781,0.0012414753,0.000027694261,0.000077074415,6.936945e-7,0.000006458355,0.0000071196423,0.0031423725],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9987787,0.000046913494,0.00016565174,0.00007620063,0.0002295715,0.0007029617],"domain_scores_gemma":[0.9996304,0.000016919375,0.00012664274,0.000053194115,0.00009680733,0.00007604494],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002738251,0.000058425787,0.00008604765,0.00006399711,0.00017764985,0.00004015843,0.00015310942,0.00007115051,0.00018996821],"category_scores_gemma":[0.000013712516,0.000052181207,0.00004495627,0.00014384065,0.000034283927,0.00035530448,0.0000022145439,0.00046623457,0.0000052437163],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031313757,0.00003284819,0.005706304,0.000008146582,0.00013382084,0.0000010291357,0.016310068,0.027940748,0.001011093,0.79657495,0.0004266129,0.15182307],"study_design_scores_gemma":[0.0006363106,0.0001107991,0.008375191,0.00002892301,0.00003897856,0.000017712444,0.008663817,0.0058321394,0.000021654618,0.97562337,0.00046639156,0.00018469125],"about_ca_topic_score_codex":0.0012154017,"about_ca_topic_score_gemma":0.0076172072,"teacher_disagreement_score":0.24654399,"about_ca_system_score_codex":0.00022409431,"about_ca_system_score_gemma":0.00206933,"threshold_uncertainty_score":0.42505804},"labels":[],"label_agreement":null},{"id":"W4236810383","doi":"10.17265/2328-2142/2015.02.001","title":"Traffic Data: Bluetooth Sensors vs. Crowdsourcing—A Comparative Study to Calculate Travel Time Reliability in Calgary, Alberta, Canada","year":2015,"lang":"en","type":"article","venue":"Journal of Traffic and Transportation Engineering","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Crowdsourcing; Bluetooth; Reliability (semiconductor); Transport engineering; Computer science; Geography; Engineering; Telecommunications; Wireless; World Wide Web","score_opus":0.0215304779682308,"score_gpt":0.26533059020389704,"score_spread":0.24380011223566625,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4236810383","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9975234,0.000046123998,0.0015076051,0.0003481631,0.0002190441,0.00026696976,0.000024050612,0.00001963786,0.00004499974],"genre_scores_gemma":[0.9985302,0.000008651244,0.0012315587,0.000022189835,0.000055249755,0.0000020785888,0.000047668993,0.000012178216,0.00009020647],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9983937,0.00008012527,0.00061933754,0.00020965483,0.00048238595,0.00021479806],"domain_scores_gemma":[0.9990727,0.00015776197,0.00015246081,0.000111558584,0.00018362206,0.0003219245],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009343017,0.00014547519,0.0003498828,0.00016945835,0.00008291446,0.00003766537,0.00017253398,0.000052069696,0.0000097321135],"category_scores_gemma":[0.000089794725,0.00014474244,0.000031222036,0.00038632166,0.000027798305,0.0003635228,0.000001686037,0.00021160409,9.905107e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000095642274,0.00010662037,0.005861566,0.000010929101,0.000025753936,0.00004309521,0.07306918,0.92043865,0.000002967037,0.000015635287,0.00018445599,0.0001454879],"study_design_scores_gemma":[0.00308419,0.00037566802,0.6709359,0.0001508392,0.00018218654,0.000007666128,0.039539643,0.28196466,0.00001199965,0.0000017097901,0.0032461588,0.00049933477],"about_ca_topic_score_codex":0.07413445,"about_ca_topic_score_gemma":0.6117479,"teacher_disagreement_score":0.6650744,"about_ca_system_score_codex":0.00013074576,"about_ca_system_score_gemma":0.0005285327,"threshold_uncertainty_score":0.932031},"labels":[],"label_agreement":null},{"id":"W4238395107","doi":"10.1177/0361198106195600121","title":"Context-Sensitive Urban Transportation Design in West Philadelphia, Pennsylvania","year":2006,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Transport engineering; Context (archaeology); Pedestrian; Work (physics); Public transport; Engineering; Principal (computer security); Upgrade; Community design; Business; Computer science; Computer security","score_opus":0.09260069509244767,"score_gpt":0.3802107092486778,"score_spread":0.2876100141562301,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4238395107","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97091156,0.00040565868,0.01485958,0.009502265,0.0008999559,0.002320915,0.00017902357,0.00010002442,0.000821044],"genre_scores_gemma":[0.99426776,0.00077295775,0.0030221837,0.00008611379,0.00046095788,0.00009588412,0.00013492006,0.000078766694,0.0010804828],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9872696,0.0034514065,0.0022915045,0.000649118,0.00494024,0.0013981349],"domain_scores_gemma":[0.9916109,0.0021496606,0.0007941949,0.00041097624,0.004602217,0.0004320941],"candidate_categories":["metaepi_narrow","sts","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.009111718,0.0003797548,0.00065697526,0.0018228535,0.0014288268,0.00025642206,0.0011142619,0.00044058717,0.0003932623],"category_scores_gemma":[0.00027598513,0.00033972386,0.00043789507,0.004299183,0.001342562,0.0014516409,0.0000022574018,0.0024592762,0.000040099],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.004040699,0.0011344414,0.792298,0.00022647338,0.0001646913,0.00066633546,0.06195322,0.05750981,0.002453894,0.052308004,0.022928381,0.0043160114],"study_design_scores_gemma":[0.0024619834,0.0003588844,0.9559348,0.00046486876,0.00006899474,3.4752745e-7,0.014159419,0.00020806566,0.0008113174,0.003919677,0.021243716,0.00036790676],"about_ca_topic_score_codex":0.11109022,"about_ca_topic_score_gemma":0.55091524,"teacher_disagreement_score":0.43982503,"about_ca_system_score_codex":0.00058156345,"about_ca_system_score_gemma":0.0016367837,"threshold_uncertainty_score":0.99990547},"labels":[],"label_agreement":null},{"id":"W4238929295","doi":"10.32920/ryerson.14654703","title":"The political economy of mass transit infrastructure investments in the greater Toronto area","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Toronto Metropolitan University; University of Calgary","funders":"","keywords":"Attractiveness; Politics; Witness; Public transport; Business; Corporate governance; Plan (archaeology); Service delivery framework; Public administration; Transit (satellite); Service (business); Public relations; Political science; Finance; Marketing","score_opus":0.020048464747241306,"score_gpt":0.2798690176887648,"score_spread":0.2598205529415235,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4238929295","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10377616,0.0003746097,0.009242848,0.018483335,0.0006787207,0.00096898637,0.000059089856,0.00006392644,0.8663523],"genre_scores_gemma":[0.99754554,0.000041773317,0.0008724962,0.001126929,0.000051927982,0.00002553325,0.00008392385,0.000004880899,0.0002469854],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9990028,0.00020608849,0.00024261748,0.00016431234,0.00018691216,0.00019727986],"domain_scores_gemma":[0.9995284,0.00010726662,0.000066600085,0.00018287303,0.00006865955,0.000046164194],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028775365,0.00009633496,0.00013319986,0.000015735004,0.00014341083,0.00012913666,0.0002980715,0.00018170394,0.00023078949],"category_scores_gemma":[0.00001688348,0.00005674941,0.00006801215,0.000052488464,0.00015363538,0.00009084076,0.000010200368,0.00017543268,4.7312736e-7],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021711972,0.000033574983,0.060238414,0.00006077672,0.00007787103,0.000008909905,0.107579455,0.010360518,0.0000037638952,0.82042193,0.0007243056,0.0004687851],"study_design_scores_gemma":[0.00068960345,0.000031058637,0.4852151,0.00023165887,0.0001285877,0.0000012151561,0.333021,0.0014998245,0.00013630404,0.16244006,0.01610441,0.00050119305],"about_ca_topic_score_codex":0.0057198834,"about_ca_topic_score_gemma":0.018903155,"teacher_disagreement_score":0.8937694,"about_ca_system_score_codex":0.000101712925,"about_ca_system_score_gemma":0.00027125483,"threshold_uncertainty_score":0.9989993},"labels":[],"label_agreement":null},{"id":"W4238980503","doi":"10.32920/ryerson.14654001","title":"Evaluating the efficiency of large scale transit infrastructure planning in Toronto Ontario: issues of governance","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Toronto Metropolitan University; University of Toronto","funders":"","keywords":"Plan (archaeology); Corporate governance; Public transport; Scale (ratio); Transit (satellite); Business; Transport engineering; Environmental planning; Regional science; Engineering; Finance; Geography","score_opus":0.024587495345229995,"score_gpt":0.36523736933735323,"score_spread":0.34064987399212326,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4238980503","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9759549,0.001629262,0.005348921,0.0001903091,0.000312155,0.00027949215,0.00003307539,0.000022215258,0.016229691],"genre_scores_gemma":[0.9906929,0.00012082801,0.008257653,0.000027858494,0.000032411182,0.000008328079,0.000055678,0.0000067849383,0.00079753576],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9983572,0.00017423737,0.00044712677,0.00024342269,0.0005963454,0.0001816906],"domain_scores_gemma":[0.9991763,0.0000977731,0.00033018304,0.00019001726,0.0001793438,0.000026337786],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0010122703,0.00011827733,0.00028538107,0.000019280733,0.00012239207,0.00003322554,0.0003012152,0.00020353381,0.0010183535],"category_scores_gemma":[0.0000896831,0.00009712901,0.00008414355,0.00014822822,0.00008828187,0.00012479452,0.000028963708,0.00027242905,1.4930137e-7],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023865217,0.000050267772,0.10693988,0.00008788026,0.00001568502,0.0000012805449,0.4335959,0.45729393,0.00010035533,0.0013373379,0.000046274487,0.00050733023],"study_design_scores_gemma":[0.0005008605,0.000062268846,0.86769503,0.001106141,0.000061524144,2.2382635e-7,0.11809404,0.010959682,0.00053590577,0.00026169073,0.0004519993,0.0002706218],"about_ca_topic_score_codex":0.42263722,"about_ca_topic_score_gemma":0.8263891,"teacher_disagreement_score":0.7607552,"about_ca_system_score_codex":0.00023962079,"about_ca_system_score_gemma":0.0007399554,"threshold_uncertainty_score":0.99989486},"labels":[],"label_agreement":null},{"id":"W4240383275","doi":"10.32920/ryerson.14665935","title":"The environmental impact of urban land use and transportation strategies in the City of Toronto","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Timeline; Environmental planning; Plan (archaeology); Land use; Process (computing); Business; Sustainable development; Urban planning; Transport engineering; Sustainable transport; Land-use planning; Population; Environmental resource management; Geography; Sustainability; Engineering; Civil engineering; Political science; Computer science; Economics","score_opus":0.022338029312564085,"score_gpt":0.2904413392876138,"score_spread":0.2681033099750497,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4240383275","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9981788,0.0005387815,0.0003112528,0.000050435436,0.000036520825,0.0001700176,0.000079562546,0.0000056532836,0.0006289853],"genre_scores_gemma":[0.99789166,0.0016699913,0.00017747068,0.000004255147,0.000012302646,0.0000055296837,0.00018845075,0.000002622281,0.000047704263],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9992794,0.00012930181,0.00020859505,0.00010291861,0.00020600097,0.00007382776],"domain_scores_gemma":[0.99955696,0.00017454558,0.00013166764,0.00009936604,0.000020905938,0.00001654568],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035319477,0.00006719811,0.00010939777,0.000009379698,0.00009053114,0.000091263115,0.00010177471,0.00008962723,0.00007307031],"category_scores_gemma":[0.000013411122,0.000039971288,0.000058617632,0.000029544683,0.00015219078,0.0002232286,0.0000030336066,0.00008493407,2.5150346e-8],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017921007,0.0000316126,0.9089448,0.000011911881,0.000020910586,6.9646126e-7,0.08404965,0.005474822,0.00002524045,0.0012623618,0.000015819985,0.00014428979],"study_design_scores_gemma":[0.00008471928,0.000015812659,0.97160584,0.000020453643,0.000017539,4.1333976e-8,0.02799391,0.000080853846,0.000010639488,0.00009132286,0.00003570133,0.000043147014],"about_ca_topic_score_codex":0.13698669,"about_ca_topic_score_gemma":0.30032638,"teacher_disagreement_score":0.1633397,"about_ca_system_score_codex":0.0000351349,"about_ca_system_score_gemma":0.0001424232,"threshold_uncertainty_score":0.8687602},"labels":[],"label_agreement":null},{"id":"W4240788109","doi":"10.1177/0361198105192600112","title":"Multiagent Learning-Based Approach to Transit Assignment Problem","year":2005,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Microsimulation; Transit (satellite); Computer science; Transport engineering; Reinforcement learning; Public transport; Adaptation (eye); Population; Operations research; Process (computing); Engineering; Artificial intelligence","score_opus":0.09898261996274811,"score_gpt":0.3954828822596817,"score_spread":0.2965002622969336,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4240788109","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7332345,0.0002830208,0.22198398,0.032746613,0.0007212959,0.0043942137,0.000092041155,0.00021728524,0.006327042],"genre_scores_gemma":[0.9708633,0.00029558616,0.025418915,0.00019149006,0.0003780841,0.0001635486,0.000047441776,0.00006469718,0.002576969],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9882184,0.0025713847,0.0015081557,0.0005506275,0.0059301555,0.0012212769],"domain_scores_gemma":[0.9935565,0.0008636988,0.00044703417,0.00036605514,0.0039163586,0.0008503731],"candidate_categories":["sts","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.008752958,0.00028994906,0.00045462616,0.0012796768,0.0019559439,0.00026103386,0.0012495127,0.00028183145,0.00039781915],"category_scores_gemma":[0.0002449718,0.00024001596,0.00043278196,0.0031561079,0.00066358317,0.00073882035,0.000003286903,0.0023420402,0.00006055156],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012437345,0.00091977074,0.07966098,0.00015713935,0.00011917538,0.000027266948,0.041519493,0.8495443,0.00069316383,0.0060607414,0.00859848,0.011455757],"study_design_scores_gemma":[0.0034695228,0.0009545684,0.48501566,0.0005309878,0.00012304401,2.7167854e-7,0.015838468,0.0034106635,0.001329469,0.0006102502,0.48812056,0.00059651205],"about_ca_topic_score_codex":0.0106999315,"about_ca_topic_score_gemma":0.056770798,"teacher_disagreement_score":0.84613365,"about_ca_system_score_codex":0.0005914988,"about_ca_system_score_gemma":0.0014373148,"threshold_uncertainty_score":0.9999596},"labels":[],"label_agreement":null},{"id":"W4241767829","doi":"10.1243/0959651011541139","title":"Traffic assignment: equilibrium models","year":2001,"lang":"en","type":"article","venue":"Proceedings of the Institution of Mechanical Engineers Part I Journal of Systems and Control Engineering","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Mathematical optimization; Calibration; Class (philosophy); Computer science; Variable (mathematics); Algorithm; Mathematical economics; Mathematics; Statistics; Artificial intelligence","score_opus":0.013619925235914843,"score_gpt":0.21454158488140318,"score_spread":0.20092165964548833,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4241767829","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.64163387,0.001846422,0.35261503,0.0006913698,0.0018697422,0.00042300325,0.000011062216,0.000059878894,0.0008496387],"genre_scores_gemma":[0.99931824,0.00017619206,0.00030751675,0.0000059104163,0.0001482288,0.000002936979,2.2183671e-7,0.0000063630137,0.000034371547],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99887407,0.0000083790155,0.00051773613,0.00006878279,0.0003943537,0.0001366681],"domain_scores_gemma":[0.9991677,0.00004641265,0.00036234935,0.000028365528,0.00030368514,0.0000915261],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000829669,0.000086782595,0.00026255896,0.000098837794,0.00006094288,0.000026258384,0.0001503215,0.00008602309,0.0000020661064],"category_scores_gemma":[0.00015136077,0.00006691248,0.00009422891,0.00020027552,0.0000472731,0.00039319554,0.0000056368413,0.00013126596,8.42982e-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":[0.000037544036,0.000019370204,0.000051206283,0.00007129287,0.000047646605,6.293503e-7,0.00050424616,0.9295139,0.003871393,0.0657135,0.000023885597,0.00014541822],"study_design_scores_gemma":[0.0018077993,0.00015319145,0.000122527,0.0011348373,0.00016762817,0.000043101147,0.0017790132,0.98936033,0.00093932846,0.00014355242,0.0041634496,0.00018525726],"about_ca_topic_score_codex":0.000016447812,"about_ca_topic_score_gemma":6.1846913e-7,"teacher_disagreement_score":0.3576844,"about_ca_system_score_codex":0.000037856233,"about_ca_system_score_gemma":0.000054281383,"threshold_uncertainty_score":0.27286115},"labels":[],"label_agreement":null},{"id":"W4241899091","doi":"10.32920/ryerson.14654565.v1","title":"Multi-Period Transportation Equilibrium Approach For Transportation Planning","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Lag; Time lag; Term (time); Period (music); Travel time; Distributed lag; Transportation planning; Computer science; Econometrics; Economics; Transport engineering; Engineering; Physics","score_opus":0.06023664899919299,"score_gpt":0.33014901541345154,"score_spread":0.26991236641425853,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4241899091","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07739908,0.00038626126,0.9165147,0.00035941336,0.00084967393,0.0013964821,0.0005911457,0.0005528355,0.0019504247],"genre_scores_gemma":[0.6518645,0.00004747968,0.31465277,0.000097362055,0.0002324083,0.0003660583,0.031210152,0.00005135089,0.0014778965],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99748135,0.00011196687,0.0006719579,0.0007831907,0.0005139469,0.00043757475],"domain_scores_gemma":[0.99871624,0.000080206926,0.00033377617,0.00024194372,0.00045415692,0.00017366625],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000549119,0.0003268854,0.0004329229,0.00016616855,0.0004181222,0.00028619208,0.00028580872,0.000686727,0.00012886728],"category_scores_gemma":[0.000054288004,0.00037764254,0.00032247894,0.00027480035,0.00011604761,0.0003819265,0.000002894205,0.00037131953,0.0000013977245],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000112122965,0.00029127064,0.014241732,0.0006960162,0.00014353312,0.000015499127,0.16775268,0.8126652,0.00032522777,0.0022682592,0.00072212756,0.00076631695],"study_design_scores_gemma":[0.009147557,0.00025471684,0.32322827,0.0016706932,0.0022357833,0.0000019307279,0.27305943,0.35642725,0.0019461198,0.00085818704,0.024948763,0.0062213205],"about_ca_topic_score_codex":0.0011008065,"about_ca_topic_score_gemma":0.002027293,"teacher_disagreement_score":0.6018619,"about_ca_system_score_codex":0.00008982914,"about_ca_system_score_gemma":0.00065833714,"threshold_uncertainty_score":0.99986756},"labels":[],"label_agreement":null},{"id":"W4242843184","doi":"10.32920/ryerson.14647236","title":"An integrated positioning system for the Toronto Transit System","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Global Positioning System; Hybrid positioning system; Positioning system; Dead reckoning; Transit (satellite); Kalman filter; Precision Lightweight GPS Receiver; Positioning technology; Automatic vehicle location; Computer science; Systems design; Navigation system; Assisted GPS; System integration; Real-time computing; Engineering; Systems engineering; Telecommunications; Transport engineering; Public transport; Node (physics)","score_opus":0.02028697836900178,"score_gpt":0.2958471246567963,"score_spread":0.2755601462877945,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4242843184","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0034927395,0.00061682134,0.9755307,0.00038166283,0.0018139204,0.0009328815,0.0001092329,0.00074768555,0.016374353],"genre_scores_gemma":[0.988075,0.000035974772,0.009764097,0.000038974064,0.00026403985,0.00017749978,0.0010187324,0.000017626424,0.000608051],"study_design_codex":"simulation_or_modeling","study_design_gemma":"qualitative","domain_scores_codex":[0.9987346,0.00022174754,0.00028547496,0.0003067509,0.00026892952,0.00018247796],"domain_scores_gemma":[0.9989963,0.00014210652,0.00012522131,0.00023266113,0.00042557644,0.00007814627],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00071742176,0.00013793043,0.00020236481,0.000020770352,0.0008686481,0.0005095477,0.000282019,0.0002702737,0.00007322334],"category_scores_gemma":[0.000014008963,0.00010487095,0.00012568323,0.00008605657,0.00004145008,0.00020491882,0.000005509426,0.00014039822,0.0000014388064],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011975365,0.00007931043,0.00042837896,0.0014586541,0.00035340159,0.000014362969,0.1574871,0.64392066,0.00007254192,0.19009306,0.0006650183,0.005307797],"study_design_scores_gemma":[0.00035493143,0.000046436402,0.0008567344,0.0014660856,0.00038085706,0.0000024988137,0.85537565,0.1365664,0.00010908018,0.000010164448,0.004398283,0.00043289308],"about_ca_topic_score_codex":0.042951595,"about_ca_topic_score_gemma":0.05657323,"teacher_disagreement_score":0.98458225,"about_ca_system_score_codex":0.00046249165,"about_ca_system_score_gemma":0.00039439366,"threshold_uncertainty_score":0.96342146},"labels":[],"label_agreement":null},{"id":"W4242866810","doi":"10.1177/0361198106198100106","title":"Advanced Activity-Based Models in Context of Planning Decisions","year":2006,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"TRIPS architecture; Context (archaeology); Metropolitan area; Transportation planning; Microsimulation; Consistency (knowledge bases); Travel behavior; Transport engineering; Operations research; Computer science; Atlanta; Engineering; Geography","score_opus":0.12940937687238843,"score_gpt":0.4238619702336571,"score_spread":0.2944525933612687,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4242866810","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.980739,0.00022849717,0.014583404,0.0023718763,0.00030460447,0.0008238133,0.000070606366,0.00002755389,0.00085066963],"genre_scores_gemma":[0.99520653,0.0002890581,0.0039575873,0.000027648093,0.000087538356,0.000044612003,0.000030342671,0.00003502003,0.00032168123],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9919278,0.0016089865,0.0014455558,0.00034088793,0.0038906173,0.00078616163],"domain_scores_gemma":[0.99298954,0.0024954993,0.0006395921,0.00031151096,0.0033294368,0.00023444109],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0057123285,0.00019445027,0.0004700892,0.0015835539,0.00073282566,0.000082611085,0.0008508156,0.00023836442,0.00010130892],"category_scores_gemma":[0.0003031446,0.00016737165,0.00031264624,0.0032299787,0.0008562668,0.0010063968,0.0000024341227,0.0014989051,0.0000034233992],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0026324354,0.00062234944,0.37072545,0.00010961951,0.00004365574,0.00009755084,0.016636081,0.5742739,0.002279831,0.022375358,0.0012490479,0.008954734],"study_design_scores_gemma":[0.002922246,0.00032253424,0.9645985,0.0009747524,0.000032444706,8.289405e-8,0.01107229,0.0021844606,0.0018952956,0.009786665,0.0059799193,0.0002308331],"about_ca_topic_score_codex":0.049331963,"about_ca_topic_score_gemma":0.23578554,"teacher_disagreement_score":0.593873,"about_ca_system_score_codex":0.00027287228,"about_ca_system_score_gemma":0.0015071675,"threshold_uncertainty_score":0.9569986},"labels":[],"label_agreement":null},{"id":"W4242955633","doi":"10.32920/ryerson.14653917.v1","title":"Addressing Challenges to Mobility Hub Implementation at Suburban Commuter Rail Parking Lots in Greater Toronto","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Transport engineering; Modal shift; Agency (philosophy); Mobility management; Business; Computer science; Telecommunications; Public transport; Engineering","score_opus":0.150402249911562,"score_gpt":0.3999190702365044,"score_spread":0.2495168203249424,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4242955633","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9722015,0.0011845772,0.0018339764,0.0024090263,0.00071200845,0.0008159323,0.00002912764,0.00018258623,0.020631218],"genre_scores_gemma":[0.9951933,0.00049624266,0.0027422972,0.0002290098,0.000114432885,0.00011151269,0.0004590021,0.000017188891,0.0006369836],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.9979599,0.00037584433,0.000422055,0.00053016294,0.00039068438,0.00032134398],"domain_scores_gemma":[0.99924415,0.000076126154,0.00013160244,0.00028904958,0.00014275374,0.00011630358],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0009154369,0.00018590568,0.00026661134,0.00006667677,0.00028743755,0.00020667409,0.00021195065,0.00023810471,0.0018177402],"category_scores_gemma":[0.000028376802,0.00019973643,0.000075516844,0.00008857387,0.000043385873,0.00028109347,0.00012032478,0.00016939356,0.000005964472],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007145809,0.00013148812,0.37238988,0.00024261595,0.00005924812,0.000024368906,0.584805,0.011615462,0.000053341937,0.00065058045,0.0009597711,0.028996773],"study_design_scores_gemma":[0.0005518969,0.000026111738,0.8955731,0.0004609613,0.00003790912,3.981749e-7,0.09608783,0.00021313089,0.00035119665,0.00013293917,0.0059761726,0.0005883766],"about_ca_topic_score_codex":0.050974026,"about_ca_topic_score_gemma":0.7161827,"teacher_disagreement_score":0.6652087,"about_ca_system_score_codex":0.0007571686,"about_ca_system_score_gemma":0.00015932246,"threshold_uncertainty_score":0.9990947},"labels":[],"label_agreement":null},{"id":"W4243039043","doi":"10.1177/0361198106194500106","title":"Statistical Investigations of Statutory Holiday Effects on Traffic Volumes","year":2006,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Regina","funders":"Natural Sciences and Engineering Research Council of Canada; University of Regina","keywords":"Traffic volume; Traffic flow (computer networking); Transport engineering; Statistics; Statutory law; Volume (thermodynamics); Wilcoxon signed-rank test; Nonparametric statistics; Econometrics; Computer science; Engineering; Mathematics; Computer security; Mann–Whitney U test","score_opus":0.058157044331679904,"score_gpt":0.3833154642554327,"score_spread":0.32515841992375283,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4243039043","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99036556,0.00015453705,0.0034876408,0.0031481008,0.000638686,0.001011575,0.00019669163,0.000052067753,0.0009451071],"genre_scores_gemma":[0.992968,0.00033156786,0.004509423,0.00004708344,0.00035358014,0.000052884632,0.00008386974,0.000046564634,0.0016069909],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9899351,0.0024841286,0.0015283258,0.00038159612,0.0048125507,0.0008582796],"domain_scores_gemma":[0.9917325,0.0033621397,0.0006274299,0.00035452217,0.0035241304,0.00039928415],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0050683776,0.00023498853,0.0004729892,0.0012103897,0.00118849,0.00013117492,0.0008372684,0.00025667303,0.00026152498],"category_scores_gemma":[0.00056119956,0.00019635523,0.00030645207,0.0024980954,0.00168731,0.00058800797,0.000002508003,0.0016361646,0.000020090103],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0021536027,0.0013494082,0.56869996,0.00090804696,0.00026419433,0.00022096072,0.02741021,0.119328216,0.002543749,0.2067331,0.05164239,0.018746166],"study_design_scores_gemma":[0.0014710302,0.0006495795,0.94779015,0.000489848,0.00007073839,1.3352056e-7,0.003213914,0.00019113827,0.0013232204,0.005977213,0.03859695,0.00022611461],"about_ca_topic_score_codex":0.018913569,"about_ca_topic_score_gemma":0.07230883,"teacher_disagreement_score":0.37909016,"about_ca_system_score_codex":0.0002601673,"about_ca_system_score_gemma":0.0015878916,"threshold_uncertainty_score":0.9876196},"labels":[],"label_agreement":null},{"id":"W4244014854","doi":"10.32920/ryerson.14654703.v1","title":"The political economy of mass transit infrastructure investments in the greater Toronto area","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Toronto Metropolitan University; University of Calgary","funders":"","keywords":"Attractiveness; Politics; Witness; Business; Corporate governance; Public transport; Service delivery framework; Plan (archaeology); Service (business); Public administration; Public relations; Finance; Political science; Marketing","score_opus":0.020048464747241306,"score_gpt":0.2798690176887648,"score_spread":0.2598205529415235,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4244014854","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10377616,0.0003746097,0.009242848,0.018483335,0.0006787207,0.00096898637,0.000059089856,0.00006392644,0.8663523],"genre_scores_gemma":[0.99754554,0.000041773317,0.0008724962,0.001126929,0.000051927982,0.00002553325,0.00008392385,0.000004880899,0.0002469854],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9990028,0.00020608849,0.00024261748,0.00016431234,0.00018691216,0.00019727986],"domain_scores_gemma":[0.9995284,0.00010726662,0.000066600085,0.00018287303,0.00006865955,0.000046164194],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028775365,0.00009633496,0.00013319986,0.000015735004,0.00014341083,0.00012913666,0.0002980715,0.00018170394,0.00023078949],"category_scores_gemma":[0.00001688348,0.00005674941,0.00006801215,0.000052488464,0.00015363538,0.00009084076,0.000010200368,0.00017543268,4.7312736e-7],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021711972,0.000033574983,0.060238414,0.00006077672,0.00007787103,0.000008909905,0.107579455,0.010360518,0.0000037638952,0.82042193,0.0007243056,0.0004687851],"study_design_scores_gemma":[0.00068960345,0.000031058637,0.4852151,0.00023165887,0.0001285877,0.0000012151561,0.333021,0.0014998245,0.00013630404,0.16244006,0.01610441,0.00050119305],"about_ca_topic_score_codex":0.0057198834,"about_ca_topic_score_gemma":0.018903155,"teacher_disagreement_score":0.8937694,"about_ca_system_score_codex":0.000101712925,"about_ca_system_score_gemma":0.00027125483,"threshold_uncertainty_score":0.9989993},"labels":[],"label_agreement":null},{"id":"W4244310130","doi":"10.1139/cjce-28-1-111","title":"Methodology for off-line assessment of advanced traffic signal control systems","year":2001,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Transportation Planning and Optimization","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":"Computer science; Key (lock); Control (management); SIGNAL (programming language); Signal timing; Reduction (mathematics); Traffic simulation; Traffic congestion; Service (business); Real-time computing; Simulation; Transport engineering; Microsimulation; Traffic signal; Engineering; Computer security","score_opus":0.03148963161133233,"score_gpt":0.3006707416175512,"score_spread":0.26918111000621886,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4244310130","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.060354862,0.0013555359,0.9362867,0.00030223525,0.00089369493,0.00017781903,0.000026602564,0.000012840956,0.00058971153],"genre_scores_gemma":[0.99271685,0.000050350078,0.0069652074,0.0000145652,0.00016882563,0.000003627284,0.0000037407053,0.000008344363,0.00006846449],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99928236,0.000055458167,0.00030882485,0.000050573253,0.000116436786,0.00018633399],"domain_scores_gemma":[0.9990373,0.0002800357,0.0001712446,0.000033970286,0.0002444592,0.00023294755],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00092332094,0.0000560726,0.00020323666,0.00020629723,0.00007290803,0.000016976697,0.00009623206,0.00005637611,0.000048462625],"category_scores_gemma":[0.00017256643,0.00005877343,0.0000625184,0.00014462289,0.000025529434,0.000107476044,2.8589142e-7,0.00008394441,9.669625e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008697556,0.000003170787,0.00073776307,0.000016408161,0.00002716144,0.000010710801,0.00085447996,0.99510986,0.00014690151,0.0018535289,0.00007046204,0.0011608592],"study_design_scores_gemma":[0.004570243,0.0006474801,0.017719354,0.00058101927,0.00024327879,0.00006597049,0.0051783845,0.81525165,0.000054835873,0.0000894492,0.15512326,0.0004750746],"about_ca_topic_score_codex":0.00056232675,"about_ca_topic_score_gemma":0.063250944,"teacher_disagreement_score":0.932362,"about_ca_system_score_codex":0.000089178335,"about_ca_system_score_gemma":0.0007384309,"threshold_uncertainty_score":0.9538423},"labels":[],"label_agreement":null},{"id":"W4244580033","doi":"10.1002/atr.1220","title":"Traffic control and management—recent methodological advances","year":2012,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","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":"Control (management); Environmental science; Computer science; Transport engineering; Engineering; Environmental planning; Artificial intelligence","score_opus":0.038198626808561106,"score_gpt":0.34760678750340684,"score_spread":0.3094081606948457,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4244580033","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.89184976,0.015488642,0.087594844,0.001820202,0.0019856067,0.00039393656,0.000009265252,0.00006887778,0.00078885036],"genre_scores_gemma":[0.91235036,0.039702214,0.047601394,0.0001333542,0.00015689727,0.000004148043,0.000011631994,0.0000064780047,0.00003354386],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99891716,0.00014112711,0.00036798147,0.00007852331,0.00030493538,0.00019028214],"domain_scores_gemma":[0.9991839,0.00017090653,0.00031343024,0.000034766075,0.0001539796,0.00014300909],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00094059587,0.00007857106,0.00017255005,0.00009885111,0.00015335069,0.000029079263,0.00006285218,0.00005597903,0.000036917354],"category_scores_gemma":[0.000042463955,0.00006897009,0.000051774263,0.0001898587,0.000075211756,0.002011787,2.969738e-7,0.00010022752,0.0000010746049],"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":[0.00077640114,0.00025021346,0.025173068,0.00009599222,0.00011731353,0.00002716882,0.04484897,0.25861552,0.00023115802,0.019063247,0.00007493032,0.650726],"study_design_scores_gemma":[0.0031363799,0.00018117244,0.75315845,0.00015203578,0.0002910321,0.0000063236193,0.034217555,0.000028171804,0.00006670763,0.0008633775,0.20763615,0.0002626706],"about_ca_topic_score_codex":0.0000015131283,"about_ca_topic_score_gemma":0.00004055998,"teacher_disagreement_score":0.7279854,"about_ca_system_score_codex":0.000024658932,"about_ca_system_score_gemma":0.000020178193,"threshold_uncertainty_score":0.28125185},"labels":[],"label_agreement":null},{"id":"W4244652998","doi":"10.32920/ryerson.14646210.v1","title":"North Toronto rapid transit line feasibility study","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Transport engineering; Public transport; Traffic congestion; Transit (satellite); Rush hour; Service (business); Travel time; Business; Engineering","score_opus":0.05947842652069528,"score_gpt":0.34620921870706983,"score_spread":0.28673079218637454,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4244652998","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93930036,0.0005129626,0.019458415,0.00058759114,0.0011374353,0.0013455256,0.00004919846,0.00043018238,0.03717832],"genre_scores_gemma":[0.9945532,0.00025295946,0.0021358982,0.00008648778,0.00018743251,0.000029970974,0.00046944787,0.000011327003,0.0022732818],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99818105,0.00031370472,0.00033254744,0.00049939204,0.000480694,0.00019263064],"domain_scores_gemma":[0.9991172,0.00005087265,0.000095369454,0.00032742036,0.00028598818,0.0001231748],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006110485,0.0001577149,0.00026782183,0.000022596874,0.00028570925,0.0001672638,0.00023344536,0.00019123229,0.0026428648],"category_scores_gemma":[0.000060601207,0.00015922551,0.00012064805,0.00012233904,0.000054048018,0.00017016713,0.0000226266,0.00022860058,0.000004890182],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007575807,0.002030411,0.5963196,0.00009586844,0.00018795006,0.00003904426,0.33434847,0.0591973,0.0000016201166,0.00034142451,0.00060826336,0.006754285],"study_design_scores_gemma":[0.0007056794,0.00013098204,0.89590085,0.000044996108,0.00018124061,1.3133322e-7,0.09973092,0.0005468892,0.000008376083,0.000058958427,0.0021539733,0.0005370036],"about_ca_topic_score_codex":0.043258887,"about_ca_topic_score_gemma":0.77585894,"teacher_disagreement_score":0.7326001,"about_ca_system_score_codex":0.00017124349,"about_ca_system_score_gemma":0.00048417528,"threshold_uncertainty_score":0.99826884},"labels":[],"label_agreement":null},{"id":"W4245184814","doi":"10.1177/0361198105191700122","title":"Trip-Chaining Trends in the United States","year":2005,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":76,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Golder Associates (Canada)","funders":"","keywords":"Chaining; TRIPS architecture; Transport engineering; Trip generation; Work (physics); Travel behavior; Business; Geography; Demographic economics; Engineering; Economics; Psychology","score_opus":0.13141920523459866,"score_gpt":0.4358474782901381,"score_spread":0.30442827305553943,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4245184814","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96201205,0.00018863386,0.0012311991,0.034198165,0.00034662912,0.00061978673,0.000044736706,0.000041312804,0.0013174632],"genre_scores_gemma":[0.99456906,0.0016021968,0.0014440243,0.0002956296,0.00037259195,0.000055683653,0.00012290826,0.000035599864,0.0015022954],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.98908037,0.003423819,0.0014195632,0.00034045518,0.0047128275,0.0010229842],"domain_scores_gemma":[0.9944934,0.0019084911,0.00044378976,0.00036780903,0.0025001778,0.00028633425],"candidate_categories":["sts","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.013294935,0.00021365556,0.0003414448,0.0025163675,0.0014800322,0.00026170138,0.001477624,0.00022046312,0.0005440096],"category_scores_gemma":[0.00026853185,0.00014751822,0.00030617532,0.0073739486,0.00090903253,0.00086555496,0.000002643657,0.0023878785,0.00001799007],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0019132289,0.0008227024,0.419253,0.00009838386,0.00015117142,0.00020326841,0.30401322,0.1590222,0.00014524396,0.034481533,0.033911522,0.045984525],"study_design_scores_gemma":[0.0015339137,0.0002489886,0.7237169,0.00019768122,0.000035734498,3.0016034e-7,0.041176073,0.00076904375,0.000054704473,0.00144874,0.23062943,0.00018850158],"about_ca_topic_score_codex":0.045267183,"about_ca_topic_score_gemma":0.31452125,"teacher_disagreement_score":0.3044639,"about_ca_system_score_codex":0.0003039421,"about_ca_system_score_gemma":0.00057690765,"threshold_uncertainty_score":0.99991363},"labels":[],"label_agreement":null},{"id":"W4245915497","doi":"10.32920/ryerson.14652288","title":"Impacts of subway pricing on fare equality among passengers: a Toronto case study","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"TRIPS architecture; Equity (law); Kilometer; Disadvantaged; Transport engineering; Transit (satellite); Business; Geography; Demographic economics; Subsidy; Public transport; Advertising; Economics; Economic growth; Engineering; Political science","score_opus":0.05120692052920646,"score_gpt":0.36600978906416026,"score_spread":0.3148028685349538,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4245915497","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9864038,0.00007697255,0.0020206887,0.00006306725,0.0003379266,0.00059449545,0.000019887584,0.000119706434,0.010363431],"genre_scores_gemma":[0.9988619,0.000040588777,0.0006039463,0.000028090846,0.00007304675,0.000020002546,0.00007459563,0.00001215218,0.0002856384],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.99801135,0.0004664958,0.0004212776,0.00037798125,0.00051632506,0.00020655131],"domain_scores_gemma":[0.9987883,0.0001829792,0.00030625716,0.00030173818,0.0002943712,0.00012635568],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010527828,0.00016396766,0.00031859623,0.00004692468,0.00023734344,0.00013273294,0.00014456296,0.00022527282,0.00035714987],"category_scores_gemma":[0.00019765735,0.00015726971,0.00010628065,0.0001362137,0.00005776599,0.00019845927,0.000035830093,0.0002221364,7.753791e-7],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025075842,0.00064967084,0.4626129,0.00016376717,0.00014687261,0.00065977935,0.51699126,0.016988156,0.000008305763,0.00095660833,0.00008655741,0.0007110698],"study_design_scores_gemma":[0.0003482704,0.00011336927,0.17916052,0.00027226412,0.00010868729,0.0000021386804,0.81925774,0.0002767368,0.0000735434,0.000023975388,0.000035456014,0.00032730936],"about_ca_topic_score_codex":0.5049551,"about_ca_topic_score_gemma":0.6896747,"teacher_disagreement_score":0.30226648,"about_ca_system_score_codex":0.00023489956,"about_ca_system_score_gemma":0.00035746928,"threshold_uncertainty_score":0.64132726},"labels":[],"label_agreement":null},{"id":"W4245926162","doi":"10.1108/9780857245670-021","title":"Congestion Modelling","year":2007,"lang":"en","type":"book-chapter","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Geology; Environmental science","score_opus":0.08759784263078096,"score_gpt":0.3093579758080745,"score_spread":0.22176013317729354,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4245926162","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000023240682,0.0000680243,0.20514996,0.00010229776,0.00017118777,0.00006781999,0.0000035676287,0.00013326487,0.79430157],"genre_scores_gemma":[0.001964106,0.00038559327,0.0076989518,0.000104306775,0.00020668867,6.2844236e-7,0.00011510484,0.000014182187,0.9895104],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993995,0.000005087515,0.00013297904,0.00012175763,0.00024204647,0.0000986249],"domain_scores_gemma":[0.99967563,0.000043419263,0.00006829566,0.000053267773,0.000103914164,0.000055449815],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000250295,0.000074677475,0.000080052596,0.00008198885,0.0001750678,0.0000259711,0.00005197769,0.00027378707,0.0014009111],"category_scores_gemma":[0.0000050692747,0.000080243204,0.00003798936,0.00001613347,0.0000630277,0.000061837556,0.0000011775464,0.000112874186,0.00013752996],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000030399785,0.000001332506,0.000008316807,0.000002495024,0.0000052525324,0.000002536387,0.0009514997,0.0476595,3.0083655e-8,0.94832677,0.0006391328,0.0024000804],"study_design_scores_gemma":[0.000058108435,0.000006411414,0.000008830259,0.00005427314,0.000025418529,1.4041673e-7,0.00011513756,0.0021213775,6.4238924e-7,0.015908388,0.9815297,0.00017155395],"about_ca_topic_score_codex":0.0003164335,"about_ca_topic_score_gemma":0.00065026747,"teacher_disagreement_score":0.9808906,"about_ca_system_score_codex":0.000036562175,"about_ca_system_score_gemma":0.000072780764,"threshold_uncertainty_score":0.99951196},"labels":[],"label_agreement":null},{"id":"W4247230689","doi":"10.1176/pn.41.7.0021","title":"Toronto Tours Designed For Wide Range of Interests","year":2006,"lang":"en","type":"article","venue":"Psychiatric News","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Range (aeronautics); Engineering; Aerospace engineering","score_opus":0.015484873111007361,"score_gpt":0.2987758831459947,"score_spread":0.2832910100349873,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4247230689","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3896881,0.0039595654,0.45180634,0.004567353,0.0029755542,0.0017356545,0.000066970366,0.0003477297,0.14485273],"genre_scores_gemma":[0.9683194,0.000085340136,0.02927238,0.00009843603,0.00032784027,0.000021339396,0.00002343698,0.000009192928,0.001842642],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9994196,0.00003581838,0.00018800751,0.00010479197,0.00012259376,0.00012916353],"domain_scores_gemma":[0.99962926,0.00010315411,0.00010439427,0.000067022185,0.000062727464,0.000033413467],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017105653,0.00005140787,0.00008792566,0.000034125147,0.0001173283,0.000014766889,0.00009327239,0.000049157363,0.00007646434],"category_scores_gemma":[0.00003357252,0.000051336105,0.000059547026,0.0001344995,0.000027304077,0.00011900496,0.0000011509603,0.000017424862,0.0000026154778],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002703308,0.00025843398,0.7461952,0.000060716655,0.000022912209,6.893488e-7,0.0091687245,0.0020001417,0.00006022483,0.05642431,0.17526948,0.010268862],"study_design_scores_gemma":[0.006039868,0.0005486139,0.6217612,0.000076424214,0.00029104177,6.22455e-7,0.013948829,0.00048159843,0.00022722302,0.023941442,0.33174613,0.000937001],"about_ca_topic_score_codex":0.021593727,"about_ca_topic_score_gemma":0.08723933,"teacher_disagreement_score":0.5786313,"about_ca_system_score_codex":0.00003056744,"about_ca_system_score_gemma":0.000061934006,"threshold_uncertainty_score":0.9849216},"labels":[],"label_agreement":null},{"id":"W4247615793","doi":"10.1177/095965180121500403","title":"Traffic assignment: Equilibrium models","year":2001,"lang":"en","type":"article","venue":"Proceedings of the Institution of Mechanical Engineers Part I Journal of Systems and Control Engineering","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Mathematical optimization; Calibration; Computer science; Class (philosophy); Variable (mathematics); Mathematical economics; Algorithm; Mathematics; Artificial intelligence; Statistics","score_opus":0.013619925235914843,"score_gpt":0.21454158488140318,"score_spread":0.20092165964548833,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4247615793","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.64163387,0.001846422,0.35261503,0.0006913698,0.0018697422,0.00042300325,0.000011062216,0.000059878894,0.0008496387],"genre_scores_gemma":[0.99931824,0.00017619206,0.00030751675,0.0000059104163,0.0001482288,0.000002936979,2.2183671e-7,0.0000063630137,0.000034371547],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99887407,0.0000083790155,0.00051773613,0.00006878279,0.0003943537,0.0001366681],"domain_scores_gemma":[0.9991677,0.00004641265,0.00036234935,0.000028365528,0.00030368514,0.0000915261],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000829669,0.000086782595,0.00026255896,0.000098837794,0.00006094288,0.000026258384,0.0001503215,0.00008602309,0.0000020661064],"category_scores_gemma":[0.00015136077,0.00006691248,0.00009422891,0.00020027552,0.0000472731,0.00039319554,0.0000056368413,0.00013126596,8.42982e-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":[0.000037544036,0.000019370204,0.000051206283,0.00007129287,0.000047646605,6.293503e-7,0.00050424616,0.9295139,0.003871393,0.0657135,0.000023885597,0.00014541822],"study_design_scores_gemma":[0.0018077993,0.00015319145,0.000122527,0.0011348373,0.00016762817,0.000043101147,0.0017790132,0.98936033,0.00093932846,0.00014355242,0.0041634496,0.00018525726],"about_ca_topic_score_codex":0.000016447812,"about_ca_topic_score_gemma":6.1846913e-7,"teacher_disagreement_score":0.3576844,"about_ca_system_score_codex":0.000037856233,"about_ca_system_score_gemma":0.000054281383,"threshold_uncertainty_score":0.27286115},"labels":[],"label_agreement":null},{"id":"W4247706144","doi":"10.31235/osf.io/3r4p6","title":"The Effect of Route-choice Strategy on Transit Travel Time Estimates","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"The Scarborough Hospital; University of Toronto","funders":"","keywords":"Context (archaeology); Transit (satellite); Heuristic; Computer science; Choice set; Selection (genetic algorithm); Public transport; Shortest path problem; Moment (physics); Heuristics; Travel time; Operations research; TRIPS architecture; Econometrics; Economics; Mathematical optimization; Transport engineering; Mathematics; Geography; Graph; Engineering","score_opus":0.015215165227105979,"score_gpt":0.30198241980937846,"score_spread":0.2867672545822725,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4247706144","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.42963925,0.0004194548,0.020722596,0.0024789828,0.0017301458,0.0030039342,0.00027027706,0.00050308777,0.5412322],"genre_scores_gemma":[0.99066,0.00006247323,0.00022133584,0.000020962443,0.00006587565,0.000016584276,0.00015265377,0.000014353561,0.008785779],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99876165,0.00019067935,0.00024968098,0.00023648633,0.00037327368,0.00018822562],"domain_scores_gemma":[0.9979665,0.0015442993,0.00015640903,0.00021320811,0.000069787115,0.000049787715],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00069413614,0.00016172789,0.00026132402,0.000045780358,0.00024350353,0.000100176374,0.00030543772,0.000253909,0.00017608609],"category_scores_gemma":[0.00011476108,0.00010642612,0.00012210592,0.00009350956,0.0001247086,0.000051066847,0.0000066798125,0.00025520957,0.00007045679],"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":[0.00017307754,0.000037165886,0.014300179,0.00017669711,0.00013140056,0.0000015887454,0.005263376,0.9621463,0.00008428946,0.0072715427,0.0017081031,0.008706296],"study_design_scores_gemma":[0.005860027,0.004000192,0.7410314,0.0024726952,0.0016927673,0.0000012653692,0.0041045877,0.19857083,0.018468706,0.005191036,0.015114258,0.0034922224],"about_ca_topic_score_codex":0.0026587977,"about_ca_topic_score_gemma":0.0006977905,"teacher_disagreement_score":0.76357543,"about_ca_system_score_codex":0.00003018267,"about_ca_system_score_gemma":0.00016476375,"threshold_uncertainty_score":0.43399313},"labels":[],"label_agreement":null},{"id":"W4248160431","doi":"10.1177/0361198106198600106","title":"Algorithm for Optimal Bid Packaging for Competitive Contracting in Public Transit","year":2006,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Procurement; Public transport; Transit (satellite); Service (business); Subsidy; Sustainability; Transport engineering; Business; Perfect competition; Market analysis; Computer science; Industrial organization; Operations research; Economics; Marketing; Microeconomics; Engineering","score_opus":0.09728309913727053,"score_gpt":0.4063050876001653,"score_spread":0.3090219884628948,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4248160431","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.63922167,0.00027171368,0.33991855,0.014115889,0.0009562332,0.0042014173,0.0005002637,0.000077413046,0.0007368203],"genre_scores_gemma":[0.955289,0.00028663353,0.04263539,0.00004499559,0.0004999255,0.00028887557,0.00015934497,0.000060329487,0.0007354996],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9931919,0.0010918318,0.0015249045,0.00043878268,0.0025153162,0.0012372778],"domain_scores_gemma":[0.99056906,0.0032415283,0.0005298085,0.00020871234,0.0051687276,0.00028219156],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.00915928,0.00022907292,0.00047826927,0.0012664401,0.0015426989,0.00028515677,0.00076187757,0.00025695376,0.00008931157],"category_scores_gemma":[0.00035464618,0.00020562085,0.00044297165,0.0021121132,0.00070207025,0.0010786164,0.000001663167,0.0012338317,0.0000027763788],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0047653676,0.0019603393,0.5243061,0.0007590434,0.00042485233,0.00017401311,0.050599754,0.06269069,0.0027821285,0.2292897,0.008738447,0.11350956],"study_design_scores_gemma":[0.008166511,0.0007479238,0.8054227,0.000685228,0.000108449305,4.4445514e-7,0.034882262,0.0046592294,0.00094231596,0.011105638,0.13272412,0.00055517105],"about_ca_topic_score_codex":0.01664252,"about_ca_topic_score_gemma":0.19287539,"teacher_disagreement_score":0.3160673,"about_ca_system_score_codex":0.00036844373,"about_ca_system_score_gemma":0.0012628714,"threshold_uncertainty_score":0.9997572},"labels":[],"label_agreement":null},{"id":"W4248365020","doi":"10.32920/ryerson.14647890","title":"The design and development of an algorithm for automatically creating transit networks in transcad to measure regional accessibility","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Toronto Metropolitan University; University of Waterloo","funders":"","keywords":"Transit (satellite); Schedule; Transport engineering; Automatic summarization; Computer science; Measure (data warehouse); Transportation planning; Service (business); Public transport; Operations research; Engineering; Database; Business; Operating system","score_opus":0.06811314807004663,"score_gpt":0.3353837486607066,"score_spread":0.26727060059065993,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4248365020","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017540697,0.00010624764,0.98071396,0.00045044147,0.00007023885,0.0009123026,0.0000040064606,0.00004311713,0.00015898014],"genre_scores_gemma":[0.24415147,0.000029118071,0.75551474,0.00004880117,0.00002849862,0.00012366794,0.00006170403,0.0000075454527,0.00003444352],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984191,0.00029968153,0.0004785093,0.0003109542,0.00030547872,0.00018626972],"domain_scores_gemma":[0.9989187,0.0005358496,0.00009700284,0.00011042384,0.00024116659,0.00009683074],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027728775,0.000114068025,0.0002154074,0.000041418938,0.00040611372,0.00013983063,0.00019286951,0.00021281425,0.000010197756],"category_scores_gemma":[0.000086483866,0.000091315684,0.00004110559,0.00014516988,0.00007572543,0.00009593267,0.000008095601,0.00014915368,3.626824e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006822206,0.00008264737,0.00058971654,0.000067142486,0.00004051875,8.988646e-7,0.10876531,0.33462834,0.000005900059,0.0003806874,0.000012629589,0.555358],"study_design_scores_gemma":[0.0005302169,0.00004074193,0.054394882,0.00044884084,0.000040510262,2.5239078e-7,0.014986513,0.928409,0.00008229546,0.00055819977,0.00018686833,0.00032167917],"about_ca_topic_score_codex":0.00026638317,"about_ca_topic_score_gemma":0.007923588,"teacher_disagreement_score":0.5937807,"about_ca_system_score_codex":0.000048369606,"about_ca_system_score_gemma":0.0008222505,"threshold_uncertainty_score":0.44215482},"labels":[],"label_agreement":null},{"id":"W4248898799","doi":"10.1177/0361198106196800104","title":"Predicting Directional Design Hourly Volume from Statutory Holiday Traffic","year":2006,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Regina","funders":"Natural Sciences and Engineering Research Council of Canada; University of Regina","keywords":"Traffic volume; Transport engineering; Recreation; Travel time; Volume (thermodynamics); Statistics; Environmental science; Road traffic; Computer science; Meteorology; Econometrics; Geography; Engineering; Mathematics; Ecology","score_opus":0.07995021905932018,"score_gpt":0.3681293791735894,"score_spread":0.28817916011426925,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4248898799","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9733823,0.00041957415,0.019526688,0.0031929458,0.001231411,0.0010386659,0.00026157597,0.0001303613,0.00081649894],"genre_scores_gemma":[0.98531157,0.00056087854,0.009621818,0.000037923593,0.00093985733,0.00006903239,0.00013752622,0.00006838537,0.003253014],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.98782444,0.0032260919,0.0016289983,0.0005258732,0.005666319,0.0011282527],"domain_scores_gemma":[0.9921849,0.0021588313,0.00068615057,0.00035421475,0.0041728,0.00044313734],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.00765904,0.0002839908,0.00045192515,0.0010572802,0.002296041,0.00034083976,0.0010699738,0.00034143226,0.00076083967],"category_scores_gemma":[0.0003254291,0.00024760398,0.00041112726,0.0026203129,0.0011531349,0.0012129887,0.0000033261967,0.0021858455,0.000040211595],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015692249,0.00053243735,0.7361238,0.00006918998,0.00018858747,0.0001394986,0.018789204,0.20153493,0.0010863079,0.0035415904,0.031098355,0.0053269025],"study_design_scores_gemma":[0.0013429653,0.0002703779,0.9433597,0.00025907878,0.000078834826,2.112304e-7,0.0053960104,0.00081443804,0.00030577756,0.00298347,0.04492056,0.00026859908],"about_ca_topic_score_codex":0.10254669,"about_ca_topic_score_gemma":0.18665197,"teacher_disagreement_score":0.2072359,"about_ca_system_score_codex":0.00047195095,"about_ca_system_score_gemma":0.0019131135,"threshold_uncertainty_score":0.9999976},"labels":[],"label_agreement":null},{"id":"W4249723867","doi":"10.2172/1462382","title":"3rd Quarter Transportation Report FY2018","year":2018,"lang":"en","type":"report","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Radioactive waste; Hazardous waste; Quarter (Canadian coin); Waste management; Environmental science; Engineering; Geography; Archaeology","score_opus":0.04113682023536411,"score_gpt":0.351941011032801,"score_spread":0.31080419079743693,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4249723867","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013167306,0.00010028568,0.012982144,0.000835294,0.0042476715,0.00043673898,0.00010056422,0.0006062524,0.9793743],"genre_scores_gemma":[0.1826535,0.001756861,0.009660289,0.00032936776,0.006678385,0.00010992911,0.012850488,0.00013708383,0.7858241],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9967142,0.00008121091,0.0007364558,0.000508609,0.001613591,0.00034597827],"domain_scores_gemma":[0.9975685,0.000059757527,0.0005351658,0.00030810537,0.0013644169,0.0001640574],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0015602248,0.00023349604,0.00032756387,0.00018627959,0.00047094806,0.0001067245,0.00019598697,0.00063888764,0.0038602029],"category_scores_gemma":[0.00018867404,0.00023121873,0.00018070248,0.0003179845,0.00023425969,0.0002596463,0.0000013202293,0.00021639696,0.00021429313],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013794631,0.000047742287,0.01023954,0.000062660074,0.00006894595,0.00017368335,0.009811251,0.000053628977,0.0000015394382,0.0009962565,0.9775398,0.0009911756],"study_design_scores_gemma":[0.00010945105,0.000032441767,0.015219316,0.00008715767,0.000110489855,0.000007746693,0.0011021264,0.000006286366,0.000008080913,0.00017539311,0.98281515,0.00032636622],"about_ca_topic_score_codex":0.008153871,"about_ca_topic_score_gemma":0.030958781,"teacher_disagreement_score":0.19355021,"about_ca_system_score_codex":0.00019633032,"about_ca_system_score_gemma":0.0023961153,"threshold_uncertainty_score":0.99845093},"labels":[],"label_agreement":null},{"id":"W4249913019","doi":"10.32920/ryerson.14665590","title":"Development of an urban travel demand model of the Town of Oakville","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"University of Toronto","keywords":"Transport engineering; Trip generation; TRIPS architecture; Transportation planning; Geographic information system; Demand forecasting; Trip distribution; Software; Travel behavior; Demand management; Mode (computer interface); Microcomputer; Computer science; Operations research; Engineering; Geography; Telecommunications","score_opus":0.039593460889615247,"score_gpt":0.284864200710571,"score_spread":0.24527073982095576,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4249913019","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9028385,0.000048439466,0.07326832,0.00007803762,0.00012796863,0.00018634462,0.000027260056,0.000012554611,0.02341253],"genre_scores_gemma":[0.9227435,0.000020072366,0.07596527,0.000010408902,0.000008455746,0.0000040893297,0.000049302864,0.0000039793513,0.0011949054],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990474,0.000051889783,0.00036470062,0.00013333764,0.00032990534,0.00007274638],"domain_scores_gemma":[0.999324,0.000019415473,0.0002610852,0.00015528475,0.00020976948,0.000030407436],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035267073,0.00006672806,0.00018491468,0.00003498633,0.00007936704,0.000008409629,0.00022155678,0.00013381353,0.00010053397],"category_scores_gemma":[0.000024505649,0.000051460058,0.000064132226,0.00010337204,0.00009342576,0.00004204969,0.000026565336,0.00007729429,5.963914e-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":[0.000014451194,0.00018256874,0.007741075,0.0001760438,0.00006656375,1.6693166e-7,0.28573126,0.6948014,0.0015283127,0.008465583,0.0000923798,0.0012002201],"study_design_scores_gemma":[0.0016358311,0.000066682915,0.27549753,0.0019022238,0.0004236069,3.1387827e-7,0.19029121,0.29260018,0.23220544,0.0031886708,0.0008641861,0.0013241487],"about_ca_topic_score_codex":0.00056279736,"about_ca_topic_score_gemma":0.0024271458,"teacher_disagreement_score":0.4022012,"about_ca_system_score_codex":0.000016670088,"about_ca_system_score_gemma":0.0009501706,"threshold_uncertainty_score":0.20984803},"labels":[],"label_agreement":null},{"id":"W4249976414","doi":"10.33423/jsis.v15i1.2736","title":"The Problem of Congestion Unreliability Cost: The Certain Error of a Mean and the Measure of Travel Time Utility Gains from Public Works","year":2020,"lang":"en","type":"article","venue":"Journal of Strategic Innovation and Sustainability","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"TRIPS architecture; Duration (music); Measure (data warehouse); Transport engineering; Travel time; Public transport; Traffic congestion; Service (business); Computer science; Statistics; Operations research; Econometrics; Economics; Mathematics; Engineering; Business; Marketing","score_opus":0.066631428423485,"score_gpt":0.3070500135423872,"score_spread":0.24041858511890218,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4249976414","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96451116,0.00020599039,0.0028286127,0.030951275,0.000029293251,0.00064232317,0.00001906411,0.0000058943124,0.00080638396],"genre_scores_gemma":[0.99966514,0.000059027807,0.00015507544,0.00006482635,0.000029038447,0.0000043311748,0.000006799093,0.000003019177,0.000012720453],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99751365,0.0008421336,0.0009411796,0.000120286975,0.00045666166,0.00012609008],"domain_scores_gemma":[0.9943863,0.0008481331,0.0010407803,0.0001221527,0.0035531207,0.000049501232],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0071382136,0.00008745942,0.00027390476,0.00004477472,0.00033604304,0.00006470792,0.00018185658,0.0001004094,0.000018193376],"category_scores_gemma":[0.0014739,0.00004602062,0.00006045692,0.0008541544,0.0014824388,0.00023666618,0.000010337681,0.0002832538,2.7691144e-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":[0.004226822,0.0003881349,0.122924425,0.00046608763,0.0002515887,0.0000016823392,0.148883,0.0048464877,0.00028216882,0.68662846,0.0001616715,0.03093948],"study_design_scores_gemma":[0.003502347,0.0003452393,0.4316356,0.00011654961,0.00016450441,0.0000017338099,0.34563285,0.015416822,0.00013241687,0.20232408,0.0005470237,0.00018085394],"about_ca_topic_score_codex":0.0004853117,"about_ca_topic_score_gemma":0.00023515146,"teacher_disagreement_score":0.4843044,"about_ca_system_score_codex":0.00004852975,"about_ca_system_score_gemma":0.00085569295,"threshold_uncertainty_score":0.54621077},"labels":[],"label_agreement":null},{"id":"W4250652486","doi":"10.32920/ryerson.14663388.v1","title":"Examining the Equitability of Public Transit Policies in Montreal, Toronto, and Vancouver","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Toronto Metropolitan University; Toronto Public Health","funders":"","keywords":"Public transport; Equity (law); Commission; Regional science; Government (linguistics); Public administration; Transit (satellite); Transit system; Public policy; Transport engineering; Political science; Business; Public economics; Geography; Economic growth; Economics; Finance; Engineering","score_opus":0.061247212803134075,"score_gpt":0.3083818341982474,"score_spread":0.2471346213951133,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4250652486","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.928803,0.0009995806,0.00364952,0.0006035189,0.0002845074,0.00024191386,0.0000151097065,0.00004084788,0.06536198],"genre_scores_gemma":[0.9982039,0.00050566875,0.0005879842,0.000048724756,0.000030072531,0.000013298476,0.000021510197,0.000004301238,0.00058453745],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.99891233,0.00023553791,0.0002608057,0.00020342156,0.00022792164,0.00015997452],"domain_scores_gemma":[0.99939764,0.00019854678,0.000092871014,0.0001556871,0.000112043504,0.000043222793],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009810713,0.00008531637,0.0001843012,0.00002874398,0.00011502744,0.00008803734,0.00014208189,0.00015455254,0.0003573324],"category_scores_gemma":[0.00012880765,0.000067089044,0.000034385972,0.00010495029,0.00018569367,0.00018772004,0.000029366069,0.00013117371,9.180386e-8],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012641331,0.00013066236,0.3963577,0.00016472572,0.000052761003,0.0000026801856,0.55970025,0.015344779,0.000016464619,0.0054377117,0.0006829077,0.02209672],"study_design_scores_gemma":[0.00014914,0.000009778201,0.819504,0.00005930249,0.00001996644,5.552962e-8,0.17882253,0.0005043356,0.000009856174,0.00045516738,0.00034504427,0.00012078084],"about_ca_topic_score_codex":0.25667447,"about_ca_topic_score_gemma":0.9496232,"teacher_disagreement_score":0.69294876,"about_ca_system_score_codex":0.00006800095,"about_ca_system_score_gemma":0.00022253534,"threshold_uncertainty_score":0.7482754},"labels":[],"label_agreement":null},{"id":"W4250689173","doi":"10.1177/0361198106198800104","title":"Rural Freeway Level of Service Based on Traveler Perception","year":2006,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"McMaster University; Florida Department of Transportation","keywords":"Transport engineering; Ordered probit; Level of service; Perception; Probit; Quality (philosophy); Probit model; Highway Capacity Manual; Service (business); Service quality; Travel behavior; Geography; Computer science; Business; Psychology; Marketing; Engineering","score_opus":0.15277300743833727,"score_gpt":0.4046792934891888,"score_spread":0.2519062860508515,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4250689173","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97815233,0.0000524208,0.009012222,0.009237925,0.00052633137,0.00093476835,0.00022721033,0.000043206524,0.0018135697],"genre_scores_gemma":[0.99456376,0.00018763982,0.0033649404,0.00011350807,0.00030121833,0.00003672585,0.00010016683,0.000046528778,0.0012854864],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9905246,0.0017612707,0.001503245,0.00032566793,0.0050752275,0.0008100065],"domain_scores_gemma":[0.99169403,0.0011700761,0.00064253004,0.00038611147,0.005824128,0.00028314572],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005629092,0.00023835001,0.0004273852,0.0012339867,0.001072782,0.00011444062,0.0009831178,0.00029494817,0.0007766262],"category_scores_gemma":[0.00016847398,0.00019524059,0.00039085245,0.0032062035,0.0007936615,0.00061987195,0.000001968485,0.001468655,0.000022754519],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0050222515,0.001729039,0.6948164,0.00059452513,0.00016429591,0.00007863,0.029452747,0.1938374,0.01061575,0.030990018,0.021640772,0.011058132],"study_design_scores_gemma":[0.0016674619,0.0004025082,0.9786507,0.00047009633,0.00005012077,8.8303246e-8,0.007826477,0.0007923609,0.00074309995,0.0020759152,0.0071187294,0.00020247452],"about_ca_topic_score_codex":0.09338065,"about_ca_topic_score_gemma":0.2591966,"teacher_disagreement_score":0.28383425,"about_ca_system_score_codex":0.00031272584,"about_ca_system_score_gemma":0.0012248008,"threshold_uncertainty_score":0.9126566},"labels":[],"label_agreement":null},{"id":"W4251576799","doi":"10.32920/ryerson.14647890.v1","title":"The design and development of an algorithm for automatically creating transit networks in transcad to measure regional accessibility","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Toronto Metropolitan University; University of Waterloo","funders":"","keywords":"Transit (satellite); Schedule; Transport engineering; Automatic summarization; Computer science; Measure (data warehouse); Transportation planning; Service (business); Public transport; Operations research; Engineering; Database; Business","score_opus":0.06811314807004663,"score_gpt":0.3353837486607066,"score_spread":0.26727060059065993,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4251576799","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017540697,0.00010624764,0.98071396,0.00045044147,0.00007023885,0.0009123026,0.0000040064606,0.00004311713,0.00015898014],"genre_scores_gemma":[0.24415147,0.000029118071,0.75551474,0.00004880117,0.00002849862,0.00012366794,0.00006170403,0.0000075454527,0.00003444352],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984191,0.00029968153,0.0004785093,0.0003109542,0.00030547872,0.00018626972],"domain_scores_gemma":[0.9989187,0.0005358496,0.00009700284,0.00011042384,0.00024116659,0.00009683074],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027728775,0.000114068025,0.0002154074,0.000041418938,0.00040611372,0.00013983063,0.00019286951,0.00021281425,0.000010197756],"category_scores_gemma":[0.000086483866,0.000091315684,0.00004110559,0.00014516988,0.00007572543,0.00009593267,0.000008095601,0.00014915368,3.626824e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006822206,0.00008264737,0.00058971654,0.000067142486,0.00004051875,8.988646e-7,0.10876531,0.33462834,0.000005900059,0.0003806874,0.000012629589,0.555358],"study_design_scores_gemma":[0.0005302169,0.00004074193,0.054394882,0.00044884084,0.000040510262,2.5239078e-7,0.014986513,0.928409,0.00008229546,0.00055819977,0.00018686833,0.00032167917],"about_ca_topic_score_codex":0.00026638317,"about_ca_topic_score_gemma":0.007923588,"teacher_disagreement_score":0.5937807,"about_ca_system_score_codex":0.000048369606,"about_ca_system_score_gemma":0.0008222505,"threshold_uncertainty_score":0.44215482},"labels":[],"label_agreement":null},{"id":"W4251854703","doi":"10.1002/net.20368","title":"A polyhedral study of the network pricing problem with connected toll arcs","year":2009,"lang":"en","type":"article","venue":"Networks","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Toll; Revenue; Flow network; Network planning and design; Mathematical optimization; Computer science; Mathematical economics; Mathematics; Operations research; Business; Computer network; Finance","score_opus":0.009393117172837029,"score_gpt":0.24659428467428937,"score_spread":0.23720116750145234,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4251854703","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97718,0.000095177726,0.010491407,0.00060979783,0.00018579369,0.00076757144,6.08362e-7,0.000119250304,0.010550411],"genre_scores_gemma":[0.9985972,0.000006817,0.0008280046,0.00015217264,0.00017877237,0.0000048474267,0.000003552495,0.000004735337,0.00022390511],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99917877,0.00012587992,0.00015404115,0.00011355388,0.00022762855,0.00020013074],"domain_scores_gemma":[0.99960476,0.000059616035,0.00012267566,0.00010642598,0.000070310685,0.00003620364],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023974432,0.00006330568,0.00010328788,0.000011966029,0.00036511838,0.000026785703,0.00013312357,0.000052630057,0.000012059579],"category_scores_gemma":[0.00001012902,0.00004240908,0.000020458447,0.00057160156,0.000047835812,0.000066107976,0.0000033439635,0.000114349525,2.932841e-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":[0.000042201165,0.000084508996,0.15700175,9.976362e-7,0.00001152338,0.0000012239796,0.013157715,0.8255485,8.302731e-7,0.002261846,0.00042234917,0.0014665469],"study_design_scores_gemma":[0.0012242039,0.00058026315,0.9689381,0.00016123845,0.00008445607,8.2818207e-7,0.007470043,0.019865409,0.0000026595433,0.00043429533,0.001030992,0.00020749377],"about_ca_topic_score_codex":0.00042889663,"about_ca_topic_score_gemma":0.0034305952,"teacher_disagreement_score":0.8119364,"about_ca_system_score_codex":0.000014877217,"about_ca_system_score_gemma":0.00005382973,"threshold_uncertainty_score":0.28082323},"labels":[],"label_agreement":null},{"id":"W4251975347","doi":"10.32920/ryerson.14649405","title":"Quantifying the impacts of the modifiable areal unit problem in travel demand models","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Toronto Metropolitan University; Polytechnique Montréal","funders":"","keywords":"Scrutiny; Scale (ratio); Geography; Unit (ring theory); Computer science; Regional science; Operations research; Transport engineering; Cartography; Engineering; Mathematics","score_opus":0.09965879940876775,"score_gpt":0.32875087245078505,"score_spread":0.22909207304201729,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4251975347","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8603895,0.00044495662,0.054364704,0.0026125675,0.0004133303,0.0011555868,0.00004157068,0.00006307217,0.080514714],"genre_scores_gemma":[0.99686074,0.00024443987,0.0020328024,0.000050610524,0.000021535796,0.000018240693,0.000038175756,0.000007940441,0.0007254859],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987338,0.00024501877,0.0002976827,0.00018911964,0.00034755922,0.00018681651],"domain_scores_gemma":[0.99931765,0.000113615075,0.00018267808,0.00021488772,0.00013550397,0.000035690675],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008668362,0.00010270696,0.00017571858,0.000050270704,0.00024432724,0.00010689634,0.00033946702,0.00018540071,0.000045115776],"category_scores_gemma":[0.000043381777,0.00006199222,0.00007981108,0.00033542988,0.000109870256,0.00012317723,0.000040800172,0.00031935947,3.6320972e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000045756983,0.000024827605,0.014790488,0.000053116684,0.000012431373,6.257812e-7,0.043227423,0.9247863,0.000016679931,0.016989948,0.00003664681,0.000056927718],"study_design_scores_gemma":[0.0009196413,0.000021024107,0.18640603,0.0021650626,0.00016681635,0.0000011119186,0.12639526,0.65182567,0.000957075,0.030240702,0.00018885628,0.0007127445],"about_ca_topic_score_codex":0.039592538,"about_ca_topic_score_gemma":0.038965933,"teacher_disagreement_score":0.27296063,"about_ca_system_score_codex":0.000033219065,"about_ca_system_score_gemma":0.00065691967,"threshold_uncertainty_score":0.97857046},"labels":[],"label_agreement":null},{"id":"W4252013762","doi":"10.1177/0361198106196400122","title":"Noniterative Approach to Dynamic Traffic Origin–Destination Estimation with Parallel Evolutionary Algorithms","year":2006,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Dynode; Estimator; Computer science; Computation; Block (permutation group theory); Algorithm; Mathematical optimization; Multiprocessing; Parallel computing; Mathematics; Statistics","score_opus":0.05645975005882906,"score_gpt":0.38211621162204623,"score_spread":0.32565646156321715,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4252013762","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8703213,0.000098420765,0.12318879,0.0028788294,0.00033906757,0.0018874287,0.00008154673,0.000097003096,0.0011076257],"genre_scores_gemma":[0.9053038,0.000113442344,0.09200027,0.000033690772,0.00020584889,0.00017958469,0.00021395295,0.000055952984,0.0018934886],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99061704,0.0016066453,0.0012920414,0.0005478286,0.004932475,0.0010039969],"domain_scores_gemma":[0.99247366,0.0007902916,0.00054939865,0.00033924213,0.005413459,0.0004339678],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0044627925,0.0003014028,0.00041579164,0.0014669187,0.0020847355,0.00029537964,0.0008795206,0.00025411375,0.00009895671],"category_scores_gemma":[0.0001543498,0.00023753547,0.00023327819,0.0043051275,0.0010262708,0.001380035,0.000003212767,0.0014838893,0.000022294416],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015108587,0.00082004623,0.11692479,0.00014370178,0.00011294052,0.000062671344,0.023941752,0.82913816,0.00015435084,0.015431061,0.0047405646,0.0070190933],"study_design_scores_gemma":[0.0016529278,0.00053765625,0.9673684,0.00035033058,0.00007302567,8.3960254e-7,0.0047500357,0.01658615,0.00003767405,0.0019076288,0.0063950145,0.00034031263],"about_ca_topic_score_codex":0.019185003,"about_ca_topic_score_gemma":0.071334705,"teacher_disagreement_score":0.8504436,"about_ca_system_score_codex":0.00063493504,"about_ca_system_score_gemma":0.0013454563,"threshold_uncertainty_score":0.9992144},"labels":[],"label_agreement":null},{"id":"W4252438427","doi":"10.32920/ryerson.14663388","title":"Examining the Equitability of Public Transit Policies in Montreal, Toronto, and Vancouver","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Toronto Metropolitan University; Toronto Public Health","funders":"","keywords":"Public transport; Equity (law); Commission; Regional science; Public administration; Government (linguistics); Transit (satellite); Transit system; Geography; Political science; Business; Transport engineering; Public economics; Finance; Economics; Engineering","score_opus":0.061247212803134075,"score_gpt":0.3083818341982474,"score_spread":0.2471346213951133,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4252438427","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.928803,0.0009995806,0.00364952,0.0006035189,0.0002845074,0.00024191386,0.0000151097065,0.00004084788,0.06536198],"genre_scores_gemma":[0.9982039,0.00050566875,0.0005879842,0.000048724756,0.000030072531,0.000013298476,0.000021510197,0.000004301238,0.00058453745],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.99891233,0.00023553791,0.0002608057,0.00020342156,0.00022792164,0.00015997452],"domain_scores_gemma":[0.99939764,0.00019854678,0.000092871014,0.0001556871,0.000112043504,0.000043222793],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009810713,0.00008531637,0.0001843012,0.00002874398,0.00011502744,0.00008803734,0.00014208189,0.00015455254,0.0003573324],"category_scores_gemma":[0.00012880765,0.000067089044,0.000034385972,0.00010495029,0.00018569367,0.00018772004,0.000029366069,0.00013117371,9.180386e-8],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012641331,0.00013066236,0.3963577,0.00016472572,0.000052761003,0.0000026801856,0.55970025,0.015344779,0.000016464619,0.0054377117,0.0006829077,0.02209672],"study_design_scores_gemma":[0.00014914,0.000009778201,0.819504,0.00005930249,0.00001996644,5.552962e-8,0.17882253,0.0005043356,0.000009856174,0.00045516738,0.00034504427,0.00012078084],"about_ca_topic_score_codex":0.25667447,"about_ca_topic_score_gemma":0.9496232,"teacher_disagreement_score":0.69294876,"about_ca_system_score_codex":0.00006800095,"about_ca_system_score_gemma":0.00022253534,"threshold_uncertainty_score":0.7482754},"labels":[],"label_agreement":null},{"id":"W4252996055","doi":"10.22215/etd/2005-10715","title":"Optimization of freight transportation in Southeast Mexico","year":2005,"lang":"en","type":"dissertation","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Heritage; Library and Archives Canada; Carleton University; ASTER","funders":"University of Ottawa","keywords":"Geography; Transport engineering; Political science; Operations research; Humanities; Engineering; Art","score_opus":0.012173108772914913,"score_gpt":0.277317118002574,"score_spread":0.26514400922965914,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4252996055","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.081546925,0.0006196011,0.033303943,0.00055780605,0.0015138849,0.0014963446,0.0002514649,0.00040024336,0.88030976],"genre_scores_gemma":[0.9558697,0.0005984249,0.016267229,0.000024795365,0.00013857329,0.00003853922,0.010512136,0.000044408636,0.016506238],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9983387,0.00008520164,0.00062362285,0.0002604244,0.00048509013,0.00020696955],"domain_scores_gemma":[0.99918675,0.00005080035,0.00035874083,0.00011008162,0.00022848937,0.00006514651],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00031881366,0.00016985032,0.00028098476,0.00039805958,0.00011842895,0.000024617566,0.00015538617,0.0004334467,0.0014008636],"category_scores_gemma":[0.000040249728,0.00018868361,0.000090874884,0.000567157,0.000045873865,0.00026502533,3.7686914e-7,0.00017076227,0.000009789045],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015607245,0.00017450104,0.011960043,0.00011488897,0.000026510706,0.0000030431095,0.06807393,0.8597063,0.000023402747,0.05706257,0.00024818364,0.0024505754],"study_design_scores_gemma":[0.0055605085,0.00022693149,0.46794838,0.002599554,0.00066041376,4.6379648e-7,0.46252775,0.032596998,0.0027081438,0.0020695347,0.019727541,0.0033738029],"about_ca_topic_score_codex":0.0020355831,"about_ca_topic_score_gemma":0.0976156,"teacher_disagreement_score":0.8743227,"about_ca_system_score_codex":0.000060642225,"about_ca_system_score_gemma":0.00032734938,"threshold_uncertainty_score":0.999512},"labels":[],"label_agreement":null},{"id":"W4253085510","doi":"10.24275/uami.f4752g76q","title":"Optimización convexa y métodos variacionales para estimar matrices origen destino","year":2019,"lang":"es","type":"dissertation","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science","score_opus":0.025983228645526726,"score_gpt":0.3240322056331739,"score_spread":0.2980489769876472,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4253085510","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.42477623,0.0061126277,0.110489614,0.0029640184,0.017970147,0.008045105,0.0012862534,0.0031831572,0.42517287],"genre_scores_gemma":[0.664112,0.03587876,0.08980327,0.0007296478,0.0011821442,0.00027318168,0.025033481,0.00031630008,0.18267126],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99582565,0.0003059267,0.00095985207,0.00096993026,0.0012613332,0.00067729724],"domain_scores_gemma":[0.9969391,0.0008052569,0.0008809439,0.00039033507,0.0006892519,0.0002951099],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00071665086,0.0006664054,0.0007457815,0.00067482283,0.00091869023,0.00043851353,0.0006577141,0.000987728,0.0030156903],"category_scores_gemma":[0.00020854874,0.0006601219,0.0002874054,0.0014300898,0.00019130071,0.00065861316,0.000014741886,0.0004576909,0.001901141],"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":[0.0016224819,0.0018120189,0.19266796,0.0041954033,0.0016093529,0.0001517313,0.06718928,0.5175833,0.00033958995,0.16356091,0.037882198,0.011385782],"study_design_scores_gemma":[0.010189965,0.0014423858,0.51709497,0.007127829,0.0054385737,0.000029704099,0.07652185,0.18821482,0.0015671699,0.0020924765,0.17715134,0.01312889],"about_ca_topic_score_codex":0.0029686796,"about_ca_topic_score_gemma":0.0002937928,"teacher_disagreement_score":0.32936847,"about_ca_system_score_codex":0.00007721387,"about_ca_system_score_gemma":0.00092118554,"threshold_uncertainty_score":0.999585},"labels":[],"label_agreement":null},{"id":"W4253352558","doi":"10.24275/uami.2f75r8145","title":"Modelos matemáticos para análisis de demanda en transporte","year":2014,"lang":"es","type":"dissertation","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Humanities; Mathematics; Physics; Philosophy","score_opus":0.014546633024380808,"score_gpt":0.30309945841810426,"score_spread":0.28855282539372346,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4253352558","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.71796143,0.00021720644,0.19224429,0.0014335042,0.0011236039,0.0010891611,0.00019150776,0.0007081236,0.08503119],"genre_scores_gemma":[0.9741595,0.0017211227,0.0043154787,0.00025369468,0.00026056063,0.00007134279,0.0034096395,0.0000650735,0.015743617],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.9970951,0.0002982907,0.0007219257,0.0005761159,0.00072469254,0.0005838957],"domain_scores_gemma":[0.9986342,0.000216139,0.00036860874,0.00024753867,0.0002502842,0.00028323883],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00086527196,0.00042527332,0.0005305288,0.00023062462,0.0006110697,0.00019524223,0.0003919694,0.0008706291,0.00096687366],"category_scores_gemma":[0.00005518532,0.0004317797,0.0002507919,0.00042906697,0.000089494184,0.00032370622,0.0000022390661,0.00033460816,0.00016956197],"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":[0.0009744977,0.0008770407,0.09668183,0.0024441583,0.0008347802,0.00007401731,0.3928194,0.34923953,0.0005844933,0.12867546,0.004593023,0.02220174],"study_design_scores_gemma":[0.005036365,0.0005383397,0.57323855,0.0027478067,0.0035993415,0.0000065009317,0.10723079,0.23781891,0.002507247,0.006472005,0.05523074,0.005573406],"about_ca_topic_score_codex":0.005118294,"about_ca_topic_score_gemma":0.0043141632,"teacher_disagreement_score":0.4765567,"about_ca_system_score_codex":0.00010153879,"about_ca_system_score_gemma":0.0004143094,"threshold_uncertainty_score":0.99994636},"labels":[],"label_agreement":null},{"id":"W4253618147","doi":"10.1002/cjs.5550300101","title":"Editor's report","year":2002,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":7,"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":"Citation; Journal editor; Computer science; Library science","score_opus":0.02162736949218906,"score_gpt":0.2521094662969446,"score_spread":0.2304820968047555,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4253618147","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014467673,0.0017634149,0.71938306,0.014054388,0.06760393,0.00030833523,0.00094667554,0.00006569171,0.18140683],"genre_scores_gemma":[0.93399286,0.00014967656,0.050335705,0.00025533116,0.006655834,4.6801276e-7,0.000025445435,0.000012878876,0.008571807],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994401,0.000025539435,0.00020110008,0.000034461715,0.00017187823,0.0001269757],"domain_scores_gemma":[0.9990754,0.000052569252,0.00015401159,0.000036541598,0.00030899883,0.0003724897],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002365571,0.000030471208,0.000060854927,0.00009248696,0.00022045162,0.000047015426,0.00007412042,0.000036384772,0.000914859],"category_scores_gemma":[0.0004475709,0.00003222318,0.000015876456,0.000104076964,0.00008713361,0.00009417829,2.6372e-7,0.00008121491,0.000014530933],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[8.4171745e-7,0.000004601873,0.01755201,0.0000025464544,0.00000958883,0.0012842005,0.007985029,0.00082781014,4.2720572e-7,0.012125464,0.95709723,0.0031102565],"study_design_scores_gemma":[0.00009038712,0.000020760946,0.0064061615,0.000014634851,0.00001575814,0.00004040717,0.0008971675,0.00013814536,8.941623e-7,0.0008228094,0.9914953,0.000057563335],"about_ca_topic_score_codex":0.0060289246,"about_ca_topic_score_gemma":0.065308705,"teacher_disagreement_score":0.9195252,"about_ca_system_score_codex":0.00006469113,"about_ca_system_score_gemma":0.0005476236,"threshold_uncertainty_score":0.99999845},"labels":[],"label_agreement":null},{"id":"W4253979363","doi":"10.32920/ryerson.14653917","title":"Addressing Challenges to Mobility Hub Implementation at Suburban Commuter Rail Parking Lots in Greater Toronto","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Transport engineering; Modal shift; Agency (philosophy); Mobility management; Placemaking; Business; Urban planning; Computer science; Telecommunications; Public transport; Engineering; Urban design; Civil engineering","score_opus":0.150402249911562,"score_gpt":0.3999190702365044,"score_spread":0.2495168203249424,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4253979363","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9722015,0.0011845772,0.0018339764,0.0024090263,0.00071200845,0.0008159323,0.00002912764,0.00018258623,0.020631218],"genre_scores_gemma":[0.9951933,0.00049624266,0.0027422972,0.0002290098,0.000114432885,0.00011151269,0.0004590021,0.000017188891,0.0006369836],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.9979599,0.00037584433,0.000422055,0.00053016294,0.00039068438,0.00032134398],"domain_scores_gemma":[0.99924415,0.000076126154,0.00013160244,0.00028904958,0.00014275374,0.00011630358],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0009154369,0.00018590568,0.00026661134,0.00006667677,0.00028743755,0.00020667409,0.00021195065,0.00023810471,0.0018177402],"category_scores_gemma":[0.000028376802,0.00019973643,0.000075516844,0.00008857387,0.000043385873,0.00028109347,0.00012032478,0.00016939356,0.000005964472],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007145809,0.00013148812,0.37238988,0.00024261595,0.00005924812,0.000024368906,0.584805,0.011615462,0.000053341937,0.00065058045,0.0009597711,0.028996773],"study_design_scores_gemma":[0.0005518969,0.000026111738,0.8955731,0.0004609613,0.00003790912,3.981749e-7,0.09608783,0.00021313089,0.00035119665,0.00013293917,0.0059761726,0.0005883766],"about_ca_topic_score_codex":0.050974026,"about_ca_topic_score_gemma":0.7161827,"teacher_disagreement_score":0.6652087,"about_ca_system_score_codex":0.0007571686,"about_ca_system_score_gemma":0.00015932246,"threshold_uncertainty_score":0.9990947},"labels":[],"label_agreement":null},{"id":"W4254364547","doi":"10.21172/1.81.052","title":"Study &amp; analysis of an urban bus and metro route using vissim simulated data","year":2016,"lang":"en","type":"article","venue":"International Journal of Latest Trends in Engineering and Technology","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"VisSim; Transport engineering; Computer science; Environmental science; Engineering; Intersection (aeronautics)","score_opus":0.038738380520787714,"score_gpt":0.35799173427648484,"score_spread":0.31925335375569713,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4254364547","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9952069,0.0001010804,0.0041375915,0.00034358347,0.0001335141,0.000014809588,0.000026987904,0.000022673737,0.000012856965],"genre_scores_gemma":[0.9970994,0.000041079646,0.0027888154,0.000001832421,0.000032739117,1.5527543e-7,0.000013888028,0.0000043108935,0.000017793694],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99934125,0.000021978352,0.00028340673,0.000107103,0.00016891502,0.000077334415],"domain_scores_gemma":[0.9994816,0.00008673851,0.00015407595,0.000092806236,0.00015091544,0.000033863595],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035934147,0.000056000375,0.00016788793,0.001797459,0.00002362674,0.000020600139,0.00024186238,0.000071946066,0.000010405142],"category_scores_gemma":[0.00017474961,0.000044946573,0.000015519909,0.0008020986,0.000053950356,0.00025057554,0.00002244516,0.000081431484,4.2540183e-8],"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":[0.000022778298,0.00010572922,0.86669356,0.0000012371711,0.00054530794,0.00004160201,0.0014061691,0.11981693,0.00047114945,0.00044519024,0.0000031970048,0.010447155],"study_design_scores_gemma":[0.003597013,0.00036424046,0.77371955,0.00026031505,0.0010937399,0.0000389162,0.0025179323,0.21502444,0.00016400572,0.00019229617,0.0026246612,0.00040287085],"about_ca_topic_score_codex":0.00016389505,"about_ca_topic_score_gemma":0.0005097965,"teacher_disagreement_score":0.095207505,"about_ca_system_score_codex":0.000031729727,"about_ca_system_score_gemma":0.000019076904,"threshold_uncertainty_score":0.1832868},"labels":[],"label_agreement":null},{"id":"W4254621146","doi":"10.1016/j.trpro.2019.05.029","title":"Modeling and managing congested transit service with heterogeneous users under monopoly","year":2019,"lang":"en","type":"article","venue":"Transportation research procedia","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Leibniz-Gemeinschaft","keywords":"Monopoly; Microeconomics; Economic surplus; Crowding; Economics; Shadow price; Willingness to pay; Profit (economics); Service (business); Constraint (computer-aided design); Externality; Value (mathematics); Computer science; Marginal cost; Crowding out; Crowds; Econometrics; Operations research; Mathematical optimization; Engineering; Mathematics; Computer security","score_opus":0.05485054292899042,"score_gpt":0.34320253467933604,"score_spread":0.2883519917503456,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4254621146","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98638,0.00010439655,0.0095931105,0.0020608788,0.000041444557,0.0006896661,0.000017772301,0.00023430098,0.00087844],"genre_scores_gemma":[0.9979745,0.00018879825,0.0011094942,0.00019410152,0.00002752195,0.000040407675,0.00013324723,0.000033727425,0.00029819607],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982082,0.000086146494,0.00020750178,0.00034453705,0.00074689504,0.0004067151],"domain_scores_gemma":[0.9991078,0.000079114114,0.00003570332,0.000107551954,0.0004872451,0.00018261692],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005671603,0.00011736587,0.00013711612,0.00019494859,0.00040573408,0.0001041077,0.00012921062,0.00010449698,0.000083121864],"category_scores_gemma":[0.0000069838147,0.000115180235,0.00001856639,0.00065596116,0.00010763949,0.0004356374,0.0000011274764,0.00024019924,0.000026328427],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002504004,0.000040347113,0.03184121,0.00022068158,0.00004735121,0.000015856926,0.048230607,0.9079362,0.00019893174,0.01061367,0.000016048143,0.00058869604],"study_design_scores_gemma":[0.007997074,0.0007794914,0.2242892,0.0009003519,0.00023700797,0.000006307451,0.15816532,0.59834695,0.0006077634,0.003960308,0.0028000528,0.0019101616],"about_ca_topic_score_codex":0.0037192,"about_ca_topic_score_gemma":0.029105354,"teacher_disagreement_score":0.30958924,"about_ca_system_score_codex":0.000037618032,"about_ca_system_score_gemma":0.00022723728,"threshold_uncertainty_score":0.9886109},"labels":[],"label_agreement":null},{"id":"W4254901266","doi":"10.1177/0361198105192600103","title":"Analysis of Factors Affecting the Frequency and Type of Activity Schedule Modification","year":2005,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Wilfrid Laurier University","funders":"","keywords":"Scheduling (production processes); Schedule; Computer science; Operations research; Engineering; Operations management","score_opus":0.15826879386411824,"score_gpt":0.44301520175215436,"score_spread":0.2847464078880361,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4254901266","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99496084,0.00020600122,0.0019040464,0.0019932694,0.00015190082,0.00052154996,0.00005177643,0.000014152112,0.00019644282],"genre_scores_gemma":[0.99741286,0.0008917889,0.0014197348,0.000008126027,0.00006965171,0.0000075643466,0.00002087134,0.000016952677,0.0001524277],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9942578,0.0014999763,0.0009743755,0.0002452572,0.002601306,0.00042129593],"domain_scores_gemma":[0.9933302,0.0017714016,0.0007883564,0.0003032698,0.0036273636,0.00017943923],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0060743922,0.00014084186,0.00040909325,0.0011114192,0.00081613724,0.000060974624,0.00061519287,0.00016317121,0.0001543481],"category_scores_gemma":[0.00047853324,0.000097305514,0.00029297586,0.0047932216,0.0010295587,0.0006594261,0.000002817352,0.000942502,0.0000011718943],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040591767,0.0001899725,0.92737365,0.00008761652,0.0004557289,0.0000019321408,0.036980484,0.020449761,0.0050183893,0.005219672,0.00010300733,0.0037138546],"study_design_scores_gemma":[0.00038732478,0.00016486469,0.98519224,0.00009319367,0.00029558045,2.8798784e-8,0.0099547375,0.00060055865,0.002424134,0.00030378657,0.00049484364,0.000088731584],"about_ca_topic_score_codex":0.0221565,"about_ca_topic_score_gemma":0.10270365,"teacher_disagreement_score":0.080547154,"about_ca_system_score_codex":0.00012120959,"about_ca_system_score_gemma":0.00058456825,"threshold_uncertainty_score":0.98435503},"labels":[],"label_agreement":null},{"id":"W4255241541","doi":"10.1201/9780203881200-40","title":"Analysis of mode split in the greater Toronto area: Long-range temporal trends and underlying travel behaviour","year":2008,"lang":"en","type":"book-chapter","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Range (aeronautics); Mode (computer interface); Geography; Computer science; Engineering; Aerospace engineering; Human–computer interaction","score_opus":0.07203167232552843,"score_gpt":0.3175276213454738,"score_spread":0.24549594901994537,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4255241541","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03401063,0.0006044308,0.001887554,0.0003397125,0.000058133824,0.00022276514,0.00017266488,0.00004604305,0.96265805],"genre_scores_gemma":[0.8553378,0.0004010905,0.00020226529,0.00004053739,0.000016343134,0.000004312402,0.00028623233,0.000009885694,0.14370154],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9989819,0.000033647917,0.00029069823,0.00021885154,0.0003456501,0.00012923361],"domain_scores_gemma":[0.9995965,0.000044740176,0.00013778408,0.00013001362,0.00004907473,0.000041906602],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021332953,0.00014430964,0.0003115363,0.00024861682,0.000139408,0.00003043272,0.0001259921,0.0002156352,0.0007389067],"category_scores_gemma":[0.000002587377,0.000109098124,0.00013328767,0.00013171863,0.00013199424,0.00013149205,0.0000032669832,0.00009519773,5.240551e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000043697546,0.00005411587,0.82768536,0.000018566641,0.00047709286,0.000057474346,0.13810594,0.0021690966,3.6432257e-7,0.025774438,0.00049652037,0.0051173586],"study_design_scores_gemma":[0.00022567177,0.000028238157,0.9938917,0.000037076978,0.00089394685,8.2352875e-7,0.003306993,0.0007155503,1.6408792e-7,0.00008921746,0.0005775385,0.00023303709],"about_ca_topic_score_codex":0.035686817,"about_ca_topic_score_gemma":0.34534365,"teacher_disagreement_score":0.82132715,"about_ca_system_score_codex":0.000059182024,"about_ca_system_score_gemma":0.000041768577,"threshold_uncertainty_score":0.97073466},"labels":[],"label_agreement":null},{"id":"W4255333502","doi":"10.1177/0361198105193200111","title":"Comparison of Second-Best and Third-Best Tolling Schemes on a Road Network","year":2005,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Queue; Queueing theory; Road pricing; Toll; Computer science; Traffic flow (computer networking); Economics; Transport engineering; Traffic congestion; Computer network; Engineering","score_opus":0.13361360627618366,"score_gpt":0.4443699977047008,"score_spread":0.3107563914285172,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4255333502","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9918758,0.00074015174,0.0007867757,0.0036478976,0.00037600668,0.00072259794,0.000045509845,0.000029145644,0.0017761077],"genre_scores_gemma":[0.99144346,0.0015921228,0.004886082,0.00005154809,0.00045058082,0.000026817648,0.000019073557,0.000037004273,0.0014933022],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9923737,0.0012440411,0.0015867428,0.0003770688,0.0035741401,0.0008442794],"domain_scores_gemma":[0.9943751,0.0013078491,0.0007075983,0.0003059794,0.0028917321,0.0004117452],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.005660547,0.00022896535,0.0005591955,0.00073853036,0.0013379076,0.00015132841,0.0007470484,0.00027033678,0.00041092935],"category_scores_gemma":[0.00025671723,0.00019067206,0.00026352928,0.0019317236,0.001024779,0.00075908995,0.000003833588,0.0018055857,0.000021237664],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0027982616,0.0009925088,0.80833,0.00032984425,0.00024461796,0.000033166587,0.046007708,0.07184195,0.0011291584,0.027839564,0.006813216,0.03364001],"study_design_scores_gemma":[0.0021661117,0.001515871,0.8493409,0.0012798909,0.00011571664,3.1734763e-7,0.023351679,0.0008287579,0.0013503688,0.0014880865,0.11818712,0.00037515818],"about_ca_topic_score_codex":0.0062617604,"about_ca_topic_score_gemma":0.121667914,"teacher_disagreement_score":0.115406156,"about_ca_system_score_codex":0.00016440006,"about_ca_system_score_gemma":0.00073364784,"threshold_uncertainty_score":0.9999622},"labels":[],"label_agreement":null},{"id":"W4255510922","doi":"10.33044/revuma.v60n1a10","title":"Trajectorial market models: arbitrage and pricing intervals","year":2019,"lang":"es","type":"article","venue":"Revista de la Unión Matemática Argentina","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Arbitrage; Mathematics; Economics; Financial economics; Econometrics; Mathematical economics","score_opus":0.010193441042504778,"score_gpt":0.2725777632385653,"score_spread":0.2623843221960605,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4255510922","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8823432,0.0014239454,0.0045049954,0.0005837025,0.00066966354,0.0006724439,0.00005970506,0.00018988794,0.10955247],"genre_scores_gemma":[0.992031,0.0032543414,0.0015158585,0.0001005824,0.00020396049,0.000007783596,0.000030271962,0.000037873167,0.0028183265],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99757427,0.00071771146,0.00047349712,0.00041344538,0.00036168788,0.0004593955],"domain_scores_gemma":[0.99886495,0.00038023462,0.00024095958,0.00023022344,0.00008931522,0.00019431152],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018261482,0.00023860417,0.00037937373,0.00011956392,0.00024454715,0.00074951013,0.0002180977,0.00024091158,0.00067991664],"category_scores_gemma":[0.000119392775,0.00024417834,0.00011494688,0.0003519332,0.00016963933,0.00036815932,0.000027385422,0.00029021828,0.00004972801],"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":[0.0003972242,0.00054418895,0.08873779,0.0031217155,0.00029082823,0.0000775386,0.023863591,0.0031298823,0.0013363239,0.8700165,0.006379838,0.002104551],"study_design_scores_gemma":[0.0042926464,0.00043662873,0.095533244,0.0056293067,0.00096385437,0.000037165213,0.0066318884,0.11263615,0.00025829172,0.008287742,0.76300824,0.0022848337],"about_ca_topic_score_codex":0.00012399236,"about_ca_topic_score_gemma":0.000004430341,"teacher_disagreement_score":0.8617288,"about_ca_system_score_codex":0.00007685972,"about_ca_system_score_gemma":0.00015433264,"threshold_uncertainty_score":0.99573034},"labels":[],"label_agreement":null},{"id":"W4255916817","doi":"10.1177/0361198105192100103","title":"Tests of a Family of Trip Table Refinements for Long-Range, Quick-Response Travel Forecasting","year":2005,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","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":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Factoring; Table (database); Range (aeronautics); Destinations; Computer science; Trip generation; Estimation; Operations research; Travel time; Transport engineering; Trip distribution; Simple (philosophy); Geography; Engineering; Data mining; Tourism; Economics; TRIPS architecture","score_opus":0.16862879069160447,"score_gpt":0.4279629682696639,"score_spread":0.25933417757805943,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4255916817","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98443526,0.00031267878,0.010162786,0.002669115,0.0003111034,0.0015013667,0.00027230996,0.000021319549,0.0003140827],"genre_scores_gemma":[0.9872782,0.0005442654,0.010349641,0.000033666845,0.00019419387,0.000082489925,0.00004097097,0.000048670438,0.0014278494],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9911935,0.0015028153,0.0021372274,0.00036047024,0.0039166505,0.0008893892],"domain_scores_gemma":[0.98832566,0.0029525068,0.0011262933,0.00033613876,0.006918969,0.00034043845],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012428305,0.00022755227,0.0006043497,0.0012175785,0.00082003424,0.00007690321,0.0009935977,0.0002520249,0.00018776655],"category_scores_gemma":[0.0010995074,0.00019424755,0.00044028027,0.0023382592,0.0008813608,0.0007821513,0.0000033593094,0.0009243757,0.0000029773141],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.026295843,0.0016271296,0.80303204,0.0012355276,0.00049893104,0.000048808288,0.06525887,0.039081376,0.0197706,0.008067221,0.008835195,0.026248468],"study_design_scores_gemma":[0.0036020258,0.0009816083,0.96838164,0.0007101658,0.000107562075,2.2644268e-7,0.009336726,0.00048104982,0.0025365432,0.00062923756,0.013015185,0.00021804025],"about_ca_topic_score_codex":0.009744995,"about_ca_topic_score_gemma":0.07272442,"teacher_disagreement_score":0.1653496,"about_ca_system_score_codex":0.00022416282,"about_ca_system_score_gemma":0.001805361,"threshold_uncertainty_score":0.9968492},"labels":[],"label_agreement":null},{"id":"W4256034555","doi":"10.1080/714040821","title":"A Multi-Attribute Performance Measurement Model for Advanced Public Transit Systems","year":2002,"lang":"en","type":"article","venue":"Journal of Intelligent Transportation Systems","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Headway; Public transport; Schedule; Transit (satellite); Novelty; Transport engineering; Engineering; Control (management); Computer science; Service (business); Work (physics); Operations research","score_opus":0.16672546232353172,"score_gpt":0.3038336016419592,"score_spread":0.1371081393184275,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4256034555","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.056755804,0.002494071,0.9378378,0.0003945287,0.0014412838,0.00083927705,0.00007818745,0.000064141816,0.00009492498],"genre_scores_gemma":[0.99432313,0.0013877256,0.0031968884,0.000028036206,0.0001669315,0.00006280918,0.000036514848,0.00002515367,0.0007728251],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968177,0.00011287424,0.0013526011,0.00019256973,0.0011813992,0.00034284062],"domain_scores_gemma":[0.9963732,0.00007157458,0.00081685814,0.000114123606,0.002384907,0.00023935102],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019347486,0.00018219044,0.00039061016,0.00028035484,0.00037940484,0.00014995273,0.00025418724,0.00014540285,0.000021733427],"category_scores_gemma":[0.00007883977,0.00016983198,0.00022013274,0.00034329112,0.000056870897,0.0008083339,4.084991e-7,0.00016465742,0.0000072067164],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000060412574,0.00017235702,0.004334157,0.0002188423,0.0000992789,0.000003862988,0.022135453,0.96983254,0.00023247096,0.0014523914,0.00045470177,0.0010035128],"study_design_scores_gemma":[0.002307903,0.00028419675,0.0024187665,0.00077321246,0.0002078958,0.000008305237,0.015922576,0.94599414,0.0002197334,0.0000058426363,0.0313998,0.00045760584],"about_ca_topic_score_codex":0.000064247615,"about_ca_topic_score_gemma":0.00027140975,"teacher_disagreement_score":0.9375673,"about_ca_system_score_codex":0.00024445972,"about_ca_system_score_gemma":0.00019983605,"threshold_uncertainty_score":0.6925547},"labels":[],"label_agreement":null},{"id":"W4256141174","doi":"10.1002/net.20362","title":"Lagrangean‐based decomposition algorithms for multicommodity network design problems with penalized constraints","year":2009,"lang":"en","type":"article","venue":"Networks","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal; Natural Sciences and Engineering Research Council of Canada","funders":"Natural Sciences and Engineering Research Council of Canada; Université de Montréal","keywords":"Mathematical optimization; Decomposition; Flow network; Convergence (economics); Relaxation (psychology); Network planning and design; Context (archaeology); Computer science; Decomposition method (queueing theory); Nonlinear system; Integer (computer science); Algorithm; Mathematics","score_opus":0.04144083009145854,"score_gpt":0.3149675041647392,"score_spread":0.27352667407328063,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4256141174","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005713601,0.0000838991,0.9964524,0.0006217675,0.00015323116,0.00090058876,0.0000073927704,0.0001978669,0.0010115317],"genre_scores_gemma":[0.76365596,0.000019848552,0.23504707,0.00055245106,0.00036016182,0.000053559274,0.00024311879,0.000010719772,0.000057087836],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988428,0.00017232391,0.0001855409,0.00020432277,0.00020260215,0.0003924094],"domain_scores_gemma":[0.99922824,0.00029561503,0.00012237112,0.00008948007,0.00015430582,0.00010996393],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00074331,0.000116860756,0.00015498228,0.000030731044,0.0006812588,0.00008872948,0.00010950626,0.000134442,0.000049386163],"category_scores_gemma":[0.000016504782,0.0001068595,0.000050564147,0.00024506965,0.00013897117,0.0001334525,0.0000011782804,0.00010751206,0.0000017332516],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001681888,0.000056346547,0.0007007356,0.000003557307,0.0000108031245,0.0000018526754,0.0006769569,0.9808728,0.0000024991555,0.0013133768,0.0016749143,0.014517935],"study_design_scores_gemma":[0.0027180838,0.0003128064,0.0058187265,0.00016106652,0.00008193643,9.119431e-7,0.00015385642,0.9852669,0.0000123552545,0.0010583883,0.004103161,0.00031180645],"about_ca_topic_score_codex":0.000057787016,"about_ca_topic_score_gemma":0.00027990865,"teacher_disagreement_score":0.7630846,"about_ca_system_score_codex":0.00003880223,"about_ca_system_score_gemma":0.00009475911,"threshold_uncertainty_score":0.5239761},"labels":[],"label_agreement":null},{"id":"W4256329829","doi":"10.5334/baw.69","title":"Gauteng","year":2016,"lang":"en","type":"book-chapter","venue":"Ubiquity Press eBooks","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Geography; Population; Census; Quarter (Canadian coin); Metropolitan area; Landlocked country; Cartography; Socioeconomics; Political science; Demography; Economics","score_opus":0.05260111449496246,"score_gpt":0.29244653894578904,"score_spread":0.23984542445082657,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4256329829","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000043270884,0.00012351513,0.0012979698,0.00011201845,0.00042982632,0.0002382295,0.000084359046,0.00027064577,0.9974391],"genre_scores_gemma":[0.0016475351,0.00016867297,0.00026214003,0.00012269625,0.0004849247,0.000010617337,0.000028960567,0.000034143755,0.9972403],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99884737,0.000034520803,0.00022111848,0.00026799855,0.00040083224,0.00022816185],"domain_scores_gemma":[0.99927694,0.00008627188,0.00017440728,0.00020869802,0.00013114285,0.00012252349],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026297083,0.00017108105,0.00019270727,0.00006305665,0.00031477798,0.00006652977,0.00023491816,0.00039979577,0.00035788],"category_scores_gemma":[0.000022214494,0.00015593815,0.00010014788,0.000002917179,0.00022400476,0.00006741786,0.000017543482,0.00018529873,0.000066109285],"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":[0.000009453655,0.000002225683,0.0000045248457,0.000015711475,0.000023559265,0.000008210407,0.0027269106,0.0000075157477,0.00000255489,0.982734,0.003269799,0.011195493],"study_design_scores_gemma":[0.00012130163,0.000009399415,0.000010835862,0.00016491034,0.000040552073,2.746671e-7,0.000016924556,0.0000026316334,0.0000299076,0.012422792,0.9869586,0.00022187292],"about_ca_topic_score_codex":0.00024357582,"about_ca_topic_score_gemma":0.00045647754,"teacher_disagreement_score":0.9836888,"about_ca_system_score_codex":0.00005619056,"about_ca_system_score_gemma":0.0001488683,"threshold_uncertainty_score":0.6358973},"labels":[],"label_agreement":null},{"id":"W4256707813","doi":"10.32920/ryerson.14647236.v1","title":"An integrated positioning system for the Toronto Transit System","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Global Positioning System; Hybrid positioning system; Positioning system; Dead reckoning; Transit (satellite); Kalman filter; Precision Lightweight GPS Receiver; Positioning technology; Computer science; Automatic vehicle location; Systems design; Real-time computing; Navigation system; Assisted GPS; System integration; Engineering; Telecommunications; Systems engineering; Transport engineering; Public transport; Node (physics)","score_opus":0.02028697836900178,"score_gpt":0.2958471246567963,"score_spread":0.2755601462877945,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4256707813","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0034927395,0.00061682134,0.9755307,0.00038166283,0.0018139204,0.0009328815,0.0001092329,0.00074768555,0.016374353],"genre_scores_gemma":[0.988075,0.000035974772,0.009764097,0.000038974064,0.00026403985,0.00017749978,0.0010187324,0.000017626424,0.000608051],"study_design_codex":"simulation_or_modeling","study_design_gemma":"qualitative","domain_scores_codex":[0.9987346,0.00022174754,0.00028547496,0.0003067509,0.00026892952,0.00018247796],"domain_scores_gemma":[0.9989963,0.00014210652,0.00012522131,0.00023266113,0.00042557644,0.00007814627],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00071742176,0.00013793043,0.00020236481,0.000020770352,0.0008686481,0.0005095477,0.000282019,0.0002702737,0.00007322334],"category_scores_gemma":[0.000014008963,0.00010487095,0.00012568323,0.00008605657,0.00004145008,0.00020491882,0.000005509426,0.00014039822,0.0000014388064],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011975365,0.00007931043,0.00042837896,0.0014586541,0.00035340159,0.000014362969,0.1574871,0.64392066,0.00007254192,0.19009306,0.0006650183,0.005307797],"study_design_scores_gemma":[0.00035493143,0.000046436402,0.0008567344,0.0014660856,0.00038085706,0.0000024988137,0.85537565,0.1365664,0.00010908018,0.000010164448,0.004398283,0.00043289308],"about_ca_topic_score_codex":0.042951595,"about_ca_topic_score_gemma":0.05657323,"teacher_disagreement_score":0.98458225,"about_ca_system_score_codex":0.00046249165,"about_ca_system_score_gemma":0.00039439366,"threshold_uncertainty_score":0.96342146},"labels":[],"label_agreement":null},{"id":"W4280621461","doi":"10.1155/2022/7568836","title":"The Effect of Key Indicators on the Operation Costs for Public Toll Roads","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","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":"China Postdoctoral Science Foundation","keywords":"Toll; Government (linguistics); Maximization; Pareto principle; Residual; Profit (economics); Profit maximization; Social Welfare; Business; Function (biology); Social welfare function; Welfare; Environmental economics; Finance; Microeconomics; Transport engineering; Economics; Operations management; Computer science; Engineering","score_opus":0.010380094676211557,"score_gpt":0.28304633353689956,"score_spread":0.272666238860688,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4280621461","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9867657,0.00015357556,0.005780031,0.00554917,0.0007610806,0.0006138539,0.0000399419,0.0000151020095,0.0003215756],"genre_scores_gemma":[0.99920315,0.00009712634,0.0003669948,0.000067613466,0.00006667879,0.000053128657,0.00005454441,0.000008463429,0.00008232123],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9985511,0.0002745081,0.00041758752,0.00007580011,0.00055604504,0.00012500568],"domain_scores_gemma":[0.9983336,0.00081146177,0.0005866455,0.00007128853,0.00015344407,0.000043603974],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019303487,0.00006828959,0.00012381392,0.000114035356,0.0011102909,0.000034840916,0.00020513825,0.000029848457,0.000031343592],"category_scores_gemma":[0.00018159907,0.000042468862,0.00011045092,0.00034152516,0.00007127436,0.00029363064,7.05325e-7,0.0001654702,2.919223e-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":[0.0017881357,0.00009248653,0.02279779,0.000023418163,0.00009865563,0.000003360013,0.029111637,0.8342917,0.00063596974,0.06931797,0.0009606341,0.04087825],"study_design_scores_gemma":[0.011130415,0.010637342,0.47771788,0.00020511444,0.000547258,0.0000053842837,0.07211591,0.0013013749,0.012010254,0.002817505,0.41078416,0.0007274186],"about_ca_topic_score_codex":0.000016823851,"about_ca_topic_score_gemma":0.00023228438,"teacher_disagreement_score":0.83299035,"about_ca_system_score_codex":0.000092109905,"about_ca_system_score_gemma":0.00013653304,"threshold_uncertainty_score":0.85395724},"labels":[],"label_agreement":null},{"id":"W4281478100","doi":"10.2139/ssrn.4129646","title":"A Novel Markov Model for Near-Term Railway Delay Prediction","year":2022,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Markov chain; Interpretability; Computer science; Markov model; Kernel (algebra); Variable-order Markov model; Heuristic; Algorithm; Markov process; Term (time); Train; Mathematical optimization; Machine learning; Artificial intelligence; Mathematics; Statistics","score_opus":0.01370924704285649,"score_gpt":0.26529280067985217,"score_spread":0.2515835536369957,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4281478100","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.058529552,0.00028441253,0.93814844,0.0011731763,0.00037651556,0.0002511957,0.000067264074,0.00007967838,0.0010897407],"genre_scores_gemma":[0.9888703,0.0002498396,0.0034936948,0.00012620557,0.00019321995,0.000048379865,0.0000677341,0.000016167467,0.006934463],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99832004,0.000060575745,0.00019072508,0.0001314978,0.00034570214,0.000951428],"domain_scores_gemma":[0.99963534,0.000038089314,0.00012473934,0.000053674292,0.000085234315,0.00006293242],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0019623658,0.000068400725,0.00007813009,0.00006395561,0.002161523,0.00007267276,0.00016883011,0.000042117277,0.00005860651],"category_scores_gemma":[0.000037011607,0.000074328666,0.00008275516,0.00017524736,0.000038825052,0.00020585426,0.0000063989114,0.0006522951,0.0000011775504],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036314433,0.00019786943,0.005207186,0.00000624424,0.00013137015,0.0000015004609,0.020688888,0.77214044,0.00023702292,0.17953297,0.0015166801,0.019976662],"study_design_scores_gemma":[0.003510587,0.00061729294,0.0015396541,0.00001503354,0.00015345795,0.00015093466,0.017280824,0.88652205,0.0000068007907,0.06582753,0.023944942,0.00043090276],"about_ca_topic_score_codex":0.00010906327,"about_ca_topic_score_gemma":0.0013994122,"teacher_disagreement_score":0.9346548,"about_ca_system_score_codex":0.00071836176,"about_ca_system_score_gemma":0.0029894388,"threshold_uncertainty_score":0.9991375},"labels":[],"label_agreement":null},{"id":"W4281566751","doi":"10.1155/2022/3536979","title":"Driver Source-Based Traffic Control Approach for Mitigating Congestion in Freeway Bottlenecks","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China; Department of Transportation of Hunan Province; U.S. Department of Transportation","keywords":"Traffic congestion; Traffic congestion reconstruction with Kerner's three-phase theory; Control (management); Computer science; Transport engineering; Traffic bottleneck; Traffic optimization; Bottleneck; Differential (mechanical device); Floating car data; Engineering; Embedded system","score_opus":0.010928291476484973,"score_gpt":0.25703460996832106,"score_spread":0.2461063184918361,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4281566751","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.70645666,0.000110040084,0.29218674,0.0004206266,0.00027947087,0.00040478908,0.00005035249,0.00003149212,0.00005979522],"genre_scores_gemma":[0.9625967,0.000013469889,0.03687996,0.00011313887,0.00008303616,0.000055539444,0.00020181501,0.000015099489,0.000041245938],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99851733,0.00014530972,0.0005536683,0.00013839106,0.00045815064,0.00018716232],"domain_scores_gemma":[0.99891394,0.00021394475,0.0005371312,0.00004755238,0.00021670767,0.00007072686],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00089374254,0.00009187561,0.00020293366,0.00022123933,0.00038132747,0.000019701225,0.00011943975,0.000055529385,0.000033787088],"category_scores_gemma":[0.0000709129,0.0001034574,0.000121314246,0.00033115843,0.000053399413,0.00034217763,3.1266208e-7,0.00022288588,1.2280987e-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":[0.00037817375,0.00011245618,0.009478284,0.000019806888,0.0000114441,0.0000044265585,0.013546143,0.9712792,0.00031446147,0.00037640735,0.00002407998,0.004455136],"study_design_scores_gemma":[0.045623176,0.002304539,0.5914838,0.0003629295,0.0006060207,0.0000087428225,0.13444254,0.18846758,0.0005485138,0.0011888134,0.033544973,0.0014184305],"about_ca_topic_score_codex":0.00003345452,"about_ca_topic_score_gemma":0.00026813016,"teacher_disagreement_score":0.7828116,"about_ca_system_score_codex":0.00013738734,"about_ca_system_score_gemma":0.00022564469,"threshold_uncertainty_score":0.42188704},"labels":[],"label_agreement":null},{"id":"W4281615978","doi":"10.1155/2022/4142517","title":"An Operation Plan Optimization Model for Integrated Customized and Conventional Bus Services Based on Cost Analysis","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","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":"Plan (archaeology); Genetic algorithm; Service (business); Public transport; Key (lock); Transport engineering; Mode (computer interface); Sustainability; Sensitivity (control systems); Service level; Operations research; Computer science; Engineering; Business","score_opus":0.014260207578482547,"score_gpt":0.2941223219110453,"score_spread":0.27986211433256275,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4281615978","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1409648,0.000022484775,0.8578526,0.0002979506,0.00015939407,0.00036942383,0.0002777276,0.000030439514,0.00002520354],"genre_scores_gemma":[0.9225956,0.000036991918,0.07362726,0.000181891,0.000036899306,0.000049084618,0.0034192023,0.000011947447,0.000041110303],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986161,0.00013542337,0.00048242518,0.00016922952,0.00047514713,0.00012164925],"domain_scores_gemma":[0.99880946,0.00009883851,0.00049405196,0.000062967636,0.00043711372,0.000097552904],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006965832,0.00010264859,0.0002156696,0.00042119276,0.00062102865,0.000057709123,0.00010780411,0.000051855917,0.000102620776],"category_scores_gemma":[0.000016036103,0.00010647943,0.00011055271,0.0004982897,0.000034257937,0.00067354896,4.493507e-7,0.0001289624,9.3018414e-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":[0.0014571266,0.00012470283,0.0028882464,0.000012580603,0.000060032755,0.0000017437706,0.00661186,0.9870583,0.00008904036,0.0010465718,0.00000766772,0.00064213324],"study_design_scores_gemma":[0.0026755875,0.00022631773,0.007546876,0.000016066737,0.00036048752,2.952713e-7,0.0052327607,0.98334587,0.00001822607,0.00011202465,0.00034842858,0.00011707926],"about_ca_topic_score_codex":0.00005325127,"about_ca_topic_score_gemma":0.0007155965,"teacher_disagreement_score":0.7842253,"about_ca_system_score_codex":0.00009878569,"about_ca_system_score_gemma":0.00020985396,"threshold_uncertainty_score":0.47765133},"labels":[],"label_agreement":null},{"id":"W4281686225","doi":"10.1155/2022/4390923","title":"Automatic Scaling Mechanism of Intermodal EDI System under Green Cloud Computing","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","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":"National Key Research and Development Program of China; Sichuan Agricultural University; China Railway","keywords":"Computer science; Cloud computing; Distributed computing; Energy consumption; Scalability; Resource allocation; Scheduling (production processes); Database; Computer network; Operating system; Engineering","score_opus":0.012627271406777007,"score_gpt":0.27324781321569247,"score_spread":0.2606205418089155,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4281686225","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8355903,0.00006421524,0.16261154,0.00018429794,0.0012208766,0.00012597533,0.000017550374,0.00004824686,0.00013699957],"genre_scores_gemma":[0.9895689,0.0000110815945,0.01017288,0.000031031377,0.00014764286,0.0000020325228,0.00003171528,0.000012062575,0.000022692158],"study_design_codex":"simulation_or_modeling","study_design_gemma":"qualitative","domain_scores_codex":[0.99820673,0.00013932923,0.00074462383,0.00009759163,0.00065349706,0.00015824701],"domain_scores_gemma":[0.9985712,0.0000984767,0.0009558328,0.00005625024,0.00025158815,0.00006661132],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00091610634,0.000081168546,0.00022735503,0.00017986071,0.00040858713,0.000012450892,0.00016331697,0.00003929614,0.000054104963],"category_scores_gemma":[0.000014569927,0.00008709153,0.00011496835,0.0003380596,0.000039755647,0.00029628337,0.0000019990282,0.00019399078,4.046367e-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":[0.000085693566,0.000056575725,0.00092842936,0.00010862281,0.000044041448,0.000020422032,0.04782502,0.8958683,0.00072495337,0.049664374,0.000009522128,0.0046640323],"study_design_scores_gemma":[0.007799608,0.0012811888,0.1840605,0.001896566,0.00083021424,0.00006526147,0.71626973,0.07009088,0.0020527926,0.0132706165,0.0013502535,0.0010324199],"about_ca_topic_score_codex":0.000145726,"about_ca_topic_score_gemma":0.00010279895,"teacher_disagreement_score":0.8257774,"about_ca_system_score_codex":0.00014479995,"about_ca_system_score_gemma":0.00015563013,"threshold_uncertainty_score":0.35514894},"labels":[],"label_agreement":null},{"id":"W4283172248","doi":"10.1155/2022/5934670","title":"Evaluation of Spatial and Temporal Performance of Deep Learning Models for Travel Demand Forecasting: Application to Bike-Sharing Demand Forecasting","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","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":"Korea Agency for Infrastructure Technology Advancement; Ministry of Land, Infrastructure and Transport","keywords":"Demand forecasting; Computer science; Demand patterns; Bike sharing; Deep learning; Outlier; Service (business); Term (time); Artificial intelligence; Index (typography); Data mining; Machine learning; Operations research; Demand management; Engineering; Transport engineering","score_opus":0.054477421183234453,"score_gpt":0.30261881151199393,"score_spread":0.24814139032875948,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283172248","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6216875,0.00010653324,0.37766665,0.00003077563,0.000077414945,0.00037634154,0.0000110999745,0.000006028909,0.00003764817],"genre_scores_gemma":[0.9794118,0.00004436679,0.020321395,0.000006676584,0.000056720637,0.00005261044,0.00008326234,0.000014712086,0.000008499301],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979718,0.000094606534,0.0007600778,0.0001646505,0.00085861696,0.00015027943],"domain_scores_gemma":[0.997649,0.00012391177,0.0010927763,0.000052818108,0.0010093653,0.00007212889],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030470209,0.00009615871,0.00024010117,0.00023891483,0.00040831027,0.000011823708,0.00010722317,0.00004519343,0.000009208685],"category_scores_gemma":[0.00013439707,0.00010727422,0.00007079511,0.00030383773,0.000038577673,0.0005498228,0.0000025688562,0.0001348013,2.5182086e-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":[0.00036611877,0.000034083536,0.030907715,0.000073414885,0.000021256354,3.2430103e-7,0.023118056,0.8968146,0.001158687,0.00021961305,4.635167e-7,0.047285683],"study_design_scores_gemma":[0.001843645,0.0006492011,0.067927726,0.00011078559,0.00027344938,0.0000029303696,0.009552439,0.9169024,0.0010769858,0.0014643053,0.000053982105,0.00014210478],"about_ca_topic_score_codex":0.000092272465,"about_ca_topic_score_gemma":0.00047010288,"teacher_disagreement_score":0.35772425,"about_ca_system_score_codex":0.00008280367,"about_ca_system_score_gemma":0.00013205464,"threshold_uncertainty_score":0.4374516},"labels":[],"label_agreement":null},{"id":"W4284971487","doi":"10.1155/2022/4252573","title":"Cooperative Control of Multistation Passenger Inflows in Case of Irregular Large-Scale Passenger Flows","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","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":"Science and Technology Commission of Shanghai Municipality; National Natural Science Foundation of China","keywords":"Control (management); Inflow; Transport engineering; Scale (ratio); Overcrowding; Computer science; Operations research; Engineering; Meteorology","score_opus":0.0076082427712602,"score_gpt":0.2755109278735899,"score_spread":0.2679026851023297,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4284971487","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96433926,0.00018503604,0.034395143,0.00022953175,0.00029707607,0.00029048498,0.00020635057,0.000009828673,0.00004728882],"genre_scores_gemma":[0.9915038,0.00007724629,0.008203941,0.00002872153,0.00003527472,0.000016151496,0.00009037876,0.000011968557,0.000032555712],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99799585,0.0002715113,0.0009567976,0.00012418766,0.00049167423,0.00015996322],"domain_scores_gemma":[0.998178,0.0001888051,0.0008939929,0.00007316865,0.000602444,0.00006361078],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009317649,0.00010015858,0.00032355133,0.00027967533,0.00018666459,0.0000071709387,0.00009320157,0.00005849637,0.00013477769],"category_scores_gemma":[0.00006848757,0.00010676614,0.00010745112,0.00056073884,0.00005624114,0.0005459963,9.2126743e-7,0.00022943065,2.1244541e-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":[0.00047730896,0.00024445733,0.016184974,0.00003178737,0.000031145497,0.00019427255,0.07610582,0.8998989,0.004568539,0.0009029585,0.000012831271,0.0013470217],"study_design_scores_gemma":[0.021538956,0.0014544791,0.64830846,0.00032293916,0.00043973065,0.00006950424,0.29586574,0.018564839,0.003432438,0.0007520902,0.008533328,0.0007174963],"about_ca_topic_score_codex":0.00020954547,"about_ca_topic_score_gemma":0.0067131836,"teacher_disagreement_score":0.88133407,"about_ca_system_score_codex":0.000083864674,"about_ca_system_score_gemma":0.00020963243,"threshold_uncertainty_score":0.43537968},"labels":[],"label_agreement":null},{"id":"W4285160254","doi":"10.1109/tfuzz.2022.3185680","title":"Assessing Spatial Synergy Between Integrated Urban Rail Transit System and Urban Form: A BULI-Based MCLSGA Model With the Wisdom of Crowds","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Fuzzy Systems","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"National Social Science Fund of China; National Natural Science Foundation of China","keywords":"Crowds; Urban rail transit; Transport engineering; Computer science; Urban transit; Geographic information system; Public transport; Economic geography; Geography; Computer security; Engineering; Remote sensing","score_opus":0.022367108213089945,"score_gpt":0.2526910160230148,"score_spread":0.23032390780992484,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285160254","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.117166795,0.000068977766,0.8798379,0.0003508675,0.00033284244,0.00055076176,0.00028098995,0.00019729648,0.001213607],"genre_scores_gemma":[0.9988726,0.0000028170962,0.00032414912,0.000027485457,0.00006139715,0.00017377095,0.000044228218,0.00003125006,0.00046231848],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978046,0.0004919833,0.0004190094,0.00030119,0.0007098901,0.00027332213],"domain_scores_gemma":[0.999041,0.00023604975,0.00023432144,0.00020742656,0.00016689692,0.00011426977],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0007709572,0.00019474595,0.00033509528,0.00020483776,0.0016249914,0.00016370288,0.00021537812,0.00010484405,0.000011063218],"category_scores_gemma":[0.000002908292,0.0001530773,0.00009026898,0.0006416134,0.00022654374,0.00028120482,5.190565e-7,0.00031934094,8.1884434e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015160127,0.00006780292,0.0004200717,0.00011465127,0.00008864947,0.0000041726435,0.015824301,0.98162776,0.00011442648,0.0008781569,0.00012995499,0.00057843793],"study_design_scores_gemma":[0.0039862767,0.00091547065,0.0010271082,0.0008105476,0.0008961335,0.000013631605,0.13467304,0.8518551,0.00095409073,0.000023772529,0.0038773834,0.0009674443],"about_ca_topic_score_codex":0.007998001,"about_ca_topic_score_gemma":0.001577558,"teacher_disagreement_score":0.88170576,"about_ca_system_score_codex":0.00020000471,"about_ca_system_score_gemma":0.00044729703,"threshold_uncertainty_score":0.99967474},"labels":[],"label_agreement":null},{"id":"W4285389445","doi":"10.1155/2022/9372011","title":"Design of Two-Regional Flexible-Route Bus Systems considering Interregional Demands","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","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":"Major Research Plan; National Natural Science Foundation of China","keywords":"Reservation; Public transport; Computer science; Transport engineering; Sensitivity (control systems); Process (computing); Function (biology); Service (business); Mode (computer interface); Transit (satellite); Operator (biology); Operations research; Mathematical optimization; Engineering; Computer network; Mathematics; Economics","score_opus":0.04288912628182687,"score_gpt":0.3095346778869257,"score_spread":0.26664555160509884,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285389445","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.43892026,0.0009522684,0.5572275,0.0005812994,0.0016489354,0.00035250527,0.000042984087,0.000057057125,0.00021721658],"genre_scores_gemma":[0.98414767,0.00012980333,0.015367597,0.000049713737,0.000104611325,0.000012131467,0.00006254114,0.000012409459,0.000113512484],"study_design_codex":"simulation_or_modeling","study_design_gemma":"qualitative","domain_scores_codex":[0.9980906,0.00017648916,0.0007142051,0.00011425999,0.0007510506,0.00015338001],"domain_scores_gemma":[0.99842393,0.0001950314,0.0008600841,0.00005879938,0.0003808336,0.000081289734],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00077202346,0.00009501748,0.00023304406,0.00023391854,0.00032195283,0.000017610502,0.00015519865,0.00003507829,0.00006831454],"category_scores_gemma":[0.000023755802,0.00010118414,0.000100793506,0.00034696618,0.000089998255,0.00048071938,0.0000012858214,0.00019800253,4.271004e-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":[0.000334508,0.000056434954,0.00503231,0.00001922326,0.000043038268,0.0000235246,0.0155921485,0.97027534,0.0008377486,0.007231033,0.00010170605,0.00045300493],"study_design_scores_gemma":[0.041760076,0.0057098637,0.36079234,0.00282478,0.0020054276,0.0005367039,0.39581206,0.031374708,0.0037104532,0.023495441,0.12843242,0.0035457064],"about_ca_topic_score_codex":0.00011957048,"about_ca_topic_score_gemma":0.0000719593,"teacher_disagreement_score":0.9389006,"about_ca_system_score_codex":0.000106091145,"about_ca_system_score_gemma":0.00036671662,"threshold_uncertainty_score":0.41261694},"labels":[],"label_agreement":null},{"id":"W4286212513","doi":"10.1155/2022/3633293","title":"How Do Individual Walk Lengths and Speeds, Together with Alighting Flow, Determine the Platform Egress Times of Train Users?","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","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":"Queue; Train; Bottleneck; Computer science; Flow (mathematics); Queueing theory; Simulation; Headway; Mathematics; Computer network; Geography","score_opus":0.016011793778175496,"score_gpt":0.25653532209623015,"score_spread":0.24052352831805465,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4286212513","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99573356,0.00032551572,0.0021288916,0.0012378959,0.00015922693,0.0001698602,0.00006120796,0.000013510271,0.00017033203],"genre_scores_gemma":[0.9901153,0.00011748578,0.00947843,0.000041654246,0.00006532458,0.000004719159,0.0000398466,0.000012048379,0.00012518466],"study_design_codex":"simulation_or_modeling","study_design_gemma":"qualitative","domain_scores_codex":[0.9987157,0.00006683726,0.00033431666,0.000100813486,0.00064589275,0.00013642866],"domain_scores_gemma":[0.9989936,0.00014932381,0.0005865326,0.00005709721,0.00015901285,0.000054487038],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006391826,0.00009309264,0.00017315154,0.0001095283,0.00039572935,0.000047499885,0.00014570582,0.000035012235,0.00006288598],"category_scores_gemma":[0.000025538047,0.000066918576,0.000054809927,0.00026840632,0.00011086593,0.00063915725,0.0000013554335,0.00019477119,5.218994e-8],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010468941,0.00013778699,0.03828258,0.000062119,0.00020701246,0.00007087931,0.35507157,0.52989703,0.0007942528,0.0040080515,0.000093347546,0.07032845],"study_design_scores_gemma":[0.0082022,0.0020100519,0.46525145,0.00042970182,0.00087578705,0.00006498145,0.48536915,0.0008482082,0.0026811988,0.0027671813,0.03071048,0.00078964187],"about_ca_topic_score_codex":0.000029116643,"about_ca_topic_score_gemma":0.0004478719,"teacher_disagreement_score":0.52904886,"about_ca_system_score_codex":0.0000247653,"about_ca_system_score_gemma":0.000099385165,"threshold_uncertainty_score":0.30436704},"labels":[],"label_agreement":null},{"id":"W4286921762","doi":"","title":"FOURTH QUARTER / ANNUAL TRANSPORTATION REPORT FISCAL YEAR 2021","year":2022,"lang":"en","type":"article","venue":"OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Quarter (Canadian coin); Fiscal year; Environmental science; Economics; Geography; Finance; Archaeology","score_opus":0.009340238185646884,"score_gpt":0.23938524286478088,"score_spread":0.230045004679134,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4286921762","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7649522,0.00012520708,0.04396653,0.002637723,0.0018063601,0.000712429,0.002952748,0.000248879,0.18259797],"genre_scores_gemma":[0.99114,0.000014565467,0.002424831,0.000040227394,0.000019823112,0.000029923427,0.0057931524,0.0000038043515,0.0005336622],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9983335,0.00005134959,0.0005625367,0.00015915239,0.00073282805,0.00016061215],"domain_scores_gemma":[0.9991257,0.000066843975,0.0003571895,0.00014560371,0.00021902693,0.00008561525],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00075488753,0.00008386814,0.00014830452,0.00018436591,0.000547044,0.000045163884,0.00014575882,0.000060342038,0.00032111187],"category_scores_gemma":[0.000055549546,0.00008808156,0.00006678279,0.00051198946,0.000330765,0.0007310558,0.0000131232455,0.00008310059,0.0000035404546],"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":[0.00089268014,0.001526593,0.08673609,0.00018335173,0.00017528192,0.000050225877,0.0035884154,0.0487555,0.0004182373,0.7505254,0.09912759,0.008020616],"study_design_scores_gemma":[0.0010681162,0.00035210417,0.3819476,0.000039130136,0.00009790175,0.000014317834,0.0021018097,0.00008588081,0.00030845456,0.00089372887,0.6126966,0.00039438688],"about_ca_topic_score_codex":0.00014545229,"about_ca_topic_score_gemma":0.00017171701,"teacher_disagreement_score":0.7496317,"about_ca_system_score_codex":0.000033141238,"about_ca_system_score_gemma":0.00014948825,"threshold_uncertainty_score":0.42074752},"labels":[],"label_agreement":null},{"id":"W4287370181","doi":"10.13140/rg.2.2.10900.94085","title":"Modeling the COVID-19 Pandemic: a Sensitivity Analysis on Input Data Using Agent-Based Transportation Simulation","year":2020,"lang":"en","type":"preprint","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); Pandemic; Sensitivity (control systems); Computer science; 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Virology; Engineering; Medicine","score_opus":0.11355045244006255,"score_gpt":0.3530827208335846,"score_spread":0.23953226839352204,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4287370181","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1036589,0.00011713275,0.88612396,0.0067591676,0.00013567862,0.0010431939,0.0012453235,0.0008858961,0.00003075723],"genre_scores_gemma":[0.95699024,0.00010652807,0.03129085,0.0050496073,0.00021500653,0.00008383597,0.006187853,0.000052322564,0.000023747814],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99574083,0.0010417321,0.00073027384,0.0010268856,0.00096417416,0.0004961216],"domain_scores_gemma":[0.9968577,0.00066571985,0.0005557488,0.0012493329,0.00023176904,0.00043973024],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0029905203,0.0004114416,0.000544152,0.0006062234,0.0011581765,0.00038733313,0.0007600181,0.000666781,0.000025540292],"category_scores_gemma":[0.0010097167,0.00040050954,0.00032165326,0.0014764847,0.0001429341,0.00032167812,0.00006715896,0.0009354029,0.0000026124078],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001339128,0.00006509041,0.043377496,0.00004904442,0.00020806778,0.000028126246,0.0043278085,0.9502558,0.000046734498,0.0008261012,0.000055522476,0.0006262911],"study_design_scores_gemma":[0.00026060894,0.000015625212,0.004969825,0.000047281534,0.0010282659,5.37455e-7,0.00051365665,0.9917284,0.000009656073,0.00051106873,0.00050912594,0.0004059139],"about_ca_topic_score_codex":0.13810015,"about_ca_topic_score_gemma":0.16214287,"teacher_disagreement_score":0.8548331,"about_ca_system_score_codex":0.00095353485,"about_ca_system_score_gemma":0.0018901931,"threshold_uncertainty_score":0.9998447},"labels":[],"label_agreement":null},{"id":"W4287747377","doi":"","title":"FY20 Second Quarter Transportation Report","year":2020,"lang":"en","type":"other","venue":"OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Quarter (Canadian coin); Transport engineering; Geography; Engineering; Archaeology","score_opus":0.011174800636001885,"score_gpt":0.24225625369496318,"score_spread":0.2310814530589613,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4287747377","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00086126267,0.00014542726,0.009457151,0.00042310052,0.00055075873,0.00030712908,0.00078698125,0.00023587617,0.9872323],"genre_scores_gemma":[0.8745997,0.00021032021,0.00894054,0.00019214665,0.00012943911,0.00005050122,0.033561856,0.00007367144,0.082241856],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9981726,0.00003320236,0.0007339806,0.0002385173,0.0006456519,0.0001760925],"domain_scores_gemma":[0.99860436,0.0000545849,0.0008328655,0.0001984495,0.00016798677,0.00014173424],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00035770983,0.00016986161,0.00031216638,0.00029510123,0.00017183275,0.000073814066,0.0001841238,0.00028054594,0.0012934968],"category_scores_gemma":[0.000062646555,0.0001706671,0.00009671618,0.000452487,0.00054576487,0.00044545645,0.0000074848085,0.000098399956,0.000014345715],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008337397,0.00017462134,0.0017578157,0.00038916213,0.00011073982,0.000013689227,0.00027542398,0.00016285032,0.00006460013,0.11942107,0.8763866,0.0011600435],"study_design_scores_gemma":[0.000252526,0.00005649127,0.011536762,0.00011591303,0.00006486288,0.0000020019365,0.00005103492,0.0000037399454,0.00009039714,0.000108673885,0.98752713,0.00019046347],"about_ca_topic_score_codex":0.00028274616,"about_ca_topic_score_gemma":0.0012544175,"teacher_disagreement_score":0.90499043,"about_ca_system_score_codex":0.0000202445,"about_ca_system_score_gemma":0.00021189162,"threshold_uncertainty_score":0.9996195},"labels":[],"label_agreement":null},{"id":"W4287906502","doi":"10.48550/arxiv.2001.01838","title":"Network-Based Analysis of Public Transportation Systems in North\\n American Cities","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Public transport; Scalability; Big data; Heuristic; Computer science; Domain (mathematical analysis); Transit system; Graph; Population; Graph theory; Information system; Data science; Data mining; Transport engineering; Theoretical computer science; Transit (satellite); Engineering; Artificial intelligence; Database; Mathematics","score_opus":0.08838796223980687,"score_gpt":0.21206075329189156,"score_spread":0.12367279105208469,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4287906502","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9154192,0.00003953831,0.083205804,0.00015507304,0.0001492653,0.0002209672,0.00017911526,0.00010222231,0.00052876817],"genre_scores_gemma":[0.9983718,0.00011670874,0.00014974689,0.00003900721,0.00003873094,0.0000016141191,0.0011945126,0.000009458814,0.000078468234],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9986153,0.00029792933,0.00029782372,0.00041671013,0.00014423119,0.0002279701],"domain_scores_gemma":[0.9989041,0.00012479004,0.00047701725,0.0001855526,0.000191522,0.000117001095],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028836488,0.00014228249,0.00044743958,0.00055884157,0.000117585376,0.000042449294,0.00030098646,0.00011924292,0.000022584696],"category_scores_gemma":[0.000028742797,0.00018773956,0.00018015614,0.004718803,0.00025397525,0.00013090228,0.000007219296,0.00021982988,0.0000015925713],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001551666,0.000015074104,0.45713454,0.000023651788,0.00010621606,0.000012578953,0.001583992,0.5324831,1.0073775e-7,0.008587909,0.000013270847,0.000024029652],"study_design_scores_gemma":[0.0001754516,0.000017593422,0.7239112,0.000029717077,0.0006284583,3.901443e-9,0.0033716047,0.27136102,2.5163808e-7,0.00014133895,0.00016963143,0.00019368537],"about_ca_topic_score_codex":0.017387211,"about_ca_topic_score_gemma":0.1658783,"teacher_disagreement_score":0.26677668,"about_ca_system_score_codex":0.00012992632,"about_ca_system_score_gemma":0.000344298,"threshold_uncertainty_score":0.98915607},"labels":[],"label_agreement":null},{"id":"W4287988197","doi":"10.46298/cst-9845","title":"Development of Indicators to Measure Transit Use Variability Based on Smart Card Data","year":2023,"lang":"en","type":"article","venue":"˜Les œCahiers scientifiques du transport","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Smart card; Ticket; Sample (material); Variance (accounting); Transit (satellite); TRIPS architecture; Service (business); Transport engineering; Computer science; Measure (data warehouse); Public transport; Geography; Statistics; Econometrics; Business; Engineering; Mathematics; Marketing; Database","score_opus":0.05868825634664842,"score_gpt":0.2879614473821595,"score_spread":0.22927319103551108,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4287988197","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.84432715,0.000004270584,0.14946067,0.00083060906,0.00058184227,0.0007070188,0.00060434703,0.0005074848,0.0029766292],"genre_scores_gemma":[0.971109,0.000008751646,0.026514487,0.000146044,0.000029695828,0.00002726238,0.0015309335,0.000021847058,0.0006120181],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99686635,0.00023017607,0.00054775615,0.0007256492,0.0012146899,0.00041538075],"domain_scores_gemma":[0.9985164,0.00019619166,0.00011986892,0.00061545067,0.00024246231,0.0003096416],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0054323175,0.00018739258,0.00026631684,0.0005886559,0.0008250412,0.000070035654,0.0007150819,0.0002053559,0.000093133225],"category_scores_gemma":[0.00022306941,0.00019179733,0.000088359855,0.0024462328,0.000479854,0.0003831763,0.0000074608806,0.00018124406,0.000021670014],"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":[0.0004388668,0.0005819078,0.7040729,0.00029877754,0.00013317315,0.00004535892,0.21617852,0.050057933,0.0009807422,0.0056889257,0.0034951728,0.018027753],"study_design_scores_gemma":[0.0007389307,0.000051059673,0.7243203,0.0003106163,0.00013208049,1.9562897e-7,0.003223468,0.0020388945,0.0024667778,0.00007220822,0.26595664,0.000688869],"about_ca_topic_score_codex":0.00077492977,"about_ca_topic_score_gemma":0.007287632,"teacher_disagreement_score":0.26246145,"about_ca_system_score_codex":0.0001315849,"about_ca_system_score_gemma":0.0008670953,"threshold_uncertainty_score":0.7821268},"labels":[],"label_agreement":null},{"id":"W4288855297","doi":"10.1155/2022/2428040","title":"Scheduling Synchronization for Overlapping Segments in Bus Lines: Speed Control and Green Extension Strategies","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","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":"National Natural Science Foundation of China","keywords":"Synchronization (alternating current); Transfer (computing); Scheduling (production processes); Computer science; Extension (predicate logic); Real-time computing; Mathematical optimization; Computer network; Parallel computing; Mathematics","score_opus":0.013929439346893438,"score_gpt":0.2885598837166366,"score_spread":0.27463044436974315,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4288855297","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.87508506,0.000331568,0.12349269,0.0004083497,0.00031026665,0.00030920617,0.000023689692,0.000017741197,0.000021427275],"genre_scores_gemma":[0.98941284,0.00018097572,0.010137251,0.00006480372,0.00008678996,0.000008169703,0.000078945195,0.000010523488,0.00001971119],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.998876,0.00006824948,0.00046972578,0.00012139136,0.00032797104,0.0001366654],"domain_scores_gemma":[0.9991286,0.000108841945,0.00042525204,0.000034595883,0.00025409408,0.00004864597],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005699519,0.00007686205,0.00016901518,0.00019570906,0.0003316757,0.000031445976,0.00006254832,0.000039072387,0.000012145741],"category_scores_gemma":[0.00004749159,0.00008530429,0.00004515746,0.00028366313,0.000030330959,0.0007907735,6.781517e-7,0.00013074154,7.766854e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035086577,0.00004125309,0.012537888,0.000031876032,0.000013005916,0.000009518279,0.01453826,0.9647853,0.0023266852,0.0010911099,0.0000021633546,0.0042720824],"study_design_scores_gemma":[0.014640766,0.00074514153,0.76922697,0.0004284391,0.00022897185,0.000009267241,0.16217265,0.04217788,0.00010121493,0.007490664,0.0022395877,0.00053843926],"about_ca_topic_score_codex":0.0001211457,"about_ca_topic_score_gemma":0.00056839705,"teacher_disagreement_score":0.9226074,"about_ca_system_score_codex":0.000093220595,"about_ca_system_score_gemma":0.00018350342,"threshold_uncertainty_score":0.34786078},"labels":[],"label_agreement":null},{"id":"W4289024130","doi":"","title":"Coûts externes des transports : des taux de couverture aux limites de la TICPE","year":2019,"lang":"fr","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ministère des Transports","funders":"","keywords":"Political science","score_opus":0.017117846395597103,"score_gpt":0.2579518858187768,"score_spread":0.2408340394231797,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4289024130","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5285199,0.004497986,0.4030943,0.0046898485,0.0004131193,0.0005341142,0.00015468766,0.0003759841,0.057720073],"genre_scores_gemma":[0.8583419,0.0067889593,0.0959446,0.0001682522,0.00005513851,0.0000546711,0.00061708555,0.00006839526,0.037961017],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.98943716,0.007757141,0.00065484503,0.0008051446,0.0005922003,0.00075348484],"domain_scores_gemma":[0.99276936,0.0033570686,0.0005389654,0.0009752259,0.0019475374,0.00041184897],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.007480105,0.0004725838,0.0004818591,0.00016874702,0.001265578,0.00057371106,0.0011408323,0.00081135804,0.0005832935],"category_scores_gemma":[0.0013966359,0.0005370029,0.00031358286,0.00065268826,0.0021589068,0.00040317906,0.00010885378,0.0007844562,0.000065958375],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005641135,0.0010717587,0.34495735,0.0007187605,0.00019113377,0.000037257258,0.33637545,0.027743064,0.0008251521,0.23184465,0.0013702767,0.054808725],"study_design_scores_gemma":[0.0018762251,0.0000040683785,0.7043456,0.011788421,0.0006584988,0.000048063164,0.0072544958,0.06538698,0.010358393,0.056820747,0.13934264,0.0021158746],"about_ca_topic_score_codex":0.012237767,"about_ca_topic_score_gemma":0.020290347,"teacher_disagreement_score":0.35938823,"about_ca_system_score_codex":0.0004235985,"about_ca_system_score_gemma":0.0014721631,"threshold_uncertainty_score":0.9997082},"labels":[],"label_agreement":null},{"id":"W4289521786","doi":"10.1155/2022/1438630","title":"Effects of Implementing Night Operation Signal Coordination on Arterials","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","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":"Fundamental Research Funds for the Central Universities","keywords":"SIGNAL (programming language); Traffic signal; Microsimulation; Transport engineering; Computer science; Traffic congestion; Metropolitan area; Control (management); Real-time computing; Operations research; Simulation; Engineering","score_opus":0.00821241609169804,"score_gpt":0.28451631625085266,"score_spread":0.2763039001591546,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4289521786","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98131466,0.00006444941,0.017078651,0.0003886411,0.0004961556,0.00030442263,0.000023021423,0.000019226447,0.00031074573],"genre_scores_gemma":[0.9972755,0.00003419957,0.002334183,0.000053081607,0.00009483296,0.000014704408,0.00009743795,0.000009336049,0.00008675229],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9983469,0.00019580938,0.00059630116,0.00009627667,0.00063166535,0.00013302205],"domain_scores_gemma":[0.99870884,0.00017604642,0.0007784139,0.000038208036,0.0002518072,0.00004667785],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008147006,0.00007466536,0.00017329365,0.00020255303,0.00043845738,0.000016622003,0.00009836513,0.00003007143,0.00018902529],"category_scores_gemma":[0.000040662566,0.00007919949,0.00008330141,0.000267644,0.000027539221,0.0004669332,8.694126e-7,0.00012383432,5.487243e-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":[0.00058204314,0.0002818579,0.0039254087,0.000070085785,0.000051606272,0.000023382985,0.03985933,0.8577331,0.07394102,0.0141871,0.00019042862,0.009154672],"study_design_scores_gemma":[0.014517635,0.004881096,0.6986748,0.0004707157,0.00068400346,0.000007906438,0.056248374,0.000864714,0.15782169,0.0048424704,0.059992123,0.0009944782],"about_ca_topic_score_codex":0.00003636147,"about_ca_topic_score_gemma":0.00008278692,"teacher_disagreement_score":0.8568683,"about_ca_system_score_codex":0.00008318365,"about_ca_system_score_gemma":0.00013579983,"threshold_uncertainty_score":0.3372304},"labels":[],"label_agreement":null},{"id":"W4289543625","doi":"10.48550/arxiv.1809.05781","title":"Modelling Latent Travel Behaviour Characteristics with Generative\\n Machine Learning","year":2018,"lang":"","type":"preprint","venue":"arXiv (Cornell University)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Latent variable; Latent variable model; Bayesian network; Generative grammar; Computer science; Machine learning; Restricted Boltzmann machine; Artificial intelligence; Generative model; Mode (computer interface); Preference; Graphical model; Latent class model; Dynamic Bayesian network; Bayesian probability; Artificial neural network; Statistics; Mathematics; Human–computer interaction","score_opus":0.08243694847824926,"score_gpt":0.20076836772598414,"score_spread":0.11833141924773488,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4289543625","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.501826,0.000021880693,0.49633163,0.000039335144,0.0003131217,0.0002724443,0.00008922838,0.00009743398,0.0010089312],"genre_scores_gemma":[0.9847312,0.0015864455,0.0025560546,0.000032715983,0.00027068064,0.0000013511924,0.0006657398,0.00005508085,0.010100728],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99705505,0.00040335907,0.00043549994,0.0012595105,0.0002582395,0.0005883198],"domain_scores_gemma":[0.9978122,0.00008259553,0.0007611205,0.0003590408,0.00063690665,0.0003480957],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0005109059,0.0005378095,0.00055709144,0.00025344756,0.0015332861,0.00019648757,0.00054295384,0.00053408486,0.00044074797],"category_scores_gemma":[0.000015974667,0.00059956085,0.00019581922,0.0007076576,0.00055005465,0.00037930554,0.00007993934,0.0010214538,0.000081478705],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020448709,0.00012575733,0.13793129,0.00003545691,0.00011822867,0.00012035388,0.009815614,0.84740627,0.000004865652,0.0041700057,0.000008181502,0.000059487516],"study_design_scores_gemma":[0.0007145573,0.00021644277,0.011229692,0.00023832328,0.00055681885,0.0000018343454,0.0020889295,0.983629,0.000023765735,0.0002750858,0.00024634463,0.0007792396],"about_ca_topic_score_codex":0.0029064745,"about_ca_topic_score_gemma":0.00054312166,"teacher_disagreement_score":0.49377558,"about_ca_system_score_codex":0.00025874493,"about_ca_system_score_gemma":0.00040378785,"threshold_uncertainty_score":0.9997666},"labels":[],"label_agreement":null},{"id":"W4290760380","doi":"10.1007/s40812-022-00225-1","title":"Ride-hailing demand elasticity: a regression discontinuity method","year":2022,"lang":"en","type":"article","venue":"Journal of Industrial and Business Economics","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Elasticity (physics); Price elasticity of demand; Price elasticity of supply; Economics; Wealth elasticity of demand; Econometrics; Revenue; Demand curve; Supply and demand; Microeconomics; Cross elasticity of demand; Regression discontinuity design; Mathematics; Statistics; Finance; Physics","score_opus":0.048206775260912826,"score_gpt":0.29742938269340646,"score_spread":0.24922260743249364,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4290760380","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9846801,0.00012949719,0.010389664,0.002785643,0.0013659198,0.00007841024,0.000017164684,0.00000979479,0.00054377795],"genre_scores_gemma":[0.9961956,0.00038166667,0.0022908614,0.00007328401,0.0008655307,0.0000016495641,0.000006648526,0.0000066178077,0.00017813897],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992012,0.0001500193,0.00035081577,0.000083527884,0.00010941789,0.00010504344],"domain_scores_gemma":[0.99913687,0.00015943292,0.00049090513,0.000033492954,0.00010853411,0.00007076719],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001413757,0.000060653805,0.00018763157,0.00010388425,0.0005471413,0.00007897695,0.00009839229,0.00006121637,0.00008132115],"category_scores_gemma":[0.00022526538,0.000052897354,0.00004077492,0.00016289322,0.00004457186,0.00030614136,0.000019147326,0.00022385348,2.205966e-7],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016570033,0.0002605268,0.13336931,0.000021159727,0.00015963036,0.000060893206,0.015770875,0.61173934,0.0001278535,0.022404235,0.003237954,0.2111912],"study_design_scores_gemma":[0.018606609,0.00079909404,0.070769474,0.0007106274,0.00091007387,0.00033704506,0.0540494,0.020868098,0.0005528144,0.020041712,0.8105552,0.0017998258],"about_ca_topic_score_codex":0.00024460736,"about_ca_topic_score_gemma":0.0001177922,"teacher_disagreement_score":0.80731726,"about_ca_system_score_codex":0.000067171946,"about_ca_system_score_gemma":0.00029252653,"threshold_uncertainty_score":0.42082238},"labels":[],"label_agreement":null},{"id":"W4292359075","doi":"","title":"Conflits autour de la « pacification » du trafic automobile - Analyse des politiques de modération de la vitesse à Paris","year":2017,"lang":"fr","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Moderation; Traffic calming; Political science; Psychology; Social psychology; Transport engineering; Engineering","score_opus":0.02112503962333664,"score_gpt":0.29140464638949554,"score_spread":0.2702796067661589,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4292359075","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4876483,0.0012637983,0.4188097,0.011194716,0.00021205237,0.00071286317,0.0011871001,0.00066727033,0.078304194],"genre_scores_gemma":[0.93253917,0.0055860076,0.051760197,0.00007604539,0.00008906607,0.00016373434,0.0037289574,0.000051012838,0.006005791],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.97722864,0.020064132,0.00074044627,0.0007766236,0.0005028302,0.00068732543],"domain_scores_gemma":[0.99153125,0.003563901,0.001112697,0.0012789195,0.002052236,0.00046099402],"candidate_categories":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.017540542,0.00043220544,0.0004544641,0.00025061597,0.002080345,0.001523308,0.0011760015,0.0009469048,0.00024600947],"category_scores_gemma":[0.0036267447,0.0005236313,0.00029388562,0.00047760495,0.0018743025,0.0006025374,0.00011757744,0.00080266973,0.0000280057],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000052074338,0.0016033471,0.18295297,0.00035985332,0.0002654325,0.000028566297,0.2683933,0.028615583,0.0033117547,0.4819391,0.0036578958,0.0288201],"study_design_scores_gemma":[0.0012878801,0.0000031285367,0.61895156,0.0028317594,0.0005816045,0.000044477616,0.0052904426,0.22948349,0.01813739,0.043451883,0.07855157,0.0013848005],"about_ca_topic_score_codex":0.030808289,"about_ca_topic_score_gemma":0.0050748256,"teacher_disagreement_score":0.4448909,"about_ca_system_score_codex":0.0007834839,"about_ca_system_score_gemma":0.0018742178,"threshold_uncertainty_score":0.9997215},"labels":[],"label_agreement":null},{"id":"W4293150014","doi":"10.1155/2022/2604150","title":"Approximating Dynamic Equilibrium Analysis in Multi-Region Network Based on Macroscopic Fundamental Diagram","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","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":"Central Public-interest Scientific Institution Basal Research Fund for Chinese Academy of Tropical Agricultural Sciences","keywords":"Computer science; Trajectory; Diagram; Mathematical optimization; Scale (ratio); Traffic network; Global Positioning System; Mathematics; Physics","score_opus":0.01585198783234048,"score_gpt":0.30481924039503167,"score_spread":0.2889672525626912,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293150014","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8758818,0.00008628505,0.122951254,0.00032588304,0.0004324571,0.00021709135,0.000018671466,0.00003130813,0.000055255136],"genre_scores_gemma":[0.9807049,0.00002237266,0.018836688,0.000113326605,0.00003443038,0.000017172628,0.00021283898,0.000012225054,0.000046040936],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99820566,0.00018988343,0.0006472703,0.00016164484,0.0005645467,0.00023100036],"domain_scores_gemma":[0.99902505,0.00011187388,0.00063023274,0.000074662064,0.00008534388,0.000072849056],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006627792,0.000105016756,0.0002475475,0.00040953397,0.00031142123,0.000029230165,0.0001373552,0.00004283331,0.00007910552],"category_scores_gemma":[0.000027926613,0.00011574508,0.00016392079,0.0015185163,0.000041747127,0.00033245338,0.0000010745593,0.0002796038,5.156048e-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":[0.0001762424,0.00013495373,0.13913488,0.000008960187,0.000029111929,0.000032306234,0.006309446,0.8525347,0.00006192353,0.00010218404,0.000004453799,0.0014707883],"study_design_scores_gemma":[0.0023303095,0.0002922349,0.8136853,0.00007348048,0.00022002548,7.5291564e-7,0.0081270775,0.17456906,0.000014642673,0.00016337847,0.00031459882,0.00020913912],"about_ca_topic_score_codex":0.00008015397,"about_ca_topic_score_gemma":0.0016829117,"teacher_disagreement_score":0.6779657,"about_ca_system_score_codex":0.0002496719,"about_ca_system_score_gemma":0.00012657576,"threshold_uncertainty_score":0.47199473},"labels":[],"label_agreement":null},{"id":"W4293255074","doi":"10.1061/ajrua6.0001234","title":"Reliability of Intergreen Interval Based on Combined Dilemma and Option Zones","year":2022,"lang":"en","type":"article","venue":"ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part A Civil Engineering","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Dilemma; Interval (graph theory); Reliability (semiconductor); Monte Carlo method; Computer science; Moment (physics); Reliability engineering; Limit (mathematics); Reliability theory; Game theory; Mathematics; Statistics; Engineering; Mathematical economics; Failure rate; Power (physics)","score_opus":0.007346378304258288,"score_gpt":0.22103468215419142,"score_spread":0.21368830384993312,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293255074","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9694119,0.00034412488,0.028899869,0.000073545336,0.00096873223,0.00016905252,0.000028816226,0.000045151628,0.0000588087],"genre_scores_gemma":[0.99925077,0.00013738884,0.0004817655,0.0000038122885,0.000081018414,0.000013262104,0.000007186856,0.000014895511,0.000009927338],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985896,0.00013717708,0.00058472535,0.000138639,0.00036402786,0.00018587189],"domain_scores_gemma":[0.9990367,0.00041253914,0.0002697738,0.00009531202,0.00008908449,0.0000965341],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017199571,0.00013745163,0.00034064366,0.0003612917,0.00010529707,0.000034943085,0.00011989295,0.000060494134,0.000006625315],"category_scores_gemma":[0.00038524403,0.00013839029,0.00006810066,0.0002987381,0.000031836862,0.00015400966,0.000015094636,0.00035841326,9.455964e-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":[0.0000871491,0.00004164861,0.017191311,0.00011088622,0.000016969758,0.00000739634,0.0026762125,0.97804505,0.000036587364,0.0016026811,0.000014119238,0.00017001698],"study_design_scores_gemma":[0.001110298,0.00030863314,0.034522876,0.00065707957,0.000038660797,0.000009257722,0.001663391,0.9589113,0.0000071515046,0.000022826158,0.0025504187,0.00019813323],"about_ca_topic_score_codex":0.00065847806,"about_ca_topic_score_gemma":0.00012927872,"teacher_disagreement_score":0.029838843,"about_ca_system_score_codex":0.0001366586,"about_ca_system_score_gemma":0.00005192873,"threshold_uncertainty_score":0.56433916},"labels":[],"label_agreement":null},{"id":"W4293564020","doi":"10.1016/j.physa.2022.128077","title":"Temporal robustness assessment framework for city-scale bus transit networks","year":2022,"lang":"en","type":"article","venue":"Physica A Statistical Mechanics and its Applications","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Robustness (evolution); Computer science; Bus rapid transit; Scale (ratio); Transport engineering; Public transport; Engineering; Geography; Cartography","score_opus":0.024012128463378607,"score_gpt":0.32666091271889897,"score_spread":0.30264878425552033,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293564020","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00038808392,0.000039508548,0.99583334,0.0017885735,0.000085630825,0.0008238956,0.00070162717,0.00007847047,0.00026090193],"genre_scores_gemma":[0.86581963,0.00004979894,0.13165538,0.00014799499,0.00013301293,0.0016017136,0.0004910436,0.000015207399,0.000086204134],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99899405,0.00007067486,0.00018839996,0.00027539127,0.00024546348,0.00022598892],"domain_scores_gemma":[0.99912786,0.00045974532,0.00008130747,0.0001012791,0.000098635726,0.00013118869],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.00024621084,0.000092455935,0.0001429488,0.000029609844,0.001568854,0.000061535306,0.00013306647,0.000048144895,0.000120096716],"category_scores_gemma":[0.000022624881,0.000105032654,0.000035430425,0.0003022817,0.00002834997,0.00006544709,0.000015184111,0.0001942177,6.0671056e-7],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011474601,0.00013308284,0.000023531493,0.000010107694,0.000010607361,2.2328011e-7,0.0005697766,0.030856105,0.0000094746865,0.96529156,0.0002892225,0.0027948534],"study_design_scores_gemma":[0.00024731786,0.00008652011,0.00028094643,0.000006640921,0.00007428466,3.391233e-7,0.0014487348,0.7740046,0.000003795847,0.19675009,0.026889343,0.00020737627],"about_ca_topic_score_codex":0.000044898803,"about_ca_topic_score_gemma":0.00004451214,"teacher_disagreement_score":0.86543155,"about_ca_system_score_codex":0.000044356602,"about_ca_system_score_gemma":0.000089762616,"threshold_uncertainty_score":0.99973094},"labels":[],"label_agreement":null},{"id":"W4293564569","doi":"10.1155/2022/9006925","title":"Decision-Making Research on Tour Lines of Tourist-Dedicated Train Based on Vague Sets and Prospect Theory","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Tourism; Operations research; Evidential reasoning approach; Line (geometry); Bounded rationality; Computer science; Decision model; Fuzzy set; Decision theory; Selection (genetic algorithm); Decision analysis; Business decision mapping; Product (mathematics); Rationality; Decision engineering; Decision-making; Index (typography); Decision support system; Fuzzy logic; Engineering; Data mining; Artificial intelligence; Operations management; Mathematics; Geography; Machine learning; Mathematical economics","score_opus":0.029708862609757594,"score_gpt":0.37785590967785676,"score_spread":0.3481470470680992,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293564569","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98895717,0.00011753595,0.009157303,0.0006306998,0.00033108916,0.00027250987,0.000060244332,0.000023375354,0.00045007796],"genre_scores_gemma":[0.99248886,0.00006187652,0.007204873,0.00008417855,0.00006837248,0.000009771702,0.00003008454,0.000013532475,0.000038461414],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9976357,0.00033554048,0.000519778,0.00015800241,0.0011794084,0.00017153464],"domain_scores_gemma":[0.9978737,0.0011309917,0.00043534304,0.00008221666,0.0003992746,0.00007842284],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026326354,0.00008787425,0.00019039745,0.00045435966,0.00051235425,0.000018707742,0.000149823,0.000052513504,0.00014262405],"category_scores_gemma":[0.00031320605,0.00008257015,0.000073015486,0.00064970826,0.000107320964,0.00020383521,0.0000011179283,0.000388146,3.5173912e-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":[0.0025603895,0.00023660071,0.004476233,0.000025978983,0.000018462133,0.00005648587,0.017779237,0.94122434,0.0003731028,0.0054202005,0.00016498513,0.02766399],"study_design_scores_gemma":[0.0058555985,0.004490048,0.88357145,0.001409838,0.00013801063,0.000006350355,0.05453322,0.003296862,0.0010580192,0.040260054,0.004869983,0.0005105979],"about_ca_topic_score_codex":0.00001675689,"about_ca_topic_score_gemma":0.00015993186,"teacher_disagreement_score":0.9379275,"about_ca_system_score_codex":0.000082122264,"about_ca_system_score_gemma":0.00024489893,"threshold_uncertainty_score":0.39406663},"labels":[],"label_agreement":null},{"id":"W4293792872","doi":"10.1155/2022/1112949","title":"Continuum Approximation Model for Transit Service Design with Stochastic Demand","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","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":"Shanxi Scholarship Council of China; National Natural Science Foundation of China","keywords":"Constraint (computer-aided design); Headway; Computer science; Reliability (semiconductor); Transit (satellite); Level of service; Mathematical optimization; Robustness (evolution); Public transport; Operations research; Transport engineering; Reliability engineering; Simulation; Engineering; Mathematics","score_opus":0.0228760530953715,"score_gpt":0.27062125562810807,"score_spread":0.24774520253273657,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293792872","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.092243135,0.000055080127,0.9063152,0.0006873299,0.00014130468,0.00047103042,0.000037204893,0.000028560053,0.000021139405],"genre_scores_gemma":[0.88700825,0.000012156621,0.112544045,0.00012204625,0.00004713101,0.000056088385,0.00010246446,0.000016145967,0.00009169979],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987655,0.000074565374,0.00040312123,0.00011855576,0.00048165716,0.00015655853],"domain_scores_gemma":[0.99876857,0.00012362826,0.00047319476,0.000048674494,0.00051305006,0.00007287047],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00067869143,0.00009183119,0.00017281645,0.00012748186,0.00051270856,0.000021726357,0.00011520493,0.000035423658,0.000019564233],"category_scores_gemma":[0.000017690576,0.0000902734,0.000061529194,0.00032893606,0.000027666621,0.0005771115,3.1444418e-7,0.00013451402,1.8050584e-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":[0.0011393045,0.000056328852,0.0000627576,0.000022285547,0.000022765666,0.0000025843945,0.045575295,0.95110244,0.00041410764,0.0009060848,0.0000406758,0.0006553959],"study_design_scores_gemma":[0.016729433,0.0021932703,0.015542665,0.000265332,0.0011119166,0.000020639312,0.06544309,0.8811225,0.00063871103,0.013391956,0.0025248285,0.0010156075],"about_ca_topic_score_codex":0.0000123322725,"about_ca_topic_score_gemma":0.0005685866,"teacher_disagreement_score":0.7947651,"about_ca_system_score_codex":0.000069237656,"about_ca_system_score_gemma":0.00027968507,"threshold_uncertainty_score":0.39433914},"labels":[],"label_agreement":null},{"id":"W4294653639","doi":"10.1155/2022/7637298","title":"Path-Based Approach for Expanding Rail Transit Network in a Metropolitan Area","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Key Research and Development Program of China","keywords":"Column generation; Metropolitan area; Train; Path (computing); Computer science; Mathematical optimization; Transit (satellite); Network planning and design; Service (business); Flow network; Transport engineering; Shortest path problem; Park and ride; Operations research; Public transport; Engineering; Computer network; Mathematics; Graph; Geography","score_opus":0.021196810093366034,"score_gpt":0.2959817186302787,"score_spread":0.2747849085369127,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4294653639","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24251208,0.00030382277,0.7555791,0.00034119608,0.0004387103,0.00042338544,0.00005418361,0.00003188609,0.0003156181],"genre_scores_gemma":[0.9292444,0.00003564303,0.070224375,0.000089813795,0.00010508278,0.00004960866,0.00020657924,0.000014314129,0.000030202164],"study_design_codex":"simulation_or_modeling","study_design_gemma":"qualitative","domain_scores_codex":[0.99851173,0.000109971406,0.0005476901,0.00013548422,0.00045466828,0.00024046093],"domain_scores_gemma":[0.99918866,0.0001388687,0.0003964598,0.00005158142,0.00014717453,0.00007725908],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00093500124,0.00009235723,0.00021922313,0.00021486422,0.00042499282,0.000018072114,0.00013064593,0.000044087945,0.000042553154],"category_scores_gemma":[0.000030113117,0.00010098983,0.000144417,0.0006144097,0.000037952832,0.00033995623,3.4034886e-7,0.00019834157,5.8119042e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000566165,0.00009853759,0.0068059782,0.000024656629,0.000012173219,0.000009488938,0.0136605585,0.9739753,0.00011376338,0.0038844927,0.00005414921,0.00079471763],"study_design_scores_gemma":[0.035638645,0.003026513,0.23142576,0.00054053206,0.0007260843,0.000011096634,0.63263917,0.035815347,0.0005373961,0.017072009,0.04061559,0.0019518371],"about_ca_topic_score_codex":0.00007517132,"about_ca_topic_score_gemma":0.00045720395,"teacher_disagreement_score":0.93815994,"about_ca_system_score_codex":0.00027195428,"about_ca_system_score_gemma":0.00021767305,"threshold_uncertainty_score":0.41182458},"labels":[],"label_agreement":null},{"id":"W4295181079","doi":"10.1155/2022/3102249","title":"Which Factors Affect User Satisfaction with ETC? Evidence from Shanghai and Beijing","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","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":"National Natural Science Foundation of China","keywords":"Toll; Beijing; Informatization; Electronic toll collection; Transport engineering; Traffic congestion; Toll road; Environmental economics; Investment (military); Business; China; Computer science; Engineering; Telecommunications; Economics","score_opus":0.017160074487308115,"score_gpt":0.28503628799044134,"score_spread":0.26787621350313323,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4295181079","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98812026,0.00025548277,0.010695865,0.00039323157,0.00031553267,0.00013620508,0.000028016646,0.000029187779,0.00002621806],"genre_scores_gemma":[0.9934188,0.00029042154,0.0060950913,0.000028446091,0.00006413121,0.000005925874,0.000054151365,0.0000118989165,0.000031103922],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99873155,0.00011929971,0.00029738044,0.00014156924,0.0005839433,0.00012626097],"domain_scores_gemma":[0.99897337,0.0002527235,0.00042757383,0.000051721156,0.00021006049,0.00008455403],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036272194,0.00009242522,0.00015553397,0.00012505466,0.0005130581,0.000037293365,0.000072266455,0.000036749505,0.00012217787],"category_scores_gemma":[0.000053858294,0.00008480325,0.00003806842,0.0003857091,0.000036182893,0.0011638119,9.4216585e-7,0.00022667689,2.511354e-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":[0.00026611635,0.000016092765,0.66079575,0.000008175023,0.000026612737,0.000008162764,0.03638587,0.29989037,0.0008970921,0.00015284968,0.000015164717,0.0015377048],"study_design_scores_gemma":[0.0005217967,0.00022190307,0.979906,0.00010416146,0.00008387066,0.0000013958489,0.018326033,0.000035043933,0.00012239524,0.000094920055,0.00047298364,0.00010949601],"about_ca_topic_score_codex":0.0010962776,"about_ca_topic_score_gemma":0.01709727,"teacher_disagreement_score":0.3191102,"about_ca_system_score_codex":0.00009117908,"about_ca_system_score_gemma":0.00013705586,"threshold_uncertainty_score":0.9540678},"labels":[],"label_agreement":null},{"id":"W4296080364","doi":"10.1155/2022/2113311","title":"An Optimization Approach considering Passengers’ Space-Time Requirements for Bus Bridging Service under URT Disruption","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Bridging (networking); Service (business); Transport engineering; Scheduling (production processes); Computer science; Megacity; Operations research; Engineering; Computer network; Business; Operations management","score_opus":0.02847358661843594,"score_gpt":0.3076421173098326,"score_spread":0.27916853069139663,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4296080364","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.30926883,0.00004799374,0.6885881,0.0010702834,0.0004216734,0.0003863618,0.000043128017,0.00006442476,0.000109213805],"genre_scores_gemma":[0.84197754,0.000051775416,0.15682831,0.00020465041,0.00015979943,0.000032582833,0.00066169654,0.000027493732,0.000056148754],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982369,0.00014194794,0.00056827045,0.00020325399,0.0006229057,0.00022674416],"domain_scores_gemma":[0.9985105,0.000077098994,0.00070193247,0.00009251151,0.00049808045,0.00011989649],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008376592,0.00012642074,0.0002073128,0.00020908078,0.0008194162,0.000058915128,0.00014894268,0.000057329195,0.000083472536],"category_scores_gemma":[0.000029081455,0.00014874093,0.00009201507,0.00047716656,0.000032652708,0.0014066626,0.000001261391,0.00016784792,4.109419e-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":[0.0002527963,0.00012226873,0.0006485918,0.000027545515,0.000031102125,0.000003508071,0.016770428,0.9774256,0.0027513597,0.0011439038,0.000024287045,0.0007986155],"study_design_scores_gemma":[0.015944311,0.0019906994,0.108714186,0.0003590348,0.0012872127,0.000042498214,0.22104602,0.6347939,0.0013849967,0.0033689644,0.008954299,0.0021139197],"about_ca_topic_score_codex":0.0000569573,"about_ca_topic_score_gemma":0.0000987325,"teacher_disagreement_score":0.5327087,"about_ca_system_score_codex":0.00019808162,"about_ca_system_score_gemma":0.00019793886,"threshold_uncertainty_score":0.630237},"labels":[],"label_agreement":null},{"id":"W4296127672","doi":"10.1016/j.trb.2022.08.009","title":"Bayesian optimization techniques for high-dimensional simulation-based transportation problems","year":2022,"lang":"en","type":"article","venue":"Transportation Research Part B Methodological","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":43,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Bayesian probability; Bayesian optimization; Computer science; Artificial intelligence","score_opus":0.30918145578619854,"score_gpt":0.4780763067218087,"score_spread":0.16889485093561013,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4296127672","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014860638,0.000043768483,0.9783139,0.0027268396,0.00027902637,0.0023940566,0.0006379539,0.0005247927,0.00021898645],"genre_scores_gemma":[0.75461125,0.00001846887,0.23683788,0.00026856508,0.0001584023,0.0019306518,0.005770581,0.000036522993,0.00036768225],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9933039,0.00284601,0.00076381734,0.00066718506,0.0017681218,0.0006509544],"domain_scores_gemma":[0.9934078,0.0050179013,0.00024440457,0.00020737633,0.0008979758,0.00022455642],"candidate_categories":["sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.009199096,0.00020833661,0.00032995516,0.00046216784,0.0029337425,0.00007792226,0.00032187736,0.00022400664,0.0024671492],"category_scores_gemma":[0.0007878329,0.00022177421,0.00016341821,0.0013234266,0.00034718472,0.00030758878,0.000002759102,0.0005316116,0.0000033986055],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043494062,0.00021591873,0.0026022012,0.00004058673,0.000018090539,0.0000063313564,0.0028511588,0.978284,0.00008135018,0.013320415,0.00051541906,0.0016296149],"study_design_scores_gemma":[0.0034037847,0.0017171757,0.024683708,0.000098323464,0.00016959338,2.9209963e-7,0.0043391366,0.82092077,0.0009946111,0.015235933,0.12724124,0.0011954483],"about_ca_topic_score_codex":0.00096668996,"about_ca_topic_score_gemma":0.0010879567,"teacher_disagreement_score":0.74147606,"about_ca_system_score_codex":0.00019443381,"about_ca_system_score_gemma":0.000451822,"threshold_uncertainty_score":0.99844474},"labels":[],"label_agreement":null},{"id":"W4296275139","doi":"10.1155/2022/9061211","title":"Comparison between Physical Parameters and Sensory Parameters Regarding Travel Behavior Based on Sensitivity Analysis","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","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":"Magyar Tudományos Akadémia","keywords":"Sensitivity (control systems); Multinomial logistic regression; Mode choice; Mode (computer interface); Travel behavior; Public transport; Computer science; Econometrics; Transport engineering; Operations research; Simulation; Mathematics; Engineering; Machine learning","score_opus":0.03371378677631693,"score_gpt":0.3296594583573865,"score_spread":0.2959456715810696,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4296275139","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9696758,0.000011634661,0.029629475,0.00024638104,0.00014034557,0.00015314497,0.00008232322,0.00002258625,0.000038334],"genre_scores_gemma":[0.98949385,0.000008683766,0.01023605,0.000060175385,0.000039834384,0.000008050344,0.00013067221,0.000010899676,0.00001178696],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9981712,0.0003267744,0.0004395295,0.0001858966,0.00070598617,0.00017060096],"domain_scores_gemma":[0.99864805,0.00047823018,0.0005432181,0.000077213124,0.00012828638,0.0001249933],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007450177,0.00011744876,0.00039419645,0.00037206692,0.0004698533,0.000030634692,0.000063743115,0.000041991603,0.000008899469],"category_scores_gemma":[0.000042524738,0.00012567009,0.00022033826,0.00064309407,0.00008592391,0.00028041936,7.7854554e-7,0.00032002103,2.1867004e-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":[0.00019774116,0.00010545648,0.19011702,0.000004908837,0.00007854664,0.000029180415,0.009912292,0.79622656,0.00025611426,0.0000705935,0.0000035440537,0.002998035],"study_design_scores_gemma":[0.0009121571,0.00037726056,0.9772481,0.000017871664,0.0013568122,7.650679e-7,0.012159305,0.0072000497,0.00040666634,0.000053330325,0.00008855696,0.00017917968],"about_ca_topic_score_codex":0.00009473618,"about_ca_topic_score_gemma":0.00017734001,"teacher_disagreement_score":0.7890265,"about_ca_system_score_codex":0.00010026833,"about_ca_system_score_gemma":0.000071593015,"threshold_uncertainty_score":0.51246774},"labels":[],"label_agreement":null},{"id":"W4296390485","doi":"10.1155/2022/6369217","title":"An Urban Bus Network Generation Algorithm Based on Particle Swarm Optimization and Force Field Properties","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China; Strategic Innovation Fund","keywords":"Particle swarm optimization; Computer science; Mathematical optimization; Network planning and design; Multi-objective optimization; Genetic algorithm; Population; Nondeterministic algorithm; NP; Algorithm; Computer network; Mathematics","score_opus":0.015198513755246973,"score_gpt":0.2567689229946228,"score_spread":0.2415704092393758,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4296390485","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.49087417,0.00020294728,0.507043,0.0009278072,0.00058380194,0.00025146583,0.000012992551,0.00004923133,0.000054576107],"genre_scores_gemma":[0.9613794,0.00008134811,0.03768744,0.00038594293,0.0002768519,0.000011415369,0.00010802323,0.0000123490745,0.0000572342],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99874896,0.00014829125,0.00036157542,0.0001408636,0.00045039333,0.00014990944],"domain_scores_gemma":[0.99928766,0.000051441024,0.00030674363,0.0000710367,0.00019126061,0.00009187463],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005210033,0.00008427113,0.0001216214,0.00007480129,0.00066931697,0.00004604174,0.00008080321,0.000040998504,0.000055089316],"category_scores_gemma":[0.000026080412,0.00008527331,0.000041343617,0.00027380747,0.000031048923,0.0006232624,6.0480545e-7,0.00014978823,1.6969918e-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":[0.00013387254,0.00007390134,0.0025021182,0.000004648094,0.0000066440693,0.000006164122,0.008493128,0.98163116,0.00034038062,0.00035273322,0.000076651624,0.0063785864],"study_design_scores_gemma":[0.0022194155,0.0020495658,0.013837242,0.000065078304,0.00011207083,0.000002588022,0.009218282,0.96726304,0.0021291666,0.00017348515,0.002598555,0.0003315373],"about_ca_topic_score_codex":0.00003161246,"about_ca_topic_score_gemma":0.00009604071,"teacher_disagreement_score":0.4705052,"about_ca_system_score_codex":0.000056272576,"about_ca_system_score_gemma":0.00011379442,"threshold_uncertainty_score":0.51479125},"labels":[],"label_agreement":null},{"id":"W4296487784","doi":"10.1111/mice.12921","title":"A methodology for scheduling within‐day roadway work zones using deep neural networks and active learning","year":2022,"lang":"en","type":"article","venue":"Computer-Aided Civil and Infrastructure Engineering","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":18,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"U.S. Department of Transportation","keywords":"Scheduling (production processes); Computer science; Schedule; Operations research; Agency (philosophy); Artificial neural network; Transport engineering; Engineering; Operations management; Artificial intelligence","score_opus":0.018885460081342034,"score_gpt":0.2552543983375592,"score_spread":0.23636893825621716,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4296487784","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27582294,0.00029715887,0.72296786,0.000040842187,0.00064052484,0.00012068941,0.0000018475323,0.00009856631,0.0000095646255],"genre_scores_gemma":[0.8252722,0.000014023268,0.17426413,0.000055964298,0.00033287072,0.000014201064,0.000020629945,0.000017877624,0.000008122093],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99899197,0.0001722773,0.00019309936,0.00025595393,0.000119690485,0.00026700422],"domain_scores_gemma":[0.999289,0.00041417035,0.000109407716,0.000052588824,0.000044828208,0.00009002858],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005226373,0.00014436625,0.00020791616,0.0001160183,0.0008926721,0.000084219406,0.00009006608,0.000084558436,0.000015129089],"category_scores_gemma":[0.0000977173,0.00015787646,0.000036922233,0.00029246966,0.000050843137,0.00018512356,0.000045984296,0.0003662716,2.389736e-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":[0.000023027995,0.0000023422529,0.0023761718,0.000014368437,0.000025672507,0.0000020248528,0.011682931,0.97318417,0.00005614383,0.0012463288,0.000003571414,0.011383272],"study_design_scores_gemma":[0.00027952323,0.00005230899,0.009510102,0.000019351122,0.000026795857,0.00000859823,0.0013855074,0.98789203,0.0000056840154,0.00017544908,0.00046678324,0.0001778374],"about_ca_topic_score_codex":0.000032988257,"about_ca_topic_score_gemma":0.000020377394,"teacher_disagreement_score":0.54944927,"about_ca_system_score_codex":0.000043024716,"about_ca_system_score_gemma":0.000023435847,"threshold_uncertainty_score":0.68658024},"labels":[],"label_agreement":null},{"id":"W4296748123","doi":"10.1061/jtepbs.0000760","title":"Leveraging Location-Based Data for Assessing Network-Level Traffic Impact of Lane Management: A Case Study of Alex Fraser Bridge","year":2022,"lang":"en","type":"article","venue":"Journal of Transportation Engineering Part A Systems","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":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Transport engineering; Traffic flow (computer networking); Bridge (graph theory); Computer science; Travel time; Traffic volume; Geography; Environmental science; Engineering; Computer network","score_opus":0.07846663192896224,"score_gpt":0.3326901514365466,"score_spread":0.2542235195075844,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4296748123","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.78984916,0.00015661049,0.20880525,0.000013413276,0.0004935225,0.00044136058,0.00021223287,0.000022314953,0.0000061556007],"genre_scores_gemma":[0.99723244,0.000005604499,0.0022596705,0.0000028941988,0.00012346111,0.000024074498,0.00031623422,0.000019721685,0.00001591225],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99816567,0.00010578818,0.0008939375,0.00014236053,0.000524275,0.00016797704],"domain_scores_gemma":[0.99847513,0.0001920218,0.0007839528,0.00017983423,0.00030029542,0.000068732435],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016265805,0.0001103692,0.000310227,0.00024736486,0.00022242162,0.000035542023,0.00023946445,0.000032990516,0.000013059054],"category_scores_gemma":[0.000023096356,0.00011663713,0.00009588111,0.00057556364,0.000014599182,0.00032220368,0.0000022036522,0.0001213139,5.0634974e-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":[0.000049465307,0.00019562848,0.022356749,0.00023382952,0.00019451257,0.00011067378,0.008164885,0.9681628,0.000005147158,0.00003688838,0.00029058408,0.00019888283],"study_design_scores_gemma":[0.005269209,0.00061741343,0.18633464,0.00071025855,0.0008357222,0.000047939662,0.055896148,0.7472612,0.0000035538078,0.00000213085,0.002609276,0.00041248673],"about_ca_topic_score_codex":0.0009322636,"about_ca_topic_score_gemma":0.00024353644,"teacher_disagreement_score":0.22090153,"about_ca_system_score_codex":0.00008418322,"about_ca_system_score_gemma":0.00022055239,"threshold_uncertainty_score":0.4756324},"labels":[],"label_agreement":null},{"id":"W4296783095","doi":"10.1155/2022/5458115","title":"Transit Signal Priority for an Integrated Traffic Signal System","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","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":"","keywords":"Transit (satellite); Dwell time; Computer science; Maximization; Transport engineering; SIGNAL (programming language); Bandwidth (computing); Real-time computing; Operations research; Simulation; Public transport; Telecommunications; Mathematical optimization; Engineering; Mathematics","score_opus":0.016250852922325996,"score_gpt":0.2833268066175855,"score_spread":0.26707595369525955,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4296783095","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7813439,0.000119376346,0.21673284,0.00024121976,0.0006942204,0.00048141778,0.00019890252,0.0001047713,0.00008336032],"genre_scores_gemma":[0.9840077,0.00001837613,0.015352384,0.000036374113,0.00016018205,0.000027739048,0.00032281905,0.000018346382,0.000056074874],"study_design_codex":"simulation_or_modeling","study_design_gemma":"qualitative","domain_scores_codex":[0.99817294,0.00018845306,0.0006436561,0.00016209215,0.0006246176,0.00020827068],"domain_scores_gemma":[0.998709,0.000103011196,0.00051293185,0.000057045763,0.00047421976,0.0001437475],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009956306,0.00011647288,0.00023961524,0.00018675686,0.0007384868,0.00003386462,0.00019090745,0.00006114496,0.00010105309],"category_scores_gemma":[0.00001034754,0.00012256144,0.00016421711,0.00041205424,0.000049534177,0.00074115826,3.4016054e-7,0.00025604927,3.6283822e-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":[0.0008273031,0.00012808664,0.00033381893,0.000036186175,0.000027692264,0.000014803695,0.031063609,0.9517841,0.0006991446,0.0019106288,0.000048183643,0.013126442],"study_design_scores_gemma":[0.020008545,0.009415183,0.092173114,0.00050183886,0.0014287798,0.000064076776,0.6556987,0.02982926,0.0015224115,0.0011302372,0.18627542,0.0019524205],"about_ca_topic_score_codex":0.000050687326,"about_ca_topic_score_gemma":0.00060357945,"teacher_disagreement_score":0.9219549,"about_ca_system_score_codex":0.00019916247,"about_ca_system_score_gemma":0.00042639807,"threshold_uncertainty_score":0.56799185},"labels":[],"label_agreement":null},{"id":"W4297337847","doi":"10.1155/2022/4947222","title":"Hybrid Scheduling Model Based on Fare Incentives for Peak Time Interval of the Metro: The Harbin Metro System","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","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":"Queueing theory; Ticket; Queue; Computer science; Schedule; Operations research; Bottleneck; Transport engineering; Incentive; Engineering; Economics; Microeconomics; Computer security; Computer network","score_opus":0.012998260429866973,"score_gpt":0.26894284837807425,"score_spread":0.2559445879482073,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4297337847","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8095388,0.00009810982,0.18787439,0.0011577143,0.0005368942,0.00044187813,0.00021345001,0.000024349876,0.00011443856],"genre_scores_gemma":[0.990445,0.0000057405587,0.009273858,0.0000859353,0.000047622634,0.000020725087,0.00004645802,0.000012664178,0.00006196596],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99842983,0.0001708777,0.00050969404,0.00010874336,0.00064938003,0.00013146526],"domain_scores_gemma":[0.9985382,0.00021788206,0.00078638224,0.00009743048,0.0003226598,0.00003747591],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00097577804,0.000088335015,0.00018681478,0.00011821589,0.0006199153,0.000019020286,0.000289378,0.00002276498,0.000018363991],"category_scores_gemma":[0.000077538105,0.000061703715,0.000234279,0.00028831227,0.00006127822,0.00024982673,0.0000015668127,0.0002029955,2.1710069e-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":[0.00039330986,0.00005111317,0.0010827808,0.000031842592,0.000026066817,0.0000012202007,0.005364955,0.9899035,0.0006493286,0.002022916,0.000020568707,0.00045240793],"study_design_scores_gemma":[0.005892268,0.0009809204,0.07352969,0.0007695724,0.0007275408,0.000001938101,0.10145439,0.8059009,0.0063738055,0.0015092576,0.0023735424,0.0004861793],"about_ca_topic_score_codex":0.00001867161,"about_ca_topic_score_gemma":0.00006301059,"teacher_disagreement_score":0.18400261,"about_ca_system_score_codex":0.00014935012,"about_ca_system_score_gemma":0.00021245198,"threshold_uncertainty_score":0.476795},"labels":[],"label_agreement":null},{"id":"W4297387604","doi":"10.9752/ts054.09-2022","title":"Mexico Transport Cost Indicator Report, September 2022","year":2022,"lang":"en","type":"report","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"BARGE; Quarter (Canadian coin); Truck; Gallon (US); Total cost; Marine transportation; Vehicle miles of travel; Economic impact analysis","score_opus":0.03796860819352247,"score_gpt":0.3423418781378418,"score_spread":0.3043732699443193,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4297387604","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0004887376,0.00041081916,0.0016474972,0.00056499493,0.003408163,0.00080742006,0.00036972508,0.0005610057,0.99174166],"genre_scores_gemma":[0.027896302,0.0028249267,0.0010035157,0.00041772088,0.0009276723,0.00045108004,0.014877869,0.00012434268,0.9514766],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99476546,0.00017887112,0.00088772643,0.00069011986,0.0030103927,0.00046744978],"domain_scores_gemma":[0.9981325,0.000105698986,0.0007575703,0.000416969,0.0003069052,0.00028033246],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0029266747,0.0002915525,0.00047853048,0.0003124817,0.0010145867,0.000057149435,0.00038971167,0.0004995544,0.081242375],"category_scores_gemma":[0.00016112314,0.00031168462,0.0002931292,0.0006236285,0.00017962982,0.00018820146,0.000014074954,0.00073443237,0.00010645832],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019926798,0.00014430482,0.12926866,0.00007737188,0.00018356652,0.001695905,0.0059562963,0.001824537,8.931391e-7,0.0010724654,0.8577515,0.002004562],"study_design_scores_gemma":[0.00013704099,0.000014342253,0.011045043,0.0000234171,0.00013308685,0.000040443236,0.0016550282,0.0000054274337,0.0000034206805,0.000030861553,0.9865198,0.00039208395],"about_ca_topic_score_codex":0.0072734593,"about_ca_topic_score_gemma":0.009542824,"teacher_disagreement_score":0.1287683,"about_ca_system_score_codex":0.0006060034,"about_ca_system_score_gemma":0.005711144,"threshold_uncertainty_score":0.99993354},"labels":[],"label_agreement":null},{"id":"W4297769474","doi":"10.5267/j.dsl.2022.6.003","title":"An efficient hybrid genetic algorithm for solving truncated travelling salesman problem","year":2022,"lang":"en","type":"article","venue":"Decision Science Letters","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":"Travelling salesman problem; Tree traversal; Genetic algorithm; Heuristic; Mathematical optimization; Christofides algorithm; Computer science; 2-opt; Lin–Kernighan heuristic; Bottleneck traveling salesman problem; Metaheuristic; Algorithm; Selection (genetic algorithm); Mathematics; Artificial intelligence","score_opus":0.019258986320652368,"score_gpt":0.3016121113861703,"score_spread":0.2823531250655179,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4297769474","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.45186415,0.000009552239,0.5468297,0.0005301026,0.0003483427,0.00027128423,0.000015071123,0.00007387438,0.000057915615],"genre_scores_gemma":[0.7647275,0.0000032885157,0.23423816,0.00085336785,0.00006543709,0.00005577252,0.000023400038,0.000010841649,0.000022282937],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974372,0.000069675094,0.00029594894,0.0005030232,0.0012454339,0.0004487586],"domain_scores_gemma":[0.99918246,0.00020515002,0.00012757191,0.0001906878,0.00012494893,0.00016915044],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0024733378,0.0000973788,0.0001104621,0.0003353239,0.0034799527,0.0002586806,0.0006667131,0.000018529745,0.00007015523],"category_scores_gemma":[0.000072205454,0.00010227735,0.000054842432,0.001133205,0.00026959163,0.00025624246,0.000019344594,0.00011248933,0.0000056878694],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013119688,0.00005136751,0.000506273,0.0000013981933,0.000001874166,0.000008388925,0.008961431,0.8880645,0.0051683076,0.00031877495,0.00034317485,0.096561395],"study_design_scores_gemma":[0.00057044724,0.00010622995,0.006686902,0.000016471953,0.000015046183,0.0000056172753,0.0040681367,0.97965115,0.00078510604,0.00048108003,0.00729063,0.00032317804],"about_ca_topic_score_codex":0.00010548321,"about_ca_topic_score_gemma":0.0000143345615,"teacher_disagreement_score":0.3128633,"about_ca_system_score_codex":0.00017025351,"about_ca_system_score_gemma":0.00022190259,"threshold_uncertainty_score":0.9978174},"labels":[],"label_agreement":null},{"id":"W4297983533","doi":"10.52825/scp.v3i.158","title":"Signal priority for improving fluidity and decreasing fuel consumption","year":2022,"lang":"en","type":"article","venue":"SUMO Conference Proceedings","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Innovation and Economic Development Trois Rivières; Université Laval","funders":"","keywords":"Computer science; Fuel efficiency; SIGNAL (programming language); Acceleration; Signal timing; Duty cycle; Traffic congestion; Real-time computing; Simulation; Traffic signal; Automotive engineering; Transport engineering; Engineering; Voltage; Electrical engineering","score_opus":0.04082086729272891,"score_gpt":0.294706923773616,"score_spread":0.25388605648088713,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4297983533","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9864194,0.00010437108,0.010531241,0.0005828017,0.00015169234,0.00045375703,0.00003386106,0.00016354158,0.0015593648],"genre_scores_gemma":[0.99562997,0.000042009007,0.0038879032,0.00006602063,0.00006422649,0.00007879214,0.000028803144,0.0000071538584,0.0001951183],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99914366,0.000020868381,0.00014488344,0.00023182401,0.0002476255,0.00021113883],"domain_scores_gemma":[0.9994737,0.00006811487,0.000107205735,0.00002450716,0.00023601756,0.00009047477],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00074533734,0.000076637356,0.000096715565,0.00006246414,0.0012119685,0.0001668084,0.00010094454,0.00004416132,0.00015518],"category_scores_gemma":[0.00013666973,0.00009114808,0.000024716273,0.00011780326,0.00011617282,0.00033572505,0.000020164507,0.00011961326,0.0000010015049],"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":[0.00026204795,0.0001019153,0.6240795,0.0004243383,0.000021174475,0.000001725147,0.104407154,0.00004089385,0.007437947,0.23224126,0.00045307478,0.030528937],"study_design_scores_gemma":[0.0072696377,0.0011690247,0.49552852,0.00026359255,0.00053207006,0.00003176235,0.20402364,0.09086821,0.002973623,0.08008941,0.11409655,0.0031539532],"about_ca_topic_score_codex":0.0004075826,"about_ca_topic_score_gemma":0.00010993729,"teacher_disagreement_score":0.15215185,"about_ca_system_score_codex":0.000068224326,"about_ca_system_score_gemma":0.00020351239,"threshold_uncertainty_score":0.9321604},"labels":[],"label_agreement":null},{"id":"W4298069779","doi":"10.1007/978-1-4614-6170-8_100722","title":"Transportation Systems","year":2014,"lang":"en","type":"book-chapter","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Business; Computer science","score_opus":0.021345980637020933,"score_gpt":0.25507740490046993,"score_spread":0.233731424263449,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4298069779","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000055739497,0.00011422794,0.027156372,0.00016033155,0.0005556631,0.0001794667,0.00002903315,0.00023546323,0.9715639],"genre_scores_gemma":[0.017743321,0.00019913395,0.00028762704,0.00007085325,0.00030471716,0.000004905481,0.0005479961,0.00002307478,0.9808184],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.999148,0.000015647094,0.00022366518,0.00016890159,0.0003318728,0.00011187378],"domain_scores_gemma":[0.99953604,0.000046458557,0.000135121,0.00009030437,0.00011807645,0.00007401591],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000213072,0.0001119101,0.00015478636,0.00007682036,0.00019152755,0.000051304196,0.00008688097,0.00029953467,0.0010340653],"category_scores_gemma":[0.000006167978,0.000115075556,0.00006264261,0.000018667824,0.00006722223,0.000051146697,3.1322358e-7,0.00010087397,0.00022150895],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000002355429,0.000001173779,0.00004193385,0.00001596935,0.000010570397,0.0000016978619,0.0013110223,0.0015478878,1.4265181e-7,0.9899256,0.006390142,0.0007514858],"study_design_scores_gemma":[0.00006781217,0.00000902217,0.00010215433,0.0000688565,0.000038156148,6.93345e-8,0.0001437258,0.00006470887,2.409294e-7,0.001555935,0.99778825,0.00016104616],"about_ca_topic_score_codex":0.0006901456,"about_ca_topic_score_gemma":0.0020446777,"teacher_disagreement_score":0.99139816,"about_ca_system_score_codex":0.000032691656,"about_ca_system_score_gemma":0.00008924259,"threshold_uncertainty_score":0.9998791},"labels":[],"label_agreement":null},{"id":"W4299131130","doi":"","title":"La variété des coûts de congestion routière. Le cas du boulevard Périphérique parisien","year":2014,"lang":"fr","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ministère des Transports","funders":"","keywords":"Boulevard; Art; Humanities; Political science; Geography; Archaeology","score_opus":0.013195281058022692,"score_gpt":0.24792600523945912,"score_spread":0.23473072418143642,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4299131130","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27014932,0.000909225,0.58259976,0.021429282,0.00058330206,0.00059602346,0.00010339217,0.00047629207,0.12315337],"genre_scores_gemma":[0.93604654,0.0024181379,0.045269173,0.00018411767,0.000099467805,0.00009558185,0.0009873185,0.00006921946,0.014830418],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9804605,0.016407682,0.0008414464,0.0009466495,0.0006498528,0.0006938687],"domain_scores_gemma":[0.9917087,0.0031860739,0.0008789812,0.0011486007,0.0026148022,0.00046283007],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.014528214,0.0004888254,0.00055525306,0.00018837278,0.0016198766,0.00071150233,0.0011761605,0.0009641212,0.00024362798],"category_scores_gemma":[0.003009016,0.00059044024,0.00028722855,0.00065349287,0.0014445238,0.00040970903,0.00023610702,0.0009764224,0.00011751779],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000045214703,0.00088216644,0.04749469,0.00025216234,0.00015577965,0.000021646818,0.17245449,0.02199231,0.000295046,0.7153472,0.0018090379,0.0392503],"study_design_scores_gemma":[0.0026281567,0.000004564323,0.47069937,0.00594231,0.00044896896,0.00007545682,0.0049974406,0.13358279,0.0050468375,0.034983624,0.33935922,0.0022312715],"about_ca_topic_score_codex":0.041025855,"about_ca_topic_score_gemma":0.03742364,"teacher_disagreement_score":0.68036354,"about_ca_system_score_codex":0.0005304265,"about_ca_system_score_gemma":0.0011856124,"threshold_uncertainty_score":0.99967986},"labels":[],"label_agreement":null},{"id":"W4301168953","doi":"10.1007/978-1-4939-7131-2_101374","title":"Transportation Systems","year":2018,"lang":"en","type":"book-chapter","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Transport engineering; Engineering","score_opus":0.02696612846317157,"score_gpt":0.26879677089716497,"score_spread":0.2418306424339934,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4301168953","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00002254695,0.00012630476,0.0043053674,0.00010498653,0.0007159841,0.00019782211,0.0000537201,0.0002394183,0.99423385],"genre_scores_gemma":[0.005420956,0.00022037668,0.0003817165,0.00005085665,0.0004872512,0.000004449364,0.00052786455,0.000022744209,0.9928838],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99914545,0.000011433013,0.00021591516,0.00017724706,0.00033385903,0.000116099895],"domain_scores_gemma":[0.9994982,0.000029495994,0.00012721737,0.00009051628,0.00018300925,0.00007160021],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00017886263,0.000110403795,0.00013312757,0.00007864837,0.000219883,0.00005517415,0.00009036418,0.00030593862,0.0051956675],"category_scores_gemma":[0.000005293838,0.0001131009,0.0000570935,0.000023442768,0.00012282871,0.00009554342,3.8531806e-7,0.00008273013,0.0004917152],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000048465804,0.0000026895686,0.00004595004,0.000015420404,0.000018520046,0.0000037052127,0.005485876,0.00025746436,2.3111471e-7,0.9675295,0.02636965,0.00026618928],"study_design_scores_gemma":[0.000062984545,0.000014150648,0.000087478445,0.000075545366,0.00003985658,8.426798e-8,0.0004431577,0.000026403635,5.241291e-7,0.0028149656,0.99627113,0.0001637353],"about_ca_topic_score_codex":0.0005597288,"about_ca_topic_score_gemma":0.0027558522,"teacher_disagreement_score":0.96990144,"about_ca_system_score_codex":0.000038290287,"about_ca_system_score_gemma":0.00012618324,"threshold_uncertainty_score":0.9957137},"labels":[],"label_agreement":null},{"id":"W4302774596","doi":"10.1155/2022/6034369","title":"Modeling Network Capacity for Urban Multimodal Transportation Applications","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","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":"Fundamental Research Funds for the Central Universities; Jiangsu University; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Mode (computer interface); Modal; Mode choice; Computer science; Multimodal transport; Flow network; Transportation planning; Public transport; Transport network; Logit; Network model; Transport engineering; Similarity (geometry); Network planning and design; Trip distribution; Operations research; Mathematical optimization; Artificial intelligence; Engineering; Machine learning; Mathematics; Computer network","score_opus":0.020509564260090315,"score_gpt":0.2845361758723533,"score_spread":0.264026611612263,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4302774596","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2672242,0.00018966266,0.7307074,0.00034282194,0.00057477236,0.0006491482,0.00018444125,0.000058080466,0.00006946932],"genre_scores_gemma":[0.90380013,0.0000802223,0.09488191,0.000084510124,0.00040076752,0.00017744188,0.00049250893,0.000021700986,0.000060797753],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99822545,0.0000797935,0.0007182703,0.0001737589,0.0005528357,0.00024988252],"domain_scores_gemma":[0.99871534,0.00010776695,0.00049390073,0.00008142991,0.00048713273,0.0001144202],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007577267,0.00011624212,0.00021395988,0.00013631136,0.0010712037,0.000022845968,0.0001824346,0.000060285176,0.000052139756],"category_scores_gemma":[0.000019708754,0.00013627518,0.00018857498,0.00045510536,0.00004444959,0.00057087967,4.111266e-7,0.00023838339,4.5015952e-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":[0.00024788512,0.00007864791,0.0020161846,0.000019458634,0.000025730342,0.0000018799167,0.016111847,0.9678853,0.00013175108,0.010457264,0.00011028022,0.0029137593],"study_design_scores_gemma":[0.016980086,0.0019143325,0.121769704,0.0002686749,0.001591901,0.00001051843,0.083278805,0.09776824,0.00029622097,0.04100217,0.6327993,0.002319997],"about_ca_topic_score_codex":0.00010550329,"about_ca_topic_score_gemma":0.0009085427,"teacher_disagreement_score":0.87011707,"about_ca_system_score_codex":0.00012937942,"about_ca_system_score_gemma":0.00020883055,"threshold_uncertainty_score":0.82389414},"labels":[],"label_agreement":null},{"id":"W4303491875","doi":"10.1155/2022/5887985","title":"Multiobjective Approach to the Transit Network Design Problem with Variable Demand considering Transit Equity","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":14,"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; Ministry of Education; Seoul National University; National Research Foundation","keywords":"Computer science; Equity (law); Network planning and design; Mathematical optimization; Trip distribution; Public transport; Multi-objective optimization; Transit (satellite); Transport engineering; Operations research; Engineering; Mathematics; Computer network; Machine learning","score_opus":0.025099649409195752,"score_gpt":0.2726644847764127,"score_spread":0.24756483536721693,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4303491875","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020576587,0.00015208551,0.9768026,0.00063006807,0.00021687768,0.0007472292,0.000020185937,0.000040940227,0.0008134461],"genre_scores_gemma":[0.7377796,0.000031625907,0.26177117,0.00017536325,0.00010124804,0.00005265541,0.000025345968,0.000015981235,0.00004699244],"study_design_codex":"simulation_or_modeling","study_design_gemma":"qualitative","domain_scores_codex":[0.9980292,0.00029567207,0.00046988486,0.00018515329,0.0007322045,0.00028791843],"domain_scores_gemma":[0.998968,0.0001722157,0.00035396044,0.00008306012,0.00030102127,0.00012173838],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016998209,0.0001299647,0.00022449384,0.00009425548,0.001154133,0.000048985225,0.00021158559,0.00004204592,0.000037568025],"category_scores_gemma":[0.000016442073,0.0001038124,0.00007147488,0.00081844366,0.00006212652,0.00048681657,0.0000014335218,0.00033226554,4.2119183e-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":[0.00074366794,0.00007896044,0.00046102208,0.000014398005,0.000054453598,0.000009872608,0.0810996,0.91373444,0.00012970157,0.0023312257,0.00008850232,0.0012541432],"study_design_scores_gemma":[0.027462322,0.008019461,0.17613506,0.0011353812,0.0034813762,0.0002447448,0.577757,0.028262235,0.0014018451,0.02258452,0.14966144,0.003854603],"about_ca_topic_score_codex":0.00010981529,"about_ca_topic_score_gemma":0.0005829446,"teacher_disagreement_score":0.88547224,"about_ca_system_score_codex":0.00011833979,"about_ca_system_score_gemma":0.00037701454,"threshold_uncertainty_score":0.8876775},"labels":[],"label_agreement":null},{"id":"W4306248836","doi":"10.1155/2022/7383396","title":"Optimizing Joint Decisions of Dynamic Pricing and Ticket Allocation for High-Speed Railway with Operators’ Risk Preference","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Natural Science Foundation of Inner Mongolia; National Natural Science Foundation of China","keywords":"Ticket; Computer science; Revenue; Revenue management; Operations research; Mathematical optimization; Dynamic programming; Stochastic programming; Train; Preference; Particle swarm optimization; Dynamic pricing; Microeconomics; Economics; Engineering; Mathematics; Algorithm","score_opus":0.020282495717414785,"score_gpt":0.2733982495096642,"score_spread":0.2531157537922494,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4306248836","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8082014,0.00012567495,0.19099677,0.00017262554,0.00014327047,0.0002775813,0.00005532262,0.000013041988,0.000014338873],"genre_scores_gemma":[0.89241433,0.00038516265,0.107042976,0.000013422494,0.000017989041,0.000011109821,0.00008181173,0.000011588466,0.000021623548],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99872327,0.00008540721,0.0005295454,0.00013469697,0.0004030529,0.00012400524],"domain_scores_gemma":[0.9984116,0.00020718729,0.0007945823,0.00006255155,0.00045609855,0.00006801371],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006852103,0.00008663356,0.00020736802,0.00018016607,0.00048515425,0.000019438523,0.00008978416,0.00003371227,0.000017856202],"category_scores_gemma":[0.00010437698,0.00007983076,0.000045633704,0.00031720055,0.00005914543,0.00048085512,0.0000012599703,0.00015552728,6.020308e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047692368,0.00006252611,0.0027215506,0.000019568002,0.00003620615,0.000002088334,0.035865355,0.9485971,0.0032626267,0.0017393165,0.000004744175,0.007212006],"study_design_scores_gemma":[0.0098751,0.0024183902,0.8821723,0.0006291002,0.0007800205,0.0000087379185,0.08762641,0.008332122,0.0029305876,0.0036574078,0.00091351394,0.00065630896],"about_ca_topic_score_codex":0.00008346772,"about_ca_topic_score_gemma":0.000534314,"teacher_disagreement_score":0.94026494,"about_ca_system_score_codex":0.000074175456,"about_ca_system_score_gemma":0.00020027105,"threshold_uncertainty_score":0.37314633},"labels":[],"label_agreement":null},{"id":"W4306687091","doi":"10.1016/j.cie.2022.108749","title":"Joint optimization of modular vehicle schedule and fair passenger flow control under heterogeneous passenger demand in a rail transit system","year":2022,"lang":"en","type":"article","venue":"Computers & Industrial Engineering","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":25,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ministry of Education and Child Care","funders":"","keywords":"Schedule; Modular design; Joint (building); Rail transit; Automotive engineering; Transit (satellite); Transport engineering; Control (management); Engineering; Flow (mathematics); Computer science; Public transport; Structural engineering; Mathematics","score_opus":0.018886203729305336,"score_gpt":0.20602381703227637,"score_spread":0.18713761330297105,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4306687091","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.34332612,0.00013710928,0.6552168,0.00028273274,0.0005520047,0.0003338128,0.0000183842,0.00011706595,0.00001594498],"genre_scores_gemma":[0.9960717,0.000006908613,0.0037089803,0.000022676859,0.00011657457,0.000025937528,0.00002409807,0.000017233113,0.000005901841],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988428,0.00015154095,0.00032643718,0.00020325824,0.00026737672,0.0002085906],"domain_scores_gemma":[0.999616,0.00009355647,0.00008529787,0.0000850368,0.000040625346,0.00007944607],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004940998,0.00011532008,0.0002403792,0.00017604412,0.00019142442,0.000039650757,0.00009211185,0.00010868845,0.00001620079],"category_scores_gemma":[0.00002240407,0.00013956492,0.000049677885,0.00033320946,0.000028877306,0.00012796749,0.0000125804145,0.00021385221,2.3404205e-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":[0.000034056706,0.00002308819,0.00028201248,0.000020017656,0.000030190342,0.000011383279,0.0019321146,0.99651283,0.00036042946,0.0004773181,0.00001151212,0.00030505814],"study_design_scores_gemma":[0.0018187431,0.000049192706,0.0004846367,0.000061238476,0.000023532524,0.000002824108,0.0007976281,0.99635404,0.00010439894,0.0000029546452,0.00015935779,0.00014146614],"about_ca_topic_score_codex":0.0001317077,"about_ca_topic_score_gemma":0.000016357859,"teacher_disagreement_score":0.65274554,"about_ca_system_score_codex":0.00013897814,"about_ca_system_score_gemma":0.00007116863,"threshold_uncertainty_score":0.5691292},"labels":[],"label_agreement":null},{"id":"W4306950013","doi":"10.1155/2022/6431231","title":"Synergistic Optimization Method for URT Network Train Connection Scheme in Peak and Off-Peak Periods","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","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":"National Natural Science Foundation of China","keywords":"Train; Transfer (computing); Connection (principal bundle); Genetic algorithm; Plan (archaeology); Computer science; Scheme (mathematics); Transfer station; Simulation; Rail network; Urban rail transit; Rail transit; Flow (mathematics); Engineering; Transport engineering; Mathematics","score_opus":0.013662642184959758,"score_gpt":0.3086503610545974,"score_spread":0.29498771886963765,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4306950013","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.251683,0.0005610641,0.7452888,0.00090563664,0.00084503874,0.00048138527,0.00004236823,0.000033059947,0.00015966927],"genre_scores_gemma":[0.74684435,0.0003139453,0.25221884,0.000094863906,0.00018823944,0.000045946956,0.00017443823,0.00001966246,0.00009971432],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9985071,0.00019091807,0.0005878412,0.00016646813,0.00034950505,0.000198197],"domain_scores_gemma":[0.99891716,0.00023694798,0.0004961205,0.00004813688,0.0002242398,0.00007741864],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014058094,0.000103004044,0.00022381089,0.0001988734,0.0005100708,0.00003029784,0.000085650616,0.000059963546,0.000076714656],"category_scores_gemma":[0.00011529077,0.000118478754,0.00007738672,0.00049723184,0.000042157986,0.0003929125,0.0000010011015,0.00019577012,7.758997e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035593926,0.000045999812,0.001687666,0.000019928691,0.000015793856,0.0000065262057,0.0132521875,0.9746441,0.0004335752,0.0040457333,0.000043801127,0.00544871],"study_design_scores_gemma":[0.016480932,0.0023438379,0.42900363,0.00043959013,0.0006017652,0.000029701705,0.13155125,0.32778525,0.00014902823,0.00827491,0.081872694,0.0014674047],"about_ca_topic_score_codex":0.000064179876,"about_ca_topic_score_gemma":0.0007906909,"teacher_disagreement_score":0.6468589,"about_ca_system_score_codex":0.00012318278,"about_ca_system_score_gemma":0.00016567565,"threshold_uncertainty_score":0.48314232},"labels":[],"label_agreement":null},{"id":"W4307178478","doi":"10.1016/j.procs.2022.09.418","title":"Use of modern technologies by public transport passengers during the COVID-19 pandemic","year":2022,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Ministerstwo Edukacji i Nauki","keywords":"Public transport; Pandemic; Coronavirus disease 2019 (COVID-19); Quarter (Canadian coin); European union; Sustainable transport; Purchasing; Business; Emerging technologies; Sample (material); Marketing; Regional science; Transport engineering; Computer science; Sustainability; Geography; Medicine; Engineering; Economic policy","score_opus":0.05812998021781502,"score_gpt":0.2846349444053883,"score_spread":0.22650496418757327,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4307178478","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8124846,0.00016532019,0.17917126,0.006736424,0.00031700992,0.00033479137,0.000039955215,0.00066520105,0.000085442814],"genre_scores_gemma":[0.9966935,0.00006000836,0.002826912,0.0002780227,0.000014959381,0.00004027572,0.0000074279533,0.000004284341,0.00007460417],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99848336,0.00005130645,0.00017798346,0.00028550226,0.0007229391,0.0002789071],"domain_scores_gemma":[0.9994012,0.00012993935,0.00012159225,0.00015826724,0.000103051425,0.00008591386],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.000971997,0.000070161586,0.000085184656,0.00014807809,0.0017738913,0.00008783088,0.0007872168,0.000030129999,0.000015238588],"category_scores_gemma":[0.00015790114,0.000059027967,0.000029712948,0.00140323,0.00090834894,0.0005870787,0.000049921247,0.00015400376,4.7550515e-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":[0.000019567779,0.00009423153,0.7815477,0.000044140794,0.000011193298,0.0000052950804,0.057325065,0.13943993,0.0016376801,0.0048721614,0.00097403623,0.014028969],"study_design_scores_gemma":[0.0025925902,0.00047433854,0.2370359,0.000055967295,0.00009972672,0.00009953632,0.028674271,0.35801208,0.0021084922,0.010554132,0.35781592,0.0024770203],"about_ca_topic_score_codex":0.00018712114,"about_ca_topic_score_gemma":0.00013297613,"teacher_disagreement_score":0.54451185,"about_ca_system_score_codex":0.00013737305,"about_ca_system_score_gemma":0.0007114849,"threshold_uncertainty_score":0.99952567},"labels":[],"label_agreement":null},{"id":"W4307371371","doi":"10.3390/su142013667","title":"Comparison of the Performance of Hybrid Traffic Signal Patterns and Conventional Alternatives When Accounting for Both Pedestrians and Vehicles","year":2022,"lang":"en","type":"article","venue":"Sustainability","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"École de Technologie Supérieure; Group for Research in Decision Analysis","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Intersection (aeronautics); Signal timing; Computer science; SIGNAL (programming language); Interval (graph theory); Real-time computing; Function (biology); Pedestrian; Pedestrian crossing; Simulation; Traffic signal; Transport engineering; Engineering; Mathematics","score_opus":0.018840301830664277,"score_gpt":0.3101180850786196,"score_spread":0.29127778324795534,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4307371371","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9985205,0.000044010183,0.0005416162,0.00047947236,0.000035459463,0.00029023807,0.00006628536,0.000008195349,0.000014228328],"genre_scores_gemma":[0.9997875,0.000003956089,0.0001043405,0.0000070927713,0.000010156975,0.000018301229,0.000010830773,0.0000023790872,0.000055441065],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9993647,0.00010863542,0.00016610888,0.000098323995,0.00017775608,0.00008451769],"domain_scores_gemma":[0.99947524,0.00016368834,0.00014466917,0.00004259592,0.00015840676,0.000015406169],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006054054,0.00003693039,0.00009014963,0.000022103675,0.00044614176,0.000011816449,0.00007570371,0.000009574745,0.000023601808],"category_scores_gemma":[0.00008398504,0.000033093336,0.000027350467,0.00005139533,0.00021397126,0.00008961592,0.000015931244,0.000053213098,4.5615596e-9],"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":[0.000042632375,0.000049987855,0.9644819,0.00013636392,0.0000059074127,6.0257456e-8,0.015138472,0.017067157,0.000007895184,0.0012075836,0.000014649417,0.0018473832],"study_design_scores_gemma":[0.00040100023,0.000107607186,0.9383072,0.000012030611,0.000019623043,2.572657e-7,0.03669666,0.022580508,0.00023163644,0.0012006412,0.0003829915,0.000059792746],"about_ca_topic_score_codex":0.0003096396,"about_ca_topic_score_gemma":0.00009501707,"teacher_disagreement_score":0.026174655,"about_ca_system_score_codex":0.000051509385,"about_ca_system_score_gemma":0.00018052927,"threshold_uncertainty_score":0.3431407},"labels":[],"label_agreement":null},{"id":"W4309985303","doi":"10.1155/2022/2134806","title":"Flexible Scheduling Model of Bus Services between Venues of the Beijing Winter Olympic Games","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","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":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Beijing; Computer science; Simulated annealing; Scheduling (production processes); Genetic algorithm; Service level; Simulation; Operations research; Real-time computing; Transport engineering; Engineering; Operations management; Algorithm; Mathematics","score_opus":0.018489339870113485,"score_gpt":0.28676430355992355,"score_spread":0.2682749636898101,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4309985303","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99000734,0.00027148065,0.009034671,0.00023115535,0.00023458523,0.000081491184,0.000052151816,0.0000097032525,0.0000774044],"genre_scores_gemma":[0.99339503,0.00006854843,0.0063818167,0.000024732291,0.00005263469,0.000002249756,0.000020862879,0.000008071041,0.00004607015],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99876475,0.000062685416,0.00050326757,0.000071604285,0.0005017455,0.00009592423],"domain_scores_gemma":[0.99877125,0.00005883652,0.0008485648,0.00006603735,0.00022408027,0.000031241783],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003826804,0.000059646147,0.00017290881,0.00010165748,0.00022277609,0.000006842763,0.00020497829,0.000030100708,0.00001660604],"category_scores_gemma":[0.00001410039,0.000053365504,0.000114720526,0.00030257655,0.000057881363,0.00035202916,0.000001961661,0.00014624468,7.887097e-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":[0.000058220598,0.000023583381,0.07119222,0.00004357202,0.000022510641,5.5772676e-7,0.033113223,0.89146334,0.0025315057,0.0003794274,0.000001643206,0.0011701841],"study_design_scores_gemma":[0.0023393221,0.00031657462,0.90207607,0.0008164943,0.0004806255,0.0000014472896,0.06615438,0.0036207603,0.016756091,0.0057401787,0.0013796746,0.000318401],"about_ca_topic_score_codex":0.000041042727,"about_ca_topic_score_gemma":0.00009526436,"teacher_disagreement_score":0.8878426,"about_ca_system_score_codex":0.000034973134,"about_ca_system_score_gemma":0.00014423732,"threshold_uncertainty_score":0.21761821},"labels":[],"label_agreement":null},{"id":"W4310818308","doi":"10.28991/cej-2022-08-11-02","title":"Modeling Sustainable Traffic Behavior: Avoiding Congestion at a Stationary Bottleneck","year":2022,"lang":"en","type":"article","venue":"Civil Engineering Journal","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Bottleneck; Traffic congestion; Traffic flow (computer networking); Negative binomial distribution; Statistics; Population; Morning; Mathematics; Simulation; Computer science; Environmental science; Engineering; Transport engineering; Physics; Demography; Computer network","score_opus":0.013474764418754687,"score_gpt":0.2407908692968676,"score_spread":0.2273161048781129,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4310818308","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9167926,0.00048300435,0.08095054,0.0002729635,0.00062734954,0.00013847147,0.000008545457,0.00019540227,0.000531159],"genre_scores_gemma":[0.9971408,0.000070307455,0.0011051276,0.000018890814,0.00014050103,0.000030377996,0.00003659694,0.00001805375,0.0014393402],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988914,0.000092453025,0.00020254958,0.00011043624,0.00041301295,0.000290163],"domain_scores_gemma":[0.9996086,0.000056818637,0.000060372688,0.000049053207,0.00011059909,0.0001145781],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.00097132253,0.00007664489,0.00008160699,0.00017829955,0.0021991709,0.000085813255,0.00011387947,0.000035027962,0.00043643036],"category_scores_gemma":[0.00010553085,0.000096469914,0.00004909732,0.00025355074,0.000013744304,0.0002952447,0.00001479154,0.00036667462,0.0000025996758],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008686274,0.00002091822,0.0004963741,0.00000706855,0.0000065704858,0.00006394533,0.008513575,0.9887485,0.000045133205,0.0015892058,0.0003160937,0.00018395646],"study_design_scores_gemma":[0.00043094502,0.000046963814,0.0015142532,0.000023750377,0.000040985826,0.00009418846,0.015827741,0.96706164,0.0000037775771,0.000054491367,0.014664459,0.00023680695],"about_ca_topic_score_codex":0.00004407191,"about_ca_topic_score_gemma":0.00006887778,"teacher_disagreement_score":0.08034824,"about_ca_system_score_codex":0.00052901276,"about_ca_system_score_gemma":0.00016540676,"threshold_uncertainty_score":0.99909985},"labels":[],"label_agreement":null},{"id":"W4311216223","doi":"10.1177/23998083221141428","title":"Defining archetypes of mixed-use developments using Google Maps API data","year":2022,"lang":"en","type":"article","venue":"Environment and Planning B Urban Analytics and City Science","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"National Research Foundation Singapore","keywords":"Archetype; Workflow; Ontology; Zoning; Computer science; Data science; Plan (archaeology); Urban planning; Database; Engineering; Civil engineering; Geography; Archaeology","score_opus":0.07925645231790814,"score_gpt":0.28945818919157407,"score_spread":0.21020173687366595,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4311216223","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9960814,0.00036284997,0.0027223285,0.00006292059,0.00008496338,0.000062085775,0.000105916675,0.000012224497,0.0005052746],"genre_scores_gemma":[0.9915077,0.000099431156,0.00795687,0.00003483179,0.000011483666,8.0938463e-7,0.00009152652,0.000003414917,0.00029389418],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99887204,0.000046672274,0.00015449894,0.00027890282,0.000462466,0.0001854193],"domain_scores_gemma":[0.99956536,0.000086282875,0.00010948019,0.00013557209,0.00001172708,0.000091601134],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011353543,0.00006729079,0.00009564642,0.00008870394,0.0012609428,0.00008766726,0.00024353317,0.000020172163,0.000033412227],"category_scores_gemma":[0.00007677805,0.00007280901,0.00000935706,0.00025999753,0.00048573693,0.00032890643,0.00015242524,0.00009158352,4.2197226e-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":[0.000007570966,0.000020946696,0.9724606,0.0000045346846,0.000008762156,0.0000030075591,0.007975006,0.016467733,0.00008541716,0.0021918386,0.00022486769,0.00054966134],"study_design_scores_gemma":[0.00042168374,0.00007072089,0.86989063,0.000055052526,0.000089081586,0.0000041968874,0.013198584,0.07014869,0.000103900304,0.00036956978,0.04520204,0.00044582764],"about_ca_topic_score_codex":0.000380171,"about_ca_topic_score_gemma":0.00001789187,"teacher_disagreement_score":0.102570005,"about_ca_system_score_codex":0.000040195748,"about_ca_system_score_gemma":0.00010917294,"threshold_uncertainty_score":0.96982795},"labels":[],"label_agreement":null},{"id":"W4311310676","doi":"10.1155/2022/3442921","title":"The Integration of Multimodal Networks: The Generalized Modal Split and Collaborative Optimization of Transportation Hubs","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Modal; Mathematical optimization; Computer science; Bilevel optimization; Multimodal transport; Optimization problem; Node (physics); Transport engineering; Engineering; Mathematics","score_opus":0.010315708705941371,"score_gpt":0.27710494044432976,"score_spread":0.26678923173838837,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4311310676","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8392435,0.0014468316,0.15649937,0.0012930179,0.0006676215,0.00061926356,0.00013740489,0.000019138004,0.00007387699],"genre_scores_gemma":[0.9777756,0.0021021971,0.01974245,0.000032937896,0.00006150981,0.000024379782,0.00021021828,0.000013013382,0.000037727026],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99788386,0.00029691798,0.000908466,0.00012289916,0.000646885,0.00014096135],"domain_scores_gemma":[0.99733174,0.00030564307,0.001398531,0.000091900525,0.00082131376,0.00005089921],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010593481,0.00011463085,0.0002314858,0.000107663735,0.00075068534,0.000025833235,0.0001815932,0.000057112054,0.00002831136],"category_scores_gemma":[0.000057146084,0.00008417488,0.000100541976,0.0007024157,0.00022588632,0.00050833006,0.0000010844649,0.00021824511,3.2657418e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007166472,0.00004905792,0.0011373466,0.000008373956,0.000040673694,0.0000014456336,0.03769483,0.94490457,0.00058003847,0.008278191,0.000027252838,0.0065615703],"study_design_scores_gemma":[0.010743812,0.0020456663,0.51103795,0.0003237259,0.0008431724,0.0000064182423,0.2969893,0.16065785,0.002719376,0.0031291065,0.010707128,0.0007965159],"about_ca_topic_score_codex":0.00017055018,"about_ca_topic_score_gemma":0.0011194502,"teacher_disagreement_score":0.78424674,"about_ca_system_score_codex":0.000053260457,"about_ca_system_score_gemma":0.00021265393,"threshold_uncertainty_score":0.57737404},"labels":[],"label_agreement":null},{"id":"W4312133933","doi":"10.1155/2022/1339598","title":"Bus Route Design Problem for Rural Tourism Objectives: A Multiobjective Approach","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","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":"Social Science Foundation of Jiangsu Province; Government of Jiangsu Province","keywords":"Tourism; TRIPS architecture; Transport engineering; Popularity; Public transport; Service (business); Rural tourism; Business; Genetic algorithm; Network planning and design; Service quality; Bus network; Quality (philosophy); Computer science; Environmental economics; Operations research; Marketing; Engineering; Tourism geography; Geography; Economics; Telecommunications","score_opus":0.016692335475457076,"score_gpt":0.2812216540361585,"score_spread":0.26452931856070144,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312133933","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.112422675,0.00020668247,0.88472915,0.00030542657,0.00056108966,0.0010969859,0.0000949106,0.0000630983,0.0005200126],"genre_scores_gemma":[0.7962399,0.00005511734,0.20307991,0.00004511233,0.00010753941,0.00011845694,0.000118995085,0.000017325663,0.00021760851],"study_design_codex":"simulation_or_modeling","study_design_gemma":"qualitative","domain_scores_codex":[0.99844134,0.00017979737,0.00049914455,0.00014273533,0.00052234856,0.00021462508],"domain_scores_gemma":[0.9985906,0.00017917479,0.0006313366,0.000055170083,0.00045754306,0.000086180924],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008612962,0.000112588015,0.00021099113,0.0001912961,0.00073793106,0.000028175034,0.00015541303,0.000050790153,0.000025074887],"category_scores_gemma":[0.000048133028,0.00011795189,0.00015357546,0.00039899032,0.000051930398,0.0006883692,8.850806e-7,0.00022610844,2.6832274e-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":[0.0008072582,0.00014472862,0.00075676065,0.00001689108,0.00004656295,0.0000072374105,0.0796995,0.9128261,0.00034965,0.0019562717,0.00010462509,0.0032843864],"study_design_scores_gemma":[0.018046182,0.0038206459,0.2766815,0.00020524442,0.00079509657,0.000036229147,0.6412179,0.0056415196,0.00094619143,0.023498869,0.027689831,0.0014207733],"about_ca_topic_score_codex":0.00008674892,"about_ca_topic_score_gemma":0.00007821081,"teacher_disagreement_score":0.9071846,"about_ca_system_score_codex":0.00021573035,"about_ca_system_score_gemma":0.0003518743,"threshold_uncertainty_score":0.56756437},"labels":[],"label_agreement":null},{"id":"W4312669565","doi":"10.2139/ssrn.4275862","title":"Assessing Trends and Patterns of the Effect of COVID-19 on Public Transit Revenues in the City of Calgary","year":2022,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); Revenue; Public transport; 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Regional science; Transit (satellite); Geography; Business; Transport engineering; Medicine; Engineering; Virology; Outbreak; Finance; Infectious disease (medical specialty)","score_opus":0.020393515190460856,"score_gpt":0.3148937007990829,"score_spread":0.29450018560862207,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312669565","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9956793,0.00028499868,0.0011759815,0.0024856266,0.000040313593,0.000056516128,0.0000064371675,0.0000026029247,0.00026819322],"genre_scores_gemma":[0.9997319,0.00015336368,0.00000474942,0.00004173841,0.000013075636,0.0000024875653,0.0000033124152,0.000002491486,0.000046900943],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9983005,0.000904585,0.00016412376,0.00005539954,0.00031802608,0.0002573934],"domain_scores_gemma":[0.99952054,0.00020707102,0.00018510425,0.0000526237,0.000015487412,0.000019180376],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037325933,0.00003866081,0.00009589902,0.00008852992,0.00030261086,0.000014283287,0.0001721254,0.000021068881,0.000019982423],"category_scores_gemma":[0.000095076604,0.000024789822,0.000050142287,0.00032670115,0.000061232626,0.00008237607,0.000003840632,0.00048365444,9.455949e-9],"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":[0.0000858096,0.00006294657,0.92620975,0.0000279989,0.00003469122,0.0000014316046,0.02578252,0.002575079,0.000105221785,0.021974059,0.000011772636,0.02312872],"study_design_scores_gemma":[0.0019699687,0.0012345546,0.9500315,0.00007458893,0.00010020232,0.000032566953,0.03552941,0.0001933787,0.00021442938,0.009243181,0.001233947,0.00014224662],"about_ca_topic_score_codex":0.0010717205,"about_ca_topic_score_gemma":0.004926454,"teacher_disagreement_score":0.023821779,"about_ca_system_score_codex":0.00013665292,"about_ca_system_score_gemma":0.0006861938,"threshold_uncertainty_score":0.27490768},"labels":[],"label_agreement":null},{"id":"W4312968868","doi":"10.18564/jasss.4923","title":"Agent-Based Model for Urban Administration: A Case Study of Bridge Construction and its Traffic Dispersion Effect","year":2022,"lang":"en","type":"article","venue":"Journal of Artificial Societies and Social Simulation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"Institute for Information and Communications Technology Promotion; Ministry of Science and ICT, South Korea","keywords":"Bridge (graph theory); Dispersion (optics); Administration (probate law); Transport engineering; Computer science; Engineering; Physics; Political science; Medicine","score_opus":0.04798007607853484,"score_gpt":0.33449470786811597,"score_spread":0.28651463178958114,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312968868","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98871917,0.000054924378,0.010323584,0.0003243199,0.00016621135,0.00037264678,0.000023089744,0.000010666805,0.0000053829676],"genre_scores_gemma":[0.99958986,0.0000058599467,0.00018109157,0.00002049835,0.00015758342,0.00000724653,0.000012092348,0.0000061623127,0.000019623294],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989388,0.00019868673,0.00034923072,0.0000918364,0.00033056753,0.00009090521],"domain_scores_gemma":[0.99914646,0.00019702052,0.0003948606,0.000019864083,0.00020104216,0.000040768376],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0007900593,0.00006731852,0.00016864704,0.000060331833,0.0016255644,0.000046971334,0.000031723022,0.000057111847,0.000008630061],"category_scores_gemma":[0.000042293326,0.000071232804,0.00008021636,0.0001228381,0.000077409364,0.0001713863,0.0000045646084,0.000094453855,2.0436028e-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":[0.00047381088,0.00017428808,0.002062437,0.00003383774,0.000027070615,0.0000081874105,0.11745569,0.87352204,0.000039202085,0.00054762256,0.00006116092,0.0055946335],"study_design_scores_gemma":[0.0012216053,0.0011768148,0.0009078728,0.0000066370403,0.00015432209,0.0000072799294,0.100216374,0.8959083,0.000013047082,0.00011920769,0.00016731181,0.000101230464],"about_ca_topic_score_codex":0.00003956403,"about_ca_topic_score_gemma":0.00016884542,"teacher_disagreement_score":0.022386238,"about_ca_system_score_codex":0.00005819693,"about_ca_system_score_gemma":0.00013086724,"threshold_uncertainty_score":0.9996742},"labels":[],"label_agreement":null},{"id":"W4313071422","doi":"10.1007/978-3-031-22832-2","title":"Web and Internet Economics","year":2022,"lang":"en","type":"book","venue":"Lecture notes in computer science","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; The Internet; World Wide Web; Data science","score_opus":0.012784317715647755,"score_gpt":0.24934398859850188,"score_spread":0.23655967088285412,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313071422","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0035436468,0.0004347832,0.9592337,0.0013158052,0.002731353,0.0003396357,0.000026799233,0.0001385155,0.03223575],"genre_scores_gemma":[0.7759623,0.0015968147,0.17647052,0.0054683867,0.0021215128,0.00004039269,0.00026519393,0.000086235166,0.037988648],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9989252,0.000040123276,0.00016477153,0.00042148237,0.00023285052,0.00021557725],"domain_scores_gemma":[0.99944264,0.00021585874,0.00009903455,0.00013300673,0.000038731378,0.00007073282],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006248257,0.00011493653,0.0001468493,0.0002597816,0.00028888043,0.00019188589,0.00044408787,0.00011100076,0.00014616153],"category_scores_gemma":[0.00004608049,0.00012540871,0.0000245752,0.00024920597,0.0005574704,0.00017140889,0.000074111296,0.00029268704,0.000003791579],"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":[0.000015091502,0.00003148816,0.0047135404,0.00003953239,0.000013142174,0.000030749732,0.04729497,0.46884456,0.0000023479015,0.020172557,0.0014169445,0.4574251],"study_design_scores_gemma":[0.0005957419,0.00017332601,0.0018113387,0.00021622707,0.000025852787,0.000010919077,0.000022155942,0.39111355,0.000024194196,0.06599269,0.53891915,0.0010948374],"about_ca_topic_score_codex":0.00014538012,"about_ca_topic_score_gemma":0.0017983005,"teacher_disagreement_score":0.7827632,"about_ca_system_score_codex":0.00027780744,"about_ca_system_score_gemma":0.0011593474,"threshold_uncertainty_score":0.5114019},"labels":[],"label_agreement":null},{"id":"W4316591874","doi":"10.1007/s00291-023-00706-2","title":"An alternative approach to address uncertainty in hub location","year":2023,"lang":"en","type":"article","venue":"OR Spectrum","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Mathematical optimization; Measure (data warehouse); Network planning and design; Flow network; Scale (ratio); Operations research; Data mining; Mathematics","score_opus":0.04089472222849712,"score_gpt":0.3383161528936501,"score_spread":0.297421430665153,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4316591874","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8676671,0.000015580827,0.024701662,0.0059876507,0.00075159885,0.0009736935,0.00003764962,0.0007840588,0.099081],"genre_scores_gemma":[0.9973437,0.000016954256,0.0005538348,0.000100092344,0.00011988984,0.00002920089,0.00010578579,0.0000069804423,0.0017235345],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99925345,0.0000751002,0.00010424917,0.00017624345,0.00020202328,0.0001889241],"domain_scores_gemma":[0.9997463,0.00003277366,0.000030111958,0.00008474921,0.000030936902,0.00007512293],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034335206,0.000050625476,0.000065441556,0.00015348496,0.0001424011,0.000046306755,0.00013780071,0.00004032086,0.000059128062],"category_scores_gemma":[0.00005784752,0.000047564983,0.000010479541,0.0011789594,0.000029487319,0.0001603092,0.0000032712112,0.000055914436,0.000069276546],"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":[0.00006347678,0.00007770027,0.0113801835,0.000007226255,0.0000046403134,0.0000074482746,0.060712837,0.88591653,0.00001885979,0.03950813,0.0008126166,0.0014903524],"study_design_scores_gemma":[0.0015521358,0.00033964802,0.6857734,0.00013689749,0.000021516795,0.0000010757461,0.07388565,0.20077154,0.00043644657,0.007825836,0.028381214,0.0008746452],"about_ca_topic_score_codex":0.004826705,"about_ca_topic_score_gemma":0.015132848,"teacher_disagreement_score":0.68514496,"about_ca_system_score_codex":0.00007408582,"about_ca_system_score_gemma":0.00009022782,"threshold_uncertainty_score":0.84444845},"labels":[],"label_agreement":null},{"id":"W4317425193","doi":"10.18280/mmep.090601","title":"School Mode Choice Classification Model Exploitation Though Artificial Intelligence Classification Application","year":2022,"lang":"en","type":"article","venue":"Mathematical Modelling and Engineering Problems","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":"Artificial neural network; Artificial intelligence; Machine learning; Mode (computer interface); Computer science; Classifier (UML); Selection (genetic algorithm)","score_opus":0.08593136248745621,"score_gpt":0.30040302767293364,"score_spread":0.21447166518547744,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4317425193","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018572967,0.000055755252,0.9788212,0.00075695984,0.00006849433,0.00042656972,0.000009773746,0.00034878388,0.0009394914],"genre_scores_gemma":[0.9272827,0.00007221426,0.07173709,0.00002889125,0.000072866,0.00048866024,0.000084999265,0.000027915925,0.00020465818],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984677,0.000059781498,0.00043665845,0.0003342148,0.00044681103,0.00025482674],"domain_scores_gemma":[0.99929726,0.00016672365,0.00012676718,0.00017509247,0.000093428236,0.00014073112],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00071598066,0.00014067498,0.00014801002,0.00014261401,0.00071061245,0.00011266152,0.00015978844,0.00008727319,0.000030355424],"category_scores_gemma":[0.00011166431,0.0001628915,0.00004004868,0.0003696064,0.00005015504,0.00028922636,0.00001599217,0.0002643454,0.00001381224],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000046266,0.0000475106,0.000020189827,0.0000489382,0.0000044231047,6.835473e-8,0.004490559,0.7018868,0.00040333092,0.29184324,0.000009950861,0.0012404181],"study_design_scores_gemma":[0.000041410407,0.000014652978,0.000035032237,0.0000262578,0.000019911151,6.075529e-7,0.0011862386,0.9146361,0.000019151621,0.08342823,0.00042094884,0.00017151161],"about_ca_topic_score_codex":0.000083644474,"about_ca_topic_score_gemma":0.000009765152,"teacher_disagreement_score":0.90870976,"about_ca_system_score_codex":0.00013526143,"about_ca_system_score_gemma":0.000064263724,"threshold_uncertainty_score":0.6642523},"labels":[],"label_agreement":null},{"id":"W4317555320","doi":"10.1155/2023/9011415","title":"A Formulated Approximated Solution for a Bus Transit System with Peak and Normal Periods","year":2023,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","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":"Weifang University of Science and Technology","keywords":"Transit system; Transit (satellite); Mathematical optimization; Applied mathematics; Computer science; Mathematics; Engineering; Public transport; Transport engineering","score_opus":0.012201616104137458,"score_gpt":0.26659912454347523,"score_spread":0.2543975084393378,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4317555320","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.80836016,0.00007770808,0.19045271,0.00033313414,0.00017919859,0.0003645208,0.00003459442,0.000112184294,0.00008580545],"genre_scores_gemma":[0.9863315,0.00011231824,0.013259613,0.000011813234,0.0000626882,0.00001562232,0.00013874187,0.000015239605,0.000052463212],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9989698,0.000029900822,0.000388406,0.0001139676,0.0003011728,0.00019676861],"domain_scores_gemma":[0.9990901,0.000058139893,0.0003091812,0.000038824506,0.00041163465,0.00009217129],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042194876,0.00009432181,0.00018292667,0.00018894508,0.00033596242,0.00003402313,0.00005583881,0.00007668395,0.0000035425276],"category_scores_gemma":[0.000015193699,0.000083641666,0.00005958646,0.00048710578,0.000055872762,0.0005241988,2.2894882e-7,0.00008412143,5.4037173e-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":[0.0037737542,0.00007909153,0.003997798,0.00056362,0.00014726324,0.00006655548,0.11575926,0.8439389,0.0055656596,0.008131062,0.00006569514,0.017911373],"study_design_scores_gemma":[0.015955042,0.0024007838,0.82523364,0.0014280055,0.0008051696,0.00005105329,0.095926546,0.04776132,0.0007238879,0.00040124296,0.008395359,0.00091795844],"about_ca_topic_score_codex":0.000050375584,"about_ca_topic_score_gemma":0.0006177579,"teacher_disagreement_score":0.82123584,"about_ca_system_score_codex":0.00004700964,"about_ca_system_score_gemma":0.00011771977,"threshold_uncertainty_score":0.3410808},"labels":[],"label_agreement":null},{"id":"W4318224794","doi":"10.1287/trsc.2023.1197","title":"In Memoriam: Bernard Gendron,1966–2022","year":2023,"lang":"en","type":"article","venue":"Transportation Science","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Université du Québec à Montréal","funders":"","keywords":"Download; Library science; Management; Economics; Computer science; World Wide Web","score_opus":0.03042451283329796,"score_gpt":0.34480658009101944,"score_spread":0.3143820672577215,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4318224794","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98199874,0.000022218708,0.0016812201,0.003332828,0.0021739686,0.0002794497,0.000030686482,0.00039987013,0.010081022],"genre_scores_gemma":[0.9963207,0.00015820023,0.0007385615,0.00017455446,0.000117985604,0.000023366732,0.00006540008,0.000008349591,0.0023928604],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9980509,0.00004121197,0.00028159583,0.00032833684,0.00090288563,0.00039506823],"domain_scores_gemma":[0.99946624,0.00006701777,0.00007534969,0.00012414897,0.00013588447,0.00013135541],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016520096,0.00008035137,0.00010139606,0.0004863067,0.00052289857,0.00006707757,0.00031759392,0.00005474581,0.0003239709],"category_scores_gemma":[0.000079597376,0.00008966598,0.000033663004,0.004520275,0.00040055788,0.00100657,0.0000015242596,0.00009783204,0.00018862002],"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":[0.00007090751,0.00012658372,0.4160204,0.00004041014,0.000009724602,0.00020185293,0.28511685,0.072016425,0.0038729843,0.20946547,0.00900446,0.0040539396],"study_design_scores_gemma":[0.00048675938,0.000020057712,0.939397,0.000022048518,0.000009003617,3.0019527e-7,0.008242411,0.0012466807,0.00049222447,0.0020185502,0.04781385,0.00025115343],"about_ca_topic_score_codex":0.0011762244,"about_ca_topic_score_gemma":0.0056942217,"teacher_disagreement_score":0.5233766,"about_ca_system_score_codex":0.00007280846,"about_ca_system_score_gemma":0.0004104251,"threshold_uncertainty_score":0.40217662},"labels":[],"label_agreement":null},{"id":"W4318253295","doi":"","title":"Fort Erie Case Study - Transition from Fixed-Route to On-Demand Transit","year":2023,"lang":"en","type":"paratext","venue":"OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Office of Energy Efficiency; Office of Energy Efficiency and Renewable Energy; U.S. Department of Energy","keywords":"Transit (satellite); Transport engineering; Transition (genetics); Computer science; Public transport; Engineering","score_opus":0.02075483088389698,"score_gpt":0.2792442028194953,"score_spread":0.25848937193559834,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4318253295","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6642867,0.00019644758,0.052511275,0.0012529098,0.004928139,0.003005274,0.014482182,0.00046397673,0.25887313],"genre_scores_gemma":[0.9787121,0.000087506945,0.0008478724,0.0001237319,0.000072173076,0.0000958687,0.016339682,0.000017693017,0.0037033684],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9973424,0.00009557468,0.00094819954,0.00038381308,0.0009440246,0.00028598748],"domain_scores_gemma":[0.99844164,0.000255615,0.0004188247,0.00030281144,0.00035891778,0.0002221813],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007615582,0.0002626396,0.00046774844,0.00051498273,0.00063131115,0.00018721657,0.0002551609,0.0003059572,0.00042069692],"category_scores_gemma":[0.000068447334,0.00026009907,0.00013002337,0.0008963357,0.00036772343,0.0006376225,0.000023019098,0.00016548172,0.0001706839],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002100478,0.0040493747,0.0010023148,0.0007965984,0.0011032961,0.00036206696,0.014080963,0.19299695,0.00017887079,0.024396118,0.74633455,0.01259839],"study_design_scores_gemma":[0.0044121225,0.0028747558,0.028967533,0.0013673976,0.0011162859,0.000039286253,0.006629911,0.00019164315,0.00071815075,0.00039075143,0.9510905,0.0022016498],"about_ca_topic_score_codex":0.002120288,"about_ca_topic_score_gemma":0.004269179,"teacher_disagreement_score":0.31442544,"about_ca_system_score_codex":0.00006446687,"about_ca_system_score_gemma":0.00027102933,"threshold_uncertainty_score":0.9999851},"labels":[],"label_agreement":null},{"id":"W4319438723","doi":"10.3311/pptr.20996","title":"Test Environments to Analyse Methodological Improvements of Cost-benefit Analysis for Transport Interventions","year":2023,"lang":"en","type":"article","venue":"Periodica Polytechnica Transportation Engineering","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Transport Canada","funders":"European Social Fund; Magyarország Kormánya; Hungarian Scientific Research Fund","keywords":"Psychological intervention; Risk analysis (engineering); Test (biology); Cost–benefit analysis; Computer science; Operations research; Transport policy; Environmental economics; Management science; Econometrics; Economics; Business; Transport engineering; Engineering; Psychology; Political science","score_opus":0.07220674434002061,"score_gpt":0.35563581901603747,"score_spread":0.28342907467601686,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319438723","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22490728,0.000026655161,0.7726867,0.00038268353,0.000090900496,0.0008308387,0.0006988921,0.00032606482,0.00004997989],"genre_scores_gemma":[0.9623539,0.000081640545,0.0354512,0.000031649633,0.000025547924,0.00042656242,0.0012617749,0.000022376646,0.00034536154],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99818486,0.00002294235,0.00070646807,0.00034571113,0.0003911004,0.0003489087],"domain_scores_gemma":[0.99907905,0.0002995391,0.00016170669,0.00019344367,0.000070577546,0.00019566891],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00086751336,0.00016513256,0.0003796553,0.0007906575,0.0002204663,0.000019583867,0.00023584822,0.00014304084,0.00015222566],"category_scores_gemma":[0.0001745497,0.00018657434,0.00048308377,0.0023926299,0.00006754149,0.00015009526,0.0000024403892,0.00009617855,0.0000072819066],"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":[0.000046336794,0.00014073862,0.11047879,0.00010115791,0.0003688749,0.0000032873227,0.005826654,0.8687757,0.011169157,0.0016246131,0.00003380849,0.0014308725],"study_design_scores_gemma":[0.0004885331,0.00012462228,0.98184437,0.00006518995,0.0009306924,6.076215e-8,0.0008849681,0.008157005,0.0014725357,0.000029880704,0.005702254,0.000299884],"about_ca_topic_score_codex":0.00030828273,"about_ca_topic_score_gemma":0.0010775287,"teacher_disagreement_score":0.8713656,"about_ca_system_score_codex":0.00005670119,"about_ca_system_score_gemma":0.000041064053,"threshold_uncertainty_score":0.7608281},"labels":[],"label_agreement":null},{"id":"W4319596638","doi":"10.1177/03611981231152248","title":"Transit Benefit Index: A Comprehensive Index for Capturing Externalities in Transit Planning","year":2023,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Transit (satellite); Public transport; Subsidy; Transport engineering; TRIPS architecture; Index (typography); Externality; Unit (ring theory); Mode choice; Cost–benefit analysis; Business; Kilometer; Economics; Engineering; Computer science; Microeconomics; Mathematics","score_opus":0.16018064639296073,"score_gpt":0.43011000681589834,"score_spread":0.2699293604229376,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319596638","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9792671,0.0002943688,0.012216752,0.005396892,0.0006912703,0.0016504474,0.00014778964,0.00010249589,0.00023289137],"genre_scores_gemma":[0.9962817,0.0010064912,0.00075663254,0.00006724955,0.000286983,0.0001812507,0.00007287384,0.0000705809,0.0012762621],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9915541,0.0010521483,0.001644384,0.0005088826,0.0038582087,0.0013823003],"domain_scores_gemma":[0.99342245,0.002225763,0.00042648046,0.00030933754,0.0032062924,0.00040968612],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.005773171,0.00029385602,0.00058440305,0.0023812694,0.0014005849,0.00023602476,0.001072656,0.00035259407,0.00012715059],"category_scores_gemma":[0.00018491934,0.0002659482,0.00045824485,0.0042271498,0.0007713863,0.0009846217,0.0000034540337,0.0018789076,0.000010479833],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002630841,0.00016747524,0.6046909,0.00041765056,0.00014305304,0.00020692451,0.09088472,0.28795797,0.000523235,0.006568254,0.0018634958,0.0039454997],"study_design_scores_gemma":[0.0027494302,0.00023277178,0.9325897,0.0008134968,0.00003869964,2.9230756e-7,0.03648189,0.0014553145,0.0001622391,0.0066338293,0.018558878,0.00028342704],"about_ca_topic_score_codex":0.032209866,"about_ca_topic_score_gemma":0.1657559,"teacher_disagreement_score":0.32789883,"about_ca_system_score_codex":0.0003223489,"about_ca_system_score_gemma":0.00090230326,"threshold_uncertainty_score":0.99997926},"labels":[],"label_agreement":null},{"id":"W4319596756","doi":"10.1177/03611981231152246","title":"Debiased Machine Learning for Estimating the Causal Effect of Urban Traffic on Pedestrian Crossing Behavior","year":2023,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Pedestrian; Copula (linguistics); Spurious relationship; Pedestrian crossing; Computer science; Confounding; Econometrics; Poison control; Crash; Statistics; Transport engineering; Machine learning; Engineering; Mathematics","score_opus":0.10546604705979132,"score_gpt":0.435455391210589,"score_spread":0.3299893441507977,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319596756","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9926196,0.00008238121,0.0021914125,0.0023241907,0.00069615786,0.0018467084,0.00009069355,0.00008250666,0.000066404],"genre_scores_gemma":[0.99699557,0.00016124346,0.0013817808,0.000015997748,0.00030682376,0.00018643895,0.00011190781,0.000058090474,0.00078214734],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9913173,0.0026540083,0.0012822155,0.00035663092,0.0034915681,0.0008982852],"domain_scores_gemma":[0.99045354,0.0061446563,0.0006776041,0.00029083257,0.0021579245,0.00027541557],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.013420957,0.00023479706,0.00045569858,0.00095056504,0.003500267,0.00028070284,0.00091080216,0.00021150823,0.00008514062],"category_scores_gemma":[0.0014467513,0.0001589495,0.0004635816,0.003127432,0.0011603339,0.00041591868,0.000003028704,0.0017622774,0.000009043973],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.004150849,0.00025668068,0.51472455,0.000564348,0.00017728472,0.00008269382,0.04634312,0.40189004,0.0012101819,0.0013698906,0.0035150691,0.025715314],"study_design_scores_gemma":[0.0046853144,0.002965214,0.9487443,0.0009916684,0.0002826714,4.0990713e-7,0.00781271,0.020063572,0.001550775,0.00033114903,0.012195333,0.0003768868],"about_ca_topic_score_codex":0.008117338,"about_ca_topic_score_gemma":0.033942383,"teacher_disagreement_score":0.43401977,"about_ca_system_score_codex":0.00017103009,"about_ca_system_score_gemma":0.00091201015,"threshold_uncertainty_score":0.9984877},"labels":[],"label_agreement":null},{"id":"W4319999521","doi":"10.23977/acss.2023.070104","title":"A simple calculation model for transport of visitors on a closed circuit","year":2023,"lang":"en","type":"article","venue":"Advances in Computer Signals and Systems","topic":"Transportation Planning and Optimization","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":"Simple (philosophy); Realization (probability); Diophantine equation; Set (abstract data type); Kinematics; Mathematical optimization; Work (physics); Applied mathematics; Dynamical systems theory; Computer science; Path (computing); Complex system; Mathematics; Statistical physics; Classical mechanics; Physics; Engineering; Discrete mathematics; Artificial intelligence; Mechanical engineering","score_opus":0.0408267630586304,"score_gpt":0.32559866357243494,"score_spread":0.2847719005138045,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319999521","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21878509,0.00020755267,0.7800061,0.000031131203,0.0002782044,0.00037621663,0.000034244888,0.000059854265,0.00022157762],"genre_scores_gemma":[0.99908733,0.00013090587,0.00051036983,0.00001811174,0.000093829985,0.000030333185,0.000052703024,0.0000058875257,0.00007053594],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99926156,0.000042416712,0.00024985467,0.0001497029,0.00017065591,0.00012583601],"domain_scores_gemma":[0.99959177,0.00018203605,0.00008643327,0.000050146053,0.000056902863,0.000032719152],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004083353,0.00005859503,0.00015059687,0.00010417477,0.00010000297,0.000015040421,0.000054499105,0.00004972697,7.4362123e-7],"category_scores_gemma":[0.0000070428073,0.000057156358,0.00003114789,0.00023211553,0.000029263541,0.00016480657,0.000001306388,0.00002651227,4.7975135e-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":[0.000015273814,0.000012245058,0.013375657,0.0000683995,0.0000038122726,8.641098e-7,0.006664958,0.96761924,0.000018742257,0.008470675,0.00003972815,0.003710433],"study_design_scores_gemma":[0.0003189528,0.000049606857,0.0067076627,0.000095955445,0.0000044471653,6.8562535e-8,0.00022960306,0.9899834,0.000008028884,0.0014124882,0.0011144367,0.00007534266],"about_ca_topic_score_codex":0.00014724652,"about_ca_topic_score_gemma":0.0001997146,"teacher_disagreement_score":0.7803022,"about_ca_system_score_codex":0.00001198348,"about_ca_system_score_gemma":0.000027713784,"threshold_uncertainty_score":0.23307686},"labels":[],"label_agreement":null},{"id":"W4320084520","doi":"10.1007/978-3-031-20719-8_8","title":"Non-proportional Substitution Patterns II: The Probit Model","year":2022,"lang":"en","type":"book-chapter","venue":"Use R!","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; McMaster University","funders":"","keywords":"Multinomial probit; Substitution (logic); Multinomial logistic regression; Econometrics; Probit; Ordered probit; Independence (probability theory); Probit model; Logit; Logistic regression; Mathematics; Economics; Statistics; Computer science","score_opus":0.044881049840660096,"score_gpt":0.26963369659079894,"score_spread":0.22475264675013884,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4320084520","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009343892,0.00013156133,0.03479919,0.0031748947,0.0016381703,0.0016986949,0.0010252775,0.00045309184,0.94773525],"genre_scores_gemma":[0.15786558,0.00039206436,0.00064446946,0.00033334928,0.00029322397,0.000054818745,0.0016504329,0.000033434993,0.8387326],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9989552,0.000013867816,0.00018931003,0.00019920077,0.0005111985,0.00013120352],"domain_scores_gemma":[0.99954414,0.000026764403,0.00015782757,0.00013846152,0.00008969258,0.000043125936],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00022782612,0.00011436335,0.000097563265,0.000058719263,0.0012413294,0.000057640173,0.0001579699,0.0001327047,0.001961829],"category_scores_gemma":[0.000010997436,0.000096994496,0.000080141624,0.000032427623,0.00010425674,0.00019772384,0.000015671474,0.00025436428,0.000025651965],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000957631,0.000014197501,0.0005352971,0.0000080978525,0.000021847864,0.0000073865695,0.004753446,0.113180734,8.990727e-7,0.87907594,0.0021861517,0.00020643018],"study_design_scores_gemma":[0.00019306119,0.000030373303,0.0010410075,0.00007771133,0.00010794921,0.0000017066372,0.00017822282,0.011273428,0.0000020858506,0.010961674,0.975745,0.00038774608],"about_ca_topic_score_codex":0.0003715102,"about_ca_topic_score_gemma":0.0008728036,"teacher_disagreement_score":0.9735589,"about_ca_system_score_codex":0.0001017808,"about_ca_system_score_gemma":0.0003300989,"threshold_uncertainty_score":0.99895054},"labels":[],"label_agreement":null},{"id":"W4320165215","doi":"10.12968/s1356-9252(23)40288-2","title":"Timing is Everything","year":2019,"lang":"en","type":"article","venue":"Traffic Technology International","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Traffic signal; Computer science; Aeronautics; Transport engineering; Engineering; Real-time computing; Telecommunications; Simulation","score_opus":0.01392818165292823,"score_gpt":0.2939817852871392,"score_spread":0.28005360363421095,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4320165215","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9466746,0.000023122755,0.0014508828,0.013401476,0.0009809597,0.000099052486,0.000006612309,0.00060469774,0.03675862],"genre_scores_gemma":[0.9907535,0.000015668687,0.002562633,0.00028954953,0.000056996272,0.000004911117,0.000016512819,0.000005973599,0.0062942677],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993682,0.0000107925025,0.0001192972,0.00015240752,0.0002168551,0.00013248507],"domain_scores_gemma":[0.9997335,0.000027162969,0.00005532815,0.00007888875,0.00008354502,0.000021594942],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00014042563,0.00005127576,0.000059854014,0.00022785705,0.00011854574,0.000027406491,0.00024741128,0.00014288696,0.0015662799],"category_scores_gemma":[0.000041360094,0.00005741385,0.000027721522,0.00022030166,0.00007943642,0.00014970903,0.000007579476,0.00012551462,0.00039359313],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005417898,0.00016403542,0.3018524,0.000014646934,0.0002024113,0.000025983296,0.058264922,0.05375516,0.00043876376,0.48023576,0.013539761,0.091451995],"study_design_scores_gemma":[0.0021137195,0.000100677746,0.029070517,0.00017555773,0.000043149917,0.000019256273,0.026880728,0.035701845,0.00087437045,0.0037108564,0.9004839,0.00082538906],"about_ca_topic_score_codex":0.000023097711,"about_ca_topic_score_gemma":0.000030576746,"teacher_disagreement_score":0.8869442,"about_ca_system_score_codex":0.000051152023,"about_ca_system_score_gemma":0.000055643148,"threshold_uncertainty_score":0.99934644},"labels":[],"label_agreement":null},{"id":"W4320176549","doi":"10.1016/j.trc.2023.104028","title":"Tolls vs tradable permits for managing travel on a bimodal congested network with variable capacities and demands","year":2023,"lang":"en","type":"article","venue":"Transportation Research Part C Emerging Technologies","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Toll; Allocative efficiency; Downtown; Toll road; Road pricing; Congestion pricing; Traffic congestion; Transport engineering; Microeconomics; Economics; Planner; Public transport; Computer science; Business; Engineering","score_opus":0.07816648507436175,"score_gpt":0.3577781183799035,"score_spread":0.27961163330554173,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4320176549","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9330428,0.0003854304,0.028637858,0.022015698,0.00037244637,0.0028201223,0.00028912022,0.0055598463,0.006876639],"genre_scores_gemma":[0.98910856,0.0011809379,0.0064431904,0.000038588332,0.00006241365,0.00051924563,0.00029170507,0.00003369441,0.002321682],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","domain_scores_codex":[0.997918,0.000087369386,0.00026628104,0.00039302013,0.00060735934,0.0007279874],"domain_scores_gemma":[0.9988449,0.0005795964,0.000073709074,0.00016568534,0.00026655672,0.000069523],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.00148402,0.00015302942,0.00021868784,0.00048085547,0.0015044743,0.00010283603,0.00021232727,0.00017920771,0.000021398935],"category_scores_gemma":[0.00014546746,0.0001418149,0.000032863078,0.001779476,0.0006628625,0.00027473655,0.0000035401936,0.0002797969,0.0000044285143],"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":[0.0008171602,0.00008988824,0.045612447,0.0004406495,0.00017298726,0.00004400697,0.03993105,0.16335009,0.00013244529,0.72533834,0.016003206,0.008067704],"study_design_scores_gemma":[0.0064439215,0.0025932018,0.15598656,0.0025126492,0.00026411613,0.000002084708,0.50245094,0.03749879,0.0018376628,0.10085707,0.18738535,0.0021676647],"about_ca_topic_score_codex":0.0005500172,"about_ca_topic_score_gemma":0.00263171,"teacher_disagreement_score":0.6244813,"about_ca_system_score_codex":0.0000396899,"about_ca_system_score_gemma":0.00014796748,"threshold_uncertainty_score":0.99979544},"labels":[],"label_agreement":null},{"id":"W4320472482","doi":"","title":"AN ODE CHARACTERISATION OF MULTI-MARGINAL OPTIMAL TRANSPORT","year":2022,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Ode; Computer science; Applied mathematics; Mathematical optimization; Mathematics; Mathematical economics","score_opus":0.022508560465826416,"score_gpt":0.2778470406430281,"score_spread":0.25533848017720173,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4320472482","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6756216,0.0001832907,0.30477324,0.0027755925,0.00038055147,0.00058374816,0.0003651842,0.00034015588,0.014976625],"genre_scores_gemma":[0.9254228,0.00041744384,0.06644603,0.000036728,0.000028037912,0.00007338177,0.003977634,0.000035240777,0.003562692],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.99364185,0.004230256,0.00058934453,0.0005770679,0.00067716604,0.0002843126],"domain_scores_gemma":[0.9965144,0.0003694005,0.00068724394,0.00088347896,0.0013679739,0.00017754933],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0056714206,0.00022594094,0.00032158947,0.0002239828,0.0006888089,0.00012527738,0.00093184947,0.00023590488,0.0007839911],"category_scores_gemma":[0.00030937153,0.00028316808,0.00017276009,0.0003728553,0.0002937638,0.0002879203,0.00008053631,0.0005239452,0.000005296322],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016445048,0.0037278386,0.12056738,0.00047987732,0.00022141081,0.00002038231,0.63633734,0.0737176,0.003999074,0.118722245,0.00025738458,0.041785005],"study_design_scores_gemma":[0.0028951324,0.0000054496613,0.75824463,0.0021719,0.00042597312,0.000004523238,0.0133349495,0.13603872,0.013742531,0.001094574,0.06969314,0.0023484742],"about_ca_topic_score_codex":0.0067846547,"about_ca_topic_score_gemma":0.004975362,"teacher_disagreement_score":0.63767725,"about_ca_system_score_codex":0.00012533565,"about_ca_system_score_gemma":0.00063853,"threshold_uncertainty_score":0.99996203},"labels":[],"label_agreement":null},{"id":"W4320712626","doi":"10.1155/2023/8275191","title":"Train Service Plan Design under the Condition of Multimodal Rail Transit Systems Integration and Interconnection","year":2023,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","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":"Lanzhou Jiaotong University; State Key Laboratory of Rail Traffic Control and Safety; Beijing Jiaotong University; Natural Science Foundation of Gansu Province; National Natural Science Foundation of China","keywords":"Interconnection; Urban rail transit; Constraint (computer-aided design); Service (business); Computer science; Transport engineering; Rail transit; Plan (archaeology); Train; Mode (computer interface); Engineering; Computer network","score_opus":0.031654224017745265,"score_gpt":0.29624135436666293,"score_spread":0.2645871303489177,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4320712626","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.59202874,0.0001105422,0.40552488,0.0014187678,0.000526362,0.0002840538,0.000026904137,0.000044934866,0.00003482078],"genre_scores_gemma":[0.9973064,0.00042117812,0.0019566019,0.000058553702,0.00008832572,0.000008274282,0.00012541276,0.00001025162,0.00002499704],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9987271,0.00019031053,0.000523679,0.00010020734,0.0003427611,0.00011596857],"domain_scores_gemma":[0.99873996,0.00028839344,0.00045969532,0.000049156846,0.00040969727,0.000053089334],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009197517,0.000089605426,0.00016930094,0.00018810795,0.00020760534,0.000032667245,0.00008139828,0.000086714565,0.000009997113],"category_scores_gemma":[0.00003305795,0.00007175297,0.000054513097,0.0005301858,0.00007284909,0.00066340656,3.2732927e-7,0.00014068802,0.0000010927159],"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":[0.00027803244,0.000025610681,0.00021717568,0.0000367449,0.000034995603,0.0000036631411,0.065087356,0.91753983,0.0069915084,0.006172054,0.00005004657,0.003562976],"study_design_scores_gemma":[0.00466819,0.0006633729,0.6106945,0.0009615054,0.00033595986,0.00001645969,0.34743133,0.024384754,0.0037467738,0.0055257957,0.0011331085,0.00043819498],"about_ca_topic_score_codex":0.00033117534,"about_ca_topic_score_gemma":0.001373305,"teacher_disagreement_score":0.8931551,"about_ca_system_score_codex":0.000035268993,"about_ca_system_score_gemma":0.00009071203,"threshold_uncertainty_score":0.2926001},"labels":[],"label_agreement":null},{"id":"W4321089587","doi":"10.1080/23249935.2023.2178240","title":"Formulation and solution method of bounded path size stochastic user equilibrium models – consistently addressing route overlap and unrealistic routes","year":2023,"lang":"en","type":"article","venue":"Transportmetrica A Transport Science","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Bounded function; Uniqueness; Robustness (evolution); Computation; Path (computing); Computer science; Mathematical optimization; Set (abstract data type); Mathematics; Algorithm","score_opus":0.0628842251296359,"score_gpt":0.33572145465016273,"score_spread":0.2728372295205268,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321089587","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6198814,0.00009518504,0.3784894,0.00023296087,0.00011571242,0.00040513094,0.00013502831,0.00022781605,0.00041740193],"genre_scores_gemma":[0.97575825,0.000108992266,0.023836901,0.000036045032,0.000020584945,0.00001835219,0.00009679878,0.000018862693,0.00010522369],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99701613,0.00007513116,0.00062223297,0.0006261478,0.0010831015,0.0005772447],"domain_scores_gemma":[0.998361,0.00054499495,0.000267489,0.00020077798,0.00032221052,0.00030351125],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020893721,0.00022130243,0.00038188347,0.0006569831,0.00075309316,0.00010113537,0.00024031838,0.0001430872,0.000010157634],"category_scores_gemma":[0.0001830763,0.00022776751,0.000085580206,0.004022273,0.0009913096,0.0014013239,0.000009369832,0.00014796399,6.698879e-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":[0.00044655797,0.00024310981,0.25753286,0.00050163467,0.00009328305,0.000050496903,0.06800605,0.41056743,0.01098342,0.2442385,0.000010883745,0.007325767],"study_design_scores_gemma":[0.0011700087,0.00010435782,0.8080746,0.00020556284,0.00027390322,0.000004332003,0.0018567466,0.17706619,0.00024381804,0.010480085,0.00004199111,0.00047843222],"about_ca_topic_score_codex":0.0021610167,"about_ca_topic_score_gemma":0.0004922547,"teacher_disagreement_score":0.5505417,"about_ca_system_score_codex":0.000070886614,"about_ca_system_score_gemma":0.000498572,"threshold_uncertainty_score":0.9288089},"labels":[],"label_agreement":null},{"id":"W4322484897","doi":"10.1016/b978-1-77467-022-4.50007-5","title":"TRANSPORTATION AND STORAGE","year":2023,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Nuclear Laboratories","funders":"","keywords":"Transport engineering; Computer science; Engineering","score_opus":0.02254504470525769,"score_gpt":0.2646487285052639,"score_spread":0.2421036838000062,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4322484897","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00010521257,0.00016024272,0.000037980266,0.0001409141,0.00033436937,0.00025418343,0.00007995553,0.0002532825,0.99863386],"genre_scores_gemma":[0.002210388,0.00042663133,0.00022575307,0.00008264435,0.00017557951,0.0000111005975,0.00021947734,0.000045748267,0.99660265],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9990716,0.000019123581,0.00021808731,0.00023929695,0.00030317798,0.00014872501],"domain_scores_gemma":[0.9995488,0.00005902462,0.00012648813,0.00009832639,0.00007043763,0.000096929296],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025093133,0.00014777126,0.00018223118,0.00011212437,0.00028351438,0.000046513505,0.000077868746,0.0002636787,0.00012018921],"category_scores_gemma":[0.00001031513,0.0001641646,0.00006208437,0.000017073862,0.00015382234,0.00005397638,0.0000017843163,0.00017598331,0.00007485241],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008774309,0.0000021195747,0.000075048876,0.000049494745,0.000036406702,0.000033219905,0.015530793,0.000059933038,0.000001203009,0.15766346,0.00026838257,0.8262712],"study_design_scores_gemma":[0.00011174114,0.000012444063,0.0008180767,0.00014570229,0.000070294445,1.7206229e-7,0.00017207075,0.000006145779,5.618604e-7,0.008497829,0.9899547,0.00021026448],"about_ca_topic_score_codex":0.000010693563,"about_ca_topic_score_gemma":0.0018707662,"teacher_disagreement_score":0.9896863,"about_ca_system_score_codex":0.000027747592,"about_ca_system_score_gemma":0.00010927465,"threshold_uncertainty_score":0.6694438},"labels":[],"label_agreement":null},{"id":"W4323342243","doi":"10.5267/j.ijiec.2023.2.003","title":"Optimization for bi-objective express transportation network design under multiple topological structures","year":2023,"lang":"en","type":"article","venue":"International Journal of Industrial Engineering Computations","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":"Sorting; Mathematical optimization; Multi-objective optimization; Computer science; Pareto principle; Sensitivity (control systems); Ranking (information retrieval); Network planning and design; Dominance (genetics); Network topology; Integer programming; Topology (electrical circuits); Engineering; Algorithm; Mathematics; Artificial intelligence","score_opus":0.06511255150463167,"score_gpt":0.3136093368642934,"score_spread":0.2484967853596617,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4323342243","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018818442,0.000018546281,0.9766957,0.0010090232,0.0030034217,0.0002633667,0.00004885983,0.00012388422,0.000018735424],"genre_scores_gemma":[0.8888705,0.000036271886,0.10910717,0.00004401469,0.0016472217,0.000017155899,0.00022891667,0.000017157532,0.000031601256],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99873567,0.000078064426,0.0004572485,0.00011733484,0.00042701178,0.0001846977],"domain_scores_gemma":[0.99781716,0.001044091,0.00027793227,0.00003761562,0.0007396954,0.00008351106],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005660187,0.00010150524,0.00014932186,0.00030699,0.00020915983,0.000101819576,0.0002259338,0.00015138304,0.000036413017],"category_scores_gemma":[0.00064399326,0.000104445244,0.00010528171,0.00044895298,0.000041404794,0.00031225002,0.000003483783,0.00017098301,0.0000012929984],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009231655,0.000018013272,0.0005155054,0.0000016122174,0.00009523705,0.0000056390754,0.0016762572,0.9870528,0.000025524201,0.008263401,0.0011830258,0.00107065],"study_design_scores_gemma":[0.0025773125,0.00014556469,0.0063638138,0.0001248981,0.00007722759,0.000004456977,0.0018066084,0.9802163,0.000114425144,0.00411491,0.0041968613,0.0002575947],"about_ca_topic_score_codex":0.000047146503,"about_ca_topic_score_gemma":0.00001670406,"teacher_disagreement_score":0.87005204,"about_ca_system_score_codex":0.00010872884,"about_ca_system_score_gemma":0.00021074379,"threshold_uncertainty_score":0.42591533},"labels":[],"label_agreement":null},{"id":"W4323538973","doi":"10.1139/cjce-2022-0320","title":"Maximizing synchronous transfer between metro and buses considering connection level","year":2023,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Simulated annealing; Public transport; Transfer (computing); Transfer station; Metro station; Computer science; Synchronization (alternating current); Arrival time; Real-time computing; Simulation; Transport engineering; Engineering; Computer network; Algorithm","score_opus":0.03837235787477098,"score_gpt":0.24227564183706637,"score_spread":0.2039032839622954,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4323538973","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8810005,0.00047573072,0.11618799,0.00071127166,0.00064687495,0.000075311,0.000024160046,0.00008205715,0.0007960987],"genre_scores_gemma":[0.9992714,0.00006215189,0.000437688,0.000014800045,0.0001690933,7.469769e-7,0.000003946721,0.000011989252,0.000028201928],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9993979,0.00001904177,0.00018831588,0.00006271122,0.00011630248,0.00021573906],"domain_scores_gemma":[0.9994227,0.00013239506,0.00003204952,0.00002961543,0.00007835008,0.00030486778],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040160722,0.00006297284,0.00012551989,0.00041228742,0.00021334301,0.00007140657,0.000056292556,0.00005537599,0.000039070528],"category_scores_gemma":[0.000191835,0.000072680974,0.000032614167,0.0003232258,0.000035751378,0.00022251128,8.7553724e-7,0.00011945139,0.000001649986],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005663561,0.0000029352525,0.2163407,0.00009309743,0.00015537694,0.00023371693,0.030273717,0.7435555,0.00034574812,0.0035881195,0.00063064875,0.004774796],"study_design_scores_gemma":[0.001956226,0.00017604946,0.8893818,0.0010963321,0.0003126266,0.00008072505,0.019995026,0.01212284,0.00077341974,0.0005677652,0.07246893,0.0010682128],"about_ca_topic_score_codex":0.0037388324,"about_ca_topic_score_gemma":0.22451739,"teacher_disagreement_score":0.7314326,"about_ca_system_score_codex":0.000082000566,"about_ca_system_score_gemma":0.00034888412,"threshold_uncertainty_score":0.78963315},"labels":[],"label_agreement":null},{"id":"W4324351621","doi":"10.1155/2023/7864340","title":"Modeling the Traveler’s Route Choice Behavior under Unexpected Accidents","year":2023,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","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":"National Natural Science Foundation of China","keywords":"Regret; Traffic congestion; Bounded rationality; Transport engineering; Computer science; Perception; Simulation; Operations research; Psychology; Engineering","score_opus":0.03553879118474401,"score_gpt":0.33700990787645374,"score_spread":0.30147111669170973,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4324351621","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9762257,0.00006323474,0.021718137,0.0009815778,0.0006521619,0.00017640174,0.000010615189,0.00008242502,0.00008978507],"genre_scores_gemma":[0.9977839,0.00032100352,0.0012923455,0.00007348006,0.00017798696,0.000010181645,0.00008836946,0.0000152736,0.00023746859],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99854416,0.00006689345,0.00050034834,0.00010901865,0.00058124674,0.00019834908],"domain_scores_gemma":[0.99909276,0.00010693233,0.00027671971,0.00007624462,0.00036464422,0.00008268778],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043100788,0.0000916345,0.00013887057,0.00015891959,0.00038761992,0.000047831123,0.0001884195,0.00007275105,0.00003088721],"category_scores_gemma":[0.000056023157,0.000074236494,0.000108075415,0.0007350392,0.000041988365,0.0006919322,5.868788e-7,0.00019232406,0.0000061992473],"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":[0.000040764375,0.000026182051,0.010073918,0.0000037324533,0.000019063611,0.000012589476,0.017731914,0.96863174,0.00048980187,0.0008277367,0.000035377507,0.0021071567],"study_design_scores_gemma":[0.00096941186,0.000046879228,0.9717739,0.00006557704,0.0001491784,0.0000014200036,0.02219395,0.0027397182,0.00008470871,0.0007110917,0.0011149407,0.0001492321],"about_ca_topic_score_codex":0.00025258434,"about_ca_topic_score_gemma":0.002908045,"teacher_disagreement_score":0.965892,"about_ca_system_score_codex":0.000057849313,"about_ca_system_score_gemma":0.00014642079,"threshold_uncertainty_score":0.30272764},"labels":[],"label_agreement":null},{"id":"W4327568934","doi":"10.1155/2023/7686208","title":"Optimization for Metro Operation Scheme of Suburban Lines: A New Method for Dealing with the Imbalanced Passenger Flow","year":2023,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","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":"Shanghai Office of Philosophy and Social Science; Jilin Office of Philosophy and Social Science","keywords":"Train; Flow (mathematics); Transport engineering; Scheme (mathematics); Computer science; Megacity; Set (abstract data type); Matching (statistics); Grid; Operations research; Engineering","score_opus":0.021282718640468615,"score_gpt":0.3360721278047874,"score_spread":0.3147894091643188,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4327568934","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.053136192,0.00011605121,0.9438259,0.0020488556,0.00020941193,0.00057371607,0.00003790879,0.000039192473,0.000012767404],"genre_scores_gemma":[0.19323283,0.000240555,0.8057915,0.000050592236,0.00022482392,0.0000315711,0.00030211447,0.000022396805,0.000103607046],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99878615,0.000046144785,0.00052482204,0.00012985576,0.00034841563,0.00016459347],"domain_scores_gemma":[0.99794245,0.00041976676,0.00062326255,0.000070536975,0.0008763425,0.000067629815],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009225372,0.00010407114,0.00022553028,0.00013956132,0.0002904679,0.0000310108,0.00011439924,0.000073919146,0.000013936316],"category_scores_gemma":[0.00013128418,0.0000779197,0.00012232948,0.00043149266,0.00003399229,0.0005300983,3.7993905e-7,0.000079445075,1.8588757e-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":[0.0006371761,0.000015895697,0.000583651,0.000040048908,0.00004762712,6.7003026e-7,0.010372204,0.9790594,0.001962871,0.0020269717,0.00013921977,0.0051142625],"study_design_scores_gemma":[0.017772295,0.0024349522,0.044297703,0.00076515594,0.001256422,0.0000030046858,0.04863656,0.841469,0.011739105,0.0026467848,0.028009256,0.000969769],"about_ca_topic_score_codex":0.000041660514,"about_ca_topic_score_gemma":0.0008503358,"teacher_disagreement_score":0.14009665,"about_ca_system_score_codex":0.000031394433,"about_ca_system_score_gemma":0.00023199603,"threshold_uncertainty_score":0.31774732},"labels":[],"label_agreement":null},{"id":"W4327965788","doi":"10.1016/j.trc.2023.104085","title":"End-to-end learning of user equilibrium with implicit neural networks","year":2023,"lang":"en","type":"article","venue":"Transportation Research Part C Emerging Technologies","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":36,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"General Motors of Canada; U.S. Department of Transportation","keywords":"Artificial neural network; Computer science; Artificial intelligence; End-to-end principle; Dead end; Human–computer interaction; Psychology; Social psychology","score_opus":0.0589451202395959,"score_gpt":0.3736704304841456,"score_spread":0.3147253102445497,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4327965788","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97943264,0.00006716279,0.010359695,0.0057567894,0.00011687328,0.0005458661,0.000025695983,0.0028528902,0.0008423732],"genre_scores_gemma":[0.99729776,0.00024876464,0.0010661101,0.000012840917,0.000034969875,0.000117696,0.00021035236,0.000027092874,0.0009844094],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99746424,0.00015324638,0.00036008438,0.0003594585,0.0009582368,0.00070472196],"domain_scores_gemma":[0.9988032,0.00034652808,0.00010796939,0.0002230108,0.00043101353,0.00008828385],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013985072,0.00013494176,0.00020578956,0.00075370976,0.00057724526,0.000055897846,0.0003926348,0.00017235929,0.00010117839],"category_scores_gemma":[0.00025418663,0.00012790995,0.00005188279,0.003857452,0.0005191306,0.00029167702,0.0000117871095,0.00050044525,0.000017480377],"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":[0.000113555405,0.000029243267,0.17515847,0.000046504254,0.00003852822,0.00002989086,0.009369331,0.78272945,0.00058457535,0.02159444,0.0020155478,0.008290482],"study_design_scores_gemma":[0.0018680751,0.0012917543,0.57902217,0.00066233875,0.00010748138,0.0000010057983,0.18795754,0.06668691,0.0047209505,0.0020977468,0.15417406,0.0014099391],"about_ca_topic_score_codex":0.0011501935,"about_ca_topic_score_gemma":0.0022579536,"teacher_disagreement_score":0.7160425,"about_ca_system_score_codex":0.000033113072,"about_ca_system_score_gemma":0.00010338788,"threshold_uncertainty_score":0.5216017},"labels":[],"label_agreement":null},{"id":"W4360584302","doi":"10.1109/access.2023.3260727","title":"Optimal Design of Integrated Semi-Flexible Transit Services in Low-Demand Conditions","year":2023,"lang":"en","type":"article","venue":"IEEE Access","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Headway; Paratransit; Schedule; Computer science; Public transport; Multi-objective optimization; Transit (satellite); Pareto principle; Operations research; Service (business); Mathematical optimization; Operator (biology); Transport engineering; Simulation; Engineering; Economics; Mathematics","score_opus":0.04304197504751441,"score_gpt":0.3482350662882809,"score_spread":0.3051930912407665,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4360584302","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9445283,0.000023763336,0.05304066,0.00027685933,0.0002171761,0.00024471886,0.00007115131,0.00021928176,0.0013781221],"genre_scores_gemma":[0.9989114,0.000094367875,0.00035636884,0.000053396223,0.000025847981,0.000021535112,0.00014427867,0.0000075535163,0.0003852638],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99920505,0.000099445104,0.00020621615,0.00013367542,0.0001850165,0.00017061402],"domain_scores_gemma":[0.9996114,0.00011653712,0.00006767818,0.00006646785,0.00009292774,0.000045020992],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003765258,0.00006489887,0.0001175955,0.00019756382,0.00014057796,0.00006203913,0.000228439,0.00008091084,0.00019036696],"category_scores_gemma":[0.000012828268,0.00006643985,0.000024974423,0.0011552535,0.000075855016,0.0004895244,0.0000027537058,0.00007014486,0.000029733932],"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":[0.000028232278,0.000033293127,0.016321003,0.000042435648,0.000009278026,0.000007509833,0.011245491,0.9710955,0.00036536274,0.0003354409,0.00044957182,0.00006685513],"study_design_scores_gemma":[0.004755942,0.00021030194,0.5437876,0.0019760467,0.00018467916,0.0000019267407,0.04362484,0.35296476,0.041327693,0.0059441486,0.0037994178,0.0014226194],"about_ca_topic_score_codex":0.0019692655,"about_ca_topic_score_gemma":0.0011449187,"teacher_disagreement_score":0.61813074,"about_ca_system_score_codex":0.000020111329,"about_ca_system_score_gemma":0.00012145649,"threshold_uncertainty_score":0.29769534},"labels":[],"label_agreement":null},{"id":"W4360933718","doi":"","title":"NREL On-Demand Transit Research and Fort Erie Case Study","year":2023,"lang":"en","type":"paratext","venue":"OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Office of Energy Efficiency; Office of Energy Efficiency and Renewable Energy; U.S. Department of Energy","keywords":"Transit (satellite); Transport engineering; Computer science; Aeronautics; Engineering; Public transport","score_opus":0.04851171788305109,"score_gpt":0.33618467518134965,"score_spread":0.28767295729829856,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4360933718","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.47194973,0.00046543375,0.0033099367,0.00067569717,0.0022467603,0.0021081632,0.0023591623,0.000228934,0.51665616],"genre_scores_gemma":[0.9832036,0.00037620385,0.000297984,0.000023493107,0.00004478781,0.000074555006,0.0037502802,0.000014067074,0.012215024],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9972424,0.0001283053,0.0007735461,0.0003452434,0.0011806706,0.00032986575],"domain_scores_gemma":[0.99809647,0.00046704474,0.00033682885,0.00027131545,0.00064192264,0.00018642604],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002183376,0.00019736742,0.0003758864,0.0007061361,0.0010278791,0.00023686136,0.00022820332,0.00029410076,0.00017809265],"category_scores_gemma":[0.00014379974,0.00018569606,0.0000632365,0.0010479057,0.0012686582,0.00056279066,0.000049258433,0.0002580632,0.00008533566],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012070208,0.0037899024,0.0031982257,0.0014975716,0.0006234036,0.0003257803,0.0054272455,0.013605991,0.000052193347,0.11627015,0.845196,0.008806515],"study_design_scores_gemma":[0.0032340593,0.0033822202,0.024593612,0.0009449277,0.00033753345,0.00008791954,0.0070405942,0.00009110543,0.00019618719,0.00071664376,0.9581067,0.0012685219],"about_ca_topic_score_codex":0.0008285158,"about_ca_topic_score_gemma":0.0018687816,"teacher_disagreement_score":0.5112539,"about_ca_system_score_codex":0.00004525903,"about_ca_system_score_gemma":0.00032028268,"threshold_uncertainty_score":0.79057187},"labels":[],"label_agreement":null},{"id":"W4361294477","doi":"10.1016/j.tra.2023.103649","title":"What do users want from transit? Qualitative analysis of current and potential users' perceptions","year":2023,"lang":"en","type":"article","venue":"Transportation Research Part A Policy and Practice","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Grounded theory; Latent Dirichlet allocation; Public transport; Perception; Topic model; Service quality; Quality (philosophy); Qualitative research; Service (business); Computer science; Marketing; Knowledge management; Business; Sociology; Psychology; Transport engineering; Engineering; Information retrieval","score_opus":0.17998013534709978,"score_gpt":0.5343680665031205,"score_spread":0.35438793115602074,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4361294477","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9795029,0.00057800487,0.003493373,0.01465781,0.0001605231,0.00038962517,0.00079481065,0.00009243325,0.00033051838],"genre_scores_gemma":[0.9700842,0.028096374,0.00037926302,0.000065432185,0.00009722042,0.000033011645,0.0010258286,0.00000964758,0.00020904401],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.99675924,0.0014264921,0.00035919514,0.0003129582,0.0008260578,0.00031606667],"domain_scores_gemma":[0.99611664,0.0029447721,0.00013035521,0.000117164614,0.0004850046,0.00020606766],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029460848,0.00010240552,0.00022391892,0.0008439645,0.00066568743,0.00020566408,0.00009089746,0.00009828635,0.0001717126],"category_scores_gemma":[0.0006549087,0.00010688711,0.00007838528,0.0029666217,0.0004985725,0.0015316785,0.0000025986467,0.00024828603,0.000010224373],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040537893,0.000111718124,0.005758418,0.000048143716,0.00050871103,0.000010401119,0.949022,0.008337609,0.00019685001,0.030318681,0.00052214134,0.0047599613],"study_design_scores_gemma":[0.00064923085,0.00007881319,0.28267294,0.00011244696,0.00093019183,1.674205e-7,0.67800343,0.0011776255,0.00001567556,0.0018940851,0.034250025,0.00021540516],"about_ca_topic_score_codex":0.014092126,"about_ca_topic_score_gemma":0.0090653375,"teacher_disagreement_score":0.2769145,"about_ca_system_score_codex":0.00002675618,"about_ca_system_score_gemma":0.0002608293,"threshold_uncertainty_score":0.9924731},"labels":[],"label_agreement":null},{"id":"W4362661197","doi":"10.1155/2023/3400555","title":"Stimulus Perception in Long-Distance Railway Mode Choice","year":2023,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","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":"Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"TRIPS architecture; Perception; Mode choice; Transport engineering; Discrete choice; Stimulus (psychology); Travel behavior; Value of time; Econometrics; Regret; Computer science; Revealed preference; Context (archaeology); Operations research; Economics; Travel time; Geography; Engineering; Public transport; Psychology; Cognitive psychology","score_opus":0.014867957424912008,"score_gpt":0.32145682061719255,"score_spread":0.30658886319228057,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4362661197","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.966452,0.000070155584,0.03183373,0.0006294002,0.0005201395,0.00014659703,0.000014404231,0.00007226197,0.00026130973],"genre_scores_gemma":[0.99561983,0.0006075047,0.003130301,0.000041322823,0.00013504438,0.000004856559,0.000114589486,0.000012466315,0.00033406788],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99863225,0.0000651046,0.00051406573,0.00012474725,0.0004610034,0.00020281106],"domain_scores_gemma":[0.9991935,0.00011560994,0.00031027535,0.00005878061,0.00023782384,0.000084004714],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046938498,0.00008652246,0.0001648594,0.00030069615,0.00013941793,0.000023562556,0.000113112656,0.00007980969,0.000052059913],"category_scores_gemma":[0.00008847218,0.000091507565,0.000073815434,0.00088735076,0.000050949468,0.000892772,3.778685e-7,0.0001736901,0.000010699835],"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":[0.00012163984,0.000038652037,0.065672085,0.000020952875,0.0000067068213,0.000038660823,0.029083882,0.8962007,0.00060239906,0.0007248776,0.000094938,0.0073944908],"study_design_scores_gemma":[0.00095492275,0.000047307585,0.9894022,0.00011449856,0.000017805229,4.950697e-7,0.005620672,0.00073591585,0.000027192213,0.00044680014,0.0025242642,0.000107959815],"about_ca_topic_score_codex":0.00021739064,"about_ca_topic_score_gemma":0.009368623,"teacher_disagreement_score":0.9237301,"about_ca_system_score_codex":0.0001054398,"about_ca_system_score_gemma":0.00012057792,"threshold_uncertainty_score":0.52279115},"labels":[],"label_agreement":null},{"id":"W4366203707","doi":"10.1155/2023/7651100","title":"Dynamic Path Optimization Based on Improved Ant Colony Algorithm","year":2023,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Jiangsu University","keywords":"Ant colony optimization algorithms; Path (computing); Algorithm; Ant colony; Computer science; Shortest path problem; Mathematical optimization; Mathematics","score_opus":0.0075683998966494,"score_gpt":0.27779582787752666,"score_spread":0.27022742798087723,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366203707","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.090846114,0.00005593897,0.90416896,0.001830779,0.0017408355,0.00046916198,0.00010393212,0.00027416498,0.0005101223],"genre_scores_gemma":[0.8653178,0.00043982372,0.13321248,0.00016654351,0.00010064471,0.000010488056,0.00050055253,0.000027932712,0.00022369802],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985449,0.000074417396,0.00050463155,0.00014774717,0.00051683985,0.00021148262],"domain_scores_gemma":[0.9987718,0.00014079924,0.00048938714,0.00007923817,0.00040522846,0.00011353559],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00055832916,0.000114040085,0.00018190575,0.00032626517,0.00026299406,0.00003407183,0.000117076896,0.00009942901,0.00004654249],"category_scores_gemma":[0.000079836034,0.00011321043,0.00010733986,0.0008179823,0.00005251643,0.00047665014,3.6053922e-7,0.00016112467,0.0000052819014],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013781621,0.00005487789,0.0004254908,0.00001060119,0.000010346857,0.00002762142,0.003821759,0.9862786,0.00018978913,0.00011933907,0.00006564905,0.008858108],"study_design_scores_gemma":[0.0031043186,0.0006561176,0.09844498,0.00023995092,0.000105528,0.0000010561226,0.0058260118,0.8867849,0.00011283081,0.0003332393,0.0040505547,0.00034052887],"about_ca_topic_score_codex":0.00002573681,"about_ca_topic_score_gemma":0.00013697396,"teacher_disagreement_score":0.7744717,"about_ca_system_score_codex":0.00010153635,"about_ca_system_score_gemma":0.00024151882,"threshold_uncertainty_score":0.46165875},"labels":[],"label_agreement":null},{"id":"W4366448446","doi":"10.1155/2023/6987787","title":"Collaborative Optimization Model for the Design and Operation of Feeder Bus Routes Based on Urban Metro","year":2023,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Fuzhou University","keywords":"Particle swarm optimization; Range (aeronautics); Computer science; Mode (computer interface); Transport engineering; Scheme (mathematics); Public transport; Bus network; Engineering; Mathematical optimization; Algorithm; Control bus","score_opus":0.021865863283228645,"score_gpt":0.29910859451740573,"score_spread":0.2772427312341771,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366448446","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024782896,0.00008369467,0.9733687,0.0010660083,0.00015467718,0.0004667548,0.000035229365,0.000023729937,0.000018288403],"genre_scores_gemma":[0.85892624,0.00026015064,0.14054807,0.00006250126,0.0000440622,0.000021490943,0.000080803686,0.000009796014,0.00004691584],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99899054,0.00007598058,0.0003917907,0.00009970133,0.0003364819,0.0001055137],"domain_scores_gemma":[0.998242,0.0005812603,0.0004070555,0.00005108117,0.000674937,0.00004363961],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00078772515,0.000081759325,0.00014830403,0.00017807544,0.00029718003,0.000032777236,0.00007392944,0.000059216018,0.000005151979],"category_scores_gemma":[0.00014217723,0.0000638969,0.000051729978,0.0005138087,0.00006354221,0.0004478491,2.8565563e-7,0.00006543487,1.72041e-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":[0.0004137224,0.000024457519,0.0006420345,0.0000130860735,0.00001840811,5.721517e-7,0.016498012,0.9800257,0.00012446842,0.0014257312,0.000099339435,0.00071445556],"study_design_scores_gemma":[0.0010508733,0.00019511282,0.009344472,0.000050489176,0.000087317494,5.2277343e-8,0.0059480355,0.9825645,0.00032864622,0.00024262379,0.000113197,0.00007469865],"about_ca_topic_score_codex":0.0000110244355,"about_ca_topic_score_gemma":0.00009886524,"teacher_disagreement_score":0.83414334,"about_ca_system_score_codex":0.000029825667,"about_ca_system_score_gemma":0.00021418468,"threshold_uncertainty_score":0.26056397},"labels":[],"label_agreement":null},{"id":"W4366726875","doi":"10.1155/2023/1974771","title":"A Collaborative Transit Signal Priority Method Considering Gaming among Traffic Participants","year":2023,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","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":"National Natural Science Foundation of China","keywords":"Weighting; Computer science; Signal timing; Robustness (evolution); Genetic algorithm; Bus priority; Sorting; Traffic congestion; Optimization problem; Mathematical optimization; Operations research; Real-time computing; Public transport; Engineering; Traffic signal; Transport engineering; Algorithm; Mathematics","score_opus":0.026186262705083552,"score_gpt":0.3447101102825012,"score_spread":0.31852384757741764,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366726875","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94766873,0.00008515352,0.051007453,0.00037928662,0.00037999026,0.00021954939,0.000022569142,0.00011741615,0.00011987197],"genre_scores_gemma":[0.9723512,0.0002378899,0.027140895,0.000026646818,0.00011018286,0.000007669071,0.000037262515,0.000016133867,0.00007213371],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99808717,0.00019037338,0.00068781705,0.00016073129,0.0005574433,0.00031645858],"domain_scores_gemma":[0.9984443,0.0003081354,0.0005090594,0.00005536879,0.00050477317,0.00017835057],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012322501,0.0001233912,0.000279289,0.00025220372,0.00033782417,0.000045117416,0.00010176334,0.00009818187,0.000048288304],"category_scores_gemma":[0.00010133834,0.00012807734,0.000111590234,0.0011310793,0.00010084748,0.0009353359,4.512654e-7,0.0002106079,0.0000035228431],"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":[0.00014062555,0.000032307995,0.0064052707,0.000022301663,0.000043618413,0.00008183643,0.10387464,0.87979907,0.0010379599,0.0003723565,0.000038758877,0.008151262],"study_design_scores_gemma":[0.0033319239,0.00029693803,0.8791037,0.00044835475,0.00037179934,0.0000035785815,0.10284972,0.0042887684,0.0026680336,0.00091505825,0.005190409,0.00053172023],"about_ca_topic_score_codex":0.00002941904,"about_ca_topic_score_gemma":0.0016333986,"teacher_disagreement_score":0.8755103,"about_ca_system_score_codex":0.000058742746,"about_ca_system_score_gemma":0.00033223868,"threshold_uncertainty_score":0.52228427},"labels":[],"label_agreement":null},{"id":"W4366765055","doi":"10.1016/j.tbs.2023.100594","title":"Does the squeaky wheel get the complaint? Linking bus performance, sociodemographic characteristics, and customer comments","year":2023,"lang":"en","type":"article","venue":"Travel Behaviour and Society","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan; McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Complaint; Service (business); Customer satisfaction; Public transport; Level of service; Business; Transport engineering; Service quality; Marketing; Engineering","score_opus":0.020096562722563552,"score_gpt":0.2718789517036039,"score_spread":0.25178238898104033,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366765055","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99576735,0.000034592667,0.000102510516,0.0030560507,0.0002635277,0.00020394777,0.00005229378,0.00009170912,0.00042804139],"genre_scores_gemma":[0.99587184,0.0027551686,0.00009353908,0.0005479434,0.00009809841,0.000017052693,0.00010079921,0.000009672804,0.00050586043],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9991296,0.00007977554,0.00015821442,0.00015327234,0.00024188386,0.00023728832],"domain_scores_gemma":[0.9995922,0.00012632714,0.00007476359,0.00009026632,0.000057806716,0.000058666326],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.00076873554,0.000100633464,0.00011189667,0.000020996325,0.0023576529,0.00014932474,0.00013091385,0.00010177206,0.000015655813],"category_scores_gemma":[0.0000065396657,0.000052888645,0.00006545369,0.00022468032,0.0005295435,0.000109337205,0.000016258504,0.00023073268,0.0000047937096],"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":[0.000003429912,0.000015737509,0.8965566,0.000014580695,0.000022306765,6.839059e-7,0.09837492,0.0000123596365,0.000009601298,0.0014345074,0.0009882143,0.0025670347],"study_design_scores_gemma":[0.00019296001,0.000008269326,0.9754595,0.000021643076,0.000053284442,5.398137e-7,0.020873388,0.001042853,0.0000028977743,0.0000904519,0.0021552034,0.00009900605],"about_ca_topic_score_codex":0.00041830778,"about_ca_topic_score_gemma":0.00013938248,"teacher_disagreement_score":0.07890288,"about_ca_system_score_codex":0.000013112882,"about_ca_system_score_gemma":0.000029093077,"threshold_uncertainty_score":0.9989411},"labels":[],"label_agreement":null},{"id":"W4367838542","doi":"10.1109/sm57895.2023.10112283","title":"Optimal Placement of Bus Stops using Particle Swarm Optimization","year":2023,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"General Motors (Canada); University of Toronto","funders":"","keywords":"Particle swarm optimization; Computer science; Selection (genetic algorithm); Lift (data mining); Local bus; School bus; Population; Travel time; Real-time computing; Engineering; Transport engineering; System bus; Control bus; Algorithm; Computer hardware; Artificial intelligence","score_opus":0.048893020686354996,"score_gpt":0.32999716324390344,"score_spread":0.28110414255754845,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4367838542","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7641743,0.000010880256,0.23105522,0.00029718198,0.00014813055,0.00013187158,0.0000052438895,0.00021501495,0.003962204],"genre_scores_gemma":[0.9667548,0.000035562043,0.031562086,0.000027555343,0.000032087864,0.0000035859646,0.00003357931,0.0000062094564,0.0015445474],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99930984,0.00004340068,0.00016265445,0.00009659575,0.00023632035,0.000151171],"domain_scores_gemma":[0.9997034,0.00004267692,0.000060278395,0.00005717944,0.00008784815,0.000048589784],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033764465,0.000042367334,0.00006524285,0.000054462937,0.00019230758,0.000020549316,0.000057198533,0.00003849054,0.0002815381],"category_scores_gemma":[0.00004438425,0.000043588458,0.000022627666,0.00055569736,0.000053522595,0.00014508743,0.0000049838527,0.000024033418,0.000014607118],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010004888,0.000016564285,0.0038974085,0.0000036863748,0.0000051260204,8.310415e-7,0.0058258325,0.98695344,0.000105446576,0.002921326,0.0001595823,0.0001007692],"study_design_scores_gemma":[0.0003426123,0.000028127854,0.0022709295,0.000015488951,0.000019907144,1.3035064e-7,0.011102531,0.98332053,0.002004193,0.00002140612,0.00076809444,0.00010602926],"about_ca_topic_score_codex":0.0005138222,"about_ca_topic_score_gemma":0.00008053395,"teacher_disagreement_score":0.20258053,"about_ca_system_score_codex":0.000025886235,"about_ca_system_score_gemma":0.00007478375,"threshold_uncertainty_score":0.30826446},"labels":[],"label_agreement":null},{"id":"W4367843760","doi":"10.1177/03611981231166384","title":"COVID-19 and Teleworking: Lessons, Current Issues and Future Directions for Transport and Land-Use Planning","year":2023,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Work (physics); Telecommuting; Incentive; Business; Land use; Environmental planning; Traffic congestion; Urban planning; Land-use planning; Transport engineering; Engineering; Geography; Economics; Civil engineering","score_opus":0.19649690417993315,"score_gpt":0.4809617935590502,"score_spread":0.28446488937911707,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4367843760","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95580316,0.0033952382,0.0016941571,0.036690004,0.0007092018,0.0013156361,0.00022997164,0.00012363805,0.000038979884],"genre_scores_gemma":[0.920716,0.07495677,0.0023665964,0.000104702296,0.0007014402,0.00014730763,0.00013680644,0.00006140602,0.00080895587],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9952969,0.00083417015,0.0008373042,0.00046085796,0.0018286344,0.00074215047],"domain_scores_gemma":[0.9953112,0.0020240229,0.00029043684,0.00018746944,0.001363034,0.0008238184],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0056498144,0.00021758305,0.00038084583,0.0010941782,0.002521241,0.000255184,0.00032773544,0.00022797936,0.00004113112],"category_scores_gemma":[0.00049739727,0.00018219356,0.0001477908,0.0019920906,0.00092744833,0.0008136249,0.0000038805497,0.0011318105,0.0000013185368],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008663324,0.00008171242,0.9125939,0.0005160515,0.00009612352,0.000046356916,0.047131214,0.0010707079,0.00006038461,0.011828091,0.0049693505,0.020739768],"study_design_scores_gemma":[0.00088011735,0.00012695223,0.6113968,0.0001793655,0.000050793093,4.6816118e-7,0.0067284484,0.00010803142,0.000008347376,0.0021562094,0.37822446,0.00014003292],"about_ca_topic_score_codex":0.012682866,"about_ca_topic_score_gemma":0.05418494,"teacher_disagreement_score":0.3732551,"about_ca_system_score_codex":0.00011685517,"about_ca_system_score_gemma":0.00083254615,"threshold_uncertainty_score":0.99877733},"labels":[],"label_agreement":null},{"id":"W4376120074","doi":"10.1016/j.tbs.2023.100590","title":"Microsimulation of activity generation, activity scheduling and shared travel choices within an activity-based travel demand modelling system","year":2023,"lang":"en","type":"article","venue":"Travel Behaviour and Society","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Division of Human Resource Management; Natural Sciences and Engineering Research Council of Canada","keywords":"Microsimulation; Travel survey; Heuristics; Travel behavior; Computer science; Scheduling (production processes); Econometrics; Markov chain Monte Carlo; Operations research; Transport engineering; Statistics; Monte Carlo method; Economics; Mathematics; Engineering; Operations management","score_opus":0.06601907584715652,"score_gpt":0.31089138950974876,"score_spread":0.24487231366259224,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4376120074","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8340392,0.000025276518,0.1652138,0.00006776114,0.000095680574,0.00030091606,0.00011252599,0.000107091924,0.0000377608],"genre_scores_gemma":[0.99423194,0.0000946975,0.005324594,0.000014873293,0.00008955796,0.000019010255,0.00012074007,0.000023655348,0.00008093134],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99844253,0.0001993757,0.00025974686,0.00043606388,0.00038500645,0.00027729504],"domain_scores_gemma":[0.9991759,0.00011839539,0.00024794307,0.00013207394,0.00016104455,0.00016463717],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010990037,0.00019912173,0.00031263797,0.000082848375,0.0010290338,0.00015880805,0.00008870435,0.00028786293,0.000004697598],"category_scores_gemma":[0.000013840391,0.00021509855,0.00010174524,0.00036798898,0.000192033,0.00069615117,0.000007094675,0.00020717057,4.9327195e-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":[0.000171343,0.00038374323,0.10825157,0.00046087295,0.0000900642,0.000003932183,0.20334883,0.50031775,0.18413772,0.0012117296,0.000023530436,0.0015988918],"study_design_scores_gemma":[0.00060840073,0.000037382415,0.37874755,0.0000499644,0.00008061306,3.889787e-7,0.0076487027,0.6037344,0.008897253,0.000007874782,5.659168e-7,0.00018691989],"about_ca_topic_score_codex":0.002127975,"about_ca_topic_score_gemma":0.0004999228,"teacher_disagreement_score":0.27049598,"about_ca_system_score_codex":0.000063963045,"about_ca_system_score_gemma":0.00017634912,"threshold_uncertainty_score":0.8771464},"labels":[],"label_agreement":null},{"id":"W4376276211","doi":"10.4337/9781800375550.00032","title":"Road pricing applications in North America","year":2023,"lang":"en","type":"book-chapter","venue":"Edward Elgar Publishing eBooks","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Revenue; Metropolitan area; Road pricing; Finance; Business; Pricing strategies; Transport engineering; Marketing; Engineering; Geography; Traffic congestion","score_opus":0.025746235180034088,"score_gpt":0.26080846708333083,"score_spread":0.23506223190329675,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4376276211","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00012232403,0.00005168826,0.0016376569,0.00054875656,0.00046047888,0.0005816273,0.000076707984,0.0006993223,0.9958214],"genre_scores_gemma":[0.010090674,0.00011859584,0.0012602594,0.00031734034,0.00081337267,0.00012350678,0.0015108129,0.00012706454,0.9856384],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9979906,0.000048136302,0.00045272752,0.0004689997,0.0006565092,0.00038304814],"domain_scores_gemma":[0.9988514,0.00018930346,0.00032492846,0.00028610206,0.00019020266,0.0001580173],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005142252,0.00023870514,0.0003071305,0.0004984982,0.00048198728,0.0007796398,0.0005047324,0.0003960677,0.000079507605],"category_scores_gemma":[0.00019162042,0.00027093219,0.00011192301,0.0001987066,0.0001656431,0.00048417796,0.000033393535,0.00070002343,0.00013580342],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027693852,0.000048033937,0.00808674,0.00016317729,0.00016932165,0.00007222683,0.1040273,0.0069177793,0.0000017659851,0.5929069,0.038901772,0.24867728],"study_design_scores_gemma":[0.00013411547,0.000010148372,0.0030843113,0.00011941603,0.00003107776,1.8201882e-7,0.0010110327,0.00005487302,4.1452017e-7,0.004364713,0.99081105,0.00037864072],"about_ca_topic_score_codex":0.0028807297,"about_ca_topic_score_gemma":0.025948184,"teacher_disagreement_score":0.9519093,"about_ca_system_score_codex":0.00018981815,"about_ca_system_score_gemma":0.00045107314,"threshold_uncertainty_score":0.9999743},"labels":[],"label_agreement":null},{"id":"W4378193589","doi":"10.1155/2023/6690346","title":"Cross-Border Travel Behavior Analysis of Hong Kong-Zhuhai-Macao Bridge Using MXL-BMA Model","year":2023,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","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":"National Key Research and Development Program of China; Science and Technology Commission of Shanghai Municipality","keywords":"Multinomial logistic regression; Mode choice; Mixed logit; Discrete choice; Mode (computer interface); Revealed preference; Nested logit; Travel behavior; Preference; Bridge (graph theory); Econometrics; Computer science; Logit; Operations research; Logistic regression; Transport engineering; Economics; Public transport; Microeconomics; Engineering","score_opus":0.036812975702713045,"score_gpt":0.41434024170496847,"score_spread":0.37752726600225545,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4378193589","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9184357,0.00005035513,0.08082686,0.000060804592,0.00024474133,0.00013221582,0.0001184203,0.000037724047,0.00009317332],"genre_scores_gemma":[0.98981935,0.00023346598,0.009357936,0.000020003305,0.000055436954,0.0000035730513,0.00021893154,0.000018755809,0.0002725654],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9979198,0.000047076144,0.0009023985,0.00017267249,0.0007156741,0.00024235477],"domain_scores_gemma":[0.9981521,0.00008199911,0.0008268241,0.0000990159,0.00071930926,0.00012076537],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005768934,0.00013042639,0.00039670296,0.00074031577,0.0002522345,0.00003727121,0.00013858624,0.00012025952,0.000093209885],"category_scores_gemma":[0.000048105285,0.00013687748,0.00031321993,0.002357368,0.00011436128,0.0009139538,8.283729e-7,0.00016409549,0.0000010883315],"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":[0.000085608306,0.000058916274,0.099637754,0.000015344309,0.00013914895,0.000018224728,0.013648056,0.8812702,0.0028057492,0.0003015585,0.0000046294863,0.0020148254],"study_design_scores_gemma":[0.00068054575,0.000039213992,0.9734996,0.000047463738,0.0011651705,6.282285e-7,0.0022532693,0.021645837,0.00038342265,0.00008364409,0.000054424894,0.0001467973],"about_ca_topic_score_codex":0.000229076,"about_ca_topic_score_gemma":0.0009998019,"teacher_disagreement_score":0.87386185,"about_ca_system_score_codex":0.00006290496,"about_ca_system_score_gemma":0.0002235158,"threshold_uncertainty_score":0.55817014},"labels":[],"label_agreement":null},{"id":"W4378573830","doi":"10.1155/2023/3917657","title":"Metamodel-Based Optimization Method for Traffic Network Signal Design under Stochastic Demand","year":2023,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","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":"Basic and Applied Basic Research Foundation of Guangdong Province; Shenzhen Science and Technology Innovation Program; National Natural Science Foundation of China","keywords":"Metamodeling; Mathematical optimization; Computer science; Benchmark (surveying); Node (physics); Flow network; Network planning and design; Traffic flow (computer networking); Optimization problem; Algorithm; Engineering; Mathematics","score_opus":0.0355020776502508,"score_gpt":0.3288972948273088,"score_spread":0.29339521717705797,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4378573830","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0070711584,0.00010647839,0.9912107,0.0005607732,0.00046004957,0.00045821653,0.000016261163,0.00010582518,0.000010513571],"genre_scores_gemma":[0.44072098,0.00005438605,0.5586347,0.0000890221,0.00021388296,0.000025100351,0.00018324102,0.000024822215,0.00005387267],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99826884,0.00019172816,0.00061906414,0.00017013236,0.00046093686,0.0002892718],"domain_scores_gemma":[0.9979402,0.00076507824,0.0005474474,0.00006105681,0.0005594548,0.00012678055],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017706187,0.00013493981,0.0002635641,0.00025054722,0.00039723222,0.000042349806,0.00012644863,0.00011687009,0.000034645327],"category_scores_gemma":[0.00006951712,0.00013547359,0.00016712213,0.000835018,0.00004686068,0.00052708725,3.2004357e-7,0.00012245108,0.0000013960349],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043077368,0.000032294396,0.000030801577,0.000018180623,0.000034572535,0.0000028783188,0.0033386694,0.9924401,0.000055955054,0.00091510854,0.00017729949,0.0025233414],"study_design_scores_gemma":[0.0023696076,0.00027840436,0.0043969653,0.00012272602,0.00033763656,7.855101e-7,0.0023745075,0.98680514,0.0000784246,0.002658379,0.0003345872,0.00024284254],"about_ca_topic_score_codex":0.0000054844854,"about_ca_topic_score_gemma":0.00008575309,"teacher_disagreement_score":0.4336498,"about_ca_system_score_codex":0.000061035345,"about_ca_system_score_gemma":0.00034613782,"threshold_uncertainty_score":0.5524453},"labels":[],"label_agreement":null},{"id":"W4380488337","doi":"10.1061/9780784484906.022","title":"How Increasing Industry Competition Benefits the Pavement Market And How Agencies Can Use It to Lower Their Pavement Expenditures","year":2023,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Infrastructure Canada","funders":"","keywords":"Competition (biology); Activity-based costing; Transport engineering; Business; Unit (ring theory); Finance; Engineering; Marketing","score_opus":0.04833576189431231,"score_gpt":0.2678356304427975,"score_spread":0.2194998685484852,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4380488337","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92235196,0.000023630457,0.00042859113,0.074642606,0.0002557139,0.00037201296,0.00008351798,0.00015518026,0.0016867724],"genre_scores_gemma":[0.98971033,0.0001263195,0.00032019982,0.00132398,0.00009850173,0.00003185329,0.000087281405,0.000007976509,0.008293555],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9990445,0.00015119,0.000083512794,0.00017939233,0.0003204781,0.0002209318],"domain_scores_gemma":[0.99945885,0.0002120369,0.00004964479,0.00012009632,0.000056318517,0.0001030447],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00061619,0.00009917815,0.00008064386,0.000075050004,0.0006592824,0.00053771655,0.00012385103,0.00009332588,0.00015563905],"category_scores_gemma":[0.0001002392,0.00006928735,0.000024260002,0.00022671025,0.00007272942,0.00022247797,0.000027420325,0.00008913287,0.0000018521868],"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":[0.00013957013,0.0001687614,0.27723932,0.000075540695,0.00024945184,0.000058415193,0.2391397,0.016739579,0.0015208876,0.08136469,0.3699728,0.013331306],"study_design_scores_gemma":[0.00040467165,0.00009596153,0.44339117,0.00016822144,0.000036884274,0.000001416843,0.31588244,0.000837317,0.00080276106,0.0002873684,0.23763894,0.00045285877],"about_ca_topic_score_codex":0.0014579642,"about_ca_topic_score_gemma":0.0128528355,"teacher_disagreement_score":0.16615185,"about_ca_system_score_codex":0.000051725165,"about_ca_system_score_gemma":0.00004734735,"threshold_uncertainty_score":0.7172184},"labels":[],"label_agreement":null},{"id":"W4380848930","doi":"10.1155/2023/6450010","title":"Design of a High-Speed Railway Passenger Train Operation Scheme considering Survivability of the Network","year":2023,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","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":"University Project of Gansu Province; Gansu Education Department","keywords":"Survivability; Train; Key (lock); Passenger train; Dedicated line; Transport engineering; Engineering; Connotation; Scheme (mathematics); Service (business); Computer science; Computer network; Automotive engineering; Reliability engineering; Computer security","score_opus":0.030428704077463932,"score_gpt":0.2875055486254951,"score_spread":0.25707684454803115,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4380848930","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94153154,0.000051983865,0.056924168,0.0006063482,0.0005433031,0.00026973552,0.000013482954,0.000026000287,0.000033413282],"genre_scores_gemma":[0.976737,0.00013974003,0.022963816,0.00001626193,0.00007654923,0.000002173907,0.000022874792,0.0000093424205,0.000032188906],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9983012,0.00023488264,0.00073708757,0.00009915007,0.00048065526,0.00014701269],"domain_scores_gemma":[0.99835306,0.0003412843,0.00071039837,0.000092224785,0.00045723215,0.00004579599],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014876997,0.0000817267,0.00023945441,0.000068767964,0.00016486339,0.000010790197,0.00012594437,0.00007620801,0.00003273133],"category_scores_gemma":[0.00016557498,0.00006713326,0.000101255144,0.0006916441,0.00012623533,0.0004064914,8.569009e-7,0.00012825201,5.3487395e-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":[0.0001355138,0.000028023163,0.013903698,0.00002779945,0.000026031908,0.00000200278,0.014353254,0.95751965,0.011555514,0.0017736962,0.00003869227,0.0006361405],"study_design_scores_gemma":[0.0015709092,0.00013646059,0.9798809,0.00025734416,0.00009278742,7.727498e-7,0.0064010667,0.0018929465,0.006038786,0.0031782181,0.0003871917,0.00016258664],"about_ca_topic_score_codex":0.000089549525,"about_ca_topic_score_gemma":0.0004809924,"teacher_disagreement_score":0.96597725,"about_ca_system_score_codex":0.000030434527,"about_ca_system_score_gemma":0.00022976277,"threshold_uncertainty_score":0.27376148},"labels":[],"label_agreement":null},{"id":"W4381681503","doi":"10.1155/2023/6506169","title":"A Battery Electric Vehicle Transportation Network Design Model with Bounded Rational Travelers","year":2023,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","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":"Fundamental Research Funds for the Central Universities; Natural Science Foundation of Anhui Province; Beijing Wuzi University; National Natural Science Foundation of China","keywords":"Heuristic; Battery (electricity); Schedule; Computer science; Bottleneck; Bounded rationality; Network planning and design; Transport engineering; Duration (music); Flow network; Government (linguistics); Operations research; Mathematical optimization; Engineering; Power (physics); Artificial intelligence; Mathematics; Computer network","score_opus":0.021789242781496344,"score_gpt":0.2690585789233258,"score_spread":0.24726933614182944,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4381681503","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5400693,0.000057823538,0.4584277,0.00074940646,0.00022076366,0.00027900716,0.000015662516,0.0001151659,0.00006517683],"genre_scores_gemma":[0.9526143,0.00034547716,0.04621613,0.00012272484,0.00019974064,0.00001837267,0.00025341852,0.000030178808,0.00019965474],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9978657,0.000100663885,0.00066328695,0.00019428608,0.000818948,0.00035713997],"domain_scores_gemma":[0.9985424,0.00018511877,0.0005496514,0.000075106174,0.0004985114,0.00014918437],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007642092,0.00016089935,0.0002470027,0.0002873679,0.00042982315,0.000052855106,0.00014148091,0.00011154129,0.000028585579],"category_scores_gemma":[0.000025467207,0.00015427636,0.000108825094,0.0014337838,0.00006822919,0.0010939629,1.7795318e-7,0.00022173862,0.0000051167044],"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":[0.0006488382,0.000039146893,0.0026938254,0.000014504725,0.000044453103,0.00003372623,0.015575729,0.97534287,0.0014562639,0.0024966905,0.00040856027,0.0012453749],"study_design_scores_gemma":[0.006441674,0.0009539875,0.8831919,0.00038283854,0.0005081465,0.000006098452,0.00751863,0.08272134,0.0012899397,0.0140294125,0.0020428316,0.0009132017],"about_ca_topic_score_codex":0.000026408226,"about_ca_topic_score_gemma":0.00064471917,"teacher_disagreement_score":0.8926216,"about_ca_system_score_codex":0.000085591804,"about_ca_system_score_gemma":0.00059568125,"threshold_uncertainty_score":0.62912077},"labels":[],"label_agreement":null},{"id":"W4381741668","doi":"10.25368/2023.103","title":"On How Traffic Signals Impact the Fundamental Diagrams of Urban Roads","year":2022,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Computer science; Diagram; Relation (database); Key (lock); SIGNAL (programming language); Traffic flow (computer networking); Function (biology); Transport engineering; Parametric statistics; Plan (archaeology); Data mining; Geography; Mathematics; Engineering; Statistics; Computer security","score_opus":0.02000579602246558,"score_gpt":0.29026783057911,"score_spread":0.2702620345566444,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4381741668","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99150115,0.000032528857,0.00019717845,0.0019640585,0.00011157937,0.00013353064,0.000025225516,0.000047421818,0.005987344],"genre_scores_gemma":[0.9974516,0.0000047628187,0.000029732595,0.00014601051,0.000025266147,0.000011223594,0.00003322499,0.000003597233,0.0022945718],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.999287,0.00013549176,0.00007261009,0.00007362008,0.00032694335,0.00010432417],"domain_scores_gemma":[0.9997106,0.00012801836,0.000054521657,0.000057849782,0.000016578126,0.000032410757],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00035470698,0.000041219126,0.0000558544,0.000029901174,0.00047923272,0.00003221673,0.00011863151,0.000014987628,0.0016917991],"category_scores_gemma":[0.000018545985,0.00002766634,0.000060043098,0.00022783162,0.00005359853,0.000054281496,0.0000035489495,0.00006473037,0.0000027128767],"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":[0.00007366507,0.00023709223,0.029159533,0.0000022811025,0.000045026067,0.0000024896854,0.07530214,0.8300488,0.000102293794,0.022387313,0.03661388,0.006025513],"study_design_scores_gemma":[0.0036019906,0.0040619215,0.4371343,0.000039706105,0.00018433086,0.000003135052,0.39178348,0.04077148,0.00081505347,0.0017621908,0.118506745,0.0013356747],"about_ca_topic_score_codex":0.00039233983,"about_ca_topic_score_gemma":0.00030809193,"teacher_disagreement_score":0.7892773,"about_ca_system_score_codex":0.000043937882,"about_ca_system_score_gemma":0.00006124304,"threshold_uncertainty_score":0.9992208},"labels":[],"label_agreement":null},{"id":"W4381802307","doi":"10.1155/2023/3513517","title":"Analytical Approximation-Based Approach for Passenger Flow Control Strategy in Oversaturated Urban Rail Transit Systems","year":2023,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","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":"Natural Science Foundation of Beijing Municipality; National Natural Science Foundation of China","keywords":"Queueing theory; Mathematical optimization; Computer science; Traffic flow (computer networking); Nonlinear system; Traffic congestion; Nonlinear programming; Relaxation (psychology); Control (management); Control theory (sociology); Engineering; Transport engineering; Mathematics","score_opus":0.019853733349060463,"score_gpt":0.28486756232847,"score_spread":0.2650138289794095,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4381802307","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15552175,0.00013466393,0.84181774,0.000583093,0.0004324388,0.00097151333,0.00014683949,0.00011687906,0.0002750917],"genre_scores_gemma":[0.9926134,0.00003841267,0.0064166742,0.000033394652,0.00013738057,0.00003649524,0.00062393886,0.000018680266,0.00008165609],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99817467,0.00011948757,0.0007625936,0.00017761799,0.0004967758,0.00026885103],"domain_scores_gemma":[0.99867505,0.00028170686,0.0003847646,0.00006752221,0.00047553526,0.000115426796],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00093027594,0.00013309895,0.00032160644,0.00038193044,0.00016535184,0.000058248763,0.000111600435,0.00014935267,0.000008867237],"category_scores_gemma":[0.00008001441,0.00012978147,0.000145555,0.00093775254,0.00005742524,0.00057257107,1.254208e-7,0.00018264576,0.00000100296],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005132734,0.00007742117,0.0030597136,0.000081152386,0.000032509757,0.000015098068,0.0048982715,0.98729783,0.00015327247,0.00321455,0.00016298257,0.000493919],"study_design_scores_gemma":[0.007929983,0.00022853178,0.0985104,0.00014892238,0.00018010942,9.67892e-7,0.014339273,0.87662315,0.000049572118,0.00023451907,0.0014449182,0.00030968108],"about_ca_topic_score_codex":0.000048831444,"about_ca_topic_score_gemma":0.00016508726,"teacher_disagreement_score":0.8370916,"about_ca_system_score_codex":0.00008905005,"about_ca_system_score_gemma":0.00031431796,"threshold_uncertainty_score":0.52923346},"labels":[],"label_agreement":null},{"id":"W4381942742","doi":"10.1155/2023/8923716","title":"Statistical Analysis of Train Operation and Passenger Distribution Based on Real Records: A Case Study of Wuhan-Guangzhou HSR","year":2023,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China; Chengdu University; Chengdu University of Information Technology","keywords":"Train; Transport engineering; Passenger train; Interval (graph theory); Service (business); Line (geometry); Computer science; Service quality; Statistical analysis; Distribution (mathematics); Automotive engineering; Simulation; Engineering; Statistics; Geography; Mathematics","score_opus":0.020937835077395366,"score_gpt":0.33733042540672786,"score_spread":0.3163925903293325,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4381942742","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9727681,0.000008601917,0.026535386,0.00009745263,0.00012416772,0.00020545725,0.00022023964,0.000019479095,0.000021070218],"genre_scores_gemma":[0.9977269,0.00011212299,0.0016736154,0.0000069083567,0.00002645356,0.000005395017,0.00043235696,0.0000073352894,0.000008920757],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99833846,0.00017785808,0.0007301552,0.00013739572,0.0005018229,0.00011431328],"domain_scores_gemma":[0.99865067,0.0003100983,0.0005453717,0.00007120204,0.00034414022,0.00007852534],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007912452,0.000088948516,0.00031145866,0.00041196082,0.00013801722,0.0000142999415,0.000047844864,0.0000645737,0.000025087313],"category_scores_gemma":[0.00009088891,0.00008534887,0.00007822214,0.0010754771,0.00006402629,0.00027987815,3.962775e-7,0.00010185296,2.1696867e-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":[0.00049060915,0.00041245777,0.09384767,0.00004263487,0.00019876916,0.00027408166,0.042861726,0.8553834,0.00034020538,0.0008161322,0.000025777112,0.0053065675],"study_design_scores_gemma":[0.0014556627,0.0008523701,0.9388517,0.00005113603,0.0008771266,0.0000017644074,0.048947804,0.008706997,0.000075581054,0.000046326226,0.000033886645,0.00009963011],"about_ca_topic_score_codex":0.00082910893,"about_ca_topic_score_gemma":0.008118895,"teacher_disagreement_score":0.84667635,"about_ca_system_score_codex":0.000039588347,"about_ca_system_score_gemma":0.000104805506,"threshold_uncertainty_score":0.45305339},"labels":[],"label_agreement":null},{"id":"W4382313243","doi":"10.1155/2023/6402531","title":"Collaborate or Compete? Time-Varying Incentives versus Tolling in Parallel Bottleneck","year":2023,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","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":"Science and Technology Commission of Shanghai Municipality; National Natural Science Foundation of China; National Science Foundation","keywords":"Incentive; Bottleneck; Business; Competition (biology); Duopoly; Revenue; Environmental economics; Industrial organization; Microeconomics; Agency (philosophy); Transport engineering; Economics; Finance; Cournot competition; Operations management; Engineering","score_opus":0.02676112318744103,"score_gpt":0.32109389538836963,"score_spread":0.2943327722009286,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382313243","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99459416,0.00011991698,0.0032286674,0.00056805304,0.0008112678,0.00017943229,0.000019118184,0.00007726287,0.0004021479],"genre_scores_gemma":[0.9847815,0.0011919689,0.013486508,0.000024587995,0.00010009317,0.0000046295318,0.00010074498,0.000014050127,0.00029590595],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.998658,0.00007973742,0.00053550943,0.000119624594,0.00039438132,0.00021274797],"domain_scores_gemma":[0.9989658,0.0002551369,0.00037770902,0.000047104317,0.00027420613,0.00008007043],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051686156,0.00009213061,0.00020531013,0.0002818795,0.00018185443,0.000034096127,0.000113070884,0.00006921381,0.00006596645],"category_scores_gemma":[0.00010693783,0.000088120214,0.000055992023,0.0013290504,0.000049918268,0.0007885488,6.370147e-7,0.00014194609,0.000013437274],"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":[0.0016022521,0.000042526306,0.012002672,0.00002469133,0.000024555075,0.0001181202,0.02827877,0.95504063,0.00077660725,0.00063643267,0.00007043553,0.0013822868],"study_design_scores_gemma":[0.015517989,0.00063746923,0.91316915,0.0010425582,0.00015405242,0.0000019847564,0.051122602,0.0034408518,0.00057058164,0.0010275756,0.0125947585,0.0007204545],"about_ca_topic_score_codex":0.000036122474,"about_ca_topic_score_gemma":0.0012240905,"teacher_disagreement_score":0.9515998,"about_ca_system_score_codex":0.000074499,"about_ca_system_score_gemma":0.00026531258,"threshold_uncertainty_score":0.3593438},"labels":[],"label_agreement":null},{"id":"W4382808809","doi":"10.2139/ssrn.4493231","title":"The Effects of COVID-19 on Public Transit in the City of Calgary: Fiscal Impacts and Equity Considerations","year":2023,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); Equity (law); Public transport; 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Transit (satellite); Business; Public economics; Economics; Political science; Medicine; Transport engineering; Engineering; Virology","score_opus":0.03141150930401422,"score_gpt":0.3368071208621268,"score_spread":0.30539561155811257,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382808809","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9671058,0.00076968904,0.0034946792,0.026900375,0.00011216958,0.0002881566,0.0000038451376,0.000024711355,0.0013005675],"genre_scores_gemma":[0.9969067,0.002861247,0.000008928441,0.00013895404,0.000023755494,0.000003531857,0.0000037464947,0.0000027605975,0.000050394785],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99851865,0.00038878486,0.00018294604,0.00006579958,0.0002992226,0.00054462417],"domain_scores_gemma":[0.9983982,0.0013361211,0.00010264544,0.00005733889,0.00004263959,0.00006307309],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038656953,0.000048198515,0.000084966516,0.00007937837,0.0006384003,0.000054400196,0.0001126366,0.000046037978,0.000006173771],"category_scores_gemma":[0.0013744812,0.000030079362,0.000036629357,0.00035960856,0.00017677211,0.00009570701,0.0000035345622,0.00043256744,4.226377e-7],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000052520412,0.000057964153,0.027802978,0.000025684962,0.000045895496,0.0000056341996,0.028682921,0.0006784089,0.00010539944,0.93911785,0.00028209543,0.0031426486],"study_design_scores_gemma":[0.0026264505,0.00095030776,0.3017455,0.00009412828,0.00008683886,0.0000426869,0.03820376,0.0007098273,0.00014811526,0.65326554,0.0018914104,0.00023541023],"about_ca_topic_score_codex":0.0006015267,"about_ca_topic_score_gemma":0.03782695,"teacher_disagreement_score":0.28585228,"about_ca_system_score_codex":0.00013086725,"about_ca_system_score_gemma":0.0020206757,"threshold_uncertainty_score":0.9797302},"labels":[],"label_agreement":null},{"id":"W4382867335","doi":"10.1155/2023/4082587","title":"A New Big Data Approach to Understanding General Traffic Impacts on Bus Passenger Delays","year":2023,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Agencia Estatal de Investigación; European Social Fund; European Regional Development Fund; Ministerio de Ciencia e Innovación","keywords":"Computer science; Transport engineering; Estimation; TRIPS architecture; Travel time; Big data; Simulation; Real-time computing; Operations research; Engineering; Data mining","score_opus":0.11311258134983086,"score_gpt":0.333905894565957,"score_spread":0.2207933132161261,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382867335","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6846546,0.00007210021,0.3094215,0.002592804,0.0016018349,0.0003438162,0.000078184814,0.00016365355,0.0010715057],"genre_scores_gemma":[0.973922,0.00025839705,0.024446273,0.00012144489,0.0005924072,0.0000019521565,0.00039612758,0.00002149618,0.00023991003],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9983927,0.000054693566,0.00045614885,0.0002077766,0.00062446226,0.00026422483],"domain_scores_gemma":[0.9990839,0.00009786149,0.00028238792,0.0001530904,0.000100426754,0.00028235288],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060555676,0.00011820986,0.00018865813,0.0003717482,0.00023918954,0.00005821913,0.00026228212,0.00008139913,0.000010879968],"category_scores_gemma":[0.0000720723,0.000113219154,0.00006711011,0.0010501246,0.000023059138,0.00061968766,0.000001293011,0.00017086777,0.000008710652],"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":[0.0001996021,0.00004248816,0.00045109942,0.000012711175,0.000029782517,0.000021600983,0.022355048,0.9615448,0.00022343206,0.0038109522,0.0023702246,0.0089382455],"study_design_scores_gemma":[0.012444819,0.0019182727,0.75159425,0.0012335331,0.0008069299,0.000020803553,0.12617527,0.012111428,0.00042512192,0.006780503,0.0842526,0.0022364953],"about_ca_topic_score_codex":0.0000611342,"about_ca_topic_score_gemma":0.000769394,"teacher_disagreement_score":0.9494334,"about_ca_system_score_codex":0.00013109637,"about_ca_system_score_gemma":0.00032037473,"threshold_uncertainty_score":0.46169433},"labels":[],"label_agreement":null},{"id":"W4382933088","doi":"10.1155/2023/3226726","title":"Heterogeneity in the Preferences of Potential Users of Automated Transit Network (ATN)","year":2023,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","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":"Public transport; Multinomial logistic regression; Mixed logit; Transport engineering; Computer science; Transit (satellite); Order (exchange); Operations research; Logistic regression; Business; Engineering","score_opus":0.0198002590128574,"score_gpt":0.3032668108528709,"score_spread":0.28346655184001346,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382933088","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9968756,0.000079677055,0.0023374492,0.00022259213,0.00025194304,0.00013262327,0.000014877959,0.00003431054,0.00005097193],"genre_scores_gemma":[0.9974991,0.00039916224,0.0019810847,0.000012355138,0.00004581374,0.0000021789397,0.000050484112,0.0000052358973,0.0000045572697],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9985299,0.00014012575,0.00063605234,0.0000741347,0.0004804169,0.00013938452],"domain_scores_gemma":[0.999045,0.00011502379,0.00053263636,0.000055371816,0.00022006912,0.000031871983],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008231558,0.000066673696,0.00019384656,0.00014279867,0.0000791494,0.000008115796,0.0001650446,0.00006469457,0.00001022463],"category_scores_gemma":[0.000026118627,0.00005328104,0.00010118213,0.0008703633,0.00008519533,0.00033715236,2.9644875e-7,0.00010407851,3.7798654e-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":[0.00015964921,0.000039521536,0.026286662,0.000025492738,0.000020420488,0.00001053273,0.02980101,0.94139075,0.00095628307,0.00032873612,0.000039511608,0.00094145566],"study_design_scores_gemma":[0.0007422549,0.00013244833,0.98203826,0.00013656623,0.0000624107,8.7351583e-7,0.014766988,0.0007320619,0.0006139632,0.00057349826,0.0001301808,0.00007048486],"about_ca_topic_score_codex":0.00014382602,"about_ca_topic_score_gemma":0.0032520413,"teacher_disagreement_score":0.9557516,"about_ca_system_score_codex":0.000011804908,"about_ca_system_score_gemma":0.00010541116,"threshold_uncertainty_score":0.21727379},"labels":[],"label_agreement":null},{"id":"W4383341844","doi":"10.20858/tp.2023.18.2.03","title":"THE EFFECT OF TRAM MANAGEMENT ON ROAD TRAFFIC FLUIDITY AND ITS INFLUENCE ON DRIVERS’ BEHAVIORS AT A CRITICAL JUNCTION IN ALGERIA","year":2023,"lang":"en","type":"article","venue":"Transport Problems","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Transport Canada","funders":"","keywords":"Transport engineering; Traffic congestion; Traffic flow (computer networking); Attendance; Public transport; Engineering; Computer science; Computer security; Economics","score_opus":0.013527945360356607,"score_gpt":0.2887394904083421,"score_spread":0.2752115450479855,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383341844","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9981377,0.000025342353,0.000012880803,0.0003604072,0.00015339722,0.0005517737,0.0000098606915,0.00010940199,0.0006392222],"genre_scores_gemma":[0.9992833,0.0002828957,0.000009406493,0.000009905044,0.000009492928,0.000072317474,0.000036323418,0.00000840844,0.0002879307],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9988464,0.00010543748,0.00023891202,0.00022358293,0.00035831935,0.00022733747],"domain_scores_gemma":[0.9996224,0.00016306971,0.00004239011,0.00008427565,0.000026519032,0.000061316234],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007677564,0.00010380224,0.00013044752,0.000105747684,0.00033811288,0.000014957588,0.00009433932,0.000082371844,0.000018344037],"category_scores_gemma":[0.000021535467,0.00008284269,0.000042407348,0.00043048803,0.00014442115,0.000109121116,0.0000021004103,0.0001042745,0.000015695696],"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":[0.0016657009,0.00035397723,0.23479293,0.0007260494,0.00007128094,0.00013711126,0.060031805,0.6554409,0.0015855966,0.016818538,0.00022407476,0.028152026],"study_design_scores_gemma":[0.00088517007,0.0006008693,0.995518,0.00021753379,0.000061516635,2.710319e-7,0.00043851673,0.00071214826,0.0004332635,0.0000485868,0.0009290089,0.00015510549],"about_ca_topic_score_codex":0.00017386442,"about_ca_topic_score_gemma":0.0013318898,"teacher_disagreement_score":0.7607251,"about_ca_system_score_codex":0.000046640645,"about_ca_system_score_gemma":0.000015856383,"threshold_uncertainty_score":0.3378227},"labels":[],"label_agreement":null},{"id":"W4384405591","doi":"10.1155/2023/5596292","title":"Bayesian Disaggregate and Aggregate Calibration of Path Logit Choice Models","year":2023,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","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":"Econometrics; Estimator; Frequentist inference; Logit; Bayesian probability; Mixed logit; Aggregate (composite); Calibration; Bayesian inference; Choice set; Discrete choice; Inference; Computer science; Sample (material); Statistics; Mathematics; Logistic regression; Artificial intelligence","score_opus":0.01898853404965746,"score_gpt":0.2848444224751015,"score_spread":0.26585588842544405,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4384405591","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9098674,0.00023007319,0.088506505,0.00069448416,0.00029600348,0.00015606725,0.000033098644,0.000049581384,0.00016680198],"genre_scores_gemma":[0.9936089,0.0015010391,0.0046293414,0.000023571167,0.00006892581,0.0000024453018,0.00006370065,0.000011074486,0.00009102017],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99881214,0.000059524056,0.00050585845,0.00010738955,0.00037554893,0.0001395427],"domain_scores_gemma":[0.99891144,0.00012021688,0.00058366434,0.000053683376,0.00023793314,0.00009308821],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038854728,0.00008260208,0.0001812959,0.00017499764,0.00013754026,0.000021091677,0.00007639489,0.00007170252,0.000011229582],"category_scores_gemma":[0.000058501922,0.00007979564,0.00006296112,0.00049294886,0.0000865165,0.0009152416,6.4353185e-7,0.00009555932,3.6775685e-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":[0.00013895259,0.000037176305,0.017112054,0.00006329927,0.000029618526,0.000019293599,0.030229783,0.93236315,0.0014044782,0.005893122,0.000050131766,0.012658938],"study_design_scores_gemma":[0.0054518036,0.0006757073,0.8532811,0.0013167786,0.00039102067,0.000005895802,0.026536422,0.059459258,0.002639439,0.045982532,0.0034883432,0.0007717284],"about_ca_topic_score_codex":0.000101267535,"about_ca_topic_score_gemma":0.00051428255,"teacher_disagreement_score":0.8729039,"about_ca_system_score_codex":0.000018659057,"about_ca_system_score_gemma":0.00009235596,"threshold_uncertainty_score":0.32539716},"labels":[],"label_agreement":null},{"id":"W4384434735","doi":"10.2139/ssrn.4511801","title":"Inclusion of People with Disabilities in Public Transit: A Case-Study Analysis of Canada and Us Policies","year":2023,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Inclusion (mineral); Transit (satellite); Public transport; Public policy; Political science; Universal design; Public administration; Regional science; Public economics; Economic growth; Sociology; Economics; Gender studies; Computer science","score_opus":0.017312377786810534,"score_gpt":0.27779329200310066,"score_spread":0.2604809142162901,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4384434735","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9981255,0.00022912088,0.00061809045,0.0006853856,0.000044584605,0.00018153306,0.000036367415,0.000011028944,0.00006838608],"genre_scores_gemma":[0.99904966,0.0007352191,0.000023997385,0.000004889374,0.00001861621,0.000006500163,0.000020627105,0.000009206845,0.00013129818],"study_design_codex":"observational","study_design_gemma":"qualitative","domain_scores_codex":[0.998046,0.00022577393,0.00040316998,0.00016618779,0.00050842715,0.0006504731],"domain_scores_gemma":[0.99921566,0.0001621161,0.00028819486,0.00010091459,0.00017373716,0.000059374328],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018272738,0.00011155438,0.00040076821,0.00050616165,0.00036568774,0.000030256753,0.00015453984,0.00008033459,0.0000076068823],"category_scores_gemma":[0.0000956206,0.0000981085,0.000061900595,0.0011144899,0.00010095817,0.000080034224,0.00004284387,0.00069039955,1.2458386e-8],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023792194,0.00006041227,0.7836159,0.00002233771,0.00049337716,0.000014541702,0.149812,0.063926496,5.7125834e-7,0.0019047396,0.0000014546533,0.00012441563],"study_design_scores_gemma":[0.00042432698,0.00017454196,0.37058386,0.00005627657,0.0007149973,0.000022599972,0.62245774,0.0009514842,0.000001112866,0.0044225655,0.000009002848,0.00018150252],"about_ca_topic_score_codex":0.96982396,"about_ca_topic_score_gemma":0.9998331,"teacher_disagreement_score":0.4726457,"about_ca_system_score_codex":0.00061433227,"about_ca_system_score_gemma":0.008923631,"threshold_uncertainty_score":0.99669486},"labels":[],"label_agreement":null},{"id":"W4384663994","doi":"10.1007/s12469-023-00325-8","title":"Effect of stochastic vehicle arrival and passenger demand on semi-flexible transit design","year":2023,"lang":"en","type":"article","venue":"Public Transport","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Headway; Heuristics; Operations research; Pareto principle; Variable (mathematics); Multi-objective optimization; Operator (biology); Public transport; Minification; Computer science; Service (business); Mathematical optimization; Set (abstract data type); Transport engineering; Engineering; Economics; Mathematics","score_opus":0.027697961050863477,"score_gpt":0.29034914125312794,"score_spread":0.26265118020226447,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4384663994","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.90240437,0.00005620695,0.09307766,0.0014664872,0.00017967088,0.00048011375,0.000034278302,0.00045638258,0.001844861],"genre_scores_gemma":[0.9992302,0.000039616094,0.00015526036,0.000034925088,0.000046646684,0.000029261757,0.00006435901,0.000016608165,0.00038312288],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9986852,0.00017347315,0.00021855658,0.00023122905,0.0003928725,0.0002986686],"domain_scores_gemma":[0.99920756,0.00042274286,0.00005751066,0.00010236424,0.00005642418,0.00015342791],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013957607,0.00012042584,0.00020513496,0.00018992662,0.00024666727,0.000026932148,0.0001084256,0.000110338566,0.000067366724],"category_scores_gemma":[0.000058400346,0.00011205253,0.0000586832,0.0006610683,0.00014649665,0.00019293938,0.0000011439097,0.00010600236,0.000013763053],"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":[0.0024600807,0.0005144974,0.35806277,0.0009500451,0.000467472,0.00014408774,0.1043969,0.4327804,0.0035604418,0.052927252,0.002261402,0.041474644],"study_design_scores_gemma":[0.009306303,0.0030331018,0.94096476,0.000505592,0.00066427054,0.000005200206,0.0030356555,0.01949698,0.008149574,0.0025747202,0.010521874,0.0017419589],"about_ca_topic_score_codex":0.00014436219,"about_ca_topic_score_gemma":0.00011658331,"teacher_disagreement_score":0.582902,"about_ca_system_score_codex":0.000015512473,"about_ca_system_score_gemma":0.00009647403,"threshold_uncertainty_score":0.45693696},"labels":[],"label_agreement":null},{"id":"W4384825607","doi":"10.1080/03155986.2023.2229603","title":"The impact of time aggregation and travel time models on time-dependent routing solutions","year":2023,"lang":"en","type":"article","venue":"INFOR Information Systems and Operational Research","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; FIFO (computing and electronics); Routing (electronic design automation); Key (lock); Traverse; Traffic congestion; Traffic flow (computer networking); Data aggregator; Volume (thermodynamics); Real-time data; Real-time computing; Computer network; Transport engineering; Wireless sensor network; Engineering","score_opus":0.07891647041592034,"score_gpt":0.3752684265727,"score_spread":0.2963519561567797,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4384825607","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7945642,0.00032254044,0.010661847,0.0038594687,0.0003483645,0.004562323,0.001028173,0.00040860634,0.18424445],"genre_scores_gemma":[0.99390376,0.00019737842,0.000030339708,0.000013026815,0.00006008688,0.00005008541,0.00039389683,0.0000050849562,0.0053463588],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981213,0.00017893022,0.00042744182,0.000084557105,0.0009239744,0.00026379817],"domain_scores_gemma":[0.9982822,0.0006600585,0.000116395655,0.00008717681,0.0007616906,0.00009248462],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0041609826,0.00007540645,0.0001068599,0.00028352175,0.0018697527,0.00052509207,0.00010070948,0.000090292706,0.000027463313],"category_scores_gemma":[0.00039968014,0.00005505424,0.000032124455,0.00047859966,0.00017305596,0.0014857908,0.00001979748,0.00012978139,0.00034586794],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009722378,0.000016881446,0.0006569179,0.000029190243,0.00005434077,4.1450915e-7,0.030860757,0.81408966,0.00015559145,0.1396172,0.007985972,0.006435841],"study_design_scores_gemma":[0.00033386427,0.00009484145,0.0055422643,0.00007587181,0.0000033535068,0.0000016816397,0.0043101623,0.98781586,0.00001585391,0.0003413579,0.0013718567,0.00009301121],"about_ca_topic_score_codex":0.0015089002,"about_ca_topic_score_gemma":0.000026347254,"teacher_disagreement_score":0.19933952,"about_ca_system_score_codex":0.00009352825,"about_ca_system_score_gemma":0.00037779563,"threshold_uncertainty_score":0.9994297},"labels":[],"label_agreement":null},{"id":"W4385060716","doi":"10.23977/acss.2023.070602","title":"Design and Implementation of Green Travel Smart Environmental Protection System","year":2023,"lang":"en","type":"article","venue":"Advances in Computer Signals and Systems","topic":"Transportation Planning and Optimization","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":"Voucher; Public transport; Business; Environmental economics; Reduction (mathematics); Point (geometry); Greenhouse gas; Carbon offset; Environmental planning; Architectural engineering; Transport engineering; Engineering; Environmental science; Economics; Ecology","score_opus":0.025694349368240894,"score_gpt":0.28659100155823913,"score_spread":0.26089665218999825,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385060716","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25608408,0.000994628,0.74165285,0.000049499828,0.000323527,0.00076810794,0.00001194786,0.000058888825,0.000056471326],"genre_scores_gemma":[0.9985121,0.0004311663,0.0009240015,0.0000037815541,0.000049059705,0.000039936076,0.000011495239,0.0000041745443,0.000024288142],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992291,0.00016769863,0.00021976496,0.00013474864,0.00014794913,0.000100768295],"domain_scores_gemma":[0.99975777,0.00006961456,0.000098781224,0.000033451823,0.0000115310995,0.00002884473],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005695243,0.000054442997,0.00011460377,0.00008859831,0.00011619428,0.00002623512,0.000033186996,0.000034650042,0.0000018671705],"category_scores_gemma":[9.829562e-7,0.000053522257,0.000009468508,0.00014200785,0.000042684536,0.00023311318,0.000004730542,0.000027194848,0.0000010223271],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000784077,0.000052585565,0.16026938,0.0011874365,0.000052135354,0.000021125366,0.093523875,0.49353927,0.0028577207,0.007983018,0.000057967605,0.24037708],"study_design_scores_gemma":[0.0017336356,0.0004303625,0.13815318,0.0006254029,0.000030088833,0.000010321621,0.059456464,0.79655224,0.0005505064,0.00033877374,0.0016881922,0.00043082066],"about_ca_topic_score_codex":0.001196671,"about_ca_topic_score_gemma":0.00015325632,"teacher_disagreement_score":0.742428,"about_ca_system_score_codex":0.00002069783,"about_ca_system_score_gemma":0.000012099511,"threshold_uncertainty_score":0.21825743},"labels":[],"label_agreement":null},{"id":"W4385275468","doi":"10.1080/03155986.2023.2229208","title":"Mathematical formulations for multi-period network design with modular capacity adjustments","year":2023,"lang":"en","type":"article","venue":"INFOR Information Systems and Operational Research","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal; Université de Montréal","funders":"Fonds de recherche du Québec – Nature et technologies","keywords":"Modular design; Time horizon; Mathematical optimization; Computer science; Network planning and design; Integer programming; Integer (computer science); Selection (genetic algorithm); Function (biology); Order (exchange); Operations research; Mathematics; Artificial intelligence; Economics","score_opus":0.2099483244810672,"score_gpt":0.4028948893331627,"score_spread":0.1929465648520955,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385275468","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01901942,0.000024119845,0.97441953,0.00056768756,0.00014477847,0.0025812539,0.00011732642,0.00016495258,0.002960934],"genre_scores_gemma":[0.9256021,0.000050570823,0.069776945,0.00010230897,0.00021792196,0.0012429511,0.0008778741,0.000014399478,0.0021149428],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99817383,0.00012193752,0.0003930619,0.000104640596,0.00084614183,0.00036037414],"domain_scores_gemma":[0.9982591,0.00036342803,0.00007458624,0.00009252207,0.0010753601,0.000134949],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.003095274,0.000087687215,0.00012416198,0.0002423958,0.00195643,0.0005633026,0.0001016986,0.00010853081,0.000022670049],"category_scores_gemma":[0.0003884137,0.00007262959,0.00002390322,0.00064897287,0.00014509782,0.0017768785,0.00001091453,0.000111076726,0.00009105231],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005475677,0.000013414518,0.0011125661,0.00008005102,0.000025071575,3.9143106e-7,0.015068237,0.5465432,0.0000031601778,0.43390185,0.0025909739,0.00060632906],"study_design_scores_gemma":[0.0010690143,0.00011136492,0.007171574,0.00010450113,0.000008804675,0.0000029650482,0.007815979,0.91650176,0.00000717259,0.0008188998,0.066213235,0.00017474616],"about_ca_topic_score_codex":0.00019322235,"about_ca_topic_score_gemma":0.000053601,"teacher_disagreement_score":0.90658265,"about_ca_system_score_codex":0.00007482355,"about_ca_system_score_gemma":0.0003287723,"threshold_uncertainty_score":0.99934286},"labels":[],"label_agreement":null},{"id":"W4385376051","doi":"10.1155/2023/8298068","title":"Study on the Shortest Reliable Path of Stochastic Time-Dependent Transportation Networks considering Waiting Time at Signalized Intersections","year":2023,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","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":"Natural Science Foundation of Shandong Province","keywords":"Shortest path problem; Dijkstra's algorithm; Travel time; Computer science; Reliability (semiconductor); Path (computing); Yen's algorithm; Mathematical optimization; Mathematics; Transport engineering; Engineering; Graph; Theoretical computer science; Computer network","score_opus":0.021386799150688272,"score_gpt":0.2793213475424983,"score_spread":0.25793454839181007,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385376051","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9864986,0.000030057736,0.012080577,0.00025222558,0.0003520165,0.00050413935,0.00003332586,0.000092777904,0.00015625662],"genre_scores_gemma":[0.9991411,0.00003988202,0.00023352838,0.00002894103,0.000090395326,0.000017956705,0.00012311364,0.000025719755,0.0002993827],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99785006,0.00013684013,0.0009045335,0.0001834618,0.00068895123,0.0002361558],"domain_scores_gemma":[0.99801743,0.00058023125,0.0008016831,0.00011071322,0.0004016835,0.00008825295],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010877362,0.00015284466,0.0002979153,0.0002303966,0.00047316548,0.000027553378,0.00014816225,0.000080111546,0.00013216167],"category_scores_gemma":[0.00010401398,0.0001305006,0.00014448194,0.0006930772,0.0000846071,0.0003135066,9.652463e-7,0.0002454618,0.000013244969],"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":[0.00033051346,0.00011002469,0.0039603324,0.0000071551294,0.00006838435,0.000031549556,0.03131022,0.9616943,0.0019814258,0.00017125937,0.00007839694,0.00025644607],"study_design_scores_gemma":[0.008840688,0.002925128,0.73775053,0.001869847,0.001465779,0.000011136175,0.19724242,0.0454101,0.001528137,0.0012384309,0.00055108767,0.0011666956],"about_ca_topic_score_codex":0.00011291014,"about_ca_topic_score_gemma":0.0014243881,"teacher_disagreement_score":0.9162842,"about_ca_system_score_codex":0.000090985915,"about_ca_system_score_gemma":0.00010232592,"threshold_uncertainty_score":0.532166},"labels":[],"label_agreement":null},{"id":"W4385377228","doi":"10.2139/ssrn.4525589","title":"Sustainable Hub Location Under Uncertainty","year":2023,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Business; Computer science; Environmental science","score_opus":0.019819416013307573,"score_gpt":0.303008057023428,"score_spread":0.2831886410101204,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385377228","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.118624374,0.0073901773,0.811005,0.038852356,0.005398102,0.0018416686,0.000027053818,0.001726185,0.015135063],"genre_scores_gemma":[0.9169907,0.007908166,0.00013127767,0.00008674435,0.0007115029,0.000019710169,0.0001620306,0.00003823469,0.07395166],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99642664,0.00024416583,0.00033592887,0.0002843134,0.0005496788,0.0021592642],"domain_scores_gemma":[0.99871445,0.00008194052,0.0003220537,0.00016151996,0.00061042776,0.00010962468],"candidate_categories":["research_integrity"],"consensus_categories":[],"category_scores_codex":[0.003903633,0.00016972293,0.00018863869,0.00023970858,0.0009938356,0.0002654436,0.0003892646,0.00033385953,0.00004239111],"category_scores_gemma":[0.00021350042,0.0001788646,0.00011311805,0.00045975193,0.00009574182,0.00019596626,0.000034754426,0.0026035446,0.000052986838],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015882562,0.000018716879,0.00064217614,0.000021046773,0.000077720324,0.000004159182,0.0034912562,0.52156895,4.2596741e-7,0.47288546,0.00053245976,0.00074176653],"study_design_scores_gemma":[0.00028127007,0.000045617184,0.0014988251,0.000084898405,0.000082748695,0.000005316175,0.095347725,0.0011347778,9.424577e-7,0.8944339,0.006780848,0.0003030931],"about_ca_topic_score_codex":0.009811583,"about_ca_topic_score_gemma":0.029454857,"teacher_disagreement_score":0.81087375,"about_ca_system_score_codex":0.0027565262,"about_ca_system_score_gemma":0.015258726,"threshold_uncertainty_score":0.9996975},"labels":[],"label_agreement":null},{"id":"W4385427038","doi":"10.1177/03611981231185140","title":"Data-Driven Multi-Criteria Assessment Framework for Analyzing the Reliability of Bus Services","year":2023,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"ELECTRE; Reliability (semiconductor); Public transport; Automatic vehicle location; Computer science; Benchmark (surveying); Process (computing); Intelligent transportation system; Ranking (information retrieval); Reliability engineering; Fuzzy logic; Bus priority; Bus network; Data mining; Operations research; Transport engineering; Multiple-criteria decision analysis; Engineering; System bus; Machine learning","score_opus":0.20267164147644218,"score_gpt":0.5030045705359836,"score_spread":0.3003329290595414,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385427038","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92693627,0.00013473105,0.05770008,0.011301017,0.00094938796,0.0019264226,0.00090410415,0.000076756674,0.00007125222],"genre_scores_gemma":[0.9637533,0.0017586186,0.03340003,0.000034291465,0.0002647646,0.00009707019,0.0003061815,0.00004366321,0.00034209073],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99173063,0.0020683275,0.0015436657,0.00047784986,0.0033485931,0.0008309526],"domain_scores_gemma":[0.9889589,0.004190128,0.0007534112,0.00084298395,0.0049890066,0.00026559696],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.015377916,0.00019651417,0.00044001715,0.00074191805,0.0017833246,0.00019851749,0.0024351962,0.00024114884,0.00016483788],"category_scores_gemma":[0.0007245196,0.00013673998,0.00032702644,0.003816279,0.0010115089,0.0009363392,0.000012802433,0.0014573026,0.000007159096],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014830878,0.00052224885,0.84925747,0.0011109888,0.00034557193,0.00003131838,0.047934372,0.06150972,0.00088443514,0.024188224,0.0073269503,0.0054056034],"study_design_scores_gemma":[0.001046384,0.00022417113,0.94667256,0.0005289174,0.00011105792,5.67327e-8,0.017804738,0.0073693027,0.00009854917,0.007752528,0.01822065,0.00017108854],"about_ca_topic_score_codex":0.01266433,"about_ca_topic_score_gemma":0.077374324,"teacher_disagreement_score":0.09741508,"about_ca_system_score_codex":0.0001616427,"about_ca_system_score_gemma":0.0011551214,"threshold_uncertainty_score":0.9995162},"labels":[],"label_agreement":null},{"id":"W4385438133","doi":"10.1016/j.aej.2023.07.065","title":"Sustainable urban development of mobility and streetscape in historic city quarters, an ancient street in Alexandria - Egypt, as a case study","year":2023,"lang":"en","type":"article","venue":"Alexandria Engineering Journal","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Sustainability; Sustainable development; Quarter (Canadian coin); Environmental planning; Transport engineering; Urban planning; Sustainable transport; Business; Civil engineering; Geography; Engineering; Political science","score_opus":0.017941272201853868,"score_gpt":0.27880495365091945,"score_spread":0.2608636814490656,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385438133","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9987488,0.00010286471,0.00056761527,0.000018734252,0.00018186838,0.0002690679,0.0000020444736,0.000048580267,0.0000604667],"genre_scores_gemma":[0.9986417,0.000022229331,0.0011340994,0.000002857849,0.00003959444,0.000014042717,0.000006372838,0.0000094709685,0.00012962209],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9985887,0.000105239626,0.00045403888,0.00019888573,0.00030200116,0.00035118186],"domain_scores_gemma":[0.99946785,0.000079211466,0.000101015095,0.000095821575,0.0000903592,0.0001657146],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017465303,0.0001157686,0.00019912142,0.0004588858,0.00021529492,0.000053122858,0.000102732265,0.00006513545,0.000029148609],"category_scores_gemma":[0.000121737154,0.0001248234,0.000022092327,0.00079590006,0.000029619305,0.0003473513,0.0000116586525,0.00020221331,5.2817023e-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":[0.000033350007,0.00031664272,0.7579274,0.000041324183,0.000016803515,0.0017003067,0.15820137,0.08083286,0.00003967137,0.00019090853,0.000082404506,0.00061693747],"study_design_scores_gemma":[0.0016942054,0.00023027344,0.8306049,0.00008037666,0.000016898874,0.000053074415,0.15753858,0.008590436,0.00001462224,0.000019500192,0.00089782866,0.00025933166],"about_ca_topic_score_codex":0.0037682427,"about_ca_topic_score_gemma":0.01430669,"teacher_disagreement_score":0.07267746,"about_ca_system_score_codex":0.00027381227,"about_ca_system_score_gemma":0.00030549878,"threshold_uncertainty_score":0.7983469},"labels":[],"label_agreement":null},{"id":"W4385705217","doi":"10.1007/s10732-023-09515-w","title":"An integrated learning and progressive hedging matheuristic for stochastic network design problem","year":2023,"lang":"en","type":"article","venue":"Journal of Heuristics","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal; Transport Canada","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Mathematical optimization; Quality (philosophy); Network planning and design; Point (geometry); Artificial intelligence; Mathematics","score_opus":0.02906346457009566,"score_gpt":0.33176446640107216,"score_spread":0.3027010018309765,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385705217","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008268716,0.00019913544,0.9907678,0.00018269595,0.00024269406,0.00022348978,0.000004734142,0.00007710401,0.000033677028],"genre_scores_gemma":[0.81669205,0.00012359857,0.1823762,0.000022146747,0.000482997,0.000008632991,0.000024135012,0.000022832062,0.00024738387],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990397,0.00016103344,0.00029363643,0.00008606369,0.0002066382,0.0002129128],"domain_scores_gemma":[0.9985514,0.0005671472,0.0003445589,0.000029886136,0.00039379235,0.00011316692],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013189949,0.00007330974,0.000150401,0.00010225879,0.0004649131,0.000106804735,0.00008444401,0.000058011552,0.000007396747],"category_scores_gemma":[0.00075555244,0.000065038075,0.000029186687,0.00030949854,0.00007712374,0.00012692447,0.0000029994512,0.0001707092,0.0000014516368],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010092745,0.000024707426,0.0016602868,0.000028276289,0.000024318448,0.00003503182,0.0069202636,0.981661,0.000010101288,0.0019344597,0.0015319309,0.006068659],"study_design_scores_gemma":[0.0026084038,0.0026113344,0.0062641534,0.0014619322,0.00058189296,0.00007608332,0.025818689,0.89258575,0.000015643487,0.028915994,0.038261674,0.00079843146],"about_ca_topic_score_codex":0.0000056445765,"about_ca_topic_score_gemma":0.0000053996328,"teacher_disagreement_score":0.80842334,"about_ca_system_score_codex":0.000026015954,"about_ca_system_score_gemma":0.00017957477,"threshold_uncertainty_score":0.35757825},"labels":[],"label_agreement":null},{"id":"W4385749309","doi":"10.1287/trsc.2021.0110","title":"Iterative Backpropagation Method for Efficient Gradient Estimation in Bilevel Network Equilibrium Optimization Problems","year":2023,"lang":"en","type":"article","venue":"Transportation Science","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Bilevel optimization; Mathematical optimization; Computer science; Backpropagation; Optimization problem; Iterative method; Iterative and incremental development; Matching (statistics); Mathematics; Artificial neural network; Artificial intelligence; Statistics","score_opus":0.04052427478692713,"score_gpt":0.34987745305525914,"score_spread":0.30935317826833203,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385749309","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06610831,0.000012893304,0.93034947,0.00078967767,0.0006122825,0.0013419379,0.00004475809,0.0003151005,0.00042557265],"genre_scores_gemma":[0.74232644,0.00002107374,0.25613037,0.00007385935,0.00006221971,0.0002947686,0.0007753374,0.000019159825,0.00029679158],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974515,0.000117991774,0.0005639714,0.00053406757,0.000776567,0.000555908],"domain_scores_gemma":[0.99872166,0.00027300834,0.00023890527,0.0001388593,0.0004948908,0.00013265893],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036915673,0.00014949334,0.00017663462,0.00054476445,0.00072261674,0.000163233,0.0002403509,0.00009279766,0.00003448685],"category_scores_gemma":[0.00022822926,0.00015837951,0.000056614917,0.0049728053,0.0002622108,0.0008375981,0.0000027069839,0.00008921209,0.000012834435],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022762059,0.00003074461,0.003338489,0.000024151963,0.000002530207,8.8095953e-7,0.021975497,0.96038496,0.00024674193,0.012177505,0.000074996584,0.0017207372],"study_design_scores_gemma":[0.0005126283,0.000042050247,0.038664825,0.000074162424,0.00001420701,1.3336268e-7,0.0010552651,0.9573845,0.00019953545,0.001536187,0.00032234465,0.000194131],"about_ca_topic_score_codex":0.00023475842,"about_ca_topic_score_gemma":0.0008548938,"teacher_disagreement_score":0.6762181,"about_ca_system_score_codex":0.00016594751,"about_ca_system_score_gemma":0.00042638564,"threshold_uncertainty_score":0.6458529},"labels":[],"label_agreement":null},{"id":"W4385965065","doi":"10.48550/arxiv.2308.00600","title":"Simulating the social influence in transport mode choices","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Universidad Nacional de Colombia; Canadian Bureau for International Education; Carnegie Mellon University; Arizona State University","keywords":"Mode (computer interface); Work (physics); Computer science; Business; Cluster (spacecraft); Mode choice; Transport engineering; Knowledge management; Operations research; Public transport; Engineering; Human–computer interaction","score_opus":0.1032847246146543,"score_gpt":0.26305134103102135,"score_spread":0.15976661641636705,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385965065","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99041945,0.000008533346,0.0055042785,0.0003259068,0.00021223712,0.00024916886,0.000033370696,0.00022998103,0.0030170835],"genre_scores_gemma":[0.9975745,0.000096036,0.00004549239,0.000052676834,0.00010001068,0.0000013745351,0.00005828046,0.000014319406,0.0020573218],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99886346,0.0001569882,0.00018535895,0.00040655793,0.00012883043,0.00025882619],"domain_scores_gemma":[0.99932545,0.00019227066,0.00016902291,0.00015824226,0.00010423685,0.00005076506],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043599776,0.00013635177,0.00016497403,0.00014028366,0.0006303323,0.000048394002,0.0004886477,0.0002624622,0.00002186395],"category_scores_gemma":[0.000060435523,0.00014349415,0.00009947642,0.0006814983,0.00020898432,0.00022875662,0.00003483106,0.00044581128,0.000018962943],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011988171,0.000011106495,0.17697133,0.000015761832,0.0000111072095,0.000029634744,0.014325347,0.7948277,6.35469e-7,0.013754252,0.000011095926,0.000030045512],"study_design_scores_gemma":[0.00049599196,0.000008675635,0.7202802,0.00017576003,0.00009929879,6.287204e-8,0.0109020695,0.25076228,0.0000020256666,0.015816303,0.00097591465,0.00048141152],"about_ca_topic_score_codex":0.012462208,"about_ca_topic_score_gemma":0.026515575,"teacher_disagreement_score":0.5440654,"about_ca_system_score_codex":0.00011812372,"about_ca_system_score_gemma":0.00024208897,"threshold_uncertainty_score":0.99411386},"labels":[],"label_agreement":null},{"id":"W4386051855","doi":"10.1177/03611981231192102","title":"Improved Random Walk Method Verified in a Large-Scale Urban Network for the Sampling of Alternatives in Route Choice Modeling","year":2023,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Random walk; Computer science; Scale (ratio); Adaptability; Mathematical optimization; Value (mathematics); Sampling (signal processing); Algorithm; Mathematics; Statistics; Machine learning","score_opus":0.14651166656180956,"score_gpt":0.45493090298897526,"score_spread":0.30841923642716573,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386051855","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8418805,0.00028771668,0.15153995,0.0034849835,0.0005416796,0.0020428323,0.0001258324,0.000034914152,0.00006160633],"genre_scores_gemma":[0.98272693,0.0017720887,0.0144559415,0.000030475485,0.00028822696,0.00022028768,0.00006234131,0.000045144054,0.00039856115],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99277824,0.0018260182,0.0016811078,0.00038102333,0.0022652086,0.0010684049],"domain_scores_gemma":[0.9911945,0.0058031133,0.0004867549,0.00028790245,0.002047113,0.00018065154],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.019646235,0.00018928535,0.00050672906,0.0010486658,0.00088503916,0.00010292701,0.0010101942,0.00020040499,0.00004534293],"category_scores_gemma":[0.0007693292,0.00014172877,0.00034330337,0.004435076,0.00032316745,0.0005793682,0.0000048806764,0.001424615,0.0000014971798],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0023326941,0.00012178621,0.26089975,0.00013557871,0.00007891168,0.000009550105,0.04415627,0.68688846,0.00045131685,0.002235935,0.00036970744,0.0023200565],"study_design_scores_gemma":[0.005745404,0.00021634572,0.76688635,0.0006371134,0.00005503769,5.767419e-8,0.025525223,0.1924468,0.00015222492,0.0035601582,0.0045619193,0.00021333632],"about_ca_topic_score_codex":0.039603684,"about_ca_topic_score_gemma":0.40300632,"teacher_disagreement_score":0.50598663,"about_ca_system_score_codex":0.00019833213,"about_ca_system_score_gemma":0.0008108201,"threshold_uncertainty_score":0.9667917},"labels":[],"label_agreement":null},{"id":"W4386208322","doi":"10.1016/j.trc.2023.104309","title":"Multi-objective multi-agent deep reinforcement learning to reduce bus bunching for multiline services with a shared corridor","year":2023,"lang":"en","type":"article","venue":"Transportation Research Part C Emerging Technologies","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":24,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Reinforcement learning; Computer science; Transit (satellite); Line (geometry); Preference; Control (management); Service (business); Public transport; State (computer science); Real-time computing; Transport engineering; Distributed computing; Engineering; Artificial intelligence; Mathematics; Algorithm","score_opus":0.09121428174535277,"score_gpt":0.40535219564624136,"score_spread":0.3141379139008886,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386208322","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.66040283,0.00017495958,0.3209762,0.0058664926,0.00036909766,0.004753474,0.00010511672,0.007215321,0.00013653484],"genre_scores_gemma":[0.9502795,0.00031609717,0.04489456,0.000030584386,0.000043988504,0.0013858973,0.00089567644,0.000047058238,0.002106675],"study_design_codex":"simulation_or_modeling","study_design_gemma":"qualitative","domain_scores_codex":[0.99697536,0.00013032508,0.00046818997,0.0006240706,0.0009216228,0.0008804163],"domain_scores_gemma":[0.9981839,0.0003509811,0.00016017875,0.00025011873,0.0009178193,0.00013698198],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0016773667,0.00022251504,0.00025312477,0.0008806819,0.0018212566,0.00013746596,0.00046418214,0.00019238236,0.00003440808],"category_scores_gemma":[0.0005599242,0.00021344036,0.00007451871,0.0024532392,0.00023796312,0.00040482677,0.000015970438,0.00044679808,0.000044121873],"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":[0.00033464987,0.000087520835,0.018662887,0.0001835891,0.00007653569,0.000022707221,0.08953825,0.8825256,0.0012477775,0.000568247,0.00038010642,0.0063721277],"study_design_scores_gemma":[0.0041209585,0.00076053827,0.070056155,0.0009651473,0.000084657964,3.836345e-7,0.49259397,0.37483817,0.005111577,0.00013982611,0.05033097,0.000997643],"about_ca_topic_score_codex":0.0022694655,"about_ca_topic_score_gemma":0.017553616,"teacher_disagreement_score":0.50768745,"about_ca_system_score_codex":0.00014042726,"about_ca_system_score_gemma":0.00016270917,"threshold_uncertainty_score":0.9994782},"labels":[],"label_agreement":null},{"id":"W4386275958","doi":"10.1155/2023/8847456","title":"Line Planning under the Operation Mode of Line Sharing between Metro and Suburban Railway","year":2023,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","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":"Gansu Education Department; Lanzhou Jiaotong University; State Key Laboratory of Rail Traffic Control and Safety; Natural Science Foundation of Gansu Province; National Natural Science Foundation of China","keywords":"Simulated annealing; Mathematical optimization; Mode (computer interface); Computer science; Line (geometry); Profit (economics); Linear programming; Operations research; Transport engineering; Engineering; Algorithm; Mathematics","score_opus":0.037013707652061606,"score_gpt":0.34201169893524946,"score_spread":0.30499799128318783,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386275958","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9500365,0.00017866203,0.04854529,0.00087980914,0.00015945286,0.00010791369,0.000017463079,0.00003056735,0.00004430039],"genre_scores_gemma":[0.9966526,0.0003137309,0.0026215538,0.0000295578,0.00017076934,0.0000020861555,0.00011680271,0.000010574375,0.00008231321],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9988404,0.000044181856,0.000518752,0.00010251325,0.00036611303,0.00012806505],"domain_scores_gemma":[0.99908084,0.00017867445,0.00039102937,0.000058433237,0.0002309975,0.00006003904],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006273688,0.00007747147,0.00017788583,0.00017797043,0.00023664394,0.00002904942,0.00010963222,0.000061900355,0.00000920997],"category_scores_gemma":[0.00004311274,0.00006185999,0.000055316963,0.00044983815,0.00006676379,0.00055089616,0.0000010998874,0.00014865045,5.9552974e-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":[0.000056879733,0.000009521726,0.04983715,0.000019193034,0.000028359571,0.0000032273576,0.019769497,0.9266762,0.00085728825,0.001321963,0.00001169252,0.0014090033],"study_design_scores_gemma":[0.001514389,0.0002287544,0.9602291,0.0002771045,0.00020342713,7.9865987e-7,0.026477512,0.0050117066,0.0020384358,0.0031782999,0.0006484618,0.0001919819],"about_ca_topic_score_codex":0.00007670801,"about_ca_topic_score_gemma":0.0003577063,"teacher_disagreement_score":0.92166454,"about_ca_system_score_codex":0.000023023094,"about_ca_system_score_gemma":0.00006528867,"threshold_uncertainty_score":0.2522577},"labels":[],"label_agreement":null},{"id":"W4386697870","doi":"10.35674/kent.1287655","title":"Ankara’da Kent İçi Toplu Ulaşımda Kullanılan Finansman Kaynaklarının Dünya Örnekleri İle Karşılaştırılması; Ankara İçin Model Önerisi","year":2023,"lang":"tr","type":"article","venue":"Kent Akademisi","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Physics","score_opus":0.043631081206258485,"score_gpt":0.3015482134447086,"score_spread":0.25791713223845014,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386697870","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9147764,0.0022138264,0.0071175965,0.023853533,0.0037519827,0.0020941463,0.0008362475,0.0021599291,0.043196354],"genre_scores_gemma":[0.9395661,0.0032148233,0.0015158209,0.0007855684,0.00094333716,0.000089621644,0.0012679504,0.00014909168,0.052467704],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99202394,0.000443129,0.0017210038,0.0017304098,0.002213012,0.0018685096],"domain_scores_gemma":[0.99667144,0.00034786647,0.00077965687,0.00092278776,0.0004900559,0.000788221],"candidate_categories":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0023948797,0.00089564465,0.0010548981,0.0006264175,0.0017085251,0.0007122177,0.001447364,0.001059323,0.00055690046],"category_scores_gemma":[0.00037066758,0.0009611536,0.0005816836,0.003046176,0.0004876091,0.0007967824,0.00016354879,0.0010047717,0.0012860431],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030401236,0.0007719338,0.08102564,0.00035682812,0.00038345647,0.00025413014,0.08665339,0.6001171,0.0005888091,0.031203559,0.19093962,0.007401551],"study_design_scores_gemma":[0.0041925097,0.0004004213,0.12128138,0.0007356175,0.00047910778,0.000011826293,0.017685918,0.5241704,0.0006441913,0.008502651,0.31848451,0.0034114905],"about_ca_topic_score_codex":0.001588722,"about_ca_topic_score_gemma":0.00079506205,"teacher_disagreement_score":0.1275449,"about_ca_system_score_codex":0.00032224678,"about_ca_system_score_gemma":0.0008026976,"threshold_uncertainty_score":0.9995911},"labels":[],"label_agreement":null},{"id":"W4386797184","doi":"10.23977/jeis.2023.080305","title":"A Path Simulator Focusing on Time Consumption-Based on the Transport Network and the Data of Public Traffic Vehicles in Shanghai","year":2023,"lang":"en","type":"article","venue":"Journal of Electronics and Information Science","topic":"Transportation Planning and Optimization","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":"Schedule; Travel time; Computer science; Public transport; Transport engineering; Graph; Construct (python library); Real-time data; Consumption (sociology); Operations research; Order (exchange); Path (computing); Real-time computing; Simulation; Engineering; Computer network; Business","score_opus":0.036383225876049065,"score_gpt":0.29299816758442865,"score_spread":0.25661494170837956,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386797184","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9934753,0.000119756674,0.0008058041,0.0051376508,0.000047812115,0.0001334283,0.0000084845315,0.000008619151,0.00026313175],"genre_scores_gemma":[0.9989452,0.00066936505,0.000052283303,0.0002999393,0.000018793122,7.882089e-7,0.000008066718,0.0000013060005,0.000004268536],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989298,0.00006973734,0.00030692853,0.000054262448,0.00047806714,0.00016121207],"domain_scores_gemma":[0.99897486,0.00047833665,0.00027873524,0.00008984211,0.000138552,0.00003969862],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006237459,0.00004223475,0.000087446606,0.0001807156,0.00040685263,0.00011191692,0.00025061975,0.000024926936,0.000006119661],"category_scores_gemma":[0.00030512465,0.000024497022,0.000016153886,0.0008349606,0.00037857555,0.0013596573,0.0000054065617,0.00012500442,0.000001448488],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017398821,0.000025140915,0.0070765982,0.00001538276,0.0000072098646,6.3277076e-7,0.010408217,0.9161936,0.000020339929,0.0546424,0.00028354223,0.011152933],"study_design_scores_gemma":[0.00081094186,0.0001022675,0.041457344,0.000091928065,0.000009515387,7.546102e-7,0.00073199713,0.95246726,0.000007882895,0.00026761944,0.003995669,0.000056798246],"about_ca_topic_score_codex":0.000008136171,"about_ca_topic_score_gemma":0.000049405455,"teacher_disagreement_score":0.054374784,"about_ca_system_score_codex":0.000027293981,"about_ca_system_score_gemma":0.0004621844,"threshold_uncertainty_score":0.31292227},"labels":[],"label_agreement":null},{"id":"W4386935174","doi":"10.1080/23249935.2023.2258996","title":"Optimal vehicle capacity and dispatching policy considering crowding in public bus transit services","year":2023,"lang":"en","type":"article","venue":"Transportmetrica A Transport Science","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates","keywords":"Public transport; Crowding; Transport engineering; Transit (satellite); Business; Bus rapid transit; Computer science; Engineering","score_opus":0.04108621947447248,"score_gpt":0.2971710170883703,"score_spread":0.2560847976138978,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386935174","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99035233,0.00008369657,0.0041805687,0.0022300056,0.000152527,0.0003579356,0.00005354148,0.0005524038,0.0020370106],"genre_scores_gemma":[0.99661356,0.0004666794,0.0025554958,0.00013545445,0.000061008537,0.00002528857,0.00004460605,0.000025450692,0.00007246246],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9962921,0.00006542832,0.0005897302,0.00077727134,0.0011363074,0.0011391259],"domain_scores_gemma":[0.998813,0.00018632403,0.0001190835,0.00022043321,0.00015548493,0.0005057173],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0023697084,0.000256092,0.0003545581,0.002498257,0.0013527533,0.00019303177,0.00054392056,0.00015164795,0.000038659662],"category_scores_gemma":[0.00008606464,0.00028069515,0.00008893231,0.0138690565,0.0011474375,0.0018704752,0.00000678972,0.00032025808,0.000009887347],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000037227597,0.00009384975,0.86356616,0.000136807,0.000013994099,0.00014510532,0.08193415,0.033167586,0.0016883276,0.015537053,0.0000021173732,0.0036776525],"study_design_scores_gemma":[0.0007363779,0.000032381846,0.98681873,0.000107429696,0.000025465151,0.0000032289604,0.0048032305,0.004633033,0.00025605698,0.00031462853,0.0018375918,0.00043181673],"about_ca_topic_score_codex":0.009793496,"about_ca_topic_score_gemma":0.00973187,"teacher_disagreement_score":0.12325263,"about_ca_system_score_codex":0.0001507774,"about_ca_system_score_gemma":0.0007056645,"threshold_uncertainty_score":0.99996454},"labels":[],"label_agreement":null},{"id":"W4386990134","doi":"10.2139/ssrn.4580914","title":"Quantifying the Impact of Bottlenecks on Network Performance Using Macroscopic Fundamental Diagram Using Clustering and Decision Tree Prediction","year":2023,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Cluster analysis; Diagram; Influence diagram; Decision tree; Computer science; Tree (set theory); Statistical physics; Data mining; Econometrics; Mathematics; Machine learning; Statistics; Physics; Combinatorics","score_opus":0.062295752994800446,"score_gpt":0.3663696009091866,"score_spread":0.30407384791438613,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386990134","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9217898,0.00077579595,0.07629733,0.000028614602,0.00080044515,0.0002163949,0.000012046045,0.00004040297,0.000039159],"genre_scores_gemma":[0.9906578,0.0076965154,0.0010647814,0.000004783732,0.0004969871,0.0000025172164,0.000017749042,0.000027485392,0.00003139535],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977451,0.00014531706,0.00041448866,0.00022939622,0.00043392158,0.0010318048],"domain_scores_gemma":[0.9991365,0.00013425092,0.00046006378,0.00012644942,0.00007905453,0.000063652056],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024260639,0.00018153929,0.00022834408,0.00014015021,0.0011975899,0.00018804621,0.0002031095,0.00019127001,0.0000053808712],"category_scores_gemma":[0.00004727769,0.0001405336,0.00014020856,0.00025193277,0.00010637589,0.00017878738,0.000049025522,0.0014775831,6.493325e-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":[0.00008233308,0.000012484015,0.13125023,0.000014172515,0.000086548986,6.657352e-7,0.0015224746,0.8550808,0.000032499425,0.00021091003,0.0000050839776,0.011701793],"study_design_scores_gemma":[0.0005870916,0.00036126783,0.16458844,0.0015281113,0.00015428485,0.00003250436,0.003688979,0.8216086,0.000008206574,0.0071681975,0.000008725503,0.0002655757],"about_ca_topic_score_codex":0.0021173817,"about_ca_topic_score_gemma":0.0046911156,"teacher_disagreement_score":0.07523254,"about_ca_system_score_codex":0.0012182033,"about_ca_system_score_gemma":0.0019659128,"threshold_uncertainty_score":0.92110145},"labels":[],"label_agreement":null},{"id":"W4387161548","doi":"10.1155/2023/2910454","title":"Expressway Usage Pattern Analysis Based on Tollgate Data: A Case Study of the Shandong Province, China","year":2023,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","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":"U.S. Department of Transportation","keywords":"Transport engineering; China; TRIPS architecture; Traffic flow (computer networking); Traffic congestion; Demand management; Computer science; Business; Geography; Engineering","score_opus":0.02518329743594576,"score_gpt":0.31687932556306,"score_spread":0.2916960281271142,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387161548","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9867881,0.000009806033,0.012295749,0.00018425539,0.00023916042,0.00030629206,0.0000915731,0.000027108183,0.00005795172],"genre_scores_gemma":[0.9993224,0.000021989486,0.0004094993,0.00002367737,0.000047517784,0.00000467802,0.00009302344,0.000009142073,0.000068076566],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9982689,0.00017571417,0.00056816,0.0001704956,0.0006815384,0.0001351996],"domain_scores_gemma":[0.9986685,0.00014102961,0.00069165276,0.0002577197,0.0001801134,0.00006097851],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007912885,0.00009439915,0.0002336897,0.00032513132,0.00028153943,0.000027395143,0.0002922325,0.000042776912,0.00002038979],"category_scores_gemma":[0.000060642386,0.0000702404,0.000117783864,0.0015723425,0.000043465985,0.0005223793,0.0000020030723,0.00015098628,5.312362e-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":[0.00009367508,0.00017720902,0.12966265,0.000014175021,0.00009347461,0.00030734262,0.022692401,0.84431523,0.000045015393,0.000008375376,0.000022699685,0.0025677814],"study_design_scores_gemma":[0.0019327747,0.00025718487,0.9617673,0.0000919184,0.00078073976,0.0000023158184,0.02613006,0.00855304,0.000091514274,0.000019535139,0.000249118,0.00012448925],"about_ca_topic_score_codex":0.0008507655,"about_ca_topic_score_gemma":0.014914789,"teacher_disagreement_score":0.83576214,"about_ca_system_score_codex":0.000024176017,"about_ca_system_score_gemma":0.00014457466,"threshold_uncertainty_score":0.8322803},"labels":[],"label_agreement":null},{"id":"W4387216474","doi":"10.1177/03611981231198845","title":"Using Wi-Fi Connection Data to Analyze Performance of the Subway System in Toronto, Canada","year":2023,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Data collection; Public transport; Bluetooth; Reliability (semiconductor); Transport engineering; Computer science; Automatic vehicle location; Measure (data warehouse); Transit (satellite); Service (business); Performance measurement; Telecommunications; Engineering; Wireless; Global Positioning System; Data mining","score_opus":0.17815192832432367,"score_gpt":0.42929028285707926,"score_spread":0.25113835453275557,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387216474","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9952635,0.00007850023,0.00032257347,0.0019585127,0.0009436246,0.00090913643,0.00020390481,0.00002724872,0.00029295575],"genre_scores_gemma":[0.9983793,0.00042705142,0.0005410044,0.00002529586,0.00013648454,0.000020750043,0.000054005144,0.00002874961,0.00038735213],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9917789,0.0015429411,0.0013154667,0.0003694978,0.0042389254,0.00075425656],"domain_scores_gemma":[0.9954046,0.0007803527,0.00047750789,0.00061828055,0.0024419506,0.00027732432],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008160229,0.0001638175,0.00036135217,0.0005955132,0.0010370716,0.00007707145,0.0017660134,0.00014568731,0.00011383276],"category_scores_gemma":[0.00035611758,0.00012339064,0.00013555598,0.005073107,0.00035901467,0.0008640151,0.000011678796,0.00092726905,0.0000035839153],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007272981,0.0000782664,0.87611264,0.0003326909,0.00007864369,0.00003871644,0.010635829,0.102785304,0.0011488429,0.0026629115,0.003996246,0.0014026251],"study_design_scores_gemma":[0.0005955568,0.00010236406,0.96200496,0.0006693464,0.00003568604,1.7763378e-7,0.024879131,0.0051603485,0.00032577198,0.00003852989,0.006053588,0.00013456275],"about_ca_topic_score_codex":0.8988001,"about_ca_topic_score_gemma":0.994601,"teacher_disagreement_score":0.09762496,"about_ca_system_score_codex":0.0010810542,"about_ca_system_score_gemma":0.0034435252,"threshold_uncertainty_score":0.79764205},"labels":[],"label_agreement":null},{"id":"W4387299441","doi":"10.5198/jtlu.2023.2268","title":"A review of the housing market-clearing process in integrated land-use and transport models","year":2023,"lang":"en","type":"review","venue":"Journal of Transport and Land Use","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Clearing; Operationalization; Disequilibrium; Interdependence; Context (archaeology); Market clearing; Computer science; Representativeness heuristic; Process (computing); Operations research; Management science; Economics; Microeconomics; Engineering; Mathematics","score_opus":0.09081487282075117,"score_gpt":0.3413157838469134,"score_spread":0.25050091102616223,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387299441","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019563224,0.97945917,0.00012600317,0.00007841655,0.00014068163,0.00045704813,0.0000841002,0.00001906146,0.00007229541],"genre_scores_gemma":[0.0051323567,0.9944786,0.00013467888,0.000041788804,0.000040612482,0.0000040151017,0.00003104032,0.000025480349,0.00011142079],"study_design_codex":"observational","study_design_gemma":"systematic_review","domain_scores_codex":[0.9981989,0.00014639526,0.0009904141,0.00015993473,0.0003320411,0.00017228641],"domain_scores_gemma":[0.9988193,0.00016041333,0.0006602749,0.00008911405,0.00017688228,0.00009399462],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012093256,0.00019568186,0.0010230017,0.00020248907,0.000115653784,0.000036994752,0.00016813424,0.00019007483,0.000012152101],"category_scores_gemma":[0.00006031507,0.00012048843,0.00022885542,0.0006352664,0.000098388715,0.000529733,0.0000023637306,0.00043030237,8.051316e-8],"study_design_candidate":"systematic_review","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032047962,0.00024384038,0.62298155,0.2378593,0.0006343615,0.00040849662,0.023063937,0.002994471,1.6828456e-7,0.00018994606,0.00029728154,0.11100615],"study_design_scores_gemma":[0.0011047833,0.00006820644,0.048925042,0.5818718,0.0029538993,0.000054328393,0.00058325654,0.0001962115,1.6932924e-7,0.00012425649,0.36351752,0.0006004956],"about_ca_topic_score_codex":0.00082223135,"about_ca_topic_score_gemma":0.0032144266,"teacher_disagreement_score":0.5740565,"about_ca_system_score_codex":0.000023602186,"about_ca_system_score_gemma":0.00042562388,"threshold_uncertainty_score":0.49133757},"labels":[],"label_agreement":null},{"id":"W4387389765","doi":"10.48550/arxiv.2310.02384","title":"Constrained Optimization with Decision-Dependent Distributions","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Air Force Office of Scientific Research; National Science Foundation; Vetenskapsrådet; European Commission; Natural Sciences and Engineering Research Council of Canada; Knut och Alice Wallenbergs Stiftelse","keywords":"Mathematical optimization; Optimization problem; Stochastic optimization; Computer science; Constrained optimization; Convergence (economics); Dual (grammatical number); Mathematics","score_opus":0.0694946494386965,"score_gpt":0.22351978657830043,"score_spread":0.15402513713960392,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387389765","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.033867143,0.0000084755575,0.96050996,0.00028049923,0.0004170805,0.00033606522,0.00029205065,0.00051383587,0.003774872],"genre_scores_gemma":[0.98926306,0.0002831565,0.006043962,0.000017507262,0.00006630023,0.0000021035942,0.0008353618,0.0000204086,0.003468142],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998751,0.0001113084,0.00016970381,0.0005420105,0.00016820888,0.00025777402],"domain_scores_gemma":[0.99885076,0.00020127991,0.00019227079,0.00028001753,0.00031190255,0.00016375918],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029837128,0.00017140554,0.00018256817,0.00018412038,0.0006063411,0.000106042084,0.00032838964,0.0002812195,0.000201919],"category_scores_gemma":[0.0001127587,0.00019174424,0.00008111911,0.0006566177,0.00027531182,0.00018182497,0.00005647761,0.0002848328,0.000054689808],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000043265147,0.000031362993,0.0086232,0.000008902739,0.00003967033,0.00008039856,0.0006330523,0.92162853,1.78607e-7,0.068644226,0.00020275782,0.00006448083],"study_design_scores_gemma":[0.003439824,0.00015778287,0.021764755,0.0009578973,0.00096487487,0.0000037813984,0.01354772,0.8952828,0.000013042339,0.058965612,0.00274999,0.002151889],"about_ca_topic_score_codex":0.0010079524,"about_ca_topic_score_gemma":0.0028611356,"teacher_disagreement_score":0.95539594,"about_ca_system_score_codex":0.0002061315,"about_ca_system_score_gemma":0.0006843014,"threshold_uncertainty_score":0.78191036},"labels":[],"label_agreement":null},{"id":"W4387396681","doi":"10.1155/2023/6678628","title":"An Activity-Based Travel Personalization Tool Driven by the Genetic Algorithm","year":2023,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","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":"Magyar Tudományos Akadémia","keywords":"Personalization; Computer science; Genetic algorithm; TRIPS architecture; Plan (archaeology); Travel behavior; Control (management); Machine learning; Transport engineering; Artificial intelligence; Engineering","score_opus":0.01291670953856495,"score_gpt":0.2911756960376092,"score_spread":0.27825898649904424,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387396681","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.58586925,0.000040223476,0.41262656,0.00086834887,0.00029370075,0.00016442162,0.000049381077,0.000055855497,0.000032238815],"genre_scores_gemma":[0.9843541,0.00017872841,0.01486382,0.00010503949,0.00014031284,0.000007908096,0.00022926404,0.000016479258,0.00010436417],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9987094,0.00012371459,0.00032013265,0.0001196043,0.0005593535,0.00016782903],"domain_scores_gemma":[0.99911535,0.00010004561,0.00034783944,0.00007551079,0.00028422213,0.000077022654],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000399168,0.00009106791,0.0001273055,0.00012566498,0.00037077206,0.00004745558,0.00015274981,0.00007183287,0.000035787187],"category_scores_gemma":[0.00002546711,0.000076863,0.00008368683,0.00060151133,0.00007485273,0.0006114266,2.108342e-7,0.00013270791,0.0000033300278],"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":[0.0001005822,0.000092250324,0.004944743,0.000016284335,0.000023008623,0.000016969638,0.026475964,0.90761226,0.006527543,0.0002634737,0.00030150556,0.053625412],"study_design_scores_gemma":[0.0014043085,0.0002498074,0.95821667,0.00006297631,0.00010939675,0.000001427591,0.010633174,0.02429885,0.0011112619,0.0003245288,0.0033598351,0.00022777761],"about_ca_topic_score_codex":0.00005596433,"about_ca_topic_score_gemma":0.00018226635,"teacher_disagreement_score":0.9532719,"about_ca_system_score_codex":0.00004573859,"about_ca_system_score_gemma":0.00018628215,"threshold_uncertainty_score":0.3134382},"labels":[],"label_agreement":null},{"id":"W4387496398","doi":"10.1287/trsc.2022.0214","title":"Probabilistic Forecasting of Bus Travel Time with a Bayesian Gaussian Mixture Model","year":2023,"lang":"en","type":"article","venue":"Transportation Science","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Polytechnique Montréal; McGill University","funders":"","keywords":"Computer science; Probabilistic logic; Markov chain Monte Carlo; Headway; Bayesian probability; Mixture model; Scheduling (production processes); Markov chain; Simulation; Mathematical optimization; Machine learning; Mathematics; Artificial intelligence","score_opus":0.03117630804305867,"score_gpt":0.28221276323373595,"score_spread":0.2510364551906773,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387496398","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.83460957,0.00000964324,0.14787862,0.0008951286,0.000098511104,0.0006338805,0.00011552953,0.0003620348,0.015397093],"genre_scores_gemma":[0.9917547,0.000006243628,0.007207797,0.000028208546,0.00001620091,0.000016704307,0.00008666951,0.00001043746,0.0008730091],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99823904,0.000022866288,0.0002861972,0.00030381107,0.00082441274,0.00032365049],"domain_scores_gemma":[0.9992082,0.00006976809,0.00015644732,0.00012291012,0.00030486862,0.00013779428],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007807923,0.00010020353,0.00014180898,0.0002590052,0.0005124094,0.000044155375,0.000245937,0.000056681743,0.00005756612],"category_scores_gemma":[0.00007708048,0.00008906964,0.00003397733,0.002740253,0.00068482035,0.0005097749,9.732054e-7,0.00007558928,0.000008652184],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000055650176,0.000042270603,0.005793472,0.000049338058,0.000006801145,0.000011832097,0.08282312,0.88201845,0.0023028865,0.02563492,0.00007022013,0.0011910272],"study_design_scores_gemma":[0.00078613235,0.00012554285,0.11459652,0.00022328127,0.000076039505,0.0000013587676,0.008063977,0.8698151,0.0014056579,0.0043258914,0.000118395605,0.00046206592],"about_ca_topic_score_codex":0.00020497687,"about_ca_topic_score_gemma":0.000985662,"teacher_disagreement_score":0.15714517,"about_ca_system_score_codex":0.00003101453,"about_ca_system_score_gemma":0.0005362595,"threshold_uncertainty_score":0.39410904},"labels":[],"label_agreement":null},{"id":"W4387517491","doi":"10.36227/techrxiv.24260680.v1","title":"A Framework for Unsupervised Multi-Objective Optimization of Transport Networks through the Use of Genetic Algorithms: Demonstrated Through the Optimization of Indian Inter-City Transport","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Discovery Air (Canada)","funders":"","keywords":"Genetic algorithm; Metaheuristic; Travel time; Computer science; Mathematical optimization; Travel behavior; Government (linguistics); Population; Transport engineering; Operations research; Engineering; Algorithm; Mathematics; Machine learning; Sociology","score_opus":0.09124159650943092,"score_gpt":0.32919820581126236,"score_spread":0.23795660930183143,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387517491","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0065721152,0.00012551581,0.9880493,0.00057940074,0.00064820016,0.0031262955,0.00060375355,0.00019128952,0.00010409899],"genre_scores_gemma":[0.50957793,0.001339093,0.48737562,0.000078858626,0.00008660277,0.00019509341,0.0011735299,0.000061091516,0.00011220665],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99624866,0.00040957044,0.0016355119,0.0006348642,0.00065156573,0.0004198091],"domain_scores_gemma":[0.9956422,0.0011064559,0.0013527921,0.0005941894,0.001235576,0.00006882932],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007399365,0.0004288789,0.00077410956,0.00014247624,0.0004526145,0.00004810562,0.00075112365,0.0009880487,0.00010488735],"category_scores_gemma":[0.00026462314,0.00031983946,0.00047430498,0.0013100964,0.0009662206,0.00037967844,0.000022633296,0.0006235641,2.483494e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018463579,0.00017138703,0.008205397,0.00017386444,0.0002937976,0.000001385399,0.070409924,0.91935295,6.74453e-7,0.0008983964,0.000031023756,0.00027656727],"study_design_scores_gemma":[0.0009058034,0.00015333561,0.017692223,0.00086715806,0.00065408903,5.653145e-7,0.015124924,0.9626027,0.00011568132,0.0013527687,0.00006937656,0.00046142645],"about_ca_topic_score_codex":0.02179991,"about_ca_topic_score_gemma":0.0058572832,"teacher_disagreement_score":0.5030058,"about_ca_system_score_codex":0.00007377954,"about_ca_system_score_gemma":0.00065938215,"threshold_uncertainty_score":0.9999254},"labels":[],"label_agreement":null},{"id":"W4387541971","doi":"10.1080/23249935.2023.2266508","title":"Assessing transportation network redundancy by integrating route diversity and spare capacity","year":2023,"lang":"en","type":"article","venue":"Transportmetrica A Transport Science","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Spare part; Redundancy (engineering); Computer science; Flow network; Travel time; Measure (data warehouse); Operations research; Transport engineering; Data mining; Engineering; Mathematics; Operations management; Mathematical optimization","score_opus":0.054404041514945274,"score_gpt":0.30532067811079644,"score_spread":0.2509166365958512,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387541971","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96288216,0.00017243274,0.031464186,0.0005602198,0.00045044863,0.00045348206,0.0001896138,0.0008955102,0.0029319513],"genre_scores_gemma":[0.9941828,0.00043817816,0.004427472,0.000113386566,0.00008920823,0.000018985907,0.00041147647,0.000025902305,0.00029258244],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99578464,0.000082315884,0.00061970757,0.00089241134,0.0015764404,0.0010444948],"domain_scores_gemma":[0.9985088,0.00019154072,0.00024113721,0.00024306249,0.0002989582,0.00051649276],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0027270215,0.00030194753,0.000381606,0.00063166174,0.0069766194,0.00018401373,0.0005796068,0.00019208556,0.00006915226],"category_scores_gemma":[0.000091237154,0.00032251005,0.00012391234,0.009692764,0.0014437471,0.0026475987,0.000010360462,0.00035822202,0.000008527798],"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":[0.000021428013,0.000053977652,0.9460811,0.00004388417,0.00001747577,0.00004066527,0.038957458,0.0048966045,0.00025291616,0.006275481,0.00021766708,0.003141354],"study_design_scores_gemma":[0.00045619157,0.000039343755,0.9894965,0.0000983141,0.00009756158,8.899416e-7,0.003989964,0.0005109848,0.00010858662,0.0005237836,0.0042227316,0.00045514002],"about_ca_topic_score_codex":0.0050121094,"about_ca_topic_score_gemma":0.0035803425,"teacher_disagreement_score":0.04341542,"about_ca_system_score_codex":0.0001472479,"about_ca_system_score_gemma":0.00036993637,"threshold_uncertainty_score":0.9999227},"labels":[],"label_agreement":null},{"id":"W4387871125","doi":"10.1109/icc45041.2023.10279338","title":"Optimized Circulation Management In Hospitals During COVID-19","year":2023,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Mitacs","keywords":"Social distance; Pandemic; Dijkstra's algorithm; Coronavirus disease 2019 (COVID-19); Computer science; Baseline (sea); Routing (electronic design automation); Guideline; Medicine; Shortest path problem; Computer network; Disease","score_opus":0.02408058963242093,"score_gpt":0.3227453957866412,"score_spread":0.29866480615422025,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387871125","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.89887255,0.000013117481,0.018828643,0.0049244473,0.00023135317,0.0005111534,0.0000030781948,0.00089581346,0.07571986],"genre_scores_gemma":[0.99184537,0.00012699123,0.0028195144,0.00013858106,0.000019575073,0.000025355748,0.000047665893,0.0000047879757,0.004972145],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9993599,0.00004717218,0.00012900948,0.00012692485,0.00018833176,0.00014866302],"domain_scores_gemma":[0.99979264,0.000038812115,0.000028510853,0.00005465336,0.000014108947,0.00007127934],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040740854,0.00003923798,0.00005360189,0.00018574532,0.00023657174,0.000034592296,0.000059732134,0.000036802947,0.00026479154],"category_scores_gemma":[0.000055094843,0.000042796535,0.000019594645,0.0006965297,0.000023270884,0.00013764747,0.0000045337247,0.000026544669,0.00007205039],"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":[0.000014824732,0.000012280369,0.079635076,0.00002294563,0.0000087726285,0.00003210132,0.014494824,0.8789243,0.00000779835,0.025907908,0.00053651555,0.0004026208],"study_design_scores_gemma":[0.0012610101,0.0000036975619,0.97335696,0.000012949634,0.000008178768,1.15231686e-7,0.013027302,0.007542489,0.00000880362,0.0016292551,0.0029924922,0.00015674609],"about_ca_topic_score_codex":0.00069102424,"about_ca_topic_score_gemma":0.00032503644,"teacher_disagreement_score":0.8937219,"about_ca_system_score_codex":0.0000834082,"about_ca_system_score_gemma":0.000032699812,"threshold_uncertainty_score":0.28992817},"labels":[],"label_agreement":null},{"id":"W4387983559","doi":"10.1515/9782763758909-005","title":"Chapitre 3 Que Peuvent Révéler les Services de Vélos en Libre-Service sur la Mobilité Urbaine Durable à Montréal et à Vancouver ?","year":2023,"lang":"fr","type":"book-chapter","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Humanities; Art","score_opus":0.016783239479793863,"score_gpt":0.24283628720630915,"score_spread":0.2260530477265153,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387983559","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.045492727,0.0032824483,0.0040469198,0.026392395,0.003138004,0.0019376951,0.0009711791,0.002156533,0.9125821],"genre_scores_gemma":[0.09169545,0.0039495183,0.0024027328,0.0016848822,0.0004274095,0.0000737294,0.00058927515,0.00017823916,0.89899874],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9968891,0.0003129991,0.0007028311,0.00073518505,0.0007446865,0.00061520387],"domain_scores_gemma":[0.9976314,0.0009408411,0.00037441668,0.0003439049,0.00040629017,0.00030316715],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001045962,0.0005713141,0.00055433856,0.00017318026,0.0005019814,0.0001591429,0.00050620385,0.0009765552,0.0037771463],"category_scores_gemma":[0.000037675494,0.00056540256,0.00025467738,0.00031052332,0.00018106852,0.00067210663,0.000059976333,0.0005971974,0.0005352181],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001277636,0.00023608393,0.0103777675,0.0011198643,0.00036693073,0.00015326384,0.07156614,0.1671361,0.00003174416,0.7398965,0.0059914845,0.0029963641],"study_design_scores_gemma":[0.0012985822,0.00017821377,0.025900895,0.0016343722,0.00044759904,0.0000062966537,0.011190982,0.024363896,0.000036546793,0.007438416,0.92589647,0.0016077072],"about_ca_topic_score_codex":0.13817477,"about_ca_topic_score_gemma":0.5308759,"teacher_disagreement_score":0.919905,"about_ca_system_score_codex":0.00016031163,"about_ca_system_score_gemma":0.00045445308,"threshold_uncertainty_score":0.99967974},"labels":[],"label_agreement":null},{"id":"W4388418033","doi":"10.1093/oso/9780199577989.003.0009","title":"Rising To The Occasion: How Transportation Green Taxes Could Yield Double Dividends For Montreal And The Province Of Quebec","year":2009,"lang":"en","type":"book-chapter","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Legislature; Dividend; Norwegian; State (computer science); Economic history; Political science; Economy; Public administration; Economics; Law","score_opus":0.02891937078362878,"score_gpt":0.2651437480043471,"score_spread":0.23622437722071832,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388418033","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002141764,0.0022326666,0.116218016,0.18870638,0.000839143,0.010074689,0.0013319085,0.00040485652,0.6780506],"genre_scores_gemma":[0.1432565,0.00026808344,0.00088428735,0.00047953532,0.00025209022,0.000034001594,0.00014971328,0.000020352434,0.85465544],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9990137,0.000020320069,0.00024851554,0.000218892,0.00035828727,0.00014026786],"domain_scores_gemma":[0.9990631,0.0003351305,0.00023688603,0.00013781167,0.0001727368,0.00005436032],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005166437,0.00014992923,0.0002291508,0.000057330406,0.00045956892,0.000096671814,0.00018041914,0.00019378429,0.0000339547],"category_scores_gemma":[0.000024767729,0.00008943002,0.00010031856,0.000047736074,0.00020948242,0.00015472354,0.000002388054,0.00011907939,8.2831247e-7],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011391195,0.000022481527,0.00028461067,0.00010106697,0.00010488722,0.000005123099,0.040334504,0.0033825499,0.0000046243645,0.8281468,0.021544537,0.10492969],"study_design_scores_gemma":[0.004828654,0.00041904027,0.015057486,0.0010459004,0.0011705586,0.0000014651506,0.008141045,0.0007817347,0.00010898331,0.028518008,0.938734,0.0011931312],"about_ca_topic_score_codex":0.061852723,"about_ca_topic_score_gemma":0.72239655,"teacher_disagreement_score":0.9171895,"about_ca_system_score_codex":0.000022578166,"about_ca_system_score_gemma":0.00015845388,"threshold_uncertainty_score":0.94439447},"labels":[],"label_agreement":null},{"id":"W4388492100","doi":"10.18757/ejtir.2002.2.4.3730","title":"Overcoming Traffic Congestion","year":2002,"lang":"en","type":"article","venue":"European journal of transport and infrastructure research","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"McMaster University","funders":"","keywords":"Traffic congestion; Balance (ability); Transport engineering; Congestion pricing; Travel behavior; Demand management; Computer science; Business; Economics; Operations research; Engineering; Psychology","score_opus":0.050712136466898,"score_gpt":0.319141164630117,"score_spread":0.268429028163219,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388492100","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9686644,0.0007453777,0.0010358113,0.001238893,0.00023315579,0.00008251893,0.0000040933764,0.000023058294,0.027972713],"genre_scores_gemma":[0.9963938,0.0014198519,0.0014273971,0.0000285394,0.00030067822,1.6631157e-7,0.0000032183743,0.000011313764,0.0004150252],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99834293,0.00046457202,0.0003019636,0.00009665722,0.00057710113,0.00021679208],"domain_scores_gemma":[0.9992751,0.00008305527,0.000101789556,0.000055249726,0.0003077494,0.00017707588],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024635636,0.000065967135,0.000116647694,0.00022087,0.00041169254,0.000055709155,0.00015824338,0.000037364494,0.00048043282],"category_scores_gemma":[0.00007948827,0.000057261634,0.000046923396,0.0002810835,0.00025862403,0.00029812063,0.0000024396575,0.0004508782,0.0000061663773],"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":[0.0004304236,0.00019396056,0.18327719,0.0001457069,0.00017874574,0.0014984434,0.2258119,0.07412921,0.000885886,0.009013655,0.02324969,0.4811852],"study_design_scores_gemma":[0.0011737873,0.00031068537,0.68203264,0.00017714362,0.000034723016,0.000049214148,0.0043628016,0.000393672,0.000017300852,0.00016993946,0.31109008,0.00018799028],"about_ca_topic_score_codex":0.000009017814,"about_ca_topic_score_gemma":0.000018934761,"teacher_disagreement_score":0.49875546,"about_ca_system_score_codex":0.000025179535,"about_ca_system_score_gemma":0.000043317872,"threshold_uncertainty_score":0.52604026},"labels":[],"label_agreement":null},{"id":"W4388691872","doi":"10.26034/lu.akwi.2019.3246","title":"Solving non-quadratic Matrices in Assignment Problems with an improved Version of Vogel's Approximation Method","year":2019,"lang":"en","type":"article","venue":"Anwendungen und Konzepte der Wirtschaftsinformatik","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Infineon Technologies (Canada)","funders":"","keywords":"Quadratic equation; Approx; Matrix (chemical analysis); Quadratic programming; Integer programming; Computation; Integer (computer science); Mathematical optimization; Quadratic assignment problem; Integer matrix; Mathematics; Computer science; Applied mathematics; Combinatorics; Algorithm; Discrete mathematics; Combinatorial optimization; Symmetric matrix; Physics; Nonnegative matrix; Geometry","score_opus":0.009728927494068751,"score_gpt":0.2760343121830478,"score_spread":0.2663053846889791,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388691872","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.68699026,0.0003978928,0.23846062,0.0004907545,0.0005515509,0.0035659955,0.000039693474,0.00028019247,0.069223024],"genre_scores_gemma":[0.94842076,0.00006308356,0.050792165,0.00009125375,0.00003053697,0.000032018463,0.00018797217,0.000019102,0.00036311868],"study_design_codex":"qualitative","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981915,0.00012785653,0.00056408654,0.00022938295,0.000543223,0.00034397544],"domain_scores_gemma":[0.99889594,0.0001297372,0.00046976155,0.0002356165,0.0001592198,0.00010974577],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010832862,0.00018392973,0.00028072437,0.0002897575,0.00020184027,0.000111025074,0.00023274097,0.00015332947,0.00013832914],"category_scores_gemma":[0.000024808256,0.0001638726,0.00004772425,0.00058846996,0.000061874314,0.0022484008,0.0000148485815,0.00015821111,0.000038806993],"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":[0.000853076,0.0007933325,0.10568551,0.0019134213,0.00043478023,0.000008856284,0.42799067,0.40802437,0.0048524668,0.011052282,0.0004960577,0.037895177],"study_design_scores_gemma":[0.007055585,0.0018780916,0.043379404,0.001336327,0.00045731026,0.0000062963754,0.06793992,0.85839957,0.0049696947,0.00090045534,0.011885581,0.0017917505],"about_ca_topic_score_codex":0.0011225122,"about_ca_topic_score_gemma":0.0010900478,"teacher_disagreement_score":0.4503752,"about_ca_system_score_codex":0.00009657709,"about_ca_system_score_gemma":0.0002106325,"threshold_uncertainty_score":0.668253},"labels":[],"label_agreement":null},{"id":"W4388912059","doi":"10.3390/su152316183","title":"Utilizing Social Media Data for Estimating Transit Performance Metrics","year":2023,"lang":"en","type":"article","venue":"Sustainability","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"York University","keywords":"Notice; Transit (satellite); Transport engineering; Computer science; Social media; Order (exchange); Public transport; Agency (philosophy); Service (business); Business; Marketing; Engineering; World Wide Web","score_opus":0.09044186820419277,"score_gpt":0.3767404781411501,"score_spread":0.2862986099369573,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388912059","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.81857735,0.00004741505,0.16686119,0.008360366,0.0012352485,0.0013286966,0.00036800254,0.001309465,0.00191227],"genre_scores_gemma":[0.98909414,0.000008624889,0.0098725585,0.000020500222,0.00023908028,0.000025351756,0.0005948089,0.000009249568,0.0001356603],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99884295,0.00007976151,0.00019852801,0.00026473016,0.00028453273,0.0003294677],"domain_scores_gemma":[0.99854976,0.000670667,0.0000596976,0.00019445122,0.00046934455,0.00005608528],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025010563,0.000067924026,0.00011292885,0.000106935,0.0010726313,0.000056224595,0.00029540606,0.00008147157,0.000016570422],"category_scores_gemma":[0.005477012,0.00007420405,0.00003541715,0.0011261391,0.00014160598,0.00040161778,0.000018213677,0.00008041648,0.0000029771159],"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":[0.00018093128,0.00014901522,0.123387486,0.001589263,0.000048086727,0.000009108931,0.32855803,0.048664663,0.0000052149703,0.04466503,0.010632013,0.44211116],"study_design_scores_gemma":[0.00093754503,0.00004022681,0.21782185,0.000025161917,0.000096470874,1.8569114e-7,0.11684683,0.6049298,0.000015995924,0.015073705,0.043716937,0.0004952963],"about_ca_topic_score_codex":0.00015089868,"about_ca_topic_score_gemma":0.00033629054,"teacher_disagreement_score":0.5562651,"about_ca_system_score_codex":0.00017741592,"about_ca_system_score_gemma":0.0005149604,"threshold_uncertainty_score":0.8249922},"labels":[],"label_agreement":null},{"id":"W4389205053","doi":"10.18280/ijsdp.181107","title":"Dynamic Modeling Impacts of Inter-Urban Railway Transportation on Regional Development: A Case Study of Sub-Urban Greater Jakarta, Indonesia","year":2023,"lang":"en","type":"article","venue":"International Journal of Sustainable Development and Planning","topic":"Transportation Planning and Optimization","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":"","keywords":"Environmental planning; Urban planning; Transport engineering; Geography; Business; Regional science; Civil engineering; Engineering","score_opus":0.027214277495284468,"score_gpt":0.29445491053282563,"score_spread":0.26724063303754114,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389205053","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9967825,0.00005970668,0.0026265762,0.000083593215,0.00019056875,0.00017705576,0.0000034772258,0.000021206282,0.00005530991],"genre_scores_gemma":[0.99898887,0.000028588485,0.0006901055,0.000018904475,0.00004524959,0.000006106525,0.000063160296,0.0000126606565,0.00014636273],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.9980556,0.00006964416,0.0008203122,0.0001515799,0.00067448104,0.0002283804],"domain_scores_gemma":[0.9982191,0.00010676362,0.00053766795,0.00004553099,0.0009956784,0.00009531598],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009671325,0.00013860919,0.00023606907,0.00096051703,0.00024126993,0.00005415993,0.00017736878,0.00007560184,0.0000073070155],"category_scores_gemma":[0.000050827002,0.00013297198,0.000045197077,0.00038982838,0.00004295044,0.00040819217,0.000007866199,0.00015168216,7.095105e-7],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004245739,0.00017461293,0.32620043,0.000072030925,0.00030718505,0.0042352653,0.62172157,0.04490313,0.000040569223,0.0003411777,0.0001333936,0.0014460993],"study_design_scores_gemma":[0.0029859613,0.00028820147,0.20260517,0.00086293113,0.000068687994,0.00010639302,0.7880457,0.0033261024,0.0004697316,0.00011056018,0.0007639401,0.0003665933],"about_ca_topic_score_codex":0.00025530212,"about_ca_topic_score_gemma":0.000203589,"teacher_disagreement_score":0.16632418,"about_ca_system_score_codex":0.00013230498,"about_ca_system_score_gemma":0.00045538074,"threshold_uncertainty_score":0.542244},"labels":[],"label_agreement":null},{"id":"W4389328479","doi":"10.1016/j.cstp.2023.101128","title":"A cross-country comparative analysis of congestion pricing systems: Lessons for decarbonizing transportation","year":2023,"lang":"en","type":"article","venue":"Case Studies on Transport Policy","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"Victoria University; University of Victoria; U.S. Department of Transportation","keywords":"Congestion pricing; Traffic congestion; Equity (law); Greenhouse gas; Context (archaeology); Road pricing; Business; Environmental economics; Government (linguistics); Climate change; Public economics; Economics; Transport engineering; Engineering","score_opus":0.10998383772175915,"score_gpt":0.4476767629931892,"score_spread":0.33769292527143,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389328479","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9943195,0.00021309209,0.0020130898,0.00037343288,0.0002944106,0.0006169356,0.0009412257,0.000245362,0.0009829731],"genre_scores_gemma":[0.9981236,0.00028762192,0.00012805848,0.000041630214,0.00012263811,0.00014057363,0.00085716054,0.000013178337,0.00028553972],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9983585,0.00006485942,0.0006165584,0.00031231443,0.00032981212,0.0003179617],"domain_scores_gemma":[0.99845386,0.0005125322,0.0003105497,0.00013232193,0.00050593086,0.00008481222],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007486119,0.00016957121,0.0005430594,0.00084963464,0.0007226785,0.00002620716,0.00008284102,0.00011023193,0.0000033684885],"category_scores_gemma":[0.0001185532,0.00017328191,0.0001922513,0.0033371164,0.00029893967,0.00017640342,8.60875e-7,0.00008244785,0.0000018403023],"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":[0.00011960573,0.000047095247,0.021067427,0.00019627971,0.0013762024,0.00011692033,0.15243794,0.7175624,0.000051603514,0.106822416,0.00004456408,0.00015756431],"study_design_scores_gemma":[0.002967895,0.0004553195,0.691892,0.0006481935,0.00704623,0.000007919496,0.26705948,0.023752674,0.00047518307,0.00033865194,0.0042098844,0.00114653],"about_ca_topic_score_codex":0.0036213156,"about_ca_topic_score_gemma":0.013288682,"teacher_disagreement_score":0.6938097,"about_ca_system_score_codex":0.00012094167,"about_ca_system_score_gemma":0.00019146562,"threshold_uncertainty_score":0.74153966},"labels":[],"label_agreement":null},{"id":"W4389504498","doi":"10.1155/2023/8862759","title":"A Class of Efficient Algorithms for the Bi-Level Demand Adjustment Problems in Congested Traffic Networks","year":2023,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","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":"Chengdu Science and Technology Program; Sichuan Province Science and Technology Support Program; National Natural Science Foundation of China","keywords":"Heuristic; Gradient descent; Algorithm; Class (philosophy); Computer science; Mathematical optimization; Mathematics; Artificial intelligence; Artificial neural network","score_opus":0.03413658035858501,"score_gpt":0.30504294316468317,"score_spread":0.27090636280609814,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389504498","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8341515,0.0008052047,0.16232473,0.0008197213,0.00093433424,0.00086050556,0.00004701821,0.000037627466,0.000019336514],"genre_scores_gemma":[0.9958777,0.0010263278,0.0028312996,0.0000211417,0.00008877605,0.000028785887,0.000066508255,0.000010852115,0.000048599504],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99867296,0.000052640724,0.0006285422,0.00010163553,0.00035667812,0.00018754216],"domain_scores_gemma":[0.9987043,0.00037453137,0.00049310597,0.00005469197,0.00032192547,0.000051412993],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009368218,0.00008274436,0.00019047412,0.0001826337,0.00012809568,0.0000116125275,0.00011701136,0.000073977746,0.0000050418494],"category_scores_gemma":[0.00004756164,0.000064409585,0.00009954116,0.0007382501,0.000072933006,0.00015306784,4.3303996e-7,0.000118911405,3.0906145e-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":[0.000112273614,0.000058154234,0.0009632014,0.000028381739,0.000024017672,0.000002534325,0.017674973,0.9680953,0.00007205821,0.00038778488,0.000037770908,0.0125435535],"study_design_scores_gemma":[0.0038170898,0.00029795265,0.81206566,0.0003663484,0.00015604576,9.862189e-7,0.017307801,0.16371015,0.0000856383,0.00017661,0.0018359397,0.0001797504],"about_ca_topic_score_codex":0.000029247049,"about_ca_topic_score_gemma":0.0014064218,"teacher_disagreement_score":0.8111025,"about_ca_system_score_codex":0.000044510158,"about_ca_system_score_gemma":0.00012064462,"threshold_uncertainty_score":0.26265466},"labels":[],"label_agreement":null},{"id":"W4389836255","doi":"10.1016/j.jtrangeo.2023.103763","title":"Impacts of public transit delays and disruptions on equity seeking groups in Toronto – A time-expanded graph approach","year":2023,"lang":"en","type":"article","venue":"Journal of Transport Geography","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Public transport; Destinations; Headway; Disadvantaged; Equity (law); Population; Travel time; Transport engineering; Travel behavior; Computer science; Business; Demographic economics; Geography; Economics; Engineering; Tourism; Demography; Economic growth; Political science; Sociology","score_opus":0.02849940578475161,"score_gpt":0.31074854228792237,"score_spread":0.28224913650317074,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389836255","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9897876,0.0003244996,0.0061644255,0.00039710986,0.000105969615,0.00016448254,0.000038120106,0.000047157017,0.0029706534],"genre_scores_gemma":[0.9978025,0.0012430142,0.00080101215,0.000028805729,0.00004759486,0.0000037587574,0.00005192166,0.000010845263,0.000010525832],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9983021,0.000108889115,0.0005652384,0.00014874736,0.0005662643,0.0003087598],"domain_scores_gemma":[0.9992109,0.00008276895,0.0002740193,0.0000838251,0.00014505145,0.00020344651],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017433945,0.000121177174,0.00030160346,0.0006382029,0.00019105893,0.00003350189,0.0001700496,0.000120336575,0.000043277774],"category_scores_gemma":[0.00002180941,0.00011239679,0.00023630231,0.0011869819,0.00015711924,0.00059685827,0.0000023827208,0.00018016035,5.4034064e-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":[0.0001643753,0.00034675468,0.96347106,0.00007579389,0.00012208604,0.00003307859,0.024991393,0.005301615,0.00034696344,0.0025979357,0.000046752455,0.0025021865],"study_design_scores_gemma":[0.00090279616,0.00017584402,0.99227893,0.000118945456,0.000076322816,0.000004191418,0.0052795894,0.00023600616,0.000010141687,0.0006027088,0.00017366098,0.00014087395],"about_ca_topic_score_codex":0.0016308484,"about_ca_topic_score_gemma":0.006830638,"teacher_disagreement_score":0.028807854,"about_ca_system_score_codex":0.000043279553,"about_ca_system_score_gemma":0.00010088978,"threshold_uncertainty_score":0.4583408},"labels":[],"label_agreement":null},{"id":"W4390097139","doi":"10.1155/2023/3770800","title":"Ecological and Real-Time Route Selection Method for Multiple Vehicles in Urban Road Network","year":2023,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Natural Science Foundation of Jiangxi Province; National Natural Science Foundation of China","keywords":"Selection (genetic algorithm); Computer science; Traffic congestion; Computation; Particle swarm optimization; Swarm behaviour; Transport engineering; Operations research; Mathematical optimization; Engineering; Algorithm; Artificial intelligence","score_opus":0.016525827346027623,"score_gpt":0.31542688287243953,"score_spread":0.2989010555264119,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390097139","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9781724,0.00004085238,0.020758634,0.000429361,0.00019497165,0.00025928323,0.000013033935,0.00006495007,0.00006650468],"genre_scores_gemma":[0.9043571,0.00051996025,0.09460846,0.000027695927,0.00019977402,0.000014699154,0.00009301431,0.000010883285,0.00016844738],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99900514,0.000093865434,0.00040904293,0.00011943269,0.00018432653,0.00018821695],"domain_scores_gemma":[0.9991217,0.00034879686,0.00027133638,0.000024312063,0.00016823265,0.000065623724],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009812196,0.000071684146,0.00017814689,0.00014027303,0.00018697846,0.000020327725,0.00005032696,0.000094622126,0.00000816894],"category_scores_gemma":[0.00010600704,0.00007046795,0.0000572203,0.00052176404,0.000027546026,0.00039444715,4.058223e-7,0.000094643474,9.547235e-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":[0.00034571148,0.000037469505,0.14444776,0.000016219903,0.000015825055,0.000008080037,0.0109226,0.82981235,0.0020520787,0.00094424747,0.00025593714,0.0111417435],"study_design_scores_gemma":[0.0011161257,0.00013926961,0.98653567,0.00004528736,0.000030491456,6.4827503e-7,0.0013913546,0.0072196596,0.000065254935,0.0013227039,0.0020442465,0.00008927113],"about_ca_topic_score_codex":0.00014796438,"about_ca_topic_score_gemma":0.002486109,"teacher_disagreement_score":0.8420879,"about_ca_system_score_codex":0.000048771017,"about_ca_system_score_gemma":0.00007478112,"threshold_uncertainty_score":0.28735995},"labels":[],"label_agreement":null},{"id":"W4390100484","doi":"10.1145/3589132.3625637","title":"One-Shot Traffic Assignment with Forward-Looking Penalization","year":2023,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"CHIST-ERA; Horizon 2020 Framework Programme; European Commission","keywords":"Metis; Baseline (sea); Computer science; Shot (pellet); Enhanced Data Rates for GSM Evolution; TRIPS architecture; Transport engineering; Operations research; Artificial intelligence; Engineering; Parallel computing; Database","score_opus":0.052077998659518655,"score_gpt":0.30985558897158166,"score_spread":0.257777590312063,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390100484","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5211935,0.000024055933,0.2851971,0.0046872087,0.00035068925,0.0006876146,0.000008984097,0.0023231898,0.18552762],"genre_scores_gemma":[0.9928972,0.000033703884,0.0010834838,0.00010486856,0.000048533297,0.000012319618,0.00010031534,0.000007902036,0.005711658],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99924934,0.00003772363,0.00009687634,0.00012122064,0.0003371454,0.00015770286],"domain_scores_gemma":[0.9997726,0.000039677958,0.000038482915,0.00004277501,0.000052356932,0.00005408867],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027797083,0.00004533826,0.00005624615,0.00007758687,0.00032734062,0.000059491507,0.000051773754,0.000038517468,0.00029674],"category_scores_gemma":[0.000017061404,0.00004196435,0.000016138414,0.00052502844,0.00003724728,0.00016206411,0.0000017637154,0.000031378735,0.00005685981],"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":[0.000044980305,0.000083807536,0.024376106,0.000023367382,0.000047026155,0.00000969574,0.0447974,0.8663895,0.00007454444,0.040255904,0.004492661,0.01940502],"study_design_scores_gemma":[0.005109617,0.00070870307,0.478667,0.0005656796,0.00036856602,0.0000021271812,0.12172637,0.17760721,0.001497187,0.001774125,0.2095923,0.002381125],"about_ca_topic_score_codex":0.00012841691,"about_ca_topic_score_gemma":0.0011849022,"teacher_disagreement_score":0.6887823,"about_ca_system_score_codex":0.00003059368,"about_ca_system_score_gemma":0.00007110995,"threshold_uncertainty_score":0.3249095},"labels":[],"label_agreement":null},{"id":"W4390533700","doi":"10.1007/s10107-023-02043-2","title":"Asymmetry in the complexity of the multi-commodity network pricing problem","year":2024,"lang":"en","type":"article","venue":"Mathematical Programming","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Computer Research Institute of Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds de recherche du Québec; Institut de Valorisation des Données","keywords":"Mathematics; Mathematical optimization; Bilevel optimization; Path (computing); Computational complexity theory; Shortest path problem; Property (philosophy); Commodity; Graph; Mathematical economics; Computer science; Optimization problem; Combinatorics; Economics; Algorithm; Finance","score_opus":0.07496112946076537,"score_gpt":0.3328110682016302,"score_spread":0.2578499387408648,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390533700","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10386779,0.0010815762,0.824037,0.022325281,0.00083285186,0.0045190533,0.000014608519,0.0007645383,0.04255726],"genre_scores_gemma":[0.9340286,0.0000025104148,0.06577059,0.000044993783,0.00005534184,0.00002456922,0.000003125198,0.000005770046,0.000064456035],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99892604,0.00021128159,0.00024876694,0.00010727013,0.00030372187,0.00020290996],"domain_scores_gemma":[0.99943405,0.00035286383,0.000050847855,0.000114602095,0.000024968922,0.000022673505],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017663102,0.000060768725,0.000104980776,0.000023714523,0.00026560284,0.000106699685,0.00023782608,0.000051304767,0.000017218717],"category_scores_gemma":[0.00015159616,0.000034258628,0.000062268526,0.00069814076,0.00035905276,0.000077297096,0.000013892514,0.00019668542,0.0000053096383],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000029324387,0.00017322894,0.0070166877,0.00032956433,0.000013013592,0.0000032862356,0.053529546,0.0026525762,0.000002212732,0.9165123,0.00020037043,0.019564278],"study_design_scores_gemma":[0.00069248147,0.000080164675,0.03626684,0.003333876,0.00017848703,0.000007668509,0.03060014,0.19262683,0.000053602394,0.68588626,0.049672313,0.00060132734],"about_ca_topic_score_codex":0.00016350266,"about_ca_topic_score_gemma":0.00051187834,"teacher_disagreement_score":0.83016086,"about_ca_system_score_codex":0.00002497009,"about_ca_system_score_gemma":0.000043881813,"threshold_uncertainty_score":0.20428292},"labels":[],"label_agreement":null},{"id":"W4390755744","doi":"10.1155/2024/6628446","title":"Optimal Reservation Volume of Urban Roads Based on Travel Reservation Strategy","year":2024,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","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":"Fujian University of Technology","keywords":"Reservation; Transport engineering; Computer science; Traffic congestion; Flow network; Traffic flow (computer networking); Transport network; Demand management; Operations research; Mathematical optimization; Engineering; Economics; Computer network; Mathematics","score_opus":0.023334523673729155,"score_gpt":0.3051188835361665,"score_spread":0.28178435986243733,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390755744","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8872507,0.00026299956,0.108700015,0.001536171,0.00077018456,0.0002616368,0.000056176443,0.00006616544,0.0010959287],"genre_scores_gemma":[0.9884013,0.00011808284,0.010584332,0.000043254753,0.00020350388,0.000004968961,0.00021082902,0.00002009197,0.0004136274],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99793327,0.00010400751,0.00080776174,0.00016926178,0.0008141916,0.00017150778],"domain_scores_gemma":[0.9985771,0.00015285284,0.00045771708,0.00009626177,0.00062570785,0.00009032277],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00088650605,0.00012311043,0.00021652052,0.00039104326,0.0001369947,0.000053892873,0.00014403347,0.00012789441,0.00009305457],"category_scores_gemma":[0.00009582816,0.00012169549,0.0001441681,0.000763023,0.00007605998,0.001156667,3.444741e-7,0.00022780387,0.000002671166],"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":[0.00040675208,0.000081081285,0.0056129745,0.00008259377,0.000022803164,0.000018853334,0.0111258095,0.9711451,0.0016593445,0.005896551,0.00045621552,0.0034919109],"study_design_scores_gemma":[0.0021728647,0.001548597,0.91948426,0.0013189029,0.00023011849,0.0000014279785,0.0116636185,0.04467988,0.0028094593,0.0012508038,0.01440691,0.00043313522],"about_ca_topic_score_codex":0.00011104066,"about_ca_topic_score_gemma":0.0002669052,"teacher_disagreement_score":0.9264652,"about_ca_system_score_codex":0.00009264046,"about_ca_system_score_gemma":0.00042303093,"threshold_uncertainty_score":0.4962598},"labels":[],"label_agreement":null},{"id":"W4391419528","doi":"","title":"Systèmes interactifs multimodaux dans le secteur du transport : un modèle formel de conception et de validation","year":2007,"lang":"fr","type":"article","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"","keywords":"Humanities; Philosophy; Computer science","score_opus":0.01475741501965526,"score_gpt":0.25537931622249693,"score_spread":0.24062190120284166,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391419528","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.37902102,0.00026543232,0.58249205,0.008198774,0.00026237895,0.00023176524,0.000042942353,0.00017144608,0.02931417],"genre_scores_gemma":[0.9339833,0.0006827489,0.05367588,0.0002385818,0.00007180268,0.000023272944,0.0007482765,0.000040785824,0.010535324],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9936167,0.00426229,0.00062182685,0.00047135292,0.00044673771,0.0005810924],"domain_scores_gemma":[0.9959392,0.0013519044,0.00045663185,0.00045996258,0.0015041621,0.00028813104],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.011012029,0.00025646298,0.00024816004,0.00016033897,0.0009363871,0.00019703616,0.00048465916,0.00031617237,0.0002319554],"category_scores_gemma":[0.0008210491,0.00031883738,0.00017542274,0.0007268996,0.00042232015,0.00090226566,0.00002822849,0.00037212294,0.000033824137],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007593382,0.0012514368,0.08737901,0.00014886992,0.000104199775,0.00002619192,0.38012215,0.013353443,0.008038806,0.46266755,0.00043879144,0.046393614],"study_design_scores_gemma":[0.0042449036,0.000007169369,0.5475453,0.0033251552,0.00040324853,0.00008165113,0.033428397,0.15416846,0.18886243,0.01023535,0.055909567,0.0017883307],"about_ca_topic_score_codex":0.017828181,"about_ca_topic_score_gemma":0.024336504,"teacher_disagreement_score":0.5549623,"about_ca_system_score_codex":0.00048099706,"about_ca_system_score_gemma":0.0006989739,"threshold_uncertainty_score":0.9999264},"labels":[],"label_agreement":null},{"id":"W4391462099","doi":"10.1016/j.trb.2024.103147","title":"Conditional forecasting of bus travel time and passenger occupancy with Bayesian Markov regime-switching vector autoregression","year":2024,"lang":"en","type":"article","venue":"Transportation Research Part B Methodological","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University Health Centre; McGill University","funders":"Canadian Statistical Sciences Institute; Natural Sciences and Engineering Research Council of Canada; China Scholarship Council","keywords":"Occupancy; Markov chain Monte Carlo; Autoregressive model; Computer science; Markov chain; Bayesian probability; Skewness; Bayesian inference; Time series; Econometrics; Engineering; Artificial intelligence; Mathematics; Machine learning","score_opus":0.25882082853003313,"score_gpt":0.44925861423322194,"score_spread":0.1904377857031888,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391462099","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.69192404,0.00070375996,0.29899693,0.003686684,0.00022675884,0.00097330153,0.00024927943,0.00036135426,0.002877902],"genre_scores_gemma":[0.9436208,0.00011534455,0.05505837,0.000025388053,0.00013388362,0.000046647583,0.00034381126,0.00001874232,0.0006369891],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9965109,0.0013481007,0.0003937112,0.00042424936,0.0009374274,0.0003855853],"domain_scores_gemma":[0.9963612,0.0029524048,0.000089721405,0.000097518285,0.00030112243,0.00019801942],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0049487343,0.00013981225,0.00026938724,0.00024343385,0.0004941758,0.00008659498,0.00011766611,0.00019360137,0.0007089529],"category_scores_gemma":[0.0005981111,0.000106576554,0.000062051,0.0006915844,0.00044593686,0.0003062387,0.000002813442,0.00043524092,0.000006035155],"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":[0.0057616658,0.0009702986,0.20930527,0.0023405012,0.0008293199,0.001960797,0.16009407,0.01678115,0.022123579,0.4831831,0.016037688,0.080612585],"study_design_scores_gemma":[0.0016388426,0.0009243658,0.91630703,0.001995813,0.00019563278,0.000015115886,0.006959671,0.048445273,0.0016669065,0.010434017,0.010578077,0.00083924364],"about_ca_topic_score_codex":0.00028834285,"about_ca_topic_score_gemma":0.00035736963,"teacher_disagreement_score":0.7070018,"about_ca_system_score_codex":0.000035202975,"about_ca_system_score_gemma":0.00031082443,"threshold_uncertainty_score":0.77625376},"labels":[],"label_agreement":null},{"id":"W4391512659","doi":"10.1016/j.tbs.2024.100746","title":"Toward understanding waiting time in an intercity station: A hazard-based approach","year":2024,"lang":"en","type":"article","venue":"Travel Behaviour and Society","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Hazard; Transport engineering; Computer science; Forensic engineering; Engineering","score_opus":0.09428655033751372,"score_gpt":0.32046427131090677,"score_spread":0.22617772097339306,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391512659","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8513075,0.00007213165,0.14447975,0.00054350164,0.00010596673,0.00020788438,0.000045330446,0.00021929346,0.0030186325],"genre_scores_gemma":[0.99497175,0.000019596691,0.0045374497,0.00006993748,0.00004106656,0.000008882078,0.00014425168,0.000010207513,0.00019684205],"study_design_codex":"qualitative","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991953,0.00007198729,0.00015053067,0.00021798075,0.00018912711,0.00017508528],"domain_scores_gemma":[0.9997778,0.000054802254,0.000022373726,0.00004269606,0.00002553227,0.00007678042],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056922564,0.00008007108,0.00009843142,0.00004301978,0.00025474717,0.00020767646,0.000056021112,0.000103798826,0.00004933652],"category_scores_gemma":[0.000006463014,0.00008340107,0.000057526217,0.00023194039,0.000109610315,0.00027602987,0.0000026561427,0.00015541384,0.0000018710975],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026789292,0.00022329546,0.13721,0.0001949091,0.000029154287,0.000025120187,0.7990621,0.004648371,0.00020566735,0.05567567,0.00073595135,0.0019629907],"study_design_scores_gemma":[0.00090867066,0.00008396474,0.1901589,0.00022281242,0.000096512544,0.0000017981391,0.25528613,0.5513328,0.000054664917,0.0012588357,0.00008252154,0.00051240966],"about_ca_topic_score_codex":0.0004485267,"about_ca_topic_score_gemma":0.00022547989,"teacher_disagreement_score":0.54668444,"about_ca_system_score_codex":0.00013859307,"about_ca_system_score_gemma":0.00013032381,"threshold_uncertainty_score":0.34009972},"labels":[],"label_agreement":null},{"id":"W4391546782","doi":"10.1007/978-3-031-34027-7_43","title":"Evaluation of Dynamic Incentive Pricing for Congestion Management in Transit System: An Agent-Based Simulation","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in civil engineering","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Incentive; Transit (satellite); Dynamic pricing; Transit system; Congestion pricing; Business; Computer science; Operations research; Microeconomics; Economics; Transport engineering; Traffic congestion; Public transport; Engineering","score_opus":0.02371389570449758,"score_gpt":0.29788454053409746,"score_spread":0.2741706448295999,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391546782","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016359247,0.00065125414,0.9900123,0.000043193773,0.00050518074,0.0014548746,0.00003532967,0.00013153009,0.0055304477],"genre_scores_gemma":[0.9970584,0.000021588285,0.002348812,0.000004760926,0.000043404172,0.000053504602,0.00029279955,0.000037005346,0.00013971401],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987005,0.00003557714,0.0003423173,0.00026650119,0.00050891016,0.00014617789],"domain_scores_gemma":[0.99939215,0.00020900882,0.000108466396,0.00009282878,0.00017186433,0.00002570889],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001124004,0.00016325165,0.0002055946,0.0005203,0.000039731614,0.000026109165,0.00007195929,0.0002530063,0.000013759037],"category_scores_gemma":[0.000057774472,0.00019283769,0.00005659417,0.00015810205,0.000017747197,0.00009594058,0.0000023215518,0.00016615367,7.4113836e-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":[0.000018265515,0.000008014142,0.00001541198,0.0006056139,0.000029301784,0.000002673671,0.0029514448,0.9762865,0.000016831696,0.006090759,1.2480236e-7,0.013975117],"study_design_scores_gemma":[0.00043019056,0.000025433403,0.00030195052,0.001888367,0.00025192715,5.1043035e-8,0.00006944049,0.9953639,0.000012803258,0.0013351896,0.00014770692,0.00017307833],"about_ca_topic_score_codex":0.0000853073,"about_ca_topic_score_gemma":0.0076306327,"teacher_disagreement_score":0.9954225,"about_ca_system_score_codex":0.00077130634,"about_ca_system_score_gemma":0.000082081446,"threshold_uncertainty_score":0.78636926},"labels":[],"label_agreement":null},{"id":"W4391579755","doi":"10.1155/2024/9392065","title":"Modeling the Peak‐Period Bus Commuting Behavior with Staggered Work Hours Using a Regret‐Minimizing Learning Method","year":2024,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","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":"Natural Science Foundation of Jiangxi Province; National Natural Science Foundation of China","keywords":"Regret; Period (music); Work (physics); Computer science; Simulation; Engineering; Physics; Machine learning; Mechanical engineering","score_opus":0.03363173790935377,"score_gpt":0.34190092431227864,"score_spread":0.3082691864029249,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391579755","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.64165246,0.00068197324,0.35682178,0.00026123945,0.0003062903,0.00014805322,0.0000032060448,0.00007001937,0.000054955945],"genre_scores_gemma":[0.88050246,0.00014294412,0.11909036,0.000018765988,0.00014969567,0.0000054383904,0.000023001101,0.000029724508,0.000037612863],"study_design_codex":"simulation_or_modeling","study_design_gemma":"qualitative","domain_scores_codex":[0.99823457,0.00020249264,0.0005671598,0.00016804754,0.00057964795,0.00024811077],"domain_scores_gemma":[0.99895763,0.00018967927,0.0003365654,0.00007888522,0.00035008512,0.00008717093],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012252238,0.0001444699,0.00021802825,0.00017851828,0.0007069537,0.00018042703,0.00015396063,0.00007043967,0.000016270871],"category_scores_gemma":[0.00005743748,0.000107953485,0.00012202375,0.0007403458,0.00006881935,0.00086256914,0.0000010205266,0.00056364114,5.3741155e-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":[0.00013641309,0.000016508473,0.004809208,0.000017039129,0.00003743935,0.00008629432,0.06645566,0.9192401,0.0002863208,0.00024005916,0.0000015028,0.008673457],"study_design_scores_gemma":[0.004732479,0.00087650324,0.07938205,0.01030146,0.003568286,0.00021043619,0.60506755,0.2891788,0.00060755515,0.00055520993,0.003649383,0.0018703257],"about_ca_topic_score_codex":0.00016554711,"about_ca_topic_score_gemma":0.00048445992,"teacher_disagreement_score":0.6300613,"about_ca_system_score_codex":0.000103096914,"about_ca_system_score_gemma":0.00026846796,"threshold_uncertainty_score":0.5437388},"labels":[],"label_agreement":null},{"id":"W4391652455","doi":"10.1007/s42524-023-0291-z","title":"Urban rail transit disruption management: Research progress and future directions","year":2024,"lang":"en","type":"article","venue":"Frontiers of Engineering Management","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"University of Illinois at Urbana-Champaign; National Natural Science Foundation of China","keywords":"Backup; Risk analysis (engineering); Scarcity; Redundancy (engineering); Resilience (materials science); Computer science; Transport engineering; Business; Engineering; Economics","score_opus":0.011423903960294718,"score_gpt":0.2807560828470523,"score_spread":0.26933217888675753,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391652455","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025663309,0.09777938,0.71662563,0.013385437,0.01351821,0.004097083,0.00008087592,0.002994013,0.12585603],"genre_scores_gemma":[0.93264765,0.014113682,0.0436013,0.000009566394,0.0004996762,0.00013819034,0.00006399016,0.00003856483,0.0088873785],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991466,0.000030605028,0.0001249474,0.00018779818,0.000315688,0.00019440304],"domain_scores_gemma":[0.9998009,0.0000125810475,0.00001308656,0.00008475499,0.000034367447,0.000054268654],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004808608,0.000071274284,0.00007689671,0.0004001372,0.00017600722,0.00009348366,0.000086753775,0.000044602984,0.000010065269],"category_scores_gemma":[0.0000024812166,0.0000767246,0.00003205417,0.0006168944,0.00007664707,0.00017928536,0.000009286542,0.000096796204,0.0000021715737],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000042335418,0.00011112753,0.003067626,0.0016325624,0.00068832224,0.00009388315,0.03966452,0.014217958,0.000009500142,0.6441124,0.09733527,0.19902448],"study_design_scores_gemma":[0.00012385719,0.000016082064,0.011026753,0.000115202725,0.000063879976,1.8054239e-7,0.0047767228,0.006270606,0.0000046154564,0.00014923484,0.97735053,0.000102334794],"about_ca_topic_score_codex":0.000021435413,"about_ca_topic_score_gemma":0.000009896972,"teacher_disagreement_score":0.9069843,"about_ca_system_score_codex":0.000049882212,"about_ca_system_score_gemma":0.0000065755285,"threshold_uncertainty_score":0.31287384},"labels":[],"label_agreement":null},{"id":"W4391798736","doi":"","title":"Bilan environnemental du transport de fret en Ile-de-France : une analyse par approche multiagents des tournées et de leur impact sur le secteur des transports","year":2023,"lang":"fr","type":"article","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ministère des Transports","funders":"","keywords":"Humanities; Art","score_opus":0.017033055918763956,"score_gpt":0.25466231292328056,"score_spread":0.23762925700451662,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391798736","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.89388824,0.0009809432,0.09667261,0.0049253916,0.00011141328,0.00030387615,0.00026125938,0.00032554872,0.0025307452],"genre_scores_gemma":[0.9635135,0.0035723967,0.021537364,0.000111637186,0.000044924618,0.00004527056,0.0011803391,0.00006163151,0.009932917],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99302965,0.0044722394,0.000578038,0.00058217644,0.00047828813,0.0008596307],"domain_scores_gemma":[0.9972994,0.0009358669,0.00030249663,0.0005285732,0.00048879045,0.0004448944],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0069182753,0.0003538556,0.0003518294,0.00021558342,0.0011781253,0.00018068595,0.0006779821,0.00029170178,0.00029538813],"category_scores_gemma":[0.0004136889,0.00040050017,0.00031821945,0.0017158886,0.00094226573,0.0006244882,0.000028651373,0.00037499628,0.000031294658],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024352057,0.00060350244,0.71792954,0.00012100135,0.000110652705,0.000044897482,0.2500123,0.01824095,0.0024212652,0.0014481152,0.0003493568,0.008694066],"study_design_scores_gemma":[0.0010783548,0.0000029983494,0.95661485,0.00072497095,0.00015001447,0.0000146011,0.005673286,0.020935407,0.00691883,0.00094653293,0.006468605,0.00047155397],"about_ca_topic_score_codex":0.061589543,"about_ca_topic_score_gemma":0.08632491,"teacher_disagreement_score":0.24433902,"about_ca_system_score_codex":0.00035595297,"about_ca_system_score_gemma":0.0007827922,"threshold_uncertainty_score":0.9998447},"labels":[],"label_agreement":null},{"id":"W4391798749","doi":"","title":"SMA et externalités environnementale avec un focus sur la pollution de l'air Session 5 : Numérique et systèmes multi-agents pour les mobilités urbaines","year":2021,"lang":"fr","type":"article","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ministère des Transports","funders":"","keywords":"Computer science; Humanities; Art","score_opus":0.019384668269077164,"score_gpt":0.26673171404315515,"score_spread":0.24734704577407798,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391798749","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5163209,0.008213016,0.3968774,0.061602425,0.00042853513,0.000442108,0.0002383644,0.00028199016,0.015595281],"genre_scores_gemma":[0.8836168,0.0028837188,0.042352058,0.0003246714,0.00003892139,0.000045779525,0.00050954625,0.0000483636,0.070180126],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9830936,0.014563356,0.0006235396,0.00067853346,0.00054580276,0.0004951316],"domain_scores_gemma":[0.99593973,0.0015790012,0.00045058536,0.0006911399,0.0010662465,0.00027327405],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.006081107,0.00033488305,0.0003209465,0.00012203956,0.0009155335,0.00030304166,0.0005102411,0.00038857618,0.0007827926],"category_scores_gemma":[0.0013828491,0.00038647547,0.00022638006,0.00064750924,0.00043290996,0.00060696685,0.0001322596,0.00043123588,0.000041385905],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006645255,0.0064961966,0.1236308,0.00053825014,0.00031860458,0.00018764903,0.31787694,0.0149273705,0.013288064,0.34953862,0.0026143363,0.17051673],"study_design_scores_gemma":[0.0062134895,0.000005817217,0.59287447,0.009421707,0.0002964931,0.00015924171,0.02361823,0.11141731,0.11639397,0.010676402,0.12669282,0.00223006],"about_ca_topic_score_codex":0.008417449,"about_ca_topic_score_gemma":0.01565379,"teacher_disagreement_score":0.46924368,"about_ca_system_score_codex":0.0002670332,"about_ca_system_score_gemma":0.0007030391,"threshold_uncertainty_score":0.99985874},"labels":[],"label_agreement":null},{"id":"W4391906376","doi":"10.1016/j.trd.2024.104120","title":"Empirical analysis of battery-electric bus transit operations in Portland, OR, USA","year":2024,"lang":"en","type":"article","venue":"Transportation Research Part D Transport and Environment","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Battery (electricity); Transit (satellite); Automotive engineering; Public transport; Engineering; Electrical engineering; Transport engineering; Environmental science; Power (physics); Physics; Thermodynamics","score_opus":0.08370455772825877,"score_gpt":0.3870545814781197,"score_spread":0.303350023749861,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391906376","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98519874,0.00042437075,0.010985262,0.0017122885,0.000070866074,0.00048706768,0.0001894063,0.000054953427,0.0008770553],"genre_scores_gemma":[0.99225885,0.005219401,0.00035476702,0.000037706115,0.0000340835,0.000090882815,0.0008236555,0.000016976628,0.0011636837],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99733335,0.00017327433,0.0006566362,0.00047758102,0.00092041754,0.0004387574],"domain_scores_gemma":[0.99936295,0.00021200233,0.000028937495,0.00014935421,0.000050204206,0.00019655282],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0013305396,0.0001582941,0.00033638504,0.001080077,0.00028164667,0.00004511792,0.00012622321,0.00016051109,0.0022652717],"category_scores_gemma":[0.0000080595855,0.00014447069,0.00013165429,0.0025217608,0.0002890963,0.00031493252,8.9154366e-7,0.00032140352,0.0000147281935],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017730762,0.0002972827,0.7571652,0.00011783425,0.00036897312,0.0001919349,0.047367465,0.18904549,0.00018822702,0.0025071132,0.00012172309,0.0024514773],"study_design_scores_gemma":[0.00038412432,0.00010884594,0.9691971,0.000068323265,0.00042199803,2.4233069e-7,0.0017547642,0.0042583435,0.00006019287,0.00006705376,0.023472583,0.00020641058],"about_ca_topic_score_codex":0.00357517,"about_ca_topic_score_gemma":0.06638244,"teacher_disagreement_score":0.21203195,"about_ca_system_score_codex":0.00007355537,"about_ca_system_score_gemma":0.00022946719,"threshold_uncertainty_score":0.9986468},"labels":[],"label_agreement":null},{"id":"W4391942860","doi":"10.1007/s12469-023-00339-2","title":"A pattern-based timetabling strategy for a short-turning metro line","year":2024,"lang":"en","type":"article","venue":"Public Transport","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; École de Technologie Supérieure; Group for Research in Decision Analysis; HEC Montréal","funders":"","keywords":"Transport engineering; Line (geometry); Computer science; Engineering; Mathematics","score_opus":0.0696865113549725,"score_gpt":0.33653875618819484,"score_spread":0.26685224483322234,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391942860","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04776149,0.0006201132,0.9424006,0.0029046913,0.00053536455,0.0005513202,0.00017113185,0.00088441256,0.0041709114],"genre_scores_gemma":[0.99406034,0.000021573765,0.003829755,0.00016661009,0.00029290983,0.00015626772,0.000747563,0.0000308501,0.00069415633],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99858046,0.000040077248,0.00030857432,0.0003263704,0.00035066606,0.00039385638],"domain_scores_gemma":[0.99937713,0.00018492727,0.00003219608,0.00010308341,0.00014141701,0.00016122051],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00091330963,0.00013532068,0.0001686011,0.00027109298,0.0003254621,0.00023339721,0.00016427529,0.000120073506,0.00033005123],"category_scores_gemma":[0.00004047346,0.00013376545,0.00015509983,0.00067965296,0.000064189844,0.0004352925,8.5096053e-7,0.00015355593,0.000009440607],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012909295,0.00045701486,0.23227441,0.00087540527,0.00047042713,0.00015954218,0.017343653,0.15095519,0.0004278959,0.054932054,0.001834689,0.5401406],"study_design_scores_gemma":[0.0013092046,0.00024732907,0.0633121,0.0003384832,0.00047237202,0.0000020382677,0.0036412447,0.16330424,0.00041677558,0.0006461238,0.7651564,0.0011536969],"about_ca_topic_score_codex":0.00045187995,"about_ca_topic_score_gemma":0.0026830148,"teacher_disagreement_score":0.94629884,"about_ca_system_score_codex":0.00004636901,"about_ca_system_score_gemma":0.0005134357,"threshold_uncertainty_score":0.5454797},"labels":[],"label_agreement":null},{"id":"W4392030281","doi":"10.32920/25267435.v1","title":"Predicting Bicycle-on-Board Transit Choice in a University Environment","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Transit (satellite); Transport engineering; Computer science; Business; Public transport; Engineering","score_opus":0.018418643344252077,"score_gpt":0.2556375404393269,"score_spread":0.23721889709507482,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392030281","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.826006,0.00009072694,0.004222305,0.005168575,0.0007016927,0.00057764683,0.000092616356,0.0006592537,0.16248117],"genre_scores_gemma":[0.9940782,0.00011246719,0.0008861471,0.000056472916,0.00008827683,0.0000029727764,0.00004927351,0.000009383091,0.0047168103],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99902475,0.00009340179,0.00013713182,0.00032046833,0.00027083256,0.00015340836],"domain_scores_gemma":[0.99967784,0.00010167004,0.000044562395,0.000102969556,0.000009944289,0.00006301347],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002496293,0.00010410142,0.00011744271,0.00016289111,0.0001267216,0.000046076588,0.00014878581,0.00021025854,0.00019335686],"category_scores_gemma":[0.000016686989,0.000116101044,0.00006134912,0.00014181025,0.000059326492,0.000040467366,0.000025773888,0.00041351153,0.000053081247],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027297681,0.000077183126,0.13888602,0.00007825604,0.000039674662,0.000043803775,0.037730865,0.81394696,0.0000031189254,0.0066086496,0.00056477456,0.0019933765],"study_design_scores_gemma":[0.0017271414,0.00012913712,0.71645504,0.0019786572,0.0003742885,2.89825e-7,0.033172265,0.083253264,0.000055841974,0.0049308324,0.15610456,0.0018186847],"about_ca_topic_score_codex":0.012291143,"about_ca_topic_score_gemma":0.012354876,"teacher_disagreement_score":0.7306937,"about_ca_system_score_codex":0.00020763556,"about_ca_system_score_gemma":0.00014037831,"threshold_uncertainty_score":0.9942861},"labels":[],"label_agreement":null},{"id":"W4392058718","doi":"10.32920/25267435","title":"Predicting Bicycle-on-Board Transit Choice in a University Environment","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Transit (satellite); Transport engineering; Business; Public transport; Engineering","score_opus":0.018418643344252077,"score_gpt":0.2556375404393269,"score_spread":0.23721889709507482,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392058718","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.826006,0.00009072694,0.004222305,0.005168575,0.0007016927,0.00057764683,0.000092616356,0.0006592537,0.16248117],"genre_scores_gemma":[0.9940782,0.00011246719,0.0008861471,0.000056472916,0.00008827683,0.0000029727764,0.00004927351,0.000009383091,0.0047168103],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99902475,0.00009340179,0.00013713182,0.00032046833,0.00027083256,0.00015340836],"domain_scores_gemma":[0.99967784,0.00010167004,0.000044562395,0.000102969556,0.000009944289,0.00006301347],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002496293,0.00010410142,0.00011744271,0.00016289111,0.0001267216,0.000046076588,0.00014878581,0.00021025854,0.00019335686],"category_scores_gemma":[0.000016686989,0.000116101044,0.00006134912,0.00014181025,0.000059326492,0.000040467366,0.000025773888,0.00041351153,0.000053081247],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027297681,0.000077183126,0.13888602,0.00007825604,0.000039674662,0.000043803775,0.037730865,0.81394696,0.0000031189254,0.0066086496,0.00056477456,0.0019933765],"study_design_scores_gemma":[0.0017271414,0.00012913712,0.71645504,0.0019786572,0.0003742885,2.89825e-7,0.033172265,0.083253264,0.000055841974,0.0049308324,0.15610456,0.0018186847],"about_ca_topic_score_codex":0.012291143,"about_ca_topic_score_gemma":0.012354876,"teacher_disagreement_score":0.7306937,"about_ca_system_score_codex":0.00020763556,"about_ca_system_score_gemma":0.00014037831,"threshold_uncertainty_score":0.9942861},"labels":[],"label_agreement":null},{"id":"W4392515967","doi":"10.1007/s11116-024-10474-9","title":"Correction to: Advances and pitfalls in measuring transportation equityTransportation","year":2024,"lang":"en","type":"article","venue":"Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":20,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"The Scarborough Hospital; University of Toronto","funders":"","keywords":"Regional science; Data science; Computer science; Operations research; Transport engineering; Management science; Engineering; Geography","score_opus":0.0205242062143959,"score_gpt":0.30209014168848675,"score_spread":0.28156593547409087,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392515967","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.91304797,0.0022691526,0.07534553,0.0012558079,0.002919552,0.00092191197,0.00009004979,0.00081187487,0.0033381616],"genre_scores_gemma":[0.9960701,0.0011840787,0.0014190299,0.0000831527,0.0001059249,0.00007433797,0.0005030957,0.00002905506,0.00053126266],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.99820626,0.000068874106,0.0004949986,0.00045261355,0.0004646559,0.0003126071],"domain_scores_gemma":[0.9994452,0.00015717624,0.00006037467,0.00007978767,0.00010320249,0.00015427552],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006251198,0.00017483924,0.00017918699,0.0004594761,0.00022905492,0.00012167757,0.00008183869,0.00011607608,0.000076454344],"category_scores_gemma":[0.00003713386,0.00020408335,0.000057302525,0.0010438912,0.000069073954,0.0012683951,2.9083145e-7,0.000198316,0.00001896916],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039593826,0.0001777643,0.26585993,0.0009137774,0.000057467863,0.00015494738,0.33757767,0.1521646,0.0023812985,0.07082844,0.0008022418,0.1686859],"study_design_scores_gemma":[0.0005348729,0.00009718018,0.95331067,0.0008320323,0.0000918887,6.9900136e-7,0.006139653,0.0021771938,0.00049974356,0.0015363467,0.034251943,0.0005277867],"about_ca_topic_score_codex":0.0026783124,"about_ca_topic_score_gemma":0.10540166,"teacher_disagreement_score":0.6874507,"about_ca_system_score_codex":0.000101698104,"about_ca_system_score_gemma":0.00014528028,"threshold_uncertainty_score":0.9109224},"labels":[],"label_agreement":null},{"id":"W4392519718","doi":"10.1016/j.ejor.2024.03.010","title":"A novel model for transfer synchronization in transit networks and a Lagrangian-based heuristic solution method","year":2024,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Lagrangian relaxation; Computer science; Dwell time; Synchronizing; Headway; Mathematical optimization; Heuristic; Scheduling (production processes); Transfer (computing); Relaxation (psychology); Synchronization (alternating current); Heuristics; Integer programming; Operations research; Simulation; Algorithm; Mathematics; Artificial intelligence; Computer network","score_opus":0.10085208267917678,"score_gpt":0.40374504069390316,"score_spread":0.3028929580147264,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392519718","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002485283,0.0005814389,0.9929652,0.0031657747,0.00008257911,0.00020827402,0.000017209382,0.000012475792,0.0004817281],"genre_scores_gemma":[0.9619602,0.0001263076,0.037352867,0.00006130599,0.00019578688,0.0000048973607,0.000032603348,0.000016570557,0.00024948025],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982651,0.0006025457,0.00031959708,0.00013717986,0.00048898946,0.00018662441],"domain_scores_gemma":[0.9987929,0.0005284016,0.000019043242,0.000033103657,0.0005393527,0.00008718337],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0071415366,0.00006006697,0.00009306982,0.00033665818,0.00030515407,0.00022265073,0.0000968385,0.00003562361,0.000021500475],"category_scores_gemma":[0.0002702814,0.000055986096,0.00004618029,0.0004325815,0.00008589928,0.00029519535,0.0000019475374,0.00026443368,0.0000012868139],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010974082,0.000039161547,0.00006155535,0.000033290115,0.000010500479,0.000015719166,0.0048886263,0.9772058,0.00045773093,0.011512492,0.0002476633,0.0054176957],"study_design_scores_gemma":[0.0005947747,0.000088171655,0.0010536707,0.0001744956,0.00001244393,0.0000041154544,0.00019127566,0.9965364,0.000010055688,0.00010415657,0.0011707913,0.000059650363],"about_ca_topic_score_codex":0.000030644722,"about_ca_topic_score_gemma":0.00021131898,"teacher_disagreement_score":0.9594749,"about_ca_system_score_codex":0.000082359555,"about_ca_system_score_gemma":0.0005840409,"threshold_uncertainty_score":0.24751283},"labels":[],"label_agreement":null},{"id":"W4392821211","doi":"10.1016/j.tra.2024.104031","title":"A collective incentive strategy to manage ridership rebound and consumer surplus in mass transit systems","year":2024,"lang":"en","type":"article","venue":"Transportation Research Part A Policy and Practice","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Incentive; Economic surplus; Revenue; Price elasticity of demand; Microeconomics; Transit (satellite); Total revenue; Economics; Transit system; Business; Transport engineering; Public transport; Finance; Engineering","score_opus":0.15746440954080834,"score_gpt":0.4670247070821425,"score_spread":0.3095602975413342,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392821211","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6524255,0.0110737,0.017703047,0.07025947,0.0008968356,0.007847386,0.0010943505,0.00063807965,0.23806158],"genre_scores_gemma":[0.9905711,0.0020224662,0.0002131767,0.00012262017,0.000110768946,0.00012263107,0.000052215386,0.000014996486,0.0067700273],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99730223,0.00108675,0.0002843756,0.00036952557,0.00054961833,0.0004075005],"domain_scores_gemma":[0.9966032,0.0027869393,0.00003977287,0.00007524112,0.00026318975,0.0002316524],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032837302,0.00011194206,0.0001600057,0.0005903198,0.0004825716,0.00048030453,0.00007153202,0.000120724144,0.00003557683],"category_scores_gemma":[0.0006261922,0.000119654265,0.000020509788,0.0017412868,0.00028097432,0.0008953041,0.0000014539734,0.0003600468,0.000015458894],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014271343,0.00010206664,0.0045327353,0.0005365631,0.00015396267,0.0003360139,0.46059102,0.0087180035,0.00008427207,0.51867133,0.0014857198,0.003361192],"study_design_scores_gemma":[0.0015947222,0.0005471982,0.06155428,0.000849831,0.00014193065,0.0000057372204,0.1548994,0.002535037,0.000033499717,0.008657612,0.76854503,0.000635709],"about_ca_topic_score_codex":0.03032667,"about_ca_topic_score_gemma":0.021510115,"teacher_disagreement_score":0.7670593,"about_ca_system_score_codex":0.0001724225,"about_ca_system_score_gemma":0.0009538404,"threshold_uncertainty_score":0.9963448},"labels":[],"label_agreement":null},{"id":"W4393610419","doi":"10.5281/zenodo.1186215","title":"A Collection Of Public Transport Network Data Sets For 25 Cities","year":2018,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Data collection; Computer science; Transport engineering; Environmental science; Geography; Statistics; Engineering; Mathematics","score_opus":0.10249064999127853,"score_gpt":0.31447179631460503,"score_spread":0.21198114632332649,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393610419","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000563347,0.000072390205,0.006559687,0.00046075037,0.00042026362,0.00080432545,0.9846891,0.0003603725,0.0065767425],"genre_scores_gemma":[0.0015440028,0.00046864388,0.00065204996,0.00005672385,0.00051304547,1.8423792e-7,0.99563116,0.00043368695,0.00070050755],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.997597,0.0004546326,0.0004295335,0.00050549046,0.0005789298,0.00043443925],"domain_scores_gemma":[0.99753565,0.0000790525,0.00035297553,0.0006643308,0.0012061174,0.0001618655],"candidate_categories":["sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0023068916,0.00016968253,0.00025555017,0.00029107404,0.0038049475,0.00038525174,0.0017437007,0.0002425532,0.006067836],"category_scores_gemma":[0.0009459482,0.00019695652,0.0000648593,0.00092202687,0.00044171588,0.00041532275,0.0001767379,0.00021582715,0.00024079648],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000052507556,0.00006718297,0.0000048460315,0.0001382232,0.000064478685,0.0000014127754,0.0012604543,0.000119904165,0.0000011404182,0.0006295593,0.99563974,0.0020205376],"study_design_scores_gemma":[0.00033873663,0.00015602415,0.00014378055,0.00007433968,0.000073810355,0.0000031043608,0.0005157291,0.00016029581,0.0000015497955,0.00012424885,0.99821645,0.00019193921],"about_ca_topic_score_codex":0.00042827672,"about_ca_topic_score_gemma":0.00031055117,"teacher_disagreement_score":0.010942022,"about_ca_system_score_codex":0.00011244446,"about_ca_system_score_gemma":0.000057076086,"threshold_uncertainty_score":0.99749196},"labels":[],"label_agreement":null},{"id":"W4393662360","doi":"10.5281/zenodo.1136378","title":"A Collection Of Public Transport Network Data Sets For 25 Cities","year":2018,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Public transport; Data collection; Transport engineering; Business; Geography; Computer science; Engineering; Mathematics; Statistics","score_opus":0.10249064999127853,"score_gpt":0.31447179631460503,"score_spread":0.21198114632332649,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393662360","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000563347,0.000072390205,0.006559687,0.00046075037,0.00042026362,0.00080432545,0.9846891,0.0003603725,0.0065767425],"genre_scores_gemma":[0.0015440028,0.00046864388,0.00065204996,0.00005672385,0.00051304547,1.8423792e-7,0.99563116,0.00043368695,0.00070050755],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.997597,0.0004546326,0.0004295335,0.00050549046,0.0005789298,0.00043443925],"domain_scores_gemma":[0.99753565,0.0000790525,0.00035297553,0.0006643308,0.0012061174,0.0001618655],"candidate_categories":["sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0023068916,0.00016968253,0.00025555017,0.00029107404,0.0038049475,0.00038525174,0.0017437007,0.0002425532,0.006067836],"category_scores_gemma":[0.0009459482,0.00019695652,0.0000648593,0.00092202687,0.00044171588,0.00041532275,0.0001767379,0.00021582715,0.00024079648],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000052507556,0.00006718297,0.0000048460315,0.0001382232,0.000064478685,0.0000014127754,0.0012604543,0.000119904165,0.0000011404182,0.0006295593,0.99563974,0.0020205376],"study_design_scores_gemma":[0.00033873663,0.00015602415,0.00014378055,0.00007433968,0.000073810355,0.0000031043608,0.0005157291,0.00016029581,0.0000015497955,0.00012424885,0.99821645,0.00019193921],"about_ca_topic_score_codex":0.00042827672,"about_ca_topic_score_gemma":0.00031055117,"teacher_disagreement_score":0.010942022,"about_ca_system_score_codex":0.00011244446,"about_ca_system_score_gemma":0.000057076086,"threshold_uncertainty_score":0.99749196},"labels":[],"label_agreement":null},{"id":"W4393710891","doi":"10.5281/zenodo.1133857","title":"A Collection Of Public Transport Network Data Sets For 25 Cities","year":2018,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Data collection; Public transport; Computer science; Geography; Transport engineering; Engineering; Mathematics; Statistics","score_opus":0.10249064999127853,"score_gpt":0.31447179631460503,"score_spread":0.21198114632332649,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393710891","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000563347,0.000072390205,0.006559687,0.00046075037,0.00042026362,0.00080432545,0.9846891,0.0003603725,0.0065767425],"genre_scores_gemma":[0.0015440028,0.00046864388,0.00065204996,0.00005672385,0.00051304547,1.8423792e-7,0.99563116,0.00043368695,0.00070050755],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.997597,0.0004546326,0.0004295335,0.00050549046,0.0005789298,0.00043443925],"domain_scores_gemma":[0.99753565,0.0000790525,0.00035297553,0.0006643308,0.0012061174,0.0001618655],"candidate_categories":["sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0023068916,0.00016968253,0.00025555017,0.00029107404,0.0038049475,0.00038525174,0.0017437007,0.0002425532,0.006067836],"category_scores_gemma":[0.0009459482,0.00019695652,0.0000648593,0.00092202687,0.00044171588,0.00041532275,0.0001767379,0.00021582715,0.00024079648],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000052507556,0.00006718297,0.0000048460315,0.0001382232,0.000064478685,0.0000014127754,0.0012604543,0.000119904165,0.0000011404182,0.0006295593,0.99563974,0.0020205376],"study_design_scores_gemma":[0.00033873663,0.00015602415,0.00014378055,0.00007433968,0.000073810355,0.0000031043608,0.0005157291,0.00016029581,0.0000015497955,0.00012424885,0.99821645,0.00019193921],"about_ca_topic_score_codex":0.00042827672,"about_ca_topic_score_gemma":0.00031055117,"teacher_disagreement_score":0.010942022,"about_ca_system_score_codex":0.00011244446,"about_ca_system_score_gemma":0.000057076086,"threshold_uncertainty_score":0.99749196},"labels":[],"label_agreement":null},{"id":"W4394018694","doi":"10.1016/j.tra.2024.104040","title":"Accounting for traffic disturbance in road infrastructure management: Optimal maintenance and rehabilitation planning for the society","year":2024,"lang":"en","type":"article","venue":"Transportation Research Part A Policy and Practice","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; McGill University","funders":"","keywords":"Sustainability; Time horizon; Agency (philosophy); Transport engineering; Environmental economics; Business; Cost–benefit analysis; Computer science; Operations research; Economics; Engineering; Finance","score_opus":0.04820544481696094,"score_gpt":0.42816216087613285,"score_spread":0.3799567160591719,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394018694","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.46882075,0.01209604,0.23776978,0.25958398,0.0009518374,0.012925751,0.0009847687,0.00061292545,0.006254167],"genre_scores_gemma":[0.9784472,0.0018601873,0.017658768,0.0002681345,0.00026754275,0.00055499614,0.000112514106,0.000017412334,0.0008132627],"study_design_codex":"qualitative","study_design_gemma":"not_applicable","domain_scores_codex":[0.9987228,0.00014699686,0.00023042598,0.00027909636,0.00029403513,0.00032665857],"domain_scores_gemma":[0.99313885,0.0065045166,0.00005580027,0.00006579567,0.00018261997,0.000052440755],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035761003,0.00008519905,0.00009140474,0.00012707677,0.0008313573,0.00026861587,0.0000817658,0.00008242575,0.0000044905655],"category_scores_gemma":[0.0010226552,0.0000725417,0.000052885367,0.000595749,0.00028277622,0.00081923086,0.0000021047324,0.00022178587,4.190245e-7],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011053641,0.000055368073,0.00277665,0.0014539273,0.0001578274,0.000008580431,0.3980629,0.13097043,0.000011385945,0.37765655,0.015718222,0.07202277],"study_design_scores_gemma":[0.0007879762,0.00009161495,0.08160979,0.00033436224,0.00007409617,9.104785e-7,0.06888634,0.04134264,0.0000010303545,0.002029756,0.8046789,0.00016253805],"about_ca_topic_score_codex":0.0005907642,"about_ca_topic_score_gemma":0.00054704625,"teacher_disagreement_score":0.7889607,"about_ca_system_score_codex":0.0000522566,"about_ca_system_score_gemma":0.0001455374,"threshold_uncertainty_score":0.6394212},"labels":[],"label_agreement":null},{"id":"W4394395127","doi":"10.6084/m9.figshare.11628603","title":"Speed and road grade dynamics of urban trips on electric and conventional bicycles","year":2020,"lang":"en","type":"dataset","venue":"Figshare","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"TRIPS architecture; Transport engineering; Electric cars; Dynamics (music); Geography; Engineering; Psychology; Automotive engineering","score_opus":0.03486408117421832,"score_gpt":0.2867106301369905,"score_spread":0.2518465489627722,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394395127","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00007836745,0.00017630156,3.722118e-7,0.00015068175,0.000033516706,0.00013839362,0.9992584,0.000017210848,0.0001467349],"genre_scores_gemma":[0.0011725624,0.0001165599,0.000015752315,0.000055283468,0.00008339627,0.0000065051886,0.99845463,0.000005632891,0.00008970424],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993047,0.00004980399,0.00014819625,0.00017570025,0.00022368175,0.0000979029],"domain_scores_gemma":[0.9995664,0.00009344246,0.00016427807,0.000054831748,0.000047805323,0.00007324237],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000040233823,0.00009571064,0.0001498879,0.00009894453,0.00012161608,0.000040804665,0.00009286593,0.00016279373,0.0067429566],"category_scores_gemma":[0.00029566072,0.00010132621,0.000031012183,0.0001977001,0.000017950157,0.000051604773,0.0000072717435,0.00014093092,0.0000572103],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000979233,0.00001467711,0.00005398626,0.00011939853,0.000012320079,0.0000029744126,0.00013937324,0.000019258758,2.939175e-7,0.00007348211,0.99940276,0.00015170997],"study_design_scores_gemma":[0.00024677988,0.000057527148,0.005180771,0.00049968995,0.000029780882,4.999124e-7,0.000114005496,0.00043307306,0.0000027820738,0.000031261312,0.9932588,0.00014505816],"about_ca_topic_score_codex":0.00014047617,"about_ca_topic_score_gemma":0.00034444575,"teacher_disagreement_score":0.0066857464,"about_ca_system_score_codex":0.000020736694,"about_ca_system_score_gemma":0.00010068725,"threshold_uncertainty_score":0.994165},"labels":[],"label_agreement":null},{"id":"W4394708860","doi":"10.48550/arxiv.2404.05894","title":"Learning Heuristics for Transit Network Design and Improvement with Deep Reinforcement Learning","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Reinforcement learning; Heuristics; Computer science; Artificial intelligence; Reinforcement; Machine learning; Engineering; Structural engineering","score_opus":0.044410415255439525,"score_gpt":0.20224070601991503,"score_spread":0.15783029076447552,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394708860","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02746967,0.00009199005,0.9703547,0.00006838851,0.00019078983,0.00064609735,0.0000018722865,0.00020193229,0.000974567],"genre_scores_gemma":[0.98917544,0.00051582174,0.005621458,0.000025360301,0.00011868341,0.00000492941,0.00008026893,0.000024643272,0.0044334135],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99886864,0.00011866951,0.0001484452,0.000471668,0.00009710749,0.00029547725],"domain_scores_gemma":[0.9993383,0.00017194645,0.00014712346,0.00009623939,0.00013322291,0.000113116694],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005480124,0.00017924825,0.00018564632,0.00008766999,0.00063896755,0.000120388526,0.00013251381,0.00018371995,0.000020143794],"category_scores_gemma":[0.000028861365,0.0001970257,0.00006264341,0.00023192899,0.00011096592,0.000068121975,0.000041519346,0.00047995753,0.0000034483598],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013271635,0.0000075602848,0.0011690202,0.000118391836,0.00008714294,0.0000251065,0.006166659,0.9743555,0.0000013075928,0.017192135,0.00004336883,0.0007010786],"study_design_scores_gemma":[0.00047206078,0.0003234985,0.000104687504,0.00021961947,0.0003831793,2.4839622e-7,0.004562928,0.98656213,0.000005248312,0.0045069535,0.0025257426,0.00033370827],"about_ca_topic_score_codex":0.00027926918,"about_ca_topic_score_gemma":0.00029476668,"teacher_disagreement_score":0.96473324,"about_ca_system_score_codex":0.00011508058,"about_ca_system_score_gemma":0.00019375248,"threshold_uncertainty_score":0.8034475},"labels":[],"label_agreement":null},{"id":"W4394720277","doi":"10.4324/9781315157375","title":"The Economics of Urban Transportation","year":2024,"lang":"en","type":"book","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Transport engineering; Regional science; Geography; Business; Engineering","score_opus":0.013175529919794584,"score_gpt":0.25001604918344417,"score_spread":0.23684051926364957,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394720277","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00006506531,0.0005655856,0.0003138294,0.0005429119,0.0005785261,0.00013697108,0.000079473095,0.00007502402,0.99764264],"genre_scores_gemma":[0.0026355265,0.0014877012,0.00014999037,0.00002616494,0.00016665498,0.0000043358036,0.0003120726,0.000013741244,0.9952038],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99945205,0.000014154335,0.0002311457,0.00010654793,0.00011345751,0.00008266002],"domain_scores_gemma":[0.9996091,0.00011827802,0.00010744507,0.000076265555,0.000061168714,0.000027739317],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023410736,0.00006750485,0.000093332696,0.000040713116,0.00017129732,0.000042283882,0.000116120136,0.0001498628,0.0001097106],"category_scores_gemma":[0.000006949615,0.000051854833,0.00007699094,0.00004366273,0.00013902807,0.00004938587,5.4828297e-7,0.000095922864,0.00003218375],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000041993435,0.0000021675332,0.000049738675,0.000021758373,0.00002572837,4.582022e-7,0.00693311,0.000562643,8.293309e-8,0.89146894,0.09957907,0.0013521205],"study_design_scores_gemma":[0.00002946542,0.000008106915,0.00009911959,0.00003757266,0.000048079437,1.5302042e-8,0.0005194374,0.000042034197,0.0000020228108,0.013819458,0.9853267,0.00006799081],"about_ca_topic_score_codex":0.00017570742,"about_ca_topic_score_gemma":0.0074738185,"teacher_disagreement_score":0.8857476,"about_ca_system_score_codex":0.000049338203,"about_ca_system_score_gemma":0.00058115745,"threshold_uncertainty_score":0.4170566},"labels":[],"label_agreement":null},{"id":"W4394784782","doi":"10.1049/itr2.12512","title":"Flexible optimal bus‐schedule bridging for metro operation‐interruption","year":2024,"lang":"en","type":"article","venue":"IET Intelligent Transport Systems","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Fuzhou University","keywords":"Bridging (networking); Scheduling (production processes); Schedule; Computer science; Engineering; Transport engineering; Computer network; Operations management","score_opus":0.0672747397271949,"score_gpt":0.35831997788837217,"score_spread":0.2910452381611773,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394784782","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03250157,0.0015434035,0.9573279,0.00056830706,0.003494023,0.0009138631,0.0001344667,0.000756999,0.0027594548],"genre_scores_gemma":[0.98781896,0.00017337146,0.002447337,0.000045793513,0.00073908817,0.00019588174,0.000638074,0.000040892235,0.007900597],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99815756,0.000067406305,0.00056564575,0.00041190363,0.000427511,0.00036998242],"domain_scores_gemma":[0.9993607,0.000106113395,0.00006555998,0.00014191904,0.00018506494,0.00014064627],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010513673,0.00018268511,0.00023267113,0.00022570784,0.00044618465,0.00030944706,0.00018905532,0.00015774107,0.00020133992],"category_scores_gemma":[0.000022084538,0.00018460886,0.00019306348,0.0003751664,0.00007221443,0.0005071039,0.0000019188815,0.00013759469,0.00011059188],"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":[0.00013596813,0.00014098693,0.0064755604,0.0007924569,0.00026719124,0.000031482217,0.047547814,0.79765266,0.00062052044,0.13738137,0.004020324,0.0049336506],"study_design_scores_gemma":[0.00055322394,0.0002516754,0.00095377705,0.0012979199,0.0003515293,0.00000925184,0.022667797,0.25811517,0.006188809,0.00015333558,0.7084301,0.0010274245],"about_ca_topic_score_codex":0.00097656227,"about_ca_topic_score_gemma":0.0006116022,"teacher_disagreement_score":0.9553174,"about_ca_system_score_codex":0.00015392964,"about_ca_system_score_gemma":0.00021737938,"threshold_uncertainty_score":0.7528131},"labels":[],"label_agreement":null},{"id":"W4394897435","doi":"10.2139/ssrn.4798017","title":"Learning Heuristics for Transit Network Design and Improvement with Deep Reinforcement Learning","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Heuristics; Download; Reinforcement learning; Computer science; World Wide Web; Artificial intelligence; Operating system","score_opus":0.010925098319338663,"score_gpt":0.2571837355029845,"score_spread":0.24625863718364585,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394897435","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004661859,0.0029028456,0.99063796,0.00050494913,0.00030059897,0.0005991887,6.35061e-7,0.000105513376,0.0002864737],"genre_scores_gemma":[0.9730664,0.011845021,0.010278425,0.00003874841,0.0007351806,0.00005749434,0.00006337418,0.000053059273,0.0038622937],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99736214,0.00017531561,0.00033468445,0.0002981688,0.00037043562,0.0014592482],"domain_scores_gemma":[0.999247,0.0001386341,0.00027716157,0.000060487833,0.00016958978,0.000107114894],"candidate_categories":["research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0035570452,0.00021065987,0.00022942186,0.000093053015,0.0010557313,0.0003069828,0.00014230821,0.0001895895,0.000010251103],"category_scores_gemma":[0.000058138412,0.00019185843,0.000083348714,0.00012832478,0.00006723891,0.00006202406,0.000020669275,0.0033220118,0.0000015169554],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013344639,0.0000066273983,0.00020320756,0.00005715291,0.00021723135,0.0000029290568,0.007684134,0.9534214,0.000002507202,0.022804422,0.00002421956,0.015442686],"study_design_scores_gemma":[0.0023833488,0.0050494145,0.00008528198,0.0011995456,0.0016328092,0.00004701442,0.0532437,0.62630874,0.00001880049,0.29406402,0.014534812,0.00143255],"about_ca_topic_score_codex":0.00016736431,"about_ca_topic_score_gemma":0.001372395,"teacher_disagreement_score":0.9803595,"about_ca_system_score_codex":0.000575653,"about_ca_system_score_gemma":0.0027518177,"threshold_uncertainty_score":0.99897736},"labels":[],"label_agreement":null},{"id":"W4394988497","doi":"10.1155/2024/9966775","title":"Influence of Expressway Construction Area Information on Drivers’ Route Choice Behaviours","year":2024,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","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":"Natural Science Foundation of Shandong Province; U.S. Department of Transportation","keywords":"Transport engineering; Computer science; Civil engineering; Engineering","score_opus":0.008993619701522076,"score_gpt":0.2726544602948344,"score_spread":0.26366084059331235,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394988497","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.989062,0.000058919846,0.009568263,0.00021822988,0.00053325813,0.00012311405,0.000058097263,0.000046194204,0.00033193597],"genre_scores_gemma":[0.9951315,0.00021952453,0.004432733,0.00003110556,0.000057763347,0.0000024943083,0.00009347208,0.000006343622,0.000025048656],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9986831,0.000035929603,0.0005751179,0.00008049674,0.0005139586,0.00011139919],"domain_scores_gemma":[0.9988331,0.0001199416,0.00045649745,0.000056800083,0.0004625329,0.00007112735],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002572719,0.00008813473,0.00014514854,0.0002957591,0.00011137675,0.00004121563,0.00009505292,0.000083794796,0.00002351949],"category_scores_gemma":[0.000069607806,0.00008619021,0.000089425695,0.00038043782,0.000095863325,0.0027543912,3.7089657e-7,0.0001777004,0.0000023528103],"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":[0.00009835872,0.000025008743,0.021371739,0.000040861865,0.000023894427,0.000007147502,0.025124893,0.9369679,0.0006478548,0.00782518,0.000047174723,0.00781998],"study_design_scores_gemma":[0.001019281,0.00030373802,0.97246474,0.0009757021,0.00018039971,0.000003971648,0.008238742,0.00017820827,0.0026379647,0.00075946865,0.0130154835,0.0002222788],"about_ca_topic_score_codex":0.00010852814,"about_ca_topic_score_gemma":0.00016782344,"teacher_disagreement_score":0.951093,"about_ca_system_score_codex":0.000070105576,"about_ca_system_score_gemma":0.00018657169,"threshold_uncertainty_score":0.35147348},"labels":[],"label_agreement":null},{"id":"W4395455715","doi":"10.1177/03611981241240762","title":"Impact of Station Design and Passengers Flow on Urban Rail Dwell Time: A Systemwide Analysis Using APC and AVL Data","year":2024,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates","keywords":"Dwell time; Transport engineering; Computer science; Transit (satellite); Urban rail transit; Flow (mathematics); Light rail transit; Engineering; Simulation; Public transport","score_opus":0.15997359784732512,"score_gpt":0.4454451472701387,"score_spread":0.28547154942281355,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4395455715","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.965911,0.0006824062,0.03104793,0.00077636895,0.00019802887,0.0009077304,0.0003853388,0.000038846607,0.000052375482],"genre_scores_gemma":[0.99128705,0.0016280946,0.0065106666,0.0000084221065,0.00011795577,0.0000128280235,0.00014532906,0.000038977385,0.00025070066],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9924637,0.0024190345,0.0011458603,0.0005177603,0.002879099,0.0005745615],"domain_scores_gemma":[0.9944024,0.0025773859,0.0003883544,0.00042094168,0.0018353604,0.00037557498],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009275999,0.00022183206,0.0005054444,0.0015550469,0.0007272571,0.00031795984,0.00063737086,0.00019913893,0.00016836746],"category_scores_gemma":[0.00030899103,0.00017105196,0.00027422182,0.0038965172,0.00074870413,0.0011045644,0.0000053769973,0.0010303562,0.000004536746],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0038488635,0.00044660823,0.435467,0.0011363929,0.003252805,0.0002663795,0.08741945,0.43802992,0.003761556,0.0028725502,0.007957271,0.015541186],"study_design_scores_gemma":[0.0012035085,0.00087686273,0.83796275,0.0011391543,0.0009765698,7.3963383e-7,0.0056678024,0.14941311,0.00016578972,0.00068447413,0.0015758993,0.00033337506],"about_ca_topic_score_codex":0.017122984,"about_ca_topic_score_gemma":0.011340124,"teacher_disagreement_score":0.4024957,"about_ca_system_score_codex":0.00025046355,"about_ca_system_score_gemma":0.0011074642,"threshold_uncertainty_score":0.9894221},"labels":[],"label_agreement":null},{"id":"W4396213978","doi":"10.2316/j.2023.203-0508","title":"CLEAN ENERGY CONSUMPTION BASED ON TIME-SHARING CARBON MEASUREMENT MODEL, 1-8.","year":2023,"lang":"en","type":"article","venue":"International Journal of Power and Energy Systems","topic":"Transportation Planning and Optimization","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":"Consumption (sociology); Energy consumption; Energy (signal processing); Environmental science; Computer science; Statistics; Engineering; Electrical engineering; Mathematics","score_opus":0.03517121791967929,"score_gpt":0.2796495545496002,"score_spread":0.24447833662992094,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396213978","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.47823116,0.0013968493,0.32485238,0.007073395,0.021762175,0.0002216622,0.000092780116,0.00066661823,0.165703],"genre_scores_gemma":[0.9985315,0.00014733047,0.0000402145,0.000105718966,0.00020751436,0.0000023653033,0.0000131391,0.000008379339,0.00094383105],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99851465,0.00006583819,0.00029096115,0.00010188961,0.00091655005,0.000110115034],"domain_scores_gemma":[0.99919957,0.000044227014,0.00019951536,0.000047052177,0.00042591302,0.000083750114],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007617233,0.00006949251,0.000107120526,0.0002778235,0.000091316804,0.00009801565,0.00015397831,0.00005687063,0.000021004913],"category_scores_gemma":[0.000043940483,0.0000648324,0.000047416575,0.000091633265,0.00003151247,0.00011766298,0.000005623093,0.000051716845,0.000002650357],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010161377,0.00003519584,0.0034011093,0.000004846608,0.000076163364,0.000030156129,0.0015775491,0.97901773,0.0002010463,0.0125467,0.0011774705,0.0018303887],"study_design_scores_gemma":[0.00071546534,0.00007832761,0.0022401258,0.00032861385,0.000022077858,0.00000614834,0.0005912605,0.9791992,0.000107648244,0.00023513098,0.016324826,0.00015120099],"about_ca_topic_score_codex":0.00077470246,"about_ca_topic_score_gemma":0.00011891663,"teacher_disagreement_score":0.5203003,"about_ca_system_score_codex":0.00011594021,"about_ca_system_score_gemma":0.00009079616,"threshold_uncertainty_score":0.26437885},"labels":[],"label_agreement":null},{"id":"W4396239215","doi":"10.1007/978-3-031-53684-7_10","title":"Understanding the Role of High-Speed Rail on Intercity Commuting: Evidence from the Shanghai Metropolitan Area","year":2024,"lang":"en","type":"book-chapter","venue":"Springer proceedings in business and economics","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Metropolitan area; Transport engineering; Geography; Economic geography; Regional science; Engineering; Archaeology","score_opus":0.0800811838415412,"score_gpt":0.2557794899176099,"score_spread":0.1756983060760687,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396239215","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.46633738,0.0037485915,0.00041455138,0.012652611,0.0015455827,0.000960698,0.00017125931,0.00016341452,0.5140059],"genre_scores_gemma":[0.99315876,0.0033214136,0.00009519448,0.00008829634,0.00021669723,0.000004232711,0.000012087352,0.000028451666,0.0030748395],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9990689,0.0000049003493,0.0003365785,0.0002965525,0.00012275348,0.00017030213],"domain_scores_gemma":[0.99915963,0.0003273948,0.00027229555,0.000107792956,0.000097399694,0.000035459165],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062182965,0.00018495813,0.00027012106,0.000114757524,0.00030348366,0.00023754401,0.00031943835,0.00018494429,0.00005194053],"category_scores_gemma":[0.000094963296,0.00013461083,0.000052722375,0.000096108924,0.0003587124,0.00020664422,0.000059460403,0.00031653405,0.00000422574],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000038410108,0.0000053294075,0.005240425,0.000035898767,0.00003898889,4.127277e-7,0.009730575,0.0004595644,0.0000032909782,0.984014,0.00007419904,0.00035894342],"study_design_scores_gemma":[0.000418062,0.00005240899,0.01713464,0.0060601514,0.0002837301,0.0000011647987,0.049463514,0.003696169,0.00006015746,0.8864589,0.035553865,0.00081724604],"about_ca_topic_score_codex":0.004324551,"about_ca_topic_score_gemma":0.0030365116,"teacher_disagreement_score":0.52682143,"about_ca_system_score_codex":0.00041034966,"about_ca_system_score_gemma":0.000069864545,"threshold_uncertainty_score":0.65374565},"labels":[],"label_agreement":null},{"id":"W4396717595","doi":"10.1080/01441647.2024.2349751","title":"What makes public transit demand management programmes successful? A systematic review of ex-post evidence","year":2024,"lang":"en","type":"review","venue":"Transport Reviews","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Public transport; Demand management; Systematic review; Transit (satellite); Business; Transport engineering; Public economics; Economics; Operations management; Engineering; Political science; MEDLINE","score_opus":0.09953196118418373,"score_gpt":0.39591084544892624,"score_spread":0.2963788842647425,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396717595","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[1.6768456e-7,0.98365533,0.00072241673,0.00057621737,0.0006523123,0.013235366,0.0000604437,0.00021497077,0.00088274776],"genre_scores_gemma":[0.000004926065,0.9937011,0.0006600052,0.00020578202,0.00008670445,0.0020576857,0.0006935227,0.00007916939,0.0025110804],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.9923198,0.0012740035,0.0038222482,0.00087373407,0.0011222941,0.00058792194],"domain_scores_gemma":[0.9968769,0.00021897141,0.0015149135,0.000811124,0.00031399398,0.00026412372],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.005998177,0.0007826162,0.0054581407,0.00041652552,0.00021240044,0.00029138863,0.0010882283,0.00034762162,0.00032237262],"category_scores_gemma":[0.00017032836,0.00054632523,0.0020039205,0.0022375197,0.00024928452,0.0011808517,0.000009810202,0.00042246922,0.0002456876],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000014586557,0.000048320417,0.000003977195,0.84649676,0.00025726383,0.000041652336,0.0014221084,4.7481356e-7,3.8729087e-9,0.001307899,0.00034989053,0.15007016],"study_design_scores_gemma":[0.00002633561,0.000016076807,7.6625895e-7,0.50903267,0.010182777,0.00000384484,0.00021963732,2.7513417e-7,1.22755255e-8,0.000006951721,0.4802422,0.00026845653],"about_ca_topic_score_codex":0.00008818704,"about_ca_topic_score_gemma":0.00088509306,"teacher_disagreement_score":0.47989228,"about_ca_system_score_codex":0.00013274037,"about_ca_system_score_gemma":0.0005239261,"threshold_uncertainty_score":0.9996988},"labels":[],"label_agreement":null},{"id":"W4396749536","doi":"10.1155/2024/6446235","title":"An Optimization Strategy for Truncating Ultralong Bus Lines Integrated with Metro Networks","year":2024,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","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":"Computer science; Transport engineering; Engineering; Operations research","score_opus":0.015607404847311355,"score_gpt":0.3114148530560257,"score_spread":0.2958074482087143,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396749536","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17710325,0.00037701178,0.8215259,0.00016540237,0.00043180812,0.00022775652,0.000023165423,0.00009828947,0.000047436184],"genre_scores_gemma":[0.8854176,0.0002361079,0.113557585,0.00002200978,0.0002990904,0.000011151673,0.00038770196,0.00002539127,0.00004339995],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987568,0.00005737108,0.0005254689,0.00016811256,0.00030232745,0.00018986945],"domain_scores_gemma":[0.9986362,0.00016259195,0.00031241414,0.000057672863,0.0007204944,0.00011066565],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045298444,0.00012801679,0.00018896155,0.00020762252,0.00022891418,0.00014732967,0.00010667089,0.000097413955,0.0000246559],"category_scores_gemma":[0.000039787283,0.00010578505,0.00008183097,0.0006434369,0.00005387163,0.0016003683,1.16358315e-7,0.00017707575,1.9881288e-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":[0.00027823367,0.000035575198,0.001506369,0.00003406248,0.00004075108,0.0000128893,0.005609158,0.9783973,0.0001373592,0.0015171214,0.000016270518,0.012414879],"study_design_scores_gemma":[0.0048810663,0.0034818081,0.074586906,0.0020197842,0.0011437646,0.000016084135,0.07878558,0.8257189,0.0008837538,0.0011575485,0.0061513726,0.0011734635],"about_ca_topic_score_codex":0.000063890264,"about_ca_topic_score_gemma":0.0012647242,"teacher_disagreement_score":0.7083143,"about_ca_system_score_codex":0.00006260155,"about_ca_system_score_gemma":0.00028465674,"threshold_uncertainty_score":0.43137893},"labels":[],"label_agreement":null},{"id":"W4396982473","doi":"10.2139/ssrn.4830661","title":"Generating Practical Last-mile Delivery Routes using a Data-informed Insertion Heuristic","year":2024,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; University of Toronto","funders":"","keywords":"Travelling salesman problem; Computer science; Operations research; Heuristic; Leverage (statistics); Backtracking; Quality (philosophy); Last mile (transportation); Customer satisfaction; Operations management; Mile; Artificial intelligence; Marketing; Business; Engineering; Algorithm; Geography","score_opus":0.045771920643298165,"score_gpt":0.3549527072568466,"score_spread":0.3091807866135484,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396982473","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5470682,0.003582875,0.44504985,0.0017732191,0.0010473811,0.00017882403,0.000018690564,0.0002556697,0.0010253252],"genre_scores_gemma":[0.98844,0.0024203258,0.007642603,0.00008128443,0.00075367966,0.0000016530277,0.00009086375,0.0000157999,0.0005538001],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982041,0.00012300875,0.00025524883,0.00017106644,0.00034134794,0.00090521196],"domain_scores_gemma":[0.99947053,0.00018185079,0.00008680882,0.000091883725,0.00009333949,0.00007557979],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017589654,0.00008441993,0.000087265784,0.00010570349,0.00069097773,0.0003559997,0.00015435353,0.00007435552,0.000036927482],"category_scores_gemma":[0.00039938808,0.00007977709,0.000038462316,0.0002861845,0.000046884514,0.0012007086,0.000011804739,0.0008017398,0.000020795893],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029456246,0.00032683893,0.016245198,0.00015994761,0.0010255936,0.00025038145,0.03818723,0.1508532,0.003522285,0.65431213,0.0041139773,0.13070866],"study_design_scores_gemma":[0.00069896574,0.00022992709,0.0010009678,0.00032928723,0.00039399366,0.00049209426,0.029558303,0.9056015,0.000031620715,0.021998208,0.039036162,0.00062894],"about_ca_topic_score_codex":0.000496951,"about_ca_topic_score_gemma":0.008005604,"teacher_disagreement_score":0.75474834,"about_ca_system_score_codex":0.0006180582,"about_ca_system_score_gemma":0.007187217,"threshold_uncertainty_score":0.9984411},"labels":[],"label_agreement":null},{"id":"W4397289277","doi":"10.1186/s40068-024-00344-9","title":"Modeling the economic cost of congestion in Addis Ababa City, Ethiopia","year":2024,"lang":"en","type":"article","venue":"ENVIRONMENTAL SYSTEMS RESEARCH","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western Forest Products","funders":"","keywords":"Geography; Water resource management; Agricultural economics; Environmental science; Economics","score_opus":0.08753444641745754,"score_gpt":0.3800074902184052,"score_spread":0.29247304380094763,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4397289277","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9897826,0.0012259325,0.00094840454,0.00055031857,0.00035752263,0.00061944494,0.000067633635,0.000029140896,0.0064190472],"genre_scores_gemma":[0.9981906,0.0004386922,0.000013697171,0.000002591836,0.00008265325,0.000050354807,0.000036251764,0.000007168678,0.0011780285],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987079,0.00036394296,0.0001953373,0.00015071397,0.0004014795,0.00018062045],"domain_scores_gemma":[0.9996316,0.00022033791,0.000016505059,0.00008501621,0.000006408411,0.00004016447],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022328922,0.000043806303,0.000071144146,0.000109247725,0.00020410234,0.00008919751,0.00013684086,0.000072897186,0.00017349324],"category_scores_gemma":[0.000024485249,0.00003666319,0.00002376821,0.00012804252,0.00019905056,0.00015094652,0.000014833317,0.00022854895,0.00008900116],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016234335,0.000024046985,0.026526954,0.000045361256,0.000015160822,0.000007008397,0.023178225,0.93527776,0.00025748598,0.012111473,0.00033462874,0.0022056778],"study_design_scores_gemma":[0.00027172052,0.000045599838,0.021490907,0.00036412553,0.000007984741,0.000001855835,0.04006436,0.9160437,0.00006369727,0.00025501385,0.021227546,0.00016348263],"about_ca_topic_score_codex":0.012443309,"about_ca_topic_score_gemma":0.001852616,"teacher_disagreement_score":0.020892918,"about_ca_system_score_codex":0.00043671665,"about_ca_system_score_gemma":0.00009099112,"threshold_uncertainty_score":0.99413294},"labels":[],"label_agreement":null},{"id":"W4398142670","doi":"10.1080/03081060.2024.2354492","title":"Development of a dynamic traffic microsimulator for on-demand transit operations within an integrated modelling system","year":2024,"lang":"en","type":"article","venue":"Transportation Planning and Technology","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Transit (satellite); Transport engineering; Transit system; Computer science; Public transport; Engineering","score_opus":0.02269753070049225,"score_gpt":0.29471770151038407,"score_spread":0.2720201708098918,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4398142670","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.61718464,0.0001987663,0.3816769,0.000108691005,0.000095873744,0.00023030491,0.000072162424,0.00041795397,0.000014698892],"genre_scores_gemma":[0.93454325,0.000010469615,0.06478256,0.0000075518506,0.000009400216,0.00005351333,0.00051712175,0.00001746521,0.000058640242],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989897,0.000022517059,0.0004160181,0.00028123797,0.00012968926,0.00016085501],"domain_scores_gemma":[0.99964786,0.000056831377,0.000050092895,0.00007033764,0.0001209994,0.000053855463],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033427947,0.0001230536,0.00017899455,0.00039814148,0.00040870628,0.000047290752,0.00008041112,0.00021030179,0.0000031343695],"category_scores_gemma":[0.0000064641426,0.00012150319,0.000028250297,0.00045303127,0.00010798426,0.0001547091,3.501611e-7,0.00012808487,9.1035747e-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":[0.000051815005,0.00003274638,0.000121923076,0.00017065437,0.000038845385,0.0000048839865,0.08389954,0.8792318,0.00073954446,0.032278202,0.000003444384,0.0034265572],"study_design_scores_gemma":[0.0005966692,0.00014459442,0.00037246506,0.0007889864,0.00009914801,0.0000016786596,0.02685797,0.9681674,0.0011147354,0.0001126235,0.0014792954,0.00026443438],"about_ca_topic_score_codex":0.000040612184,"about_ca_topic_score_gemma":0.00088833016,"teacher_disagreement_score":0.31735864,"about_ca_system_score_codex":0.000038829483,"about_ca_system_score_gemma":0.0002353206,"threshold_uncertainty_score":0.49547562},"labels":[],"label_agreement":null},{"id":"W4398544468","doi":"10.7910/dvn/a1uglt","title":"A cruise-and-crash model of the cost of ruling","year":2023,"lang":"en","type":"dataset","venue":"Harvard Dataverse","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Cruise; Crash; Aeronautics; Computer science; Environmental science; Engineering; Oceanography; Geology; Programming language","score_opus":0.03958091295869075,"score_gpt":0.2909628114603949,"score_spread":0.2513818985017041,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4398544468","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00012312928,6.080232e-7,0.00032598784,0.000013040133,0.00025556743,0.00020271068,0.9989006,0.000021609954,0.00015676892],"genre_scores_gemma":[0.0004880409,0.000681353,0.00041138832,0.00004816068,0.000044584787,0.000008425485,0.99787027,0.000009199644,0.00043859682],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9990495,0.00006814345,0.00024565012,0.00015725977,0.0003579271,0.00012155131],"domain_scores_gemma":[0.9991644,0.000104156985,0.000274166,0.0003260756,0.0000865493,0.000044667486],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036929638,0.000087868306,0.00017793075,0.00007412028,0.00016286071,0.000019040108,0.00035369926,0.00014568554,0.00024011229],"category_scores_gemma":[0.00028906684,0.000074445044,0.000056775425,0.00021716484,0.00022257351,0.00010368145,0.00004873811,0.00013109269,0.0007146621],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000070394976,0.000013820031,0.000103153565,0.00007263657,0.000017814722,8.464924e-7,0.0007038355,0.014797118,0.000005015614,0.0002625877,0.9839693,0.000046857254],"study_design_scores_gemma":[0.000135629,0.0000038882586,0.00022788448,0.00013658701,0.00009135817,7.4171176e-8,0.000508273,0.0020018364,0.000006515388,0.000054249256,0.99675554,0.00007816411],"about_ca_topic_score_codex":0.0032305166,"about_ca_topic_score_gemma":0.0028533544,"teacher_disagreement_score":0.012795281,"about_ca_system_score_codex":0.000016492284,"about_ca_system_score_gemma":0.00023569974,"threshold_uncertainty_score":0.9185774},"labels":[],"label_agreement":null},{"id":"W4398773105","doi":"10.1515/9782760625273-008","title":"Développement Urbain Durable, Débat Public et Urbanisme À Montréal","year":2008,"lang":"fr","type":"book-chapter","venue":"Les Presses de l'Université de Montréal eBooks","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Geography","score_opus":0.02078019584012462,"score_gpt":0.2083482032784331,"score_spread":0.18756800743830848,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4398773105","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011033076,0.018858531,0.014430884,0.011302886,0.00050161855,0.000950353,0.00048856146,0.0005755117,0.9418586],"genre_scores_gemma":[0.5141788,0.036862653,0.012239831,0.0014543714,0.00039359718,0.000037518996,0.00094739336,0.00023595964,0.43364984],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99637806,0.00034078225,0.00059715845,0.000782244,0.000878042,0.0010237274],"domain_scores_gemma":[0.99771905,0.0003120664,0.0005913991,0.00044050108,0.00030577809,0.0006312351],"candidate_categories":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00041163925,0.00071291823,0.00062276976,0.00029566084,0.0020776435,0.00016728588,0.0007364229,0.0009910887,0.0019022902],"category_scores_gemma":[0.00004919246,0.0008543618,0.0003888686,0.00009885394,0.0006750112,0.00032860978,0.0001653366,0.00076493353,0.0001864382],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00085377356,0.0002975978,0.019530566,0.00036875,0.0014581078,0.0044920756,0.11443942,0.044717778,0.00037530746,0.6169381,0.10782381,0.088704705],"study_design_scores_gemma":[0.001346227,0.00015442216,0.0100372005,0.00029246937,0.0004100967,0.00009238756,0.0018628536,0.0013853904,0.00009671606,0.002153462,0.98113114,0.001037645],"about_ca_topic_score_codex":0.04412903,"about_ca_topic_score_gemma":0.0974529,"teacher_disagreement_score":0.87330735,"about_ca_system_score_codex":0.0017515764,"about_ca_system_score_gemma":0.0005351989,"threshold_uncertainty_score":0.9993907},"labels":[],"label_agreement":null},{"id":"W4398839298","doi":"10.7910/dvn/qftapm","title":"Bus Stop Spacings for Transit Providers in Canada","year":2023,"lang":"en","type":"dataset","venue":"Harvard Dataverse","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Transit (satellite); Transport engineering; Business; Computer science; Telecommunications; Engineering; Public transport","score_opus":0.019960371852818196,"score_gpt":0.2599524075947785,"score_spread":0.23999203574196032,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4398839298","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000038159644,4.8811035e-7,0.00018010694,0.000109862456,0.0008209075,0.000564609,0.9981596,0.00004900325,0.000077255936],"genre_scores_gemma":[0.00007112129,0.00016368492,0.00023809838,0.00024177809,0.00010783219,0.00006286402,0.9984866,0.000016682237,0.00061137404],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99860996,0.00006200147,0.00026147187,0.00032448178,0.00039368123,0.00034843187],"domain_scores_gemma":[0.99926597,0.00018358619,0.00012226646,0.00025076047,0.00006580734,0.00011164022],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00036437876,0.0001526254,0.00022398592,0.00015006238,0.00020810189,0.00007168539,0.00031366484,0.00016077144,0.0006166779],"category_scores_gemma":[0.0002565378,0.00017652595,0.00004511141,0.0003203663,0.00005301371,0.00025300324,0.000008213522,0.00018222978,0.0020579332],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029023608,0.000009205453,0.000055615557,0.000090899994,0.000013898025,0.00004423907,0.0008252971,0.0018624852,1.8911219e-7,0.00004225287,0.9969229,0.00010400634],"study_design_scores_gemma":[0.00036533942,0.00000903673,0.000210735,0.00008129894,0.00004676815,1.7405547e-7,0.0034130372,0.000074497555,6.344342e-7,0.000015602405,0.9955786,0.00020430953],"about_ca_topic_score_codex":0.9912675,"about_ca_topic_score_gemma":0.99907935,"teacher_disagreement_score":0.007811831,"about_ca_system_score_codex":0.00044200895,"about_ca_system_score_gemma":0.003504132,"threshold_uncertainty_score":0.9987191},"labels":[],"label_agreement":null},{"id":"W4399030518","doi":"10.1007/978-981-99-7976-9_50","title":"Approaches for Modelling Travel Time Uncertainty","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in civil engineering","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Time travel; Computer science; Artificial intelligence","score_opus":0.032216677222651986,"score_gpt":0.234066456585911,"score_spread":0.201849779363259,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399030518","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000008639609,0.0012325352,0.9142352,0.00023610353,0.00034552478,0.0003387239,0.000060865154,0.00020493112,0.083337516],"genre_scores_gemma":[0.7432596,0.0007400547,0.0644533,0.00016443491,0.003365719,0.00021839641,0.0017839964,0.00059385016,0.18542065],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99900585,0.0000044100348,0.00022943351,0.00030952904,0.00020057448,0.00025016983],"domain_scores_gemma":[0.99944407,0.00032172038,0.000047407553,0.00009995792,0.000034064935,0.000052809384],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002522795,0.00022776384,0.0002508991,0.0002156854,0.00008049069,0.000062059626,0.00012486741,0.00044245223,0.000079927384],"category_scores_gemma":[0.000053153588,0.00024423006,0.0001181261,0.00007956082,0.00003145656,0.000053038526,0.0000049268183,0.0003691748,0.000013179596],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000050627814,0.0000023627179,0.0000010003057,0.00011842683,0.000024326388,0.0000030362085,0.003399233,0.92671776,0.0000031712216,0.06859921,0.00001961146,0.0011067763],"study_design_scores_gemma":[0.00009573799,0.00001032728,0.0000010023065,0.00042061953,0.000049095477,4.0670972e-7,0.000012272487,0.94457734,0.0000085517995,0.026529286,0.0279834,0.00031196204],"about_ca_topic_score_codex":0.000074055184,"about_ca_topic_score_gemma":0.0009441447,"teacher_disagreement_score":0.8497819,"about_ca_system_score_codex":0.0001323443,"about_ca_system_score_gemma":0.00008060554,"threshold_uncertainty_score":0.9959412},"labels":[],"label_agreement":null},{"id":"W4399122541","doi":"10.1155/2024/8131887","title":"Optimizing Transit Network Departure Frequency considering Congestion Effects","year":2024,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","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":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Transit (satellite); Transport engineering; Computer science; Urban transit; Environmental science; Public transport; Engineering","score_opus":0.010187650322493516,"score_gpt":0.2763419518501044,"score_spread":0.2661543015276109,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399122541","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.48342407,0.0114418,0.49533793,0.001822152,0.005715082,0.00051287527,0.000018074512,0.00037288415,0.0013551158],"genre_scores_gemma":[0.94273806,0.0008825001,0.05574885,0.00006664628,0.0004562004,0.0000049702644,0.00004180148,0.000019961928,0.000041007454],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99866253,0.00008513037,0.0004961322,0.00014855343,0.00038476984,0.00022285398],"domain_scores_gemma":[0.9991164,0.00027763378,0.00020485176,0.000052817566,0.0002285377,0.000119731725],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047928598,0.00012293713,0.00020535424,0.00014174746,0.00023411587,0.00008536856,0.000081762366,0.00012070225,0.000033512737],"category_scores_gemma":[0.0000537604,0.000121795994,0.00013383951,0.00043127013,0.00006345283,0.0010586223,2.6713303e-7,0.0002782227,0.0000030191684],"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":[0.00006335598,0.000017422984,0.0018822401,0.00012158214,0.000057705434,0.00022182762,0.031125046,0.94704527,0.0009427061,0.0103890225,0.00007561158,0.00805824],"study_design_scores_gemma":[0.011370226,0.002364926,0.60913444,0.019296864,0.0040450795,0.00019270094,0.056021303,0.005916785,0.005743889,0.06421551,0.21785028,0.0038480207],"about_ca_topic_score_codex":0.000027208678,"about_ca_topic_score_gemma":0.0007915467,"teacher_disagreement_score":0.94112843,"about_ca_system_score_codex":0.000069995585,"about_ca_system_score_gemma":0.000215418,"threshold_uncertainty_score":0.49666965},"labels":[],"label_agreement":null},{"id":"W4399122559","doi":"10.1155/2024/4250807","title":"Route Guidance Model with Limited Overlap on Freeway Network under Traffic Incidents","year":2024,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","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":"Key Research and Development Program of Hunan Province of China; Natural Science Foundation of Shandong Province; Central South University; National Natural Science Foundation of China","keywords":"Transport engineering; Computer science; Traffic simulation; Traffic model; Simulation; Engineering; Computer network; Microsimulation","score_opus":0.014830308025740478,"score_gpt":0.28394544361215784,"score_spread":0.26911513558641736,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399122559","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.86811584,0.00055758574,0.12879142,0.0009106006,0.00073661184,0.00017766564,0.000024170427,0.00012221263,0.00056392106],"genre_scores_gemma":[0.98176783,0.0003372173,0.01699245,0.00018401454,0.0002496947,0.0000044472554,0.00006021221,0.000024966097,0.00037919305],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9984075,0.00004795198,0.00047669723,0.00017454835,0.0006731805,0.00022014887],"domain_scores_gemma":[0.99916637,0.000109331246,0.00027205163,0.000077531295,0.00026034255,0.00011437411],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003695994,0.00013493675,0.0001846375,0.00015177013,0.00020256233,0.00007621962,0.0001271231,0.00009024593,0.000017965172],"category_scores_gemma":[0.00001620007,0.000115017494,0.00009706643,0.0005663474,0.000059960366,0.000886471,3.3979077e-7,0.0002490247,0.0000038342623],"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":[0.0002915921,0.00003823754,0.0013545532,0.00001958585,0.00004423829,0.000045547204,0.010247662,0.9783966,0.000050240928,0.0069615417,0.0002245765,0.0023256503],"study_design_scores_gemma":[0.00829611,0.0025962142,0.75464225,0.007825177,0.0012133016,0.000025882124,0.022412932,0.15621188,0.0003081434,0.01482645,0.02957532,0.0020663175],"about_ca_topic_score_codex":0.000018267572,"about_ca_topic_score_gemma":0.001112664,"teacher_disagreement_score":0.8221847,"about_ca_system_score_codex":0.00011579346,"about_ca_system_score_gemma":0.00030308115,"threshold_uncertainty_score":0.46902773},"labels":[],"label_agreement":null},{"id":"W4399145395","doi":"10.1051/e3sconf/202452903012","title":"Capacity of heterogeneous traffic in urban areas: A level of service estimation","year":2024,"lang":"en","type":"article","venue":"E3S Web of Conferences","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Horizon College and Seminary","funders":"","keywords":"Estimation; Transport engineering; Service (business); Computer science; Geography; Business; Engineering","score_opus":0.08027793684466468,"score_gpt":0.30612068576526424,"score_spread":0.22584274892059958,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399145395","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9951383,0.00016300501,0.0011022383,0.00030018773,0.00011092829,0.00009195798,0.00006329985,0.000027958853,0.0030021102],"genre_scores_gemma":[0.99912727,0.00003180561,0.00074431434,0.000008429005,0.0000117049185,0.0000033709352,0.00002064791,0.0000027230399,0.000049729806],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992888,0.000063832034,0.00027538108,0.0000971209,0.00019700012,0.000077883735],"domain_scores_gemma":[0.99956304,0.00011573211,0.000103775754,0.000051877767,0.00014175578,0.000023801576],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027219066,0.000052543564,0.00014368776,0.00012958814,0.000025233026,0.000013783424,0.00010263753,0.000061000184,0.00009655837],"category_scores_gemma":[0.00004900092,0.000050557268,0.00003136138,0.00037464322,0.00011155552,0.00011613438,0.000002210526,0.00004486024,0.0000014190822],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010517423,0.00032334458,0.09539181,0.0013628677,0.0001166145,0.000007686694,0.22624359,0.48367485,0.0017354334,0.15946074,0.00025949717,0.0313184],"study_design_scores_gemma":[0.0012033208,0.00037533123,0.22601333,0.0032521773,0.00016908263,0.0000025001607,0.018682446,0.7259812,0.014295913,0.006510658,0.0028687695,0.0006453234],"about_ca_topic_score_codex":0.0031010807,"about_ca_topic_score_gemma":0.015258232,"teacher_disagreement_score":0.24230629,"about_ca_system_score_codex":0.000010009152,"about_ca_system_score_gemma":0.00065788336,"threshold_uncertainty_score":0.8514452},"labels":[],"label_agreement":null},{"id":"W4399175319","doi":"10.1007/s40864-024-00213-9","title":"Identifying Station Importance in Urban Rail Transit Networks Using a Combination of Centrality and Time Reliability Measures: A Case Study in Beijing, China","year":2024,"lang":"en","type":"article","venue":"Urban Rail Transit","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Beijing; Centrality; TOPSIS; Reliability (semiconductor); Metric (unit); Ranking (information retrieval); Computer science; Exploit; Transport engineering; Urban rail transit; Operations research; Statistics; Engineering; Mathematics; China; Geography; Artificial intelligence; Operations management","score_opus":0.029394642443060615,"score_gpt":0.30416227816126035,"score_spread":0.27476763571819973,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399175319","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9639172,0.0007037262,0.034012,0.00016738607,0.00014220735,0.0008225571,0.000026083208,0.00009384618,0.00011499006],"genre_scores_gemma":[0.9993496,0.000049858834,0.00041341168,0.000011144841,0.000030740346,0.000014412724,0.000049247938,0.000018525223,0.00006305579],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.997629,0.00051423087,0.0006817153,0.00045114657,0.00042406714,0.00029986884],"domain_scores_gemma":[0.99943256,0.00014168586,0.00011133212,0.00013904573,0.00008859402,0.00008675301],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002301252,0.00016831714,0.0003021454,0.0002516156,0.00020319807,0.00011650366,0.00008986952,0.00014131783,0.000032447875],"category_scores_gemma":[0.00006202681,0.00019079543,0.00006357565,0.0009918868,0.00013620255,0.0006526256,0.0000030092192,0.00027298133,5.137864e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010266745,0.00036892487,0.6635678,0.00018042639,0.000030496449,0.0003903665,0.27648365,0.057060532,0.00012865393,0.0008106455,0.000035919424,0.00083995855],"study_design_scores_gemma":[0.003335199,0.00017250796,0.7059477,0.0007187732,0.00022605801,0.000023011256,0.02629695,0.26164052,0.00003232335,0.00089495286,0.00012251262,0.00058950466],"about_ca_topic_score_codex":0.009626321,"about_ca_topic_score_gemma":0.029767819,"teacher_disagreement_score":0.25018668,"about_ca_system_score_codex":0.00018968641,"about_ca_system_score_gemma":0.00016189042,"threshold_uncertainty_score":0.9969687},"labels":[],"label_agreement":null},{"id":"W4399411027","doi":"10.1155/2024/4420668","title":"Exploring the Impact of a Railway Corridor on Intermodal Service Networks and Industrial Location: A Land‐Use Transport Interaction Modeling Approach","year":2024,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Transport engineering; Land use; Service (business); Computer science; Civil engineering; Engineering; Business","score_opus":0.0972813254552709,"score_gpt":0.3203771769641977,"score_spread":0.2230958515089268,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399411027","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8778421,0.00011227597,0.12094994,0.00018942877,0.0006344887,0.00018376243,0.000012265234,0.00002990641,0.000045785546],"genre_scores_gemma":[0.99846715,0.0004098603,0.00068867084,0.0000190538,0.0003036263,0.000010481426,0.00007443365,0.000015791858,0.000010921101],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987536,0.00007567381,0.0005694448,0.0001435988,0.0003227733,0.0001349002],"domain_scores_gemma":[0.9990526,0.00018077844,0.00026409517,0.00006853352,0.00035797723,0.000076032],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051622675,0.00012395385,0.00019619585,0.0001915598,0.000147488,0.00006358514,0.00009606405,0.00007392598,0.0000073275264],"category_scores_gemma":[0.000034821453,0.0000901002,0.00012219563,0.0005962672,0.000043117707,0.0016868656,5.7450643e-7,0.00037155888,2.6977193e-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":[0.00063611363,0.000042201296,0.0041017425,0.000031454,0.00006506494,0.0000060628204,0.026770744,0.9623083,0.000033780103,0.00034332304,0.000006102822,0.00565514],"study_design_scores_gemma":[0.0028989,0.00083267316,0.27995163,0.0025358768,0.000487024,0.000024463627,0.026201373,0.6858443,0.000070466995,0.000260566,0.00042394086,0.00046880072],"about_ca_topic_score_codex":0.0006538854,"about_ca_topic_score_gemma":0.00043378043,"teacher_disagreement_score":0.276464,"about_ca_system_score_codex":0.00008029255,"about_ca_system_score_gemma":0.00018807797,"threshold_uncertainty_score":0.36741796},"labels":[],"label_agreement":null},{"id":"W4399497615","doi":"10.1287/trsc.2023.0110","title":"A Sampling Strategy for High-Dimensional, Simulation-Based Transportation Optimization Problems","year":2024,"lang":"en","type":"article","venue":"Transportation Science","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Sampling (signal processing); Mathematical optimization; Computer science; Optimization problem; Sampling distribution; Bayesian optimization; Probability distribution; Importance sampling; Data mining; Algorithm; Mathematics; Statistics; Monte Carlo method","score_opus":0.06460351217906143,"score_gpt":0.36020488523953004,"score_spread":0.29560137306046863,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399497615","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.062174905,0.00012549991,0.9343687,0.00071000797,0.0006142298,0.0009424141,0.0002722401,0.00061686535,0.00017510781],"genre_scores_gemma":[0.95627797,0.000018271669,0.041568466,0.00012581884,0.0001103269,0.00013201317,0.0015447091,0.00003132444,0.00019109588],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99727607,0.000041368174,0.00058326364,0.0006560165,0.0010021153,0.00044119137],"domain_scores_gemma":[0.9982129,0.0005768581,0.00014309143,0.00015105808,0.0007277113,0.00018842111],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012865207,0.00019621172,0.00017670423,0.0004858492,0.0011649991,0.00039594612,0.00024134548,0.0001322556,0.00022323694],"category_scores_gemma":[0.000113093876,0.00020802784,0.00009905347,0.0020514682,0.00042924462,0.0015388426,4.323932e-7,0.0001214351,0.000011432519],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000032652173,0.00003636222,0.0004840324,0.000083934836,0.000007859342,0.0000017240535,0.0056667444,0.964087,0.000269215,0.027442193,0.000020576686,0.0018676897],"study_design_scores_gemma":[0.00053387857,0.000079561294,0.009907897,0.00018123319,0.00008249267,6.391125e-8,0.0005961695,0.98398185,0.00027670604,0.0015543943,0.002454796,0.0003509453],"about_ca_topic_score_codex":0.0006307311,"about_ca_topic_score_gemma":0.001460291,"teacher_disagreement_score":0.89410305,"about_ca_system_score_codex":0.00012986753,"about_ca_system_score_gemma":0.0012022351,"threshold_uncertainty_score":0.8960349},"labels":[],"label_agreement":null},{"id":"W4399564080","doi":"10.1109/isorc61049.2024.10551354","title":"Analyzing Public Transit Schedule Deviations: A Case Study on Montreal Using Real-Time Data","year":2024,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Trent University","funders":"","keywords":"Public transport; Computer science; Schedule; Transit (satellite); Real-time computing; Transport engineering; Operations research; Engineering","score_opus":0.12039722393592259,"score_gpt":0.38298062129342214,"score_spread":0.2625833973574996,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399564080","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93261105,0.000096441945,0.049362607,0.0011136439,0.00017196423,0.000404364,0.0000669164,0.0006329691,0.01554005],"genre_scores_gemma":[0.9959913,0.000020694612,0.003029125,0.000020310017,0.00009283949,0.0000045659012,0.00013387998,0.000011414064,0.0006958687],"study_design_codex":"qualitative","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989298,0.00014639148,0.00019505707,0.00031370966,0.00023956776,0.00017548146],"domain_scores_gemma":[0.9994451,0.00014107284,0.000028093937,0.00023442754,0.00006364463,0.000087676795],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007463764,0.0000795798,0.000096120035,0.00018865074,0.00055024266,0.0003810211,0.00015679238,0.000049668513,0.0002290045],"category_scores_gemma":[0.0000640746,0.00007444302,0.000026124419,0.000746154,0.000032228923,0.0007505116,0.000007419069,0.0000803656,0.000038882565],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010761768,0.0034817916,0.18366252,0.0001636971,0.001613174,0.014629942,0.48729536,0.140446,0.0006922473,0.0945572,0.007986753,0.065363675],"study_design_scores_gemma":[0.0008904867,0.00017258714,0.009533922,0.00014944769,0.00053252385,0.000072580406,0.17303649,0.8115269,0.0000119720335,0.00023667509,0.0031542105,0.0006821925],"about_ca_topic_score_codex":0.0252023,"about_ca_topic_score_gemma":0.03065968,"teacher_disagreement_score":0.6710809,"about_ca_system_score_codex":0.000065538,"about_ca_system_score_gemma":0.00024351553,"threshold_uncertainty_score":0.98702824},"labels":[],"label_agreement":null},{"id":"W4399564601","doi":"10.1016/j.scs.2024.105553","title":"A cloud-oriented data-analysis framework to analyze peak demand dynamics in institutional building clusters","year":2024,"lang":"en","type":"article","venue":"Sustainable Cities and Society","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"BC Hydro (Canada); University of British Columbia; Okanagan University College; University of British Columbia, Okanagan Campus","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Cloud computing; Dynamics (music); Computer science; Data science; Econometrics; Regional science; Economics; Geography; Sociology","score_opus":0.012144687875850482,"score_gpt":0.30286752788423027,"score_spread":0.2907228400083798,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399564601","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.29701456,0.0010726984,0.6932361,0.004766046,0.0003438506,0.00032203045,0.00017375329,0.00017173646,0.0028992095],"genre_scores_gemma":[0.9848818,0.0003309896,0.010681611,0.00039442853,0.00012701159,0.000012135511,0.00029902437,0.000007703252,0.0032652877],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","domain_scores_codex":[0.99882054,0.00006183138,0.00019982745,0.00032877154,0.00026628166,0.00032274929],"domain_scores_gemma":[0.99943024,0.00015948956,0.000028430073,0.00014602531,0.00013716791,0.000098676894],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010088924,0.00009313102,0.00016914566,0.00016419611,0.00069589866,0.00029276824,0.00014817009,0.00012080746,0.000040504874],"category_scores_gemma":[0.00017885907,0.00009550893,0.00009627242,0.0018970642,0.00017554336,0.00041479076,0.000051177507,0.00016909119,7.540128e-7],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012063112,0.000013533225,0.010073545,0.000125133,0.0002999773,0.00002603335,0.074314,0.08909165,1.6030928e-7,0.823934,0.0018714493,0.00023840735],"study_design_scores_gemma":[0.00014670668,0.000018845787,0.0055888337,0.0000797767,0.00042634728,4.6569133e-7,0.58907825,0.3470464,3.0808462e-7,0.0068441434,0.0505127,0.00025721005],"about_ca_topic_score_codex":0.0039125215,"about_ca_topic_score_gemma":0.0027798917,"teacher_disagreement_score":0.8170899,"about_ca_system_score_codex":0.00042843694,"about_ca_system_score_gemma":0.00029840638,"threshold_uncertainty_score":0.59145886},"labels":[],"label_agreement":null},{"id":"W4399889799","doi":"10.1155/2024/8905446","title":"A Bilevel Programming Model for Designing a Collaborative Network for Regional Railway Transportation and Logistics: The Case of the Beijing‐Tianjin‐Hebei Region in China","year":2024,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Social Science Fund of China","keywords":"Beijing; China; Transport engineering; Bilevel optimization; Business; Computer science; Civil engineering; Regional science; Environmental planning; Operations research; Engineering; Geography","score_opus":0.030520719364782074,"score_gpt":0.3133320465964765,"score_spread":0.28281132723169444,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399889799","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17157945,0.0008843189,0.82444894,0.001550382,0.0003161835,0.0011070466,0.000090008994,0.000018330456,0.000005330714],"genre_scores_gemma":[0.9202867,0.00022054181,0.079151794,0.000034177516,0.00012819133,0.00007270799,0.000054984153,0.000016708578,0.00003418787],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987107,0.00007900898,0.0006492494,0.00016437202,0.00020849373,0.00018819557],"domain_scores_gemma":[0.99846756,0.00050409394,0.00050080044,0.000060919112,0.00042056027,0.000046076377],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00093050505,0.00011836416,0.0002054169,0.00010600124,0.0003724939,0.000048076457,0.00009895132,0.00009294265,6.791428e-7],"category_scores_gemma":[0.00010632952,0.00008279882,0.00012664573,0.0005870974,0.00016366337,0.00045751643,3.7460433e-7,0.00016039675,1.6195822e-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":[0.0004508635,0.000027787173,0.0015673899,0.00013970437,0.000036602498,0.000027784125,0.07167488,0.89961654,0.00007684793,0.015147413,0.00009224892,0.011141964],"study_design_scores_gemma":[0.012935982,0.0016546062,0.19020344,0.0069509866,0.0023584731,0.00013530377,0.120390676,0.51471126,0.00044413775,0.13777891,0.01103746,0.00139876],"about_ca_topic_score_codex":0.000068440735,"about_ca_topic_score_gemma":0.0065663042,"teacher_disagreement_score":0.74870723,"about_ca_system_score_codex":0.00005317269,"about_ca_system_score_gemma":0.0003728738,"threshold_uncertainty_score":0.36641517},"labels":[],"label_agreement":null},{"id":"W4400182414","doi":"10.32866/001c.120430","title":"Revealed Preferences for Utilitarian Cycling Energy Expenditure versus Travel Time","year":2024,"lang":"en","type":"article","venue":"Findings","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Cycling; Energy expenditure; Environmental science; Transport engineering; Geography; Medicine; Engineering; Internal medicine; Forestry","score_opus":0.03522097947697218,"score_gpt":0.3087053917155565,"score_spread":0.27348441223858433,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400182414","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5766689,0.0028893813,0.09631461,0.008067775,0.012314093,0.0014342922,0.0007649349,0.0024086519,0.29913735],"genre_scores_gemma":[0.9855089,0.0000422862,0.0018483433,0.000037306043,0.0002955683,0.00003077121,0.00022184345,0.000011181319,0.0120038],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992912,0.000026807369,0.00013183754,0.00019918043,0.0001632562,0.00018771335],"domain_scores_gemma":[0.9995733,0.0002669759,0.000022473192,0.000049719343,0.000030350906,0.000057196234],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025986464,0.00007279532,0.000088582776,0.00007673915,0.00033998018,0.00011551638,0.000108663524,0.00011186734,0.0003006406],"category_scores_gemma":[0.00008054131,0.00007081869,0.00005851104,0.00016339094,0.000047495203,0.00019871845,0.000002422964,0.000060198432,0.00003060271],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006179505,0.00009349823,0.0034342653,0.00021768869,0.0002514411,0.00002686907,0.21994622,0.0010938734,0.001237912,0.64789534,0.09535321,0.029831715],"study_design_scores_gemma":[0.002144009,0.000356326,0.014521781,0.00065733626,0.00024710374,0.0000013246937,0.01693594,0.0122178,0.0011024597,0.016409658,0.9343655,0.0010407455],"about_ca_topic_score_codex":0.0003364947,"about_ca_topic_score_gemma":0.00029400186,"teacher_disagreement_score":0.8390123,"about_ca_system_score_codex":0.00003600312,"about_ca_system_score_gemma":0.00010127062,"threshold_uncertainty_score":0.32918036},"labels":[],"label_agreement":null},{"id":"W4400183149","doi":"10.2139/ssrn.4881689","title":"Subway-Emergency Bridging Strategy for Bus-Scheduling Considering Passenger Travel Behavior","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Bridging (networking); Transport engineering; Scheduling (production processes); Computer science; Automotive engineering; Engineering; Operations management; Computer security","score_opus":0.03676990018308829,"score_gpt":0.3318313501089281,"score_spread":0.2950614499258398,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400183149","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.61550444,0.018251449,0.35100284,0.0031336336,0.006769329,0.001496203,0.00011777151,0.00044796756,0.0032763395],"genre_scores_gemma":[0.98574406,0.008431545,0.001528277,0.000016549217,0.001517656,0.00008071358,0.00008846784,0.0000674006,0.0025253221],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99636894,0.00011718196,0.00063225254,0.000436975,0.0004417754,0.0020028595],"domain_scores_gemma":[0.9990197,0.00006876822,0.00034364368,0.00013320846,0.0002845745,0.00015012381],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0026319337,0.0002809492,0.0003155224,0.00023674477,0.00079966424,0.00030230096,0.0003032418,0.0003599047,0.00008875234],"category_scores_gemma":[0.00009992207,0.00030599022,0.00032481633,0.00019458943,0.00006135055,0.00014497543,0.000030741885,0.0034680318,0.000010816877],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012484366,0.00024066144,0.009527639,0.00050287426,0.0011436623,0.00007798958,0.023973653,0.26036566,0.0007785103,0.6395272,0.0006380617,0.063099265],"study_design_scores_gemma":[0.0016555777,0.00034030245,0.0048913336,0.0010716917,0.0024177113,0.0001800377,0.0776207,0.020726137,0.00025608606,0.88499194,0.0032321992,0.0026162765],"about_ca_topic_score_codex":0.00070941413,"about_ca_topic_score_gemma":0.0075661046,"teacher_disagreement_score":0.37023962,"about_ca_system_score_codex":0.0007939114,"about_ca_system_score_gemma":0.007064425,"threshold_uncertainty_score":0.9999392},"labels":[],"label_agreement":null},{"id":"W4400420891","doi":"10.1016/j.procs.2024.06.051","title":"Computational Architecture of an Integrated Urban Model Considering Physical-Virtual Activity Spaces","year":2024,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Dalhousie University","funders":"Dalhousie University","keywords":"Computer science; Flexibility (engineering); Microsimulation; Architecture; Process (computing); Traffic flow (computer networking); Land use; Transport engineering","score_opus":0.017537473052587564,"score_gpt":0.28931499717760734,"score_spread":0.27177752412501976,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400420891","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.44663584,0.000008357762,0.5525796,0.00021181605,0.00016053325,0.0000679934,0.000008361896,0.0001439604,0.00018352405],"genre_scores_gemma":[0.9112685,0.0000014903233,0.088527,0.0000350274,0.000118545526,0.000003623766,0.0000075310527,0.000005168689,0.000033163295],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998837,0.00003415649,0.00010636216,0.00031928963,0.00051221735,0.00019099585],"domain_scores_gemma":[0.99943924,0.00013077696,0.00004688901,0.0000735899,0.00019864258,0.00011083524],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003809501,0.000086281296,0.00010507825,0.00017328339,0.0002878922,0.00024684885,0.00024759074,0.000031180298,0.0000034867012],"category_scores_gemma":[0.000045273304,0.00007792037,0.00003001096,0.00089849194,0.0006571147,0.00075733504,0.000018883695,0.00012735589,0.0000025626284],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000056214258,0.000038839695,0.00025183757,0.000017222732,0.0000037718385,0.0000018215696,0.038149055,0.91526,0.0002863117,0.018084327,0.000039248484,0.0278619],"study_design_scores_gemma":[0.00005387708,0.000045391273,0.0008152095,0.000048280126,0.0000053207527,0.0000011292241,0.00020279131,0.9952723,0.0004435662,0.0028731986,0.00014306615,0.00009588351],"about_ca_topic_score_codex":0.00007325929,"about_ca_topic_score_gemma":0.000058668105,"teacher_disagreement_score":0.4646326,"about_ca_system_score_codex":0.00003634907,"about_ca_system_score_gemma":0.0009479129,"threshold_uncertainty_score":0.31775004},"labels":[],"label_agreement":null},{"id":"W4400650463","doi":"10.1111/itor.13515","title":"Municipal street‐sweeping area generation with route optimization","year":2024,"lang":"en","type":"article","venue":"International Transactions in Operational Research","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Lakeridge Health; Ontario Tech University","funders":"Mitacs","keywords":"Merge (version control); Computer science; Tabu search; Mathematical optimization; Operations research; Heuristic; Macro; Differential evolution; Ant colony optimization algorithms; Metaheuristic; Algorithm; Artificial intelligence; Mathematics","score_opus":0.1355280868514501,"score_gpt":0.43015988401197824,"score_spread":0.2946317971605281,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400650463","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.035642087,0.00022328104,0.90165156,0.009960628,0.0012251221,0.0006145814,0.000120693614,0.00024143781,0.0503206],"genre_scores_gemma":[0.98039496,0.00044016473,0.012662962,0.00007483639,0.0003891679,0.00017625322,0.00048643278,0.000023294897,0.005351906],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972999,0.00027252614,0.00032935137,0.00038798805,0.0014029257,0.00030727184],"domain_scores_gemma":[0.99872553,0.00036044096,0.000023401844,0.00011523063,0.0006829197,0.00009247008],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0015062907,0.00011651354,0.00009596937,0.0008454708,0.0007174811,0.0006162094,0.0002590087,0.0001244765,0.0021741956],"category_scores_gemma":[0.000114836366,0.00011609512,0.00004585462,0.001125173,0.00021127758,0.001259317,0.0000044952517,0.0004758429,0.000033337034],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000035205474,0.00007512713,0.0011543151,0.0000064966684,0.000040586136,0.00002722251,0.005912462,0.95883024,0.00006471706,0.030977605,0.00011325714,0.0027627985],"study_design_scores_gemma":[0.00037689944,0.00004835867,0.0008476611,0.00017379275,0.000010178671,0.000006604218,0.0028807563,0.9656111,0.00009306637,0.00018182477,0.029577874,0.00019188615],"about_ca_topic_score_codex":0.0031099888,"about_ca_topic_score_gemma":0.01666288,"teacher_disagreement_score":0.9447529,"about_ca_system_score_codex":0.0005627546,"about_ca_system_score_gemma":0.0007170902,"threshold_uncertainty_score":0.99873793},"labels":[],"label_agreement":null},{"id":"W4400703707","doi":"10.1787/5e336b71-fr","title":"La faible densité de population du Canada a plutôt favorisé l’investissement dans les infrastructures routières","year":2023,"lang":"fr","type":"other","venue":"Études économiques de l'OCDE. Canada","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Humanities; Political science; Geography; Art","score_opus":0.009302581389744217,"score_gpt":0.21548404706688065,"score_spread":0.20618146567713644,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400703707","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.86640316,0.0011705667,0.003463025,0.0042681084,0.0039842,0.0011241339,0.0012712342,0.00073768763,0.11757789],"genre_scores_gemma":[0.9703537,0.0006783682,0.0029369106,0.0008333191,0.00068626896,0.00006154604,0.00043847624,0.00027055442,0.023740817],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9972742,0.00039524224,0.00061307865,0.0005692241,0.00039276635,0.00075544044],"domain_scores_gemma":[0.9982648,0.00045206043,0.0004670343,0.0002640959,0.000103362596,0.00044868197],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00036852373,0.0005112167,0.0005145415,0.0001409941,0.00058543205,0.00017755662,0.00043977206,0.0005016014,0.001090534],"category_scores_gemma":[0.00020539123,0.00058937195,0.00009354857,0.00032763448,0.00019613978,0.00016679507,0.000031616313,0.0003992693,0.0000038492713],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":true,"study_design_scores_codex":[0.000026103482,0.000023323055,0.6083863,0.00017920358,0.0003136132,0.00029439075,0.010279157,0.20003018,0.00001592512,0.10142703,0.07269154,0.006333282],"study_design_scores_gemma":[0.0002668014,0.000029155004,0.46079794,0.0002831797,0.00016016951,0.000013288722,0.0041756337,0.0032223489,0.000046587873,0.00051684596,0.5297814,0.00070663216],"about_ca_topic_score_codex":0.99986947,"about_ca_topic_score_gemma":0.9999972,"teacher_disagreement_score":0.45708987,"about_ca_system_score_codex":0.007736284,"about_ca_system_score_gemma":0.011605199,"threshold_uncertainty_score":0.9998226},"labels":[],"label_agreement":null},{"id":"W4400857209","doi":"10.1016/j.ecolecon.2024.108306","title":"The economic cost of a 130 km/h speed limit: Insights for cost-benefit analyses","year":2024,"lang":"en","type":"article","venue":"Ecological Economics","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Transport Canada","funders":"","keywords":"Limit (mathematics); Economics; Environmental science; Natural resource economics; Environmental economics; Mathematics","score_opus":0.08272224197730789,"score_gpt":0.35587882163128537,"score_spread":0.2731565796539775,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400857209","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9704241,0.00051089184,0.0011179722,0.0034003945,0.0015789934,0.0010386029,0.00021187306,0.00014929881,0.02156788],"genre_scores_gemma":[0.99689007,0.0011373416,0.00046394035,0.00012982672,0.00018327769,0.000048083104,0.00006616484,0.000008453616,0.0010728427],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99922097,0.00003198126,0.0003217164,0.00020150791,0.000032892207,0.00019093856],"domain_scores_gemma":[0.99854666,0.0011532025,0.00010260382,0.00009073768,0.000037305792,0.00006951831],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036483802,0.00008152619,0.00016862458,0.000049744427,0.00037053728,0.00015307833,0.00018730646,0.00012049296,0.00017310104],"category_scores_gemma":[0.000098664874,0.000060646664,0.0001221435,0.00006978545,0.00018313673,0.00014819393,0.000008995917,0.000069522815,0.000051462375],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007588425,0.000042003245,0.0015136059,0.00001537909,0.00011495808,0.0000020514906,0.0022258451,0.32664937,0.000012020483,0.65629447,0.0023955393,0.010658885],"study_design_scores_gemma":[0.00049318396,0.00017587951,0.022551987,0.000018835184,0.000094282004,5.6638305e-7,0.0026040466,0.16699144,0.00019752939,0.024254492,0.7823245,0.0002932784],"about_ca_topic_score_codex":0.00014103057,"about_ca_topic_score_gemma":0.00601878,"teacher_disagreement_score":0.7799289,"about_ca_system_score_codex":0.00020522789,"about_ca_system_score_gemma":0.00021573206,"threshold_uncertainty_score":0.33586207},"labels":[],"label_agreement":null},{"id":"W4400920841","doi":"10.1155/2024/2921485","title":"Generalization of Beckmann’s Transformation for Traffic Assignment Models with Asymmetric Cost Functions","year":2024,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","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":"Generalization; Transformation (genetics); Computer science; Transport engineering; Mathematical optimization; Mathematics; Engineering; Chemistry; Mathematical analysis","score_opus":0.020877160342761884,"score_gpt":0.28912842733448363,"score_spread":0.26825126699172175,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400920841","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10990736,0.000415036,0.88789964,0.0004222686,0.00045874334,0.00048249032,0.00008232618,0.00004749775,0.0002846578],"genre_scores_gemma":[0.98370844,0.0004582923,0.0152110355,0.000017203509,0.00010394443,0.000030637868,0.00032587556,0.000018133047,0.0001264252],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99859184,0.000038795366,0.0006214691,0.00011822803,0.0004880752,0.00014159428],"domain_scores_gemma":[0.99888027,0.00011007273,0.00033023456,0.00004258892,0.0005619478,0.00007487933],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044756723,0.00010278998,0.00018386045,0.00043036125,0.00016443651,0.00004010613,0.000066070155,0.00007744136,0.000013740699],"category_scores_gemma":[0.00001472078,0.0000903969,0.00012231396,0.0007649512,0.000049438215,0.0014512938,1.3684605e-7,0.00009511916,4.9181295e-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":[0.00022760798,0.000058588885,0.00008791508,0.00010708632,0.000053298634,0.0000020499874,0.01927865,0.93849766,0.00015389659,0.012990283,0.00007805359,0.028464887],"study_design_scores_gemma":[0.026901485,0.008887654,0.08036934,0.0061668134,0.0063833203,0.000046141493,0.15469104,0.50095505,0.009667612,0.017604044,0.18509968,0.0032278101],"about_ca_topic_score_codex":0.000019071722,"about_ca_topic_score_gemma":0.0004036846,"teacher_disagreement_score":0.8738011,"about_ca_system_score_codex":0.00010064941,"about_ca_system_score_gemma":0.00026588375,"threshold_uncertainty_score":0.36862785},"labels":[],"label_agreement":null},{"id":"W4400981439","doi":"10.1061/jtepbs.teeng-8396","title":"Microcirculation Bus Routes Design and Coordinated Schedules Considering the Impact of Shared Bicycles","year":2024,"lang":"en","type":"article","venue":"Journal of Transportation Engineering Part A Systems","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Microcirculation; Medicine","score_opus":0.025302935734907,"score_gpt":0.2788555948080454,"score_spread":0.2535526590731384,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400981439","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.68900114,0.003536279,0.3065932,0.00011135992,0.0004651503,0.00019414045,0.000029133365,0.000060450086,0.000009106318],"genre_scores_gemma":[0.9972042,0.00016000221,0.002485532,0.0000013229188,0.0001002583,0.000004420102,0.000018634537,0.000013173662,0.0000124632115],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990342,0.0000654092,0.0004963559,0.00007928942,0.00021274915,0.00011194844],"domain_scores_gemma":[0.99919933,0.00028091072,0.00020947355,0.000043712975,0.00020714219,0.00005942002],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00076740107,0.00009242244,0.00018296161,0.00018024565,0.00009373825,0.00012021552,0.000066719374,0.0000663101,0.000008252108],"category_scores_gemma":[0.00005082331,0.00006866138,0.00009631609,0.00030550544,0.000041970456,0.00036828325,4.491266e-7,0.000108999004,4.55167e-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":[0.000014750157,0.000007892074,0.009139877,0.00010975327,0.00013018477,0.00000961118,0.00877013,0.9775005,0.00254126,0.0015920282,0.00008394201,0.000100083475],"study_design_scores_gemma":[0.0011603454,0.00025283982,0.3530013,0.0040938314,0.00050279393,0.000059509144,0.007706335,0.6272621,0.0012509043,0.0001570522,0.0040056007,0.0005474084],"about_ca_topic_score_codex":0.00031683044,"about_ca_topic_score_gemma":0.000018202398,"teacher_disagreement_score":0.35023838,"about_ca_system_score_codex":0.00004571987,"about_ca_system_score_gemma":0.00014779414,"threshold_uncertainty_score":0.27999297},"labels":[],"label_agreement":null},{"id":"W4401283857","doi":"10.1016/j.tre.2024.103679","title":"A method of time-varying demand distribution estimation for high-speed railway networks with user equilibrium model","year":2024,"lang":"en","type":"article","venue":"Transportation Research Part E Logistics and Transportation Review","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Estimation; Distribution (mathematics); Travel time; Computer science; Transport engineering; Econometrics; Economics; Engineering; Mathematics","score_opus":0.0803540560281141,"score_gpt":0.40456547103995094,"score_spread":0.3242114150118368,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401283857","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0021279666,0.005639226,0.9875467,0.0013899293,0.00007450883,0.0017133628,0.0012379623,0.00015182262,0.000118521144],"genre_scores_gemma":[0.86445844,0.03404889,0.07798798,0.0001401912,0.00012501887,0.00040420305,0.021843826,0.00007702579,0.0009144254],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99723715,0.00023029922,0.00083042705,0.0004946566,0.00078385114,0.00042364365],"domain_scores_gemma":[0.9978476,0.00070587907,0.00019619358,0.00015910542,0.000905783,0.00018543724],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027305658,0.00021751632,0.00044102917,0.00015958647,0.0003851392,0.000106624066,0.00013346062,0.00017171919,0.00009089024],"category_scores_gemma":[0.00009113386,0.00019640842,0.00010557341,0.00095142255,0.0003316392,0.00044910848,9.253899e-7,0.0002574834,0.0000034469574],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018611031,0.00007243846,0.0006600054,0.0044455472,0.00009849371,0.000011272214,0.0019827313,0.83136517,0.000057144145,0.15302947,0.0017968527,0.006294786],"study_design_scores_gemma":[0.001439172,0.00036563494,0.005190054,0.0073246215,0.0011066868,0.0000010470217,0.0002658943,0.9556167,0.00015570139,0.0053786226,0.022493746,0.000662095],"about_ca_topic_score_codex":0.00034735288,"about_ca_topic_score_gemma":0.00068910205,"teacher_disagreement_score":0.9095587,"about_ca_system_score_codex":0.000044702534,"about_ca_system_score_gemma":0.0003521376,"threshold_uncertainty_score":0.8009303},"labels":[],"label_agreement":null},{"id":"W4401316767","doi":"10.2139/ssrn.4915558","title":"Abnormal Metro Passenger Demand is Predictable from Alighting and Boarding Correlation","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; McGill University","funders":"","keywords":"Correlation; Business; Transport engineering; Advertising; Engineering; Mathematics; Geometry","score_opus":0.009343481425475638,"score_gpt":0.26348177562827063,"score_spread":0.254138294202795,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401316767","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8294381,0.033385783,0.12033399,0.00418362,0.003198136,0.0004120439,0.000095127136,0.00032400823,0.008629173],"genre_scores_gemma":[0.9875677,0.008211812,0.0008198367,0.00005534505,0.0012475827,0.0000065636455,0.00009111867,0.000028578872,0.0019714532],"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.997489,0.00017411202,0.00038245425,0.00034112015,0.00048421018,0.0011291031],"domain_scores_gemma":[0.99926174,0.00012374898,0.00028820598,0.00009457246,0.00011816331,0.000113564056],"candidate_categories":["research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0023613893,0.0001890304,0.00023511868,0.00018775978,0.0007012971,0.00045888373,0.00016433766,0.00034235985,0.00007286414],"category_scores_gemma":[0.000079283316,0.00018718233,0.00010905993,0.0001723958,0.00005768677,0.00024156546,0.000052355437,0.002888535,0.000013017443],"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":[0.00028469457,0.00014035375,0.41014642,0.00026926713,0.0039525633,0.00007713897,0.16652492,0.10962171,0.0001542414,0.25768295,0.00691699,0.044228762],"study_design_scores_gemma":[0.0010786349,0.0001317113,0.010290411,0.0010585713,0.0014652883,0.000029624507,0.03794343,0.044459455,0.00007229496,0.8916437,0.010728698,0.0010981854],"about_ca_topic_score_codex":0.0020801555,"about_ca_topic_score_gemma":0.0036994792,"teacher_disagreement_score":0.6339608,"about_ca_system_score_codex":0.00062284915,"about_ca_system_score_gemma":0.0021833144,"threshold_uncertainty_score":0.9994118},"labels":[],"label_agreement":null},{"id":"W4401342981","doi":"10.54324/j.mtl.v11i2.1386","title":"Trip Attraction Rate Estimation Using Open Data Sources for Smart Transportation Planning","year":2024,"lang":"en","type":"article","venue":"Jurnal Manajemen Transportasi & Logistik (JMTRANSLOG)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Encana (Canada)","funders":"","keywords":"Estimation; tar (computing); Transport engineering; Aggregate (composite); Computer science; Sample (material); Engineering","score_opus":0.2036715721304766,"score_gpt":0.4238067445153483,"score_spread":0.22013517238487168,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401342981","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.054396402,0.0010243735,0.93447024,0.0026375337,0.0015992828,0.0024598292,0.001110086,0.00068932417,0.0016129167],"genre_scores_gemma":[0.9670544,0.0002869486,0.0221841,0.00020625643,0.0004221405,0.0001385214,0.008735887,0.0000957693,0.0008759606],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9962817,0.00020853675,0.0011280179,0.0010478947,0.00063006446,0.0007037813],"domain_scores_gemma":[0.9983708,0.00041279767,0.00031422105,0.00044936928,0.00019373444,0.00025905046],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0028131083,0.00041411648,0.0004943645,0.00036545238,0.001321777,0.00087574206,0.0011731109,0.00033807268,0.00015020039],"category_scores_gemma":[0.000067106375,0.0003969804,0.00020230403,0.0008103999,0.00026353583,0.002993391,0.0000072008324,0.00042089424,0.000008736263],"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":[0.0019891278,0.00048094278,0.028778259,0.0016037687,0.0009272808,0.0004324543,0.041046463,0.80060107,0.0017696457,0.06519248,0.002252692,0.05492583],"study_design_scores_gemma":[0.0062982445,0.00068370934,0.1343626,0.0025415197,0.0042456524,0.000026253685,0.011727506,0.3153918,0.0008974873,0.013995499,0.5064827,0.0033470388],"about_ca_topic_score_codex":0.0028080312,"about_ca_topic_score_gemma":0.0068314825,"teacher_disagreement_score":0.91265804,"about_ca_system_score_codex":0.00014453924,"about_ca_system_score_gemma":0.0006575571,"threshold_uncertainty_score":0.99997836},"labels":[],"label_agreement":null},{"id":"W4401362047","doi":"10.36227/techrxiv.172296521.19751375/v1","title":"Multi-mode traffic network equilibrium and system optimization with tradable credit scheme","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Mathematical optimization; Pareto principle; Computer science; Mode (computer interface); Correctness; Scheme (mathematics); Genetic algorithm; Heuristic; Function (biology); Logit; Mathematics; Algorithm","score_opus":0.021437602992170027,"score_gpt":0.2785236936743914,"score_spread":0.2570860906822214,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401362047","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15133022,0.00448666,0.7761676,0.0031687738,0.007559836,0.003489859,0.0002516108,0.0065361504,0.047009297],"genre_scores_gemma":[0.52087677,0.00017852742,0.4696311,0.00004261629,0.0007670832,0.00007999039,0.0005208144,0.000064411644,0.0078387065],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99837196,0.00009839253,0.00030252046,0.0005286282,0.0003764663,0.00032204177],"domain_scores_gemma":[0.99935436,0.000057348763,0.00013515813,0.00017212862,0.00013206567,0.00014893537],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041624787,0.00022977311,0.00027736335,0.000096945776,0.00023221919,0.0003980124,0.00015812748,0.00036874565,0.000056628345],"category_scores_gemma":[0.0000139923195,0.00020050544,0.00005209061,0.0003065874,0.00012672356,0.00013613836,0.0000404067,0.00036181984,0.0000073947567],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023027436,0.000016308668,0.00036262066,0.0003126745,0.000049254715,0.00000863387,0.0037776688,0.99173445,0.0000014663523,0.002200197,0.0014276919,0.00008598364],"study_design_scores_gemma":[0.00024672825,0.000021591248,0.000097672186,0.0006408371,0.000110182285,0.0000016589877,0.0022574314,0.99543405,0.0000020727039,0.000025953756,0.0008701368,0.00029167233],"about_ca_topic_score_codex":0.00081264804,"about_ca_topic_score_gemma":0.0014606816,"teacher_disagreement_score":0.36954653,"about_ca_system_score_codex":0.000104110964,"about_ca_system_score_gemma":0.00041567153,"threshold_uncertainty_score":0.81763744},"labels":[],"label_agreement":null},{"id":"W4401416121","doi":"10.1109/icra57147.2024.10611313","title":"A Neural-Evolutionary Algorithm for Autonomous Transit Network Design","year":2024,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Artificial neural network; Evolutionary algorithm; Evolutionary computation; Evolutionary acquisition of neural topologies; Artificial intelligence; Time delay neural network","score_opus":0.030297315161058158,"score_gpt":0.2896223023822787,"score_spread":0.25932498722122055,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401416121","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000050506525,0.0006293751,0.9933053,0.0016240552,0.00059612805,0.0002833193,0.000014198857,0.0005045575,0.0029925122],"genre_scores_gemma":[0.12551054,0.000099716744,0.85648483,0.00046044824,0.0010360243,0.00011116052,0.00013589559,0.000024729114,0.016136667],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99939495,0.00004708874,0.00010907238,0.00014728234,0.00011239124,0.0001892352],"domain_scores_gemma":[0.9996604,0.00019566236,0.000011205745,0.00003712772,0.00004165615,0.00005395453],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029611535,0.000054864653,0.000058954676,0.00003489919,0.00033376153,0.000074896765,0.00006006564,0.00006109656,0.00016947612],"category_scores_gemma":[0.0000081748185,0.000052393934,0.00005349279,0.00024009947,0.000042647564,0.00017340647,8.5371204e-7,0.000044103876,0.000017695675],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012959181,0.000019924175,0.000065548826,0.000014548741,0.000030545343,0.000010245452,0.008261169,0.6428066,0.000003455412,0.04914941,0.063298546,0.23632705],"study_design_scores_gemma":[0.00009115999,0.000031410313,0.0002574934,0.00001447236,0.000023604218,7.8612254e-7,0.0005223448,0.8567332,0.000003513212,0.0022922435,0.1399319,0.00009783986],"about_ca_topic_score_codex":0.00018574946,"about_ca_topic_score_gemma":0.00010384774,"teacher_disagreement_score":0.23622921,"about_ca_system_score_codex":0.00003218196,"about_ca_system_score_gemma":0.00016148003,"threshold_uncertainty_score":0.25670576},"labels":[],"label_agreement":null},{"id":"W4401710730","doi":"10.1007/978-3-031-60419-5_12","title":"Phased Approach to Improving Rural All-Way Stop Controlled Intersection in Pelham, Ontario","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in civil engineering","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Regional Municipality of Niagara; Toronto Metropolitan University","funders":"","keywords":"Intersection (aeronautics); Engineering; Transport engineering; Operations research","score_opus":0.011441391067895245,"score_gpt":0.2318023675503693,"score_spread":0.22036097648247405,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401710730","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0033525259,0.0009134297,0.6327739,0.0003582859,0.0027269914,0.0017130154,0.000021570391,0.0005557813,0.3575845],"genre_scores_gemma":[0.9823854,0.000015668225,0.0021043858,0.00007740187,0.00031437358,0.00006383837,0.00010615053,0.00007011636,0.01486264],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99859744,0.000015232608,0.00040046353,0.00035955998,0.00029776033,0.00032956933],"domain_scores_gemma":[0.9994924,0.00017780121,0.00007662856,0.00012553528,0.00004128031,0.000086343534],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00038216385,0.0003031381,0.0004357818,0.00064702675,0.000053536154,0.000109103625,0.0001524356,0.00043133606,0.000092650655],"category_scores_gemma":[0.00013614492,0.00031897106,0.00012567382,0.00016956423,0.00002123054,0.000107668326,0.000010054066,0.0007694317,0.000008263312],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009319856,0.000017705855,0.00009099595,0.000092052964,0.000051994368,0.000017005481,0.038501564,0.952073,0.0001695964,0.0072058863,0.000013215468,0.0016737938],"study_design_scores_gemma":[0.026245805,0.00088163797,0.0032089085,0.013310454,0.0013908416,0.000040096795,0.002950632,0.5955257,0.00043774568,0.017780919,0.3276235,0.010603794],"about_ca_topic_score_codex":0.012993857,"about_ca_topic_score_gemma":0.40159565,"teacher_disagreement_score":0.9790329,"about_ca_system_score_codex":0.0009740101,"about_ca_system_score_gemma":0.00015259197,"threshold_uncertainty_score":0.9999262},"labels":[],"label_agreement":null},{"id":"W4401710764","doi":"10.1007/978-3-031-60419-5_4","title":"Novel Transit Driver Advisory System for Supporting e-Bus Operations","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in civil engineering","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Transit (satellite); Transport engineering; Bus rapid transit; Advisory committee; Computer science; Aeronautics; Public transport; Engineering; Political science; Public administration","score_opus":0.013936454257641562,"score_gpt":0.24840632884584604,"score_spread":0.23446987458820448,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401710764","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000037150952,0.00065812946,0.9637824,0.0003014847,0.0011176257,0.0005200188,0.00014224372,0.00043332955,0.033007573],"genre_scores_gemma":[0.95908284,0.00008021759,0.01876184,0.000065981294,0.0010298468,0.00009841874,0.0004872795,0.00018015606,0.020213446],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99877733,0.0000046048317,0.00037216197,0.00032843833,0.00023079832,0.00028668193],"domain_scores_gemma":[0.9994505,0.00023320389,0.00005232278,0.000114933646,0.00008272404,0.00006629526],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00027144325,0.00024179957,0.00027198286,0.00027073146,0.00015977827,0.00009517354,0.00013227756,0.00042543706,0.00007001561],"category_scores_gemma":[0.00009720433,0.00026843493,0.00013540637,0.00010537352,0.000029112118,0.00010143486,0.0000049787714,0.00038274357,0.0000095712885],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000350631,0.0000035758399,0.000006496699,0.0003517243,0.00003413485,0.000009569141,0.0056619933,0.9158971,0.00016245483,0.077168845,0.000015043029,0.0006855905],"study_design_scores_gemma":[0.0015768149,0.00010679667,0.00010796695,0.00820877,0.0007180524,0.000020790698,0.0004883494,0.746642,0.00047875257,0.00333601,0.23554902,0.0027666804],"about_ca_topic_score_codex":0.00013674649,"about_ca_topic_score_gemma":0.007945583,"teacher_disagreement_score":0.95904565,"about_ca_system_score_codex":0.00020789693,"about_ca_system_score_gemma":0.00013789537,"threshold_uncertainty_score":0.9999768},"labels":[],"label_agreement":null},{"id":"W4402161806","doi":"10.1016/j.tre.2024.103720","title":"Corrigendum to “A method of time-varying demand distribution estimation for high-speed railway networks with user equilibrium model” [Transp. Res. Part E: Logist. Transp. Rev. 189 (2024) 103679]","year":2024,"lang":"en","type":"erratum","venue":"Transportation Research Part E Logistics and Transportation Review","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Estimation; Travel time; Distribution (mathematics); Transport engineering; Engineering; Mathematics; Systems engineering; Mathematical analysis","score_opus":0.09182193908064216,"score_gpt":0.3913850321155749,"score_spread":0.2995630930349327,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402161806","genre_codex":"methods","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00006327493,0.018357173,0.9537626,0.0031135324,0.0036054805,0.0062385676,0.013478054,0.0003062488,0.0010750853],"genre_scores_gemma":[0.021224914,0.30743828,0.09532729,0.0012557375,0.0022157133,0.003706171,0.36217678,0.00083429855,0.20582083],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9917731,0.0006543214,0.0025236167,0.0016062862,0.002216218,0.001226447],"domain_scores_gemma":[0.9948224,0.000603201,0.0008268983,0.0006044969,0.0024727103,0.0006702904],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0049911547,0.0008548549,0.0017107124,0.0005086561,0.0008266132,0.00024124795,0.00054165494,0.0010013328,0.0005575181],"category_scores_gemma":[0.00026665296,0.0008191337,0.00039502786,0.0022404492,0.0007395688,0.0005024938,0.0000039961456,0.0014682172,0.000023898414],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00070764107,0.00020323375,0.00024034407,0.017596334,0.0003869861,0.00005608437,0.0028431732,0.45146504,0.00001892059,0.023669528,0.49982384,0.0029888933],"study_design_scores_gemma":[0.002035097,0.00090459036,0.0013636593,0.025411384,0.0043454133,0.0000020825628,0.0002667044,0.111780606,0.000045334014,0.0021600712,0.84972334,0.0019617255],"about_ca_topic_score_codex":0.0012579687,"about_ca_topic_score_gemma":0.005677119,"teacher_disagreement_score":0.8584353,"about_ca_system_score_codex":0.00018604721,"about_ca_system_score_gemma":0.0013880528,"threshold_uncertainty_score":0.99942595},"labels":[],"label_agreement":null},{"id":"W4402168784","doi":"10.32920/26866435.v1","title":"Transit-Supportive Policy: Evaluating Municipal Plans in Medium-Sized Canadian Municipalities","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Transit (satellite); Business; Environmental planning; Transit system; Transport engineering; Public transport; Geography; Engineering","score_opus":0.06660839520079637,"score_gpt":0.38575563242125505,"score_spread":0.3191472372204587,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402168784","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5522185,0.00055927254,0.00014077095,0.041255422,0.0023469974,0.001461235,0.0011260002,0.00062923477,0.4002626],"genre_scores_gemma":[0.9867675,0.00019592542,0.0012565893,0.00048561118,0.00035925402,0.00010897094,0.0007453357,0.0000357376,0.010045081],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.997204,0.00044274458,0.00060993305,0.0004726725,0.0006131198,0.00065749575],"domain_scores_gemma":[0.9989097,0.00022554648,0.000116406845,0.0002598234,0.00013912263,0.00034940027],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0018960783,0.00026122818,0.00037854185,0.00090767833,0.00031334689,0.00020941104,0.00044840598,0.00055921613,0.0007959546],"category_scores_gemma":[0.00033677995,0.00027655053,0.00013068378,0.0005579272,0.00021503048,0.00009360675,0.000044481138,0.0009031204,0.000045248773],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000370469,0.0000271818,0.0030065796,0.00018177979,0.00012298915,0.00009167702,0.8173999,0.09907502,0.000007022988,0.07757661,0.00083419675,0.001640004],"study_design_scores_gemma":[0.0029001632,0.00020529951,0.060218785,0.004133239,0.0009006178,0.0000052738446,0.6607164,0.10185175,0.00012083559,0.13111357,0.03394518,0.0038888943],"about_ca_topic_score_codex":0.9720685,"about_ca_topic_score_gemma":0.99646705,"teacher_disagreement_score":0.43454903,"about_ca_system_score_codex":0.00067175826,"about_ca_system_score_gemma":0.013887599,"threshold_uncertainty_score":0.99996865},"labels":[],"label_agreement":null},{"id":"W4402188988","doi":"10.32920/26866435","title":"Transit-Supportive Policy: Evaluating Municipal Plans in Medium-Sized Canadian Municipalities","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Business; Transit (satellite); Environmental planning; Environmental economics; Transport engineering; Environmental science; Public transport; Economics; Engineering","score_opus":0.06660839520079637,"score_gpt":0.38575563242125505,"score_spread":0.3191472372204587,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402188988","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5522185,0.00055927254,0.00014077095,0.041255422,0.0023469974,0.001461235,0.0011260002,0.00062923477,0.4002626],"genre_scores_gemma":[0.9867675,0.00019592542,0.0012565893,0.00048561118,0.00035925402,0.00010897094,0.0007453357,0.0000357376,0.010045081],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.997204,0.00044274458,0.00060993305,0.0004726725,0.0006131198,0.00065749575],"domain_scores_gemma":[0.9989097,0.00022554648,0.000116406845,0.0002598234,0.00013912263,0.00034940027],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0018960783,0.00026122818,0.00037854185,0.00090767833,0.00031334689,0.00020941104,0.00044840598,0.00055921613,0.0007959546],"category_scores_gemma":[0.00033677995,0.00027655053,0.00013068378,0.0005579272,0.00021503048,0.00009360675,0.000044481138,0.0009031204,0.000045248773],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000370469,0.0000271818,0.0030065796,0.00018177979,0.00012298915,0.00009167702,0.8173999,0.09907502,0.000007022988,0.07757661,0.00083419675,0.001640004],"study_design_scores_gemma":[0.0029001632,0.00020529951,0.060218785,0.004133239,0.0009006178,0.0000052738446,0.6607164,0.10185175,0.00012083559,0.13111357,0.03394518,0.0038888943],"about_ca_topic_score_codex":0.9720685,"about_ca_topic_score_gemma":0.99646705,"teacher_disagreement_score":0.43454903,"about_ca_system_score_codex":0.00067175826,"about_ca_system_score_gemma":0.013887599,"threshold_uncertainty_score":0.99996865},"labels":[],"label_agreement":null},{"id":"W4402206941","doi":"10.32920/26866615","title":"For a Greater Region: How Has Rail Coverage Changed in the Greater Toronto &amp; Hamilton Area?","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Toronto Metropolitan University; York University","funders":"","keywords":"Transport engineering; Economic geography; Environmental science; Engineering; Economics","score_opus":0.1233322543243222,"score_gpt":0.3156013401497577,"score_spread":0.1922690858254355,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402206941","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28719154,0.0032546686,0.14799736,0.26292115,0.010198586,0.014179058,0.0008298386,0.0022344587,0.27119333],"genre_scores_gemma":[0.9515052,0.00024072037,0.00078257715,0.0008615088,0.00045116598,0.0005104598,0.00047083144,0.000026833028,0.045150694],"study_design_codex":"qualitative","study_design_gemma":"not_applicable","domain_scores_codex":[0.9985397,0.00015655803,0.00020636067,0.00046341572,0.000338994,0.0002950061],"domain_scores_gemma":[0.99935806,0.00009810487,0.000091768234,0.0003008728,0.00009837571,0.00005280668],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006546122,0.00021498611,0.00023282532,0.00007432017,0.00028503168,0.0006647686,0.000318857,0.00035245757,0.00021941018],"category_scores_gemma":[0.000030596446,0.00014887295,0.00015799869,0.000116848045,0.000100086094,0.00012587028,0.000028310578,0.00024502733,0.000014429988],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002019822,0.000070275644,0.005189805,0.00045727927,0.00012411598,0.000064086125,0.82330215,0.0023252142,0.0000041786147,0.007705822,0.15889157,0.0016635475],"study_design_scores_gemma":[0.0013625172,0.000090146335,0.017953157,0.00103022,0.00028012742,0.0000056631734,0.023154713,0.0010284901,0.00002753681,0.011169062,0.9427582,0.0011401523],"about_ca_topic_score_codex":0.007360274,"about_ca_topic_score_gemma":0.11081445,"teacher_disagreement_score":0.8001474,"about_ca_system_score_codex":0.00018282514,"about_ca_system_score_gemma":0.00014912245,"threshold_uncertainty_score":0.9992498},"labels":[],"label_agreement":null},{"id":"W4402255331","doi":"10.32920/26950432","title":"On-Demand Public Transit Systems: Demand Analysis, Network Modelling, and Sustainability Evaluation","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Sustainability; Public transport; Transit (satellite); Transit system; Environmental economics; Demand management; Business; On demand; Transport engineering; Computer science; Economics; Engineering; Commerce","score_opus":0.03881623130140239,"score_gpt":0.32554375714026945,"score_spread":0.28672752583886707,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402255331","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.076536424,0.006659196,0.893189,0.0027360504,0.0009161451,0.001932651,0.000048851944,0.0004190321,0.017562622],"genre_scores_gemma":[0.9969324,0.0005127173,0.0005167219,0.00003972633,0.00023158923,0.00011765297,0.00043955134,0.000015750667,0.0011938789],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969161,0.0007186555,0.0004718787,0.0007051192,0.0008529824,0.0003352613],"domain_scores_gemma":[0.998391,0.0002240272,0.0001590036,0.00027714367,0.0007776535,0.00017118154],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0049571535,0.0002252717,0.00040447462,0.00033596836,0.0004765054,0.0007385738,0.00015674102,0.0004405001,0.00011787886],"category_scores_gemma":[0.000117731084,0.00021042427,0.00016585315,0.00084634474,0.00013280645,0.00011222735,0.000028212957,0.00041057632,0.0000045011857],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011287061,0.00002089945,0.0027443124,0.00020894995,0.00034056092,0.0000015771118,0.005418664,0.910333,6.954081e-9,0.08012514,0.0006043055,0.00019129108],"study_design_scores_gemma":[0.00012392754,0.000013761726,0.00082981825,0.00007878617,0.0015957244,9.428587e-8,0.0018438324,0.9166264,8.377786e-8,0.07775266,0.0009029112,0.00023202851],"about_ca_topic_score_codex":0.0031025447,"about_ca_topic_score_gemma":0.003470274,"teacher_disagreement_score":0.920396,"about_ca_system_score_codex":0.0002822197,"about_ca_system_score_gemma":0.0010035945,"threshold_uncertainty_score":0.85808533},"labels":[],"label_agreement":null},{"id":"W4402255565","doi":"10.32920/26950432.v1","title":"On-Demand Public Transit Systems: Demand Analysis, Network Modelling, and Sustainability Evaluation","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Sustainability; Public transport; Transit (satellite); Transit system; Environmental economics; Business; Demand management; Transport engineering; Natural resource economics; Economics; Engineering; Macroeconomics","score_opus":0.03881623130140239,"score_gpt":0.32554375714026945,"score_spread":0.28672752583886707,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402255565","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.076536424,0.006659196,0.893189,0.0027360504,0.0009161451,0.001932651,0.000048851944,0.0004190321,0.017562622],"genre_scores_gemma":[0.9969324,0.0005127173,0.0005167219,0.00003972633,0.00023158923,0.00011765297,0.00043955134,0.000015750667,0.0011938789],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969161,0.0007186555,0.0004718787,0.0007051192,0.0008529824,0.0003352613],"domain_scores_gemma":[0.998391,0.0002240272,0.0001590036,0.00027714367,0.0007776535,0.00017118154],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0049571535,0.0002252717,0.00040447462,0.00033596836,0.0004765054,0.0007385738,0.00015674102,0.0004405001,0.00011787886],"category_scores_gemma":[0.000117731084,0.00021042427,0.00016585315,0.00084634474,0.00013280645,0.00011222735,0.000028212957,0.00041057632,0.0000045011857],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011287061,0.00002089945,0.0027443124,0.00020894995,0.00034056092,0.0000015771118,0.005418664,0.910333,6.954081e-9,0.08012514,0.0006043055,0.00019129108],"study_design_scores_gemma":[0.00012392754,0.000013761726,0.00082981825,0.00007878617,0.0015957244,9.428587e-8,0.0018438324,0.9166264,8.377786e-8,0.07775266,0.0009029112,0.00023202851],"about_ca_topic_score_codex":0.0031025447,"about_ca_topic_score_gemma":0.003470274,"teacher_disagreement_score":0.920396,"about_ca_system_score_codex":0.0002822197,"about_ca_system_score_gemma":0.0010035945,"threshold_uncertainty_score":0.85808533},"labels":[],"label_agreement":null},{"id":"W4402322434","doi":"10.2139/ssrn.4948391","title":"Learning Heuristics for Transit Network Design and Improvement with Deep Reinforcement Learning","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Reinforcement learning; Heuristics; Computer science; Artificial intelligence; Deep learning; Learning network; Machine learning","score_opus":0.010925098319338663,"score_gpt":0.2571837355029845,"score_spread":0.24625863718364585,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402322434","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004661859,0.0029028456,0.99063796,0.00050494913,0.00030059897,0.0005991887,6.35061e-7,0.000105513376,0.0002864737],"genre_scores_gemma":[0.9730664,0.011845021,0.010278425,0.00003874841,0.0007351806,0.00005749434,0.00006337418,0.000053059273,0.0038622937],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99736214,0.00017531561,0.00033468445,0.0002981688,0.00037043562,0.0014592482],"domain_scores_gemma":[0.999247,0.0001386341,0.00027716157,0.000060487833,0.00016958978,0.000107114894],"candidate_categories":["research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0035570452,0.00021065987,0.00022942186,0.000093053015,0.0010557313,0.0003069828,0.00014230821,0.0001895895,0.000010251103],"category_scores_gemma":[0.000058138412,0.00019185843,0.000083348714,0.00012832478,0.00006723891,0.00006202406,0.000020669275,0.0033220118,0.0000015169554],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013344639,0.0000066273983,0.00020320756,0.00005715291,0.00021723135,0.0000029290568,0.007684134,0.9534214,0.000002507202,0.022804422,0.00002421956,0.015442686],"study_design_scores_gemma":[0.0023833488,0.0050494145,0.00008528198,0.0011995456,0.0016328092,0.00004701442,0.0532437,0.62630874,0.00001880049,0.29406402,0.014534812,0.00143255],"about_ca_topic_score_codex":0.00016736431,"about_ca_topic_score_gemma":0.001372395,"teacher_disagreement_score":0.9803595,"about_ca_system_score_codex":0.000575653,"about_ca_system_score_gemma":0.0027518177,"threshold_uncertainty_score":0.99897736},"labels":[],"label_agreement":null},{"id":"W4402637704","doi":"10.2139/ssrn.4961592","title":"Learning Heuristics for Transit Network Design and Improvement with Deep Reinforcement Learning","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Reinforcement learning; Heuristics; Computer science; Transit (satellite); Artificial intelligence; Engineering; Transport engineering; Public transport","score_opus":0.010925098319338663,"score_gpt":0.2571837355029845,"score_spread":0.24625863718364585,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402637704","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004661859,0.0029028456,0.99063796,0.00050494913,0.00030059897,0.0005991887,6.35061e-7,0.000105513376,0.0002864737],"genre_scores_gemma":[0.9730664,0.011845021,0.010278425,0.00003874841,0.0007351806,0.00005749434,0.00006337418,0.000053059273,0.0038622937],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99736214,0.00017531561,0.00033468445,0.0002981688,0.00037043562,0.0014592482],"domain_scores_gemma":[0.999247,0.0001386341,0.00027716157,0.000060487833,0.00016958978,0.000107114894],"candidate_categories":["research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0035570452,0.00021065987,0.00022942186,0.000093053015,0.0010557313,0.0003069828,0.00014230821,0.0001895895,0.000010251103],"category_scores_gemma":[0.000058138412,0.00019185843,0.000083348714,0.00012832478,0.00006723891,0.00006202406,0.000020669275,0.0033220118,0.0000015169554],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013344639,0.0000066273983,0.00020320756,0.00005715291,0.00021723135,0.0000029290568,0.007684134,0.9534214,0.000002507202,0.022804422,0.00002421956,0.015442686],"study_design_scores_gemma":[0.0023833488,0.0050494145,0.00008528198,0.0011995456,0.0016328092,0.00004701442,0.0532437,0.62630874,0.00001880049,0.29406402,0.014534812,0.00143255],"about_ca_topic_score_codex":0.00016736431,"about_ca_topic_score_gemma":0.001372395,"teacher_disagreement_score":0.9803595,"about_ca_system_score_codex":0.000575653,"about_ca_system_score_gemma":0.0027518177,"threshold_uncertainty_score":0.99897736},"labels":[],"label_agreement":null},{"id":"W4402666251","doi":"10.1016/j.heliyon.2024.e38223","title":"Evolutionary game analysis of carbon emission reduction in transportation infrastructure construction under dual regulation","year":2024,"lang":"en","type":"article","venue":"Heliyon","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Ontario Ministry of Transportation; Ministry of Finance; National Natural Science Foundation of China; Innovative Research Group Project of the National Natural Science Foundation of China","keywords":"Dual (grammatical number); Reduction (mathematics); Carbon fibers; Dual purpose; Natural resource economics; Business; Engineering; Computer science; Economics; Mathematics; Mechanical engineering","score_opus":0.009301716287888057,"score_gpt":0.26921896172167553,"score_spread":0.25991724543378747,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402666251","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9910574,0.0007817452,0.005795351,0.00032474042,0.00058372493,0.00016209992,0.000032554097,0.00014028122,0.001122081],"genre_scores_gemma":[0.9976612,0.00048580533,0.0008615939,0.000006400427,0.00009726393,0.0000073245938,0.0006324187,0.000009063778,0.00023897694],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9987699,0.000108968,0.00037064534,0.000242599,0.00037317903,0.00013474801],"domain_scores_gemma":[0.99960726,0.00004251522,0.0001049601,0.00008330552,0.00011596451,0.000046007517],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002523507,0.000091072914,0.00015797956,0.00078561704,0.00008239456,0.000026210433,0.000037563223,0.00019072619,0.00014929596],"category_scores_gemma":[0.00001691121,0.00009841456,0.00008111057,0.002317251,0.00011047427,0.0003067704,0.0000010760989,0.00012615646,9.61123e-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":[0.00013876906,0.000053367137,0.1126123,0.00024930012,0.0002198585,0.000006268139,0.0345371,0.8065134,0.010891329,0.02803847,0.000092718336,0.0066470806],"study_design_scores_gemma":[0.0002245392,0.000027098316,0.9583059,0.00033670312,0.0004398037,0.0000016211903,0.0057759485,0.030964695,0.0005308856,0.0021805393,0.0010347433,0.00017754274],"about_ca_topic_score_codex":0.00064338703,"about_ca_topic_score_gemma":0.0010092664,"teacher_disagreement_score":0.8456936,"about_ca_system_score_codex":0.00017802481,"about_ca_system_score_gemma":0.0001718666,"threshold_uncertainty_score":0.4013229},"labels":[],"label_agreement":null},{"id":"W4402701601","doi":"","title":"THIRD QUARTER TRANSPORTATION REPORT FISCAL YEAR 2024","year":2024,"lang":"en","type":"article","venue":"OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Quarter (Canadian coin); Fiscal year; Geography; Economics; Archaeology; Finance","score_opus":0.010987708466137572,"score_gpt":0.25338443114326037,"score_spread":0.2423967226771228,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402701601","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28027427,0.00047966183,0.07337876,0.0029262055,0.0033077307,0.0005829167,0.0008186944,0.00062687666,0.6376049],"genre_scores_gemma":[0.9935617,0.000048995862,0.0022044592,0.000028261686,0.000029829724,0.000013788352,0.0027263705,0.0000040800915,0.0013825092],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9985667,0.000023071425,0.00056442095,0.00016852953,0.00052878994,0.00014851439],"domain_scores_gemma":[0.9993626,0.00008887493,0.00016043051,0.00012825144,0.00017408439,0.000085746266],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006214479,0.0000901773,0.00014011718,0.00021339016,0.00020197005,0.00013427567,0.00010541742,0.00011194055,0.00010642672],"category_scores_gemma":[0.00005516048,0.00008321446,0.00007488229,0.0005314664,0.00039668332,0.00096566643,0.0000052128244,0.00007454343,0.000014817637],"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":[0.00014241402,0.00024233594,0.011887253,0.00034019936,0.000096291755,0.0000277074,0.0008532773,0.0020535777,0.00033536003,0.8817872,0.098971054,0.0032633557],"study_design_scores_gemma":[0.00048600655,0.00018195155,0.22423995,0.00031913765,0.00015984126,0.000013398769,0.00042357948,0.00018591394,0.0008436893,0.0024442512,0.77030283,0.00039944082],"about_ca_topic_score_codex":0.00010341022,"about_ca_topic_score_gemma":0.00019095054,"teacher_disagreement_score":0.8793429,"about_ca_system_score_codex":0.000022867584,"about_ca_system_score_gemma":0.0001445537,"threshold_uncertainty_score":0.33933872},"labels":[],"label_agreement":null},{"id":"W4402741155","doi":"10.1016/j.cjph.2024.09.033","title":"Higher-order properties of multi-modal public transit networks","year":2024,"lang":"en","type":"article","venue":"Chinese Journal of Physics","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Modal; Public transport; Transit (satellite); Order (exchange); Computer science; Transport engineering; Materials science; Business; Engineering","score_opus":0.035387091261346044,"score_gpt":0.2928830461837097,"score_spread":0.25749595492236366,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402741155","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6697685,0.006125142,0.31630287,0.003589768,0.0025682766,0.00014969247,0.0000088947845,0.00009042769,0.0013964105],"genre_scores_gemma":[0.99778324,0.00015151521,0.0010215243,0.000032104166,0.0006787255,6.704692e-7,0.0000032678843,0.000009999997,0.00031894355],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99925303,0.000054283773,0.00025600562,0.000061317594,0.00026559524,0.00010978397],"domain_scores_gemma":[0.99942625,0.00003527689,0.0001161243,0.00004327323,0.00031768327,0.000061392406],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002745761,0.00007361188,0.00015360952,0.000052955078,0.000097508404,0.00007143967,0.000120245146,0.00005135227,0.00002775755],"category_scores_gemma":[0.000030324223,0.00004913743,0.00010296868,0.0004985773,0.00010051458,0.00048862986,0.000002291341,0.00016559346,0.0000014872511],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013545391,0.0006435092,0.028478445,0.00035245385,0.00047307895,0.0000495923,0.09211565,0.78887826,0.0039861156,0.04012222,0.0014919516,0.043273285],"study_design_scores_gemma":[0.008219173,0.0010498422,0.17835884,0.004694446,0.0011900514,0.000039544637,0.011939997,0.6913038,0.0029975905,0.021182662,0.07639543,0.002628618],"about_ca_topic_score_codex":0.00004095973,"about_ca_topic_score_gemma":0.00004777668,"teacher_disagreement_score":0.32801473,"about_ca_system_score_codex":0.000018969005,"about_ca_system_score_gemma":0.000200037,"threshold_uncertainty_score":0.20037663},"labels":[],"label_agreement":null},{"id":"W4402769734","doi":"10.1155/2024/8249757","title":"Measuring Travel Time Reliability for Urban Residents’ Commutes via the Integration of Information Entropy and Standard Deviation","year":2024,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","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":"National Natural Science Foundation of China","keywords":"Standard deviation; Reliability (semiconductor); Entropy (arrow of time); Transport engineering; Travel time; Reliability engineering; Computer science; Statistics; Environmental science; Econometrics; Mathematics; Engineering; Thermodynamics; Physics","score_opus":0.014114584278814136,"score_gpt":0.2724427599961614,"score_spread":0.2583281757173473,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402769734","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4254954,0.0004817368,0.5718737,0.0011861984,0.00036876163,0.0004368548,0.00006742082,0.000029980536,0.000059949823],"genre_scores_gemma":[0.986336,0.00029763274,0.013115723,0.000020132235,0.00007232316,0.000007635866,0.00011878142,0.000006434916,0.000025366578],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.99873215,0.000074347,0.00063378393,0.00006972197,0.00039861846,0.000091378766],"domain_scores_gemma":[0.99857396,0.00029444526,0.00040393922,0.0000558719,0.00063187495,0.00003990191],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011909221,0.000075396885,0.0001496388,0.00013910927,0.00019720088,0.0000672919,0.000078052755,0.000059732836,0.000009161741],"category_scores_gemma":[0.0001910456,0.000058244186,0.00007912208,0.00023053927,0.00007520096,0.0017043045,4.9751213e-7,0.000116251795,5.2972143e-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":[0.0032415648,0.00011581068,0.01776487,0.000987145,0.00024680252,0.0000037259965,0.4583985,0.2921604,0.026811266,0.054652907,0.0008020551,0.14481497],"study_design_scores_gemma":[0.0028789337,0.0008582782,0.91030926,0.0013933833,0.00068379217,0.0000043866426,0.02481919,0.011213156,0.014734948,0.023556702,0.009143772,0.00040419638],"about_ca_topic_score_codex":0.000075679556,"about_ca_topic_score_gemma":0.0001790335,"teacher_disagreement_score":0.8925444,"about_ca_system_score_codex":0.00006760134,"about_ca_system_score_gemma":0.00013209986,"threshold_uncertainty_score":0.2375129},"labels":[],"label_agreement":null},{"id":"W4403350198","doi":"10.1016/j.tbs.2024.100922","title":"Heterogeneity in route choice during peak hours: Implications on travel demand management","year":2024,"lang":"en","type":"article","venue":"Travel Behaviour and Society","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"National Natural Science Foundation of China","keywords":"Demand management; Business; Transport engineering; Economics; Engineering","score_opus":0.02668054870868127,"score_gpt":0.31716815101043044,"score_spread":0.2904876023017492,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403350198","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9940207,0.00014729322,0.0013669448,0.00057620136,0.00010838793,0.00023374036,0.000052052874,0.00009560528,0.003399095],"genre_scores_gemma":[0.99771667,0.00062480854,0.0005484251,0.000080455065,0.00006050975,0.00003582342,0.000046534176,0.0000118815715,0.0008748968],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99912375,0.000040007584,0.00016465607,0.00027799208,0.0001736831,0.00021993229],"domain_scores_gemma":[0.9997378,0.000043887852,0.000023455998,0.00009065615,0.000019067242,0.000085138585],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027913976,0.00010117964,0.00009954246,0.000049211943,0.00037143767,0.00012006672,0.000082848856,0.00009557379,0.000016566837],"category_scores_gemma":[0.0000035367966,0.000104623155,0.0000851829,0.0002370916,0.0000733884,0.0001388659,0.0000060275247,0.00014472172,0.0000044689345],"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":[0.000012481024,0.00022518,0.88373345,0.0001300739,0.00008188257,0.00002119264,0.08824031,0.0009631564,0.00040361198,0.022962198,0.0005741574,0.0026522737],"study_design_scores_gemma":[0.0002615527,0.000010249001,0.9946278,0.0000652406,0.00004327823,7.352704e-7,0.004515405,0.000106010564,0.00007089831,0.000069408874,0.00010782498,0.000121618374],"about_ca_topic_score_codex":0.0005153017,"about_ca_topic_score_gemma":0.00068751804,"teacher_disagreement_score":0.1108943,"about_ca_system_score_codex":0.00008807342,"about_ca_system_score_gemma":0.00003221453,"threshold_uncertainty_score":0.42664084},"labels":[],"label_agreement":null},{"id":"W4403431223","doi":"10.1155/2024/8712813","title":"Analysing the Environmental and Social Impacts of a Novel User‐Based Transit Signal Priority Strategy in a Connected Vehicle Environment","year":2024,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","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":"Horizon 2020 Framework Programme","keywords":"Transit (satellite); Transport engineering; SIGNAL (programming language); Computer science; Environmental science; Public transport; Engineering","score_opus":0.013524559621875473,"score_gpt":0.2713671428944948,"score_spread":0.25784258327261933,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403431223","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9848008,0.00034750244,0.014019141,0.0005969562,0.00004320208,0.00012764831,0.000045093133,0.000009248453,0.000010367186],"genre_scores_gemma":[0.9986592,0.00015570669,0.001072553,0.00002304441,0.000038619415,0.0000016613726,0.00003685194,0.0000076804245,0.000004704311],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99889934,0.00006668696,0.00044659295,0.000106226275,0.00035798093,0.00012315362],"domain_scores_gemma":[0.9995708,0.00011948974,0.00021196908,0.000030089594,0.000024316221,0.000043340766],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042695634,0.00008270982,0.00016252432,0.00012928272,0.00012723518,0.000030594467,0.000059936716,0.00006223407,0.00003703097],"category_scores_gemma":[0.000004513383,0.000069223526,0.000080957434,0.00018201354,0.00012893266,0.00041288804,3.515028e-7,0.00017144077,2.0680518e-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":[0.0004123044,0.00025672675,0.025870986,0.0000883114,0.00007885047,0.000037422764,0.06907967,0.7934296,0.103266895,0.0012043652,0.0000039818565,0.0062708803],"study_design_scores_gemma":[0.0018823338,0.00017225703,0.97853917,0.00013435262,0.0001774242,0.0000014152245,0.0115389945,0.0052425396,0.0016096525,0.00012801358,0.00043774713,0.00013610878],"about_ca_topic_score_codex":0.00009517408,"about_ca_topic_score_gemma":0.00066119595,"teacher_disagreement_score":0.9526682,"about_ca_system_score_codex":0.00007134755,"about_ca_system_score_gemma":0.00012774495,"threshold_uncertainty_score":0.28228533},"labels":[],"label_agreement":null},{"id":"W4403451844","doi":"10.1080/21680566.2024.2416181","title":"Bayesian inference for link travel time correlation of a bus route","year":2024,"lang":"en","type":"article","venue":"Transportmetrica B Transport Dynamics","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; China Scholarship Council","keywords":"Link (geometry); Inference; Travel time; Computer science; Transport engineering; Bayesian probability; Correlation; Statistics; Engineering; Artificial intelligence; Mathematics; Computer network","score_opus":0.012818783977917126,"score_gpt":0.2818648155491053,"score_spread":0.26904603157118817,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403451844","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014997851,0.00034972717,0.97515607,0.00072795036,0.0004987873,0.0008185688,0.0015026226,0.00041585765,0.0055325744],"genre_scores_gemma":[0.9828814,0.0003565701,0.010802861,0.000029992278,0.00011631486,0.00004951533,0.0032321764,0.000054405766,0.0024767625],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976862,0.00004121739,0.0008019961,0.00046493404,0.00057554885,0.00043008543],"domain_scores_gemma":[0.9987154,0.00047127582,0.00015097464,0.00019952362,0.00027434833,0.0001884703],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000687053,0.0002511579,0.0004195323,0.00067585835,0.00025269773,0.000042263982,0.0002727257,0.00034170612,0.00019531224],"category_scores_gemma":[0.000053249016,0.0002713124,0.00031957254,0.0021966088,0.000212167,0.00042983962,9.099007e-7,0.00023745444,0.0000121791945],"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":[0.00063678686,0.0004996753,0.20561424,0.0014434523,0.0005134796,0.00013251076,0.043971557,0.15451697,0.00015345735,0.5079878,0.00014935869,0.084380716],"study_design_scores_gemma":[0.0018070703,0.00043471108,0.19790764,0.00065315986,0.0012457873,0.00000372945,0.0012363861,0.7723273,0.000110521134,0.007405191,0.015573063,0.0012954337],"about_ca_topic_score_codex":0.0009261298,"about_ca_topic_score_gemma":0.0019065222,"teacher_disagreement_score":0.9678835,"about_ca_system_score_codex":0.00014634193,"about_ca_system_score_gemma":0.000574214,"threshold_uncertainty_score":0.9999739},"labels":[],"label_agreement":null},{"id":"W4403685681","doi":"10.1080/01441647.2024.2416652","title":"Week-long activity-based modelling: a review of the existing models and datasets and a comprehensive conceptual framework","year":2024,"lang":"en","type":"review","venue":"Transport Reviews","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"TRIPS architecture; Replicate; Conceptual model; Conceptual framework; Computer science; Scheduling (production processes); Operations research; Management science; Transport engineering; Data science; Engineering; Operations management","score_opus":0.26595430859694535,"score_gpt":0.41809933276292055,"score_spread":0.1521450241659752,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403685681","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000004218973,0.97533196,0.021125982,0.00012824792,0.00015303172,0.0022141917,0.0007149343,0.000050594474,0.00027684058],"genre_scores_gemma":[0.000043556134,0.9953401,0.0034591246,0.00021585755,0.00006667426,0.0001214496,0.0006462799,0.000037965103,0.000068983245],"study_design_codex":"systematic_review","study_design_gemma":"not_applicable","domain_scores_codex":[0.99715495,0.0005934315,0.0010398282,0.00055118056,0.00040617192,0.0002544597],"domain_scores_gemma":[0.998319,0.00033869848,0.0007201954,0.0004070286,0.00008331031,0.00013180442],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0010593826,0.00042654737,0.0020825055,0.000079668585,0.00027713072,0.00003423005,0.00030913408,0.0002969031,0.000028285674],"category_scores_gemma":[0.000058443293,0.00028161443,0.00045746443,0.0006588579,0.00053843274,0.00017174378,0.000013356528,0.00063274096,0.0000039970123],"study_design_candidate":"systematic_review","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007972029,0.0000693619,0.000025640376,0.5576599,0.00014072188,0.000018985738,0.004362553,0.00082349265,3.174375e-8,0.011243211,0.000786317,0.4248618],"study_design_scores_gemma":[0.000041181338,0.000009638104,0.0000018934372,0.26481056,0.0019347504,0.0000018834031,0.00003028495,0.00035605262,2.8069111e-8,0.000106348765,0.7324929,0.00021443557],"about_ca_topic_score_codex":0.00022537734,"about_ca_topic_score_gemma":0.000108414606,"teacher_disagreement_score":0.7317066,"about_ca_system_score_codex":0.00003364241,"about_ca_system_score_gemma":0.000447754,"threshold_uncertainty_score":0.9999636},"labels":[],"label_agreement":null},{"id":"W4403764039","doi":"10.3390/data9110123","title":"Sustainable Transportation Characteristics Diary—Example of Older (50+) Cyclists","year":2024,"lang":"en","type":"article","venue":"Data","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Transport engineering; Geography; Gerontology; Medicine; Engineering","score_opus":0.03274830892476594,"score_gpt":0.3137914969987436,"score_spread":0.28104318807397766,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403764039","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6905236,0.0022478818,0.25875717,0.0043981513,0.0029360985,0.0015793603,0.017573448,0.0014588498,0.020525435],"genre_scores_gemma":[0.98304325,0.00017239101,0.00092488993,0.000028887462,0.00009836282,0.0000040764735,0.013319992,0.000009341692,0.0023988236],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99925387,0.000039394436,0.00017984648,0.00018836728,0.00019521949,0.00014327886],"domain_scores_gemma":[0.999526,0.00009763013,0.000043352575,0.00022442693,0.000066518005,0.000042050375],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005085923,0.000050734816,0.000079319936,0.00005938287,0.00011842561,0.00006654159,0.00021806317,0.000051604904,0.00017571962],"category_scores_gemma":[0.000067078116,0.00005241289,0.000016975471,0.00024869805,0.00006154364,0.00052363373,0.000004871372,0.000055452365,0.000013062375],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000691911,0.00026731825,0.09263642,0.0014393129,0.0001404096,0.00019626216,0.17884803,0.0005664053,0.0003913439,0.60671407,0.08835106,0.03038015],"study_design_scores_gemma":[0.00012047044,0.000009393421,0.26736388,0.000091803675,0.000068013534,2.0363596e-7,0.0040935352,0.0015962475,0.00002898768,0.00051538285,0.72596365,0.00014843183],"about_ca_topic_score_codex":0.006754747,"about_ca_topic_score_gemma":0.0009780759,"teacher_disagreement_score":0.6376126,"about_ca_system_score_codex":0.000015028854,"about_ca_system_score_gemma":0.0001366004,"threshold_uncertainty_score":0.99985933},"labels":[],"label_agreement":null},{"id":"W4403919561","doi":"10.1109/sm63044.2024.10733519","title":"Towards Frictionless Public Transit: A Brief Review of Automatic Fare Collection","year":2024,"lang":"en","type":"review","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Public transport; Computer science; Transit (satellite); Data collection; Transport engineering; Engineering; Mathematics; Statistics","score_opus":0.0716186416374211,"score_gpt":0.3764870733363676,"score_spread":0.3048684316989465,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403919561","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[4.722479e-8,0.95202094,0.003172176,0.00068397966,0.00055294274,0.00083948893,0.000064046355,0.00035670245,0.042309694],"genre_scores_gemma":[0.0000050479207,0.99437153,0.00082392676,0.000081767874,0.00008727367,0.00011704522,0.0003731768,0.000027423708,0.004112781],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99790853,0.0003237525,0.0007899804,0.00028432408,0.0005034627,0.0001899391],"domain_scores_gemma":[0.9991365,0.00006977987,0.00029709836,0.00015530422,0.00024817392,0.00009311701],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000771696,0.00021688473,0.0010031796,0.00030948583,0.00023928194,0.000088654364,0.00020412816,0.00027674748,0.0011285706],"category_scores_gemma":[0.00014022546,0.0001733053,0.0004726686,0.0023867374,0.00007125287,0.00017072585,0.0000048957413,0.00021121251,0.000047896436],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[2.1564084e-7,0.000025227635,6.14872e-7,0.20733061,0.00009759044,0.0000020604884,0.0011236857,0.0000012906193,1.9044142e-9,0.010786934,0.012051485,0.76858026],"study_design_scores_gemma":[0.000030479076,0.000011648142,0.0000020521436,0.098430865,0.0011640827,0.0000026578632,0.0001717371,0.000034699497,2.0550248e-8,0.00004500412,0.8999484,0.00015833748],"about_ca_topic_score_codex":0.00060554175,"about_ca_topic_score_gemma":0.00064838835,"teacher_disagreement_score":0.88789696,"about_ca_system_score_codex":0.00016521625,"about_ca_system_score_gemma":0.0018899712,"threshold_uncertainty_score":0.9997845},"labels":[],"label_agreement":null},{"id":"W4403980699","doi":"10.2139/ssrn.4963186","title":"Comparative Analysis of Evolutionary Algorithms for Optimizing Vehicle Routing in Smart Cities","year":2024,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Vehicle routing problem; Computer science; Evolutionary algorithm; Routing (electronic design automation); Algorithm; Artificial intelligence; Computer network","score_opus":0.025295117554767294,"score_gpt":0.3233603560733162,"score_spread":0.29806523851854894,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403980699","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.58578074,0.007396541,0.4010887,0.0013189685,0.00045136458,0.00030896935,0.000044100954,0.000108340886,0.0035022656],"genre_scores_gemma":[0.99649817,0.0006350871,0.002212032,0.000009009862,0.00007698501,0.0000057036223,0.000029372433,0.0000044226404,0.000529206],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99873036,0.00008586482,0.00025679136,0.00011061421,0.00019201552,0.0006243665],"domain_scores_gemma":[0.9995714,0.0001886854,0.00008000184,0.000031285745,0.000100007244,0.000028650444],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014012968,0.000058426445,0.00017212222,0.00037997836,0.00024885824,0.00004784635,0.00008683225,0.000046103734,0.000016469936],"category_scores_gemma":[0.000032653446,0.000058774716,0.000120485,0.0009463355,0.000058972215,0.00024219754,0.0000025552206,0.00035053832,7.4939163e-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":[0.00006397295,0.00005249407,0.03547276,0.00001490344,0.0012101218,0.000002742937,0.056890454,0.2892013,0.000056076482,0.61289936,0.000084907835,0.004050915],"study_design_scores_gemma":[0.00065899565,0.00020361129,0.029356914,0.0001648869,0.0008256292,0.000005045238,0.15861067,0.76276165,0.000050164395,0.045530822,0.0015254989,0.00030611982],"about_ca_topic_score_codex":0.00065415434,"about_ca_topic_score_gemma":0.006154452,"teacher_disagreement_score":0.5673685,"about_ca_system_score_codex":0.00045194625,"about_ca_system_score_gemma":0.0012779162,"threshold_uncertainty_score":0.34343287},"labels":[],"label_agreement":null},{"id":"W4403984041","doi":"10.1016/j.jue.2024.103714","title":"The city-wide effects of tolling downtown drivers: Evidence from London’s congestion charge","year":2024,"lang":"en","type":"article","venue":"Journal of Urban Economics","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Downtown; Congestion pricing; Transport engineering; Business; Traffic congestion; Geography; Engineering; Archaeology","score_opus":0.01674314867214784,"score_gpt":0.256945019863802,"score_spread":0.24020187119165418,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403984041","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98779607,0.0048355274,0.003080673,0.0021489437,0.0017721783,0.00008203748,0.000007665477,0.00001640961,0.00026048822],"genre_scores_gemma":[0.9918772,0.0068188733,0.0006448746,0.000038027996,0.0003194364,5.9966163e-7,0.0000022752786,0.0000053779845,0.00029332767],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993195,0.00006676678,0.0003344547,0.000076725686,0.0001033625,0.00009921926],"domain_scores_gemma":[0.9977673,0.0017283952,0.00029030046,0.000058148613,0.0000975324,0.00005830148],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00068051106,0.000055072673,0.0001287111,0.00006341799,0.00016273047,0.00013587865,0.00016031195,0.000057648063,0.000039700484],"category_scores_gemma":[0.00039083554,0.000044032462,0.00008655138,0.0000793974,0.00008000644,0.0005383873,0.0000037351858,0.00013824795,0.000006512828],"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":[0.0004572292,0.00012395685,0.66533995,0.00023775113,0.00089891616,0.000107799024,0.15529807,0.063274756,0.00089862524,0.03735901,0.021286769,0.054717172],"study_design_scores_gemma":[0.0031805444,0.0009924159,0.43854067,0.009156431,0.0014295258,0.000018314144,0.011788055,0.03948848,0.012784957,0.010799535,0.47052792,0.0012931667],"about_ca_topic_score_codex":0.00023059723,"about_ca_topic_score_gemma":0.00029285593,"teacher_disagreement_score":0.44924113,"about_ca_system_score_codex":0.000107250766,"about_ca_system_score_gemma":0.00020440712,"threshold_uncertainty_score":0.17955917},"labels":[],"label_agreement":null},{"id":"W4404208195","doi":"10.1155/2024/6995651","title":"A Route Diversity–Based Approach for Estimating Vulnerability of Stations in a Multimodal Public Transport Network","year":2024,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","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":"Natural Science Foundation of Inner Mongolia","keywords":"Multimodal transport; Vulnerability (computing); Public transport; Diversity (politics); Computer science; Transport engineering; Engineering; Computer security; Sociology","score_opus":0.030696714377754945,"score_gpt":0.3144788673164585,"score_spread":0.28378215293870357,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404208195","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22612616,0.00012540085,0.77257234,0.00029935557,0.00034399645,0.00032907506,0.00008767197,0.00003527333,0.000080721664],"genre_scores_gemma":[0.68088007,0.000012619604,0.3187904,0.000014130233,0.000076235636,0.000011609533,0.00019776494,0.000008575002,0.000008567243],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9983912,0.000077035525,0.00074162474,0.00016812507,0.00040977227,0.00021225106],"domain_scores_gemma":[0.9988017,0.00030676997,0.00033483267,0.00006065246,0.00041001584,0.000086027096],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012063008,0.00010329936,0.0002499623,0.00025400933,0.00025457493,0.00002401304,0.0001284096,0.00008657684,0.000012748957],"category_scores_gemma":[0.000094587864,0.00010612863,0.00017116302,0.0007278518,0.00008565307,0.0008928167,6.470884e-7,0.00018399439,9.975132e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013799522,0.00010960897,0.060860332,0.00017731026,0.000024918392,0.0000059907397,0.020234587,0.91280645,0.00004171812,0.0028309915,0.000010720467,0.0027593877],"study_design_scores_gemma":[0.004361753,0.00037745538,0.6885656,0.00074550457,0.0003478332,0.0000012611849,0.0120339,0.2868797,0.000068418325,0.004934414,0.0012287349,0.00045541616],"about_ca_topic_score_codex":0.00016958725,"about_ca_topic_score_gemma":0.0022463785,"teacher_disagreement_score":0.6277053,"about_ca_system_score_codex":0.00009989137,"about_ca_system_score_gemma":0.00043517965,"threshold_uncertainty_score":0.43278},"labels":[],"label_agreement":null},{"id":"W4404214212","doi":"10.1016/j.sftr.2024.100370","title":"Modelling severe weather impact on traffic mobility and evaluating cross-regional applicability to functionally distinct segments in cold region highway networks","year":2024,"lang":"en","type":"article","venue":"Sustainable Futures","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Regina","funders":"","keywords":"Cold weather; Transport engineering; Meteorology; Environmental science; Computer science; Geography; Engineering","score_opus":0.022473916137628894,"score_gpt":0.33433073195765645,"score_spread":0.31185681582002756,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404214212","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9804261,0.00037468376,0.016955765,0.0005474965,0.00015585913,0.0007297138,0.000009660756,0.0001679227,0.00063280016],"genre_scores_gemma":[0.9967643,0.000018631417,0.00034851374,0.00008070488,0.0002650152,0.00009539664,0.000043135373,0.00001605552,0.0023682301],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99835616,0.0001427702,0.00023749046,0.0004729837,0.00039086584,0.0003996978],"domain_scores_gemma":[0.9992429,0.0002773878,0.000042937918,0.00012834361,0.0001747384,0.00013366358],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012611871,0.0001571124,0.00014775519,0.0001186675,0.0005895174,0.00027401568,0.0000939594,0.00014191974,0.000044978828],"category_scores_gemma":[0.000067401845,0.00013662851,0.00006474142,0.0005316421,0.00009928863,0.00025579234,0.000011971875,0.00021496734,0.0000024573983],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002400568,0.00005900152,0.01446695,0.00006728622,0.000012863417,0.000012516061,0.0049958904,0.9714933,9.870379e-7,0.006511504,0.0005578466,0.0015817874],"study_design_scores_gemma":[0.00062731764,0.00025635952,0.13682091,0.00017442924,0.000032007418,0.0000021432413,0.010670743,0.84099984,0.0000018023604,0.0028781479,0.007091495,0.00044478083],"about_ca_topic_score_codex":0.0015669732,"about_ca_topic_score_gemma":0.00045072864,"teacher_disagreement_score":0.13049346,"about_ca_system_score_codex":0.0004874879,"about_ca_system_score_gemma":0.00029867384,"threshold_uncertainty_score":0.5571549},"labels":[],"label_agreement":null},{"id":"W4404430951","doi":"10.1080/03081060.2024.2422400","title":"Electric vehicle drivers’ choices of expressway usage and peak avoidance: an empirical analysis considering the random effects among individuals","year":2024,"lang":"en","type":"article","venue":"Transportation Planning and Technology","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"National Natural Science Foundation of China","keywords":"Transport engineering; Poison control; Human factors and ergonomics; Empirical research; Electric vehicle; Engineering; Psychology; Statistics; Mathematics; Environmental health; Power (physics); Medicine; Physics","score_opus":0.012658631297771634,"score_gpt":0.29842569656701656,"score_spread":0.28576706526924495,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404430951","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9922379,0.001971456,0.004740731,0.00037262047,0.000051891904,0.00018923248,0.000016079919,0.00027771454,0.00014234129],"genre_scores_gemma":[0.99926835,0.00020431954,0.00034132734,0.000020728212,0.000017725635,0.000023412753,0.000058571677,0.000008416496,0.00005711561],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9989784,0.000119351585,0.0002473066,0.00027130486,0.00019851662,0.00018513924],"domain_scores_gemma":[0.99914503,0.0005581485,0.00009650845,0.000094097595,0.000049067334,0.0000571307],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00050692237,0.00011202803,0.00023725364,0.00051549345,0.00037885614,0.00007426919,0.00010162145,0.0001814942,0.000011566356],"category_scores_gemma":[0.00006230463,0.00009066431,0.00004057826,0.0013944587,0.00036182156,0.00028211178,0.000002179783,0.00021133941,4.5279526e-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":[0.000017549777,0.000011844092,0.96689296,0.00005990043,0.00017405415,0.000016890124,0.025738554,0.0029952459,0.0007187698,0.0018387304,0.000024271816,0.0015112497],"study_design_scores_gemma":[0.0007313051,0.000106094645,0.98266464,0.000119609744,0.0007711224,0.0000010665296,0.004406498,0.007819043,0.0015729421,0.0006500565,0.0009770181,0.00018060887],"about_ca_topic_score_codex":0.00025665332,"about_ca_topic_score_gemma":0.00036759878,"teacher_disagreement_score":0.021332055,"about_ca_system_score_codex":0.000007880695,"about_ca_system_score_gemma":0.00004225545,"threshold_uncertainty_score":0.3697183},"labels":[],"label_agreement":null},{"id":"W4404542482","doi":"10.1007/978-981-97-9644-1_36","title":"A Freight Forecasting Method for Multimodal Transport Networks Using a High-Fidelity Integrated Modeling Approach","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in civil engineering","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary; University of Waterloo","funders":"","keywords":"Multimodal transport; Fidelity; Computer science; Operations research; Transport engineering; Engineering; Telecommunications","score_opus":0.036888213417550594,"score_gpt":0.27401296297103317,"score_spread":0.23712474955348256,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404542482","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0001266081,0.001015759,0.992624,0.000035480116,0.00067257596,0.0006514766,0.000087170985,0.00036171428,0.0044252165],"genre_scores_gemma":[0.49593884,0.000053201158,0.50184256,0.000029919493,0.00067960477,0.000048385802,0.0006262575,0.00015933764,0.00062189356],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99796134,0.00002127066,0.00060520536,0.000616516,0.000305878,0.00048978295],"domain_scores_gemma":[0.99903244,0.00042487035,0.000121036566,0.00016370797,0.00015655613,0.000101419624],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008436488,0.00044874664,0.00054734666,0.00038215015,0.00019133929,0.00007944734,0.0001976076,0.0008802555,0.00003158183],"category_scores_gemma":[0.00016277375,0.00046989336,0.0002249189,0.0002569216,0.00003208194,0.00013030248,0.00000921868,0.000998275,4.4862745e-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":[0.000020646205,0.000006936766,0.000016373599,0.000224865,0.000066787194,0.000011481268,0.0026970012,0.9848941,0.000012132492,0.010040727,0.0000014141784,0.0020075347],"study_design_scores_gemma":[0.00022679182,0.000011630725,0.0000020205491,0.000933185,0.0001596096,0.0000026507248,0.000027202568,0.99233186,0.0000057338793,0.0050373287,0.00078383234,0.0004781793],"about_ca_topic_score_codex":0.0018339866,"about_ca_topic_score_gemma":0.0053433967,"teacher_disagreement_score":0.49581224,"about_ca_system_score_codex":0.00031489728,"about_ca_system_score_gemma":0.0001727434,"threshold_uncertainty_score":0.9997753},"labels":[],"label_agreement":null},{"id":"W4404762447","doi":"10.3390/su162310378","title":"City-Level Integrated Traffic Management with User Preferences Under Connected Environment","year":2024,"lang":"en","type":"article","venue":"Sustainability","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Transport engineering; Computer science; Business; Engineering","score_opus":0.021633134718957434,"score_gpt":0.2772396769384638,"score_spread":0.25560654221950635,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404762447","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94359875,0.000104324085,0.04800529,0.0027691333,0.00013693575,0.0007854205,0.000026710013,0.00048167259,0.0040917746],"genre_scores_gemma":[0.9919109,0.00003894163,0.0014982909,0.000035328,0.000019989935,0.0000518648,0.00007219346,0.0000084682715,0.006364058],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9988322,0.00014814029,0.00015526355,0.00032089296,0.0003033795,0.00024010852],"domain_scores_gemma":[0.9995448,0.000079287965,0.000026313433,0.000135084,0.00013913345,0.00007540656],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045243994,0.0001075044,0.00009578436,0.0000784993,0.00025443346,0.00012736411,0.0001112151,0.00006843354,0.00049542973],"category_scores_gemma":[0.00003807413,0.000084398154,0.000031690655,0.0003907616,0.0002551423,0.00019219775,0.0000073673486,0.00011957885,0.000010356321],"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":[0.00033678266,0.0005958745,0.082206555,0.00079372467,0.00038090264,0.00013714997,0.07141642,0.29545248,0.0000019442853,0.4856529,0.002081007,0.060944263],"study_design_scores_gemma":[0.00086847565,0.0002471073,0.60903484,0.00016786897,0.00022817975,0.0000011133407,0.15364532,0.0038474277,0.000023151992,0.020640984,0.21053109,0.00076443807],"about_ca_topic_score_codex":0.00052805786,"about_ca_topic_score_gemma":0.0011352359,"teacher_disagreement_score":0.5268283,"about_ca_system_score_codex":0.0005265975,"about_ca_system_score_gemma":0.00035323115,"threshold_uncertainty_score":0.5424608},"labels":[],"label_agreement":null},{"id":"W4405391258","doi":"10.2139/ssrn.5056234","title":"Dynamic Passenger Crowding and Operations in Urban Rail Transit Systems: A Validated Framework of Data-Driven Agent-Based Simulation","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Urban rail transit; Crowding; Transit (satellite); Transport engineering; Transit system; Computer science; Rail transit; Public transport; Engineering","score_opus":0.027657097540984537,"score_gpt":0.3348894731871739,"score_spread":0.3072323756461893,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405391258","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3137226,0.0065694605,0.6768653,0.0011680822,0.0006663861,0.00060740934,0.00019572377,0.000102787766,0.000102242564],"genre_scores_gemma":[0.99574697,0.0015669158,0.0018944949,0.0000134853935,0.00010625089,0.0000098639375,0.0004948065,0.000026974081,0.00014022662],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976072,0.00036886663,0.0005731871,0.00036083505,0.0003897393,0.0007002004],"domain_scores_gemma":[0.9991761,0.00018227375,0.00019997367,0.00021906002,0.00015412996,0.00006847133],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021404473,0.00017389575,0.00029249254,0.00035564994,0.00025941155,0.00028507996,0.0003247534,0.0003489978,0.000010755176],"category_scores_gemma":[0.000124014,0.00018054254,0.00006392227,0.00036781986,0.00007414944,0.00023197138,0.000031277243,0.0022577376,0.0000019736183],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022830283,0.000030828603,0.0009831167,0.00009360662,0.00010118036,0.0000044445014,0.0059260787,0.9602644,0.000024986524,0.032152537,0.000006414246,0.0003896031],"study_design_scores_gemma":[0.000350936,0.000037638765,0.0005561686,0.0009884834,0.00020391308,0.0000026638616,0.0047905245,0.98117614,0.0000017621802,0.011488058,0.00017015723,0.00023353723],"about_ca_topic_score_codex":0.0014193327,"about_ca_topic_score_gemma":0.013561809,"teacher_disagreement_score":0.68202436,"about_ca_system_score_codex":0.0006227698,"about_ca_system_score_gemma":0.0038626897,"threshold_uncertainty_score":0.9808872},"labels":[],"label_agreement":null},{"id":"W4405395180","doi":"10.2139/ssrn.5052651","title":"Dynamic Passenger Crowding and Operations in Urban Rail Transit Systems: A Validated Framework of Data-Driven Agent-Based Simulation","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Crowding; Transit (satellite); Transport engineering; Transit system; Urban rail transit; Computer science; Public transport; Passenger transport; Engineering; Psychology","score_opus":0.027657097540984537,"score_gpt":0.3348894731871739,"score_spread":0.3072323756461893,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405395180","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3137226,0.0065694605,0.6768653,0.0011680822,0.0006663861,0.00060740934,0.00019572377,0.000102787766,0.000102242564],"genre_scores_gemma":[0.99574697,0.0015669158,0.0018944949,0.0000134853935,0.00010625089,0.0000098639375,0.0004948065,0.000026974081,0.00014022662],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976072,0.00036886663,0.0005731871,0.00036083505,0.0003897393,0.0007002004],"domain_scores_gemma":[0.9991761,0.00018227375,0.00019997367,0.00021906002,0.00015412996,0.00006847133],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021404473,0.00017389575,0.00029249254,0.00035564994,0.00025941155,0.00028507996,0.0003247534,0.0003489978,0.000010755176],"category_scores_gemma":[0.000124014,0.00018054254,0.00006392227,0.00036781986,0.00007414944,0.00023197138,0.000031277243,0.0022577376,0.0000019736183],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022830283,0.000030828603,0.0009831167,0.00009360662,0.00010118036,0.0000044445014,0.0059260787,0.9602644,0.000024986524,0.032152537,0.000006414246,0.0003896031],"study_design_scores_gemma":[0.000350936,0.000037638765,0.0005561686,0.0009884834,0.00020391308,0.0000026638616,0.0047905245,0.98117614,0.0000017621802,0.011488058,0.00017015723,0.00023353723],"about_ca_topic_score_codex":0.0014193327,"about_ca_topic_score_gemma":0.013561809,"teacher_disagreement_score":0.68202436,"about_ca_system_score_codex":0.0006227698,"about_ca_system_score_gemma":0.0038626897,"threshold_uncertainty_score":0.9808872},"labels":[],"label_agreement":null},{"id":"W4405411779","doi":"10.21203/rs.3.rs-5457598/v1","title":"Modelling the Efficiency of Transportation Survey Recruitment and Reminder Methods","year":2024,"lang":"en","type":"preprint","venue":"Research Square","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Transport engineering; Business; Computer science; Engineering","score_opus":0.35630186596957686,"score_gpt":0.5302131901819706,"score_spread":0.1739113242123937,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405411779","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.62664783,0.01338813,0.34023327,0.0033948233,0.0013247789,0.0052958694,0.0010205901,0.00023371993,0.008460987],"genre_scores_gemma":[0.989707,0.0020642679,0.0070639667,0.00000473632,0.0000644208,0.00012239939,0.0003293146,0.000018206876,0.0006256729],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99603176,0.002028247,0.00033156734,0.00037056548,0.00095834344,0.0002794998],"domain_scores_gemma":[0.9975874,0.0013960757,0.00009198882,0.00022248703,0.0006198295,0.00008224515],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012873987,0.000109312445,0.00018202576,0.00022099311,0.00042442302,0.00012669819,0.000241616,0.00024376292,0.000039344362],"category_scores_gemma":[0.00024287046,0.0000836838,0.00006791024,0.00062333967,0.00036006517,0.000039471175,0.00003238094,0.0008425608,0.0000029228183],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008031724,0.00008597006,0.005328728,0.0011867405,0.00008040885,0.0000063726593,0.16711728,0.80060524,0.000025185025,0.014444941,0.0006452138,0.010393576],"study_design_scores_gemma":[0.0008065492,0.00035592425,0.079659455,0.0056001097,0.0003215727,7.280496e-7,0.06447208,0.7311308,0.0014398858,0.08996878,0.02497684,0.001267318],"about_ca_topic_score_codex":0.01585647,"about_ca_topic_score_gemma":0.003060268,"teacher_disagreement_score":0.3630592,"about_ca_system_score_codex":0.00007464652,"about_ca_system_score_gemma":0.000591627,"threshold_uncertainty_score":0.990697},"labels":[],"label_agreement":null},{"id":"W4405597648","doi":"10.5198/jtlu.2024.2513","title":"Investigating the impacts of telecommuting on the spatial, temporal, and modal distribution of travel using an agent-based transport simulation model","year":2024,"lang":"en","type":"article","venue":"Journal of Transport and Land Use","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Environment and Climate Change Canada","keywords":"Telecommuting; Transport engineering; Population; Paratransit; Traffic congestion; Travel behavior; Computer science; Modal; Traffic flow (computer networking); Mode choice; Demand management; Traffic simulation; Microsimulation; Work (physics); Geography; Engineering; Public transport; Computer security; Economics","score_opus":0.06586218497661447,"score_gpt":0.31688251485707536,"score_spread":0.25102032988046086,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405597648","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92645425,0.00010688685,0.07287023,0.00033837132,0.000044214317,0.00010004348,0.000068984096,0.000007301745,0.000009745581],"genre_scores_gemma":[0.9992548,0.00006550534,0.00054497534,0.000032511103,0.0000441516,4.0134213e-7,0.000047131012,0.0000073319893,0.0000031421928],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99904203,0.00007346784,0.00042266454,0.00007795813,0.000283829,0.00010006458],"domain_scores_gemma":[0.999301,0.00021299518,0.00024815966,0.00005669497,0.00011138037,0.000069750306],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00094447803,0.000082456485,0.00015781498,0.000059654732,0.00024441123,0.00003618786,0.00006648244,0.00006332417,0.0000040396258],"category_scores_gemma":[0.000035086137,0.000048901387,0.000063993604,0.00015641948,0.00014679908,0.00031510452,6.696925e-7,0.00015825516,1.2858156e-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":[0.00006104913,0.00003920612,0.27711552,0.000059412305,0.000024624796,0.0000036437366,0.011631174,0.7098902,0.00038708787,0.0003437685,0.0000023512653,0.00044194018],"study_design_scores_gemma":[0.00032164808,0.00010954706,0.28194383,0.00037691236,0.00015542164,0.0000017551873,0.0011510699,0.71534765,0.00029746277,0.00017970102,0.00003858755,0.00007641982],"about_ca_topic_score_codex":0.0016975887,"about_ca_topic_score_gemma":0.000986357,"teacher_disagreement_score":0.07280063,"about_ca_system_score_codex":0.000013798424,"about_ca_system_score_gemma":0.0001866465,"threshold_uncertainty_score":0.25662577},"labels":[],"label_agreement":null},{"id":"W4405659567","doi":"10.1016/j.trip.2024.101310","title":"Investigating the impacts of connected vehicle technology on the flow of trucks at the busiest Canada-U.S. border crossings","year":2024,"lang":"en","type":"article","venue":"Transportation Research Interdisciplinary Perspectives","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Truck; Flow (mathematics); Transport engineering; Business; Geography; Economic geography; Engineering; Automotive engineering; Mathematics","score_opus":0.03379207814728748,"score_gpt":0.39385603382918244,"score_spread":0.360063955681895,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405659567","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92669904,0.0006868841,0.000102547165,0.06918197,0.000115203075,0.00043446996,0.00013960215,0.000057810805,0.0025824879],"genre_scores_gemma":[0.9988363,0.0000672881,0.00006903528,0.00003970811,0.000049188944,0.000047178524,0.00003210417,0.000015624637,0.0008435685],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.99789923,0.0003219207,0.00034652115,0.00026306958,0.0008562483,0.00031303315],"domain_scores_gemma":[0.9973785,0.0015753664,0.00011123049,0.0002470348,0.0006287826,0.0000590529],"candidate_categories":["sts"],"consensus_categories":["sts"],"category_scores_codex":[0.0014261372,0.00011490469,0.0001424875,0.00018111232,0.0017892221,0.00008478905,0.00041838698,0.00008580456,0.00034278908],"category_scores_gemma":[0.0003809259,0.00006256401,0.000071005976,0.0016987228,0.0027912532,0.00014719582,0.00002035079,0.00049779325,0.0000033575302],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018060814,0.0000897414,0.015271307,0.000121733225,0.00017741264,0.000017001452,0.8378729,0.010347027,0.0038851362,0.12220325,0.008251375,0.0015824739],"study_design_scores_gemma":[0.00028762274,0.00023840355,0.28033262,0.0005544525,0.00004583981,0.0000010465689,0.7008467,0.0040360047,0.0041987426,0.0058727222,0.0034042632,0.00018163516],"about_ca_topic_score_codex":0.07513854,"about_ca_topic_score_gemma":0.64865303,"teacher_disagreement_score":0.57351446,"about_ca_system_score_codex":0.00022665245,"about_ca_system_score_gemma":0.0010877454,"threshold_uncertainty_score":0.9999226},"labels":[],"label_agreement":null},{"id":"W4405694969","doi":"10.1155/atr/2344587","title":"Key Characteristics of Multimodal Public Transport Across the Entire Commuting Process: Quantitative Evidence from Shanghai","year":2024,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","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":"National Natural Science Foundation of China","keywords":"Public transport; Key (lock); Process (computing); Transport engineering; Computer science; Regional science; Process management; Business; Engineering; Geography; Computer security","score_opus":0.03739124246274659,"score_gpt":0.35905835753023274,"score_spread":0.32166711506748613,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405694969","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9559262,0.0017182953,0.039577693,0.0015905441,0.0007154354,0.00019134024,0.00020045752,0.00004904838,0.000031024585],"genre_scores_gemma":[0.99127233,0.0012448785,0.0071059894,0.000038293063,0.00015332666,0.0000063708153,0.00013749664,0.000017795806,0.000023538041],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.99794114,0.000109817236,0.0008816913,0.00016475294,0.00068152905,0.00022106747],"domain_scores_gemma":[0.9978895,0.0005158819,0.0006641207,0.00009534625,0.0007489242,0.000086226275],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001000322,0.00013345346,0.00026920333,0.00009703087,0.0002873238,0.00007775005,0.00028229522,0.00009283249,0.000049719274],"category_scores_gemma":[0.00021128965,0.00010524085,0.00016427963,0.0005712975,0.00021952114,0.001671212,8.3762825e-7,0.00031740777,0.0000017627667],"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":[0.00042178293,0.00012926676,0.08330332,0.00023003767,0.00014903843,0.00006640462,0.84154266,0.042017817,0.002544237,0.0032107143,0.000018427254,0.026366288],"study_design_scores_gemma":[0.00067260873,0.0002120726,0.90224725,0.0024606031,0.00020307995,0.0000020147463,0.088048525,0.0013537188,0.0007084968,0.00093783083,0.0028824906,0.0002713156],"about_ca_topic_score_codex":0.00030587552,"about_ca_topic_score_gemma":0.0017635068,"teacher_disagreement_score":0.8189439,"about_ca_system_score_codex":0.00004935612,"about_ca_system_score_gemma":0.0003376018,"threshold_uncertainty_score":0.42915973},"labels":[],"label_agreement":null},{"id":"W4406115484","doi":"10.1016/j.seps.2025.102155","title":"Mathematical programming in public bus transit design and operations: Emerging technologies and sustainability – A review","year":2025,"lang":"en","type":"review","venue":"Socio-Economic Planning Sciences","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates; University of Calgary","keywords":"Sustainability; Transit (satellite); Public transport; Computer science; Transport engineering; Business; Emerging technologies; Engineering; Engineering management","score_opus":0.07162862975306573,"score_gpt":0.39028872308694396,"score_spread":0.31866009333387824,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406115484","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00004602639,0.9882934,0.006778797,0.0026305104,0.00007165962,0.0015682138,0.000008509021,0.00020010679,0.00040279134],"genre_scores_gemma":[0.00045641456,0.99006903,0.008993315,0.000032518594,0.000015896976,0.00030090704,0.00001838435,0.000008118173,0.00010542292],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9977303,0.00042282743,0.0006574651,0.0005982466,0.00016293679,0.00042818635],"domain_scores_gemma":[0.99867254,0.00090730033,0.00018009933,0.00012574263,0.00005104108,0.00006327633],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0041878507,0.00024674687,0.0008880544,0.00041218955,0.001052728,0.00041427623,0.00036945532,0.00027168248,0.000011987977],"category_scores_gemma":[0.0010882039,0.0002197512,0.000084426254,0.0006550458,0.0012137252,0.0005281975,0.000033287353,0.0002807764,0.0000017910802],"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":[0.0000010970025,0.000022402572,0.00058998447,0.018073082,0.00002770312,0.00000665666,0.0064653745,0.0014922845,4.7634527e-9,0.025901224,0.00009833349,0.94732183],"study_design_scores_gemma":[0.00042559125,0.00011852843,0.000057463385,0.113382526,0.000693906,0.000023028426,0.105058305,0.003919208,8.3919915e-8,0.016632477,0.7580439,0.0016450032],"about_ca_topic_score_codex":0.00013059867,"about_ca_topic_score_gemma":0.000077034616,"teacher_disagreement_score":0.94567686,"about_ca_system_score_codex":0.00026971978,"about_ca_system_score_gemma":0.0020795914,"threshold_uncertainty_score":0.89611936},"labels":[],"label_agreement":null},{"id":"W4406195436","doi":"10.1016/j.trpro.2024.12.199","title":"Development of A Demand Model for School Trips in Colombo, Sri Lanka","year":2025,"lang":"en","type":"article","venue":"Transportation research procedia","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Research Council Canada; University of Moratuwa; Natural Sciences and Engineering Research Council of Canada; Mitacs; Transport Canada","keywords":"Sri lanka; TRIPS architecture; Transport engineering; Engineering; Socioeconomics; Economics","score_opus":0.10283340059651146,"score_gpt":0.43849536509075393,"score_spread":0.33566196449424246,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406195436","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8913099,0.00011336204,0.102001086,0.0007351044,0.000065495115,0.0018270183,0.00005924862,0.00011129841,0.003777465],"genre_scores_gemma":[0.9657055,0.000061304694,0.032531187,0.0000353523,0.000012689922,0.00039106453,0.00014616494,0.0000073282804,0.0011094086],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.9985647,0.000053194155,0.00042444962,0.0002021776,0.00046195227,0.0002934865],"domain_scores_gemma":[0.99906725,0.00017449683,0.000060865696,0.00006408105,0.00054944475,0.00008385125],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001813629,0.000067823305,0.00014235808,0.000409215,0.00028232526,0.000021872534,0.0001509035,0.000114017,0.000024322117],"category_scores_gemma":[0.00027479016,0.00007340762,0.00003127312,0.00089974725,0.000105354746,0.00016729186,0.0000011395219,0.00014714258,0.000003032408],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00096763304,0.0005694823,0.1774955,0.0013396073,0.00007421722,0.0000040214777,0.53638947,0.09536148,0.0014302423,0.17713448,0.0031113534,0.006122531],"study_design_scores_gemma":[0.0075220936,0.00012352275,0.7867283,0.000955915,0.000060038554,3.7222186e-8,0.06969458,0.09048379,0.004067464,0.0124341,0.027310291,0.00061983883],"about_ca_topic_score_codex":0.00020563499,"about_ca_topic_score_gemma":0.020635063,"teacher_disagreement_score":0.60923284,"about_ca_system_score_codex":0.00008653942,"about_ca_system_score_gemma":0.0020569528,"threshold_uncertainty_score":0.9972358},"labels":[],"label_agreement":null},{"id":"W4406226585","doi":"10.1016/j.trpro.2024.12.145","title":"Passenger Satisfaction across Multiple Public Transit Modes","year":2025,"lang":"en","type":"article","venue":"Transportation research procedia","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Philippine-American Educational Foundation","keywords":"Public transport; Transport engineering; Transit (satellite); Business; Computer science; Engineering","score_opus":0.09720589831529805,"score_gpt":0.4354391435083239,"score_spread":0.3382332451930259,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406226585","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9332758,0.00019518303,0.047603145,0.008387357,0.00030147145,0.0011697212,0.00014114231,0.000889841,0.008036295],"genre_scores_gemma":[0.99599826,0.0002839672,0.0012753105,0.000103689476,0.00007997498,0.00019745762,0.00022450894,0.000021324446,0.0018155056],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9974656,0.00020748924,0.00036097024,0.00037943115,0.00093127333,0.00065521215],"domain_scores_gemma":[0.99831784,0.00033358578,0.00005840911,0.00015014833,0.00096025696,0.00017974453],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0016274713,0.00012654069,0.00015388436,0.00031029378,0.0014351299,0.00020447808,0.0001992028,0.00021234021,0.00013582603],"category_scores_gemma":[0.0003212278,0.00013757756,0.000071602124,0.0016267975,0.00033899397,0.00074980914,0.0000016935796,0.0003939384,0.00004196443],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015838284,0.00017453679,0.7484544,0.00021970201,0.000070106966,0.000011668268,0.058711894,0.005720582,0.0010381733,0.15656595,0.00395436,0.024920242],"study_design_scores_gemma":[0.00086286745,0.00002765118,0.9474856,0.000063954765,0.000014623083,6.27251e-8,0.018351905,0.0010149118,0.00043251738,0.0015854061,0.029967725,0.00019276617],"about_ca_topic_score_codex":0.0039133513,"about_ca_topic_score_gemma":0.08635278,"teacher_disagreement_score":0.1990312,"about_ca_system_score_codex":0.00011537635,"about_ca_system_score_gemma":0.00060852576,"threshold_uncertainty_score":0.9998649},"labels":[],"label_agreement":null},{"id":"W4406226868","doi":"10.1016/j.trpro.2024.12.117","title":"Sustainable public bus transit systems: Proof-of-concept","year":2025,"lang":"en","type":"article","venue":"Transportation research procedia","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Calgary","funders":"Alberta Innovates; National Key Research and Development Program of China; Mitacs; Tongji University; Natural Sciences and Engineering Research Council of Canada; Technische Universiteit Delft","keywords":"Public transport; Transit (satellite); Proof of concept; Transport engineering; Bus rapid transit; Computer science; Transit system; Computer security; Business; Engineering","score_opus":0.055810896158976483,"score_gpt":0.3784711966848776,"score_spread":0.3226603005259011,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406226868","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.45228893,0.0074986964,0.328584,0.023831481,0.0015949531,0.013428891,0.0003773095,0.002321769,0.17007399],"genre_scores_gemma":[0.9859662,0.000069467,0.00040193336,0.000028949773,0.00005583096,0.00021859186,0.00017413305,0.000017116308,0.013067799],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9973833,0.00023678732,0.00047009526,0.00030708712,0.00097413285,0.00062858016],"domain_scores_gemma":[0.9971738,0.00022301676,0.00009406194,0.0001555577,0.0022004195,0.0001531485],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019093585,0.00011117252,0.0002035458,0.0005876063,0.00070193724,0.00012359851,0.00030882348,0.00017852815,0.00008785558],"category_scores_gemma":[0.0003177683,0.00011757742,0.00006197691,0.0024400603,0.00044801028,0.00057775306,0.000001791308,0.00028492164,0.0000046661635],"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":[0.000064466185,0.00017603705,0.012215929,0.00074552075,0.00004642163,0.000016455899,0.04330056,0.010652519,0.00003060339,0.9266182,0.003837976,0.0022953153],"study_design_scores_gemma":[0.004472384,0.00047779808,0.15478948,0.001085763,0.00018277665,4.794893e-7,0.38107732,0.0026309013,0.0063582542,0.0139154745,0.43390527,0.0011040935],"about_ca_topic_score_codex":0.004166643,"about_ca_topic_score_gemma":0.0026294999,"teacher_disagreement_score":0.91270274,"about_ca_system_score_codex":0.00010479287,"about_ca_system_score_gemma":0.0018566183,"threshold_uncertainty_score":0.6298746},"labels":[],"label_agreement":null},{"id":"W4406227063","doi":"10.1016/j.trpro.2024.12.091","title":"Data Driven Synchronization Strategies of a Bus Line in a Transit Network","year":2025,"lang":"en","type":"article","venue":"Transportation research procedia","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Polytechnique Montréal","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Polytechnique Montréal","keywords":"Transit (satellite); Line (geometry); Synchronization (alternating current); Computer science; Bus rapid transit; Transport engineering; Computer network; Real-time computing; Public transport; Engineering","score_opus":0.09164851402244263,"score_gpt":0.4301667841043678,"score_spread":0.3385182700819252,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406227063","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.55920446,0.0017835794,0.40884712,0.0054079406,0.00052049046,0.0036593163,0.00066730153,0.0006558264,0.019253945],"genre_scores_gemma":[0.99496245,0.0005707696,0.0029724268,0.000022210435,0.000066125845,0.000044820048,0.0011185122,0.000012366708,0.00023033765],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99805766,0.00018494569,0.00045986308,0.00031889,0.00061843364,0.0003602017],"domain_scores_gemma":[0.9988594,0.00022239315,0.00007880421,0.00022583765,0.00055050006,0.00006307238],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016251126,0.000088134075,0.00017387651,0.00035021087,0.00024276187,0.00005544029,0.00042036525,0.00013965883,0.00006131343],"category_scores_gemma":[0.00015997572,0.00009777825,0.000024245162,0.0025007946,0.00025876702,0.00063518225,0.000003626587,0.0002557025,0.0000036666622],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020802401,0.00025875584,0.11350265,0.0005447332,0.000047667698,0.00001676605,0.03910499,0.5977824,0.00010021667,0.24071212,0.002044861,0.0056768544],"study_design_scores_gemma":[0.0027976492,0.00018273613,0.84186256,0.0013406373,0.000087637054,1.18412515e-7,0.06510802,0.06361045,0.00011824592,0.012406402,0.012060495,0.00042501526],"about_ca_topic_score_codex":0.0028790298,"about_ca_topic_score_gemma":0.11181094,"teacher_disagreement_score":0.72835994,"about_ca_system_score_codex":0.00005006235,"about_ca_system_score_gemma":0.0014254483,"threshold_uncertainty_score":0.9043962},"labels":[],"label_agreement":null},{"id":"W4406230579","doi":"10.1016/j.trpro.2024.12.055","title":"Systemwide Variations and Factors Affecting Mixture Transit Travel Time Distributions","year":2025,"lang":"en","type":"article","venue":"Transportation research procedia","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Polytechnique Montréal; Ministry of Transportation of Ontario","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Transport engineering; Transit (satellite); Travel time; Environmental science; Engineering; Public transport","score_opus":0.031846639107453736,"score_gpt":0.3609018161380692,"score_spread":0.3290551770306155,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406230579","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.77303416,0.00036599865,0.20694008,0.00520233,0.0003122119,0.0021206818,0.00067875907,0.0007885688,0.0105572045],"genre_scores_gemma":[0.9965077,0.000085914,0.00064425427,0.000024893008,0.000047792233,0.00005622349,0.00064418884,0.000014543311,0.0019745054],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99822485,0.00021549994,0.00030858457,0.00031414823,0.0005421965,0.00039471732],"domain_scores_gemma":[0.99864817,0.00052785745,0.000059052447,0.00010211052,0.0005035907,0.000159215],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0011263116,0.00012237894,0.00017133777,0.00031530685,0.0014909237,0.00013050047,0.00013878033,0.00018360993,0.00007910572],"category_scores_gemma":[0.0002950123,0.00012618495,0.00005302244,0.0013400527,0.00022934031,0.00033209694,0.0000014582598,0.00032785672,0.000013466678],"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":[0.00010242166,0.00031856156,0.14808632,0.0008260167,0.00019536515,0.000016718639,0.22172995,0.002483973,0.0025847391,0.6161868,0.005644906,0.0018242376],"study_design_scores_gemma":[0.0005629269,0.000036740217,0.9775572,0.00022738919,0.00007985113,1.6042183e-7,0.015654117,0.00081559754,0.0004896437,0.0015847657,0.0027735415,0.00021807927],"about_ca_topic_score_codex":0.0012006646,"about_ca_topic_score_gemma":0.0033270917,"teacher_disagreement_score":0.8294709,"about_ca_system_score_codex":0.000083754,"about_ca_system_score_gemma":0.000447819,"threshold_uncertainty_score":0.999809},"labels":[],"label_agreement":null},{"id":"W4406569568","doi":"10.2139/ssrn.5102788","title":"Reducing Traffic with “Carrots”: A Review of the Evidence","year":2025,"lang":"en","type":"review","venue":"SSRN Electronic Journal","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Engineering Link (Canada)","funders":"","keywords":"Computer science; Business; Transport engineering; Engineering","score_opus":0.027130175727087855,"score_gpt":0.34576260709412154,"score_spread":0.3186324313670337,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406569568","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000020358416,0.99743813,0.00040591537,0.00054060883,0.00018043836,0.0005274496,0.0000032091164,0.000019611392,0.00088260137],"genre_scores_gemma":[0.00005265916,0.99673235,0.00006868914,0.000048566533,0.00011845628,0.000016993019,0.000005242762,0.000012914769,0.002944095],"study_design_codex":"design_other","study_design_gemma":"systematic_review","domain_scores_codex":[0.9972493,0.0006638874,0.000543503,0.00018483968,0.0005123815,0.0008461081],"domain_scores_gemma":[0.9984885,0.00027890253,0.0007942938,0.00019565492,0.00019906361,0.00004357792],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036076796,0.00017197272,0.00066980976,0.000100984165,0.00044867973,0.00003307323,0.0005892868,0.0001228877,0.000025887044],"category_scores_gemma":[0.0005111814,0.00010060562,0.0003616859,0.001062125,0.00011511792,0.0001360162,0.000007533717,0.0016084552,0.0000019783795],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004143773,0.00001416416,0.0000057501993,0.027214767,0.00014021953,9.326863e-7,0.0005469136,0.00019130278,6.9846258e-9,0.011359859,0.00023361633,0.96028835],"study_design_scores_gemma":[0.000053702555,0.000043472854,0.0000016902054,0.53619874,0.0010945271,0.00003843602,0.0004089244,9.984667e-7,2.8466898e-8,0.00022854307,0.46179253,0.00013839503],"about_ca_topic_score_codex":0.00008017264,"about_ca_topic_score_gemma":0.0010210213,"teacher_disagreement_score":0.96014994,"about_ca_system_score_codex":0.0006193378,"about_ca_system_score_gemma":0.026705667,"threshold_uncertainty_score":0.978812},"labels":[],"label_agreement":null},{"id":"W4406614472","doi":"10.1016/j.trc.2025.105000","title":"Understanding bus delay patterns under different temporal and weather conditions: A Bayesian Gaussian mixture model","year":2025,"lang":"en","type":"article","venue":"Transportation Research Part C Emerging Technologies","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Calgary; McGill University","funders":"China Scholarship Council","keywords":"Bayesian probability; Mixture model; Gaussian; Computer science; Meteorology; Environmental science; Artificial intelligence; Geography","score_opus":0.11695735520686484,"score_gpt":0.38928738264339774,"score_spread":0.2723300274365329,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406614472","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22367753,0.0004014886,0.7379158,0.032514486,0.00017547487,0.00084925926,0.00017281219,0.0017490262,0.0025440857],"genre_scores_gemma":[0.9960467,0.0010870842,0.0012411985,0.00007462515,0.000014191956,0.000135074,0.00023257262,0.000016594431,0.001151909],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99809486,0.00013121568,0.0003317885,0.00039760701,0.0005530622,0.000491462],"domain_scores_gemma":[0.99930847,0.00017990136,0.000075002186,0.00020921856,0.00015425266,0.000073161784],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0005743808,0.0001687558,0.00019099534,0.0006613374,0.0013061183,0.00012929138,0.0002442071,0.00027710674,0.000069820286],"category_scores_gemma":[0.000077349585,0.00016034511,0.00005373306,0.0008215945,0.0005822586,0.00028164976,0.000008527237,0.00044573337,0.0000019103034],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005207473,0.000104123465,0.17227423,0.00012413342,0.00009648919,0.00002024291,0.009814879,0.019657344,0.00012973117,0.7928103,0.0041176574,0.0007987985],"study_design_scores_gemma":[0.0024875565,0.00016982417,0.078663215,0.0017072553,0.00019728349,9.182535e-7,0.3194566,0.04191126,0.00092401885,0.53953725,0.0137029635,0.0012418296],"about_ca_topic_score_codex":0.00069929974,"about_ca_topic_score_gemma":0.013203798,"teacher_disagreement_score":0.7723692,"about_ca_system_score_codex":0.00016012776,"about_ca_system_score_gemma":0.0001602119,"threshold_uncertainty_score":0.99999404},"labels":[],"label_agreement":null},{"id":"W4406669607","doi":"10.32388/wozspd","title":"Review of: \"Selfish Routing on Transportation Networks With Supply and Demand Constraints\"","year":2025,"lang":"en","type":"peer-review","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Routing (electronic design automation); Supply and demand; Operations research; Computer science; Economics; Business; Computer network; Microeconomics; Engineering","score_opus":0.016390605229497965,"score_gpt":0.3021099303264421,"score_spread":0.28571932509694414,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406669607","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000037810332,0.5726226,0.078548715,0.08003823,0.0016849416,0.0046800724,0.0012783195,0.0004125189,0.2606968],"genre_scores_gemma":[0.0025743167,0.93088895,0.0029481626,0.01241343,0.00013578711,0.000052500003,0.0058488636,0.000022583554,0.0451154],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99836373,0.00014971619,0.00055264815,0.0003286354,0.0004038936,0.00020135609],"domain_scores_gemma":[0.99874,0.00026771834,0.00039874,0.00013471932,0.00037608237,0.00008270152],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009551092,0.00020938968,0.0005842814,0.00007633211,0.0001958811,0.000027131058,0.0001261446,0.00020872336,0.0005760937],"category_scores_gemma":[0.00012759269,0.00017482982,0.000083007464,0.00042138473,0.0002363438,0.00008259653,0.0000011585926,0.0002532696,0.0000010065842],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023141989,0.00004404507,0.0021120736,0.03755579,0.000120921584,0.0000080270975,0.0009895704,0.0013306874,6.218305e-8,0.010074824,0.91535634,0.03238452],"study_design_scores_gemma":[0.00030065957,0.00006532352,0.0011964135,0.24737343,0.0007134631,6.637244e-7,0.00023263779,0.000055382487,9.574277e-7,0.000017321296,0.7497158,0.00032793515],"about_ca_topic_score_codex":0.0006122337,"about_ca_topic_score_gemma":0.0029838956,"teacher_disagreement_score":0.35826635,"about_ca_system_score_codex":0.000026076863,"about_ca_system_score_gemma":0.00041984176,"threshold_uncertainty_score":0.7129353},"labels":[],"label_agreement":null},{"id":"W4406689841","doi":"10.1016/j.cstp.2025.101375","title":"Bus user itinerary choice: Can crowding information help shift riders?","year":2025,"lang":"en","type":"article","venue":"Case Studies on Transport Policy","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University; Polytechnique Montréal","funders":"Mitacs","keywords":"Crowding; Transport engineering; Computer science; Business; Engineering; Psychology","score_opus":0.022652676284242265,"score_gpt":0.3457678747724808,"score_spread":0.3231151984882385,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406689841","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9003359,0.00034030972,0.0014414468,0.019138139,0.0011194474,0.0006552869,0.00020836451,0.00056873827,0.076192364],"genre_scores_gemma":[0.99455744,0.00026924876,0.00015236576,0.0028652318,0.0002732448,0.000041502364,0.00012908436,0.000008959769,0.0017029148],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9987433,0.00005324315,0.0004265762,0.00018782711,0.00025443296,0.00033463415],"domain_scores_gemma":[0.9992955,0.00016947203,0.00011896663,0.00014493089,0.00016905394,0.00010210549],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031115557,0.00017243881,0.00022085627,0.00039939937,0.001111306,0.000050377326,0.00010950271,0.0001069448,0.000024269666],"category_scores_gemma":[0.000254986,0.00017435578,0.00008815577,0.0008457824,0.00020993408,0.0004802248,0.0000040837663,0.00016164528,0.00001537296],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001793764,0.00016575088,0.11902495,0.00052438193,0.0005762674,0.0009388057,0.39239517,0.032869305,0.00000822849,0.4292201,0.013857747,0.010239909],"study_design_scores_gemma":[0.0046444,0.00022562052,0.25586876,0.0009951168,0.00055232417,0.00003832852,0.1553211,0.0001949517,0.0004917583,0.002971952,0.5769519,0.0017437662],"about_ca_topic_score_codex":0.006761195,"about_ca_topic_score_gemma":0.01444615,"teacher_disagreement_score":0.5630942,"about_ca_system_score_codex":0.00015854122,"about_ca_system_score_gemma":0.0003883054,"threshold_uncertainty_score":0.9998529},"labels":[],"label_agreement":null},{"id":"W4406755942","doi":"10.1016/j.retrec.2025.101519","title":"An importance-performance analysis of public transport to the university campus based on best-worst scaling","year":2025,"lang":"en","type":"article","venue":"Research in Transportation Economics","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"University of Cambridge; University of Alberta","keywords":"Public transport; Scaling; Transport engineering; Computer science; Public university; Engineering; Political science; Mathematics; Public administration","score_opus":0.06098057230926785,"score_gpt":0.35291137348244067,"score_spread":0.2919308011731728,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406755942","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9858699,0.0000109289385,0.006680424,0.0028859358,0.00007227948,0.00034760498,0.00011064991,0.00002647278,0.003995822],"genre_scores_gemma":[0.99854016,0.00021740985,0.00054133154,0.00010686134,0.00001462003,0.000010498776,0.0003663317,0.000006509954,0.00019626613],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9984956,0.00022892172,0.0003788527,0.00030093256,0.00027222186,0.00032350133],"domain_scores_gemma":[0.99902356,0.0002602239,0.00008650261,0.0002640563,0.00023743488,0.00012825464],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027863404,0.00008560384,0.0002001406,0.0014379795,0.0004648209,0.00003196923,0.00039023807,0.0001005585,0.000079133904],"category_scores_gemma":[0.000029704253,0.000088585024,0.000083527615,0.0038059454,0.00018985859,0.00032968266,8.5616966e-7,0.00020757322,0.000003920565],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000064188906,0.0000688704,0.43858653,0.000011038464,0.000030514499,0.0000013708371,0.0029503335,0.54525626,0.0000016588273,0.010625799,0.000005962245,0.0023974457],"study_design_scores_gemma":[0.0003648119,0.000056014218,0.89308155,0.000045563746,0.00008084249,5.6731504e-9,0.008119312,0.092353635,0.00003737139,0.00005021558,0.005701481,0.00010923102],"about_ca_topic_score_codex":0.002557412,"about_ca_topic_score_gemma":0.09333509,"teacher_disagreement_score":0.45449498,"about_ca_system_score_codex":0.0002471326,"about_ca_system_score_gemma":0.0006646275,"threshold_uncertainty_score":0.9232092},"labels":[],"label_agreement":null},{"id":"W4407171640","doi":"10.4018/979-8-3693-7250-0.ch017","title":"Urban Transportation Planning","year":2025,"lang":"en","type":"book-chapter","venue":"Advances in computational intelligence and robotics book series","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Maple Leaf Foods","funders":"","keywords":"Environmental planning; Transportation planning; Transport engineering; Business; Geography; Engineering","score_opus":0.022813872156876545,"score_gpt":0.30751785845578733,"score_spread":0.2847039862989108,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407171640","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000038917565,0.035620727,0.41949716,0.0013732142,0.00080039277,0.00046681688,0.00009796602,0.00016502418,0.5419748],"genre_scores_gemma":[0.018087165,0.093508646,0.073457025,0.0010233026,0.00042883088,0.00003476332,0.0020030176,0.000072367344,0.8113849],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99868876,0.000022808132,0.00045854636,0.00032419374,0.00032234218,0.00018334754],"domain_scores_gemma":[0.99917316,0.00032105576,0.00018709873,0.000071877446,0.00018510914,0.000061692466],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015271943,0.00021326699,0.00025456457,0.00023850394,0.00034243552,0.0000617167,0.0001294303,0.00022183926,0.000072605915],"category_scores_gemma":[0.000027512055,0.0002449512,0.00005113274,0.00008979456,0.00038139024,0.00093701284,0.0000055031282,0.0002555147,0.0000061036562],"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":[0.000016080401,0.000004582061,0.00037322842,0.000037822498,0.0000078510975,0.000008849808,0.0023203525,0.5062864,1.4310787e-8,0.48864144,0.00018000415,0.0021233768],"study_design_scores_gemma":[0.000113249516,0.000073472336,0.00044113427,0.0013232528,0.00006210496,0.0000015436356,0.0014688438,0.0049498007,0.0000033360825,0.30714172,0.68385154,0.00056998286],"about_ca_topic_score_codex":0.000027418073,"about_ca_topic_score_gemma":0.0005892461,"teacher_disagreement_score":0.68367153,"about_ca_system_score_codex":0.000060328843,"about_ca_system_score_gemma":0.00023624462,"threshold_uncertainty_score":0.99888194},"labels":[],"label_agreement":null},{"id":"W4407179658","doi":"10.1007/s11116-025-10585-x","title":"The effect of crowding level information provision on the revealed route choice of transit riders","year":2025,"lang":"en","type":"article","venue":"Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Transport engineering; Transit (satellite); Crowding; Public transport; Travel behavior; Business; Regional science; Geography; Engineering; Psychology","score_opus":0.01713307099507351,"score_gpt":0.2992565217901676,"score_spread":0.2821234507950941,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407179658","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9641608,0.000038271897,0.026501006,0.0025871326,0.00025496748,0.00090950297,0.00006394903,0.000057505957,0.00542688],"genre_scores_gemma":[0.99942607,0.000043048814,0.00010819279,0.00005864382,0.000011230187,0.000024408368,0.00010913509,0.0000036597587,0.00021559691],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99888414,0.00015557147,0.00041408895,0.00008226541,0.00034498557,0.00011894085],"domain_scores_gemma":[0.9985689,0.0009142666,0.00023542334,0.000117171556,0.00014502769,0.000019233583],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009862602,0.00008230337,0.00012395799,0.00008507309,0.0004549225,0.000027757744,0.00015268513,0.00007184281,0.000010469559],"category_scores_gemma":[0.00018805724,0.00005208939,0.0000771089,0.0003641692,0.00012791193,0.00032829918,4.4525422e-7,0.00009542361,0.0000016622403],"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":[0.0019445696,0.000080742924,0.3024121,0.000801758,0.00022168514,8.833143e-7,0.09995561,0.18749817,0.0018902111,0.32753828,0.0015290587,0.07612694],"study_design_scores_gemma":[0.0013643266,0.00022662459,0.972488,0.00043406183,0.00017590004,2.6853849e-8,0.0039305356,0.0012279514,0.012105629,0.0004483043,0.007450096,0.00014855312],"about_ca_topic_score_codex":0.001179765,"about_ca_topic_score_gemma":0.0021205666,"teacher_disagreement_score":0.6700759,"about_ca_system_score_codex":0.000026253967,"about_ca_system_score_gemma":0.00008848601,"threshold_uncertainty_score":0.3498942},"labels":[],"label_agreement":null},{"id":"W4407264778","doi":"10.1016/j.jtrangeo.2025.104142","title":"Towards a better understanding of changes in cost per riders for bus routes before and after the COVID-19 pandemic in Montréal, Canada","year":2025,"lang":"en","type":"article","venue":"Journal of Transport Geography","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"McGill University","funders":"Social Sciences and Humanities Research Council; Social Sciences and Humanities Research Council of Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Coronavirus disease 2019 (COVID-19); Pandemic; 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Aeronautics; Transport engineering; Business; Engineering; Geography; Virology; Medicine; Outbreak; Infectious disease (medical specialty)","score_opus":0.02235194861368617,"score_gpt":0.2772893401622394,"score_spread":0.2549373915485532,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407264778","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9703502,0.0010971889,0.0082360115,0.019708974,0.00016449047,0.00030393916,0.00006455191,0.0000051568345,0.000069483016],"genre_scores_gemma":[0.9978762,0.00061141886,0.00030782787,0.0011489941,0.000020649504,0.000013382884,0.0000066496477,0.0000037440352,0.000011109732],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99919367,0.000039974842,0.00031497038,0.00008213726,0.00021592597,0.00015333462],"domain_scores_gemma":[0.9995666,0.00012614019,0.00014249665,0.000040389776,0.00006150071,0.00006283093],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00068509846,0.00007142865,0.00018088937,0.00033244205,0.00010613875,0.000013298141,0.00009381869,0.00006066776,0.0000102449285],"category_scores_gemma":[0.00002749591,0.000054021853,0.00006898404,0.00038347818,0.00012715523,0.000098772005,0.000001185876,0.00012261457,5.006773e-9],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018249406,0.000013404922,0.9870402,0.000045483437,0.000031334184,0.000011252022,0.009815373,0.0021226145,0.0000019993604,0.00018323021,0.00012628868,0.00042633063],"study_design_scores_gemma":[0.0009255162,0.000035839996,0.9768561,0.000116479736,0.00006317602,0.0000015932364,0.012576453,0.000034814362,0.0000024812991,0.0014066477,0.007914371,0.0000665051],"about_ca_topic_score_codex":0.26113543,"about_ca_topic_score_gemma":0.98239547,"teacher_disagreement_score":0.7212601,"about_ca_system_score_codex":0.00012504957,"about_ca_system_score_gemma":0.00049281935,"threshold_uncertainty_score":0.7437847},"labels":[],"label_agreement":null},{"id":"W4407270370","doi":"10.2139/ssrn.5129409","title":"Long-Term Simulation of Voluntary Congestion Management Schemes: A Modelling Framework and Application for Large Road Networks","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Transport Canada","funders":"","keywords":"Term (time); Congestion management; Turnover; Computer science; Scheme (mathematics); Transport engineering; Business; Operations management; Operations research; Engineering; Economics; Management","score_opus":0.012968288276556576,"score_gpt":0.31144427011030706,"score_spread":0.2984759818337505,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407270370","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026582738,0.0029130727,0.9691717,0.00017252426,0.0002293209,0.00076749735,0.000018419929,0.000042768013,0.000101958125],"genre_scores_gemma":[0.9796831,0.013719074,0.0056372113,0.000028014163,0.00025585666,0.000055662607,0.00028019713,0.00001258255,0.0003283144],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982908,0.00008506855,0.00039470592,0.0002692099,0.00024410669,0.0007161329],"domain_scores_gemma":[0.99902767,0.00011521576,0.00044024305,0.00012548923,0.00024234672,0.00004903087],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001680302,0.00014574455,0.00020811884,0.00016588795,0.00042364062,0.000060732174,0.00017086792,0.00034696126,0.0000034138195],"category_scores_gemma":[0.0000286432,0.00016567357,0.00009165762,0.00015794572,0.000037596794,0.00012425381,0.000024840952,0.0010445787,2.1022674e-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":[0.000072362454,0.00003325459,0.006416657,0.00010132107,0.00013021204,1.6173216e-7,0.00084654946,0.84133095,1.387026e-7,0.13155459,0.000002064016,0.019511726],"study_design_scores_gemma":[0.00041794364,0.0000358188,0.0027467576,0.0005039883,0.0002563368,5.161761e-7,0.00092817436,0.88799304,7.3705786e-7,0.10671386,0.0002274222,0.00017542072],"about_ca_topic_score_codex":0.00012108291,"about_ca_topic_score_gemma":0.0007339774,"teacher_disagreement_score":0.9635345,"about_ca_system_score_codex":0.00038098445,"about_ca_system_score_gemma":0.0006870636,"threshold_uncertainty_score":0.6755972},"labels":[],"label_agreement":null},{"id":"W4407780861","doi":"10.1016/j.rtbm.2025.101311","title":"Assessing complementary and competing interactions between transit and shared transportation modes","year":2025,"lang":"en","type":"article","venue":"Research in Transportation Business & Management","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ministère des Transports; Centrale des Syndicats du Québec; Société de Transport de Montréal; Transport Canada; Polytechnique Montréal","funders":"Fonds de recherche du Québec – Nature et technologies; Fonds de recherche du Québec; Institut de Valorisation des Données; Canada Research Chairs","keywords":"Transit (satellite); Transport engineering; Computer science; Business; Public transport; Engineering","score_opus":0.12233474157320835,"score_gpt":0.4433119041666152,"score_spread":0.32097716259340686,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407780861","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9421975,0.00023856702,0.044308804,0.0063203694,0.00011844673,0.0010510354,0.00008054358,0.00012684941,0.005557861],"genre_scores_gemma":[0.99296665,0.00090175285,0.0048785103,0.000042580792,0.000032608394,0.00006075789,0.0009925119,0.000012706857,0.00011192011],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.997978,0.0002008605,0.00051672943,0.00039982726,0.00051650207,0.00038810656],"domain_scores_gemma":[0.9991121,0.00035332076,0.00007588495,0.000099173834,0.00026845105,0.000091014925],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018268598,0.0001362026,0.00019929565,0.0008283714,0.00077713514,0.00030447738,0.00012519916,0.00006334062,0.00003971447],"category_scores_gemma":[0.000014063477,0.0001615935,0.000024984407,0.0016024234,0.00022559521,0.0013290733,0.0000032135285,0.00023060742,9.1298665e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014509777,0.00021735956,0.72643,0.0014460055,0.00015187851,0.00006453392,0.031657837,0.012779743,0.00026506057,0.09654702,0.000103762184,0.13019171],"study_design_scores_gemma":[0.0007734049,0.0000049598684,0.9612069,0.0006057847,0.000054589982,5.6996498e-8,0.03266894,0.000332092,0.000018799385,0.0012096295,0.002989556,0.00013528188],"about_ca_topic_score_codex":0.0039128633,"about_ca_topic_score_gemma":0.023313805,"teacher_disagreement_score":0.23477691,"about_ca_system_score_codex":0.00010553911,"about_ca_system_score_gemma":0.00007709645,"threshold_uncertainty_score":0.99450815},"labels":[],"label_agreement":null},{"id":"W4408157881","doi":"10.1177/00375497251318743","title":"Development of a cumulative prospect theory-based departure time choice model for dynamic traffic microsimulation","year":2025,"lang":"en","type":"article","venue":"SIMULATION","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Microsimulation; Cumulative prospect theory; Computer science; Econometrics; Operations research; Economics; Transport engineering; Engineering; Prospect theory; Microeconomics","score_opus":0.01822289331738506,"score_gpt":0.343335252887904,"score_spread":0.3251123595705189,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408157881","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.31376234,0.000016593687,0.6850361,0.00008538008,0.00004806317,0.000643303,0.000019887006,0.00008752872,0.0003007972],"genre_scores_gemma":[0.9563478,4.570212e-7,0.04135937,0.00004786558,0.000012295841,0.000032151223,0.00036545488,0.0000092681,0.001825305],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991101,0.00006311438,0.00031312017,0.0001864323,0.00018421604,0.0001430155],"domain_scores_gemma":[0.99894255,0.00050865737,0.00016333743,0.000080643615,0.00027760488,0.00002718128],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004868782,0.000094636125,0.00012948582,0.00014211406,0.00037961925,0.000025587007,0.00007378768,0.000117897354,0.000015421498],"category_scores_gemma":[0.0002097134,0.00010049395,0.000055132885,0.00032652813,0.000050254042,0.00015472568,0.0000024281994,0.00004478939,0.0000023260918],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011938236,0.00004470909,0.0005531443,0.000031496573,0.000014468142,2.2599256e-8,0.010723494,0.9813777,0.00030303464,0.0031696027,0.000008241733,0.0036546874],"study_design_scores_gemma":[0.0006793192,0.000009213431,0.0053888224,0.000056535122,0.000034252953,2.62464e-9,0.00009856266,0.9914438,0.00017366296,0.0012490716,0.00076781353,0.00009893134],"about_ca_topic_score_codex":0.000006529095,"about_ca_topic_score_gemma":0.00035370328,"teacher_disagreement_score":0.64367676,"about_ca_system_score_codex":0.00012487014,"about_ca_system_score_gemma":0.00040374597,"threshold_uncertainty_score":0.40980244},"labels":[],"label_agreement":null},{"id":"W4408712310","doi":"10.1109/itsc58415.2024.10920102","title":"Spatiotemporal Decomposition and Analysis of Vehicular Arrival on Green in A Signalized Arterial using Trajectories","year":2024,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Decomposition; Computer science; Arrival time; Environmental science; Real-time computing; Transport engineering; Engineering; Chemistry","score_opus":0.02398605302075547,"score_gpt":0.33030618496157355,"score_spread":0.3063201319408181,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408712310","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9866104,0.000028917384,0.01269559,0.00014332517,0.000073998235,0.000060717633,0.00001257691,0.00003311818,0.00034133458],"genre_scores_gemma":[0.9985759,0.000006940116,0.0012754701,0.000014709467,0.000030900126,0.0000013068534,0.00006122472,0.0000026273733,0.000030894564],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994805,0.000070985036,0.00014112532,0.00010358512,0.0001423969,0.00006144399],"domain_scores_gemma":[0.99984866,0.000053818214,0.000025998523,0.000026775497,0.000022305392,0.000022411314],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001959546,0.00004055578,0.00010798203,0.00026471016,0.0000551738,0.000040196563,0.000021179929,0.000042804895,0.00008698351],"category_scores_gemma":[0.0000064445,0.000038407103,0.000037103277,0.000628163,0.00004424262,0.00011673847,0.000001036725,0.000028724999,3.2345704e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009901987,0.000244886,0.37453452,0.00016839961,0.0012418594,0.00008115123,0.16067909,0.36731464,0.023038154,0.064037904,0.000062020925,0.0076071694],"study_design_scores_gemma":[0.00066620973,0.00009591119,0.4376814,0.00014172259,0.0007912452,3.0171e-7,0.0020515514,0.5556365,0.001278523,0.0010625165,0.00034120667,0.00025284302],"about_ca_topic_score_codex":0.009507546,"about_ca_topic_score_gemma":0.0051817927,"teacher_disagreement_score":0.1883219,"about_ca_system_score_codex":0.000022704178,"about_ca_system_score_gemma":0.000043949138,"threshold_uncertainty_score":0.99708825},"labels":[],"label_agreement":null},{"id":"W4408741853","doi":"10.1016/j.trd.2025.104711","title":"Urban transit decarbonization via stochastic programming and statistical inference under uncertainty","year":2025,"lang":"en","type":"article","venue":"Transportation Research Part D Transport and Environment","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Inference; Statistical inference; Transit (satellite); Environmental science; Computer science; Engineering; Transport engineering; Mathematics; Statistics; Public transport; Artificial intelligence","score_opus":0.03620375804472445,"score_gpt":0.34205844123693263,"score_spread":0.3058546831922082,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408741853","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24223924,0.00039022387,0.753757,0.0018182509,0.00009449645,0.0009723533,0.00009033446,0.00012356293,0.00051455654],"genre_scores_gemma":[0.9960672,0.0009335454,0.0015612867,0.00006890221,0.000034098637,0.0001249806,0.0006743165,0.000015624224,0.0005200058],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9977473,0.00016122947,0.00042172373,0.00048689466,0.0006952313,0.0004876455],"domain_scores_gemma":[0.9991864,0.00028697387,0.000050237,0.0001317111,0.00008690777,0.0002577674],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008903062,0.00018119646,0.00021570217,0.00021440488,0.0008452415,0.00006443371,0.00010052698,0.0001636778,0.00018079064],"category_scores_gemma":[0.000015160905,0.00019226148,0.000034195702,0.00038594383,0.00081990566,0.00023016917,0.0000019722008,0.00029196273,0.000004648393],"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":[0.0005508991,0.00058158825,0.42802152,0.00040244276,0.00021616441,0.000049245507,0.05049317,0.25413033,0.0002020908,0.21782969,0.00030423352,0.04721863],"study_design_scores_gemma":[0.0015260461,0.00015214569,0.95243526,0.00018780076,0.00019338897,3.2019412e-7,0.0054822974,0.0049348255,0.000021893045,0.0044848104,0.030118296,0.00046290396],"about_ca_topic_score_codex":0.001723149,"about_ca_topic_score_gemma":0.004281722,"teacher_disagreement_score":0.753828,"about_ca_system_score_codex":0.0000745256,"about_ca_system_score_gemma":0.00015702513,"threshold_uncertainty_score":0.78401953},"labels":[],"label_agreement":null},{"id":"W4408832963","doi":"10.5539/jsd.v18n2p100","title":"Characterizing and Estimating Operational Cost Elements and Performance Metrics for Public Transport Modes in Sub-Saharan African Cities","year":2025,"lang":"en","type":"article","venue":"Journal of Sustainable Development","topic":"Transportation Planning and Optimization","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":"Public transport; Business; Environmental economics; Natural resource economics; Economic geography; Econometrics; Geography; Regional science; Environmental planning; Economics; Transport engineering; Engineering","score_opus":0.023978983029517578,"score_gpt":0.2755050994454458,"score_spread":0.25152611641592826,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408832963","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97151583,0.00022240853,0.026602315,0.0009825319,0.00006283163,0.00028019102,0.0000023869552,0.000006661412,0.00032486644],"genre_scores_gemma":[0.9638945,0.0003374437,0.035253245,0.00007744494,0.0000206898,0.00002527772,0.000015715814,0.0000036396773,0.00037205598],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.999079,0.000023636443,0.0004087066,0.00008230377,0.00020019768,0.00020618062],"domain_scores_gemma":[0.9993133,0.0000769421,0.00015373583,0.000018414989,0.0003828623,0.00005475374],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001331524,0.00006537444,0.00013399014,0.00047344956,0.00045654943,0.00011212795,0.0000614914,0.00003577691,0.0000033226947],"category_scores_gemma":[0.00014605868,0.000064772685,0.000012551943,0.00039260063,0.0000432754,0.00063882605,0.0000054226743,0.00007403736,3.5604415e-8],"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":[0.00017233909,0.00009295896,0.89014125,0.0005320087,0.000076684984,0.000024427762,0.043639403,0.0030609097,0.000101276746,0.009442248,0.00012107408,0.052595444],"study_design_scores_gemma":[0.0013200298,0.000056798166,0.9161911,0.00024178563,0.000022884042,0.0000024615842,0.04823738,0.008489505,0.00033757716,0.00022940549,0.02468716,0.00018390095],"about_ca_topic_score_codex":0.000029320647,"about_ca_topic_score_gemma":0.00006057021,"teacher_disagreement_score":0.05241154,"about_ca_system_score_codex":0.00018936006,"about_ca_system_score_gemma":0.00079651974,"threshold_uncertainty_score":0.35114554},"labels":[],"label_agreement":null},{"id":"W4408914682","doi":"10.1080/03155986.2025.2478699","title":"Perspectives on optimizing transport systems with supply-dependent demand","year":2025,"lang":"en","type":"article","venue":"INFOR Information Systems and Operational Research","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Supply and demand; Computer science; Business; Economics; Microeconomics","score_opus":0.03235381392536392,"score_gpt":0.3486807839498791,"score_spread":0.3163269700245152,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408914682","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20546058,0.0017447073,0.12128342,0.0047941986,0.0011377877,0.005226854,0.00031514498,0.0003760103,0.6596613],"genre_scores_gemma":[0.99427575,0.00028956105,0.00034512812,0.000076799195,0.00007754575,0.00018084314,0.00015371107,0.000004749366,0.0045959274],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","domain_scores_codex":[0.9978393,0.00015571961,0.00041745708,0.00015070396,0.0011651155,0.00027173958],"domain_scores_gemma":[0.998235,0.00024983395,0.00007344657,0.00011415875,0.001219131,0.00010845172],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0022587858,0.0001071601,0.00014886157,0.00052997825,0.0013720398,0.0007784727,0.00013259539,0.00011781903,0.000026151427],"category_scores_gemma":[0.00011959164,0.000087370034,0.00002126143,0.0005053204,0.00018586588,0.001665364,0.0000072636108,0.00022510039,0.00002768923],"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":[0.00015230716,0.00002268999,0.007028688,0.00010305202,0.00004007001,0.0000017581251,0.03988155,0.21672069,0.000003225442,0.7347819,0.00096019503,0.0003038452],"study_design_scores_gemma":[0.0030706606,0.00040194142,0.027687056,0.0012747742,0.000032312597,0.000011498503,0.5979728,0.04511977,0.000049173,0.00009737022,0.32369673,0.0005858925],"about_ca_topic_score_codex":0.0022872002,"about_ca_topic_score_gemma":0.00017488483,"teacher_disagreement_score":0.78881514,"about_ca_system_score_codex":0.00016213178,"about_ca_system_score_gemma":0.00064295885,"threshold_uncertainty_score":0.99992806},"labels":[],"label_agreement":null},{"id":"W4409019266","doi":"10.5539/jmr.v17n1p51","title":"Multi-Criteria Decision Making and Graph Theory-Based Optimization of the Lahore Metro System: A Metric Dimension, VIKOR, and PROMETHEE Approach","year":2025,"lang":"en","type":"article","venue":"Journal of Mathematics Research","topic":"Transportation Planning and Optimization","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":"Mathematics; Dimension (graph theory); Mathematical optimization; Metric (unit); Graph; Combinatorics; Operations management","score_opus":0.06536168522529141,"score_gpt":0.4100925016857395,"score_spread":0.3447308164604481,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409019266","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16766323,0.00064726314,0.8304376,0.0001807522,0.000092335074,0.00038122313,0.000004098779,0.000009823625,0.0005836818],"genre_scores_gemma":[0.69213426,0.00006618946,0.3077192,0.000006138309,0.000010158602,0.0000034372654,5.451891e-7,0.0000054405077,0.000054642314],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99779344,0.0006657027,0.0004611587,0.00010479408,0.0008297259,0.00014519734],"domain_scores_gemma":[0.9970842,0.0015806956,0.00030653004,0.00012782027,0.0008502399,0.00005052232],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008578776,0.000069034475,0.00021972397,0.00085689424,0.00041710975,0.0001078336,0.00021493225,0.00008531122,0.000006623137],"category_scores_gemma":[0.00230775,0.000044168974,0.00005482947,0.0016152575,0.0002351351,0.00011865758,0.000030336996,0.00021425249,1.0944275e-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":[0.0011442523,0.0028246902,0.0571626,0.0084964195,0.0006999748,0.000021928248,0.09684933,0.5441455,0.0027220636,0.26559192,0.0009608205,0.019380474],"study_design_scores_gemma":[0.0025929909,0.00021057985,0.013474115,0.005118013,0.0002911145,0.000014061345,0.07544781,0.88571703,0.000522406,0.01627976,0.00010157808,0.00023051533],"about_ca_topic_score_codex":0.000015730417,"about_ca_topic_score_gemma":0.000011010409,"teacher_disagreement_score":0.52447104,"about_ca_system_score_codex":0.000056690616,"about_ca_system_score_gemma":0.00018736754,"threshold_uncertainty_score":0.3208113},"labels":[],"label_agreement":null},{"id":"W4409248016","doi":"10.1080/00140139.2025.2481606","title":"Passenger information function preferences based on travel frequency and expertise","year":2025,"lang":"en","type":"article","venue":"Ergonomics","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Research Councils UK; Workplace Safety and Insurance Board","keywords":"Function (biology); Transport engineering; Travel behavior; Aeronautics; Engineering; Computer science; Psychology","score_opus":0.012023856458200703,"score_gpt":0.24732068092650472,"score_spread":0.23529682446830402,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409248016","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.62663287,0.00008484607,0.21343131,0.0022269792,0.0013362493,0.0004153856,0.00002675061,0.00021212002,0.15563346],"genre_scores_gemma":[0.99799615,0.000050294086,0.0010646601,0.0005342684,0.000027226743,0.0000134352,0.000052749456,0.0000017505541,0.00025947962],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9996247,0.000031758133,0.0001235558,0.00007437114,0.00006742413,0.00007814557],"domain_scores_gemma":[0.9997895,0.00004723438,0.000044154345,0.000051692776,0.00003794547,0.000029513718],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014012199,0.00004837009,0.000050974802,0.00009363082,0.00021571749,0.00006959573,0.000039376493,0.00006514674,0.00003662277],"category_scores_gemma":[0.000046462294,0.000049166054,0.000013886782,0.00010577699,0.000038828177,0.00033452886,0.0000013088655,0.00004686122,0.000008261078],"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":[0.000768299,0.00017395896,0.13261057,0.000093976494,0.000058139718,8.020144e-7,0.05124254,0.06942477,0.000111590605,0.4219121,0.0076444545,0.3159588],"study_design_scores_gemma":[0.00088693696,0.000103197184,0.9268252,0.00008433848,0.0000368226,4.526886e-8,0.0058165677,0.04064793,0.0001472909,0.002900258,0.022287484,0.0002638919],"about_ca_topic_score_codex":0.00035337894,"about_ca_topic_score_gemma":0.0003816054,"teacher_disagreement_score":0.79421467,"about_ca_system_score_codex":0.000043608514,"about_ca_system_score_gemma":0.00011888182,"threshold_uncertainty_score":0.20049335},"labels":[],"label_agreement":null},{"id":"W4409263765","doi":"10.2139/ssrn.5203734","title":"&lt;div&gt; &lt;div&gt; Generating and Leveraging Crowdsourced Travel Time&amp;nbsp;&lt;span&gt;Information&lt;/span&gt; &lt;/div&gt;&lt;/div&gt;","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; York University","funders":"","keywords":"Span (engineering); Engineering","score_opus":0.013673683201638018,"score_gpt":0.25678687678357803,"score_spread":0.24311319358194,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409263765","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4848728,0.027888287,0.40411994,0.009617464,0.008202636,0.0059693707,0.0013810489,0.0025689632,0.05537952],"genre_scores_gemma":[0.91215324,0.02355583,0.01515899,0.0017119445,0.0050212466,0.00030704896,0.0034928364,0.00042998418,0.038168855],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.98280686,0.0014110134,0.00378309,0.0020840568,0.0035715334,0.0063434555],"domain_scores_gemma":[0.99172586,0.000693115,0.0032717919,0.0012678445,0.0019101922,0.0011311697],"candidate_categories":["metaepi_narrow","sts","scholarly_communication","research_integrity","insufficient_payload"],"consensus_categories":["metaepi_narrow","research_integrity"],"category_scores_codex":[0.008621778,0.0020795057,0.0022601217,0.0018601227,0.005949721,0.002892657,0.0024656553,0.0020475704,0.0010042057],"category_scores_gemma":[0.0011282708,0.0022594598,0.0011214957,0.0019880459,0.0008023584,0.0026078683,0.0004801215,0.006532412,0.00029386804],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":true,"study_design_scores_codex":[0.0015615292,0.0013593322,0.008661119,0.0018430623,0.0070527545,0.00019864186,0.15213588,0.540243,0.01695397,0.13114935,0.03995967,0.09888167],"study_design_scores_gemma":[0.025040274,0.002080514,0.032754075,0.007915028,0.0070907674,0.0015352014,0.024401959,0.17594138,0.0017473394,0.06099811,0.640926,0.019569354],"about_ca_topic_score_codex":0.000740365,"about_ca_topic_score_gemma":0.007084336,"teacher_disagreement_score":0.60096633,"about_ca_system_score_codex":0.0038720274,"about_ca_system_score_gemma":0.012435485,"threshold_uncertainty_score":0.99995196},"labels":[],"label_agreement":null},{"id":"W4409464209","doi":"10.1155/atr/5576254","title":"Analysis of Cascading Failure in Urban Metro Networks: A Dynamic Perspective Incorporating Changes in Travel Decisions","year":2025,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Key Research and Development Program of China; Nanjing Tech University; National Natural Science Foundation of China","keywords":"Perspective (graphical); Transport engineering; Computer science; Risk analysis (engineering); Engineering; Business","score_opus":0.009891549804507875,"score_gpt":0.3055227092856369,"score_spread":0.29563115948112906,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409464209","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.91489196,0.00034514358,0.083629034,0.00069925503,0.00014048605,0.00014456487,0.000013961835,0.000007898259,0.00012770276],"genre_scores_gemma":[0.9909522,0.00024788332,0.008696547,0.000022199283,0.000015711876,0.0000042904553,0.000037258273,0.000004626417,0.000019291341],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9986479,0.00012299031,0.0006696294,0.00013732594,0.00027876138,0.00014337144],"domain_scores_gemma":[0.99863195,0.00031877094,0.0005783254,0.000057034693,0.00037175877,0.000042126918],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007506117,0.000088969115,0.0003718521,0.0017367618,0.00009591134,0.000014434464,0.0001121586,0.000091908434,0.0000083045425],"category_scores_gemma":[0.00022949978,0.00009222341,0.00011903932,0.004038408,0.000053589367,0.00038220204,6.40541e-7,0.00024332173,3.9695244e-8],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000925873,0.000048301536,0.20634857,0.0000054780685,0.00009045567,0.000012349629,0.031676497,0.7571728,0.00016965457,0.0030751452,0.0000021375374,0.0013059892],"study_design_scores_gemma":[0.0007257482,0.000043580392,0.88731235,0.00029995028,0.0002973657,1.01843675e-7,0.101107545,0.009276056,0.00003409632,0.0008000788,0.000019936311,0.000083192084],"about_ca_topic_score_codex":0.0005868151,"about_ca_topic_score_gemma":0.2739843,"teacher_disagreement_score":0.7478968,"about_ca_system_score_codex":0.0002423283,"about_ca_system_score_gemma":0.00016509481,"threshold_uncertainty_score":0.7392636},"labels":[],"label_agreement":null},{"id":"W4409478016","doi":"10.1016/j.tre.2025.104131","title":"Retrofit or new construction? Strategic budget allocation to improve transportation network redundancy under uncertain disruptions","year":2025,"lang":"en","type":"article","venue":"Transportation Research Part E Logistics and Transportation Review","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Research Grants Council, University Grants Committee; University Grants Committee; National Natural Science Foundation of China","keywords":"Redundancy (engineering); Transport engineering; Strategic planning; Flow network; Budget constraint; Computer science; Business; Operations research; Engineering; Economics; Reliability engineering; Microeconomics; Mathematical optimization","score_opus":0.12024543801667649,"score_gpt":0.42961644401564797,"score_spread":0.3093710059989715,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409478016","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.036588803,0.021302428,0.84090567,0.069244236,0.0035915803,0.013890538,0.0031656811,0.001441195,0.009869847],"genre_scores_gemma":[0.8157813,0.12436832,0.029195432,0.0037847422,0.000696764,0.0011344695,0.011913843,0.00011799898,0.013007117],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9950273,0.0004234598,0.0015427037,0.00094656536,0.00118528,0.0008746759],"domain_scores_gemma":[0.9968307,0.00052548194,0.00033376363,0.0004061353,0.0012877703,0.0006161347],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0019958827,0.0004078221,0.00063169445,0.00042185473,0.0014821345,0.00021425063,0.00032676957,0.00032973348,0.0006916298],"category_scores_gemma":[0.00017529515,0.00040323858,0.00016800853,0.0032605375,0.0005543362,0.00047862716,0.0000016416965,0.0005824466,0.000033107757],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003720996,0.00016420078,0.010191319,0.0019080959,0.00016307447,0.000032921016,0.0033401605,0.037471533,0.000049929065,0.9254922,0.012710193,0.008104238],"study_design_scores_gemma":[0.004633886,0.00087544404,0.348899,0.01139341,0.0019397404,0.0000018902867,0.01392051,0.000655357,0.00010254474,0.09551744,0.51974046,0.0023202864],"about_ca_topic_score_codex":0.0038910822,"about_ca_topic_score_gemma":0.054758187,"teacher_disagreement_score":0.8299748,"about_ca_system_score_codex":0.00016038628,"about_ca_system_score_gemma":0.0017198643,"threshold_uncertainty_score":0.9998419},"labels":[],"label_agreement":null},{"id":"W4409512809","doi":"10.1155/atr/7897020","title":"Optimization of Skip‐Stop Train Schedule in Urban Rail Transit Under Virtual Coupling","year":2025,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; Beijing Municipal Natural Science Foundation","keywords":"Schedule; Transport engineering; Rail transit; Transit (satellite); Computer science; Urban transit; Urban rail transit; Simulation; Public transport; Engineering; Operating system","score_opus":0.010407528240805364,"score_gpt":0.2856262046497053,"score_spread":0.2752186764088999,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409512809","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4919488,0.00020888589,0.50644225,0.00056142005,0.0003563393,0.00015619073,0.000010469989,0.0000195334,0.00029609236],"genre_scores_gemma":[0.97972995,0.00028975843,0.01969224,0.000060208444,0.000044790184,0.0000033011925,0.000056323483,0.000010450872,0.00011298552],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9984064,0.000055463115,0.00085202,0.00013466993,0.0003846429,0.00016680242],"domain_scores_gemma":[0.9989081,0.00012849268,0.00045830323,0.00006538623,0.00037934023,0.000060353657],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000632395,0.00010876873,0.00027500218,0.00042462596,0.00011613272,0.000017618695,0.00013065903,0.00012710685,0.00003770636],"category_scores_gemma":[0.000061895684,0.00011811429,0.00010833718,0.0008244605,0.00009406758,0.0006895808,3.9816646e-7,0.00019612613,2.8626704e-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":[0.00023839888,0.00010037236,0.0055525494,0.000027478929,0.00002308083,0.0000048028364,0.01621691,0.9587164,0.0009541684,0.017355023,0.0000120769855,0.0007987567],"study_design_scores_gemma":[0.01906564,0.00088835234,0.7690644,0.0028903098,0.00063099706,0.0000027594094,0.14449723,0.044150442,0.008121282,0.0065406524,0.0030657903,0.0010821193],"about_ca_topic_score_codex":0.000057527683,"about_ca_topic_score_gemma":0.00096195145,"teacher_disagreement_score":0.9145659,"about_ca_system_score_codex":0.000087955224,"about_ca_system_score_gemma":0.00035228737,"threshold_uncertainty_score":0.48165607},"labels":[],"label_agreement":null},{"id":"W4409535974","doi":"10.3390/land14040886","title":"Development of a Differential Spatial Economic Modeling Method for Improved Land Use and Multimodal Transportation Planning","year":2025,"lang":"en","type":"article","venue":"Land","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary; University of Waterloo","funders":"National Natural Science Foundation of China","keywords":"Differential (mechanical device); Land use; Environmental planning; Environmental resource management; Environmental science; Computer science; Civil engineering; Engineering","score_opus":0.02778426502211276,"score_gpt":0.317196169041792,"score_spread":0.2894119040196792,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409535974","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.49920693,0.00000815381,0.5005182,0.00002962339,0.000067965004,0.00011119924,0.000027655815,0.000013341396,0.000016927155],"genre_scores_gemma":[0.87132293,0.0000057722154,0.12841207,0.000010476421,0.00002585273,0.000013926159,0.00015668797,0.0000038159287,0.000048487145],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995284,0.000019675994,0.00019454687,0.00012196768,0.000041364343,0.000094061244],"domain_scores_gemma":[0.99976194,0.000089574765,0.00005623511,0.000030013516,0.00003553909,0.00002671361],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015179922,0.000055207893,0.00011064653,0.000056225766,0.0001787876,0.000034396562,0.000032750846,0.000059413607,0.000005393203],"category_scores_gemma":[0.00001658015,0.00005477455,0.000020651982,0.000029159368,0.000015021213,0.00010362991,0.0000012949685,0.000029375997,8.178285e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006372026,0.0000545643,0.62724924,0.00013794996,0.00012768724,4.831645e-7,0.058414474,0.28250355,0.0019285679,0.0013473466,0.000014869371,0.027584089],"study_design_scores_gemma":[0.0013064115,0.000011495154,0.13200843,0.000052271684,0.000047706537,2.5431389e-8,0.00047835388,0.86513114,0.00024698593,0.0001526539,0.00046274078,0.00010180911],"about_ca_topic_score_codex":0.0047475686,"about_ca_topic_score_gemma":0.025325054,"teacher_disagreement_score":0.5826276,"about_ca_system_score_codex":0.000016636975,"about_ca_system_score_gemma":0.00010535932,"threshold_uncertainty_score":0.99246025},"labels":[],"label_agreement":null},{"id":"W4409838993","doi":"10.1016/j.trd.2025.104751","title":"Equity implications of emerging mobility services and public transit coopetition: A review","year":2025,"lang":"en","type":"review","venue":"Transportation Research Part D Transport and Environment","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; Indian Institute of Science","keywords":"Coopetition; Equity (law); Transit (satellite); Business; Public transport; Industrial organization; Transport engineering; Economics; Engineering; Market economy; Political science","score_opus":0.13429046724493507,"score_gpt":0.43030127738630214,"score_spread":0.2960108101413671,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409838993","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00012029804,0.9925412,0.0008882664,0.001486905,0.00005367193,0.002196258,0.0009633826,0.00006526193,0.0016847819],"genre_scores_gemma":[0.0018227766,0.993499,0.00024138989,0.000055739587,0.000039484912,0.0006079234,0.003355013,0.000024740986,0.0003539345],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9962259,0.00042280217,0.0011889383,0.00072578114,0.0008904573,0.00054612115],"domain_scores_gemma":[0.9985154,0.00031643297,0.00028588038,0.00036838884,0.00017014117,0.00034372282],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002294866,0.00033765656,0.0011156684,0.00033165468,0.0007509615,0.00004000223,0.00031435658,0.00031275838,0.00056281703],"category_scores_gemma":[0.000015505118,0.00033457516,0.00024032702,0.00083000376,0.0007926026,0.00031012812,0.000007688143,0.00045910102,0.000005322304],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027347702,0.000520734,0.0064834277,0.24206555,0.00030347673,0.000012884546,0.0055852197,0.00007380477,7.642917e-7,0.020808058,0.00026100795,0.7238577],"study_design_scores_gemma":[0.00021110733,0.000038810645,0.0069801738,0.020368902,0.0007520367,4.520303e-7,0.00053474325,0.0000016075082,2.7537627e-7,0.00027190862,0.9705495,0.00029046278],"about_ca_topic_score_codex":0.00075892947,"about_ca_topic_score_gemma":0.0035183805,"teacher_disagreement_score":0.9702885,"about_ca_system_score_codex":0.00008718904,"about_ca_system_score_gemma":0.0005026505,"threshold_uncertainty_score":0.99991065},"labels":[],"label_agreement":null},{"id":"W4409868956","doi":"10.1155/atr/6620828","title":"Data‐Driven Approach for Passenger Assignment in Urban Rail Transit Networks: Insights From Passenger Route Choices and Itinerary Choices","year":2025,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","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":"Sichuan Province Science and Technology Support Program; National Key Research and Development Program of China; Jiangsu Development and Reform Commission; National Natural Science Foundation of China","keywords":"Transport engineering; Transit (satellite); Rail transit; Urban rail transit; Computer science; Passenger transport; Public transport; Operations research; Engineering","score_opus":0.01893657345100655,"score_gpt":0.2868641185516143,"score_spread":0.2679275451006078,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409868956","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.40280145,0.0036330817,0.590276,0.0009360767,0.00072239456,0.00075183925,0.00025358665,0.000053379794,0.00057222176],"genre_scores_gemma":[0.9667517,0.0011400166,0.030288812,0.00013343916,0.00027925443,0.000022082906,0.0012434613,0.000013918381,0.00012734336],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9983914,0.000105779916,0.00069768704,0.0002944391,0.00031579263,0.00019491838],"domain_scores_gemma":[0.9988545,0.00033401503,0.00042062116,0.00012916385,0.00017034732,0.000091318274],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003401919,0.00015468693,0.00031964775,0.00021092445,0.0002039628,0.00007190685,0.0002314057,0.00014062604,0.0000074796726],"category_scores_gemma":[0.000037260324,0.00014600814,0.00006765284,0.00035584052,0.000069799775,0.0014634954,0.0000016795067,0.00021725784,9.7953134e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00046857962,0.00019032603,0.041272067,0.00006967307,0.00012872776,0.000011631337,0.014452505,0.9359144,0.0004192965,0.0022114757,0.00024548155,0.00461585],"study_design_scores_gemma":[0.005927673,0.00013926942,0.9061326,0.0005249573,0.00041555217,4.460914e-7,0.009759805,0.030552207,0.00010291327,0.00131721,0.04467664,0.0004507769],"about_ca_topic_score_codex":0.00016019496,"about_ca_topic_score_gemma":0.003627405,"teacher_disagreement_score":0.9053622,"about_ca_system_score_codex":0.00005424356,"about_ca_system_score_gemma":0.0001492559,"threshold_uncertainty_score":0.5954039},"labels":[],"label_agreement":null},{"id":"W4409889158","doi":"10.1287/ijoc.2024.0686","title":"Solving Combinatorial Pricing Problems Using Embedded Dynamic Programming Models","year":2025,"lang":"en","type":"article","venue":"INFORMS journal on computing","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science; Dynamic programming; Mathematical optimization; Combinatorial algorithms; Integer programming; Combinatorial optimization; Mathematics; Algorithm","score_opus":0.02275743366881434,"score_gpt":0.3168890661005752,"score_spread":0.2941316324317609,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409889158","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.37760767,0.00007467683,0.608067,0.00019246215,0.0026533979,0.000307544,5.949691e-7,0.00020588553,0.010890792],"genre_scores_gemma":[0.9787007,0.000016161786,0.02084855,0.0001330618,0.00018686817,9.839464e-7,0.000004258068,0.000010708788,0.000098715864],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99838734,0.00006166252,0.000547375,0.00014039758,0.00044467952,0.00041855135],"domain_scores_gemma":[0.99903524,0.00015002105,0.00037197894,0.00007681673,0.00026430152,0.000101644284],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0013323692,0.00013065535,0.00018327913,0.00030326814,0.0019085427,0.0005439837,0.00021475065,0.000110007844,0.00000538874],"category_scores_gemma":[0.00014507903,0.000121866666,0.0000859148,0.0005859897,0.0000581663,0.0007079299,0.000018965979,0.0004418164,0.00000193175],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016574577,0.000029974288,0.0017403102,0.000023718017,0.000027929567,0.00000606092,0.013884985,0.93101764,0.000018549594,0.021490943,0.000016805656,0.031726487],"study_design_scores_gemma":[0.0008643906,0.00005166354,0.0004498674,0.0010878845,0.000029065497,0.000007363441,0.004678795,0.98379207,0.000016306401,0.0070389067,0.0017565837,0.00022711307],"about_ca_topic_score_codex":0.000112827125,"about_ca_topic_score_gemma":0.000030957457,"teacher_disagreement_score":0.601093,"about_ca_system_score_codex":0.00030548463,"about_ca_system_score_gemma":0.00042090518,"threshold_uncertainty_score":0.99939084},"labels":[],"label_agreement":null},{"id":"W4409977473","doi":"10.1155/atr/3990405","title":"An Optimization Model of Urban Transportation Travel Carbon Footprint Based on Game Theory","year":2025,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","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":"Government of Jiangsu Province; National Natural Science Foundation of China","keywords":"Carbon footprint; Game theory; Transport engineering; Footprint; Travel behavior; Computer science; Operations research; Engineering; Economics; Geography; Greenhouse gas; Microeconomics; Ecology","score_opus":0.010436880575093706,"score_gpt":0.2771134975973008,"score_spread":0.2666766170222071,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409977473","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.30133212,0.000052084433,0.69671965,0.0002109756,0.00030226517,0.00025617587,0.00003886023,0.000038879592,0.0010490267],"genre_scores_gemma":[0.96573037,0.00010595543,0.033757567,0.00008163468,0.000047598453,0.000008279168,0.00017091508,0.00001890389,0.000078802776],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979332,0.00015433483,0.0009230599,0.00020666559,0.0005946947,0.00018803128],"domain_scores_gemma":[0.9981573,0.00014424202,0.00074891764,0.00015355273,0.00068647397,0.00010950465],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00077045633,0.00016741779,0.0003173592,0.000484887,0.00012719406,0.000021102864,0.00019586412,0.00015964005,0.000011960711],"category_scores_gemma":[0.00005606673,0.00017277831,0.00015690777,0.00055341027,0.00010737896,0.00033442752,1.750515e-7,0.00020525191,9.6415725e-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":[0.0009545005,0.00023353157,0.003319037,0.000047586862,0.00002552732,0.0000030586643,0.014040315,0.95418423,0.0017583063,0.022697063,0.0000020423595,0.0027348225],"study_design_scores_gemma":[0.0075586936,0.0012902785,0.2654807,0.0011446154,0.00079624925,3.2229758e-7,0.018776411,0.6808524,0.012311407,0.010856544,0.00018096276,0.00075140083],"about_ca_topic_score_codex":0.000058135935,"about_ca_topic_score_gemma":0.0003224044,"teacher_disagreement_score":0.66439825,"about_ca_system_score_codex":0.00008996633,"about_ca_system_score_gemma":0.00046740842,"threshold_uncertainty_score":0.7045695},"labels":[],"label_agreement":null},{"id":"W4410091476","doi":"10.1155/atr/5625483","title":"Leveraging Ant Colony and Particle Swarm Optimization Algorithms for Assessing the Transit‐Oriented Development Potential: A Case Study of Dhaka, Bangladesh","year":2025,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","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":"Ant colony optimization algorithms; Particle swarm optimization; Parallel metaheuristic; Metaheuristic; Computer science; Algorithm; Transit (satellite); Multi-swarm optimization; Mathematical optimization; Operations research; Engineering; Transport engineering; Public transport; Mathematics","score_opus":0.02084458275722714,"score_gpt":0.330233765938915,"score_spread":0.3093891831816879,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410091476","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.60683924,0.00008597073,0.39228407,0.00024640645,0.00021388508,0.0003119146,0.0000025299294,0.000010634099,0.0000053493577],"genre_scores_gemma":[0.95406646,0.000050791026,0.04576445,0.000034091365,0.000025870391,0.000015022962,0.000016163747,0.000007503811,0.000019652907],"study_design_codex":"simulation_or_modeling","study_design_gemma":"qualitative","domain_scores_codex":[0.9987507,0.000088049164,0.00062878017,0.00012404495,0.00027450715,0.00013391509],"domain_scores_gemma":[0.9987956,0.00014834682,0.00042134468,0.000051643667,0.0005348468,0.00004821268],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000675627,0.000088037545,0.00018093178,0.0001085541,0.0005277892,0.000046120367,0.00006378604,0.000045426445,0.0000029705068],"category_scores_gemma":[0.00004085923,0.000074721494,0.00004820483,0.00038436952,0.000048761372,0.0005001706,7.633127e-7,0.0000911008,1.9770862e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013115144,0.0001932477,0.0061972993,0.000031197225,0.00006265097,0.00006533961,0.103052706,0.8828317,0.00020991912,0.0001114025,0.0000042574566,0.0071091405],"study_design_scores_gemma":[0.010702101,0.00069590355,0.18862891,0.00049283536,0.00093286554,0.000047245245,0.7502609,0.043730646,0.0024827905,0.00017768772,0.0014545018,0.00039362002],"about_ca_topic_score_codex":0.00011693841,"about_ca_topic_score_gemma":0.0005209391,"teacher_disagreement_score":0.839101,"about_ca_system_score_codex":0.000046606543,"about_ca_system_score_gemma":0.00021866601,"threshold_uncertainty_score":0.4059381},"labels":[],"label_agreement":null},{"id":"W4410100021","doi":"10.1155/atr/7410746","title":"A Framework for Prioritizing the Connected Vehicle Infrastructure Service in Mixed Autonomy Traffic: A Fuzzy‐Analytic Hierarchy Process Approach","year":2025,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","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":"Korean National Police Agency","keywords":"Analytic hierarchy process; Autonomy; Hierarchy; Fuzzy logic; Service (business); Process (computing); Computer science; Transport engineering; Process management; Operations research; Business; Engineering; Artificial intelligence; Marketing; Economics; Political science","score_opus":0.01262059145762028,"score_gpt":0.3067163149334411,"score_spread":0.2940957234758208,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410100021","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.87375593,0.00024267612,0.122147046,0.0026879597,0.00033952575,0.00059975276,0.000015099912,0.000040355324,0.00017163514],"genre_scores_gemma":[0.9537597,0.000047690894,0.04575545,0.00024645161,0.00007809509,0.000030671363,0.00004755867,0.000010130274,0.000024261934],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99866927,0.00007863178,0.0006072777,0.00015652871,0.00027917768,0.00020911508],"domain_scores_gemma":[0.9985408,0.0003700482,0.00040702813,0.00007689711,0.00055004016,0.00005515075],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054197613,0.00011429356,0.0002399955,0.0002283284,0.00028608207,0.000051029496,0.00019770442,0.00013391425,0.000003830756],"category_scores_gemma":[0.00016707096,0.00009569473,0.000093220566,0.0010589274,0.000058073285,0.00047429797,5.8296814e-7,0.00033151815,1.4489382e-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":[0.00031509262,0.000059067002,0.003319374,0.00014695637,0.000039590243,0.0000035474047,0.07569376,0.8983781,0.000066184395,0.014678179,0.0000087852895,0.0072913705],"study_design_scores_gemma":[0.0045343125,0.0001243155,0.8048219,0.0010629355,0.00034832652,0.0000025676177,0.11585125,0.013977568,0.0001283193,0.055511747,0.0032540923,0.0003826334],"about_ca_topic_score_codex":0.00002161276,"about_ca_topic_score_gemma":0.00078467285,"teacher_disagreement_score":0.88440055,"about_ca_system_score_codex":0.00011258513,"about_ca_system_score_gemma":0.0006952651,"threshold_uncertainty_score":0.3902318},"labels":[],"label_agreement":null},{"id":"W4410177028","doi":"10.1016/j.sftr.2025.100653","title":"Decision-making in bus-transit systems: A comprehensive approach based on stochastic multi-criteria acceptability analysis","year":2025,"lang":"en","type":"article","venue":"Sustainable Futures","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Université de Sherbrooke","funders":"Fundação para a Ciência e a Tecnologia; Canada Research Chairs; Université de Sherbrooke","keywords":"Computer science; Operations research; Bus rapid transit; Transit (satellite); Transport engineering; Public transport; Engineering","score_opus":0.012784704530168554,"score_gpt":0.33290018163437896,"score_spread":0.3201154771042104,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410177028","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08095235,0.0004291325,0.91274464,0.0002720164,0.00033932342,0.00091140985,0.000021333834,0.00015087516,0.0041788914],"genre_scores_gemma":[0.9925403,0.000004309144,0.006658723,0.00020135885,0.000042175063,0.00007025198,0.00005061337,0.000007689725,0.00042456045],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982009,0.00032901435,0.00033417807,0.000399597,0.0003401099,0.00039618247],"domain_scores_gemma":[0.9985754,0.0005421609,0.00008251728,0.00023957592,0.00050185324,0.000058512578],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00064226927,0.000151151,0.0003169578,0.00081468315,0.000611626,0.00020375004,0.00022198065,0.00015129683,0.000082675564],"category_scores_gemma":[0.0005075709,0.00014863467,0.000115299175,0.0026685162,0.00010140157,0.00016946484,0.000009886789,0.0001666063,0.0000011884484],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016214167,0.0001457323,0.0066703167,0.00013707041,0.000052049912,0.000013462607,0.0076136147,0.97885656,6.6335434e-7,0.0054921294,0.00019430135,0.0006619828],"study_design_scores_gemma":[0.00089937507,0.00002505088,0.2419897,0.00012568137,0.00022424551,7.575272e-8,0.19756825,0.55668885,8.638714e-7,0.00040009958,0.0018134096,0.00026438982],"about_ca_topic_score_codex":0.0036547026,"about_ca_topic_score_gemma":0.001767311,"teacher_disagreement_score":0.91158795,"about_ca_system_score_codex":0.00029007535,"about_ca_system_score_gemma":0.00038209246,"threshold_uncertainty_score":0.60611457},"labels":[],"label_agreement":null},{"id":"W4410266979","doi":"10.1016/j.trip.2025.101463","title":"Synthetic multi-criteria decision analysis (S-MCDA): A new framework for participatory transportation planning","year":2025,"lang":"en","type":"article","venue":"Transportation Research Interdisciplinary Perspectives","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Fonds de recherche du Québec; Environment and Climate Change Canada; Canada Research Coordinating Committee","keywords":"Multiple-criteria decision analysis; Decision analysis; Citizen journalism; Transportation planning; Management science; Computer science; Environmental planning; Operations research; Engineering; Geography; Transport engineering; Economics","score_opus":0.13753081837785675,"score_gpt":0.5207127029020053,"score_spread":0.3831818845241486,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410266979","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20869769,0.0005741393,0.7857167,0.0028021543,0.00029266623,0.0009187492,0.00014863168,0.00022261018,0.00062667765],"genre_scores_gemma":[0.9422558,0.00009872251,0.05516536,0.0000375523,0.00012656864,0.00027609346,0.00035805104,0.000028321409,0.0016535454],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.9966647,0.00032816295,0.0006866072,0.000828084,0.0008035025,0.00068895397],"domain_scores_gemma":[0.997081,0.0014970672,0.00014798342,0.0003318744,0.00064774265,0.00029436982],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0017531924,0.00024414778,0.0004119682,0.0014610204,0.0017490098,0.00020392399,0.00040502966,0.0002854614,0.00039572644],"category_scores_gemma":[0.00037207585,0.00026108752,0.00037574518,0.0030028967,0.0004800926,0.0005529079,0.000006630504,0.00044787186,0.0000114091945],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002192222,0.0007039583,0.052991398,0.00012894656,0.0010140778,0.00003151751,0.6854512,0.04323434,0.00015440409,0.2050944,0.0031250215,0.005878503],"study_design_scores_gemma":[0.001638048,0.00023906604,0.61358136,0.00075115496,0.0008600255,8.0743895e-8,0.3168689,0.008714629,0.00017723333,0.05257388,0.004074569,0.0005210242],"about_ca_topic_score_codex":0.0007446178,"about_ca_topic_score_gemma":0.009235979,"teacher_disagreement_score":0.7335581,"about_ca_system_score_codex":0.00027238153,"about_ca_system_score_gemma":0.00056708616,"threshold_uncertainty_score":0.99998415},"labels":[],"label_agreement":null},{"id":"W4410339715","doi":"10.2139/ssrn.5246855","title":"Why We Change Routes: Psychological Crowding and Transit Decisions in Toronto","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Crowding; Transit (satellite); Psychology; Transport engineering; Public transport; Engineering; Cognitive psychology","score_opus":0.04552427732732554,"score_gpt":0.36422879094179755,"score_spread":0.318704513614472,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410339715","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26318958,0.17717114,0.41876382,0.076348156,0.005832157,0.0029080957,0.00014030488,0.00058995775,0.055056766],"genre_scores_gemma":[0.8179622,0.17968462,0.0008148412,0.00045468353,0.00032584034,0.000025781152,0.000020542786,0.000009655667,0.00070179487],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9977013,0.00022769126,0.00033881355,0.00031721054,0.00029137093,0.0011236487],"domain_scores_gemma":[0.99944997,0.00013828852,0.00013472824,0.000105401006,0.00007436064,0.0000972258],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00178537,0.00016318202,0.00024586043,0.0001426958,0.00038958472,0.00013952535,0.00029009377,0.00037141808,0.00004944712],"category_scores_gemma":[0.00010475337,0.00015706173,0.000094802,0.00016683548,0.00006577994,0.00024362757,0.000020208146,0.0019041982,0.0000010250684],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002720655,0.00034880845,0.022263162,0.000041344312,0.00021063391,0.000034755234,0.09007301,0.0038661698,0.000008442621,0.64913064,0.000744008,0.23300697],"study_design_scores_gemma":[0.0032534369,0.000470875,0.03841331,0.0028567403,0.00030889086,0.000066143424,0.1111799,0.0013575803,0.0000031499687,0.7904895,0.05006448,0.0015359654],"about_ca_topic_score_codex":0.008239593,"about_ca_topic_score_gemma":0.31345055,"teacher_disagreement_score":0.5547727,"about_ca_system_score_codex":0.0012857763,"about_ca_system_score_gemma":0.00093280594,"threshold_uncertainty_score":0.9983646},"labels":[],"label_agreement":null},{"id":"W4410357252","doi":"10.1145/3672608.3707908","title":"Refining Urban Microscopic Traffic Simulations Accuracy Using a Customized Random Walk Model for Dynamic Stochastic Route Choice","year":2025,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University Canada West","funders":"","keywords":"Random walk; Computer science; Refining (metallurgy); Stochastic process; Stochastic modelling; Simulation; Statistics; Mathematics; Materials science","score_opus":0.03245388798078445,"score_gpt":0.36573916590774963,"score_spread":0.33328527792696516,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410357252","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23939236,0.000054490472,0.7590022,0.0003151953,0.00019191878,0.00039752363,0.000040803356,0.00018595862,0.00041957645],"genre_scores_gemma":[0.96334904,0.000003005092,0.028974285,0.00016585506,0.000029745102,0.000029853698,0.00012672467,0.000012499034,0.007308989],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99896,0.00006443075,0.00032160027,0.00024417773,0.00014504285,0.00026474378],"domain_scores_gemma":[0.99846077,0.0011012951,0.0001128017,0.00011245349,0.00015467861,0.000057981026],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028666444,0.000117318436,0.00020202236,0.0001675723,0.0009193455,0.0001195126,0.00013530257,0.00010948806,0.000023600433],"category_scores_gemma":[0.00064782094,0.0001207099,0.00008552616,0.00038043447,0.00006360419,0.00028204347,0.000005306117,0.00008521688,0.0000016899967],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010262698,0.00002316341,0.00037605534,0.00001266804,0.000020698108,8.588817e-8,0.0040690512,0.9930924,0.00019991412,0.0014180797,0.000080788304,0.00060445524],"study_design_scores_gemma":[0.0037280216,0.0000049584505,0.00026023877,0.00006799502,0.00010501358,4.036171e-8,0.00042618086,0.99481136,0.0000041245867,0.00013611761,0.00032824604,0.00012771813],"about_ca_topic_score_codex":0.00051041885,"about_ca_topic_score_gemma":0.0043575712,"teacher_disagreement_score":0.7300279,"about_ca_system_score_codex":0.00014127615,"about_ca_system_score_gemma":0.00046423517,"threshold_uncertainty_score":0.7070955},"labels":[],"label_agreement":null},{"id":"W4410518468","doi":"10.1177/03611981251334620","title":"Panel Regression Analysis of Disruption Frequency of Urban Rail Transit Systems","year":2025,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"China Scholarship Council","keywords":"Regression analysis; Transit (satellite); Transport engineering; Engineering; Public transport; Statistics; Mathematics","score_opus":0.10134974923261636,"score_gpt":0.41640696685296186,"score_spread":0.3150572176203455,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410518468","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9766871,0.0008330359,0.017635724,0.0017557994,0.0007446434,0.0010111635,0.00021308556,0.000032486983,0.0010870008],"genre_scores_gemma":[0.9956991,0.0016952265,0.0010229683,0.000013657066,0.00007865512,0.000037221085,0.00008890816,0.000024198538,0.0013400833],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.990063,0.0024022874,0.0023043544,0.00039172036,0.0041771713,0.0006614844],"domain_scores_gemma":[0.9902692,0.0014697957,0.0010901209,0.0004824156,0.006426858,0.00026163927],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007421922,0.00022928977,0.00084177614,0.0033553692,0.00079939427,0.00008752252,0.0010810846,0.00033519254,0.00019862781],"category_scores_gemma":[0.0003571211,0.00018025404,0.00072808086,0.00953031,0.0012033507,0.0006741347,0.0000031606644,0.0012070535,0.0000020348957],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001191722,0.00046251641,0.84333605,0.0006225053,0.0013171055,0.000025844924,0.028162997,0.03624922,0.0049473266,0.07960635,0.0019729724,0.0021053888],"study_design_scores_gemma":[0.0011387957,0.0002241605,0.9766028,0.0010276907,0.0008091754,4.877624e-8,0.013554118,0.00062304194,0.0008577732,0.0015596073,0.003433296,0.00016950772],"about_ca_topic_score_codex":0.046372637,"about_ca_topic_score_gemma":0.072310016,"teacher_disagreement_score":0.13326673,"about_ca_system_score_codex":0.00023833921,"about_ca_system_score_gemma":0.0014078049,"threshold_uncertainty_score":0.9599776},"labels":[],"label_agreement":null},{"id":"W4410551115","doi":"10.4337/9781802208016.fm4","title":"Contributors","year":2025,"lang":"en","type":"book-chapter","venue":"Edward Elgar Publishing eBooks","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Dalhousie University","keywords":"Geography","score_opus":0.017827530255103863,"score_gpt":0.25929991360324595,"score_spread":0.24147238334814208,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410551115","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000053048434,0.0001825072,0.0012681613,0.0015784649,0.0019686336,0.00029938103,0.00012353537,0.0005463429,0.9940277],"genre_scores_gemma":[0.0021913128,0.000045565008,0.0006448792,0.00070353394,0.00080395263,0.00001126994,0.00044448135,0.000037155416,0.99511784],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9981332,0.000057735622,0.0003800661,0.00039467827,0.0006768576,0.00035748922],"domain_scores_gemma":[0.9986259,0.00024426452,0.00026816133,0.00026141203,0.00042472855,0.00017558735],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00092652824,0.00024757642,0.00032520053,0.00030206752,0.00063281704,0.0013862195,0.000495574,0.0007806241,0.00045969005],"category_scores_gemma":[0.0005476731,0.0002771273,0.00017282389,0.00004229868,0.00018490899,0.00048894685,0.000029025974,0.0006650187,0.000054216795],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009074578,0.0000031978318,0.00005592898,0.0000190777,0.000055465633,0.0000115083085,0.0025852479,0.000024021643,2.6022144e-7,0.7928254,0.19119853,0.013212278],"study_design_scores_gemma":[0.00018629144,0.00000892527,0.000035827652,0.00023078981,0.000062728104,2.4587544e-7,0.00012954745,0.000008287328,0.0000029999596,0.014846152,0.9842117,0.0002765005],"about_ca_topic_score_codex":0.0007179116,"about_ca_topic_score_gemma":0.0015113237,"teacher_disagreement_score":0.79301316,"about_ca_system_score_codex":0.00019732848,"about_ca_system_score_gemma":0.0009737162,"threshold_uncertainty_score":0.9999681},"labels":[],"label_agreement":null},{"id":"W4410551567","doi":"10.5194/icuc12-911","title":"Improving Subkilometer Modelling with GEM : case of Paris ","year":2025,"lang":"en","type":"preprint","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Environment and Climate Change Canada","funders":"","keywords":"Computer science","score_opus":0.027585246792095588,"score_gpt":0.2905448335307175,"score_spread":0.2629595867386219,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410551567","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05647405,0.000067487286,0.91072905,0.00012364118,0.00016187294,0.00021752778,0.00003779483,0.00010266473,0.03208592],"genre_scores_gemma":[0.8809571,0.00007198536,0.11370674,0.0000271891,0.000037058322,0.000012389021,0.00004692278,0.0000060223765,0.0051345853],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999234,0.000048668855,0.00021180631,0.00021702876,0.00016199621,0.00012647684],"domain_scores_gemma":[0.9993881,0.00007842839,0.00015879882,0.00014222701,0.00019019764,0.000042248554],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029014063,0.000093860814,0.00016408318,0.0001195198,0.0001515883,0.00004499626,0.0000975717,0.0001703676,0.00007291716],"category_scores_gemma":[0.000009294452,0.00008236059,0.000047665566,0.00015970417,0.00006236296,0.00006587859,0.000019091687,0.00017580327,6.926814e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017281245,0.000019209037,0.0025819978,0.00014070944,0.00003275609,0.000042411157,0.013880055,0.97458756,8.270343e-7,0.006765962,0.00006851043,0.0018626939],"study_design_scores_gemma":[0.0008996973,0.000093357805,0.00026751205,0.0009902394,0.0006436066,0.000010783064,0.025886718,0.9624316,0.00040461164,0.0026013094,0.004620679,0.0011499005],"about_ca_topic_score_codex":0.041750386,"about_ca_topic_score_gemma":0.0050663743,"teacher_disagreement_score":0.82448304,"about_ca_system_score_codex":0.000027834898,"about_ca_system_score_gemma":0.00034336364,"threshold_uncertainty_score":0.96463066},"labels":[],"label_agreement":null},{"id":"W4410732374","doi":"10.1016/j.trd.2025.104835","title":"Potential greenhouse gas emission reduction from active transportation: Comparing travel behavior patterns","year":2025,"lang":"en","type":"article","venue":"Transportation Research Part D Transport and Environment","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"China Scholarship Council","keywords":"Greenhouse gas; Reduction (mathematics); Environmental science; Ecology; Mathematics; Biology","score_opus":0.043914475982973745,"score_gpt":0.32591546933607185,"score_spread":0.2820009933530981,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410732374","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9847487,0.00014514987,0.010918454,0.0013821219,0.00032394144,0.0010010174,0.0005373744,0.00014915867,0.00079403823],"genre_scores_gemma":[0.99061894,0.003666283,0.00059108017,0.000030339195,0.00014903565,0.00027400622,0.003320473,0.000032702086,0.0013171085],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9965642,0.00020367063,0.0007070844,0.0007676896,0.0011472877,0.0006100305],"domain_scores_gemma":[0.99907243,0.00008865892,0.00012851763,0.00025552732,0.000118363685,0.00033649645],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00062259444,0.000287391,0.00035805278,0.00032398096,0.0012734905,0.000066267916,0.00022014619,0.0002876583,0.0008295512],"category_scores_gemma":[0.000005838957,0.000320905,0.00013917063,0.00041146803,0.0004786118,0.0005151652,0.0000018324994,0.0005205535,0.000014429191],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013139521,0.0012500259,0.9005746,0.00017556138,0.00024869113,0.00018770622,0.05671929,0.010710416,0.0031677915,0.0038790875,0.0006935832,0.02107928],"study_design_scores_gemma":[0.0014686587,0.00007172564,0.976396,0.00016443402,0.000249129,2.576508e-7,0.0103723,0.00013403114,0.0013940149,0.00049184216,0.008933022,0.00032454895],"about_ca_topic_score_codex":0.0111215105,"about_ca_topic_score_gemma":0.00904041,"teacher_disagreement_score":0.07582142,"about_ca_system_score_codex":0.00014267932,"about_ca_system_score_gemma":0.00016500376,"threshold_uncertainty_score":0.9999243},"labels":[],"label_agreement":null},{"id":"W4410779197","doi":"10.1155/atr/6691768","title":"Travel Mode Choices for Connecting Urban Rail Transit System During Irregular Time Periods: A Case Study in Beijing","year":2025,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","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":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Beijing; Transport engineering; Mode choice; Travel time; Mode (computer interface); Transit (satellite); Urban rail transit; Rail transit; Transit time; Environmental science; Computer science; Geography; Public transport; Engineering; China","score_opus":0.01058052566402165,"score_gpt":0.3011599254778819,"score_spread":0.2905793998138603,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410779197","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9590117,0.00015915371,0.03965135,0.00010683095,0.00028304395,0.00062272255,0.000018284281,0.00004274167,0.000104201914],"genre_scores_gemma":[0.99494416,0.000014774541,0.004806906,0.000009935948,0.00008478651,0.000021818107,0.000018235369,0.000015056453,0.000084313084],"study_design_codex":"simulation_or_modeling","study_design_gemma":"qualitative","domain_scores_codex":[0.99840295,0.000100566365,0.00081317447,0.00019035408,0.00027652498,0.00021644539],"domain_scores_gemma":[0.99904746,0.00017138547,0.00041185349,0.00006763675,0.00023280478,0.00006883747],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000593187,0.000132334,0.00033238603,0.00038344893,0.0004721571,0.000054402884,0.000109041495,0.00008367143,0.0000047656245],"category_scores_gemma":[0.00004868573,0.0001415388,0.00012360634,0.00049781665,0.000029055223,0.0007076347,4.181321e-7,0.00016162814,1.961569e-7],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00074346503,0.00022808343,0.032169856,0.0003444961,0.00010341988,0.00069766724,0.20180081,0.7571663,0.004420373,0.0009260914,0.0000023364175,0.0013970691],"study_design_scores_gemma":[0.011543533,0.00040725002,0.28490797,0.0014626128,0.00050363404,0.000068504094,0.69478023,0.0046255128,0.0009789795,0.00008438934,0.00017394057,0.00046344858],"about_ca_topic_score_codex":0.00032960973,"about_ca_topic_score_gemma":0.0051070526,"teacher_disagreement_score":0.7525408,"about_ca_system_score_codex":0.0001278772,"about_ca_system_score_gemma":0.0001557934,"threshold_uncertainty_score":0.5771785},"labels":[],"label_agreement":null},{"id":"W4410875383","doi":"10.5267/j.dsl.2025.3.012","title":"Context sensitive transit oriented development assessment: AHP weighted TOD standards for regional railway hubs in Thailand","year":2025,"lang":"en","type":"article","venue":"Decision Science Letters","topic":"Transportation Planning and Optimization","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":"Analytic hierarchy process; Context (archaeology); Transport engineering; Transit-oriented development; Transit (satellite); Regional development; Business; Computer science; Engineering; Regional science; Operations research; Geography; Public transport","score_opus":0.01931079762940687,"score_gpt":0.3486038644290518,"score_spread":0.32929306679964493,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410875383","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4290474,0.000013691078,0.5607779,0.0078023914,0.00041654197,0.00038941938,0.000022556951,0.000042584263,0.0014874939],"genre_scores_gemma":[0.9643304,0.000012759109,0.03176866,0.003606982,0.000024028697,0.000031293104,0.000032522395,0.0000051834136,0.0001882206],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99750686,0.000079594625,0.00037010544,0.00044776755,0.0012296682,0.000366026],"domain_scores_gemma":[0.99871504,0.00052345893,0.00009009117,0.00012330904,0.0004494533,0.00009867408],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028886115,0.00011586001,0.00017798557,0.0005135262,0.00091826724,0.00015795669,0.00026382902,0.000067164925,0.000027176677],"category_scores_gemma":[0.0002443013,0.00010704033,0.00004771325,0.001705663,0.00038950882,0.00045920277,0.0000100701645,0.00011119898,0.0000024949907],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002246912,0.000528786,0.12996013,0.0000675737,0.00011378745,0.00014060255,0.21914367,0.017045433,0.015324602,0.14101082,0.056019284,0.41839838],"study_design_scores_gemma":[0.006088852,0.0000734736,0.32801893,0.00073370716,0.000031776824,0.000002015512,0.020842187,0.007471488,0.0037780297,0.0012776676,0.6309424,0.0007395042],"about_ca_topic_score_codex":0.00010849075,"about_ca_topic_score_gemma":0.0012969037,"teacher_disagreement_score":0.5749231,"about_ca_system_score_codex":0.00043030252,"about_ca_system_score_gemma":0.0014700877,"threshold_uncertainty_score":0.7062662},"labels":[],"label_agreement":null},{"id":"W4410889974","doi":"10.5267/j.ijiec.2025.5.003","title":"Mixed traffic optimization considering road reservation travel based on the bi-level programming model","year":2025,"lang":"en","type":"article","venue":"International Journal of Industrial Engineering Computations","topic":"Transportation Planning and Optimization","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":"Reservation; Computer science; Mathematical optimization; Programming paradigm; Traffic model; Transport engineering; Operations research; Engineering; Mathematics; Computer network","score_opus":0.09298425618787723,"score_gpt":0.30520583520289296,"score_spread":0.21222157901501573,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410889974","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.032320496,0.000013644686,0.9558564,0.009391553,0.001724266,0.00021523041,0.000017022996,0.000059858856,0.00040154348],"genre_scores_gemma":[0.9515334,0.000009445679,0.047829237,0.00018769858,0.00029188403,0.000010635371,0.000042798212,0.000011716587,0.00008318046],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985108,0.00009266764,0.00053646055,0.00011386978,0.0005994593,0.00014678568],"domain_scores_gemma":[0.998219,0.0005620588,0.0002857402,0.00007226156,0.0008026891,0.00005820978],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00085371855,0.000111502144,0.0001355831,0.0005251752,0.00026674528,0.00021246713,0.00032771027,0.00011871817,0.000017049839],"category_scores_gemma":[0.0013903988,0.0001012746,0.000098917044,0.0005292997,0.000050976043,0.00026513293,0.000007645469,0.00032285077,8.817984e-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":[0.00003512244,0.000053266587,0.00014018895,0.0000021264616,0.000059467096,0.0000033855106,0.00077288086,0.98044425,0.000014578384,0.010646209,0.00052066223,0.0073078335],"study_design_scores_gemma":[0.00078929955,0.000021990421,0.00051679736,0.00026676786,0.000029093848,0.0000011233999,0.0005458383,0.9968557,0.00005427725,0.00007851374,0.0007522724,0.00008833937],"about_ca_topic_score_codex":0.000046739155,"about_ca_topic_score_gemma":0.00002064673,"teacher_disagreement_score":0.91921294,"about_ca_system_score_codex":0.00019707899,"about_ca_system_score_gemma":0.0006456498,"threshold_uncertainty_score":0.41298583},"labels":[],"label_agreement":null},{"id":"W4411067570","doi":"10.1061/9780784486207.068","title":"Evaluation of Transit Signal Priority Strategies and the Impact of Background Signal Timing","year":2025,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"SIGNAL (programming language); Computer science; Transit (satellite); Real-time computing; Transport engineering; Public transport; Engineering","score_opus":0.059407565004115176,"score_gpt":0.38318847804466555,"score_spread":0.32378091304055034,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411067570","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9121113,0.00019670144,0.051483683,0.00019521646,0.000024113966,0.00024173269,0.0000046452255,0.000015004732,0.035727605],"genre_scores_gemma":[0.9994838,0.00002063877,0.00038884213,0.0000053454014,0.000009230211,0.00000255373,0.000004705622,0.0000011931098,0.00008364669],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99911696,0.00025784696,0.00016095157,0.000066407985,0.0003339684,0.000063887266],"domain_scores_gemma":[0.9993705,0.00019270852,0.00006741334,0.00003820569,0.0003167254,0.000014448151],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021879328,0.00004069248,0.00009647469,0.000042769178,0.00013143371,0.000038762766,0.00005202172,0.00003934158,0.0002598313],"category_scores_gemma":[0.00002430891,0.000026921729,0.000045857912,0.00019526646,0.0002432352,0.00021577043,0.000001548601,0.000037041264,1.6363707e-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":[0.00048592067,0.000109454224,0.015513714,0.000057691777,0.00027026364,2.1127637e-7,0.06437961,0.60678154,0.0013964529,0.28043482,0.00014635273,0.030423943],"study_design_scores_gemma":[0.0046685566,0.00013380684,0.6340096,0.00012911345,0.0007020177,2.348085e-7,0.092695825,0.22844459,0.0009249188,0.038063727,0.000037373673,0.00019023087],"about_ca_topic_score_codex":0.0043701613,"about_ca_topic_score_gemma":0.0011151885,"teacher_disagreement_score":0.6184959,"about_ca_system_score_codex":0.00002393219,"about_ca_system_score_gemma":0.0006831824,"threshold_uncertainty_score":0.6606406},"labels":[],"label_agreement":null},{"id":"W4411334025","doi":"10.1016/j.ijtst.2025.06.001","title":"EcoRouteQ: A Reinforcement Learning Framework for Green Route Recommendations","year":2025,"lang":"en","type":"article","venue":"International Journal of Transportation Science and Technology","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Research Council Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Reinforcement; Reinforcement learning; Business; Engineering; Transport engineering; Computer science; Operations management; Artificial intelligence; Structural engineering","score_opus":0.017828835252900778,"score_gpt":0.3585817779203076,"score_spread":0.3407529426674068,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411334025","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07963109,0.00012389816,0.8352991,0.080873296,0.0018174319,0.00025542005,0.000019231627,0.00008715882,0.0018934279],"genre_scores_gemma":[0.9769179,0.00029511916,0.021951053,0.00024979125,0.000057177225,0.0000102204385,0.000018987963,0.0000029859984,0.00049673836],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99891394,0.000014499673,0.00038622587,0.00012639318,0.0004234795,0.00013547423],"domain_scores_gemma":[0.9974035,0.00013423787,0.00029526677,0.000046526588,0.0020748354,0.00004562349],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009442027,0.000057616224,0.000102692764,0.00090978097,0.00041750792,0.000072449715,0.00037267618,0.000089929665,0.00002417368],"category_scores_gemma":[0.000531334,0.00005779437,0.000035885994,0.00083613675,0.00035618912,0.0005182733,0.0000038764156,0.00015747793,6.205129e-7],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006041855,0.000031108884,0.033158313,0.0000064651667,0.000056503704,0.0000039402066,0.004669376,0.002931813,0.00020787507,0.9000139,0.00022149694,0.058638785],"study_design_scores_gemma":[0.0027329293,0.0003873145,0.036935184,0.0006679338,0.00018294892,0.000009782758,0.02699409,0.003126589,0.002711682,0.23726052,0.68857634,0.00041469187],"about_ca_topic_score_codex":0.000091981456,"about_ca_topic_score_gemma":0.00024728902,"teacher_disagreement_score":0.89728683,"about_ca_system_score_codex":0.00010097282,"about_ca_system_score_gemma":0.00057518913,"threshold_uncertainty_score":0.32111755},"labels":[],"label_agreement":null},{"id":"W4411746054","doi":"10.1155/atr/2393859","title":"Assessing the Determinant Factors Influencing Transport Mode Choice: A Case of Debre Berhan City","year":2025,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","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":"Mode choice; Mode (computer interface); Chemistry; Transport engineering; Public transport; Computer science; Engineering","score_opus":0.020410648120832382,"score_gpt":0.3631116518155317,"score_spread":0.3427010036946993,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411746054","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.976584,0.00012830041,0.02228394,0.00023143513,0.0003687035,0.00016090511,0.000016104928,0.000020553078,0.00020603856],"genre_scores_gemma":[0.99638474,0.00008491273,0.0033641155,0.000044781456,0.000043673,0.0000027769993,0.000020768224,0.00000836284,0.000045889592],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99846697,0.00008129688,0.0008036616,0.00012369026,0.0003466665,0.00017769006],"domain_scores_gemma":[0.99836063,0.000276856,0.00072003517,0.00009837153,0.00047342633,0.000070680304],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004957316,0.00012232733,0.00025756753,0.0001947442,0.00043292882,0.000033739372,0.00015997986,0.000096262025,0.000013877953],"category_scores_gemma":[0.00009342041,0.00009392767,0.00016264226,0.00054742,0.00013786094,0.0011607927,5.590438e-7,0.00023131518,7.383192e-8],"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":[0.000099565026,0.00011274632,0.49882552,0.000115931995,0.00009131484,0.00021707334,0.10690513,0.37889874,0.0023175776,0.0013789687,0.00001082091,0.011026621],"study_design_scores_gemma":[0.00075645826,0.000052132105,0.95341694,0.00036902007,0.0002589115,0.000010807674,0.041440133,0.00016440822,0.0021844269,0.00056670915,0.0006430188,0.00013703511],"about_ca_topic_score_codex":0.0011460453,"about_ca_topic_score_gemma":0.00853514,"teacher_disagreement_score":0.45459142,"about_ca_system_score_codex":0.000070093534,"about_ca_system_score_gemma":0.00038662416,"threshold_uncertainty_score":0.47628087},"labels":[],"label_agreement":null},{"id":"W4411932625","doi":"10.2139/ssrn.5334821","title":"Why We Change Routes: Psychological Crowding and Transit Decisions in Toronto","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Crowding; Transit (satellite); Psychology; Transport engineering; Applied psychology; Cognitive psychology; Engineering; Public transport","score_opus":0.04552427732732554,"score_gpt":0.36422879094179755,"score_spread":0.318704513614472,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411932625","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26318958,0.17717114,0.41876382,0.076348156,0.005832157,0.0029080957,0.00014030488,0.00058995775,0.055056766],"genre_scores_gemma":[0.8179622,0.17968462,0.0008148412,0.00045468353,0.00032584034,0.000025781152,0.000020542786,0.000009655667,0.00070179487],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9977013,0.00022769126,0.00033881355,0.00031721054,0.00029137093,0.0011236487],"domain_scores_gemma":[0.99944997,0.00013828852,0.00013472824,0.000105401006,0.00007436064,0.0000972258],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00178537,0.00016318202,0.00024586043,0.0001426958,0.00038958472,0.00013952535,0.00029009377,0.00037141808,0.00004944712],"category_scores_gemma":[0.00010475337,0.00015706173,0.000094802,0.00016683548,0.00006577994,0.00024362757,0.000020208146,0.0019041982,0.0000010250684],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002720655,0.00034880845,0.022263162,0.000041344312,0.00021063391,0.000034755234,0.09007301,0.0038661698,0.000008442621,0.64913064,0.000744008,0.23300697],"study_design_scores_gemma":[0.0032534369,0.000470875,0.03841331,0.0028567403,0.00030889086,0.000066143424,0.1111799,0.0013575803,0.0000031499687,0.7904895,0.05006448,0.0015359654],"about_ca_topic_score_codex":0.008239593,"about_ca_topic_score_gemma":0.31345055,"teacher_disagreement_score":0.5547727,"about_ca_system_score_codex":0.0012857763,"about_ca_system_score_gemma":0.00093280594,"threshold_uncertainty_score":0.9983646},"labels":[],"label_agreement":null},{"id":"W4412060066","doi":"10.1016/j.trc.2025.105239","title":"Abnormal metro passenger demand is predictable from alighting and boarding correlation","year":2025,"lang":"en","type":"article","venue":"Transportation Research Part C Emerging Technologies","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Transport engineering; Correlation; Engineering; Aeronautics; Computer science; Mathematics","score_opus":0.03937342396818918,"score_gpt":0.3663101789195726,"score_spread":0.32693675495138347,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412060066","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92339826,0.0019476565,0.051936466,0.013374372,0.000412562,0.0006709916,0.00010487594,0.0020667645,0.006088041],"genre_scores_gemma":[0.9931486,0.0013698414,0.004208662,0.00004360815,0.00004195295,0.00007770429,0.00016206646,0.000011320693,0.00093619304],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9980078,0.00014800527,0.00037635083,0.00039545965,0.0006180156,0.0004543727],"domain_scores_gemma":[0.99888873,0.00047046706,0.00009500308,0.00018531298,0.00030814274,0.00005235786],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0013374297,0.00012856837,0.00018151487,0.0005596254,0.0013669501,0.00013892908,0.00021757529,0.00025567965,0.00009448353],"category_scores_gemma":[0.00035294043,0.00013546935,0.000042468677,0.0012691447,0.00038965425,0.0005351623,0.0000103193015,0.00039325055,0.0000070073206],"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":[0.00006746042,0.00004718221,0.9024251,0.00006821303,0.00011635721,0.000012167718,0.0183898,0.004202893,0.00035945867,0.050486706,0.011438019,0.0123866135],"study_design_scores_gemma":[0.0021760333,0.0001230438,0.44640696,0.0011425243,0.00025725405,1.5473721e-7,0.18451828,0.019768985,0.010625612,0.039238505,0.29479578,0.0009468874],"about_ca_topic_score_codex":0.002548004,"about_ca_topic_score_gemma":0.0025647741,"teacher_disagreement_score":0.45601818,"about_ca_system_score_codex":0.00006200267,"about_ca_system_score_gemma":0.00012694299,"threshold_uncertainty_score":0.9999331},"labels":[],"label_agreement":null},{"id":"W4412639231","doi":"10.1177/10812865251343512","title":"On minimizing cyclists’ ascent times: Part II","year":2025,"lang":"en","type":"article","venue":"Mathematics and Mechanics of Solids","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Mount Royal University; Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Mathematical analysis; Computer science","score_opus":0.017592681862264203,"score_gpt":0.28752126002258543,"score_spread":0.26992857816032123,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412639231","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.47715306,0.0007216896,0.20350334,0.005762872,0.0018087301,0.00111674,0.00006509304,0.00035082677,0.30951765],"genre_scores_gemma":[0.98145056,0.0004984479,0.014258576,0.00030176964,0.000031025833,0.0000060227458,0.000012184067,0.000008972249,0.0034324373],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9994456,0.000012625084,0.00018810427,0.000090992864,0.00015601353,0.00010663196],"domain_scores_gemma":[0.9996523,0.000092761475,0.00007883021,0.000084716485,0.00005531935,0.00003601659],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033214377,0.000058746675,0.00012256992,0.00006269757,0.00024060467,0.000014945003,0.00007047437,0.000062031126,0.000040482613],"category_scores_gemma":[0.000105612235,0.00005593722,0.000029286899,0.00011817319,0.000024722422,0.000034552933,0.000010900495,0.000044994496,0.0000017637913],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004060995,0.000077822035,0.000008157072,0.0000652466,0.000011345293,3.8099887e-7,0.0074363207,0.0003047545,0.000093286086,0.9902925,0.001314034,0.00039210616],"study_design_scores_gemma":[0.0016676472,0.000303402,0.00007544693,0.002416574,0.00026041336,8.309586e-7,0.026532339,0.07052108,0.00519056,0.83107007,0.061335497,0.0006261479],"about_ca_topic_score_codex":0.000016146692,"about_ca_topic_score_gemma":0.00001773785,"teacher_disagreement_score":0.5042975,"about_ca_system_score_codex":0.0000122479405,"about_ca_system_score_gemma":0.000047844027,"threshold_uncertainty_score":0.22810537},"labels":[],"label_agreement":null},{"id":"W4412754658","doi":"10.11159/iccste25.191","title":"Investigation of the Applicability of Surrogate Models in Transportation Network Design Problems","year":2025,"lang":"en","type":"article","venue":"Proceedings of the International Conference on Civil, Structural and Transportation Engineering","topic":"Transportation Planning and Optimization","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":"Computer science; Surrogate model; Machine learning","score_opus":0.02782811867500478,"score_gpt":0.23973529538579935,"score_spread":0.21190717671079456,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412754658","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99436116,0.00002181255,0.0037428096,0.0007377746,0.00018975737,0.00040658776,0.00003375387,0.000017950286,0.0004884023],"genre_scores_gemma":[0.9987211,0.000040221927,0.0011450904,0.000014649187,0.000010969673,0.000020059317,0.00001402323,0.0000043004943,0.00002955102],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9990066,0.000011320398,0.00042833833,0.0001439204,0.0003131161,0.00009668977],"domain_scores_gemma":[0.9992765,0.000062389474,0.00026313614,0.000043821605,0.0003339106,0.000020211137],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031063327,0.00009265647,0.00014314319,0.00008099896,0.00007011103,0.000014604083,0.00024096506,0.00006517831,0.000007510075],"category_scores_gemma":[0.000031656993,0.00006961437,0.000052681677,0.00042584623,0.00012983456,0.00024215282,0.000002409098,0.0001126626,1.9525157e-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":[0.00004707899,0.000006041937,0.11657117,0.00014389146,0.000020991649,2.2975843e-8,0.005829005,0.53160375,0.004901094,0.34071255,0.00000483541,0.00015956833],"study_design_scores_gemma":[0.0004551516,0.000019744872,0.86100256,0.0007906276,0.00003848973,7.3331286e-8,0.0008714534,0.07677686,0.010908505,0.049010452,0.000014693796,0.000111395726],"about_ca_topic_score_codex":0.00036675806,"about_ca_topic_score_gemma":0.0009165627,"teacher_disagreement_score":0.7444314,"about_ca_system_score_codex":0.000026353266,"about_ca_system_score_gemma":0.00007388943,"threshold_uncertainty_score":0.28387916},"labels":[],"label_agreement":null},{"id":"W4413022634","doi":"10.70121/001c.143043","title":"Downsview Runway Analysis for Enhancing the Integration of a Linear Urban Space","year":2025,"lang":"en","type":"article","venue":"Scholarly review .","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Runway; Space (punctuation); Computer science; Geography; Cartography","score_opus":0.03319912060811084,"score_gpt":0.3557347598548942,"score_spread":0.3225356392467834,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413022634","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004201509,0.16090603,0.80428374,0.017319633,0.0003584941,0.002254821,0.00004164439,0.000105411076,0.010528717],"genre_scores_gemma":[0.87107253,0.090731114,0.021105103,0.003494969,0.00018124898,0.00031439154,0.00028061404,0.000015071288,0.012804988],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99909335,0.00019651797,0.0003131452,0.00012725014,0.00017411618,0.00009561785],"domain_scores_gemma":[0.9990812,0.00019051002,0.00016300715,0.0001622691,0.00037883758,0.000024187435],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002310347,0.000059095397,0.00021942754,0.000088158486,0.00037088414,0.00007630403,0.00016946583,0.0000447817,0.000047915342],"category_scores_gemma":[0.0011303375,0.000040837804,0.00017970058,0.0016225565,0.000044607477,0.00036541524,0.0000037841753,0.00010328484,0.0000038164007],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008337478,0.00025165352,0.08515336,0.0068989834,0.0020225325,0.0000011715351,0.05298428,0.004061367,0.0019550736,0.66550636,0.025752513,0.15532932],"study_design_scores_gemma":[0.00037218962,0.000052255287,0.02360475,0.0064147357,0.0038340685,8.330305e-8,0.0028899948,0.0008955612,0.00066390156,0.0009216483,0.9600806,0.00027020628],"about_ca_topic_score_codex":0.00025500343,"about_ca_topic_score_gemma":0.0020323982,"teacher_disagreement_score":0.9343281,"about_ca_system_score_codex":0.000024393226,"about_ca_system_score_gemma":0.0001462257,"threshold_uncertainty_score":0.28525785},"labels":[],"label_agreement":null},{"id":"W4413070814","doi":"10.1007/978-3-031-95284-5_60","title":"Hard Shoulder Bus Priority Measures on Motorways and Type 1 Dual Carriageways","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in mobility","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Transport engineering; Context (archaeology); Public transport; Legislation; Engineering; Transport network; Traffic congestion; Irish; Dual (grammatical number); Civil engineering; Business; Geography","score_opus":0.03007817343716072,"score_gpt":0.2933051619633307,"score_spread":0.26322698852616994,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413070814","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03586517,0.005968436,0.037170846,0.003939271,0.005697035,0.00486091,0.0007917217,0.0010205205,0.9046861],"genre_scores_gemma":[0.966594,0.00072077627,0.001919146,0.00059739646,0.00046433392,0.000023703047,0.00018039919,0.000039828763,0.029460449],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99842566,0.00010825216,0.00028279863,0.00052550656,0.00043689867,0.00022087566],"domain_scores_gemma":[0.99887586,0.00047775626,0.000100629906,0.00027238842,0.00019320089,0.000080194644],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00066898373,0.0002498171,0.0003361242,0.00012830965,0.00026319164,0.00006036173,0.000124486,0.00070747524,0.0002253637],"category_scores_gemma":[0.0008599003,0.00024395928,0.00007168066,0.0001066062,0.00025554394,0.00005734732,0.000014282574,0.0006276097,0.0000043599175],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0023124372,0.00070924725,0.15042923,0.0014496681,0.0006168977,0.00020399044,0.088046916,0.13627613,0.00022028388,0.2856698,0.005441416,0.32862398],"study_design_scores_gemma":[0.0024312579,0.0006956724,0.18868011,0.001858518,0.0006353752,0.0000028833342,0.0002329482,0.00095839414,0.00052459945,0.24013577,0.56058455,0.003259948],"about_ca_topic_score_codex":0.0009336846,"about_ca_topic_score_gemma":0.007014436,"teacher_disagreement_score":0.9307288,"about_ca_system_score_codex":0.0001737869,"about_ca_system_score_gemma":0.00034324752,"threshold_uncertainty_score":0.9948371},"labels":[],"label_agreement":null},{"id":"W4413085476","doi":"10.1155/atr/4103948","title":"Evaluating Impacts of Public Transit and Automobiles During Connected and Automated Vehicle Adoption","year":2025,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","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":"Software deployment; Equity (law); Public transport; Transport engineering; Transit (satellite); Business; Engineering; Political science","score_opus":0.020214011884339567,"score_gpt":0.3389653056310556,"score_spread":0.318751293746716,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413085476","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9960452,0.00056423916,0.0026154385,0.0003844124,0.0001154059,0.00014332317,0.000008843331,0.00007050541,0.00005268882],"genre_scores_gemma":[0.993036,0.00058329245,0.0063086506,0.000013712071,0.00001750702,0.0000021843741,0.000019046745,0.0000057941434,0.000013815017],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9989088,0.00009755581,0.0005056912,0.000102210906,0.0002591943,0.00012650383],"domain_scores_gemma":[0.9989147,0.00011948606,0.00040096501,0.000038682225,0.0004501325,0.000076039374],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000584606,0.00007752134,0.00018716666,0.00023731812,0.00019845513,0.00003052473,0.000046103436,0.00007308455,0.000008247237],"category_scores_gemma":[0.00013740631,0.0000781178,0.00003824774,0.00039396062,0.00009328583,0.00070672686,5.3906297e-7,0.00009688132,6.3001224e-8],"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":[0.0010017034,0.00025122825,0.2893375,0.0008552131,0.00026064564,0.00002470963,0.098369524,0.28914747,0.25979632,0.0117942635,0.000015269587,0.04914616],"study_design_scores_gemma":[0.0017024205,0.00011083539,0.9882461,0.0002878532,0.00008982095,0.000001415725,0.0058292593,0.0015266732,0.001817789,0.0002709051,0.000040087256,0.00007684332],"about_ca_topic_score_codex":0.000056185054,"about_ca_topic_score_gemma":0.00047556116,"teacher_disagreement_score":0.6989086,"about_ca_system_score_codex":0.00003364024,"about_ca_system_score_gemma":0.0001613733,"threshold_uncertainty_score":0.31855515},"labels":[],"label_agreement":null},{"id":"W4413130549","doi":"10.14419/zr181f21","title":"Assessing The Impact of The Homotopy Perturbation Method on Computational Performance in AI Systems","year":2025,"lang":"en","type":"article","venue":"International Journal of Basic and Applied Sciences","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science; Robustness (evolution); Artificial intelligence; Reinforcement learning; Fuzzy logic; Computation; Machine learning; Homotopy perturbation method; Perturbation (astronomy); Homotopy; Algorithm; Mathematics","score_opus":0.023852143868005302,"score_gpt":0.38985834313406603,"score_spread":0.3660061992660607,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413130549","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9654535,0.000061314786,0.019592313,0.003040596,0.00045721672,0.00009500197,0.000002262872,0.0000031441095,0.011294637],"genre_scores_gemma":[0.99897194,0.000020806401,0.00074702344,0.00013437677,0.000052186682,0.0000015303348,6.607703e-7,9.550038e-7,0.000070515765],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.999065,0.00009378007,0.00024225605,0.000064807886,0.00047197693,0.0000622185],"domain_scores_gemma":[0.99917173,0.000359395,0.00024489133,0.000025484815,0.00018470066,0.000013783429],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014406181,0.000041551295,0.00007608554,0.00014115928,0.0002644481,0.00016590179,0.00028153593,0.00002401123,0.0000053624426],"category_scores_gemma":[0.0000657344,0.000021879643,0.000038510596,0.00032592996,0.00023110588,0.0002490148,0.0000075698645,0.000104672064,1.5874087e-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":[0.000018100653,0.000027205811,0.072774276,0.000002814513,0.000017242377,2.2761846e-7,0.0026420841,0.83649606,0.00004510813,0.083195016,0.00007405379,0.0047078067],"study_design_scores_gemma":[0.0003969406,0.000044488046,0.9031225,0.0002499337,0.000010331724,0.0000039990537,0.0054056924,0.0839885,0.00011415773,0.00635594,0.0002527426,0.00005476215],"about_ca_topic_score_codex":0.000101386155,"about_ca_topic_score_gemma":0.000010732522,"teacher_disagreement_score":0.83034825,"about_ca_system_score_codex":0.0000568567,"about_ca_system_score_gemma":0.00035898414,"threshold_uncertainty_score":0.20339479},"labels":[],"label_agreement":null},{"id":"W4413213988","doi":"10.2139/ssrn.5386984","title":"A Quantitative Assessment of Daily Transportation Energy Demand and Electrification Potential Across the Dwelling Types in the Greater Toronto and Hamilton Area","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Electrification; Energy demand; Transport engineering; Geography; Regional science; Natural resource economics; Engineering; Economics; Electricity","score_opus":0.014942941724024165,"score_gpt":0.3176897361728761,"score_spread":0.30274679444885194,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413213988","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8164931,0.007308011,0.17424098,0.000993838,0.00014214199,0.00030001585,0.00003177531,0.000013907951,0.00047624882],"genre_scores_gemma":[0.9710827,0.028248278,0.00029090623,0.000037268674,0.000045886514,0.000018568819,0.000064313426,0.000005668654,0.00020641513],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9982514,0.00037836065,0.00034780294,0.00020543218,0.00030455968,0.000512439],"domain_scores_gemma":[0.9993035,0.00012806109,0.000312777,0.000093004135,0.00013968437,0.000022934955],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026418297,0.00012883,0.00017572237,0.00006265242,0.00046729206,0.00012698973,0.00019694005,0.00014999388,0.0000037604534],"category_scores_gemma":[0.00001854183,0.000088420784,0.000052547523,0.00014105304,0.000120412915,0.00016037504,0.0000033046686,0.00072570035,3.517804e-8],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036201783,0.00013705996,0.036423188,0.00009526696,0.00042930647,0.000004741585,0.23489127,0.092978984,0.00019463548,0.6235865,0.00002417119,0.0108728735],"study_design_scores_gemma":[0.0025431714,0.00072751753,0.5012462,0.00073720794,0.0009112173,0.00002512213,0.259309,0.05019196,0.00014226763,0.18210964,0.0011764759,0.00088020245],"about_ca_topic_score_codex":0.0059457,"about_ca_topic_score_gemma":0.09895352,"teacher_disagreement_score":0.464823,"about_ca_system_score_codex":0.00024500064,"about_ca_system_score_gemma":0.0010469484,"threshold_uncertainty_score":0.9174882},"labels":[],"label_agreement":null},{"id":"W4413293266","doi":"10.1016/j.jpubtr.2025.100135","title":"Bit by bit: A method for using bus data to develop plan bus priority interventions in Portland, Oregon, USA","year":2025,"lang":"en","type":"article","venue":"Journal of Public Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Social Sciences and Humanities Research Council of Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Bit (key); Plan (archaeology); Computer science; Psychological intervention; Telecommunications; Computer security; Psychology; Geography","score_opus":0.16634714802352896,"score_gpt":0.43223609744905217,"score_spread":0.2658889494255232,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413293266","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23890358,0.00012220343,0.7571282,0.0027522212,0.00039976503,0.0003073407,0.00029579762,0.000017595028,0.00007328796],"genre_scores_gemma":[0.8173238,0.00005608352,0.18122748,0.00017423638,0.000084622494,0.000011056826,0.00085345504,0.000011784115,0.0002575155],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99807584,0.0001603983,0.0009480577,0.00021911698,0.0003497724,0.00024683724],"domain_scores_gemma":[0.99819756,0.00018355243,0.0004610753,0.00013805964,0.00087947334,0.00014026057],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003081694,0.00011020447,0.0002574419,0.00061279983,0.0002317718,0.00012799358,0.00039162053,0.00012422228,0.000039932496],"category_scores_gemma":[0.00061861554,0.000112040485,0.00008382439,0.0016636539,0.000036125362,0.0010504073,0.000003534954,0.00016859015,6.770266e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005874319,0.0010297392,0.80795026,0.0005306932,0.00041168503,0.00004887854,0.036239736,0.024463931,0.0018484346,0.048525654,0.013491205,0.06487233],"study_design_scores_gemma":[0.0031692048,0.00015466515,0.88303554,0.00083819963,0.00034847972,0.0000045925794,0.007344905,0.003221252,0.00021569605,0.0013043854,0.09992399,0.00043910893],"about_ca_topic_score_codex":0.0015223401,"about_ca_topic_score_gemma":0.048379116,"teacher_disagreement_score":0.5784202,"about_ca_system_score_codex":0.00012886756,"about_ca_system_score_gemma":0.0010415878,"threshold_uncertainty_score":0.9689855},"labels":[],"label_agreement":null},{"id":"W4413304015","doi":"10.1155/atr/3607727","title":"On the Application of Probabilistic Route Choice Models to Urban Rail Transit Networks Containing Small‐Scale OD Trip Data","year":2025,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China; McGill University","keywords":"Transport engineering; Probabilistic logic; Scale (ratio); Computer science; Rail transit; Transit (satellite); Public transport; Engineering; Geography; Artificial intelligence; Cartography","score_opus":0.03363936408944567,"score_gpt":0.30029800946756346,"score_spread":0.2666586453781178,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413304015","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18334666,0.000117499345,0.81371194,0.0015149363,0.00023722126,0.0005292357,0.00004513502,0.000025305268,0.00047208852],"genre_scores_gemma":[0.9924874,0.00007031007,0.006833224,0.00025264223,0.00008243955,0.000017375633,0.00015981679,0.000010436113,0.00008639067],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9984639,0.000104474406,0.00070415647,0.00020328807,0.0003570375,0.00016711294],"domain_scores_gemma":[0.9982588,0.00051134155,0.00048566272,0.00024163527,0.0004249349,0.000077599085],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008968544,0.00011435455,0.00024474083,0.00013987794,0.00023044218,0.000029010876,0.00042660526,0.000086034896,0.0000065593154],"category_scores_gemma":[0.00011664179,0.00009541691,0.00007629612,0.0006437054,0.000067183,0.00046977255,0.0000015423784,0.00020683851,3.8635113e-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":[0.00041467228,0.00006584539,0.001406504,0.00002055353,0.00003074714,8.888194e-7,0.010695155,0.9449679,0.000117632255,0.03532466,0.000081313876,0.0068741157],"study_design_scores_gemma":[0.012966537,0.0019984758,0.48031855,0.0035935652,0.0021839028,0.0000025623654,0.052095383,0.34708095,0.0009607947,0.0639228,0.033267472,0.0016090265],"about_ca_topic_score_codex":0.00020736281,"about_ca_topic_score_gemma":0.0042804074,"teacher_disagreement_score":0.8091407,"about_ca_system_score_codex":0.00006248995,"about_ca_system_score_gemma":0.00020729052,"threshold_uncertainty_score":0.38909888},"labels":[],"label_agreement":null},{"id":"W4413332685","doi":"10.1016/j.ejtl.2025.100162","title":"Optimal departure time choices as quantiles and expectiles of the travel time distribution","year":2025,"lang":"en","type":"article","venue":"EURO Journal on Transportation and Logistics","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Quantile; Travel time; Distribution (mathematics); Environmental science; Computer science; Statistics; Operations research; Econometrics; Engineering; Mathematics; Transport engineering","score_opus":0.012376710756746798,"score_gpt":0.27956546266229604,"score_spread":0.26718875190554925,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413332685","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96801215,0.00043069271,0.024140788,0.0025087334,0.00034838152,0.00021887492,0.0002436101,0.000044102937,0.0040526823],"genre_scores_gemma":[0.9974045,0.00048480718,0.00030662716,0.00014869255,0.00004251584,0.000001177211,0.00007440675,0.000004570483,0.001532688],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99919933,0.00009716922,0.00026274784,0.00011149652,0.00022187708,0.00010736876],"domain_scores_gemma":[0.9994227,0.00017056846,0.00017246355,0.00004951111,0.00012386037,0.000060843937],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002475796,0.00008594976,0.000126523,0.000051231786,0.00047351373,0.00006284746,0.00008046256,0.00007741496,0.000055738146],"category_scores_gemma":[0.00014363074,0.000065534055,0.000044819666,0.00018271306,0.00022912577,0.00009342186,7.9881414e-7,0.00015745428,0.0000022250345],"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":[0.0014328846,0.0007719245,0.1709006,0.00028080118,0.00036866506,0.0000924231,0.056555044,0.11532632,0.0051166355,0.62577116,0.0097735245,0.013610016],"study_design_scores_gemma":[0.0007757914,0.00014010374,0.97635365,0.00020356056,0.0001772876,0.000004846665,0.0025672966,0.0015511641,0.0006017408,0.001213876,0.016230956,0.00017971279],"about_ca_topic_score_codex":0.00004866038,"about_ca_topic_score_gemma":0.000081562066,"teacher_disagreement_score":0.80545306,"about_ca_system_score_codex":0.00001253765,"about_ca_system_score_gemma":0.000093940864,"threshold_uncertainty_score":0.36419326},"labels":[],"label_agreement":null},{"id":"W4413540759","doi":"10.18280/ijsdp.200735","title":"Sustainable Urban Mobility Through Cycling Pathways","year":2025,"lang":"en","type":"article","venue":"International Journal of Sustainable Development and Planning","topic":"Transportation Planning and Optimization","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":"Universiti Sultan Zainal Abidin; Kementerian Tenaga, Teknologi Hijau dan Air; Tenaga Nasional Berhad","keywords":"Cycling; Environmental science; Environmental planning; Geography; Forestry","score_opus":0.018464374404193035,"score_gpt":0.2974952863268674,"score_spread":0.2790309119226744,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413540759","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93851256,0.0010912449,0.028022904,0.0010049818,0.0006620314,0.00015949887,0.0000011882468,0.000040450126,0.03050511],"genre_scores_gemma":[0.98359686,0.00008783784,0.005936618,0.0002019196,0.00016050598,0.000004341215,0.000013234282,0.0000050254557,0.0099936435],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","domain_scores_codex":[0.9986987,0.00005838749,0.00043531635,0.00012232775,0.00040455692,0.00028069437],"domain_scores_gemma":[0.9981287,0.00016852704,0.00023727455,0.000039875365,0.001364608,0.00006102355],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012118735,0.00009271605,0.0001440634,0.00030740848,0.00053592765,0.00022352039,0.00023753228,0.0000761458,0.00002664938],"category_scores_gemma":[0.00045971046,0.0000921165,0.00003871459,0.00026937,0.00006876676,0.000786599,0.000031532676,0.0001661081,7.070681e-7],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003527507,0.0001531564,0.17145775,0.00013883911,0.00032587914,0.0022070662,0.3245987,0.010013615,0.000020311192,0.48101443,0.0054946253,0.0042228703],"study_design_scores_gemma":[0.0008677696,0.000024194163,0.038110383,0.0002802137,0.0000275012,0.0000068256313,0.5793043,0.0000926733,0.0003649421,0.010336183,0.37038225,0.00020277123],"about_ca_topic_score_codex":0.0002060697,"about_ca_topic_score_gemma":0.000008154826,"teacher_disagreement_score":0.47067827,"about_ca_system_score_codex":0.00027218706,"about_ca_system_score_gemma":0.00095323153,"threshold_uncertainty_score":0.41219765},"labels":[],"label_agreement":null},{"id":"W4413726043","doi":"10.64628/aam.xumasmuuf","title":"Transit networks are key to smart growth in suburbs","year":2018,"lang":"en","type":"preprint","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Key (lock); Smart growth; Transit (satellite); Computer science; Business; Transport engineering; Computer security; Engineering; Public transport; Urban planning; Civil engineering","score_opus":0.02245708343984518,"score_gpt":0.28049858684123846,"score_spread":0.25804150340139326,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413726043","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28227833,0.00018035009,0.4980648,0.012365174,0.0035929705,0.0015962753,0.000059766608,0.0007874106,0.20107494],"genre_scores_gemma":[0.99253434,0.00010661094,0.003260976,0.00058410596,0.00033076142,0.000031657193,0.00012449334,0.000014977013,0.0030121028],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9987666,0.00011546726,0.00024923915,0.00034217085,0.00026112463,0.0002653966],"domain_scores_gemma":[0.9994498,0.000048898786,0.00007670447,0.00012812708,0.00016221605,0.0001342481],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00053509156,0.00013336688,0.0001991724,0.00014442783,0.0001451859,0.000096243326,0.0002477124,0.0003539926,0.0003673331],"category_scores_gemma":[0.000055001743,0.00014045353,0.00005903393,0.00032851673,0.00006490839,0.00006993582,0.000016078784,0.00025951807,0.0000389499],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006309158,0.0000758676,0.5749545,0.000050915656,0.00002578107,0.000018401688,0.04232558,0.35485408,5.6143307e-7,0.006305065,0.020739622,0.000586545],"study_design_scores_gemma":[0.000630273,0.00006256105,0.9506614,0.0007770121,0.000064465145,2.8997096e-7,0.0059521585,0.011772468,0.000027367561,0.003416976,0.02542234,0.0012126439],"about_ca_topic_score_codex":0.011522493,"about_ca_topic_score_gemma":0.06523293,"teacher_disagreement_score":0.710256,"about_ca_system_score_codex":0.000067467496,"about_ca_system_score_gemma":0.00013121864,"threshold_uncertainty_score":0.99505985},"labels":[],"label_agreement":null},{"id":"W4414030191","doi":"10.20944/preprints202509.0441.v1","title":"Decarbonizing Urban Transportation: A Case Study of Montreal","year":2025,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Business; Transport engineering; Regional science; Environmental planning; Geography; Engineering","score_opus":0.10768629606682774,"score_gpt":0.3749529798906745,"score_spread":0.2672666838238468,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414030191","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98244154,0.00008148193,0.0003468255,0.00011025063,0.0005121244,0.00096212805,0.000071776965,0.0002110504,0.015262799],"genre_scores_gemma":[0.99777013,0.00006318595,0.00026637744,0.000019711764,0.00010072305,0.000118662334,0.00009082319,0.000014059637,0.0015563046],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99770945,0.00027750936,0.00067383813,0.00062882487,0.0004742954,0.0002360506],"domain_scores_gemma":[0.9985075,0.00013209796,0.00040349056,0.0005207127,0.0003232119,0.000113006055],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007174219,0.00022072213,0.00039702034,0.00022389581,0.00030960524,0.00002181746,0.00036187324,0.00029645723,0.0001714639],"category_scores_gemma":[0.00010233142,0.00026193418,0.00013947312,0.00036049008,0.0000979707,0.000103247316,0.000042553813,0.00043759428,0.000009596631],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003179657,0.0003353955,0.7781746,0.00008875442,0.00011296816,0.00030433558,0.19489367,0.025352037,0.0000056461786,0.00035376218,0.000018561836,0.00032847977],"study_design_scores_gemma":[0.0010504423,0.000040512805,0.9163674,0.00031116087,0.0004895484,0.0000040518935,0.07952765,0.0003817012,0.0001723064,0.0003663803,0.00082628336,0.00046254782],"about_ca_topic_score_codex":0.11378617,"about_ca_topic_score_gemma":0.07810739,"teacher_disagreement_score":0.13819283,"about_ca_system_score_codex":0.00008338309,"about_ca_system_score_gemma":0.00049825007,"threshold_uncertainty_score":0.9999833},"labels":[],"label_agreement":null},{"id":"W4414036062","doi":"10.1093/imanum/draf067","title":"An ODE characterization of multi-marginal optimal transport with pairwise cost functions","year":2025,"lang":"en","type":"article","venue":"IMA Journal of Numerical Analysis","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Centre National de la Recherche Scientifique; Agence Nationale de la Recherche","keywords":"Mathematics; Ode; Characterization (materials science); Pairwise comparison; Applied mathematics; Mathematical optimization; Statistics","score_opus":0.013515396359522435,"score_gpt":0.2977670344118323,"score_spread":0.28425163805230985,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414036062","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3423639,0.000014170866,0.65688145,0.00049275666,0.00004549131,0.00004576246,0.000022249505,0.000012407298,0.00012182126],"genre_scores_gemma":[0.99032575,0.00004760622,0.009074071,0.000069530804,0.00003920204,0.0000024803296,0.00010517413,0.0000048815473,0.0003313161],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99879295,0.00012721459,0.0004628042,0.00012041077,0.00036165482,0.00013494382],"domain_scores_gemma":[0.99879014,0.000047126618,0.00039879253,0.00008856774,0.0005528294,0.00012255507],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036446674,0.00008595385,0.00031869952,0.00044381001,0.00019952419,0.000028286164,0.00014605351,0.00005182586,0.00016353506],"category_scores_gemma":[0.00002828853,0.00007020549,0.00019225298,0.0020950532,0.00012276552,0.00040587806,0.0000013021622,0.00012476218,9.896004e-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":[0.00043483303,0.000688103,0.5697943,0.000013645325,0.001165193,0.000018663832,0.0044421833,0.41502962,0.0015279251,0.00023181684,0.00002641259,0.0066273157],"study_design_scores_gemma":[0.00080002646,0.00019920336,0.9516584,0.00004615149,0.0026823953,0.0000018677015,0.0026488632,0.038945068,0.00015824188,0.0000050429553,0.0027082507,0.00014648386],"about_ca_topic_score_codex":0.00036642162,"about_ca_topic_score_gemma":0.00020635244,"teacher_disagreement_score":0.64796185,"about_ca_system_score_codex":0.000042377127,"about_ca_system_score_gemma":0.0002139512,"threshold_uncertainty_score":0.2862897},"labels":[],"label_agreement":null},{"id":"W4414063016","doi":"10.28924/2291-8639-23-2025-222","title":"Mastering Complexity: Fuzzy Logic-Driven Optimization for Multi-Objective Transport Solutions Using LINGO Software","year":2025,"lang":"en","type":"article","venue":"International Journal of Analysis and Applications","topic":"Transportation Planning and Optimization","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":"Taif University","keywords":"Fuzzy logic; Variance (accounting); Software; Stratified sampling; Randomized response; Work (physics); Face (sociological concept)","score_opus":0.07769313103438896,"score_gpt":0.37493529347103866,"score_spread":0.2972421624366497,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414063016","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005067049,0.00011026821,0.9933513,0.0007951944,0.00011143867,0.00019221385,0.000083912215,0.000022315784,0.00026632933],"genre_scores_gemma":[0.62509465,0.00022767174,0.37405896,0.00012633324,0.0001605105,0.000025367874,0.00013499348,0.000005788922,0.00016570427],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999069,0.000036212077,0.00041518972,0.00014207067,0.00022325151,0.000114280556],"domain_scores_gemma":[0.99835193,0.00009203489,0.00032897037,0.00006094341,0.0011076528,0.000058458427],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003034515,0.0000771329,0.00018419183,0.00053631177,0.00049167796,0.00008080062,0.00019308149,0.00006286666,0.000022846394],"category_scores_gemma":[0.0000623022,0.00007947521,0.00018336375,0.0006286861,0.00011823141,0.00021988263,0.000009118387,0.00007787343,2.4701103e-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":[0.000033557,0.00011996061,0.0262666,0.000007771921,0.00095605804,8.7858297e-7,0.0014839636,0.92942137,0.00007690984,0.038383476,0.000016320766,0.0032331177],"study_design_scores_gemma":[0.0024100419,0.00006316142,0.062265437,0.00019315995,0.0042872285,0.000006728465,0.007710943,0.8996152,0.000119415716,0.010118239,0.012724279,0.00048613676],"about_ca_topic_score_codex":0.00022138272,"about_ca_topic_score_gemma":0.00050809706,"teacher_disagreement_score":0.6200276,"about_ca_system_score_codex":0.00009323129,"about_ca_system_score_gemma":0.00012988303,"threshold_uncertainty_score":0.37816387},"labels":[],"label_agreement":null},{"id":"W4414276449","doi":"10.1287/msom.2024.1317","title":"Machine Learning–Augmented Optimization of Large Bilevel and Two-Stage Stochastic Programs: Application to Cycling Network Design","year":2025,"lang":"en","type":"article","venue":"Manufacturing & Service Operations Management","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Bilevel optimization; Representation (politics); Network planning and design; Exploit; Work (physics); Function (biology); Quality (philosophy); Stochastic programming; Decision problem","score_opus":0.015195676926077149,"score_gpt":0.28599040143827903,"score_spread":0.2707947245122019,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414276449","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008591074,0.000051202107,0.98738474,0.0011578168,0.00009446437,0.0015157133,0.000007668622,0.00016706043,0.0010302913],"genre_scores_gemma":[0.8621478,0.00006174197,0.13465485,0.0004780594,0.00003758111,0.00027336442,0.0003221124,0.000016219477,0.0020082877],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987256,0.00014526564,0.00033176137,0.0003134422,0.00022789631,0.00025602189],"domain_scores_gemma":[0.9994945,0.00005457638,0.000087190165,0.00016891354,0.00012551175,0.000069331196],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006866418,0.00013795726,0.00014582422,0.00020976106,0.00086486555,0.00015060043,0.0001608992,0.000049888047,0.00003599638],"category_scores_gemma":[0.000018563527,0.0001510036,0.00002229233,0.00060582143,0.000022793462,0.00019059713,0.00005101438,0.00009225154,0.00000586164],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026208832,0.00007050632,0.00039593966,0.00007327961,0.0000433274,4.6672577e-7,0.002137096,0.982402,0.000008976837,0.009024422,0.000014596934,0.0058031813],"study_design_scores_gemma":[0.00085362326,0.000036090605,0.00231946,0.00017064871,0.000108650136,8.752272e-8,0.0023168453,0.98961776,0.00011901239,0.00012878359,0.004127603,0.00020143815],"about_ca_topic_score_codex":0.0010661846,"about_ca_topic_score_gemma":0.0026540577,"teacher_disagreement_score":0.8535567,"about_ca_system_score_codex":0.00006908865,"about_ca_system_score_gemma":0.000033756536,"threshold_uncertainty_score":0.66519344},"labels":[],"label_agreement":null},{"id":"W4414350770","doi":"10.1287/trsc.2024.0840","title":"A Heteroscedastic Robust Bayesian Optimization Method for Solving Simulation-Based Transportation Problems","year":2025,"lang":"en","type":"article","venue":"Transportation Science","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"Government of Jiangsu Province; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Heteroscedasticity; Optimization problem; Bayesian probability; Gaussian process; Ranking (information retrieval); Constraint (computer-aided design); Bayesian optimization; Probabilistic logic; Bayesian inference","score_opus":0.03307681624404186,"score_gpt":0.35239952365310645,"score_spread":0.3193227074090646,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414350770","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003412947,0.0000281381,0.99291974,0.0009238509,0.00045269795,0.0011880556,0.00009917368,0.000336431,0.0006389565],"genre_scores_gemma":[0.7476216,0.000008537026,0.25115755,0.00030402557,0.000033864537,0.00015190049,0.00044835624,0.000016737768,0.0002573935],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973506,0.00006694175,0.00067988946,0.000652882,0.000751732,0.0004979574],"domain_scores_gemma":[0.99761784,0.0007580706,0.0002777139,0.00020688862,0.00097456836,0.0001648941],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0016900245,0.00020966024,0.00023443277,0.00061965076,0.0016968942,0.0002396046,0.00036141076,0.00014939961,0.000089936824],"category_scores_gemma":[0.000349573,0.00023724801,0.00012247756,0.0025233112,0.00038426564,0.001090908,7.218881e-7,0.000115824405,0.0000020185664],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000050853232,0.00005593964,0.0044845804,0.00007816934,0.00000806195,4.927608e-7,0.005663798,0.97655827,0.00037060885,0.011263347,0.000019147681,0.0014467468],"study_design_scores_gemma":[0.0007782752,0.00003963566,0.0134369815,0.00013473506,0.00009019187,1.8995756e-8,0.00089719216,0.982289,0.00048477884,0.0005656547,0.0010193818,0.00026415568],"about_ca_topic_score_codex":0.00049665465,"about_ca_topic_score_gemma":0.002817211,"teacher_disagreement_score":0.7442087,"about_ca_system_score_codex":0.00015938986,"about_ca_system_score_gemma":0.0011384736,"threshold_uncertainty_score":0.99960274},"labels":[],"label_agreement":null},{"id":"W4414370479","doi":"10.1093/jeea/jvaf039","title":"Pricing Congestion to Increase Traffic: The Case of Bogotá","year":2025,"lang":"en","type":"article","venue":"Journal of the European Economic Association","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Welfare; Congestion pricing; Deadweight loss; Traffic congestion","score_opus":0.008764013777441397,"score_gpt":0.2566588712316002,"score_spread":0.2478948574541588,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414370479","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97267574,0.000023452729,0.0006245575,0.0052832393,0.0007775736,0.00010867572,0.0000050905987,0.000008942913,0.020492725],"genre_scores_gemma":[0.99800473,0.00003347313,0.0001307481,0.00020805284,0.00018254294,3.4137662e-7,7.613915e-7,0.0000043695336,0.0014349995],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99873966,0.0006820873,0.00036519868,0.00005039271,0.000083165214,0.00007947687],"domain_scores_gemma":[0.99867356,0.00035600553,0.0007638376,0.00007077508,0.00010559954,0.000030248466],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036038763,0.000041867286,0.000093440794,0.0000675355,0.00028726496,0.00004503024,0.00017974516,0.000026396501,0.000010986461],"category_scores_gemma":[0.00092044036,0.00002914162,0.00007706454,0.0001329533,0.000023194476,0.00013493763,0.000009373767,0.00010682128,0.000006719992],"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":[0.000106327,0.00009079684,0.06497173,0.000017181326,0.0002644169,0.00004325179,0.044547368,0.79728997,0.000108119995,0.0078049954,0.025884088,0.058871765],"study_design_scores_gemma":[0.0022333364,0.00013169818,0.867655,0.0004189345,0.000653402,0.00006364772,0.025251387,0.005272964,0.0002551576,0.0009883706,0.09673292,0.0003432146],"about_ca_topic_score_codex":0.000218085,"about_ca_topic_score_gemma":0.00079999847,"teacher_disagreement_score":0.80268323,"about_ca_system_score_codex":0.00037537256,"about_ca_system_score_gemma":0.00015316057,"threshold_uncertainty_score":0.22094388},"labels":[],"label_agreement":null},{"id":"W4414393026","doi":"10.1155/atr/9728885","title":"Optimization of Transit Route and Frequency for Integrated Urban–Rural Transit Network","year":2025,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","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":"Wuhan University of Science and Technology; Wuhan University; National Natural Science Foundation of China","keywords":"Transit (satellite); Urban transit; Genetic algorithm; Plan (archaeology); Public transport; Transit system; Travel time; Bus network; Promotion (chess)","score_opus":0.007414907967706511,"score_gpt":0.2684764024418114,"score_spread":0.2610614944741049,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414393026","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14854665,0.000712029,0.84905857,0.0005646627,0.00048224378,0.0003368245,0.000052426454,0.0000272657,0.00021933072],"genre_scores_gemma":[0.89281356,0.0005995516,0.10618126,0.000059087306,0.00007969216,0.000007346138,0.00016227372,0.0000104768615,0.00008677333],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99873275,0.000057803234,0.0007107176,0.00010991953,0.00022347782,0.00016531571],"domain_scores_gemma":[0.99865043,0.0001350262,0.00043275548,0.000053262756,0.0006635636,0.000064949105],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042009205,0.00011251181,0.00028064594,0.00017076213,0.0001880613,0.000021139032,0.00009451773,0.000115223906,0.000016060118],"category_scores_gemma":[0.00004603923,0.00011124764,0.00011808835,0.0005220034,0.000094562354,0.0006025964,2.2052139e-7,0.00011841685,5.191841e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041641796,0.00003801449,0.009202082,0.0000651991,0.000059037447,0.0000016229395,0.0109602865,0.9651937,0.0003959195,0.009186511,0.00006686931,0.004414361],"study_design_scores_gemma":[0.030953659,0.0026808176,0.760059,0.0058744056,0.003948756,0.000008646293,0.110907555,0.027061852,0.0038788097,0.03097751,0.021616107,0.0020328735],"about_ca_topic_score_codex":0.00010320458,"about_ca_topic_score_gemma":0.00091401266,"teacher_disagreement_score":0.9381318,"about_ca_system_score_codex":0.000041869316,"about_ca_system_score_gemma":0.00024245257,"threshold_uncertainty_score":0.4536547},"labels":[],"label_agreement":null},{"id":"W4414402505","doi":"10.22215/cujs.v5i2.5323","title":"Substance Use and Long-Haul Truckers","year":2025,"lang":"en","type":"article","venue":"Carleton undergraduate journal of science.","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Canadian Centre on Substance Use and Addiction; Carleton University","funders":"","keywords":"Substance use; Harm; Truck; Work (physics); Addiction; Stigma (botany); Substance abuse","score_opus":0.02135498510630778,"score_gpt":0.29285012648525555,"score_spread":0.27149514137894776,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414402505","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9730716,0.00045733183,0.012147839,0.009105113,0.0010473077,0.00008438713,0.0000017539164,0.000024359944,0.004060322],"genre_scores_gemma":[0.99542016,0.0008439865,0.0017038393,0.00029792977,0.000041442494,4.3219322e-7,5.421289e-7,0.0000031621623,0.0016885275],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99873394,0.000059935664,0.00028806183,0.00014225813,0.0005495013,0.00022630623],"domain_scores_gemma":[0.9988435,0.00013518342,0.00021728333,0.000078432284,0.00056538184,0.0001602568],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012209314,0.000072333765,0.00013744301,0.00037877326,0.00068412186,0.0002885614,0.00025415712,0.00004438487,0.00000772653],"category_scores_gemma":[0.00032734877,0.000064482694,0.000045181503,0.0012480572,0.0015050786,0.0013001428,0.0000060860334,0.00013531066,9.128289e-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":[0.00015550069,0.00010468546,0.7302461,0.000042220243,0.00007939942,0.00011091822,0.026906675,0.00862155,0.0027221679,0.20876765,0.010938091,0.011305031],"study_design_scores_gemma":[0.0029893073,0.0002471397,0.8812634,0.00075162394,0.0002400258,0.00004789297,0.027217017,0.0010813494,0.0028311287,0.034332488,0.04828363,0.0007150191],"about_ca_topic_score_codex":0.00011196744,"about_ca_topic_score_gemma":0.00041115156,"teacher_disagreement_score":0.17443517,"about_ca_system_score_codex":0.00012040204,"about_ca_system_score_gemma":0.00073135603,"threshold_uncertainty_score":0.5545525},"labels":[],"label_agreement":null},{"id":"W4414580030","doi":"10.38094/jocef602110","title":"Analyzing Transit Stop Service Areas Using Geographic Information Systems (GIS): A Case Study of UET Taxila","year":2025,"lang":"en","type":"article","venue":"Journal of Civil Engineering Frontiers","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Rimouski","funders":"","keywords":"Transit (satellite); Public transport; Geographic information system; Transit system; Service (business); Kilometer; Rail transit; Information system; Flow network","score_opus":0.008626836812915518,"score_gpt":0.23927727860180828,"score_spread":0.23065044178889277,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414580030","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.57848996,0.0002728049,0.42030317,0.00005469587,0.00062055903,0.00011828853,0.0000032487653,0.000019722687,0.000117554664],"genre_scores_gemma":[0.997753,0.000027412358,0.0021544204,0.000011012504,0.000035090445,0.0000016070508,0.0000018664416,0.0000052260166,0.000010319714],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989167,0.00006495233,0.0005424719,0.000054644654,0.00028581833,0.00013545423],"domain_scores_gemma":[0.999062,0.0000454929,0.00033875357,0.00006951206,0.00041504393,0.00006919126],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007602972,0.00008812822,0.0002386405,0.00070804753,0.0001419992,0.0000682127,0.000111796166,0.00006827517,0.0000034998488],"category_scores_gemma":[0.00007182122,0.0000913764,0.00006836435,0.00092556706,0.000016084758,0.0006864504,0.0000028289744,0.0001499643,8.695026e-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":[0.000017760458,0.00003724585,0.019678915,0.000091355214,0.00014172701,0.00004828872,0.020273803,0.9592712,0.000029013641,0.00020111058,0.000093905044,0.000115701856],"study_design_scores_gemma":[0.0028853547,0.00023862805,0.017491477,0.0015051899,0.0009862162,0.00017243937,0.34427935,0.6275951,0.000032760527,0.00002794604,0.0042844242,0.0005010612],"about_ca_topic_score_codex":0.0026592563,"about_ca_topic_score_gemma":0.00095101766,"teacher_disagreement_score":0.4192631,"about_ca_system_score_codex":0.00007655832,"about_ca_system_score_gemma":0.00013873228,"threshold_uncertainty_score":0.40200177},"labels":[],"label_agreement":null},{"id":"W4414661800","doi":"10.1080/23249935.2025.2563182","title":"Railway booking demand forecasting for revenue management: a deep probabilistic approach","year":2025,"lang":"en","type":"article","venue":"Transportmetrica A Transport Science","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Demand forecasting; Probabilistic logic; Revenue; Probabilistic forecasting; Economic forecasting","score_opus":0.030645699340362143,"score_gpt":0.2920402080063593,"score_spread":0.26139450866599717,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414661800","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027302288,0.00033021005,0.90253466,0.00030292783,0.0004351195,0.0021803738,0.00008491604,0.0003075181,0.06652198],"genre_scores_gemma":[0.9395037,0.0001059874,0.057297315,0.00014556637,0.00005049698,0.0002736008,0.0000978191,0.000020031974,0.0025054808],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9967432,0.00004263934,0.000637399,0.0008589528,0.00085665984,0.00086116075],"domain_scores_gemma":[0.99875695,0.00020266406,0.00017422697,0.00031031767,0.00032879933,0.00022706998],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.002736669,0.00024688902,0.00034029566,0.0011732312,0.0020113483,0.00010851307,0.0007863696,0.00013283607,0.000024979361],"category_scores_gemma":[0.00014947128,0.0002598333,0.00018440449,0.0068164505,0.0009385462,0.0006280998,0.000004902935,0.00017413292,0.0000019538265],"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":[0.00063815294,0.0011011206,0.14062272,0.0026038298,0.00026319642,0.000107716,0.056362662,0.12663366,0.00019985378,0.5923276,0.0003705322,0.07876896],"study_design_scores_gemma":[0.012827328,0.0004721064,0.57544,0.0021484445,0.003200499,0.000014334645,0.016842468,0.092618175,0.0007793323,0.035890184,0.25524455,0.004522597],"about_ca_topic_score_codex":0.00018984618,"about_ca_topic_score_gemma":0.00043652082,"teacher_disagreement_score":0.9122014,"about_ca_system_score_codex":0.00016748454,"about_ca_system_score_gemma":0.0004809777,"threshold_uncertainty_score":0.9999854},"labels":[],"label_agreement":null},{"id":"W4414728602","doi":"10.1007/978-3-031-95111-4_23","title":"Understanding Urban Mobility: Dynamics of Transport Planning and Modal Preferences","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in civil engineering","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Public transport; Modal; Transportation planning; Modal shift; Mode (computer interface); Urban planning; Private transport; Originality; Mode choice","score_opus":0.03120942585192579,"score_gpt":0.24648886197300016,"score_spread":0.21527943612107436,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414728602","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00089995284,0.0012586305,0.9066574,0.00012427739,0.00031272083,0.0002816629,0.00007965617,0.0001370819,0.09024862],"genre_scores_gemma":[0.996649,0.00016332799,0.00146077,0.000010182835,0.000054067128,0.0000044576486,0.00011421446,0.000020939586,0.0015230294],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990286,0.000008017874,0.00029687915,0.00025297777,0.0002297011,0.00018382448],"domain_scores_gemma":[0.9993869,0.00034278247,0.000093446586,0.00009750312,0.00003367447,0.00004570616],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021581084,0.00019724773,0.00030761262,0.00029280066,0.000079154706,0.000018924575,0.00010916381,0.0003869258,0.000034909543],"category_scores_gemma":[0.000068668116,0.00022160816,0.000052107996,0.00011138536,0.000087414126,0.000076565484,0.00000614774,0.00036162676,9.46057e-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":[0.000015912714,0.000007281322,0.01327678,0.00036042047,0.00004624954,0.000008141409,0.009275196,0.8284962,0.0000033555916,0.1478693,0.000004849406,0.0006362916],"study_design_scores_gemma":[0.0021959979,0.00028075554,0.015929637,0.018592704,0.0007708329,0.0000048685015,0.0017053147,0.61320406,0.00011005106,0.33584183,0.007602934,0.003761038],"about_ca_topic_score_codex":0.00018604203,"about_ca_topic_score_gemma":0.0079905335,"teacher_disagreement_score":0.99574906,"about_ca_system_score_codex":0.00020247258,"about_ca_system_score_gemma":0.000105806066,"threshold_uncertainty_score":0.9036919},"labels":[],"label_agreement":null},{"id":"W4414730616","doi":"10.1007/978-3-031-95111-4_19","title":"Machine Learning for Sustainable Transportation Management: Predicting Bike Sharing System Ridership with Geospatial Features","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in civil engineering","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Geospatial analysis; Poisson regression; Support vector machine; Poisson distribution; Set (abstract data type); Ensemble learning; Random forest; Predictive modelling; Strengths and weaknesses","score_opus":0.0062351697598779935,"score_gpt":0.21016266496386798,"score_spread":0.20392749520398998,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414730616","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00011852049,0.0013981459,0.9020967,0.000117662734,0.00031139684,0.0013379307,0.00003999437,0.0007557228,0.09382394],"genre_scores_gemma":[0.93933207,0.00018481645,0.005497289,0.000022806176,0.00034364,0.0001344923,0.001053219,0.00011882268,0.053312875],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984508,0.000012826184,0.00032628697,0.00044464093,0.00032809674,0.00043736023],"domain_scores_gemma":[0.9992255,0.00029045634,0.00016519213,0.00012959236,0.00012991372,0.000059358725],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00038916687,0.00031688175,0.00032230566,0.00047626827,0.00035735228,0.00012352418,0.00018912833,0.0003670978,0.000023066435],"category_scores_gemma":[0.00010389799,0.00033799367,0.000087679735,0.00020450675,0.000025334923,0.00016643315,0.0000067327533,0.00056614866,4.0258985e-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":[0.000053373537,0.0000035283072,0.0015251468,0.0019130065,0.00008078107,0.000038374288,0.0036099772,0.8970258,4.3026867e-7,0.09478398,0.000008369176,0.00095724064],"study_design_scores_gemma":[0.00513196,0.00040838646,0.004622271,0.022728117,0.0017107031,0.000005929549,0.003741694,0.8367302,0.0000888873,0.0046832208,0.1165058,0.0036428722],"about_ca_topic_score_codex":0.000837691,"about_ca_topic_score_gemma":0.013743395,"teacher_disagreement_score":0.9392135,"about_ca_system_score_codex":0.00028248536,"about_ca_system_score_gemma":0.000073129624,"threshold_uncertainty_score":0.9999072},"labels":[],"label_agreement":null},{"id":"W4414731065","doi":"10.1007/978-3-031-95111-4_25","title":"An Analysis of Waiting Time in a Common-Line Problem Considering Waiting Time at Destination","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in civil engineering","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Arrival time; Travel time; Scope (computer science); Plan (archaeology); Duration (music); Time allocation; Work (physics)","score_opus":0.010709204703396239,"score_gpt":0.24917976513332904,"score_spread":0.2384705604299328,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414731065","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32888028,0.0035832736,0.3784716,0.0017258904,0.00083654706,0.004242457,0.0006885568,0.0027023524,0.27886903],"genre_scores_gemma":[0.99193555,0.000030145637,0.00592123,0.000024361352,0.00006271472,0.0000100919215,0.00061223167,0.00003696099,0.0013666977],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984249,0.000042931533,0.0006206976,0.0003376432,0.00029997452,0.0002738858],"domain_scores_gemma":[0.99855816,0.0008711114,0.00025703356,0.00015848056,0.00010207323,0.000053150266],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00061146455,0.0002478922,0.00057930424,0.00115169,0.000102397295,0.000037658996,0.00015687049,0.0003662735,0.00020158212],"category_scores_gemma":[0.0003098356,0.0003035584,0.00010572668,0.000613331,0.000044943856,0.00012502237,0.000018671231,0.0003758294,0.0000021443798],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006779762,0.000009784246,0.0023006203,0.00009543792,0.00008163543,0.000012227073,0.0029548688,0.9901057,0.00032550617,0.0024181637,0.0000018846624,0.0016873741],"study_design_scores_gemma":[0.00017284647,0.000015259164,0.0005230397,0.0010650817,0.00021592567,4.1125023e-7,0.00001128608,0.99684775,0.00013866651,0.00051403,0.00020356063,0.00029213008],"about_ca_topic_score_codex":0.0004897322,"about_ca_topic_score_gemma":0.018674288,"teacher_disagreement_score":0.6630553,"about_ca_system_score_codex":0.00032095023,"about_ca_system_score_gemma":0.00009779197,"threshold_uncertainty_score":0.99994165},"labels":[],"label_agreement":null},{"id":"W4415054187","doi":"10.1016/j.energy.2025.138852","title":"A quantitative assessment of daily transportation energy demand and electrification potential across the dwelling types in the Greater Toronto and Hamilton Area","year":2025,"lang":"en","type":"article","venue":"Energy","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Electrification; Vehicle miles of travel; Greenhouse gas; Energy consumption; Incentive; Consumption (sociology); Fossil fuel; Private transport; Electricity","score_opus":0.013392340548029054,"score_gpt":0.307455036356552,"score_spread":0.29406269580852296,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415054187","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.88059145,0.0011069851,0.11541111,0.0006318541,0.00006852285,0.00007931301,0.000009600337,0.000014267755,0.0020868995],"genre_scores_gemma":[0.99813914,0.0010617229,0.000361068,0.0001006185,0.000011785722,0.000017387547,0.000043802247,0.0000025821673,0.00026190342],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9993352,0.00017462786,0.00015101259,0.000117094394,0.00012834837,0.00009369343],"domain_scores_gemma":[0.9996846,0.00013034382,0.00006624696,0.000056362423,0.000050772924,0.000011690315],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004703692,0.000053333704,0.000072996125,0.000028191249,0.00024214867,0.000043608612,0.00006494434,0.00004758597,0.000005260503],"category_scores_gemma":[0.000010349134,0.000036172485,0.000014969273,0.00016525097,0.000090786896,0.00012759902,7.425267e-7,0.00003231697,2.1907091e-8],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018167934,0.00008000124,0.036933076,0.000024358153,0.00006377733,0.000003111315,0.14632718,0.04753317,0.0015493967,0.7597111,0.00010235601,0.007490776],"study_design_scores_gemma":[0.0006894294,0.00009923489,0.92546487,0.000072714596,0.00007823794,4.002197e-7,0.032327887,0.03165707,0.0009830121,0.0027263775,0.0057391822,0.00016156473],"about_ca_topic_score_codex":0.010414446,"about_ca_topic_score_gemma":0.046233688,"teacher_disagreement_score":0.8885318,"about_ca_system_score_codex":0.000019159588,"about_ca_system_score_gemma":0.000040753097,"threshold_uncertainty_score":0.9961753},"labels":[],"label_agreement":null},{"id":"W4415095202","doi":"10.58110/wp-8d85","title":"Evaluation of Development Policies of a Tourist Territory Confronted with Intensive Urban Development using a Multi-Agent Modeling","year":2025,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Zoning; Tourism; Baseline (sea); Competition (biology); Urban economics; Urban planning; Land use; Vitality","score_opus":0.06095264116204579,"score_gpt":0.30494265510233853,"score_spread":0.24399001394029274,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415095202","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4562783,0.00060245045,0.5367193,0.00044232688,0.00015217983,0.00072125904,0.000039910963,0.00007819483,0.0049660667],"genre_scores_gemma":[0.81259817,0.000059666414,0.18589559,0.000020021214,0.000008548732,0.000049709335,0.00033566434,0.000013595286,0.0010190492],"study_design_codex":"qualitative","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99455875,0.00295405,0.0007642025,0.0004142887,0.0010800964,0.00022862366],"domain_scores_gemma":[0.98638374,0.00026636926,0.00076598977,0.00048716986,0.012004899,0.000091806425],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00725824,0.00022243818,0.00036398886,0.00032620755,0.00042489756,0.00007705551,0.00047492058,0.0002072359,0.000022702377],"category_scores_gemma":[0.0010360056,0.00023560529,0.0000781413,0.00030876114,0.00025169522,0.000096322154,0.0001561705,0.0002368181,6.659583e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000052841733,0.00069429097,0.013314985,0.0004587712,0.00050355186,0.000001266929,0.7444286,0.21772431,0.00064518594,0.004732787,0.000105614825,0.017337814],"study_design_scores_gemma":[0.0019100843,0.0000010098283,0.012024107,0.012475699,0.00053461036,9.108692e-7,0.021423487,0.91579044,0.031997405,0.00017788442,0.0028782561,0.00078612816],"about_ca_topic_score_codex":0.009660015,"about_ca_topic_score_gemma":0.019042345,"teacher_disagreement_score":0.7230051,"about_ca_system_score_codex":0.00046363202,"about_ca_system_score_gemma":0.005062096,"threshold_uncertainty_score":0.99885756},"labels":[],"label_agreement":null},{"id":"W4415258162","doi":"10.1016/j.cstp.2025.101630","title":"Automated enforcement of bus-only lanes and crossings with policy and implementation insights from a Calgary case study","year":2025,"lang":"en","type":"article","venue":"Case Studies on Transport Policy","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Calgary Laboratory Services; SAIT Polytechnic; University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; Indian Institute of Science","keywords":"Enforcement; License; Process (computing); Global Positioning System; Agency (philosophy); Transit (satellite); Public transport; Pedestrian crossing","score_opus":0.01889047060213372,"score_gpt":0.3767994815001459,"score_spread":0.3579090108980122,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415258162","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99701124,0.0002869726,0.000065673186,0.00033760237,0.000040942,0.0005634204,0.000081770624,0.00013626809,0.0014760998],"genre_scores_gemma":[0.99905694,0.00019377559,0.00018635599,0.00022182902,0.000054925298,0.000033002772,0.000057657922,0.000008852005,0.00018667121],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.99897313,0.00006900613,0.0003267587,0.00026333958,0.00018843214,0.00017933668],"domain_scores_gemma":[0.9994111,0.00013099231,0.00013182824,0.00010150925,0.0001434399,0.00008114324],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014154699,0.00016248359,0.00026616832,0.00031084032,0.00075881084,0.0000334983,0.000037575894,0.000049518614,0.0000063782672],"category_scores_gemma":[0.000028613902,0.00013612321,0.000022442551,0.00054546905,0.00036273283,0.00013565751,0.000005764492,0.00006409937,2.3008027e-7],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028633678,0.0002789428,0.20159276,0.00018703408,0.00073749904,0.008678786,0.74404275,0.000699877,0.000022679631,0.036437847,0.000108189175,0.006927309],"study_design_scores_gemma":[0.007376929,0.0011270354,0.19523747,0.00032238037,0.00079561793,0.00042330413,0.79055303,0.00033309966,0.00022241994,0.0004976749,0.0024878443,0.0006232067],"about_ca_topic_score_codex":0.17035271,"about_ca_topic_score_gemma":0.14677821,"teacher_disagreement_score":0.046510283,"about_ca_system_score_codex":0.000072298004,"about_ca_system_score_gemma":0.0004342155,"threshold_uncertainty_score":0.86879086},"labels":[],"label_agreement":null},{"id":"W4415300083","doi":"10.1080/21680566.2025.2561863","title":"Learning heuristics for transit network design and improvement with deep reinforcement learning","year":2025,"lang":"en","type":"article","venue":"Transportmetrica B Transport Dynamics","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Reinforcement learning; Heuristics; Learning network; Network planning and design; Public transport; Transit (satellite)","score_opus":0.009609201457943787,"score_gpt":0.246766158077909,"score_spread":0.23715695661996522,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415300083","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0140971,0.00022090308,0.9817715,0.0003666778,0.00016456474,0.0014451225,0.000011097367,0.00034713472,0.0015758637],"genre_scores_gemma":[0.9436359,0.0010293984,0.05206836,0.00012349039,0.0000545675,0.00014043962,0.00071462244,0.00004592775,0.00218729],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99755734,0.00009048321,0.00063726335,0.00053265045,0.00047657665,0.00070569385],"domain_scores_gemma":[0.9987687,0.00040942722,0.00019333848,0.00014730521,0.00027868242,0.00020252987],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001130313,0.00032574832,0.00043800214,0.00036255334,0.0012945111,0.000064825,0.000199814,0.0002338435,0.000028101736],"category_scores_gemma":[0.000037950136,0.00033194828,0.00012438645,0.0016699296,0.00022502751,0.00021032576,0.0000017676946,0.00040670286,6.893768e-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":[0.00051239243,0.000053514214,0.050370086,0.00013646303,0.00013327946,0.000013375742,0.0045806975,0.9141099,0.0000030949573,0.01853008,0.000010593452,0.011546528],"study_design_scores_gemma":[0.007006247,0.0025736461,0.043290567,0.0004696048,0.0020735862,0.00000237781,0.010453163,0.9022661,0.000054859825,0.001568512,0.028543415,0.0016979473],"about_ca_topic_score_codex":0.00041860825,"about_ca_topic_score_gemma":0.0022394976,"teacher_disagreement_score":0.9297032,"about_ca_system_score_codex":0.00016387834,"about_ca_system_score_gemma":0.0003075125,"threshold_uncertainty_score":0.9999133},"labels":[],"label_agreement":null},{"id":"W4415445434","doi":"10.1016/j.tra.2025.104730","title":"Modelling inter-activity duration to capture weekly activity-travel dynamics: instilling inherent dynamics within daily travel demand models","year":2025,"lang":"en","type":"article","venue":"Transportation Research Part A Policy and Practice","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Copula (linguistics); Duration (music); Travel behavior; Mode (computer interface); Dependency (UML); Travel time; Scheduling (production processes); Mode choice","score_opus":0.1060473943975471,"score_gpt":0.4167225617253332,"score_spread":0.3106751673277861,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415445434","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14311078,0.000044870023,0.82350653,0.0186844,0.0001633978,0.0008457869,0.00018808476,0.00008362697,0.013372508],"genre_scores_gemma":[0.99156046,0.0004918654,0.0044420306,0.0003471163,0.00011384584,0.00011471228,0.0002348496,0.000023571352,0.0026715545],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9967209,0.00090830756,0.0004834465,0.0005433081,0.00079908845,0.00054493453],"domain_scores_gemma":[0.9971755,0.0013826808,0.00021964601,0.0002075793,0.0007168146,0.00029778344],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0032833035,0.00022512622,0.00028333178,0.00064083614,0.0012814573,0.0003278989,0.0002007647,0.0002822257,0.000013845922],"category_scores_gemma":[0.00072697917,0.00024852727,0.00006072038,0.0014573341,0.00023231446,0.0020009305,0.0000076665965,0.00075998023,0.0000055811297],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011526747,0.00021709946,0.0005791231,0.00012987826,0.00007883743,0.000012109693,0.06641157,0.6546637,0.000120854784,0.27270344,0.00015587252,0.003774873],"study_design_scores_gemma":[0.000716815,0.00010496067,0.0040239072,0.00025624235,0.00008988517,0.000001212671,0.028989857,0.95824,0.00012582196,0.0046734447,0.0024291768,0.0003487111],"about_ca_topic_score_codex":0.042025365,"about_ca_topic_score_gemma":0.06412607,"teacher_disagreement_score":0.84844965,"about_ca_system_score_codex":0.00039833697,"about_ca_system_score_gemma":0.00096610846,"threshold_uncertainty_score":0.9999967},"labels":[],"label_agreement":null},{"id":"W4415555204","doi":"10.1007/978-3-031-95115-2_19","title":"Data Driven Method for Assessing the Temporal Quality of Intermodal Connections at Suburban Rail Stations","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in civil engineering","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Punctuality; Public transport; Train; Schedule; Metric (unit); Reliability (semiconductor); Scheduling (production processes); Quality (philosophy); Service (business); Data-driven","score_opus":0.05885097763444291,"score_gpt":0.37230546757100286,"score_spread":0.31345448993655994,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415555204","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00006747536,0.00022807345,0.98874396,0.00073874,0.00044879026,0.0003619003,0.00070680835,0.00008079038,0.008623486],"genre_scores_gemma":[0.77137786,0.00012864052,0.20908535,0.00016519417,0.0005191837,0.00008174756,0.0052068504,0.00009775624,0.01333743],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988438,0.000063377396,0.00042789374,0.00028605957,0.00021194981,0.00016696018],"domain_scores_gemma":[0.99642044,0.002879346,0.00021288448,0.00033108096,0.00012487662,0.00003135584],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008860395,0.00016438702,0.00028384873,0.00017815114,0.00025205794,0.000054038992,0.00034508816,0.0002454185,0.0000620382],"category_scores_gemma":[0.0011895641,0.00015336637,0.00008553814,0.00013393305,0.00006824573,0.00013777865,0.000038275237,0.00030414792,3.3874528e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001455214,0.000013482908,0.0013142031,0.00024557798,0.000115576804,0.0000011268835,0.006360361,0.9246694,0.00008233255,0.063484155,0.00044367806,0.0032555491],"study_design_scores_gemma":[0.0013475473,0.000050873517,0.0050672274,0.0021854853,0.0004907642,0.0000018393116,0.00051541586,0.7251577,0.0001498619,0.01881083,0.24488331,0.0013391392],"about_ca_topic_score_codex":0.00069206976,"about_ca_topic_score_gemma":0.048508164,"teacher_disagreement_score":0.7796586,"about_ca_system_score_codex":0.00014309332,"about_ca_system_score_gemma":0.00022366743,"threshold_uncertainty_score":0.96885407},"labels":[],"label_agreement":null},{"id":"W4415555249","doi":"10.1007/978-3-031-95115-2_4","title":"Enhancing Passenger-Oriented Public Transport: Integrating Service Quality Dimensions into Network Design","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in civil engineering","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Headway; Crowding; Network planning and design; Public transport; Quality (philosophy); Intersection (aeronautics); Service quality","score_opus":0.019443380890788446,"score_gpt":0.263390856083578,"score_spread":0.24394747519278953,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415555249","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000804736,0.00092347595,0.9818626,0.0008884858,0.0009288165,0.00041251554,0.000013466808,0.00044590796,0.014444269],"genre_scores_gemma":[0.8061757,0.0005002421,0.18524374,0.0008696487,0.0011755681,0.000092583316,0.0007708622,0.00018709285,0.0049845474],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99773383,0.00009615037,0.0007089284,0.00050279085,0.0004432108,0.00051507936],"domain_scores_gemma":[0.9976485,0.001490992,0.00021027344,0.00026350914,0.00025086373,0.0001358551],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0011354783,0.00041309747,0.0005099621,0.0003320739,0.00037246587,0.00006317649,0.00024439802,0.00069967366,0.00013364639],"category_scores_gemma":[0.0007253991,0.00045717222,0.00012719017,0.000606679,0.000041600837,0.00017898604,0.000015433434,0.001000029,0.000004394871],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011920303,0.0000107969745,0.000327378,0.0001980963,0.000059050213,0.0000133449585,0.011558429,0.93128383,0.00009454492,0.05460464,0.000030429414,0.0018075486],"study_design_scores_gemma":[0.0044778455,0.00031005515,0.00412436,0.040427066,0.0013839058,0.00000958388,0.0022694883,0.35896036,0.0012876008,0.08033985,0.4934987,0.012911178],"about_ca_topic_score_codex":0.0012833328,"about_ca_topic_score_gemma":0.11133583,"teacher_disagreement_score":0.80609524,"about_ca_system_score_codex":0.00029513278,"about_ca_system_score_gemma":0.00042318652,"threshold_uncertainty_score":0.999788},"labels":[],"label_agreement":null},{"id":"W4416183561","doi":"10.1109/mt-its68460.2025.11223558","title":"Evaluating the Impact of Truck Platooning on Italy's Highway Network","year":2025,"lang":"","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canadian University Press; Ministero dell'Università e della Ricerca","keywords":"Truck; Limiting; Toll; Fuel efficiency; Consumption (sociology)","score_opus":0.04697504940068864,"score_gpt":0.3986074143078554,"score_spread":0.35163236490716676,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416183561","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.79725474,0.00037843385,0.0052723857,0.0008438052,0.0010488249,0.0004878379,0.000013515876,0.00008679002,0.19461368],"genre_scores_gemma":[0.99276376,0.000062759296,0.0010588483,0.00013439069,0.00016024981,0.000006687068,0.000015706157,0.000008224619,0.005789375],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979955,0.0003599402,0.00053729635,0.00026951783,0.00046892278,0.00036884903],"domain_scores_gemma":[0.9980983,0.0011262762,0.00030701136,0.00022159347,0.00018930569,0.000057501853],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018804984,0.00017322759,0.00024869305,0.000082863764,0.0010522433,0.00011482336,0.000282924,0.00014513485,0.00085837813],"category_scores_gemma":[0.00026486942,0.00011378688,0.00023491561,0.0013351367,0.00020611288,0.00015530948,0.0000126694185,0.00021874989,0.000014464056],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010741883,0.000040331484,0.029701576,0.000009890225,0.0001241179,3.478595e-7,0.008047704,0.91532445,0.000025556197,0.03860927,0.0024502564,0.005559097],"study_design_scores_gemma":[0.0014797982,0.0012767605,0.44348904,0.0011093218,0.00033705356,2.8828356e-7,0.009685807,0.5346839,0.00021658142,0.0066882274,0.00054573367,0.00048751247],"about_ca_topic_score_codex":0.0018602897,"about_ca_topic_score_gemma":0.00021228958,"teacher_disagreement_score":0.41378748,"about_ca_system_score_codex":0.00009803425,"about_ca_system_score_gemma":0.00059879856,"threshold_uncertainty_score":0.93986386},"labels":[],"label_agreement":null},{"id":"W4416306428","doi":"10.2139/ssrn.5763965","title":"Multivariate Insights into Users’ Preferences on Transit Development Choices in Emerging Cities","year":2025,"lang":"","type":"preprint","venue":"SSRN Electronic Journal","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"TRIPS architecture; Public transport; Multivariate probit model; Work (physics); Probit; Modal; Traffic congestion; Ordered probit; Probit model; Multivariate analysis","score_opus":0.020268722905231407,"score_gpt":0.29156820074891093,"score_spread":0.2712994778436795,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416306428","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93954843,0.0067423424,0.03991608,0.0014790748,0.0028215002,0.0009559858,0.000009510526,0.0001330302,0.00839403],"genre_scores_gemma":[0.9617249,0.032499965,0.0011935913,0.00009481592,0.00029197198,0.00004959454,0.00006209325,0.000025272922,0.004057805],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.99306077,0.0007692456,0.0014736237,0.00085714244,0.0011090501,0.0027301726],"domain_scores_gemma":[0.99819183,0.00028125264,0.0008288664,0.00021309136,0.00030136792,0.00018358878],"candidate_categories":["metaepi_narrow","sts","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0029981972,0.0006434205,0.0007161359,0.0011233863,0.0017327113,0.0003815282,0.0009887036,0.0006235644,0.000095030635],"category_scores_gemma":[0.00009774931,0.00063019345,0.00022960917,0.00091853604,0.0001856819,0.00055729796,0.000046420715,0.005512566,0.000017281136],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004515059,0.00037337444,0.012104592,0.00020101736,0.0008149326,0.000010146325,0.5049606,0.31036887,0.00002078188,0.058179338,0.000010475528,0.112504385],"study_design_scores_gemma":[0.00791904,0.0011636099,0.14391449,0.012390133,0.0007160725,0.000012426956,0.41756546,0.008400472,0.000640996,0.36224052,0.040040765,0.0049960045],"about_ca_topic_score_codex":0.0059074126,"about_ca_topic_score_gemma":0.15930612,"teacher_disagreement_score":0.30406117,"about_ca_system_score_codex":0.002870881,"about_ca_system_score_gemma":0.017283332,"threshold_uncertainty_score":0.99961495},"labels":[],"label_agreement":null},{"id":"W4416396310","doi":"10.1016/j.tbs.2025.101171","title":"Analyzing sequential activity and travel decisions with interpretable deep inverse reinforcement learning","year":2025,"lang":"en","type":"article","venue":"Travel Behaviour and Society","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Singapore-MIT Alliance for Research and Technology Centre; University of Florida","keywords":"Interpretability; Reinforcement learning; Travel behavior; Function (biology); Multinomial logistic regression; TRIPS architecture; Artificial neural network; Behavioral modeling; Deep learning","score_opus":0.019240713605036992,"score_gpt":0.2930527176155078,"score_spread":0.2738120040104708,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416396310","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.74398017,0.00003762303,0.2525397,0.0001730085,0.00005145239,0.00013340628,0.0000029563175,0.000039896917,0.003041794],"genre_scores_gemma":[0.99396515,0.00037068914,0.0027694877,0.00006220827,0.000012908167,0.000006149005,0.000021708645,0.0000054994725,0.0027862275],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9992806,0.000055131834,0.00012429566,0.00020084827,0.00015320696,0.00018596505],"domain_scores_gemma":[0.9996778,0.00006380979,0.00005323569,0.00005695051,0.000060033923,0.000088200795],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033455805,0.000095773656,0.00013804229,0.000040228042,0.0007934141,0.00010539068,0.00005142145,0.00009875236,0.000028957442],"category_scores_gemma":[0.000021685093,0.00008956289,0.00005128082,0.0002087459,0.0001827333,0.00021093758,0.000010995715,0.00019139706,6.1360197e-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":[0.00016211905,0.00013194824,0.6990821,0.00005138509,0.00023538369,0.000009673104,0.25790897,0.017990904,0.0017946474,0.009951849,0.00049463374,0.012186426],"study_design_scores_gemma":[0.0013770364,0.00009273394,0.9051126,0.00016344806,0.00034371106,0.0000014736884,0.06402471,0.027895097,0.0003276155,0.0001038868,0.00022656347,0.0003311234],"about_ca_topic_score_codex":0.0016423523,"about_ca_topic_score_gemma":0.0011717229,"teacher_disagreement_score":0.24998495,"about_ca_system_score_codex":0.000044838467,"about_ca_system_score_gemma":0.000105656785,"threshold_uncertainty_score":0.610238},"labels":[],"label_agreement":null},{"id":"W4416522196","doi":"10.1016/j.tra.2025.104747","title":"A comprehensive analysis of duplication in public transportation","year":2025,"lang":"en","type":"article","venue":"Transportation Research Part A Policy and Practice","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"WSP (Canada); York University","funders":"","keywords":"Gene duplication; Service (business); Schedule; Public transport; Perspective (graphical); Trajectory; Service level; Service provider","score_opus":0.15165920819556228,"score_gpt":0.48949171350008747,"score_spread":0.3378325053045252,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416522196","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8681919,0.0008557591,0.016482277,0.06663991,0.00013411476,0.0016574607,0.00041268519,0.0001601608,0.045465745],"genre_scores_gemma":[0.99588597,0.0019212005,0.0007437622,0.00029500568,0.000026061325,0.00007565066,0.00056959636,0.0000054366396,0.00047729342],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99760896,0.000780068,0.00049322797,0.00027176455,0.00054842344,0.00029757048],"domain_scores_gemma":[0.99610066,0.0025931695,0.00015804799,0.00014349377,0.0009057229,0.000098880046],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002101778,0.000087022716,0.00023118222,0.0019722446,0.00033439804,0.00005999731,0.000120487675,0.0001309217,0.000059383667],"category_scores_gemma":[0.0009786871,0.00009969432,0.000060157825,0.0071960115,0.00032603092,0.0007336107,9.920982e-7,0.0002274851,0.0000021275516],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039662808,0.00026004046,0.08461733,0.00010981699,0.0003489623,0.0000059046984,0.045448106,0.008175714,0.0001759968,0.8556726,0.00031499978,0.0044738925],"study_design_scores_gemma":[0.0004941696,0.000029677827,0.76560193,0.00004186142,0.00023100329,4.2439954e-8,0.011903886,0.0005806988,0.00003211928,0.0012992553,0.21968792,0.00009741508],"about_ca_topic_score_codex":0.02290605,"about_ca_topic_score_gemma":0.03829716,"teacher_disagreement_score":0.85437334,"about_ca_system_score_codex":0.000065658605,"about_ca_system_score_gemma":0.00060631416,"threshold_uncertainty_score":0.9836005},"labels":[],"label_agreement":null},{"id":"W4416573488","doi":"10.7202/1121801ar","title":"Le 3 <sup>e</sup>  lien à Québec : vers une nouvelle ère de mobilité ? Analyse des impacts potentiels à partir d’une simulation multi-agent","year":2025,"lang":"en","type":"article","venue":"Canadian Journal of Regional Science","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"Polytechnique Montréal; Université Laval","funders":"","keywords":"Lien; ESPACE; Work (physics); Limiting","score_opus":0.02384553840211306,"score_gpt":0.289664186713857,"score_spread":0.26581864831174395,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416573488","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9712737,0.00025593542,0.019094875,0.007575119,0.00013862406,0.00011252718,0.000007526301,0.000014457075,0.0015272301],"genre_scores_gemma":[0.9964152,0.00003585324,0.0026485347,0.00030192104,0.000043965094,0.0000012592948,0.0000028152535,0.0000042158877,0.0005462573],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9986428,0.00007880201,0.00029364624,0.00017008222,0.00039335605,0.00042131884],"domain_scores_gemma":[0.9982918,0.00013060922,0.00017466056,0.00009827769,0.0005577074,0.0007469647],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0010445481,0.000087917346,0.00013099247,0.00042473484,0.001523113,0.00014510541,0.00037861877,0.00006405797,0.000032160086],"category_scores_gemma":[0.0005962002,0.000086202104,0.000090766494,0.001954678,0.0011698639,0.0006141566,0.0000043856116,0.00012951218,0.0000033200145],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014008692,0.000021880884,0.06637777,0.000005388799,0.000014550111,0.000023731529,0.02775121,0.90181756,0.00006414027,0.0014441827,0.0004199495,0.0020456375],"study_design_scores_gemma":[0.0015746622,0.00015287356,0.656607,0.00041272023,0.00013756123,0.000018860424,0.098926656,0.18627664,0.0002473997,0.0017078469,0.05345545,0.00048235286],"about_ca_topic_score_codex":0.52287316,"about_ca_topic_score_gemma":0.7007593,"teacher_disagreement_score":0.7155409,"about_ca_system_score_codex":0.0008004245,"about_ca_system_score_gemma":0.023343312,"threshold_uncertainty_score":0.9997768},"labels":[],"label_agreement":null},{"id":"W4417070475","doi":"10.1155/atr/7174563","title":"Univariate Probability Density Estimation With Partially Monotone Neural Networks: A Case Study on Shopping Activity Durations","year":2025,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","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":"Fundamental Research Funds for the Central Universities","keywords":"Univariate; Robustness (evolution); Probability density function; Monotonic function; Probability distribution; Monotone polygon; Cumulative distribution function; TRIPS architecture; Focus (optics); Artificial neural network","score_opus":0.019182382899295164,"score_gpt":0.31090929997751254,"score_spread":0.2917269170782174,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417070475","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6664302,0.000006322319,0.33248505,0.00040065518,0.00018194689,0.00041755263,0.0000026670184,0.000036281388,0.000039327337],"genre_scores_gemma":[0.99327093,0.000009826449,0.0065763616,0.000038649305,0.00004472757,0.000014484036,0.00001505392,0.000007713111,0.000022274764],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99861413,0.00021480577,0.00047886648,0.00018390692,0.0003460649,0.00016220805],"domain_scores_gemma":[0.99863225,0.00017923643,0.00047808458,0.00011401996,0.00051534554,0.00008104689],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00059388316,0.00012751475,0.00021976973,0.00016654246,0.00054372847,0.000059456816,0.00007730231,0.00006635856,0.000004984989],"category_scores_gemma":[0.000081279046,0.00011558604,0.000062924235,0.0006246148,0.00006750085,0.0009251525,7.4252443e-7,0.00024164157,1.9232779e-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":[0.0006724833,0.0003535194,0.036101524,0.00001158089,0.000048854403,0.00026290354,0.014156191,0.94250023,0.00004938175,0.00090321654,0.000002608671,0.0049374816],"study_design_scores_gemma":[0.0027242424,0.0007550964,0.9197679,0.0001717966,0.00036672872,0.000023899294,0.02160946,0.053621516,0.00009579381,0.00059432816,0.000029910532,0.00023928347],"about_ca_topic_score_codex":0.00031834003,"about_ca_topic_score_gemma":0.012992958,"teacher_disagreement_score":0.88887876,"about_ca_system_score_codex":0.00012041061,"about_ca_system_score_gemma":0.00023122077,"threshold_uncertainty_score":0.7250376},"labels":[],"label_agreement":null},{"id":"W4417149909","doi":"10.1177/03611981251391715","title":"Decomposition and Sensitivity Analysis of Bus Travel Times","year":2025,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Bureau de Coopération Interuniversitaire; Bell (Canada)","funders":"","keywords":"Dwell time; Transit (satellite); Travel time; Sensitivity (control systems); Database transaction; Reliability (semiconductor); Service (business); Variable (mathematics); Decomposition","score_opus":0.05490021962968468,"score_gpt":0.42245758623819596,"score_spread":0.3675573666085113,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417149909","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9796224,0.00024212389,0.013895957,0.00411966,0.00029231946,0.00057950657,0.000100091675,0.0000215294,0.0011263868],"genre_scores_gemma":[0.99580485,0.0012715881,0.0018624496,0.000042727483,0.000049878247,0.000015527416,0.00004252298,0.000014585551,0.00089590315],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9935526,0.0020538187,0.001147141,0.0003177569,0.0024094814,0.00051919476],"domain_scores_gemma":[0.9932674,0.0019366918,0.00044606882,0.0002642313,0.0038598115,0.00022575217],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0072239335,0.00015985822,0.00052405853,0.0024213728,0.0009704386,0.00010253975,0.0003489343,0.00020943533,0.00015956955],"category_scores_gemma":[0.00028144146,0.0001359144,0.00036839177,0.0060183555,0.0012161034,0.0005197666,0.0000032928078,0.000959197,0.0000017163957],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014452362,0.00039407137,0.87267184,0.0002544248,0.001454349,0.000065276115,0.023218453,0.021113027,0.0037194567,0.06617557,0.002323965,0.0071643246],"study_design_scores_gemma":[0.0007090483,0.00012080864,0.9859301,0.0002381942,0.00051258155,8.215032e-8,0.0063975924,0.0008058225,0.00082450744,0.00208101,0.0022659227,0.000114349794],"about_ca_topic_score_codex":0.028596483,"about_ca_topic_score_gemma":0.15523385,"teacher_disagreement_score":0.12663737,"about_ca_system_score_codex":0.00015082826,"about_ca_system_score_gemma":0.0008116044,"threshold_uncertainty_score":0.9778722},"labels":[],"label_agreement":null},{"id":"W4417213063","doi":"10.1016/j.trc.2025.105484","title":"Statistical inference of boarding and alighting counts in transit systems with incomplete data","year":2025,"lang":"en","type":"article","venue":"Transportation Research Part C Emerging Technologies","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Exfo Electro-Optical Engineering (Canada); McGill University","funders":"","keywords":"Statistical inference; Transit (satellite); Statistical analysis; Transit system; Statistical model; Inference; Complete information; Public transport","score_opus":0.12147919891877015,"score_gpt":0.4384878932874818,"score_spread":0.3170086943687116,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417213063","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6484345,0.0027175916,0.31431475,0.007452101,0.00038360708,0.0018906099,0.00079963787,0.0015742435,0.02243296],"genre_scores_gemma":[0.9963239,0.0005350294,0.002912495,0.0000029165417,0.000004013327,0.000018955576,0.000116345786,0.000003945333,0.00008243195],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9989744,0.00007817862,0.00022752302,0.00019986452,0.000330165,0.00018988659],"domain_scores_gemma":[0.99932295,0.00032040523,0.000042360352,0.00016116757,0.00013620402,0.000016917631],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009158175,0.000054871256,0.00012489143,0.00031442178,0.000178069,0.0000310409,0.00022016944,0.000073735035,0.0000112186035],"category_scores_gemma":[0.00019836532,0.00005140867,0.000005232082,0.0007388606,0.00038342914,0.00016386408,0.000005922937,0.00017355486,9.78249e-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":[0.00006956441,0.000044823762,0.3908785,0.0003675442,0.00004120008,0.000021786036,0.005694005,0.0077997115,0.00008112471,0.5820523,0.0011058323,0.011843604],"study_design_scores_gemma":[0.0031076337,0.00029085684,0.5261676,0.0056187636,0.00015418597,6.8172415e-7,0.18890339,0.04916486,0.00065771275,0.015907677,0.20894042,0.001086222],"about_ca_topic_score_codex":0.0017157724,"about_ca_topic_score_gemma":0.0032315229,"teacher_disagreement_score":0.56614465,"about_ca_system_score_codex":0.000018484421,"about_ca_system_score_gemma":0.000141387,"threshold_uncertainty_score":0.25937462},"labels":[],"label_agreement":null},{"id":"W48780797","doi":"","title":"Tools and Methods for a Transportation Household Survey","year":2008,"lang":"en","type":"article","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Public transport; Survey data collection; Travel survey; Transport engineering; Transportation planning; Computer science; Intelligent transportation system; Software; Sample (material); Planner; Travel behavior; Task (project management); Operations research; Engineering; Systems engineering","score_opus":0.04870422374430391,"score_gpt":0.3170527857244906,"score_spread":0.2683485619801867,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W48780797","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15606016,0.0003975227,0.8410229,0.00082963065,0.000081250975,0.0006366838,0.00034915173,0.00043790543,0.00018478233],"genre_scores_gemma":[0.6088499,0.00065384485,0.38852814,0.00053194596,0.000060405167,0.00027922334,0.00038923553,0.00003067099,0.0006766459],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9985828,0.00029030084,0.0003032475,0.0002730122,0.00018559163,0.0003650929],"domain_scores_gemma":[0.9987911,0.0005665622,0.00013387279,0.00017045894,0.00014349131,0.00019448684],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016757104,0.00014506448,0.00021029677,0.00016759592,0.00073155406,0.00008980093,0.00014987533,0.00021131225,0.000010936349],"category_scores_gemma":[0.00044098834,0.00015963388,0.0000733965,0.00038231083,0.00015121719,0.00042340567,0.0000033716283,0.00012631125,7.088579e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036343958,0.00021569243,0.84408593,0.00005502587,0.000069639114,0.000019514011,0.021494206,0.0121492585,0.0011623198,0.054612726,0.004301046,0.061471213],"study_design_scores_gemma":[0.0004972902,0.00006667102,0.9764576,0.000017300346,0.000028917702,0.000004702702,0.00036375775,0.0064167604,0.0007165545,0.0008324568,0.014315758,0.00028221842],"about_ca_topic_score_codex":0.019308772,"about_ca_topic_score_gemma":0.02569388,"teacher_disagreement_score":0.45278975,"about_ca_system_score_codex":0.00008738655,"about_ca_system_score_gemma":0.00023732639,"threshold_uncertainty_score":0.9920847},"labels":[],"label_agreement":null},{"id":"W560580117","doi":"","title":"OPUS: High-Performance Automated Fare Collection for Quebec's Public Transport","year":2012,"lang":"en","type":"article","venue":"Public transport international","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Public transport; Opus; Software deployment; Ticket; Revenue; Smart card; Transport engineering; General partnership; Business; Bus rapid transit; Transit (satellite); Telecommunications; Finance; Engineering; Computer science; Computer security","score_opus":0.02805708844949968,"score_gpt":0.28617459389885597,"score_spread":0.2581175054493563,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W560580117","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8971183,0.00009844176,0.06291863,0.015679725,0.005521333,0.0010332915,0.00056665135,0.002357668,0.014705966],"genre_scores_gemma":[0.98652065,0.00009304626,0.002839989,0.00028554056,0.0006444191,0.00025033453,0.0041854144,0.00003628663,0.0051443507],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9976621,0.000050140632,0.0005314235,0.00031827035,0.00077335077,0.0006646862],"domain_scores_gemma":[0.99863964,0.00007916113,0.00019926175,0.00014251795,0.00062882015,0.00031059663],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00088637334,0.0002100707,0.00021174113,0.00038324454,0.0007295097,0.000113026035,0.00041107944,0.0002294811,0.00097648427],"category_scores_gemma":[0.000070136644,0.00022792211,0.00013582774,0.00063573744,0.00014522036,0.0025248276,0.0000027993697,0.00015934571,0.000026094032],"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":[0.0001072475,0.00043870576,0.8917575,0.00004324843,0.00017166264,0.0000017243709,0.0085739065,0.0014286048,0.00003795231,0.08997667,0.0032831884,0.0041795764],"study_design_scores_gemma":[0.0009519073,0.000028497554,0.675998,0.00002247096,0.00003823626,0.0000022016911,0.0007341585,0.002398329,0.00008116181,0.000053673906,0.31938556,0.0003058604],"about_ca_topic_score_codex":0.0037431663,"about_ca_topic_score_gemma":0.010614769,"teacher_disagreement_score":0.31610236,"about_ca_system_score_codex":0.00031002413,"about_ca_system_score_gemma":0.0005413148,"threshold_uncertainty_score":0.99993676},"labels":[],"label_agreement":null},{"id":"W561118968","doi":"","title":"DESIGN AND IMPLEMENTATION OF TRANSIT PRIORITY AT SIGNALIZED INTERSECTIONS: A PRIMER OF TRANSIT MANAGERS AND A REVIEW OF NORTH AMERICAN EXPERIENCE","year":2000,"lang":"en","type":"review","venue":"","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":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Bus priority; Transit (satellite); Transport engineering; Schedule; Traffic congestion; Level of service; Bus rapid transit; Public transport; Computer science; Engineering","score_opus":0.040341679569913656,"score_gpt":0.37434259632721567,"score_spread":0.33400091675730204,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W561118968","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016771551,0.97371554,0.022634722,0.000028015485,0.000021700613,0.0016309331,0.00004966745,0.000022946466,0.00021931125],"genre_scores_gemma":[0.0031171013,0.99366915,0.0030055144,0.00002255734,0.0000048494057,0.000051731007,0.000062887244,0.000009763952,0.00005646589],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99825525,0.0003653375,0.00076427433,0.00023922916,0.00025722277,0.00011867293],"domain_scores_gemma":[0.99901515,0.00014715998,0.0005866215,0.00009989042,0.00009067502,0.00006049985],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037773314,0.00016077653,0.0009482088,0.000137498,0.00008733882,0.000006598422,0.000094041134,0.000047881706,0.00023421062],"category_scores_gemma":[0.000011434841,0.00014249122,0.0001338488,0.00063679746,0.00040638424,0.00012339628,0.0000034695663,0.00006206801,2.3902194e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000037343474,0.000031288855,0.0006901015,0.025972307,0.000109793866,8.015697e-7,0.017242149,0.000040926836,0.0000012669827,0.000034789944,0.000028856542,0.95581037],"study_design_scores_gemma":[0.0027573095,0.0013870706,0.017869893,0.12301277,0.010277276,0.000019709554,0.018957488,0.000095773656,0.00015760503,0.000024261035,0.8232259,0.002214947],"about_ca_topic_score_codex":0.004365673,"about_ca_topic_score_gemma":0.0038565765,"teacher_disagreement_score":0.9535954,"about_ca_system_score_codex":0.000034384073,"about_ca_system_score_gemma":0.00017350014,"threshold_uncertainty_score":0.65996206},"labels":[],"label_agreement":null},{"id":"W561364930","doi":"","title":"TRAFFIC MANAGEMENT SYSTEM FOR TURKISH HIGHWAYS","year":2006,"lang":"en","type":"article","venue":"PROCEEDINGS OF THE 13th ITS WORLD CONGRESS, LONDON, 8-12 OCTOBER 2006","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Toll; Turkish; Transport engineering; Quarter (Canadian coin); Data collection; Management system; Computer science; Business; Engineering; Operations management; Geography","score_opus":0.010837453442314867,"score_gpt":0.23237818893790801,"score_spread":0.22154073549559314,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W561364930","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.52020234,0.0023189783,0.00045255647,0.007615759,0.0062036174,0.006434947,0.00039903258,0.0024297927,0.45394298],"genre_scores_gemma":[0.94635427,0.000033175253,0.001585875,0.00009002118,0.0003531147,0.00010795903,0.00002891444,0.00003471108,0.051411986],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99805444,0.000016894597,0.00052067055,0.0003608378,0.00058910355,0.00045804083],"domain_scores_gemma":[0.99874747,0.00007120735,0.00047309848,0.00011779888,0.00051487697,0.00007556257],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056005234,0.00021425965,0.00027810017,0.00019206933,0.0006207707,0.00017619292,0.00058048643,0.00009712939,0.000026303507],"category_scores_gemma":[0.000033579716,0.0001805295,0.00016698097,0.0007147263,0.00018844512,0.00037443262,0.000027602237,0.00011465335,0.000011171142],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022168111,0.00028158908,0.05840722,0.0013816485,0.00015824998,0.000003164714,0.0019164953,0.00992224,0.0002539886,0.5413729,0.38398787,0.002092956],"study_design_scores_gemma":[0.0038533749,0.000060669445,0.048783496,0.0013584511,0.0006117097,0.0000035124935,0.002803686,0.0029802306,0.006651017,0.0013533311,0.9305029,0.0010376328],"about_ca_topic_score_codex":0.00032617885,"about_ca_topic_score_gemma":0.0013405845,"teacher_disagreement_score":0.54651505,"about_ca_system_score_codex":0.000121665085,"about_ca_system_score_gemma":0.000054122436,"threshold_uncertainty_score":0.736178},"labels":[],"label_agreement":null},{"id":"W562290735","doi":"","title":"Pacific Transit Trifecta","year":2008,"lang":"en","type":"article","venue":"Railway age","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Light rail transit; Transit (satellite); Light rail; Rail transit; Transport engineering; Public transport; Transit system; Rapid transit; Service (business); Transit-oriented development; West coast; Engineering; Business; Geology; Oceanography","score_opus":0.026593507116154436,"score_gpt":0.2659457583833047,"score_spread":0.23935225126715026,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W562290735","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5199831,0.00020595157,0.015942229,0.001823853,0.0005332964,0.00028552447,0.000019189907,0.00057506276,0.4606318],"genre_scores_gemma":[0.98714745,0.00016300312,0.00075467717,0.00006700455,0.00009367899,0.0000040162054,0.000031012125,0.000005756807,0.011733388],"study_design_codex":"qualitative","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994203,0.000056689554,0.00009294635,0.00010574169,0.0001815779,0.00014274224],"domain_scores_gemma":[0.99977607,0.000043514003,0.00002399772,0.00006466696,0.000025993106,0.00006574591],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015801974,0.000047370464,0.000069515714,0.00004065897,0.0005010722,0.000016832406,0.00007138619,0.000059007107,0.00038629337],"category_scores_gemma":[0.000027206026,0.000048139413,0.000040498475,0.00021286313,0.00011343285,0.000112930815,6.828223e-7,0.000058471316,0.00008394168],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018264935,0.00042375404,0.10035762,0.00004791404,0.00008908916,0.0012054053,0.71982753,0.010143505,0.0016609837,0.070392676,0.079525605,0.016143253],"study_design_scores_gemma":[0.0009612262,0.00005223335,0.116114825,0.000022702017,0.000024353654,0.000005578874,0.0047377804,0.00015181141,0.00027326494,0.00043767403,0.87685204,0.00036651845],"about_ca_topic_score_codex":0.00021959087,"about_ca_topic_score_gemma":0.00027740057,"teacher_disagreement_score":0.79732645,"about_ca_system_score_codex":0.000014017791,"about_ca_system_score_gemma":0.000058843234,"threshold_uncertainty_score":0.42296416},"labels":[],"label_agreement":null},{"id":"W563012626","doi":"","title":"Application of Dynamic Traffic Assignment (DTA) Model to Evaluate Network Traffic Impact During Bridge Closure - A Case Study in Edmonton, Alberta","year":2014,"lang":"en","type":"article","venue":"Transportation 2014: Past, Present, Future - 2014 Conference and Exhibition of the Transportation Association of Canada // Transport 2014 : Du passé vers l'avenir - 2014 Congrès et Exposition de 'Association des transports du Canada","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":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Traffic flow (computer networking); Allowance (engineering); Transport engineering; Bridge (graph theory); Traffic generation model; Computer science; Queue; Traffic congestion reconstruction with Kerner's three-phase theory; Reliability (semiconductor); Traffic congestion; Calibration; Engineering; Operations research; Real-time computing; Computer network; Operations management; Statistics","score_opus":0.007678336015126368,"score_gpt":0.24571431899085058,"score_spread":0.2380359829757242,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W563012626","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9781876,0.00008028531,0.013148153,0.003949637,0.00052672304,0.0022994655,0.0015620693,0.00007796193,0.00016808668],"genre_scores_gemma":[0.99622846,0.0005239251,0.00034180743,0.00021082796,0.0002199779,0.00025362286,0.0018616376,0.00008340692,0.00027635126],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.991825,0.0009977905,0.0025970566,0.00093870406,0.0026080152,0.0010334732],"domain_scores_gemma":[0.993878,0.0005488055,0.0028706077,0.0004876005,0.0016706018,0.0005443739],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.003084356,0.0007262632,0.00121825,0.00035676037,0.0007443363,0.0000704596,0.0004600281,0.0005545961,0.00018764506],"category_scores_gemma":[0.00007191594,0.00077052903,0.0004332261,0.00081947574,0.00017157524,0.0009591272,0.000004075778,0.00054817076,0.0000013870916],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047907516,0.00036877635,0.46963885,0.00024239297,0.00030411402,0.00003353361,0.02001707,0.5022309,0.00027821868,0.0005261584,0.005468424,0.00041247078],"study_design_scores_gemma":[0.004283773,0.0002240578,0.9764387,0.00023314673,0.00083051855,0.0000066244293,0.0031748938,0.011763101,0.0001720572,0.000108655586,0.001964977,0.00079952047],"about_ca_topic_score_codex":0.7353514,"about_ca_topic_score_gemma":0.99472225,"teacher_disagreement_score":0.5067998,"about_ca_system_score_codex":0.002546739,"about_ca_system_score_gemma":0.0041149016,"threshold_uncertainty_score":0.9994746},"labels":[],"label_agreement":null},{"id":"W563637631","doi":"","title":"Smart Commute 404-7 Program","year":2007,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Corporation; Business; Transport engineering; Smart growth; Public administration; Finance; Engineering; Urban planning; Political science; Civil engineering","score_opus":0.024152542364408,"score_gpt":0.3458995116224909,"score_spread":0.3217469692580829,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W563637631","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07076091,0.00002030623,0.03581217,0.0013013993,0.00025534717,0.00021896628,0.00000105207,0.0006859659,0.8909439],"genre_scores_gemma":[0.9688226,0.000009521102,0.022293117,0.00023170108,0.000054616332,0.000003157631,0.00001893222,0.0000030068352,0.008563366],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.99959064,0.000016291928,0.00007758015,0.000053168143,0.0001305145,0.0001317984],"domain_scores_gemma":[0.99979925,0.00004032079,0.000018198092,0.000043350592,0.000042550997,0.000056322668],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046893544,0.000026017682,0.000030526513,0.000026472755,0.00022137853,0.000028520124,0.00005685981,0.000036565307,0.00024304143],"category_scores_gemma":[0.000018637165,0.000024384675,0.000015686597,0.00016040166,0.00005309879,0.00007593137,0.0000012838976,0.000035309193,0.00004215173],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031297415,0.00025725333,0.38373512,0.000007323168,0.000017986305,0.000013733491,0.032410033,0.00038962378,0.00002078788,0.27250192,0.014839473,0.29577544],"study_design_scores_gemma":[0.00015337495,0.000027225837,0.16306306,0.000004989974,0.0000053618483,1.7673473e-7,0.0036777132,0.00011107782,0.00006982414,0.00030690237,0.83248866,0.00009165677],"about_ca_topic_score_codex":0.0010018583,"about_ca_topic_score_gemma":0.0068767127,"teacher_disagreement_score":0.8980617,"about_ca_system_score_codex":0.000011111148,"about_ca_system_score_gemma":0.000025387952,"threshold_uncertainty_score":0.3837367},"labels":[],"label_agreement":null},{"id":"W563999843","doi":"","title":"Toronto Plans Network of Light-Rail Systems","year":2007,"lang":"en","type":"article","venue":"Civil engineering","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Commission; Light rail; Agency (philosophy); Transport engineering; Light rail transit; Transit (satellite); Rail transit; Business; Engineering; Telecommunications; Finance; Public transport; Sociology","score_opus":0.007965364331318456,"score_gpt":0.2291606882527225,"score_spread":0.22119532392140406,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W563999843","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.082038425,0.0037796944,0.7150277,0.00010619876,0.0040962263,0.0003383834,0.00001313149,0.0006749462,0.19392534],"genre_scores_gemma":[0.99854976,0.000040074086,0.000766231,0.0000049253495,0.0003134518,0.0000019210759,0.0000068299414,0.000007355034,0.00030946624],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99945676,0.000007484543,0.00015047344,0.000065465436,0.00014390607,0.00017590915],"domain_scores_gemma":[0.9997487,0.000059488684,0.00003815111,0.000056606965,0.0000405172,0.00005654972],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043374774,0.000046269262,0.00007885859,0.000014493476,0.00006448352,0.000013159729,0.000066095134,0.000055816887,0.000029896375],"category_scores_gemma":[0.00003066972,0.000050538918,0.00002118881,0.00011335895,0.000009028465,0.00010371195,0.0000018142096,0.0000338051,0.000002195169],"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":[0.00000469124,0.000008190745,0.007686069,0.00003667828,0.000014063518,0.0000034240004,0.009037017,0.9493964,0.0001994754,0.03240489,0.00096560176,0.00024356025],"study_design_scores_gemma":[0.0010510227,0.00010417183,0.17585513,0.00093946484,0.00008673222,0.0000034461636,0.015768277,0.050473016,0.00066422665,0.00007954667,0.75394255,0.0010323917],"about_ca_topic_score_codex":0.00095103227,"about_ca_topic_score_gemma":0.009365697,"teacher_disagreement_score":0.9165113,"about_ca_system_score_codex":0.000050120067,"about_ca_system_score_gemma":0.000020435373,"threshold_uncertainty_score":0.5226279},"labels":[],"label_agreement":null},{"id":"W564070673","doi":"10.3141/2530-03","title":"Revenue and Policy Implications from Emerging Fuel Sale Trends in Metro Vancouver, British Columbia, Canada","year":2015,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Pacific Insight Electronics (Canada)","funders":"","keywords":"Revenue; Fuel tax; Payment; Tax revenue; Kilometer; Vehicle miles of travel; Business; Per capita; Liability; Finance; Transport engineering; Natural resource economics; Economics; Public economics; Engineering","score_opus":0.07363503355433289,"score_gpt":0.3871460467249452,"score_spread":0.3135110131706123,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W564070673","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99041545,0.0004469422,0.0003061633,0.006787704,0.00056178716,0.00041958652,0.0005523351,0.000020436964,0.0004895927],"genre_scores_gemma":[0.9939359,0.0012590941,0.0013462703,0.000086090964,0.00027354053,0.000045393746,0.000085992906,0.00003442805,0.0029333152],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9936394,0.0012801542,0.0010799462,0.00038038372,0.0028588923,0.00076122757],"domain_scores_gemma":[0.9955052,0.0007113126,0.00037820265,0.0002738659,0.0025087565,0.00062267465],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003786897,0.00014098243,0.0003622719,0.00078945974,0.0008198654,0.0003295517,0.000685445,0.00017174729,0.00019854194],"category_scores_gemma":[0.00046603786,0.00017204616,0.0001287479,0.003901338,0.00050970836,0.0007174028,0.0000046236864,0.0012480723,0.0000015938904],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013829806,0.00009067021,0.9296991,0.000028335056,0.000041349063,0.000069315654,0.008599926,0.0012992871,0.00002130491,0.00040228514,0.048558038,0.011052039],"study_design_scores_gemma":[0.0012239317,0.00008541487,0.9232509,0.00016693129,0.000025106156,8.7874085e-8,0.012930846,0.000037135716,0.0000057689676,0.004603719,0.05751757,0.00015258412],"about_ca_topic_score_codex":0.99533135,"about_ca_topic_score_gemma":0.9999588,"teacher_disagreement_score":0.010899455,"about_ca_system_score_codex":0.00078633183,"about_ca_system_score_gemma":0.004073014,"threshold_uncertainty_score":0.7225352},"labels":[],"label_agreement":null},{"id":"W565474738","doi":"","title":"MEDIAN BUS LANE DESIGN IN VANCOUVER, BC: THE #98 B-LINE","year":2003,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Transport engineering; Headway; Transit (satellite); Intersection (aeronautics); Travel time; Offset (computer science); Median; Line (geometry); Telecommunications; Geography; Computer science; Public transport; Engineering; Mathematics","score_opus":0.030869487900829287,"score_gpt":0.283363478557338,"score_spread":0.25249399065650874,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W565474738","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02224391,0.00023022766,0.39959195,0.0064470135,0.0021398184,0.00074766256,0.0000045785355,0.00031863237,0.5682762],"genre_scores_gemma":[0.9757636,0.00008730308,0.0114487065,0.00036295582,0.000044879383,0.000007815299,0.0000055231694,0.000004339636,0.012274844],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99940854,0.0001541422,0.00009475727,0.0000734326,0.00014790025,0.00012121419],"domain_scores_gemma":[0.9996995,0.00016128623,0.000022961323,0.00005305724,0.00002794456,0.00003530625],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006504422,0.000036659618,0.000046974026,0.00002971164,0.00014376467,0.00001709873,0.00007151353,0.000041084633,0.000601449],"category_scores_gemma":[0.00014449036,0.000025291294,0.000011698362,0.00025416331,0.00004669833,0.00007093951,6.7516214e-7,0.000053509222,0.000026897116],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006311821,0.00027976485,0.1473616,0.0000126603745,0.000031129566,0.000050523562,0.110732056,0.2965852,0.000043512704,0.31212953,0.12077712,0.01193379],"study_design_scores_gemma":[0.0032560497,0.0001784089,0.06571821,0.00007298389,0.000058264508,0.0000013338991,0.07006617,0.009761455,0.0011287216,0.034942057,0.81384116,0.0009751978],"about_ca_topic_score_codex":0.0038227425,"about_ca_topic_score_gemma":0.08249437,"teacher_disagreement_score":0.9535197,"about_ca_system_score_codex":0.000017459975,"about_ca_system_score_gemma":0.000089527566,"threshold_uncertainty_score":0.93424773},"labels":[],"label_agreement":null},{"id":"W565708351","doi":"","title":"DESIGN & CONSTRUCTION OF THE MILLENNIUM LINE","year":2001,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Line (geometry); Engineering; Procurement; Computer science; Construction engineering; Systems engineering; Operations research; Economics; Management; Mathematics","score_opus":0.031838501164820496,"score_gpt":0.2788844281683452,"score_spread":0.24704592700352468,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W565708351","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15887909,0.000105690095,0.7035738,0.005145845,0.0008030005,0.00029072244,0.0000020251869,0.00013320672,0.13106659],"genre_scores_gemma":[0.9813081,0.0001692731,0.015264947,0.00006919046,0.00003598167,0.0000011914677,0.0000013054181,0.0000015719553,0.003148429],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.99971235,0.000047249956,0.00006562463,0.000035042187,0.00009451862,0.00004522444],"domain_scores_gemma":[0.999837,0.00003130781,0.000032876236,0.000037091977,0.00004923035,0.000012517978],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013739882,0.00001724252,0.00002567801,0.00001245618,0.0001244668,0.000005071293,0.00004529304,0.000024807558,0.0002087614],"category_scores_gemma":[0.000022785975,0.000011807455,0.000013745726,0.00016079572,0.00008618106,0.00004259387,9.1389126e-7,0.000018784402,0.0000029197724],"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":[0.00014559188,0.00015698791,0.36474684,0.000014533049,0.000044744305,0.0000023597638,0.05125075,0.255168,0.0013698263,0.27319098,0.0120346695,0.041874733],"study_design_scores_gemma":[0.00317729,0.00028225686,0.33962652,0.00018999659,0.00020421144,0.000015186817,0.07050042,0.018090084,0.029685842,0.019539902,0.517719,0.0009692879],"about_ca_topic_score_codex":0.00034017806,"about_ca_topic_score_gemma":0.00022383613,"teacher_disagreement_score":0.822429,"about_ca_system_score_codex":0.00000543007,"about_ca_system_score_gemma":0.000039358696,"threshold_uncertainty_score":0.22857909},"labels":[],"label_agreement":null},{"id":"W565964238","doi":"","title":"Modeling Commuters’ Response to Pre-Trip Information Provided for Prolonged and Large-Scale Network Disruptions: Case study of West LRT Construction in the City Calgary, Canada","year":2012,"lang":"en","type":"article","venue":"Transportation Research Board 91st Annual MeetingTransportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Transport engineering; TRIPS architecture; Scale (ratio); Quality (philosophy); Travel behavior; Sample (material); Traffic congestion; Perception; Geography; Computer science; Engineering","score_opus":0.056130365215050056,"score_gpt":0.3838431198488567,"score_spread":0.32771275463380667,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W565964238","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97835106,0.00008022616,0.012233213,0.0018064923,0.00020004979,0.006751787,0.00041893093,0.00007430098,0.00008395035],"genre_scores_gemma":[0.99310523,0.000061012946,0.004451043,0.00009420894,0.00014258432,0.0016467131,0.000422583,0.000030911935,0.000045726443],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99047035,0.0031770763,0.0014033767,0.0004921907,0.0029868572,0.0014701505],"domain_scores_gemma":[0.9937743,0.002252437,0.00023387221,0.00038693481,0.0028680307,0.00048444828],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.02028218,0.0002749838,0.0004164662,0.00084837613,0.0023986076,0.00019328427,0.00044074462,0.00023990445,0.000027807944],"category_scores_gemma":[0.00094619056,0.00026203674,0.00008061215,0.0026656603,0.00035128894,0.0017368535,0.000010245766,0.0009349059,0.0000018602486],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.003833548,0.0005436483,0.4962667,0.0002255425,0.000046226498,0.000048669106,0.45528483,0.040424008,0.000024433357,0.0017690179,0.00049212226,0.0010412598],"study_design_scores_gemma":[0.0024212466,0.0006587791,0.50727606,0.0001679195,0.0000550889,0.00000295687,0.48067835,0.0043736403,0.000014855671,0.00012981129,0.0039036702,0.0003176512],"about_ca_topic_score_codex":0.72160906,"about_ca_topic_score_gemma":0.9712245,"teacher_disagreement_score":0.24961545,"about_ca_system_score_codex":0.00030185876,"about_ca_system_score_gemma":0.0012950482,"threshold_uncertainty_score":0.9999832},"labels":[],"label_agreement":null},{"id":"W566954275","doi":"10.1016/j.trb.2014.09.016","title":"A semi-analytical approach for solving the bottleneck model with general user heterogeneity","year":2014,"lang":"en","type":"article","venue":"Transportation Research Part B Methodological","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Bottleneck; Mathematical optimization; Computer science; Class (philosophy); Linear bottleneck assignment problem; Projection (relational algebra); Mathematics; Algorithm; Assignment problem; Generalized assignment problem; Weapon target assignment problem; Artificial intelligence","score_opus":0.4635511716640937,"score_gpt":0.4915859650213623,"score_spread":0.028034793357268606,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W566954275","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17777385,0.000018395465,0.8180333,0.0015802819,0.000034560777,0.00067124784,0.00003091571,0.00010344217,0.0017540264],"genre_scores_gemma":[0.68770486,0.000042939217,0.30953467,0.00027769752,0.00024207307,0.00037902998,0.0002594993,0.000021263673,0.0015379543],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9957992,0.0017259929,0.0003587455,0.00048528335,0.0009948604,0.0006359329],"domain_scores_gemma":[0.9957097,0.0032512369,0.00008427461,0.00023413476,0.0004998319,0.00022082827],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011377835,0.00014853085,0.00027180018,0.000090535985,0.0012491589,0.000113382775,0.00034163473,0.00020710668,0.00006242991],"category_scores_gemma":[0.0011524452,0.00009381619,0.00013113041,0.00046750452,0.00066583115,0.00017352043,0.0000036552833,0.00039308218,0.0000035577932],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047475944,0.00014831912,0.029346637,0.000043631648,0.00006384495,0.0000020775117,0.0047541694,0.71904826,0.00012793286,0.24298264,0.0022692706,0.00073847186],"study_design_scores_gemma":[0.0013452619,0.00038348197,0.07302063,0.000029265166,0.00014691515,8.106204e-7,0.002286817,0.88487005,0.0003726319,0.005057108,0.03202851,0.00045849124],"about_ca_topic_score_codex":0.00023642545,"about_ca_topic_score_gemma":0.00092952466,"teacher_disagreement_score":0.509931,"about_ca_system_score_codex":0.000039234164,"about_ca_system_score_gemma":0.00017781153,"threshold_uncertainty_score":0.9607647},"labels":[],"label_agreement":null},{"id":"W567445262","doi":"","title":"Calgary Transit: Overcoming the Last Mile (Poster)","year":2014,"lang":"fr","type":"article","venue":"Transportation 2014: Past, Present, Future - 2014 Conference and Exhibition of the Transportation Association of Canada // Transport 2014 : Du passé vers l'avenir - 2014 Congrès et Exposition de 'Association des transports du Canada","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Transport engineering; Transit (satellite); Last mile (transportation); Mile; Public transport; TRIPS architecture; Pedestrian; Intersection (aeronautics); Grid; Computer science; Engineering; Geography","score_opus":0.0066747033678040794,"score_gpt":0.20491935740743666,"score_spread":0.1982446540396326,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W567445262","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8240371,0.002297244,0.038582813,0.10442971,0.009155455,0.0038803164,0.012684748,0.00030747987,0.004625158],"genre_scores_gemma":[0.98298216,0.0060699736,0.00041534708,0.001918135,0.0010418547,0.00015757149,0.0045898044,0.00013956266,0.0026855946],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.98875165,0.0016684721,0.0031646548,0.0011825353,0.0036960596,0.0015366566],"domain_scores_gemma":[0.9902168,0.0012548382,0.00419742,0.0007151119,0.002855323,0.0007605404],"candidate_categories":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.003528087,0.0011397399,0.0015166467,0.00027513123,0.0016750152,0.00016973671,0.00087456394,0.0011172803,0.00154874],"category_scores_gemma":[0.00009289007,0.001118254,0.0007974271,0.0008704234,0.0006794698,0.0018032759,0.000006318783,0.0011633694,0.00000802917],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007609451,0.00054463523,0.675622,0.0014761543,0.0012746335,0.00004795167,0.030553397,0.044467513,0.0007421162,0.025132162,0.217789,0.0015895214],"study_design_scores_gemma":[0.0038931787,0.00018793417,0.74134845,0.0006336721,0.0019474343,0.000007014329,0.0032221284,0.00093184697,0.0013088203,0.0006307394,0.24467498,0.0012138302],"about_ca_topic_score_codex":0.7039971,"about_ca_topic_score_gemma":0.98561984,"teacher_disagreement_score":0.28162277,"about_ca_system_score_codex":0.0025484872,"about_ca_system_score_gemma":0.006060012,"threshold_uncertainty_score":0.99962467},"labels":[],"label_agreement":null},{"id":"W568365136","doi":"","title":"Integrated Passenger and Commercial Vehicle Model for Assessing the Benefits of Dedicated Truck-Only Lanes on the Freeways","year":2012,"lang":"en","type":"article","venue":"2012 CONFERENCE AND EXHIBITION OF THE TRANSPORTATION ASSOCIATION OF CANADA - TRANSPORTATION: INNOVATIONS AND OPPORTUNITIES","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Truck; Context (archaeology); Transport engineering; Key (lock); Macro; Operations research; Computer science; Engineering; Geography","score_opus":0.06951751882445957,"score_gpt":0.2699589220010849,"score_spread":0.20044140317662534,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W568365136","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97934115,0.00012633484,0.0035570515,0.0147835715,0.00013930758,0.00042107925,0.0012275659,0.000017004644,0.00038692117],"genre_scores_gemma":[0.99806225,0.00031838194,0.00025287832,0.00043687312,0.00003511518,0.00003514756,0.00048084502,0.000009982288,0.00036853814],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99848086,0.00011268729,0.0006202956,0.00011739957,0.00048111676,0.00018767103],"domain_scores_gemma":[0.9976033,0.000507383,0.0007657039,0.00010333634,0.00095849665,0.00006176212],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008995564,0.00013468202,0.00021816113,0.00008885578,0.0007053107,0.000036283553,0.00009838044,0.00011653222,0.00004246958],"category_scores_gemma":[0.000085307845,0.000094046765,0.000045148576,0.0002991353,0.0002913423,0.0004772934,0.0000013171554,0.00013064222,3.0860235e-8],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000050583854,0.0000633266,0.20592141,0.00011264787,0.00008745905,8.7760846e-8,0.024472747,0.0018720336,0.00017771576,0.7620727,0.0017731744,0.0033961476],"study_design_scores_gemma":[0.00053844356,0.000027501299,0.97594845,0.00015323801,0.00017185241,1.578011e-7,0.01721553,0.0019208809,0.00030970245,0.0012993827,0.0022599204,0.00015492168],"about_ca_topic_score_codex":0.014638649,"about_ca_topic_score_gemma":0.23071565,"teacher_disagreement_score":0.77002704,"about_ca_system_score_codex":0.00003547003,"about_ca_system_score_gemma":0.00072309404,"threshold_uncertainty_score":0.991923},"labels":[],"label_agreement":null},{"id":"W568454711","doi":"","title":"The Big Move: Transforming Transportation in the Greater Toronto and Hamilton Area - Modelling and Measuring Against the Triple Bottom Line","year":2009,"lang":"en","type":"article","venue":"2009 ANNUAL CONFERENCE AND EXHIBITION OF THE TRANSPORTATION ASSOCIATION OF CANADA - TRANSPORTATION IN A CLIMATE OF CHANGE","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Triple bottom line; Plan (archaeology); Stakeholder; Process (computing); Process management; Transport engineering; Quality (philosophy); Big data; Transportation planning; Development plan; Computer science; Measure (data warehouse); Stakeholder engagement; Operations research; Engineering management; Business; Engineering; Sustainable development; Civil engineering; Geography; Management; Economics","score_opus":0.03981239840399184,"score_gpt":0.24802565636435558,"score_spread":0.20821325796036375,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W568454711","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9923315,0.00048956537,0.0006520891,0.004679841,0.00014552145,0.0008016627,0.0006595633,0.000014832658,0.00022544467],"genre_scores_gemma":[0.9947259,0.004613934,0.00009068593,0.00021910315,0.00002994056,0.00004635546,0.00022700289,0.000009907862,0.000037161542],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.9974592,0.00020913957,0.0009664225,0.0002344009,0.000823093,0.00030777138],"domain_scores_gemma":[0.99840605,0.00029384933,0.00070471974,0.00012931053,0.00040852107,0.000057533427],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015073324,0.0001941808,0.0003108329,0.00008721553,0.00045620961,0.00004385985,0.00017912034,0.00015611302,0.0000069714238],"category_scores_gemma":[0.000034227953,0.00013335966,0.00007373374,0.00039841552,0.0001582752,0.00056304695,5.402399e-7,0.0002078528,2.58174e-8],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015365713,0.00024333294,0.17419045,0.00050209434,0.0001109968,0.000007290217,0.7378573,0.03620955,0.0006210133,0.03080177,0.0001216026,0.017798018],"study_design_scores_gemma":[0.0018100917,0.000096760574,0.9509208,0.00037289178,0.00011530154,1.5559986e-7,0.04269417,0.002196802,0.00040633464,0.0006632465,0.0005084121,0.00021500106],"about_ca_topic_score_codex":0.07692304,"about_ca_topic_score_gemma":0.86245275,"teacher_disagreement_score":0.7855297,"about_ca_system_score_codex":0.00010021733,"about_ca_system_score_gemma":0.00022995686,"threshold_uncertainty_score":0.9292238},"labels":[],"label_agreement":null},{"id":"W569996643","doi":"","title":"Customer Focus: Keeping Its Customers Happy is the Secret to GO Transit's Success","year":2007,"lang":"en","type":"article","venue":"Mass transit","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Public transport; Transit (satellite); Service (business); Work (physics); Commission; Customer service; Business; Focus (optics); Marketing; Telecommunications; Advertising; Transport engineering; Engineering; Finance","score_opus":0.0188909602403692,"score_gpt":0.2925694546320202,"score_spread":0.27367849439165104,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W569996643","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14698875,0.0005197888,0.55341077,0.050548207,0.0014315718,0.0017587062,0.00014409843,0.00080892985,0.24438919],"genre_scores_gemma":[0.99428695,0.00006623645,0.00085944956,0.0023163017,0.00018282198,0.000014517421,0.000017217102,0.000026572616,0.0022299578],"study_design_codex":"qualitative","study_design_gemma":"not_applicable","domain_scores_codex":[0.99807715,0.00010119696,0.00032163825,0.00032538697,0.00061784184,0.0005568064],"domain_scores_gemma":[0.9991883,0.00018311627,0.00006177853,0.00017292242,0.00015128942,0.00024260207],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011737971,0.00017272607,0.00018890463,0.0001609519,0.000771779,0.00012451658,0.00035775706,0.00016204822,0.00044614795],"category_scores_gemma":[0.000028724764,0.00015098695,0.00012226378,0.0009733881,0.00012253529,0.0003067761,0.000002439293,0.00021580518,0.00027091082],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006314029,0.00019196533,0.022342443,0.0001491948,0.00029536482,0.00013042927,0.7939696,0.0903583,0.0051313345,0.028677134,0.012860065,0.045262784],"study_design_scores_gemma":[0.0011769916,0.000084231295,0.037810665,0.00018486331,0.00023552032,0.0000026298073,0.017619925,0.0009723908,0.007518896,0.00023978963,0.93310136,0.0010527613],"about_ca_topic_score_codex":0.00079469365,"about_ca_topic_score_gemma":0.0045740185,"teacher_disagreement_score":0.9202413,"about_ca_system_score_codex":0.00006615468,"about_ca_system_score_gemma":0.000077569704,"threshold_uncertainty_score":0.61570686},"labels":[],"label_agreement":null},{"id":"W570643029","doi":"10.1068/b35107","title":"Development of Prototype Urbansim Models","year":2010,"lang":"en","type":"article","venue":"Environment and Planning B Planning and Design","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Transport Canada","funders":"","keywords":"Computer science; Process (computing); Reputation; Operations research; Management science; Data science; Process management; Systems engineering; Engineering; Political science","score_opus":0.048326010911666245,"score_gpt":0.26455756561516774,"score_spread":0.2162315547035015,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W570643029","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7112988,0.00057207764,0.28253862,0.000067026085,0.00014141368,0.0004313741,0.000003850076,0.000087849665,0.004858978],"genre_scores_gemma":[0.8764118,0.000024677034,0.123207904,0.000019597805,0.000042434734,0.00001745677,0.000019010857,0.000008221904,0.00024891956],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.99919987,0.000050086306,0.00019254666,0.00018501579,0.00020023469,0.0001722389],"domain_scores_gemma":[0.99960726,0.00012659866,0.00008851419,0.000064487256,0.0000111712825,0.00010197845],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00064015994,0.00010272902,0.00012416039,0.00006971731,0.00043152922,0.000035397934,0.00005725874,0.000108486696,0.000028356128],"category_scores_gemma":[0.000023926335,0.00010047581,0.000011599916,0.000049742408,0.0001370276,0.00014739316,0.000007985463,0.00014366567,0.0000019870802],"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":[0.00049202755,0.00013854868,0.26380852,0.00008402266,0.000100718375,0.00003324,0.48522097,0.21375944,0.006328434,0.010831019,0.0010067172,0.018196356],"study_design_scores_gemma":[0.006244447,0.0009842505,0.42869568,0.0014464761,0.0003692989,0.000030123922,0.07091623,0.14024122,0.010782594,0.010481191,0.32566464,0.0041438397],"about_ca_topic_score_codex":0.00002182249,"about_ca_topic_score_gemma":0.000002884875,"teacher_disagreement_score":0.41430473,"about_ca_system_score_codex":0.0000060524444,"about_ca_system_score_gemma":0.00006579735,"threshold_uncertainty_score":0.40972847},"labels":[],"label_agreement":null},{"id":"W570868792","doi":"","title":"NEW STARTS FOR LIGHT RAIL","year":2000,"lang":"en","type":"article","venue":"Mass transit","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Light rail; Light rail transit; Transport engineering; Service (business); Business; Traffic congestion; Engineering; Public transport; Marketing","score_opus":0.014075843867630328,"score_gpt":0.262942145433473,"score_spread":0.24886630156584266,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W570868792","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04221382,0.00031885554,0.5883141,0.056222484,0.000815874,0.0011349092,0.00011462977,0.0008123252,0.31005296],"genre_scores_gemma":[0.8881844,0.00009379506,0.025591685,0.0004975538,0.00037248267,0.000017176286,0.00009230771,0.000017571208,0.08513302],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995025,0.000019307348,0.000091670474,0.00010607147,0.00013071974,0.00014977729],"domain_scores_gemma":[0.99980336,0.000026826418,0.000012384338,0.0000493309,0.000025141217,0.000082932114],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00011489398,0.00004755047,0.000062851104,0.000021792534,0.00022157098,0.000037473197,0.00006507713,0.000053962503,0.0030322277],"category_scores_gemma":[0.000004542068,0.00004937979,0.000042315016,0.00010689887,0.00001602273,0.000114840695,1.2541396e-7,0.000029825205,0.00003502382],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00069741404,0.00014988024,0.0059815533,0.00008292803,0.00011485931,0.00001921231,0.24916321,0.06081315,0.0016030053,0.04462598,0.26311705,0.37363178],"study_design_scores_gemma":[0.0003814055,0.000019204752,0.0011561223,0.000007878845,0.000015837539,5.8297577e-8,0.00028424326,0.00011905132,0.00015271516,0.00087278325,0.9969069,0.000083794665],"about_ca_topic_score_codex":0.00022428914,"about_ca_topic_score_gemma":0.0013413377,"teacher_disagreement_score":0.8459706,"about_ca_system_score_codex":0.000011293783,"about_ca_system_score_gemma":0.00007310684,"threshold_uncertainty_score":0.99787915},"labels":[],"label_agreement":null},{"id":"W571343414","doi":"","title":"Adaptive Signal Control – Is it Worth it?","year":2013,"lang":"en","type":"article","venue":"ITS International","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"SIGNAL (programming language); Control (management); Plan (archaeology); Traffic signal; Computer science; Telecommunications; Transport engineering; Real-time computing; Computer security; Engineering; Geography; Artificial intelligence","score_opus":0.028249560448359707,"score_gpt":0.30086107619455593,"score_spread":0.27261151574619624,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W571343414","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03974755,0.00009819248,0.100488044,0.106642194,0.0025004728,0.0007865769,0.00025086873,0.0002513545,0.74923474],"genre_scores_gemma":[0.97513884,0.00001789547,0.0008073768,0.0033948373,0.00028054888,0.000024980027,0.00003363467,0.0000051922507,0.020296711],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991481,0.000036675512,0.0001495701,0.00013191816,0.00040392787,0.00012982146],"domain_scores_gemma":[0.9993919,0.000087228436,0.00007234556,0.000038193168,0.0003438177,0.00006652788],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00013595622,0.00005971492,0.000059956536,0.00006066035,0.00015422783,0.000091729016,0.00017510055,0.000054757616,0.015274454],"category_scores_gemma":[0.000046446665,0.00005996219,0.00003944644,0.00008279135,0.00005263108,0.00038187284,0.0000037527377,0.00007118768,0.0007500843],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014363514,0.0002752726,0.06271895,0.000006296831,0.00038438226,0.000017836264,0.08226183,0.026193555,0.00020021186,0.18504477,0.6229784,0.019774847],"study_design_scores_gemma":[0.0028496783,0.0001174315,0.12336406,0.00012800643,0.000056996003,0.0000026308937,0.01603293,0.08725278,0.00028095813,0.004504845,0.7646809,0.00072875834],"about_ca_topic_score_codex":0.00049945636,"about_ca_topic_score_gemma":0.00028007998,"teacher_disagreement_score":0.93539125,"about_ca_system_score_codex":0.0000439533,"about_ca_system_score_gemma":0.000081396654,"threshold_uncertainty_score":0.98562574},"labels":[],"label_agreement":null},{"id":"W571388159","doi":"","title":"York Region ITS Strategic Plan","year":2008,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Software deployment; Plan (archaeology); Strategic planning; Traffic congestion; Business; Intelligent transportation system; Regional planning; Process (computing); Transport engineering; Transportation planning; Environmental planning; Geography; Urban planning; Engineering; Computer science; Civil engineering; Marketing","score_opus":0.12995166390253263,"score_gpt":0.29249751466452767,"score_spread":0.16254585076199504,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W571388159","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2913929,0.000057447607,0.0018833232,0.00083677436,0.000167778,0.00008080618,0.0000012731815,0.0002597913,0.7053199],"genre_scores_gemma":[0.9819511,0.00009128249,0.00044702995,0.000112735,0.00006844938,0.0000016524074,0.0000150111455,0.000002528715,0.01731019],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99961853,0.000025879399,0.000064151034,0.00007179341,0.00012583447,0.000093799084],"domain_scores_gemma":[0.99984705,0.000018948422,0.000022007167,0.000034729124,0.0000299784,0.000047282785],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005856532,0.000030155188,0.00003505262,0.000024110333,0.00033834085,0.000013339141,0.000050553357,0.000042397,0.00022870532],"category_scores_gemma":[0.0000071309646,0.000028082393,0.0000142931185,0.0001345845,0.000039947612,0.00008089042,6.072052e-7,0.00003131995,0.000070587215],"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":[0.000034380588,0.00008374544,0.081850834,0.000008494627,0.000014315965,0.00009281836,0.070839584,0.008372614,0.000035265948,0.80867153,0.029446965,0.00054943067],"study_design_scores_gemma":[0.003078404,0.00034788193,0.32256025,0.00012756257,0.000070532595,0.000063801504,0.110929914,0.005957369,0.00086496875,0.011504214,0.5425044,0.0019907316],"about_ca_topic_score_codex":0.00045261177,"about_ca_topic_score_gemma":0.0004905438,"teacher_disagreement_score":0.79716736,"about_ca_system_score_codex":0.000009128575,"about_ca_system_score_gemma":0.000070468785,"threshold_uncertainty_score":0.26022783},"labels":[],"label_agreement":null},{"id":"W572633464","doi":"","title":"UBC TRANSIT TERMINAL - REAL-TIME TERMINAL MANAGEMENT","year":2006,"lang":"en","type":"article","venue":"PROCEEDINGS OF THE 13th ITS WORLD CONGRESS, LONDON, 8-12 OCTOBER 2006","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Terminal (telecommunication); Transit (satellite); Transport engineering; Service (business); Footprint; Order (exchange); Bus rapid transit; Occupancy; Computer science; Engineering; Operations research; Public transport; Telecommunications; Business; Geography; Architectural engineering; Marketing; Finance","score_opus":0.008593736012969026,"score_gpt":0.2391426128186383,"score_spread":0.23054887680566927,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W572633464","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.44499862,0.00028469454,0.000032734853,0.003533807,0.00078079605,0.001182859,0.0000854649,0.00038589086,0.5487151],"genre_scores_gemma":[0.78751254,0.00011628326,0.0015021046,0.00015503618,0.00035783506,0.000039969236,0.00003093816,0.00004042906,0.21024486],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.99764484,0.000027749289,0.00057528965,0.00041322128,0.0008081402,0.00053073687],"domain_scores_gemma":[0.99890876,0.00004832691,0.00044429273,0.00014452674,0.00034985336,0.00010426879],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051913783,0.0002673061,0.000312681,0.00024011,0.0005755403,0.00018036523,0.00068878016,0.000108704415,0.0003382812],"category_scores_gemma":[0.00002240215,0.0002299128,0.00016815017,0.00079054275,0.0003589491,0.0005584503,0.000035804143,0.00018142797,0.000044434095],"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":[0.0006761817,0.0009707906,0.4721007,0.000929365,0.00028402638,0.000082014674,0.0062255817,0.0024009794,0.0042269547,0.17006132,0.33395785,0.008084216],"study_design_scores_gemma":[0.0041672,0.00011902174,0.45957625,0.0014337815,0.00097505347,0.000028146118,0.0012538832,0.001083932,0.010771029,0.005284026,0.51356596,0.0017417453],"about_ca_topic_score_codex":0.0013114023,"about_ca_topic_score_gemma":0.0012823021,"teacher_disagreement_score":0.34251392,"about_ca_system_score_codex":0.000083562234,"about_ca_system_score_gemma":0.00007254943,"threshold_uncertainty_score":0.93755716},"labels":[],"label_agreement":null},{"id":"W574326559","doi":"","title":"Tour-based mode choice modeling technique: US practices","year":2008,"lang":"en","type":"article","venue":"EUROPEAN TRANSPORT CONFERENCE 2008; PROCEEDINGS","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Mode choice; Public transport; Transport engineering; Metropolitan area; Mode (computer interface); TRIPS architecture; Transit (satellite); Variety (cybernetics); Occupancy; Computer science; Engineering; Geography; Architectural engineering; Artificial intelligence","score_opus":0.07621110963871311,"score_gpt":0.3128648773865819,"score_spread":0.2366537677478688,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W574326559","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.63529783,0.0000703335,0.13858409,0.0008767862,0.0001731815,0.00072220206,0.00003021953,0.0011762285,0.22306913],"genre_scores_gemma":[0.98199576,0.00021050434,0.015640782,0.0002975578,0.00022214012,0.000033854245,0.00008431205,0.00005154342,0.0014635287],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9977655,0.000061722225,0.0004893375,0.00052833173,0.0006632044,0.00049192767],"domain_scores_gemma":[0.9984684,0.00003848581,0.0004357298,0.00012903528,0.0006795793,0.00024877698],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007736022,0.00025777644,0.00023947381,0.00018317258,0.0009868727,0.00009607378,0.00047075225,0.00012255524,0.00028129556],"category_scores_gemma":[0.00019652623,0.00027560486,0.00009728389,0.0005144244,0.00025602395,0.0009744363,0.0000048874317,0.00037345482,0.000053286298],"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":[0.00048175588,0.0008835105,0.75975466,0.00037936785,0.0001187281,0.00048400715,0.10884819,0.09831804,0.0058821645,0.017847195,0.004891069,0.0021113334],"study_design_scores_gemma":[0.0063784984,0.0007948877,0.2787377,0.0019270447,0.0006900517,0.00009561187,0.012878009,0.2842822,0.005297023,0.00070594746,0.402112,0.006101024],"about_ca_topic_score_codex":0.0018260279,"about_ca_topic_score_gemma":0.00046982316,"teacher_disagreement_score":0.48101693,"about_ca_system_score_codex":0.00004932632,"about_ca_system_score_gemma":0.00045158653,"threshold_uncertainty_score":0.9999696},"labels":[],"label_agreement":null},{"id":"W574684527","doi":"","title":"NEW ORLEANS REINTRODUCES STREETCARS","year":2001,"lang":"en","type":"article","venue":"Metrologia","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Downtown; Quarter (Canadian coin); Plan (archaeology); Transit (satellite); Tourism; Service (business); Public transport; Transport engineering; Business; Political science; Public administration; Geography; Engineering; Archaeology; Law","score_opus":0.022044919179710666,"score_gpt":0.2930154144907284,"score_spread":0.2709704953110177,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W574684527","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.71989614,0.00039192132,0.09590324,0.011833378,0.0010763237,0.0002044568,0.000005932564,0.0007100554,0.16997854],"genre_scores_gemma":[0.9809917,0.00011983123,0.0086204205,0.00022172862,0.00031126163,0.0000019068606,0.000016082216,0.000004712624,0.009712309],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.99933726,0.0000659909,0.0001001182,0.00013690624,0.00017802452,0.00018167887],"domain_scores_gemma":[0.9997071,0.00005692813,0.000040874573,0.00008450144,0.000033192562,0.00007741159],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029504643,0.000050528375,0.000073557116,0.000065594315,0.00020201877,0.000035874054,0.00012424556,0.000065263084,0.00086406],"category_scores_gemma":[0.00015044418,0.000047064255,0.000029129735,0.00036394186,0.000046097106,0.00011233868,0.0000027072892,0.000065120475,0.0000662204],"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":[0.00008090565,0.000043387692,0.8133176,0.0000026253556,0.000038132694,0.000032300053,0.008410887,0.012338169,0.0001552887,0.05570194,0.050410114,0.05946865],"study_design_scores_gemma":[0.0006074872,0.000120947865,0.30288878,0.0000075965554,0.00004739371,0.0000013607215,0.003736188,0.00025546353,0.00013984207,0.002600816,0.68931544,0.0002787152],"about_ca_topic_score_codex":0.0017919631,"about_ca_topic_score_gemma":0.002289163,"teacher_disagreement_score":0.6389053,"about_ca_system_score_codex":0.000022420867,"about_ca_system_score_gemma":0.00007273658,"threshold_uncertainty_score":0.94608516},"labels":[],"label_agreement":null},{"id":"W574904311","doi":"","title":"NEW TRB SPECIAL REPORT: MAKING TRANSIT WORK: INSIGHT FROM WESTERN EUROPE, CANADA, AND THE UNITED STATES","year":2001,"lang":"en","type":"article","venue":"TR news","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Public transport; Transit (satellite); Work (physics); Western europe; Political science; Public administration; Regional science; Economic growth; Economy; European union; Geography; Business; Engineering; International trade; Law; Economics","score_opus":0.023745059330470844,"score_gpt":0.25919026162703457,"score_spread":0.23544520229656374,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W574904311","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93170995,0.00026269004,0.013531288,0.019802712,0.00086079125,0.00032313925,0.000023090837,0.0001224573,0.033363894],"genre_scores_gemma":[0.9704581,0.00074416783,0.0009726625,0.0025931275,0.0019055322,0.0000038935173,0.00039003912,0.000024738993,0.022907777],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991451,0.00011764869,0.00020356315,0.00014784676,0.00023454557,0.00015132639],"domain_scores_gemma":[0.99955964,0.00012821707,0.000092447575,0.000093674294,0.000048133636,0.00007787951],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014232344,0.000075445,0.00011031614,0.00003423596,0.00028287517,0.00009864397,0.00009637816,0.00003710321,0.00022382983],"category_scores_gemma":[0.000041283263,0.00005757765,0.000016745598,0.00047410117,0.00007275947,0.00009408221,0.000003081889,0.000085849264,0.000003691794],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008119973,0.00003315278,0.61713624,0.0000055425867,0.00009244109,0.0011257726,0.2435867,0.021391904,0.00000338721,0.0035587123,0.06891443,0.043339726],"study_design_scores_gemma":[0.00064986566,0.0000039747756,0.111370474,0.000021106738,0.000027944552,0.0000023698074,0.0014968478,0.0000797121,0.0000014670778,0.00014748194,0.8861008,0.00009792966],"about_ca_topic_score_codex":0.84156185,"about_ca_topic_score_gemma":0.97809523,"teacher_disagreement_score":0.8171864,"about_ca_system_score_codex":0.000022130505,"about_ca_system_score_gemma":0.0002653024,"threshold_uncertainty_score":0.24507797},"labels":[],"label_agreement":null},{"id":"W575722655","doi":"","title":"Integrating Frequent Transit Service and Corridor-Based Transit Supportive Environments inteh Metro Vancouver Region","year":2009,"lang":"en","type":"article","venue":"2009 ANNUAL CONFERENCE AND EXHIBITION OF THE TRANSPORTATION ASSOCIATION OF CANADA - TRANSPORTATION IN A CLIMATE OF CHANGE","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Transit (satellite); Metropolitan area; Transport engineering; Service (business); Public transport; Key (lock); Business; Geography; Telecommunications; Computer science; Engineering; Computer security; Marketing","score_opus":0.02189905960596463,"score_gpt":0.2418192321328864,"score_spread":0.21992017252692175,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W575722655","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99007934,0.000073801515,0.0032947958,0.0041175894,0.00026795268,0.0006555261,0.0011506933,0.00002305895,0.00033721406],"genre_scores_gemma":[0.9984492,0.00028418782,0.00036185296,0.00039026974,0.000017812083,0.000022510498,0.00041792312,0.000011307503,0.000044956778],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9975313,0.00016326945,0.00096989225,0.00026714904,0.0007875201,0.00028088567],"domain_scores_gemma":[0.99808115,0.00014784501,0.0010537857,0.00010853027,0.0005074516,0.00010121525],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00053673075,0.00020229837,0.00041090953,0.00018762892,0.00016738505,0.000013739052,0.00012805812,0.00018669669,0.000058662667],"category_scores_gemma":[0.000044614946,0.00020170347,0.00008632194,0.00051897025,0.00010763398,0.0005383462,6.530468e-7,0.00016824313,9.318377e-8],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002761276,0.0008310078,0.65636176,0.001316147,0.00020620054,0.000019792069,0.2823216,0.018746562,0.004022827,0.02478919,0.001155868,0.007467763],"study_design_scores_gemma":[0.0026281923,0.0002094089,0.9659801,0.00058329484,0.00018446322,7.08753e-8,0.025234682,0.00064848235,0.0033071246,0.0005572623,0.0003732628,0.0002936246],"about_ca_topic_score_codex":0.057019442,"about_ca_topic_score_gemma":0.86438227,"teacher_disagreement_score":0.8073628,"about_ca_system_score_codex":0.00015153042,"about_ca_system_score_gemma":0.00037653683,"threshold_uncertainty_score":0.94925994},"labels":[],"label_agreement":null},{"id":"W576145352","doi":"","title":"Building the Case: Improving the Credibility and Reliability of Travel Demand Models to Meet Changing Needs","year":2012,"lang":"en","type":"article","venue":"2012 CONFERENCE AND EXHIBITION OF THE TRANSPORTATION ASSOCIATION OF CANADA - TRANSPORTATION: INNOVATIONS AND OPPORTUNITIES","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Credibility; Investment (military); Context (archaeology); Sustainability; Government (linguistics); Business; Demand management; Demand forecasting; Finance; Transport engineering; Economics; Marketing; Engineering; Political science; Geography","score_opus":0.04321803538352324,"score_gpt":0.2529344030138536,"score_spread":0.20971636763033036,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W576145352","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9684214,0.00015974404,0.021695863,0.008022224,0.00017882283,0.0004955775,0.0006172595,0.00001489217,0.00039420134],"genre_scores_gemma":[0.9985133,0.00016076532,0.00067719375,0.00025280242,0.00003936204,0.000032992433,0.00007741253,0.000008927825,0.0002372533],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9981678,0.00016428599,0.00076664827,0.00014690969,0.00050316675,0.00025115287],"domain_scores_gemma":[0.9976688,0.00036472242,0.00073363946,0.00015770168,0.0009763502,0.00009879584],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020022532,0.00014543922,0.00024665202,0.0001596264,0.0008590194,0.00003362639,0.000110155095,0.00010590742,0.00003024844],"category_scores_gemma":[0.00008246109,0.00010639382,0.00004828216,0.000631786,0.00030825293,0.0007288938,0.0000034306076,0.00011479874,1.4696737e-8],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004876039,0.000053968248,0.059885196,0.0002513909,0.000061749764,9.879917e-7,0.095723785,0.0041008648,0.0004324514,0.8373445,0.00041548949,0.0016807972],"study_design_scores_gemma":[0.0010197446,0.00007914152,0.77564305,0.0003023718,0.00056060374,0.000005069609,0.20584382,0.0026656615,0.0017894555,0.009262308,0.0023322965,0.0004964683],"about_ca_topic_score_codex":0.041181378,"about_ca_topic_score_gemma":0.20900047,"teacher_disagreement_score":0.82808226,"about_ca_system_score_codex":0.00005660446,"about_ca_system_score_gemma":0.0005189791,"threshold_uncertainty_score":0.96520346},"labels":[],"label_agreement":null},{"id":"W576279887","doi":"","title":"Duration Modelling of Calgary Household Weekday and Weekend Activities: How Different Are They?","year":2007,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Duration (music); Names of the days of the week; Weekend effect; Geography; Medicine","score_opus":0.042749208457019086,"score_gpt":0.25179582512352255,"score_spread":0.20904661666650348,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W576279887","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6522608,0.000029647817,0.34316298,0.000233869,0.00006153448,0.00007545281,0.000004066136,0.00005446509,0.0041172234],"genre_scores_gemma":[0.996724,0.00011879243,0.0018985666,0.00002586789,0.000040196766,0.0000015981393,0.0000114803115,0.000005782388,0.0011737274],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99942493,0.000029965066,0.0001233687,0.000104159495,0.00019640643,0.00012119147],"domain_scores_gemma":[0.9996621,0.00010599683,0.00009775182,0.00005045114,0.000031106676,0.000052609314],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026334348,0.000058441397,0.00008950157,0.000060910323,0.00017358772,0.00003419123,0.000038653532,0.00006672193,0.0000133754675],"category_scores_gemma":[0.000012483447,0.000050350056,0.000021157786,0.00006567921,0.00005775316,0.00020871028,0.0000025631978,0.000051225994,1.7057383e-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":[0.00031970735,0.00049059896,0.48052993,0.00022436609,0.0001197068,0.000018738616,0.22714524,0.12846509,0.0028513297,0.1410119,0.0006891928,0.018134193],"study_design_scores_gemma":[0.0023388905,0.0002500061,0.676223,0.00037291815,0.00017198188,0.0000025902157,0.16699548,0.12977475,0.01415335,0.004353214,0.004101216,0.0012626207],"about_ca_topic_score_codex":0.00074828666,"about_ca_topic_score_gemma":0.003364777,"teacher_disagreement_score":0.34446323,"about_ca_system_score_codex":0.000018348386,"about_ca_system_score_gemma":0.00001724077,"threshold_uncertainty_score":0.20532157},"labels":[],"label_agreement":null},{"id":"W576958285","doi":"","title":"BUS RAPID TRANSIT","year":2001,"lang":"en","type":"article","venue":"Mass transit","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Bus rapid transit; Metropolitan area; Transport engineering; Transit (satellite); Public transport; Rapid transit; Traffic congestion; Transit system; Rail transit; Business; Telecommunications; Engineering; Geography","score_opus":0.020423000221677853,"score_gpt":0.26630799276699696,"score_spread":0.2458849925453191,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W576958285","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14481772,0.00059685635,0.49696007,0.017669305,0.00095605396,0.000524595,0.000047096797,0.000999963,0.33742836],"genre_scores_gemma":[0.99434805,0.0003580064,0.0019013626,0.0003124253,0.00013770023,0.0000073055317,0.00004803535,0.000011836353,0.002875273],"study_design_codex":"qualitative","study_design_gemma":"not_applicable","domain_scores_codex":[0.9990918,0.00006948308,0.00015079747,0.00016660617,0.00027320377,0.00024812607],"domain_scores_gemma":[0.9996871,0.000036628768,0.000025322744,0.00008936813,0.000055246634,0.00010637698],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00024367248,0.00008299507,0.00010527328,0.000062708954,0.00033967316,0.00004938558,0.00011906469,0.00009583138,0.0013062634],"category_scores_gemma":[0.000009021539,0.00008853459,0.000065199994,0.00033643338,0.00008306808,0.00018386297,3.841686e-7,0.00008752581,0.00007466153],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00084484817,0.0006370168,0.1276048,0.00012591772,0.0002240209,0.000637204,0.36435792,0.15839171,0.005730112,0.08504722,0.011677311,0.2447219],"study_design_scores_gemma":[0.0011447318,0.00006282651,0.03841276,0.000033958633,0.00007764728,0.0000030600386,0.0037577746,0.0007766126,0.00030847645,0.001054266,0.9539446,0.00042325386],"about_ca_topic_score_codex":0.0004122209,"about_ca_topic_score_gemma":0.0010508335,"teacher_disagreement_score":0.9422673,"about_ca_system_score_codex":0.000021498197,"about_ca_system_score_gemma":0.000055590208,"threshold_uncertainty_score":0.99960667},"labels":[],"label_agreement":null},{"id":"W577379265","doi":"","title":"Design Guidance for Freeway Transitions to Signalized Intersections: Some Application Heuristics","year":2009,"lang":"en","type":"article","venue":"Transportation Research Board 88th Annual MeetingTransportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Heuristics; Intersection (aeronautics); Transport engineering; Set (abstract data type); Work (physics); Section (typography); Computer science; Operations research; Transition (genetics); Engineering","score_opus":0.08122569214229929,"score_gpt":0.41918025262523595,"score_spread":0.33795456048293665,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W577379265","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.084669456,0.00030060078,0.88213,0.020029906,0.0004132748,0.008893085,0.0011733678,0.0009352115,0.0014550918],"genre_scores_gemma":[0.94759876,0.00035246796,0.045023873,0.00066492753,0.00062750984,0.0025559636,0.0013900354,0.00010444351,0.001682022],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.98909557,0.0018844871,0.0015814723,0.0014148058,0.0039293393,0.0020943107],"domain_scores_gemma":[0.9888422,0.0021215933,0.00027340403,0.0006147001,0.0069782385,0.001169902],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.010676984,0.0004720508,0.00059280905,0.00197013,0.0037728366,0.00037832325,0.00096739177,0.0005098407,0.0001902763],"category_scores_gemma":[0.0011992697,0.0005503216,0.00030884417,0.0040389765,0.00076072896,0.0013808355,0.0000040944697,0.0011872024,0.00013512427],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.013437403,0.0026644368,0.008738807,0.0007577332,0.0003493516,0.0001281747,0.29012352,0.29193956,0.02307483,0.26438507,0.07820247,0.02619864],"study_design_scores_gemma":[0.01232927,0.007962494,0.36462703,0.0018768659,0.00046328598,0.0000013020045,0.11674161,0.010832729,0.0096729705,0.090572044,0.3806099,0.0043104906],"about_ca_topic_score_codex":0.008649396,"about_ca_topic_score_gemma":0.02279604,"teacher_disagreement_score":0.8629293,"about_ca_system_score_codex":0.00045566933,"about_ca_system_score_gemma":0.0011879235,"threshold_uncertainty_score":0.9996948},"labels":[],"label_agreement":null},{"id":"W577751950","doi":"","title":"Evaluating the Impact of Travel Demand Management Strategies on GHGs for Downtown Commuters in Rail Catchment Areas","year":2012,"lang":"en","type":"article","venue":"Transportation Research Board 91st Annual MeetingTransportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Downtown; Greenhouse gas; Public transport; Transport engineering; Mode choice; Land use; Population; Transit (satellite); TRIPS architecture; Catchment area; Business; Environmental science; Geography; Engineering; Drainage basin; Civil engineering","score_opus":0.14753944464117225,"score_gpt":0.4905458445086879,"score_spread":0.34300639986751563,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W577751950","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9844683,0.00024951168,0.0026428131,0.0015360583,0.00019576114,0.004196457,0.0004412587,0.00009522782,0.0061746347],"genre_scores_gemma":[0.99379414,0.00051368965,0.0029182772,0.0000359369,0.00018540895,0.0011689721,0.00065742474,0.00006214644,0.0006640189],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.98968554,0.0021980465,0.0013486062,0.00067707343,0.0041535683,0.001937182],"domain_scores_gemma":[0.99350274,0.0028699492,0.0003341909,0.0004956114,0.0023117752,0.00048575792],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.019813804,0.0003781021,0.00051467697,0.0012966107,0.0016815163,0.00021399895,0.00079883187,0.0002876052,0.00025827836],"category_scores_gemma":[0.00033867362,0.00031528884,0.0003531397,0.0024644174,0.0011317431,0.0011017408,0.000007419029,0.0010629711,0.00001934244],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.005457285,0.0017526267,0.39663115,0.0008291297,0.00042997583,0.000027092423,0.31284776,0.16329132,0.0010631725,0.103056975,0.0033105097,0.011302982],"study_design_scores_gemma":[0.002221275,0.0010208645,0.8781961,0.00041687235,0.000054034692,9.399875e-8,0.11441492,0.00088387553,0.00033076908,0.0013853572,0.00072526664,0.00035056108],"about_ca_topic_score_codex":0.041947316,"about_ca_topic_score_gemma":0.024591243,"teacher_disagreement_score":0.48156494,"about_ca_system_score_codex":0.00049347605,"about_ca_system_score_gemma":0.0008801322,"threshold_uncertainty_score":0.9999299},"labels":[],"label_agreement":null},{"id":"W578391238","doi":"10.1007/s10109-015-0222-6","title":"Testing block subdivision algorithms on block designs","year":2015,"lang":"en","type":"article","venue":"Journal of Geographical Systems","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Subdivision; Block (permutation group theory); Grid; Computer science; Algorithm; Scale (ratio); Minimum bounding box; Database; Data mining; Engineering; Geography; Mathematics; Artificial intelligence; Cartography; Civil engineering","score_opus":0.08353725387069073,"score_gpt":0.3169565352879216,"score_spread":0.2334192814172309,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W578391238","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97992843,0.0007762887,0.0064748176,0.0011113334,0.0029558158,0.00032401265,0.000007811861,0.00012863759,0.008292867],"genre_scores_gemma":[0.99779046,0.000018854498,0.0013839682,0.000036185123,0.00065900513,0.0000016346977,0.0000016096513,0.000009097026,0.000099211495],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9978272,0.00030445575,0.00051862246,0.00011572997,0.0009828964,0.0002511274],"domain_scores_gemma":[0.99801105,0.0003411145,0.00043108934,0.00007826077,0.00076239376,0.00037606485],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002543215,0.000095692674,0.00022133644,0.0002495375,0.0002781423,0.00013140099,0.00019005757,0.00013912305,0.0000035568808],"category_scores_gemma":[0.0006769206,0.000076479206,0.000098935845,0.0007522505,0.00008545091,0.00016787209,0.0000048175716,0.0002489195,0.000008925773],"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":[0.00032225612,0.0007195272,0.70993793,0.000060055536,0.0002033678,0.0003753112,0.011911831,0.24812047,0.00026038967,0.012135029,0.010827798,0.0051260106],"study_design_scores_gemma":[0.0119798565,0.010527208,0.63807595,0.005098392,0.00076430204,0.0009368894,0.044663258,0.05826769,0.00016417852,0.0043264655,0.22245131,0.0027444854],"about_ca_topic_score_codex":0.0005992338,"about_ca_topic_score_gemma":0.000026282463,"teacher_disagreement_score":0.2116235,"about_ca_system_score_codex":0.000047854795,"about_ca_system_score_gemma":0.00016946984,"threshold_uncertainty_score":0.31187314},"labels":[],"label_agreement":null},{"id":"W579683765","doi":"","title":"Funding Shortfalls are Key Concern","year":2006,"lang":"en","type":"article","venue":"Metrologia","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Salary; Transit (satellite); Quarter (Canadian coin); Business; Key (lock); Order (exchange); Finance; Operations management; Transport engineering; Operations research; Marketing; Computer science; Public transport; Computer security; Economics; Engineering; Geography","score_opus":0.04105162250339803,"score_gpt":0.30185939019352415,"score_spread":0.26080776769012615,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W579683765","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8420371,0.00026905935,0.02093067,0.0013303164,0.00050503016,0.0001334062,0.0000146486445,0.00041466198,0.13436514],"genre_scores_gemma":[0.9950452,0.000011229001,0.001770125,0.00014812939,0.0001735013,0.0000045338343,0.000027404489,0.0000045521615,0.0028153167],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99934506,0.000063781314,0.00011278375,0.00011864119,0.0001713205,0.00018841792],"domain_scores_gemma":[0.999727,0.00006777834,0.000067883826,0.000056073728,0.00004718876,0.000034055574],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003394518,0.000049874187,0.00008054399,0.000055304037,0.0002626667,0.00004491078,0.00009148107,0.00007678936,0.00022321346],"category_scores_gemma":[0.00006848134,0.000048057853,0.000029701523,0.00022018934,0.00008211574,0.00009327179,0.0000027914748,0.000060346276,0.00004388146],"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":[0.000007647033,0.000018642984,0.9551989,0.0000020132127,0.0000058030128,0.000007899649,0.0008245282,0.006153866,0.000060888447,0.033105217,0.004193378,0.00042120164],"study_design_scores_gemma":[0.00026373903,0.000027273029,0.8949829,0.000008968736,0.00002238776,2.796657e-7,0.0018160752,0.0003327626,0.00009503637,0.0014440117,0.100835286,0.00017130059],"about_ca_topic_score_codex":0.0014461413,"about_ca_topic_score_gemma":0.0021425267,"teacher_disagreement_score":0.15300815,"about_ca_system_score_codex":0.000036701258,"about_ca_system_score_gemma":0.000026141628,"threshold_uncertainty_score":0.2444031},"labels":[],"label_agreement":null},{"id":"W580898299","doi":"","title":"Modelling Traffic Operations for Performance Measurement of the Port Mann-Highway 1 Project","year":2009,"lang":"en","type":"article","venue":"2009 ANNUAL CONFERENCE AND EXHIBITION OF THE TRANSPORTATION ASSOCIATION OF CANADA - TRANSPORTATION IN A CLIMATE OF CHANGE","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Transport engineering; Port (circuit theory); Suite; Traffic congestion; Engineering; Throughput; Gateway (web page); Process (computing); Computer science; Operations research; Telecommunications; Geography","score_opus":0.05029547493734449,"score_gpt":0.2646940740570473,"score_spread":0.21439859911970283,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W580898299","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99378294,0.000059568654,0.0011216245,0.0019203929,0.00022443246,0.00105534,0.0016060865,0.000014840574,0.00021478155],"genre_scores_gemma":[0.9987573,0.00036006424,0.0004426767,0.00006107031,0.000021648633,0.00005085886,0.00023481106,0.000008332642,0.000063234715],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99751854,0.00009886183,0.00097618636,0.00018088402,0.0010004446,0.0002250617],"domain_scores_gemma":[0.997371,0.0000747683,0.00089166383,0.000121123,0.0015013396,0.00004007525],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00093432836,0.00014012476,0.00031533878,0.00013126686,0.00022445376,0.00000960931,0.00016835678,0.00013002437,0.000015951864],"category_scores_gemma":[0.000052907628,0.00012053455,0.000116524534,0.00050798996,0.00009027933,0.00039883223,7.2478434e-7,0.00010042493,3.2131375e-8],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000941497,0.00046725097,0.08104656,0.0011197204,0.00010857813,4.998059e-7,0.13812302,0.74299955,0.0010981624,0.031599615,0.00050424517,0.001991282],"study_design_scores_gemma":[0.0029784443,0.00028190092,0.95832026,0.0010637315,0.0002618878,8.7662876e-8,0.014887978,0.015668439,0.00526186,0.00022125311,0.0007103559,0.00034381452],"about_ca_topic_score_codex":0.02786688,"about_ca_topic_score_gemma":0.4975411,"teacher_disagreement_score":0.8772737,"about_ca_system_score_codex":0.00011876566,"about_ca_system_score_gemma":0.0008479465,"threshold_uncertainty_score":0.97860664},"labels":[],"label_agreement":null},{"id":"W581241149","doi":"","title":"An Approach to Incorporate Uncertainty and Risk in Transportation Investment Decision Making: Detroit River International Crossing Case Study","year":2011,"lang":"en","type":"article","venue":"Transportation Research Board 90th Annual MeetingTransportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Investment (military); Risk analysis (engineering); Option value; Economics; Policy analysis; Business; Actuarial science; Operations research; Engineering; Microeconomics; Incentive","score_opus":0.10084820366166235,"score_gpt":0.41476423916109095,"score_spread":0.3139160354994286,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W581241149","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98422813,0.000057856432,0.00890338,0.00014732924,0.00026376767,0.003675226,0.0005550483,0.0002868602,0.001882401],"genre_scores_gemma":[0.96443844,0.00021442697,0.033594415,0.00010297054,0.00015144044,0.0007457339,0.000521476,0.00010187639,0.00012924554],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9873614,0.0028217155,0.001815645,0.0018716813,0.004441875,0.0016877335],"domain_scores_gemma":[0.993098,0.0009450137,0.00041262014,0.000632623,0.0035243414,0.0013873585],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.012982568,0.00053710316,0.0006138212,0.0030239082,0.002978166,0.0006182901,0.00090090005,0.00045274556,0.00019787709],"category_scores_gemma":[0.0006928456,0.000591583,0.00014335761,0.00411264,0.0015569002,0.0022444578,0.000011054193,0.0017443941,0.000027121661],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013504997,0.0014120924,0.6050431,0.000055407898,0.00006602565,0.0009916413,0.36034435,0.02152554,0.000028493801,0.0031822345,0.00012564316,0.0058749514],"study_design_scores_gemma":[0.002090444,0.0007265796,0.7759145,0.00018645526,0.000060056176,0.000002371808,0.21579798,0.0015131866,0.000023082172,0.0020490482,0.0010942605,0.0005420286],"about_ca_topic_score_codex":0.20353717,"about_ca_topic_score_gemma":0.39819312,"teacher_disagreement_score":0.19465595,"about_ca_system_score_codex":0.0005118817,"about_ca_system_score_gemma":0.0010955242,"threshold_uncertainty_score":0.9996536},"labels":[],"label_agreement":null},{"id":"W581424200","doi":"10.18757/ejtir.2002.2.4.3726","title":"Agent Behaviour Issues Arising with Urban System Micro-Simulation","year":2002,"lang":"en","type":"article","venue":"European journal of transport and infrastructure research","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Social Sciences and Humanities Research Council of Canada; University of Toronto; Oregon Department of Transportation; Natural Sciences and Engineering Research Council of Canada; Northwestern University; U.S. Department of Transportation","keywords":"Work (physics); Representation (politics); Computer science; Management science; Land use; Transport policy; Transport system; Risk analysis (engineering); Operations research; Transport engineering; Public transport; Business; Engineering; Civil engineering","score_opus":0.045057900107770506,"score_gpt":0.3245160222828826,"score_spread":0.2794581221751121,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W581424200","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.979893,0.0011223935,0.006846909,0.0006095902,0.00017762082,0.00016500906,0.000008209865,0.000040056897,0.011137178],"genre_scores_gemma":[0.9955273,0.00029258023,0.0034076304,0.000012214465,0.00026917126,2.6765443e-7,0.000006758385,0.000018469167,0.00046560168],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99808335,0.00044508025,0.00035719053,0.00013278636,0.00073599914,0.00024557835],"domain_scores_gemma":[0.9990559,0.00004480011,0.0001607082,0.00007949774,0.00047391467,0.00018518623],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017791484,0.00009421708,0.0001548894,0.00023842213,0.0005283261,0.00010108698,0.00016609114,0.000040936535,0.00012612573],"category_scores_gemma":[0.000019571971,0.000073072864,0.0000435081,0.00031692724,0.00025252122,0.00031075135,0.000003000052,0.00040625618,0.000004165527],"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":[0.0003114599,0.000081933315,0.78821605,0.000144651,0.000107608656,0.0011782199,0.122210875,0.068160035,0.00036184854,0.0017226741,0.0032908872,0.014213742],"study_design_scores_gemma":[0.0012439026,0.00050146104,0.9192621,0.0005635148,0.00008943892,0.00005938483,0.0143522,0.0009640694,0.00008574241,0.000029529421,0.06259052,0.00025810924],"about_ca_topic_score_codex":0.00004429981,"about_ca_topic_score_gemma":0.000019380985,"teacher_disagreement_score":0.13104607,"about_ca_system_score_codex":0.00005198537,"about_ca_system_score_gemma":0.000046216956,"threshold_uncertainty_score":0.40635103},"labels":[],"label_agreement":null},{"id":"W581470350","doi":"","title":"Metrolinx: Governing the Growth of Transportation in Canada's Largest Urban Region","year":2009,"lang":"en","type":"article","venue":"Public transport international","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"TRIPS architecture; Plan (archaeology); Transit (satellite); Work (physics); Transport engineering; Transportation planning; Agency (philosophy); Investment (military); Public transport; Business; Government (linguistics); Rail transit; Journey to work; Regional planning; Environmental planning; Urban planning; Geography; Engineering; Political science; Civil engineering","score_opus":0.015489052097006897,"score_gpt":0.23939322355917458,"score_spread":0.22390417146216768,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W581470350","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.91815466,0.00015211586,0.01368877,0.038444217,0.00092854426,0.00038078064,0.00030508527,0.000099218196,0.027846595],"genre_scores_gemma":[0.99844444,0.000088023444,0.00019945549,0.00040215603,0.00009831673,0.000008725708,0.00053064723,0.000006869979,0.00022134488],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9983886,0.000053903732,0.00042718172,0.00017852573,0.0007278571,0.00022395434],"domain_scores_gemma":[0.9993218,0.00007963837,0.00019505678,0.000087565306,0.00024467363,0.00007121911],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004019261,0.00009769453,0.00013348737,0.00013490541,0.00012937901,0.000022433343,0.00035761547,0.000059810533,0.00013441914],"category_scores_gemma":[0.000057703546,0.0000885157,0.000062957755,0.00046214391,0.000058260437,0.0003869399,6.1149694e-7,0.00015616132,0.0000010264699],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031324424,0.000054615808,0.91047394,0.0000038103758,0.000016345557,0.000013060028,0.0031057817,0.0013010551,0.000006977964,0.08347449,0.00053016696,0.0009884484],"study_design_scores_gemma":[0.0003699512,0.000011409093,0.97797066,0.000020822019,0.000011262249,5.4104936e-7,0.0007775899,0.00026042486,0.000022903121,0.00023826801,0.020218616,0.00009757752],"about_ca_topic_score_codex":0.65300167,"about_ca_topic_score_gemma":0.93807584,"teacher_disagreement_score":0.28507414,"about_ca_system_score_codex":0.00027722534,"about_ca_system_score_gemma":0.0010054577,"threshold_uncertainty_score":0.36095652},"labels":[],"label_agreement":null},{"id":"W581740004","doi":"","title":"City of Ottawa Transportation Master Plan 2013","year":2014,"lang":"fr","type":"article","venue":"Transportation 2014: Past, Present, Future - 2014 Conference and Exhibition of the Transportation Association of Canada // Transport 2014 : Du passé vers l'avenir - 2014 Congrès et Exposition de 'Association des transports du Canada","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Master plan; Plan (archaeology); Sustainability; Action plan; Transportation planning; Public administration; Environmental planning; Urban planning; Land-use planning; Population; Sustainable transport; Population growth; Business; Political science; Land use; Economic growth; Transport engineering; Engineering; Geography; Management; Civil engineering; Sociology; Economics; Archaeology","score_opus":0.009595776676357262,"score_gpt":0.20704346497490375,"score_spread":0.19744768829854648,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W581740004","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8903334,0.0014153218,0.026936924,0.0494796,0.0060173855,0.0028571414,0.020349456,0.00021654165,0.002394232],"genre_scores_gemma":[0.9801279,0.0061971075,0.0007857481,0.00082918635,0.00079253106,0.00013443644,0.008311987,0.00014174441,0.0026793934],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.98772204,0.0013762636,0.0041570724,0.0012740684,0.003977478,0.0014931017],"domain_scores_gemma":[0.9872711,0.0009852913,0.006328011,0.0007084376,0.0038674928,0.00083971536],"candidate_categories":["metaepi_narrow","research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.003341979,0.0012156855,0.0019180669,0.00040807074,0.00097088155,0.000104958235,0.00080777454,0.0013301624,0.0022443677],"category_scores_gemma":[0.00008713759,0.0013383729,0.00085287064,0.00094889814,0.0006832477,0.0020378914,0.0000044239,0.0010155723,0.000006148565],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006256005,0.0005217923,0.7856618,0.0015930696,0.0009347325,0.000025924062,0.01584742,0.032400336,0.0005831508,0.022188978,0.13897114,0.00064603286],"study_design_scores_gemma":[0.0043495563,0.0002583255,0.82709986,0.0007397833,0.0019974243,0.000003490969,0.0026685116,0.0006439388,0.0023971885,0.0007477341,0.15782969,0.0012645359],"about_ca_topic_score_codex":0.75950074,"about_ca_topic_score_gemma":0.9862648,"teacher_disagreement_score":0.22676408,"about_ca_system_score_codex":0.0021359117,"about_ca_system_score_gemma":0.0057972735,"threshold_uncertainty_score":0.9999663},"labels":[],"label_agreement":null},{"id":"W582791806","doi":"","title":"Bus Rapid Transit … Past, Present, Future: Evolution into a Worldwide Winner","year":2005,"lang":"en","type":"article","venue":"ITE 2005 Annual Meeting and Exhibit Compendium of Technical PapersInstitute of Transportation Engineers (ITE)ARRB Group Ltd.","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Bus rapid transit; Downtown; Transport engineering; Transit (satellite); Public transport; Paratransit; Bus priority; Service (business); Engineering; Operations research; Telecommunications; Computer science; Business; Geography; Marketing","score_opus":0.005077531733797743,"score_gpt":0.22753658307003627,"score_spread":0.22245905133623853,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W582791806","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.73980427,0.015045087,0.16790578,0.040750638,0.0034960299,0.0038846051,0.002013695,0.0027384746,0.024361443],"genre_scores_gemma":[0.97521365,0.00050009973,0.022723496,0.000093843024,0.0007025315,0.000018825018,0.0005641685,0.000033639764,0.00014975575],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99706495,0.00010894589,0.0010160672,0.0005134178,0.0007711008,0.0005255086],"domain_scores_gemma":[0.99866265,0.00016587005,0.00034495853,0.00023656718,0.00027451012,0.0003154454],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00085081375,0.00034490557,0.0005337323,0.00040504494,0.00039676993,0.000033730936,0.00030273423,0.00034334138,0.00007664215],"category_scores_gemma":[0.000038958628,0.00036756488,0.00024283347,0.00073517,0.00049174327,0.00081623,0.000005810713,0.00034646277,0.0000040741515],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00164764,0.0015772827,0.016703667,0.0013283816,0.00048642282,0.00005365332,0.07729737,0.76462847,0.044908196,0.040950187,0.011975616,0.03844314],"study_design_scores_gemma":[0.0070262025,0.00068423303,0.1937778,0.0017619336,0.0010474324,0.000016653756,0.020459354,0.0042624124,0.0035313505,0.0002494896,0.76474977,0.002433372],"about_ca_topic_score_codex":0.0009970206,"about_ca_topic_score_gemma":0.0034462237,"teacher_disagreement_score":0.760366,"about_ca_system_score_codex":0.00012802168,"about_ca_system_score_gemma":0.00012639971,"threshold_uncertainty_score":0.99987763},"labels":[],"label_agreement":null},{"id":"W582989559","doi":"","title":"DRIVE ON A DIET : RESTRUCTURING LANES LEADS TO IMPROVED SAFETY AND QUALITY OF LIFE","year":2004,"lang":"en","type":"article","venue":"Roads & bridges/Roads & bridges (Des Plaines, Ill. Online)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Transport engineering; Restructuring; Engineering; Road traffic; Civil engineering; Business","score_opus":0.029294775763406715,"score_gpt":0.30696514349145215,"score_spread":0.27767036772804543,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W582989559","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.989382,0.0005127486,0.0035368209,0.0035268269,0.0004574533,0.0006745407,0.0006971841,0.00034966393,0.00086272456],"genre_scores_gemma":[0.9913091,0.0009522276,0.004774615,0.000881377,0.00061235524,0.000018590643,0.0006237655,0.00006144562,0.0007665032],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9961543,0.0003086774,0.0011296711,0.0008452093,0.00079523056,0.00076690805],"domain_scores_gemma":[0.9975482,0.00039314138,0.0004878162,0.0004764727,0.0005247902,0.0005695324],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001076003,0.0004810815,0.0007976347,0.00032776152,0.00082620274,0.00013692335,0.000463299,0.0003111854,0.00003179682],"category_scores_gemma":[0.0014971545,0.00048505078,0.00018728655,0.00070972444,0.000564431,0.00040287865,0.000067329136,0.00037291812,0.000014341843],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.009873634,0.0016353867,0.51772904,0.0010956725,0.00045272903,0.00009132591,0.06557834,0.3068736,0.0073455432,0.046046518,0.0010704597,0.04220777],"study_design_scores_gemma":[0.0022764076,0.00053238857,0.98516315,0.00041145287,0.00007436365,0.000004404494,0.0028443788,0.0005643773,0.00074624585,0.00020145791,0.006400178,0.0007812184],"about_ca_topic_score_codex":0.017714966,"about_ca_topic_score_gemma":0.015211173,"teacher_disagreement_score":0.4674341,"about_ca_system_score_codex":0.00026684545,"about_ca_system_score_gemma":0.00044379677,"threshold_uncertainty_score":0.9997601},"labels":[],"label_agreement":null},{"id":"W583721976","doi":"","title":"Household Trip Patterns and Travel Characteristics in Lethbridge, Alberta","year":2011,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Census; Geography; Transport engineering; Travel behavior; Population; Travel time; Survey methodology; Engineering; Statistics; Demography; Mathematics; Sociology","score_opus":0.04753169156163012,"score_gpt":0.25712289737039523,"score_spread":0.2095912058087651,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W583721976","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9546229,0.00000552944,0.0039178967,0.00021093142,0.00010398119,0.000085889034,0.000014015462,0.00003296928,0.04100586],"genre_scores_gemma":[0.9961465,0.00008351073,0.000456473,0.00015846304,0.00002480955,0.0000032009516,0.000020041125,0.0000046902583,0.003102335],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.999569,0.000029512225,0.00012083978,0.00009474888,0.000075573014,0.00011031172],"domain_scores_gemma":[0.99982744,0.000039096587,0.000030746156,0.000040923343,0.000012901438,0.000048906382],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001490691,0.000042608513,0.000069702524,0.000045002485,0.00006675406,0.000018457253,0.00004577545,0.000049619888,0.00014697324],"category_scores_gemma":[0.000029103623,0.000041492734,0.000010039142,0.00007779467,0.000036287747,0.00010147356,0.0000018799424,0.00004552567,0.000005117087],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009416437,0.000033643602,0.958409,0.0000045888924,0.0000021594155,0.000004039705,0.030875737,0.000002911209,0.000005364444,0.009250085,0.00010279265,0.0013002611],"study_design_scores_gemma":[0.00015554113,0.000009056062,0.99732983,0.000009087343,0.0000036481063,1.9635432e-7,0.0014425701,0.00006274775,0.000024384964,0.000049239366,0.0008493925,0.000064276705],"about_ca_topic_score_codex":0.02282072,"about_ca_topic_score_gemma":0.022740947,"teacher_disagreement_score":0.04152355,"about_ca_system_score_codex":0.000007576908,"about_ca_system_score_gemma":0.00002304312,"threshold_uncertainty_score":0.9950915},"labels":[],"label_agreement":null},{"id":"W584115241","doi":"","title":"EXPLAINING THE SUSPECT BEHAVIOR OF TRAVEL DEMAND FORECASTERS","year":2004,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Suspect; Feeling; Public relations; Work (physics); Evasion (ethics); Psychology; Social psychology; Political science; Positive economics; Economics; Criminology; Engineering","score_opus":0.03135348286598967,"score_gpt":0.29302744670093783,"score_spread":0.2616739638349482,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W584115241","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9097125,0.000025220032,0.0386806,0.0009236896,0.0001242767,0.00017252505,0.0000029185592,0.000040885727,0.050317407],"genre_scores_gemma":[0.99761325,0.000010487965,0.0018179994,0.00004647836,0.000020770642,0.0000058103005,0.0000040617424,0.0000025463232,0.00047861857],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9996221,0.00001942002,0.000092065784,0.000053015545,0.00013205773,0.000081340695],"domain_scores_gemma":[0.9998315,0.00003771367,0.00003667576,0.000041159787,0.0000300807,0.000022873155],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021406423,0.0000287888,0.00004267097,0.00002067546,0.00019008301,0.000014005577,0.00006622628,0.000024634279,0.00008798477],"category_scores_gemma":[0.000016756147,0.00002006287,0.000023626639,0.00010932869,0.00008277422,0.00007581041,0.000001167178,0.000027755192,0.0000025130985],"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":[0.000033691842,0.00012387006,0.14098673,0.000009365752,0.00002405983,0.00000915497,0.3343526,0.038237303,0.000509914,0.48072502,0.0002946139,0.0046936753],"study_design_scores_gemma":[0.002177327,0.00017900781,0.68266714,0.00008599335,0.00014843784,0.000003308407,0.30023807,0.00030487354,0.008780374,0.0030892293,0.0019299934,0.00039625572],"about_ca_topic_score_codex":0.00083741726,"about_ca_topic_score_gemma":0.0018641461,"teacher_disagreement_score":0.5416804,"about_ca_system_score_codex":0.000012038184,"about_ca_system_score_gemma":0.000049100057,"threshold_uncertainty_score":0.1461984},"labels":[],"label_agreement":null},{"id":"W585070670","doi":"","title":"Joint or Solo: Structural Equations Model of Household Activity Time Allocation Patterns","year":2008,"lang":"en","type":"article","venue":"Transportation Research Board 87th Annual MeetingTransportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Demographics; Interdependence; Joint (building); Time allocation; Wife; Discrete choice; Representation (politics); Work (physics); Structural equation modeling; Econometrics; Survey data collection; Demographic economics; Computer science; Economics; Statistics; Mathematics; Demography; Engineering; Sociology; Machine learning","score_opus":0.17982109382130493,"score_gpt":0.3980514297020188,"score_spread":0.21823033588071386,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W585070670","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97112435,0.00005615165,0.021009918,0.001880644,0.00015974998,0.0020829586,0.001620032,0.00042244242,0.001643723],"genre_scores_gemma":[0.9884704,0.00074996357,0.0045083337,0.00005420779,0.00019347006,0.00034705384,0.0010873914,0.00010439839,0.0044847983],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.98779094,0.0018035726,0.0015481138,0.0011073716,0.00601821,0.0017318085],"domain_scores_gemma":[0.9910922,0.0016063047,0.0005070357,0.00064047513,0.005356496,0.0007974887],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.006278934,0.00044718088,0.00072922243,0.0016753908,0.0031482268,0.00012809047,0.0007996082,0.00054103223,0.0007167739],"category_scores_gemma":[0.0010978545,0.00044852257,0.00030519624,0.0030026915,0.0018631218,0.001747398,0.0000117647305,0.0015115796,0.00006658359],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0047414345,0.001494354,0.16666837,0.001198323,0.0004110371,0.00032558196,0.26954687,0.49053675,0.019443257,0.033336863,0.007378307,0.0049188724],"study_design_scores_gemma":[0.0048108497,0.0013560472,0.88958895,0.0007869255,0.00017721708,0.0000016786081,0.029542636,0.057167307,0.009635771,0.0022868128,0.0029716084,0.0016742154],"about_ca_topic_score_codex":0.029214272,"about_ca_topic_score_gemma":0.0378223,"teacher_disagreement_score":0.72292054,"about_ca_system_score_codex":0.00034436944,"about_ca_system_score_gemma":0.0027503325,"threshold_uncertainty_score":0.9997966},"labels":[],"label_agreement":null},{"id":"W585123178","doi":"","title":"Light Rail Update: Light Rail Systems Throughout North America Expand to Accommodate City Growth","year":2011,"lang":"en","type":"article","venue":"Mass transit","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Light rail; Rail transit; Light rail transit; Transit (satellite); Public transport; Transport engineering; Bay; Rapid transit; Orange (colour); Engineering; Telecommunications; Geography; Civil engineering","score_opus":0.028068192635367414,"score_gpt":0.2601628531685098,"score_spread":0.23209466053314237,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W585123178","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4225282,0.0006070751,0.38370237,0.016723659,0.0032158375,0.0021954833,0.00037194724,0.0017508582,0.16890459],"genre_scores_gemma":[0.99570215,0.00012769907,0.0023453692,0.0005224942,0.00017185183,0.000044679997,0.000103889135,0.000026650781,0.0009551972],"study_design_codex":"qualitative","study_design_gemma":"not_applicable","domain_scores_codex":[0.99821484,0.00016357296,0.00035318435,0.00038335202,0.00044629592,0.00043878276],"domain_scores_gemma":[0.9992102,0.000031742213,0.00010771664,0.00023573269,0.0001509573,0.0002636884],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002617954,0.00020317163,0.00029842532,0.00011075093,0.0005227536,0.00013058822,0.0003712725,0.00013082325,0.00037983264],"category_scores_gemma":[0.000019218529,0.00019393266,0.00009070077,0.0005710865,0.00006813457,0.00044479864,0.0000063575003,0.0001535334,0.00023757466],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007358331,0.0005951884,0.29920074,0.00023366215,0.0003657547,0.00016287311,0.6428927,0.008043279,0.0012355088,0.013870098,0.027419515,0.0052448176],"study_design_scores_gemma":[0.0020663156,0.00026963273,0.1288858,0.00016339646,0.0003055761,0.00000278386,0.014121287,0.0003806485,0.0031232801,0.00053817505,0.8483791,0.0017640045],"about_ca_topic_score_codex":0.0030880545,"about_ca_topic_score_gemma":0.0024415671,"teacher_disagreement_score":0.82095957,"about_ca_system_score_codex":0.00004725281,"about_ca_system_score_gemma":0.00008560544,"threshold_uncertainty_score":0.7908344},"labels":[],"label_agreement":null},{"id":"W586589260","doi":"","title":"BUS RAPID TRANSIT: INSTITUTIONAL ISSUES AND STRATEGIES FOR RESOLUTION","year":2001,"lang":"en","type":"article","venue":"8th World Congress on Intelligent Transport SystemsITS America, ITS Australia, ERTICO (Intelligent Transport Systems and Services - Europe)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Bus rapid transit; Transit (satellite); Public transport; Outreach; Stakeholder; Transport engineering; Enforcement; Software deployment; Business; Champion; Computer science; Engineering; Public relations; Political science; Economic growth; Economics","score_opus":0.05329943042586167,"score_gpt":0.31146418964031564,"score_spread":0.258164759214454,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W586589260","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8624051,0.016665133,0.08490719,0.0033107873,0.009075995,0.009878892,0.002171814,0.0016746516,0.009910415],"genre_scores_gemma":[0.97419024,0.007512147,0.00024727915,0.00024554742,0.0005321936,0.00027155803,0.00088367914,0.00010488571,0.016012501],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99362534,0.00036144262,0.0021161467,0.0014266925,0.0012740887,0.0011962737],"domain_scores_gemma":[0.99707013,0.00020965142,0.00061817764,0.00048037872,0.00085719366,0.00076444406],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0010820677,0.00091679033,0.0012288205,0.0006516372,0.0014758554,0.00044963605,0.0005831366,0.00033856966,0.00029445734],"category_scores_gemma":[0.00001307553,0.00087505573,0.000295219,0.001207217,0.00061235443,0.001098846,0.000008790765,0.0004441135,0.00006689104],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0059862738,0.0023218913,0.15106133,0.013053239,0.0024048535,0.0013857099,0.08058056,0.37687784,0.001030815,0.35460788,0.0016120216,0.009077607],"study_design_scores_gemma":[0.001364662,0.00066145783,0.021652278,0.0033390976,0.0007197744,0.0000683992,0.013559558,0.007056734,0.00038070526,0.00006788578,0.9493005,0.0018289129],"about_ca_topic_score_codex":0.016153894,"about_ca_topic_score_gemma":0.01321253,"teacher_disagreement_score":0.9476885,"about_ca_system_score_codex":0.00012490994,"about_ca_system_score_gemma":0.00024837287,"threshold_uncertainty_score":0.9998241},"labels":[],"label_agreement":null},{"id":"W586865191","doi":"","title":"Study of highway traffic peaking due to statutory holidays.","year":2006,"lang":"en","type":"dissertation","venue":"oURspace (University of Regina)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Faculty of Graduate Studies and Research, University of Regina; University of Regina","keywords":"Statutory law; Transport engineering; Road traffic; Engineering; Forensic engineering; Business; Political science; Law","score_opus":0.01342776125152832,"score_gpt":0.24797607013576956,"score_spread":0.23454830888424125,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W586865191","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.977771,0.00004965903,0.00033931958,0.016070472,0.00031579632,0.00039489937,0.000026840233,0.000084288586,0.004947764],"genre_scores_gemma":[0.80556387,0.000033550496,0.0012616168,0.0000042732627,0.000045714252,5.680314e-7,0.0003157278,0.000019291201,0.19275536],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.9988657,0.00012294191,0.000028697077,0.00026475586,0.00052656,0.00019135058],"domain_scores_gemma":[0.9989786,0.00005077665,0.0004065068,0.00016627171,0.00030982573,0.0000879985],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024754705,0.00013408232,0.00032781024,0.00038135285,0.00035853693,0.000016306763,0.00029010337,0.00019395279,0.000009240737],"category_scores_gemma":[0.00002294035,0.00019098425,0.00008677528,0.00051240454,0.000070758666,0.00017135634,0.000005813401,0.0001475041,0.0000059913446],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040750523,0.00054001395,0.0036423167,0.00031738382,0.00020201427,0.00025315135,0.53514147,0.094306305,0.00008336658,0.003749912,0.35465902,0.0066975686],"study_design_scores_gemma":[0.00064061227,0.00022549907,0.039876368,0.00021563187,0.0002058437,2.5295978e-7,0.5735436,0.000033231958,0.000010512774,0.0000016834224,0.38496545,0.00028127583],"about_ca_topic_score_codex":0.001245483,"about_ca_topic_score_gemma":0.081493236,"teacher_disagreement_score":0.1878076,"about_ca_system_score_codex":0.000093795046,"about_ca_system_score_gemma":0.00031978294,"threshold_uncertainty_score":0.9352671},"labels":[],"label_agreement":null},{"id":"W587028126","doi":"","title":"TWO HANDS BETTER THAN ONE","year":2004,"lang":"en","type":"article","venue":"Cargo systems","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Subtitle; Harbour; Yard; Shore; Quarter (Canadian coin); Containerization; Port (circuit theory); Telecommunications; Computer science; Aeronautics; Gantry crane; Dredging; Order (exchange); Operations research; Operations management; Marine engineering; Engineering; Business; Geography; Archaeology; Oceanography; Container (type theory); Geology; Finance; Electrical engineering","score_opus":0.024341360692925116,"score_gpt":0.2798238437189182,"score_spread":0.2554824830259931,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W587028126","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8661318,0.00046360973,0.01802411,0.0015591902,0.001655379,0.00035588493,0.000014466382,0.00035357702,0.11144198],"genre_scores_gemma":[0.99741024,0.000010389272,0.00027905655,0.00006774024,0.00041734666,0.000012147633,0.000025270014,0.000006400466,0.001771389],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99938107,0.000043640954,0.00011739938,0.000104449224,0.0002111826,0.00014227377],"domain_scores_gemma":[0.9997356,0.000016117272,0.00004495078,0.00007746467,0.000062145125,0.0000637387],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021139672,0.000046686855,0.00008193779,0.000035941153,0.0002539721,0.00007324068,0.000075190335,0.000050336337,0.000015778434],"category_scores_gemma":[0.000014846829,0.00004716735,0.000027246677,0.0001379355,0.0000446767,0.000107254404,0.0000015824013,0.00004422277,0.000060759823],"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":[0.0000308125,0.00014726384,0.281147,0.000117247386,0.00010454401,0.000043453303,0.112210415,0.148743,0.0005179499,0.4521786,0.0034550233,0.0013046986],"study_design_scores_gemma":[0.021854552,0.0006172757,0.38123605,0.0027758575,0.0004466768,0.000022060465,0.109091304,0.0016481752,0.002790162,0.008700412,0.4671598,0.0036576777],"about_ca_topic_score_codex":0.0042334846,"about_ca_topic_score_gemma":0.000933338,"teacher_disagreement_score":0.4637048,"about_ca_system_score_codex":0.000051929594,"about_ca_system_score_gemma":0.00007040848,"threshold_uncertainty_score":0.63997906},"labels":[],"label_agreement":null},{"id":"W588160890","doi":"","title":"NOT ENOUGH HOURS IN A DAY","year":2003,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Logbook; Duty; Rest (music); Order (exchange); Perspective (graphical); Service (business); Business; Feeling; Labour economics; Operations management; Economics; Finance; Law; Marketing; Political science; Computer science; Psychology","score_opus":0.022884177784274033,"score_gpt":0.2915745679781976,"score_spread":0.26869039019392355,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W588160890","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22136956,0.000022876198,0.007226009,0.0014021205,0.0002122345,0.0000987632,0.000001447712,0.000102484235,0.7695645],"genre_scores_gemma":[0.9925307,0.000016053864,0.0023916091,0.00021253579,0.000010292669,0.000002126476,0.000002764466,0.0000017774729,0.00483214],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99965274,0.000059426282,0.000056374218,0.000056085595,0.00008952113,0.000085837666],"domain_scores_gemma":[0.99988973,0.000033903187,0.000011174183,0.000026838215,0.000013528799,0.000024796369],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030651616,0.00002067503,0.000029117597,0.00002907978,0.00006562151,0.000014256395,0.000029006902,0.000028146613,0.00037153717],"category_scores_gemma":[0.00007686303,0.000020077185,0.000008637119,0.0001642791,0.000020866368,0.00007522581,2.9804008e-7,0.000026264634,0.000023843673],"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":[0.0000043734585,0.000066586894,0.20545176,0.0000015015855,0.000002912077,0.0000071817362,0.07982312,0.008207511,0.00003142833,0.702655,0.0017553844,0.0019932133],"study_design_scores_gemma":[0.00096734177,0.000029779865,0.55760986,0.000027301601,0.000008727481,3.5439413e-7,0.02272148,0.00016157849,0.0011706786,0.0022320324,0.4147058,0.00036508695],"about_ca_topic_score_codex":0.00087161246,"about_ca_topic_score_gemma":0.0036654884,"teacher_disagreement_score":0.77116114,"about_ca_system_score_codex":0.000012423047,"about_ca_system_score_gemma":0.000052103787,"threshold_uncertainty_score":0.40680715},"labels":[],"label_agreement":null},{"id":"W590767920","doi":"","title":"Congestion Relief Using HOT Lanes","year":2007,"lang":"en","type":"article","venue":"ITE 2007 Annual Meeting and ExhibitInstitute of Transportation Engineers (ITE)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Toll; Transport engineering; Traffic congestion; Congestion pricing; Sound (geography); Occupancy; Transit (satellite); Business; Computer science; Environmental science; Engineering; Civil engineering; Public transport","score_opus":0.01269716997963785,"score_gpt":0.2658062587329037,"score_spread":0.2531090887532659,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W590767920","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94992614,0.00042395596,0.0419795,0.00014036268,0.00065045175,0.00022458343,0.00015130643,0.00022993842,0.0062737833],"genre_scores_gemma":[0.9854328,0.00021311056,0.013487629,0.00006715553,0.0002300094,0.0000022371753,0.0003282411,0.000017225515,0.00022158342],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99843025,0.000037014946,0.00052647915,0.00025769658,0.00040438407,0.00034415227],"domain_scores_gemma":[0.9990687,0.0001492447,0.00021173838,0.00008505339,0.00030926752,0.00017602919],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011276202,0.00017254935,0.0002204012,0.0002497561,0.00032923606,0.000030241968,0.00007959741,0.00018805829,0.000024340703],"category_scores_gemma":[0.0001113443,0.00019511266,0.00006555432,0.00040274236,0.00019548633,0.00044321347,0.0000012257319,0.00014692928,0.0000027223534],"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":[0.000474107,0.00022741637,0.10432547,0.0004874049,0.000126127,0.00012857154,0.1275924,0.7115507,0.0036437246,0.042786196,0.00070731586,0.007950556],"study_design_scores_gemma":[0.0066691213,0.0005111299,0.6865723,0.0035851111,0.0009608466,0.000025667006,0.106645286,0.009300789,0.009031224,0.0003355161,0.17309237,0.0032706086],"about_ca_topic_score_codex":0.0019198923,"about_ca_topic_score_gemma":0.0012425437,"teacher_disagreement_score":0.70224994,"about_ca_system_score_codex":0.000035973426,"about_ca_system_score_gemma":0.00009928364,"threshold_uncertainty_score":0.7956463},"labels":[],"label_agreement":null},{"id":"W591488975","doi":"","title":"Lessons Learned on Managed Lanes: HOV to HOT","year":2007,"lang":"en","type":"article","venue":"ITE 2007 Annual Meeting and ExhibitInstitute of Transportation Engineers (ITE)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Toll; Occupancy; Transport engineering; Road pricing; Business; Traffic congestion; Pricing strategies; Congestion pricing; Marketing; Engineering; Civil engineering","score_opus":0.020177927020245748,"score_gpt":0.2946735999054016,"score_spread":0.27449567288515586,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W591488975","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92267615,0.0001470691,0.046089035,0.0018214014,0.00073739677,0.00045302275,0.00033300038,0.000396898,0.027346015],"genre_scores_gemma":[0.9918535,0.00013933303,0.006271195,0.00029750686,0.00018271984,0.0000064924525,0.00031348606,0.000020871274,0.00091490283],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9982611,0.000041126783,0.00047259295,0.00033831436,0.00047889387,0.00040799275],"domain_scores_gemma":[0.9990683,0.0001934704,0.0001430855,0.000120272714,0.00020606989,0.00026880938],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011821736,0.00019579747,0.0002414048,0.0003588606,0.00034009607,0.000036074027,0.00012090738,0.00016059478,0.000029636123],"category_scores_gemma":[0.00014294346,0.00021603575,0.00007522882,0.00055322866,0.000117349,0.0002572558,0.0000015823072,0.00017912955,0.000014473795],"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":[0.00091810053,0.000332183,0.019381177,0.00037615965,0.00012066542,0.00018454307,0.18714575,0.6609689,0.0015323763,0.09440783,0.003227132,0.03140519],"study_design_scores_gemma":[0.004675601,0.00070685573,0.43981773,0.0022906503,0.00039500967,0.000004164125,0.10308339,0.0009716457,0.0051869024,0.000273685,0.4402292,0.0023651833],"about_ca_topic_score_codex":0.0009916958,"about_ca_topic_score_gemma":0.0019511709,"teacher_disagreement_score":0.6599972,"about_ca_system_score_codex":0.00003441627,"about_ca_system_score_gemma":0.000066866996,"threshold_uncertainty_score":0.8809683},"labels":[],"label_agreement":null},{"id":"W591673872","doi":"","title":"Disruption, Congestion and Mitigation: Charaterisation of Strategic Road Infrastructure Using Partial Itinerary Data","year":2014,"lang":"fr","type":"article","venue":"Transportation 2014: Past, Present, Future - 2014 Conference and Exhibition of the Transportation Association of Canada // Transport 2014 : Du passé vers l'avenir - 2014 Congrès et Exposition de 'Association des transports du Canada","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Transport engineering; TRIPS architecture; Bridge (graph theory); Public transport; Traffic congestion; Strategic planning; Business; Transit (satellite); Transportation planning; Transportation infrastructure; Survey data collection; Sample (material); Engineering; Marketing","score_opus":0.01580555707932554,"score_gpt":0.2353007174252718,"score_spread":0.21949516034594627,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W591673872","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95287627,0.000864914,0.013407367,0.016872697,0.0027450798,0.001395703,0.011314469,0.00008797361,0.00043555562],"genre_scores_gemma":[0.9805179,0.00548868,0.0008557691,0.00041963148,0.00080575305,0.00005408849,0.01125787,0.00008728488,0.0005129823],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9912576,0.0012419974,0.0028954085,0.0010879228,0.002576951,0.00094009965],"domain_scores_gemma":[0.9907145,0.00051346025,0.004832476,0.0006429253,0.0027345868,0.0005620881],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002632157,0.00085633097,0.0013040022,0.00026644804,0.0008741315,0.00011676717,0.0005882736,0.0009626659,0.00084505393],"category_scores_gemma":[0.00008085586,0.000943475,0.00031782314,0.000640108,0.0006110257,0.002261172,0.0000085729425,0.00068322645,0.0000014757728],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007070222,0.00036789046,0.84553987,0.001991265,0.0009279214,0.000020666277,0.015050447,0.07187675,0.0021065744,0.025136089,0.0349278,0.0013476958],"study_design_scores_gemma":[0.0037558542,0.00019546492,0.94867754,0.0007650663,0.002018796,0.0000079154615,0.0034266864,0.007851091,0.0013484843,0.0010896107,0.029775612,0.0010878714],"about_ca_topic_score_codex":0.5794411,"about_ca_topic_score_gemma":0.9073383,"teacher_disagreement_score":0.32789725,"about_ca_system_score_codex":0.0014021932,"about_ca_system_score_gemma":0.0051462175,"threshold_uncertainty_score":0.99930155},"labels":[],"label_agreement":null},{"id":"W592211146","doi":"10.71781/3130","title":"Les relations entre le transport actif et l'environnement urbain : le cas d'usagers du train de banlieue de Montréal","year":2010,"lang":"fr","type":"dissertation","venue":"Open MIND","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Humanities; Art; Political science","score_opus":0.01848240631407182,"score_gpt":0.2907445326956798,"score_spread":0.27226212638160796,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W592211146","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8867505,0.00035763794,0.007996988,0.008265938,0.00045407203,0.0009270192,0.00035604,0.000014449132,0.09487734],"genre_scores_gemma":[0.9225632,0.00022799637,0.023150094,0.000073004485,0.00011315666,0.00005783487,0.0047583864,0.000048859143,0.049007475],"study_design_codex":"qualitative","study_design_gemma":"not_applicable","domain_scores_codex":[0.9976387,0.00036979635,0.00057090225,0.0005520037,0.0003834271,0.00048518184],"domain_scores_gemma":[0.9988318,0.00020160228,0.0004014937,0.00020331379,0.00008892628,0.00027284687],"candidate_categories":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0012430684,0.00033959074,0.00034999166,0.00012134176,0.0015121406,0.00020574144,0.0005118139,0.00073921256,0.004407352],"category_scores_gemma":[0.00007493826,0.00041062772,0.00018632166,0.00031118473,0.0001944199,0.0005989332,0.000007916017,0.0006723825,0.00010881762],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018989852,0.000846053,0.051627986,0.000045316523,0.00021757268,0.000111026886,0.67029965,0.17978135,0.0015213288,0.01601246,0.00074346736,0.07860388],"study_design_scores_gemma":[0.0022674734,0.00011215775,0.35210535,0.00035294713,0.00067765656,0.000008455645,0.21512164,0.006303324,0.0017559407,0.00031210756,0.41976154,0.0012214044],"about_ca_topic_score_codex":0.13513914,"about_ca_topic_score_gemma":0.5241132,"teacher_disagreement_score":0.45517802,"about_ca_system_score_codex":0.00034039657,"about_ca_system_score_gemma":0.002303029,"threshold_uncertainty_score":0.99983454},"labels":[],"label_agreement":null},{"id":"W593880998","doi":"","title":"Cross Border Transportation Patterns at the Western Cascade Gateway and Trade Corridor: Implications for Mitigating the Impact of Delay on Regional Supply Chains","year":2008,"lang":"en","type":"article","venue":"","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":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Gateway (web page); Border crossing; Commodity; Geography; Immigration; Business; Computer science; Finance; Archaeology","score_opus":0.03731702633606384,"score_gpt":0.36771762264919083,"score_spread":0.330400596313127,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W593880998","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9869306,0.000038811486,0.005182906,0.0066017755,0.00003287825,0.0004404613,0.00045754234,0.000035578112,0.00027942905],"genre_scores_gemma":[0.99751806,0.00013415414,0.00012716326,0.0003649093,0.000053116164,0.00006075499,0.0003222258,0.00001084575,0.0014087668],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9991938,0.0000539915,0.00023884891,0.00016228117,0.00016997138,0.00018111271],"domain_scores_gemma":[0.99921095,0.00039838155,0.00014375604,0.00011533251,0.000073519324,0.000058067904],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022063697,0.00009491757,0.0000986031,0.000027820923,0.0011989145,0.000029506355,0.000100498764,0.000067172354,0.00005301561],"category_scores_gemma":[0.000021502794,0.000056227116,0.00009785772,0.00013248781,0.00030176865,0.000119554556,0.000001368664,0.00007011566,6.4898677e-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":[0.000036174595,0.000035573114,0.9331169,0.000006769724,0.00002747748,4.3915492e-7,0.042388022,0.020984957,0.00013179395,0.0016447612,0.0011529123,0.00047418478],"study_design_scores_gemma":[0.00033520465,0.000057941805,0.99667144,0.000014266928,0.00001650907,0.0000047498875,0.0010543991,0.00044249467,0.00014927027,0.000036096837,0.0011380434,0.00007957673],"about_ca_topic_score_codex":0.0039069066,"about_ca_topic_score_gemma":0.007915241,"teacher_disagreement_score":0.06355452,"about_ca_system_score_codex":0.000040438346,"about_ca_system_score_gemma":0.000094348994,"threshold_uncertainty_score":0.9221202},"labels":[],"label_agreement":null},{"id":"W593897669","doi":"","title":"The Toronto Metro: History, Demand, Performance","year":2011,"lang":"en","type":"article","venue":"Transportation Research Board 90th Annual MeetingTransportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"TRIPS architecture; Population; Metropolitan area; Transport engineering; Track (disk drive); Work (physics); Geography; Business; Engineering; Demography","score_opus":0.09734220556256362,"score_gpt":0.3727075863657461,"score_spread":0.27536538080318246,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W593897669","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8856716,0.0033386697,0.0017927381,0.0024025652,0.001458385,0.0034270417,0.00020663321,0.0008997849,0.10080257],"genre_scores_gemma":[0.98118633,0.005281038,0.0025988878,0.00011447132,0.00034091444,0.00065564684,0.00027500285,0.00010024475,0.009447444],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.98683715,0.0024831258,0.0013635869,0.0010722316,0.005784453,0.0024594385],"domain_scores_gemma":[0.99071836,0.0017062502,0.00031265456,0.0007329397,0.0053514116,0.0011783714],"candidate_categories":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.015992083,0.00041727652,0.00043677358,0.00061099645,0.005065608,0.00022400371,0.0014353899,0.00044923907,0.0023624706],"category_scores_gemma":[0.0009066511,0.00037622178,0.00026098138,0.001713584,0.0025952046,0.0019944855,0.000010589919,0.0015642385,0.00023097603],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0036942777,0.0007811291,0.38603398,0.00038407458,0.0002869795,0.00017128802,0.3922216,0.0011587198,0.00026443577,0.14338587,0.053225257,0.018392406],"study_design_scores_gemma":[0.0010174984,0.00050681713,0.5272991,0.00014242019,0.00004914293,1.5699531e-7,0.07230047,0.00017479778,0.0003502266,0.00082291564,0.39680633,0.00053013884],"about_ca_topic_score_codex":0.113860376,"about_ca_topic_score_gemma":0.37868994,"teacher_disagreement_score":0.34358108,"about_ca_system_score_codex":0.0012292496,"about_ca_system_score_gemma":0.0020478838,"threshold_uncertainty_score":0.999869},"labels":[],"label_agreement":null},{"id":"W594689090","doi":"","title":"On Street Transit Priority Measures - Putting Buses First in Winnipeg","year":2008,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Transit (satellite); Transport engineering; Public transport; Plan (archaeology); Bus priority; Park and ride; Quality (philosophy); Bus rapid transit; Business; Engineering; Geography","score_opus":0.03689828636184427,"score_gpt":0.276410550945693,"score_spread":0.23951226458384872,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W594689090","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95521605,0.000040091614,0.004868401,0.0017033583,0.00012964365,0.00015916806,0.000004550659,0.00020161146,0.03767712],"genre_scores_gemma":[0.9976317,0.00007454929,0.0015552994,0.00012912207,0.00003934587,0.0000030177414,0.0000075226376,0.000004264742,0.0005551765],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99925715,0.000051165378,0.00013422055,0.00013128917,0.00027341128,0.00015276766],"domain_scores_gemma":[0.99972,0.00011013938,0.000027561047,0.000053974716,0.000041058654,0.000047314494],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024681934,0.00005660993,0.00007726718,0.000065786844,0.00039598913,0.000016622213,0.00007414338,0.00006020505,0.0000978173],"category_scores_gemma":[0.000104693194,0.000054743676,0.000024478915,0.00025192174,0.00006919958,0.00014764564,9.2090124e-7,0.000073714014,0.000009988709],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030097262,0.00014215367,0.85871035,0.000009508272,0.0000075808725,0.00003304812,0.058416404,0.061111037,0.000009860009,0.017453494,0.0014752506,0.002601228],"study_design_scores_gemma":[0.00082953146,0.000060218124,0.97635764,0.000080947524,0.000008936645,6.4165295e-7,0.004279237,0.0006006013,0.0001911982,0.0007490627,0.01657353,0.00026845717],"about_ca_topic_score_codex":0.0060084388,"about_ca_topic_score_gemma":0.064427584,"teacher_disagreement_score":0.1176473,"about_ca_system_score_codex":0.00003079686,"about_ca_system_score_gemma":0.000080350015,"threshold_uncertainty_score":0.95264417},"labels":[],"label_agreement":null},{"id":"W594725858","doi":"","title":"TORONTO IN TRANSIT: A TALE OF TWO SYSTEMS","year":2003,"lang":"en","type":"article","venue":"Railway age","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Transit (satellite); Transit system; Transport engineering; Rail transit; Bus rapid transit; Rapid transit; Light rail transit; Mile; Public transport; Urban transit; Business; Engineering; Geography","score_opus":0.016735342227073527,"score_gpt":0.28916722924881216,"score_spread":0.27243188702173865,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W594725858","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.62683487,0.0014924922,0.009979454,0.00007916137,0.0005320513,0.00036836293,0.000016617947,0.00008524478,0.36061177],"genre_scores_gemma":[0.99774724,0.000043918502,0.00049204176,0.000009495634,0.00001625201,0.0000052180826,0.0000065630998,0.0000034283394,0.0016758237],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994664,0.000098231496,0.00013274177,0.00007329922,0.00012886361,0.00010047055],"domain_scores_gemma":[0.9998314,0.00003211235,0.000034636294,0.000049591126,0.000021634676,0.00003064079],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032869563,0.000035016536,0.000082880724,0.000020335776,0.000050236074,0.000012091885,0.00004601257,0.000036414465,0.00014091047],"category_scores_gemma":[0.00002586643,0.000036411417,0.000020503936,0.00011475564,0.00003588228,0.000118278884,3.607487e-7,0.000025817397,0.0000035054431],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003500874,0.0002535581,0.054233048,0.00011521785,0.000025973666,0.0000702698,0.23742342,0.095113315,0.0016036924,0.60846215,0.0008043025,0.001860062],"study_design_scores_gemma":[0.012563193,0.00046476515,0.16957125,0.0010264899,0.00015011907,0.0000069858197,0.17978224,0.003818816,0.0032184138,0.0038891295,0.6234556,0.00205302],"about_ca_topic_score_codex":0.010183052,"about_ca_topic_score_gemma":0.037399434,"teacher_disagreement_score":0.6226513,"about_ca_system_score_codex":0.000035189747,"about_ca_system_score_gemma":0.000045995082,"threshold_uncertainty_score":0.9964082},"labels":[],"label_agreement":null},{"id":"W595637624","doi":"","title":"Regional Sustainable Transportation Master Plan (Poster)","year":2014,"lang":"fr","type":"article","venue":"Transportation 2014: Past, Present, Future - 2014 Conference and Exhibition of the Transportation Association of Canada // Transport 2014 : Du passé vers l'avenir - 2014 Congrès et Exposition de 'Association des transports du Canada","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Sustainable community; Sustainable transport; Downtown; Business; Public transport; Transportation planning; Plan (archaeology); Transport engineering; Carbon footprint; Environmental planning; Realm; Sustainable development; Sustainability; Engineering; Political science; Geography","score_opus":0.009281547591007487,"score_gpt":0.20550993092244993,"score_spread":0.19622838333144244,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W595637624","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.85131985,0.0018604142,0.026761908,0.09078181,0.006491547,0.0039531454,0.014845022,0.0003492743,0.0036370168],"genre_scores_gemma":[0.97319746,0.005977977,0.0005473409,0.0016693131,0.0010905195,0.00019035945,0.010374576,0.00016722547,0.006785247],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9875409,0.0014365447,0.003623915,0.0014149647,0.0040970794,0.0018865773],"domain_scores_gemma":[0.9881839,0.00095034373,0.005056747,0.0007040195,0.0041402467,0.0009647413],"candidate_categories":["metaepi_narrow","sts","research_integrity","insufficient_payload"],"consensus_categories":["metaepi_narrow"],"category_scores_codex":[0.0032998018,0.001299072,0.0017317422,0.00041726165,0.0014450426,0.00018338999,0.0008205293,0.0013613977,0.001954577],"category_scores_gemma":[0.00008295578,0.0014384519,0.00081474707,0.00097107835,0.0006308797,0.0025411148,0.000005188841,0.0011176093,0.000009431766],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010410409,0.00057565403,0.649453,0.0020413725,0.0010663922,0.00007592179,0.025447428,0.033598438,0.00040242405,0.039864857,0.24571559,0.0007179184],"study_design_scores_gemma":[0.0044358736,0.00024407648,0.68803376,0.0006171958,0.0017827813,0.000005847235,0.0050909254,0.0004675005,0.0009422323,0.0010242856,0.29598606,0.0013694589],"about_ca_topic_score_codex":0.7195322,"about_ca_topic_score_gemma":0.97623986,"teacher_disagreement_score":0.25670764,"about_ca_system_score_codex":0.003247595,"about_ca_system_score_gemma":0.007240193,"threshold_uncertainty_score":0.9999761},"labels":[],"label_agreement":null},{"id":"W597886962","doi":"","title":"An Integrated Approach to Managing Travel Demand in Downtown Calgary","year":2007,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Downtown; TRIPS architecture; Population; Business; Transport engineering; Service (business); Engineering; Geography; Marketing","score_opus":0.01851238653020366,"score_gpt":0.2922647709835939,"score_spread":0.27375238445339023,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W597886962","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06951438,0.000007385922,0.6554326,0.00014159897,0.000045856996,0.00012800405,7.210057e-7,0.00007412636,0.27465537],"genre_scores_gemma":[0.96097124,0.000004600243,0.037376735,0.0002743763,0.000024457948,0.0000032778546,0.000040403494,0.0000042883817,0.0013006155],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.9993492,0.000040561954,0.00013780622,0.00014187071,0.00014360184,0.00018697818],"domain_scores_gemma":[0.9997606,0.000023859286,0.000016134238,0.000054690317,0.000032752756,0.00011195002],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008532067,0.000049392453,0.0000632936,0.0001463488,0.00009882831,0.00003739752,0.0000923081,0.000055661454,0.00007524347],"category_scores_gemma":[0.000016367063,0.00004524577,0.000011372389,0.0004911258,0.00002675925,0.00015350574,0.000001295576,0.00006333332,0.0000065019303],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018912555,0.0006064212,0.26934892,0.000024233195,0.000018115012,0.000036912188,0.2932047,0.09767705,0.0004284998,0.21213196,0.00096844626,0.12536563],"study_design_scores_gemma":[0.00051826483,0.000045816414,0.90273464,0.000027410062,0.00000911092,6.450781e-7,0.07202494,0.019402305,0.0002114578,0.00039297572,0.004305583,0.00032684443],"about_ca_topic_score_codex":0.007923991,"about_ca_topic_score_gemma":0.014131106,"teacher_disagreement_score":0.89145684,"about_ca_system_score_codex":0.0000368919,"about_ca_system_score_gemma":0.00003648379,"threshold_uncertainty_score":0.9986823},"labels":[],"label_agreement":null},{"id":"W598722581","doi":"","title":"Optimize Signal Priority Strategy to Improve Transit Mobility","year":2011,"lang":"en","type":"article","venue":"18th ITS World CongressTransCoreITS AmericaERTICO - ITS EuropeITS Asia-Pacific","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Transit (satellite); Preemption; Intersection (aeronautics); VisSim; Transport engineering; Bus priority; SIGNAL (programming language); Public transport; Computer science; Engineering","score_opus":0.04111377852644457,"score_gpt":0.28419373721305075,"score_spread":0.24307995868660617,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W598722581","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23773186,0.0013409241,0.026500419,0.0031026057,0.0030328473,0.0066458522,0.0008205767,0.0027155252,0.71810937],"genre_scores_gemma":[0.98520434,0.00017250504,0.0031871633,0.00024438632,0.0002616178,0.00011507119,0.000060759547,0.00012421036,0.010629921],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9936622,0.00076954084,0.0012847185,0.0015178903,0.001238497,0.0015271631],"domain_scores_gemma":[0.9965258,0.00021556186,0.000420474,0.000693015,0.0009295067,0.0012156276],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0010329587,0.00075419043,0.0008527244,0.0005034001,0.0010336783,0.00037288433,0.0010832775,0.00022873585,0.003486268],"category_scores_gemma":[0.00016851413,0.0007613252,0.00031332838,0.002596032,0.00052128,0.0011309024,0.000025738434,0.00076050055,0.0005377573],"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":[0.010031222,0.0096709225,0.037549876,0.0013061215,0.0021769365,0.0029103123,0.3067388,0.071344286,0.092619315,0.14609621,0.010829202,0.3087268],"study_design_scores_gemma":[0.012731075,0.006484695,0.52635616,0.0015772767,0.002510654,0.00006237503,0.050002582,0.0140040135,0.078938164,0.00067362015,0.2934432,0.013216157],"about_ca_topic_score_codex":0.00056636863,"about_ca_topic_score_gemma":0.0006614314,"teacher_disagreement_score":0.7474725,"about_ca_system_score_codex":0.00011341573,"about_ca_system_score_gemma":0.0006771057,"threshold_uncertainty_score":0.99948376},"labels":[],"label_agreement":null},{"id":"W600853639","doi":"","title":"Transfer Optimization Model for Intermodal Transit Services into the Suburbs","year":2008,"lang":"en","type":"article","venue":"Transportation Research Board 87th Annual MeetingTransportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Schedule; Transit (satellite); Transfer (computing); Computer science; Modal; Operations research; Transport engineering; Public transport; Genetic algorithm; Travel time; Service (business); Engineering; Business","score_opus":0.07590018077152863,"score_gpt":0.3873930371058333,"score_spread":0.3114928563343047,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W600853639","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.60962135,0.0004399379,0.37027687,0.01074894,0.00037876083,0.0052873376,0.0007427257,0.00066066755,0.001843415],"genre_scores_gemma":[0.97587764,0.002069606,0.014399119,0.0003651367,0.00039804855,0.0015499621,0.0018117363,0.00015224665,0.0033764846],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9881596,0.0016637952,0.0015582027,0.0013407957,0.0051469845,0.002130618],"domain_scores_gemma":[0.9898342,0.0018733145,0.00018438397,0.00064566004,0.006656964,0.0008054747],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.009046519,0.00053047657,0.00062597846,0.0012393611,0.007297684,0.00031223713,0.0014092348,0.0006065332,0.00029209015],"category_scores_gemma":[0.0003006475,0.0004899747,0.00044854576,0.0030437927,0.0024585694,0.0021889894,0.000007118243,0.0016171491,0.00004289551],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015395194,0.00027950772,0.010580027,0.0003725727,0.00010641661,0.00004015952,0.3153571,0.6488446,0.0002871211,0.019766552,0.0017264499,0.0011000092],"study_design_scores_gemma":[0.010539543,0.001699222,0.09806666,0.0010419379,0.000416015,0.0000023832445,0.22230238,0.5581819,0.0016845601,0.00735977,0.095641084,0.003064545],"about_ca_topic_score_codex":0.025006024,"about_ca_topic_score_gemma":0.08187749,"teacher_disagreement_score":0.3662563,"about_ca_system_score_codex":0.00030480573,"about_ca_system_score_gemma":0.0016233486,"threshold_uncertainty_score":0.9997552},"labels":[],"label_agreement":null},{"id":"W602299156","doi":"","title":"Sharing the Street in Urban Areas: The Example of the 'Code de la Rue' (Street Use Code) in France","year":2012,"lang":"en","type":"article","venue":"2012 CONFERENCE AND EXHIBITION OF THE TRANSPORTATION ASSOCIATION OF CANADA - TRANSPORTATION: INNOVATIONS AND OPPORTUNITIES","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":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Context (archaeology); Pedestrian; Transport engineering; Variety (cybernetics); Urban planning; Space (punctuation); Business; Geography; Environmental planning; Engineering; Civil engineering; Computer science","score_opus":0.043355862118658564,"score_gpt":0.2552609521496965,"score_spread":0.21190509003103797,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W602299156","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9931526,0.00014861317,0.00056269654,0.0044172774,0.00014551992,0.0003688599,0.0006986619,0.000010869619,0.0004948584],"genre_scores_gemma":[0.99842554,0.00036721738,0.000087255816,0.00028389192,0.00002707503,0.000037926216,0.0001899826,0.000009566819,0.0005715462],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.998011,0.00025901344,0.00078182557,0.00014135249,0.00056367565,0.0002431366],"domain_scores_gemma":[0.9980363,0.0005071458,0.0007856819,0.00018305075,0.00043735895,0.000050456645],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015128064,0.00013679847,0.00021503809,0.00011082728,0.00040916677,0.000036418645,0.0002192313,0.00012947002,0.00004569967],"category_scores_gemma":[0.00010067512,0.00009498825,0.000053587122,0.00064389646,0.00038406896,0.00064797635,0.0000029298335,0.00022485499,2.7723406e-8],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010031805,0.00003696855,0.7763131,0.00003714526,0.000018371795,2.3969872e-7,0.018734578,0.0013205379,0.000037049762,0.20301095,0.00032402846,0.00015699587],"study_design_scores_gemma":[0.00040637792,0.000007820221,0.9790697,0.00016402273,0.000050870476,1.2749732e-7,0.012416657,0.00012800256,0.00016023964,0.0008873333,0.0066040545,0.00010476202],"about_ca_topic_score_codex":0.17370103,"about_ca_topic_score_gemma":0.81941175,"teacher_disagreement_score":0.6457107,"about_ca_system_score_codex":0.000089528294,"about_ca_system_score_gemma":0.00086173095,"threshold_uncertainty_score":0.83180135},"labels":[],"label_agreement":null},{"id":"W602334682","doi":"","title":"EXPERIENCES WITH BUS BULBS","year":2001,"lang":"en","type":"article","venue":"ITE 2001 Annual Meeting and ExhibitInstitute of Transportation Engineers (ITE)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Transport engineering; Weaving; Installation; Transit (satellite); Engineering; Public transport; Telecommunications","score_opus":0.009369731709767865,"score_gpt":0.23891459848904048,"score_spread":0.2295448667792726,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W602334682","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96885306,0.00036849373,0.020039104,0.0003003875,0.00022646847,0.0002664203,0.000083999475,0.00022913165,0.00963295],"genre_scores_gemma":[0.9922841,0.00045080725,0.0062538125,0.0000666667,0.00014246174,0.000034998808,0.00019349257,0.000017565424,0.0005560831],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.9985027,0.000036764464,0.00038294925,0.00028439474,0.00043953955,0.0003536611],"domain_scores_gemma":[0.99921906,0.000078984376,0.00016185515,0.00008995785,0.0002593971,0.00019074963],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039674554,0.00018143957,0.00022604967,0.00017742814,0.00034619882,0.000040958916,0.00010628507,0.00011713721,0.000051281742],"category_scores_gemma":[0.00003682179,0.00017520576,0.0000508111,0.0005528521,0.00028700253,0.000546162,0.0000010678531,0.00011349629,0.0000025974155],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038259308,0.00023380885,0.0778339,0.00027025698,0.000121092555,0.00019165667,0.6501749,0.25027606,0.0002906809,0.008591053,0.00070590276,0.010928054],"study_design_scores_gemma":[0.0042535504,0.00052428554,0.053444467,0.0015317942,0.00035807915,0.000027760701,0.66240203,0.0020007873,0.0017688803,0.000079657606,0.27168792,0.0019207789],"about_ca_topic_score_codex":0.0011092876,"about_ca_topic_score_gemma":0.0015348764,"teacher_disagreement_score":0.27098203,"about_ca_system_score_codex":0.000019235564,"about_ca_system_score_gemma":0.00012678641,"threshold_uncertainty_score":0.7144683},"labels":[],"label_agreement":null},{"id":"W602589453","doi":"","title":"Edmonton: Changing the Way We Grow and the Way We Move","year":2009,"lang":"en","type":"article","venue":"2009 ANNUAL CONFERENCE AND EXHIBITION OF THE TRANSPORTATION ASSOCIATION OF CANADA - TRANSPORTATION IN A CLIMATE OF CHANGE","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Plan (archaeology); Transit-oriented development; Land use; Master plan; Chart; Urban planning; Environmental planning; Transportation planning; Transport engineering; Business; Transit (satellite); Development plan; Engineering; Public transport; Civil engineering; Geography","score_opus":0.01898973278933934,"score_gpt":0.2450711472733272,"score_spread":0.22608141448398789,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W602589453","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9288307,0.000860533,0.00042126156,0.06521844,0.00042475996,0.0012993147,0.0015580872,0.000040386258,0.0013465288],"genre_scores_gemma":[0.99543077,0.003682937,0.0000793382,0.0003581217,0.000047492475,0.000036804027,0.00017167855,0.000009355429,0.00018350921],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.9975349,0.00023622195,0.00083303434,0.00021327123,0.0008649287,0.00031764852],"domain_scores_gemma":[0.9978134,0.00036389704,0.0010392215,0.00013814653,0.00057816185,0.00006719356],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012832171,0.0001788947,0.00036784785,0.0001450638,0.0003868245,0.000027724636,0.00019294448,0.00015200683,0.00005386567],"category_scores_gemma":[0.00007805037,0.0001279703,0.000101527614,0.0006268998,0.00025608833,0.00047095065,0.0000015450989,0.00018052568,1.5819101e-7],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001590416,0.00020830329,0.06160314,0.00050899666,0.00014150332,0.0000041495355,0.5826234,0.003586493,0.00032020052,0.33815077,0.0009592975,0.010303302],"study_design_scores_gemma":[0.0033599723,0.000090519046,0.93566835,0.0005298038,0.00018885946,1.9198303e-7,0.053625517,0.00051839167,0.000601126,0.0033996594,0.0017590978,0.00025852816],"about_ca_topic_score_codex":0.037311547,"about_ca_topic_score_gemma":0.5626723,"teacher_disagreement_score":0.8740652,"about_ca_system_score_codex":0.00007725917,"about_ca_system_score_gemma":0.00023296205,"threshold_uncertainty_score":0.9690991},"labels":[],"label_agreement":null},{"id":"W603848625","doi":"","title":"ITS Deployment in Johannesburg","year":2010,"lang":"en","type":"article","venue":"17th ITS World CongressITS JapanITS AmericaERTICO","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Software deployment; Procurement; Event (particle physics); Corporation; Business; Road map; Transport engineering; Operations management; Engineering; Geography; Marketing; Finance; Cartography","score_opus":0.02054903585379762,"score_gpt":0.29953868282101836,"score_spread":0.27898964696722073,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W603848625","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93997365,0.00039350297,0.00015887542,0.0048419484,0.001936836,0.00063331344,0.000016688446,0.00045035535,0.051594816],"genre_scores_gemma":[0.98724353,0.000099489465,0.00053688965,0.0006849976,0.00022864982,0.000059110247,0.000020896703,0.00003264573,0.01109379],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9977765,0.00016127569,0.00045746702,0.0004193219,0.000569353,0.0006161029],"domain_scores_gemma":[0.9987831,0.00020657115,0.00021311414,0.00022642079,0.0002802233,0.00029055358],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00046572328,0.00021576096,0.0002825813,0.00048660685,0.00041525642,0.00015373366,0.00036608055,0.000109256696,0.0012063658],"category_scores_gemma":[0.00043958062,0.00021735177,0.00006081483,0.0019192522,0.00019050932,0.00049082236,0.000022737635,0.00043897232,0.00026793825],"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":[0.000413424,0.0016989899,0.59323496,0.0001287815,0.00020313487,0.00040346634,0.08552475,0.008535399,0.030888787,0.16770831,0.013737174,0.09752285],"study_design_scores_gemma":[0.003058859,0.00013678441,0.45121804,0.00039932487,0.00013945831,0.000008319877,0.005312471,0.005952034,0.0105061,0.0003684926,0.5209407,0.0019593905],"about_ca_topic_score_codex":0.00044551137,"about_ca_topic_score_gemma":0.009733858,"teacher_disagreement_score":0.5072036,"about_ca_system_score_codex":0.000053280022,"about_ca_system_score_gemma":0.00022982084,"threshold_uncertainty_score":0.9997067},"labels":[],"label_agreement":null},{"id":"W607022369","doi":"","title":"HYBRID TAXI PILOT TRIAL AND ITS IMPACT ON AIR QUALITY","year":2011,"lang":"en","type":"article","venue":"CONGRESS - DUBAI 2011","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Air quality index; Taxis; Greenhouse gas; Air pollution; Government (linguistics); Urbanization; Business; Gasoline; Agency (philosophy); Transport engineering; Environmental science; Engineering; Environmental planning; Environmental protection; Waste management; Economic growth; Geography; Meteorology; Economics","score_opus":0.10047989218610998,"score_gpt":0.3672419906060205,"score_spread":0.2667620984199105,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W607022369","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98005086,0.00013701331,0.00011700534,0.0001345402,0.0009103152,0.00033309043,0.000067341185,0.00012046059,0.01812935],"genre_scores_gemma":[0.9975882,0.00008431184,0.000072066054,0.00010246964,0.00015363273,0.000014630899,0.000029561954,0.0000097051325,0.0019454124],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99893975,0.00019674141,0.00020544957,0.00021055306,0.0002319089,0.00021558859],"domain_scores_gemma":[0.9993714,0.00010749311,0.0001344943,0.00012135562,0.00010436904,0.00016086562],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00055978907,0.00011044069,0.0001566486,0.000058981877,0.00028107324,0.00003115363,0.00012798252,0.00005598473,0.0011208458],"category_scores_gemma":[0.0001335565,0.00009429216,0.000048090566,0.00005514872,0.000117660595,0.00022860439,0.000006494955,0.000094963805,0.0001295856],"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":[0.12517197,0.003920092,0.20262457,0.00018733555,0.0006394043,0.0002567612,0.119946964,0.0010902201,0.0003263404,0.42355612,0.09748206,0.024798153],"study_design_scores_gemma":[0.044531256,0.0041487245,0.91790146,0.00014638381,0.00017990626,0.000004089726,0.0018919954,0.00046038083,0.002642474,0.0039418326,0.022585794,0.0015656995],"about_ca_topic_score_codex":0.0024772105,"about_ca_topic_score_gemma":0.00028213675,"teacher_disagreement_score":0.7152769,"about_ca_system_score_codex":0.000023610944,"about_ca_system_score_gemma":0.000098919474,"threshold_uncertainty_score":0.9997923},"labels":[],"label_agreement":null},{"id":"W607487340","doi":"","title":"The road challenge network and public transit - the complementary challenge in greater Montreal","year":2011,"lang":"en","type":"article","venue":"24th World Road CongressWorld Road Association (PIARC)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Public transport; Metropolitan area; Transport engineering; Bus rapid transit; Transit (satellite); Traffic congestion; Service (business); Business; Telecommunications; Computer science; Engineering; Geography; Marketing","score_opus":0.03723553284411946,"score_gpt":0.2597323208163674,"score_spread":0.22249678797224792,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W607487340","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18559133,0.013509499,0.00045765477,0.26209816,0.006409437,0.005867232,0.00025630632,0.0012939683,0.5245164],"genre_scores_gemma":[0.98734456,0.0021923417,0.00011167866,0.00056637876,0.00051248,0.00015261714,0.00008856964,0.00003449389,0.008996863],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99591696,0.0010063485,0.000716975,0.00050012354,0.00084159797,0.0010180009],"domain_scores_gemma":[0.9983232,0.00038255326,0.0005148167,0.000339101,0.00023862191,0.00020172462],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0026524768,0.00031276725,0.0003623018,0.00019595059,0.002307802,0.00032354667,0.00056548347,0.00016271435,0.00046211504],"category_scores_gemma":[0.00011095278,0.00023020204,0.00012935449,0.0008941988,0.00028343924,0.00055886083,0.00004766424,0.00052040146,0.000028893743],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020665565,0.00030786652,0.6769781,0.000020872316,0.0004070021,0.000030605857,0.096787564,0.0007333859,5.538219e-7,0.050371226,0.011813067,0.16234308],"study_design_scores_gemma":[0.0014227666,0.00004816529,0.8882586,0.00007245105,0.00008674997,4.7638986e-7,0.005656155,0.0018237315,0.0000012866086,0.0022318652,0.099987596,0.00041015644],"about_ca_topic_score_codex":0.01749389,"about_ca_topic_score_gemma":0.5248226,"teacher_disagreement_score":0.8017532,"about_ca_system_score_codex":0.0002870264,"about_ca_system_score_gemma":0.0001530302,"threshold_uncertainty_score":0.9989911},"labels":[],"label_agreement":null},{"id":"W607619281","doi":"","title":"Incorporating Cycling in Ottawa-Gatineau Travel Forecasting Model","year":2013,"lang":"en","type":"article","venue":"2013 CONFERENCE AND EXHIBITION OF THE TRANSPORTATION ASSOCIATION OF CANADA - TRANSPORTATION: BETTER - FASTER - SAFER","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Cycling; TRIPS architecture; Transport engineering; Population; Range (aeronautics); Geography; Engineering; Computer science","score_opus":0.018900459029308453,"score_gpt":0.22150565959334959,"score_spread":0.20260520056404113,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W607619281","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9779091,0.000020455253,0.010507813,0.008938187,0.00021382955,0.0006547477,0.00026181503,0.000029104327,0.0014649563],"genre_scores_gemma":[0.9954662,0.000024863964,0.0028319692,0.00048407196,0.000042716336,0.000060332728,0.00035527739,0.00002133615,0.0007132817],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99698555,0.0001532901,0.0012662242,0.0003094258,0.0009379859,0.00034749272],"domain_scores_gemma":[0.9976757,0.00018893188,0.0011160037,0.00015128407,0.0007492406,0.00011882426],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060436985,0.00022639098,0.00038466247,0.00016063383,0.00031090702,0.000058845166,0.0001942269,0.00020305449,0.00025192255],"category_scores_gemma":[0.000057617086,0.00022074144,0.00010281406,0.0003899256,0.00012742456,0.0008548833,0.0000017087133,0.00024387642,0.0000011350188],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006372849,0.00014054017,0.82351464,0.00027859636,0.00010197408,0.0000034409481,0.0467275,0.10240067,0.0029078724,0.019157477,0.002357945,0.0023456437],"study_design_scores_gemma":[0.0015174627,0.000032802844,0.9608083,0.000431309,0.00010419071,2.0366767e-7,0.006450424,0.025343014,0.001434799,0.0031994213,0.00025138323,0.00042673646],"about_ca_topic_score_codex":0.17006648,"about_ca_topic_score_gemma":0.80251235,"teacher_disagreement_score":0.6324459,"about_ca_system_score_codex":0.00015438869,"about_ca_system_score_gemma":0.00076985155,"threshold_uncertainty_score":0.90015745},"labels":[],"label_agreement":null},{"id":"W607792280","doi":"","title":"Variable Messaging for East-West Highway Corridors through Canada’s Mountain National Parks; Prospects for Multi-Agency Collaboration and Integration","year":2005,"lang":"en","type":"article","venue":"12th World Congress on Intelligent Transport SystemsITS AmericaITS JapanERTICO","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Dissemination; Context (archaeology); Agency (philosophy); Conceptual framework; Transport engineering; Geography; Environmental resource management; Environmental planning; Engineering; Environmental science; Telecommunications; Archaeology; Sociology","score_opus":0.035359551538029624,"score_gpt":0.3035145993007727,"score_spread":0.26815504776274307,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W607792280","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.070007935,0.001108466,0.88169485,0.011256401,0.006457883,0.012961351,0.0023178817,0.00094865344,0.013246575],"genre_scores_gemma":[0.9760692,0.000055702138,0.013134788,0.0006949234,0.0003076049,0.0005937508,0.0007575597,0.000048992304,0.008337495],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99705523,0.000110567875,0.00088651484,0.0006285102,0.0007869983,0.00053219707],"domain_scores_gemma":[0.99768883,0.00035130218,0.00045517666,0.0001619702,0.0011373563,0.00020537661],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007008473,0.00034099157,0.00045152247,0.00020014698,0.0009346364,0.00016380455,0.00021694035,0.000101503014,0.000080218946],"category_scores_gemma":[0.00020426947,0.00033909082,0.00007002671,0.0006983302,0.0001513281,0.0005925215,0.0000036807546,0.00014873946,0.0000043132404],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018451187,0.0012596911,0.034581747,0.0009158227,0.0005638784,0.000013229175,0.062437646,0.35215816,0.0012755023,0.52131534,0.016425593,0.007208263],"study_design_scores_gemma":[0.006010502,0.0008915574,0.017911188,0.0023384267,0.00070482434,0.000008952945,0.029124925,0.1871401,0.009254645,0.0007015668,0.7428467,0.0030665586],"about_ca_topic_score_codex":0.060297694,"about_ca_topic_score_gemma":0.58611244,"teacher_disagreement_score":0.90606123,"about_ca_system_score_codex":0.00060745556,"about_ca_system_score_gemma":0.0012824114,"threshold_uncertainty_score":0.9999061},"labels":[],"label_agreement":null},{"id":"W608439703","doi":"","title":"ALTERNATIVES FOR PROMOTING PUBLIC TRANSPORT INTEGRATION IN THE AMERICAS","year":2000,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Public transport; Transport engineering; Business; Operator (biology); Key (lock); Service (business); Population; Regional science; Economic growth; Environmental planning; Geography; Engineering; Marketing; Computer science; Economics; Computer security; Sociology","score_opus":0.04025984108695903,"score_gpt":0.33446558961480216,"score_spread":0.29420574852784315,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W608439703","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7737193,0.000022082584,0.06830984,0.01982601,0.00007660015,0.00077980006,0.000007956584,0.00013702612,0.13712138],"genre_scores_gemma":[0.99539346,0.000030166539,0.0026088404,0.00031480577,0.00003942614,0.000046837868,0.000046041307,0.0000029237178,0.0015174793],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.999487,0.0000624234,0.00011728221,0.000086617925,0.00013328329,0.000113422524],"domain_scores_gemma":[0.9998015,0.00008303362,0.000025864858,0.000036998626,0.000034604862,0.000018004761],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005043328,0.000036421352,0.000043323205,0.00003450863,0.00019207865,0.00004207016,0.000101285805,0.000022957163,0.00030424108],"category_scores_gemma":[0.000035855683,0.00002519535,0.000023547855,0.00023899421,0.000049253667,0.00024232481,1.7759254e-7,0.000040310675,0.000004142142],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031088417,0.00016736066,0.053024244,0.0000067758892,0.000007839811,0.000002295816,0.43697453,0.0018628201,0.0000325,0.10360858,0.00039387043,0.4038881],"study_design_scores_gemma":[0.0019630617,0.00028163695,0.41357228,0.00010766347,0.0000370485,0.0000016592626,0.15156707,0.026944218,0.00033077036,0.008671165,0.39586154,0.00066188275],"about_ca_topic_score_codex":0.000724234,"about_ca_topic_score_gemma":0.003957596,"teacher_disagreement_score":0.40322623,"about_ca_system_score_codex":0.000011749443,"about_ca_system_score_gemma":0.000033920336,"threshold_uncertainty_score":0.33312264},"labels":[],"label_agreement":null},{"id":"W608805360","doi":"","title":"RESEARCH PAYS OFF: NIGHTTIME CONSTRUCTION: DEVELOPING A SPECIFICATION FOR ROAD WORK AT NIGHT","year":2005,"lang":"en","type":"article","venue":"TR news","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Nova scotia; Transport engineering; Work (physics); Engineering; Geography","score_opus":0.10105126489654946,"score_gpt":0.3761743754787889,"score_spread":0.2751231105822394,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W608805360","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.52405334,0.0019230639,0.11838594,0.13562644,0.0021143216,0.0034007693,0.00005333476,0.0010501043,0.2133927],"genre_scores_gemma":[0.757606,0.0009703726,0.18956956,0.0003932265,0.002340792,0.00019046562,0.00029440515,0.00003812933,0.048596986],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9988143,0.0001248677,0.00022672105,0.00021910528,0.00034232004,0.00027268846],"domain_scores_gemma":[0.99933493,0.00015452279,0.00007309094,0.00011785587,0.00024178003,0.00007783674],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007005106,0.00006721645,0.000093651564,0.000108849265,0.00097600353,0.00007097827,0.00012829353,0.00010110582,0.00033890683],"category_scores_gemma":[0.0000807201,0.00006725455,0.00003820831,0.0005502081,0.00015273076,0.00020158538,0.000005531555,0.00009065393,0.00020974035],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003925196,0.000102398895,0.03488942,0.000033281092,0.000048533766,0.0000032131602,0.053174995,0.0036461425,0.00019951654,0.24617559,0.16365278,0.49768162],"study_design_scores_gemma":[0.00030444915,0.000011571428,0.011658583,0.000026203057,0.000005784915,6.131732e-7,0.0019662182,0.00014035535,0.00020613476,0.00050500175,0.985058,0.000117092],"about_ca_topic_score_codex":0.000117112286,"about_ca_topic_score_gemma":0.0024006767,"teacher_disagreement_score":0.82140523,"about_ca_system_score_codex":0.00026997318,"about_ca_system_score_gemma":0.00017493505,"threshold_uncertainty_score":0.7506729},"labels":[],"label_agreement":null},{"id":"W608945925","doi":"","title":"Development of a Microsimulation Model of Weekend Travel by Households in Calgary","year":2007,"lang":"en","type":"article","venue":"11th World Conference on Transport ResearchWorld Conference on Transport Research Society","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Microsimulation; Econometrics; Calibration; Monte Carlo method; Estimation; Aggregate (composite); Survey data collection; Work (physics); Computer science; Transport engineering; Statistics; Economics; Engineering; Mathematics","score_opus":0.1856484905704804,"score_gpt":0.39199519453188975,"score_spread":0.20634670396140933,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W608945925","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.87642235,0.0001221414,0.04868342,0.0023212316,0.00011799318,0.0025371998,0.0003257202,0.00018538439,0.06928456],"genre_scores_gemma":[0.98560065,0.0008889545,0.0072853076,0.00006811074,0.000035354813,0.00009702692,0.000526772,0.00006244653,0.0054353913],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9904538,0.00035767854,0.0018918465,0.001105209,0.004313658,0.0018778312],"domain_scores_gemma":[0.9962516,0.0005678895,0.00035257646,0.00063296565,0.0015277094,0.000667271],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.009809571,0.0005051451,0.00089519814,0.0010541875,0.0007509891,0.000055628305,0.0011880234,0.00045064284,0.0005029384],"category_scores_gemma":[0.000055661752,0.0005295972,0.00036111753,0.003149495,0.0017530723,0.00043702265,0.000016442864,0.0019096215,0.000010570924],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0056759785,0.0058069546,0.12703313,0.0014140747,0.0003742887,0.000109937944,0.21330813,0.012712987,0.111121304,0.48661965,0.0009749703,0.034848593],"study_design_scores_gemma":[0.013012377,0.0015929505,0.69069374,0.0068191485,0.00015425844,0.0000011625553,0.04936106,0.04772246,0.16711168,0.011300096,0.008099059,0.004132007],"about_ca_topic_score_codex":0.0034317528,"about_ca_topic_score_gemma":0.04890898,"teacher_disagreement_score":0.5636606,"about_ca_system_score_codex":0.00044960456,"about_ca_system_score_gemma":0.004125974,"threshold_uncertainty_score":0.99971557},"labels":[],"label_agreement":null},{"id":"W609371968","doi":"","title":"Smart Commute Initiative - Establishment of a Multijurisdictional Workplace-based Transportation Demand Management Program Serving the Greater Toronto Area and Hamilton","year":2007,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Gridlock; Traffic congestion; Demand management; Business; Population; Transport engineering; Engineering; Economics; Environmental health; Medicine; Political science","score_opus":0.021796237841176062,"score_gpt":0.2860634912417502,"score_spread":0.2642672534005741,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W609371968","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8107069,0.0001805046,0.14810216,0.0009957625,0.00020095376,0.001688894,0.000037363865,0.00025556344,0.03783191],"genre_scores_gemma":[0.9870327,0.000069090755,0.012232858,0.00018415129,0.000017718883,0.000043726075,0.000120295554,0.0000060548377,0.00029341943],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99906576,0.00006486517,0.00024702586,0.00014024135,0.00031479215,0.00016733851],"domain_scores_gemma":[0.9995243,0.00012474529,0.00010392561,0.000080524194,0.000109142755,0.000057348097],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00077427947,0.00008644078,0.000089390735,0.000043277152,0.00034839945,0.000047048106,0.00007975947,0.000054811815,0.000103875886],"category_scores_gemma":[0.000004564911,0.00006634878,0.000032224823,0.0001583316,0.0001235533,0.00025261764,0.0000020092168,0.000050985927,4.4602385e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005702311,0.00048529875,0.7681995,0.00015395641,0.00021233284,0.00001615828,0.18092854,0.014505097,0.000019887299,0.011483363,0.0007138679,0.022711731],"study_design_scores_gemma":[0.0008795939,0.00007666011,0.9687077,0.00009277841,0.00007906926,1.4311715e-7,0.021183135,0.0025272148,0.00015921106,0.000077088844,0.0060836715,0.00013377346],"about_ca_topic_score_codex":0.0023108583,"about_ca_topic_score_gemma":0.04084808,"teacher_disagreement_score":0.20050813,"about_ca_system_score_codex":0.0000626552,"about_ca_system_score_gemma":0.000026986145,"threshold_uncertainty_score":0.97665393},"labels":[],"label_agreement":null},{"id":"W610867851","doi":"","title":"Solving the Stochastic Multiclass Traffic Assignment Problem with Asymmetric Interactions and Vehicle Restrictions","year":2015,"lang":"en","type":"article","venue":"Transportation Research Board 94th Annual MeetingTransportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Path (computing); Mathematical optimization; Computer science; Flow network; Sensitivity (control systems); Logit; Algorithm; Mathematics; Machine learning; Engineering","score_opus":0.08037243175632645,"score_gpt":0.38501235414405893,"score_spread":0.30463992238773246,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W610867851","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9551652,0.0004091367,0.021006951,0.013272282,0.00032045934,0.0037763051,0.00024276243,0.0005758269,0.005231052],"genre_scores_gemma":[0.9922418,0.00022967633,0.0038161443,0.00007603806,0.00023410452,0.00072619057,0.00023891538,0.00007625324,0.0023609065],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9897074,0.0019864615,0.00094245066,0.0009507328,0.0048570987,0.0015558243],"domain_scores_gemma":[0.99091464,0.002677417,0.0002540743,0.0003853551,0.0046732016,0.0010953066],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.008466429,0.00035391346,0.0003728561,0.001457743,0.003728004,0.00054345076,0.00054569007,0.00024372504,0.00007979414],"category_scores_gemma":[0.00084122695,0.0002940285,0.000109123386,0.004979489,0.0017397639,0.0013333301,0.000010659791,0.0018563103,0.000058066453],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0029940193,0.0013665472,0.12825882,0.00032302222,0.00041646103,0.00022770352,0.325458,0.45869786,0.00027986208,0.042211253,0.016914675,0.022851797],"study_design_scores_gemma":[0.0059493314,0.0018175622,0.5240527,0.0007163062,0.0002614734,0.0000030691076,0.38050228,0.01652733,0.00013463033,0.0012813831,0.06746472,0.0012892213],"about_ca_topic_score_codex":0.025445,"about_ca_topic_score_gemma":0.11004015,"teacher_disagreement_score":0.4421705,"about_ca_system_score_codex":0.00048489118,"about_ca_system_score_gemma":0.0015580026,"threshold_uncertainty_score":0.9999512},"labels":[],"label_agreement":null},{"id":"W612234685","doi":"","title":"Success by any other name would smell as sweet: different perspectives on success in public transport systems","year":2006,"lang":"en","type":"article","venue":"Transport Research Forum","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Public transport; Set (abstract data type); Meaning (existential); Transport system; Transport engineering; Public relations; Sociology; Business; Computer science; Engineering; Psychology; Political science","score_opus":0.04136144277795073,"score_gpt":0.35268409962665687,"score_spread":0.31132265684870614,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W612234685","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9359353,0.0008994592,0.0008961774,0.005379473,0.00019673225,0.001068831,0.0002089512,0.00023091404,0.05518414],"genre_scores_gemma":[0.9900917,0.00037731617,0.000017750563,0.00006273709,0.00014007857,0.00016934784,0.0004034501,0.00005768948,0.008679973],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99469304,0.0003946418,0.0006107446,0.0007549944,0.0020864708,0.001460122],"domain_scores_gemma":[0.99868417,0.00018947801,0.000108049375,0.0003295338,0.00036003563,0.00032872774],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0018740182,0.00030164164,0.00041448677,0.0006402792,0.0007796988,0.00018226926,0.00065981556,0.00048048206,0.00073515135],"category_scores_gemma":[0.000031016792,0.00027993895,0.00014585792,0.0012371433,0.0005942998,0.0006108947,0.0000043954733,0.0010541823,0.000058137353],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017914869,0.0007863302,0.89422226,0.00006570698,0.000028456292,0.000065418826,0.011539055,0.002415669,0.00009917915,0.0895233,0.000930355,0.00014509515],"study_design_scores_gemma":[0.0021151237,0.00037608572,0.84443545,0.00029917085,0.000025533715,0.0000013976147,0.043732442,0.00029344327,0.00033263338,0.0010026508,0.10654997,0.0008360698],"about_ca_topic_score_codex":0.06479299,"about_ca_topic_score_gemma":0.04671606,"teacher_disagreement_score":0.10561962,"about_ca_system_score_codex":0.00036356467,"about_ca_system_score_gemma":0.00036359107,"threshold_uncertainty_score":0.99996525},"labels":[],"label_agreement":null},{"id":"W612364093","doi":"","title":"Design-Build of a Major Transportation Project in York Region","year":2014,"lang":"fr","type":"article","venue":"Transportation 2014: Past, Present, Future - 2014 Conference and Exhibition of the Transportation Association of Canada // Transport 2014 : Du passé vers l'avenir - 2014 Congrès et Exposition de 'Association des transports du Canada","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Scope (computer science); Transport engineering; Process (computing); Transit (satellite); Work (physics); Bus rapid transit; Pace; Engineering; Public transport; Computer science; Geography","score_opus":0.011147134567908163,"score_gpt":0.21888047876059372,"score_spread":0.20773334419268555,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W612364093","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8600013,0.0019502479,0.07820011,0.037280243,0.005506621,0.0051426636,0.010131993,0.00024118269,0.0015456211],"genre_scores_gemma":[0.98530185,0.0059864237,0.0013250455,0.0005776012,0.0005343931,0.00020945084,0.0043949573,0.00013848106,0.0015317883],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9879743,0.0018365888,0.0040491833,0.0012571618,0.003440688,0.0014421004],"domain_scores_gemma":[0.98914754,0.0010389814,0.0057302304,0.0006398677,0.0028689504,0.0005744089],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.003600209,0.0011349685,0.0018678237,0.00055617944,0.0007350003,0.0000895595,0.0007442645,0.0012820517,0.0006611123],"category_scores_gemma":[0.00009875483,0.0012629041,0.00068296003,0.0013412478,0.00057354,0.0017339649,0.0000037351588,0.0009972182,0.0000029755136],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013844601,0.0007540003,0.7682874,0.002097925,0.00083308073,0.00005537629,0.031068353,0.08084483,0.0007646479,0.02010734,0.09293166,0.0008709292],"study_design_scores_gemma":[0.0069644083,0.00038533474,0.91043115,0.0014127401,0.0020558683,0.000005750494,0.004769356,0.0018177019,0.0028634816,0.0009667802,0.06674267,0.0015847557],"about_ca_topic_score_codex":0.7821162,"about_ca_topic_score_gemma":0.98155284,"teacher_disagreement_score":0.19943672,"about_ca_system_score_codex":0.002623431,"about_ca_system_score_gemma":0.007820696,"threshold_uncertainty_score":0.9989821},"labels":[],"label_agreement":null},{"id":"W612594187","doi":"","title":"INTRODUCING VIDEO TOLL COLLECTION","year":2002,"lang":"en","type":"article","venue":"9th World Congress on Intelligent Transport SystemsITS America, ITS Japan, ERTICO (Intelligent Transport Systems and Services-Europe)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Toll; Electronic toll collection; Toll road; Data collection; Transport engineering; Telecommunications; Computer science; Business; Engineering; Medicine; Sociology","score_opus":0.02342092833906427,"score_gpt":0.25129200375952526,"score_spread":0.22787107542046098,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W612594187","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6918595,0.033888593,0.0662276,0.00777984,0.054975472,0.020290669,0.0014068903,0.0069702417,0.116601184],"genre_scores_gemma":[0.91218674,0.0077854446,0.00015116058,0.0006354633,0.00078627444,0.00026145516,0.00038018922,0.00020376634,0.07760952],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.99060875,0.0007125894,0.0030077805,0.0020297065,0.0020578862,0.0015832816],"domain_scores_gemma":[0.99530226,0.0003501692,0.0010574656,0.0009606127,0.0012444523,0.0010850114],"candidate_categories":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0013370883,0.0011884157,0.0017033056,0.001136439,0.0018246409,0.0004203646,0.00096714165,0.00039085996,0.0012720692],"category_scores_gemma":[0.000042844113,0.0011569168,0.0004138442,0.0033582866,0.00038908818,0.000901329,0.000017499899,0.00080624147,0.00063040183],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0026691929,0.004127789,0.39371228,0.010882912,0.0032034125,0.0013989161,0.17920297,0.3409989,0.0009978121,0.034926128,0.010885246,0.016994452],"study_design_scores_gemma":[0.0012121969,0.0005612371,0.008619116,0.0038823895,0.0007631299,0.000056421366,0.009938796,0.028403977,0.00084649964,0.00000746212,0.94337624,0.0023325281],"about_ca_topic_score_codex":0.009865564,"about_ca_topic_score_gemma":0.009504486,"teacher_disagreement_score":0.932491,"about_ca_system_score_codex":0.00028329485,"about_ca_system_score_gemma":0.00016236848,"threshold_uncertainty_score":0.9996409},"labels":[],"label_agreement":null},{"id":"W612797586","doi":"","title":"MULTIMODAL RAPID TRANSIT GROWS IN OTTAWA","year":2004,"lang":"en","type":"article","venue":"Mass transit","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Transit (satellite); Light rail transit; Rail transit; Public transport; Transport engineering; Transit system; Light rail; Bus rapid transit; Rapid transit; Urban transit; Capital city; Capital (architecture); Business; Engineering; Telecommunications; Geography","score_opus":0.013066912571100662,"score_gpt":0.2565614284234157,"score_spread":0.24349451585231507,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W612797586","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5331288,0.00035136283,0.38173243,0.01765305,0.0007898936,0.00090284325,0.000059666803,0.00064944394,0.0647325],"genre_scores_gemma":[0.9933822,0.00005534929,0.005998203,0.00020391066,0.00006651817,0.000012772923,0.00003807104,0.000012521311,0.00023045976],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99892664,0.000071727416,0.00022932042,0.00020965376,0.0002775138,0.0002851398],"domain_scores_gemma":[0.9997214,0.00003255077,0.000030017864,0.00008457957,0.000036892998,0.00009455929],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033526166,0.00010193044,0.00013815348,0.00011419625,0.00019509,0.000038385413,0.00013083471,0.00012869276,0.00022953926],"category_scores_gemma":[0.000013430737,0.00011187839,0.00006566126,0.00038430293,0.000093181894,0.00024442674,5.131179e-7,0.0001397382,0.000030080193],"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":[0.00015560738,0.00035736788,0.031353526,0.000059302754,0.00003873127,0.00015957573,0.18101864,0.7284305,0.002542523,0.041760586,0.0002180403,0.013905563],"study_design_scores_gemma":[0.025091954,0.00047740975,0.7469024,0.0005364512,0.00023144418,0.0000070505016,0.026516864,0.0047485908,0.006601178,0.017319698,0.16854161,0.0030253625],"about_ca_topic_score_codex":0.0030792716,"about_ca_topic_score_gemma":0.013653374,"teacher_disagreement_score":0.7236819,"about_ca_system_score_codex":0.00006654298,"about_ca_system_score_gemma":0.0001130052,"threshold_uncertainty_score":0.7618903},"labels":[],"label_agreement":null},{"id":"W613164464","doi":"","title":"COMPARATIVE ANALYSIS OF THE MASSACHUSETTS HIGHWAY SYSTEM","year":2000,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Revenue; Mile; Government (linguistics); State (computer science); County government; Highway system; Transport engineering; Business; Quarter (Canadian coin); State government; Investment (military); Geography; Local government; Finance; Engineering; Political science; Public administration; Mathematics; Law","score_opus":0.022173881751321053,"score_gpt":0.2889150729835004,"score_spread":0.2667411912321793,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W613164464","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.44742513,0.000015401687,0.0011222474,0.00048154374,0.0000668563,0.00009547947,0.000019477991,0.00008322316,0.55069065],"genre_scores_gemma":[0.9917747,0.00000315507,0.00025547278,0.0000281275,0.000008592927,0.0000016190269,0.000009570754,7.2310115e-7,0.007918042],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99952036,0.0000901339,0.0001035157,0.000057596506,0.00017104796,0.000057347897],"domain_scores_gemma":[0.9997873,0.00004114744,0.00003987541,0.00006954988,0.00004293627,0.000019225441],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014154908,0.00002620519,0.00009516005,0.000039118826,0.00016647713,0.0000110961,0.00008683207,0.000024000019,0.0009035303],"category_scores_gemma":[0.0000027146502,0.000017074275,0.000057436395,0.0008337267,0.00006154312,0.00004562968,7.4065713e-7,0.00002041134,0.00001171467],"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":[0.000022367925,0.000059538394,0.24265969,0.000010830957,0.00069674215,6.7842586e-7,0.093874805,0.469186,0.00002935098,0.18912382,0.0035684889,0.00076767855],"study_design_scores_gemma":[0.00019834956,0.000010391746,0.92261773,0.00002338116,0.00082926813,5.7224778e-8,0.020669293,0.022213373,0.00027209317,0.000029177494,0.033005036,0.00013186494],"about_ca_topic_score_codex":0.0014034421,"about_ca_topic_score_gemma":0.0030493692,"teacher_disagreement_score":0.67995805,"about_ca_system_score_codex":0.000016043574,"about_ca_system_score_gemma":0.000026257216,"threshold_uncertainty_score":0.98930234},"labels":[],"label_agreement":null},{"id":"W613742084","doi":"","title":"Freeway Interchange Planning: A Public Involvement Success Story","year":2014,"lang":"fr","type":"article","venue":"Transportation 2014: Past, Present, Future - 2014 Conference and Exhibition of the Transportation Association of Canada // Transport 2014 : Du passé vers l'avenir - 2014 Congrès et Exposition de 'Association des transports du Canada","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Stakeholder; Participatory planning; Project team; Plan (archaeology); Business; Process (computing); Public participation; Process management; Stakeholder engagement; Transportation planning; Project planning; Project management; Public relations; Engineering; Knowledge management; Political science; Transport engineering; Computer science; Civil engineering","score_opus":0.013204636187044922,"score_gpt":0.22263886055195378,"score_spread":0.20943422436490886,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W613742084","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.81921935,0.0024835407,0.038934276,0.11049088,0.009095238,0.003266745,0.012949699,0.0003267489,0.0032335008],"genre_scores_gemma":[0.9817037,0.005118094,0.0004929741,0.0014862453,0.0010668299,0.00020004739,0.0067553995,0.00013922405,0.0030374853],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9884014,0.0018320567,0.0029935848,0.0012728757,0.0038742893,0.0016257538],"domain_scores_gemma":[0.9889443,0.0009943614,0.0048239157,0.0006695995,0.0035886923,0.0009791623],"candidate_categories":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00406649,0.0011533218,0.001584933,0.00042682784,0.0014116459,0.00017849184,0.0008748762,0.0011782623,0.001751682],"category_scores_gemma":[0.00012239149,0.0012736885,0.0006936031,0.0008789907,0.00061298703,0.0022515366,0.000007958339,0.0011912386,0.0000068650133],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004592902,0.0004975279,0.724646,0.001366242,0.0009827747,0.00003912515,0.02506842,0.021459613,0.00028722186,0.021080377,0.20314044,0.0009729864],"study_design_scores_gemma":[0.0039297203,0.0002372886,0.6689262,0.0007711386,0.0014303711,0.0000033735691,0.0045199897,0.0007865268,0.000742303,0.0006205542,0.3167364,0.0012961513],"about_ca_topic_score_codex":0.670469,"about_ca_topic_score_gemma":0.9829368,"teacher_disagreement_score":0.3124678,"about_ca_system_score_codex":0.0037959295,"about_ca_system_score_gemma":0.0071677696,"threshold_uncertainty_score":0.99988836},"labels":[],"label_agreement":null},{"id":"W614212321","doi":"","title":"New BRT Projects Gather Momentum Across the U.S.","year":2010,"lang":"en","type":"article","venue":"Metrologia","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Bus rapid transit; Transport engineering; Transit (satellite); Compressed natural gas; Plan (archaeology); Capital cost; Propulsion; Business; Public transport; Computer science; Operations management; Aeronautics; Geography; Engineering","score_opus":0.02055061753406172,"score_gpt":0.3142065625777642,"score_spread":0.29365594504370246,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W614212321","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9631177,0.000043528657,0.005872266,0.005924768,0.0011266398,0.00023469704,0.000005784574,0.00020942652,0.023465168],"genre_scores_gemma":[0.98868173,0.000008195605,0.0024406526,0.0003160674,0.00020735884,0.000008788408,0.0000057756274,0.000004883607,0.008326546],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.99938613,0.0000499526,0.00007934154,0.00010848268,0.00017539898,0.00020067452],"domain_scores_gemma":[0.9996949,0.00006692377,0.000044566517,0.000109352106,0.000036133446,0.00004812637],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000608343,0.000048516285,0.00005363878,0.000018266242,0.0004015291,0.000069490445,0.0001847318,0.00008185387,0.000376287],"category_scores_gemma":[0.00014338842,0.00003170332,0.000028085035,0.00023428877,0.00008306885,0.000080470316,0.0000056823374,0.00015808294,0.00006086535],"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":[0.00006807006,0.000093627095,0.71838814,0.000006351785,0.00005812148,0.0000088658135,0.113024935,0.001016506,0.002651983,0.07866243,0.053868927,0.03215206],"study_design_scores_gemma":[0.00050781766,0.00006357355,0.4297804,0.0000033325223,0.000022510854,7.9051307e-7,0.0061629135,0.00018546499,0.00049747026,0.0017498725,0.5608238,0.00020209177],"about_ca_topic_score_codex":0.0013813399,"about_ca_topic_score_gemma":0.003846846,"teacher_disagreement_score":0.50695485,"about_ca_system_score_codex":0.000008731275,"about_ca_system_score_gemma":0.00008014226,"threshold_uncertainty_score":0.4120079},"labels":[],"label_agreement":null},{"id":"W615738457","doi":"","title":"Automated Metro Operation: Greater Capacity and Safer, More Efficient Transport","year":2011,"lang":"en","type":"article","venue":"Public transport international","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Train; SAFER; Transport engineering; Flexibility (engineering); Transit (satellite); Public transport; Transit system; Rail transit; Engineering; Computer science; Economics","score_opus":0.051864902114312016,"score_gpt":0.2775369205908563,"score_spread":0.22567201847654425,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W615738457","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9401214,0.00003184568,0.019259183,0.0028160654,0.00084660115,0.00028375728,0.00019325456,0.0008236946,0.035624217],"genre_scores_gemma":[0.99452776,0.00002778011,0.0039193323,0.0002720518,0.00011177313,0.000034293287,0.00045795424,0.000017566068,0.00063146313],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9983077,0.0000470973,0.00038606834,0.0003519752,0.00061089586,0.00029624754],"domain_scores_gemma":[0.99926394,0.000021034091,0.00009193212,0.00012142552,0.0002895697,0.0002120737],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004855522,0.00016614691,0.00016397207,0.0002334498,0.00036213445,0.000056470926,0.00026464756,0.00013817342,0.002031203],"category_scores_gemma":[0.000021344105,0.00016483037,0.00007937417,0.00025390173,0.0002675627,0.0005014694,0.000003294086,0.00013714946,0.000018080835],"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":[0.00007099466,0.00022132922,0.9022694,0.000013384715,0.00012222011,0.000028243147,0.04470175,0.0011861033,0.00002321001,0.050399378,0.00015563954,0.00080830115],"study_design_scores_gemma":[0.0006466316,0.00002120287,0.9774535,0.000016360527,0.0000388829,0.000004081104,0.0011509622,0.0041831313,0.00008267164,0.000073910225,0.016080813,0.00024789167],"about_ca_topic_score_codex":0.0017341872,"about_ca_topic_score_gemma":0.0016655562,"teacher_disagreement_score":0.07518402,"about_ca_system_score_codex":0.00007466949,"about_ca_system_score_gemma":0.00014474672,"threshold_uncertainty_score":0.9988811},"labels":[],"label_agreement":null},{"id":"W616274087","doi":"","title":"Pembina Highway Buffered Bike Lane Project - A Solution to Managing Cyclists, Buses and Pedestrians at Transit Stops","year":2013,"lang":"en","type":"article","venue":"2013 CONFERENCE AND EXHIBITION OF THE TRANSPORTATION ASSOCIATION OF CANADA - TRANSPORTATION: BETTER - FASTER - SAFER","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Transport engineering; Pedestrian; Cycling; Truck; Ring road; Twin cities; Engineering; Geography","score_opus":0.013535080510740583,"score_gpt":0.22160932690306775,"score_spread":0.20807424639232716,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W616274087","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9668958,0.00005193428,0.0048022466,0.025543146,0.00030233705,0.0011053667,0.000625744,0.00006316963,0.0006102369],"genre_scores_gemma":[0.99530464,0.0001625228,0.0008315787,0.0007083611,0.000051093677,0.000086794134,0.00060705724,0.000024293524,0.0022236342],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99720997,0.00019225718,0.0008638372,0.00039776973,0.0009508737,0.0003852874],"domain_scores_gemma":[0.99820995,0.00014726631,0.00062584487,0.00018498691,0.0006573019,0.00017462883],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003748948,0.00026733679,0.00040231363,0.00019116781,0.0005191651,0.00007622833,0.00017676002,0.00019603477,0.00035617605],"category_scores_gemma":[0.000027699098,0.00025419926,0.00009975937,0.00042769196,0.00014200389,0.00066204875,0.00000289462,0.00016527754,0.0000030825151],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00068447145,0.00039117812,0.7484716,0.00108311,0.0005992267,0.000013505067,0.15559813,0.0056849257,0.015493789,0.019168323,0.045031436,0.007780278],"study_design_scores_gemma":[0.0012662183,0.00006621873,0.98689276,0.00023909759,0.00019783706,4.835073e-7,0.0031417098,0.00020659086,0.0009829139,0.0004755588,0.006153353,0.00037724164],"about_ca_topic_score_codex":0.24886075,"about_ca_topic_score_gemma":0.76638,"teacher_disagreement_score":0.51751924,"about_ca_system_score_codex":0.00019989042,"about_ca_system_score_gemma":0.0004871882,"threshold_uncertainty_score":0.999991},"labels":[],"label_agreement":null},{"id":"W616420750","doi":"","title":"TRANSIT AND SCHOOL TRANSPORTATION: EXPERIENCE AND ALTERNATIVE APPROACHES FROM THE LITERATURE","year":2000,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Transit (satellite); Transport engineering; Public transport; Business; Transit system; Service (business); Mass transportation; Engineering; Marketing","score_opus":0.0263204613838952,"score_gpt":0.2575041734856993,"score_spread":0.2311837121018041,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W616420750","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9845587,0.0007159264,0.0017111737,0.0028013922,0.00003509706,0.00014943401,0.00004349824,0.000056217104,0.009928567],"genre_scores_gemma":[0.9955043,0.00075364317,0.0014600194,0.00033184318,0.00007741357,0.000011380278,0.00005825492,0.0000033571803,0.0017997699],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.99949145,0.000046709774,0.00008806081,0.00015581714,0.00013491415,0.00008302213],"domain_scores_gemma":[0.9997819,0.00006467154,0.000016703954,0.000048465674,0.00002027657,0.00006795741],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008174288,0.000059373993,0.00005527383,0.000010725761,0.00033858587,0.00013105963,0.00006225876,0.000047693884,0.0008410271],"category_scores_gemma":[0.00000716207,0.000041359322,0.000012980366,0.00012593571,0.0001598595,0.0003106992,3.4011705e-7,0.000069853304,0.0000035476965],"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":[0.000056944784,0.000021426935,0.070243835,0.000004784295,0.000021235273,0.000006034914,0.8931974,0.0014497711,0.000012055359,0.012068147,0.0003161176,0.022602234],"study_design_scores_gemma":[0.00080109824,0.000031897718,0.8899349,0.000094320996,0.00004352601,0.0000010175829,0.06475402,0.0012461715,0.000121980636,0.0029922847,0.03966021,0.00031858927],"about_ca_topic_score_codex":0.001347494,"about_ca_topic_score_gemma":0.0014318934,"teacher_disagreement_score":0.8284434,"about_ca_system_score_codex":0.000004004664,"about_ca_system_score_gemma":0.000017337776,"threshold_uncertainty_score":0.9208657},"labels":[],"label_agreement":null},{"id":"W616445489","doi":"","title":"GOVERNANCE AND BUSINESS MODELS FOR DEPLOYMENT AND MANAGEMENT OF A MULTI-AGENCY INTEGRATED TRANSIT FARE SYSTEM IN THE GREATER TORONTO AREA","year":2004,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Software deployment; Agency (philosophy); Business; Corporate governance; Finance; Engineering","score_opus":0.03171278540970823,"score_gpt":0.2620388944659916,"score_spread":0.23032610905628337,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W616445489","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25585797,0.00049583154,0.74044913,0.00032935862,0.000038325154,0.0008389,0.000040854593,0.000037882073,0.0019117597],"genre_scores_gemma":[0.99313205,0.0003627892,0.0062916344,0.000026131485,0.0000027426183,0.000042175063,0.0000062719128,0.0000031094858,0.0001330857],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99959594,0.000016194113,0.00011384448,0.000100554564,0.00009801642,0.00007545909],"domain_scores_gemma":[0.9998571,0.0000088485485,0.00003146438,0.00004029423,0.000048261838,0.000013987364],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014783806,0.0000493002,0.00006770368,0.0000081047465,0.000074655465,0.000023793424,0.00004818926,0.00002623121,0.0000020312125],"category_scores_gemma":[0.0000011088893,0.000033211418,0.000009931931,0.00008027354,0.000026416243,0.00019745846,0.0000012125081,0.000011621393,4.142761e-8],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023320754,0.0002585326,0.015874488,0.001355344,0.00009397104,0.000021054753,0.222669,0.32783964,0.00002881198,0.41923046,0.00003429341,0.012361175],"study_design_scores_gemma":[0.008000717,0.00009865224,0.78196704,0.0016389124,0.00017613843,0.0000024253281,0.1467253,0.059615236,0.00006504334,0.00052835996,0.00075596134,0.00042623273],"about_ca_topic_score_codex":0.009342139,"about_ca_topic_score_gemma":0.021504937,"teacher_disagreement_score":0.76609254,"about_ca_system_score_codex":0.000050619612,"about_ca_system_score_gemma":0.000013370135,"threshold_uncertainty_score":0.9972547},"labels":[],"label_agreement":null},{"id":"W616470829","doi":"","title":"Service and Cost Comparisons of Bus Rapid Transit and Light Rail Transit","year":2004,"lang":"en","type":"article","venue":"10th World Conference on Transport ResearchWorld Conference on Transport Research SocietyIstanbul Technical University","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Bus rapid transit; Light rail transit; Service (business); Transit (satellite); Transport engineering; Public transport; Level of service; Computer science; Engineering; Business","score_opus":0.12488648886547062,"score_gpt":0.3474744889039451,"score_spread":0.2225880000384745,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W616470829","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3558328,0.0007495886,0.04966405,0.24096432,0.00029991593,0.009869042,0.002135873,0.0019066046,0.3385778],"genre_scores_gemma":[0.9899381,0.005405785,0.0013704622,0.0001943419,0.000044218763,0.000028919869,0.00023801348,0.000047201185,0.0027329908],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99277824,0.00060864823,0.00079139124,0.0014066863,0.002853645,0.0015614111],"domain_scores_gemma":[0.9956606,0.0005392714,0.00019154557,0.0006980928,0.0016705723,0.0012398857],"candidate_categories":["metaepi_narrow","sts","research_integrity"],"consensus_categories":["sts"],"category_scores_codex":[0.003162383,0.0005700376,0.00096383726,0.0009708939,0.0018916029,0.00012592819,0.0012414273,0.0005139029,0.0008584073],"category_scores_gemma":[0.00005133283,0.0006134875,0.00029155964,0.0034096763,0.0036633722,0.0005861055,0.00002889644,0.0025495845,0.000016268532],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0028297906,0.0011834807,0.00709457,0.0004549403,0.00017468413,0.00035515844,0.016085317,0.0008332011,0.0026511822,0.9635006,0.0004997595,0.00433727],"study_design_scores_gemma":[0.029507333,0.005690419,0.5422312,0.008167345,0.0009274055,0.000025557969,0.10159106,0.0013382899,0.006263151,0.043209832,0.25465438,0.0063939956],"about_ca_topic_score_codex":0.0069987956,"about_ca_topic_score_gemma":0.08388603,"teacher_disagreement_score":0.9202908,"about_ca_system_score_codex":0.00043474557,"about_ca_system_score_gemma":0.0022809722,"threshold_uncertainty_score":0.99975157},"labels":[],"label_agreement":null},{"id":"W616644696","doi":"","title":"County-Wide Active Transportation System (CWATS)","year":2013,"lang":"en","type":"article","venue":"2013 CONFERENCE AND EXHIBITION OF THE TRANSPORTATION ASSOCIATION OF CANADA - TRANSPORTATION: BETTER - FASTER - SAFER","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Transport engineering; Plan (archaeology); Pedestrian; Master plan; Transportation planning; Sustainable transport; Environmental planning; Business; Engineering; Geography; Sustainability","score_opus":0.008539754129175719,"score_gpt":0.20379519790251985,"score_spread":0.19525544377334414,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W616644696","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97801894,0.00004416628,0.0043626484,0.01214715,0.00067799486,0.0011487777,0.0017403378,0.00009986142,0.0017600997],"genre_scores_gemma":[0.99593824,0.00009024399,0.00054056046,0.0006373866,0.00007328712,0.0001105098,0.0012550723,0.000032585835,0.001322085],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9960569,0.0002343852,0.0013202579,0.00042830294,0.00151108,0.00044905415],"domain_scores_gemma":[0.9963959,0.00026681775,0.0013372016,0.00025169324,0.0015449735,0.00020339979],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00044366447,0.0003299039,0.0005159796,0.0001659866,0.00051675306,0.00007013482,0.00027921988,0.00030164485,0.00077176344],"category_scores_gemma":[0.000034457724,0.0003130301,0.00018843879,0.00051400636,0.00020933949,0.0011481128,0.000001190941,0.00026784145,0.000005935758],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037651014,0.0003227455,0.8327785,0.0010482365,0.00066365546,0.000010236152,0.07221567,0.009600478,0.0026422364,0.047630157,0.029104074,0.0036074598],"study_design_scores_gemma":[0.0011946729,0.000041625706,0.98028857,0.0003165231,0.00025288694,1.8279873e-7,0.00897063,0.00020426478,0.0017054933,0.00042947268,0.0062137977,0.0003818777],"about_ca_topic_score_codex":0.21764593,"about_ca_topic_score_gemma":0.698864,"teacher_disagreement_score":0.481218,"about_ca_system_score_codex":0.00031886934,"about_ca_system_score_gemma":0.0009370168,"threshold_uncertainty_score":0.99993217},"labels":[],"label_agreement":null},{"id":"W616709343","doi":"","title":"Fast Times at Edmonton Transit System","year":2006,"lang":"en","type":"article","venue":"Metrologia","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Bus rapid transit; Transport engineering; Attrition; Transit (satellite); Transit system; Rail transit; Service (business); Business; Mile; Security system; Light rail transit; Public transport; Telecommunications; Computer science; Engineering; Computer security; Geography; Marketing","score_opus":0.008564038208286157,"score_gpt":0.23509669763092753,"score_spread":0.22653265942264136,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W616709343","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6012335,0.00035260347,0.02702263,0.0010013145,0.00045138548,0.00022025955,0.000026223646,0.0007862686,0.36890584],"genre_scores_gemma":[0.9903814,0.0000045114875,0.0012608514,0.000040718554,0.00011773129,0.000006743578,0.000047801517,0.0000051852753,0.00813504],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9992907,0.00008407408,0.00012944484,0.00013010597,0.00018962909,0.00017605748],"domain_scores_gemma":[0.9997669,0.000044486223,0.000048732807,0.00006550097,0.000040425974,0.000033960314],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029033737,0.0000591763,0.00009451399,0.000054974746,0.00033095237,0.000027873266,0.00008690039,0.00009000404,0.00018927258],"category_scores_gemma":[0.000011274783,0.000054303517,0.00004033132,0.00019197159,0.000063674044,0.00007541331,0.0000020675002,0.00004289452,0.00009360152],"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":[0.00027886312,0.00016715174,0.5617088,0.00007674782,0.000065971835,0.00007323565,0.010500442,0.12479228,0.0016674497,0.26075563,0.036441114,0.0034723135],"study_design_scores_gemma":[0.0019163417,0.00021898474,0.8122929,0.00005178393,0.00017500827,0.0000044854232,0.0065872106,0.005222875,0.0012555672,0.0005282272,0.17100042,0.0007462086],"about_ca_topic_score_codex":0.0024193588,"about_ca_topic_score_gemma":0.0034084772,"teacher_disagreement_score":0.38914794,"about_ca_system_score_codex":0.000087968925,"about_ca_system_score_gemma":0.000021801794,"threshold_uncertainty_score":0.3657363},"labels":[],"label_agreement":null},{"id":"W617279286","doi":"10.1061/jtepbs.0000108","title":"Two-Stage Bicycle Traffic Assignment Model","year":2017,"lang":"en","type":"article","venue":"Journal of Transportation Engineering Part A Systems","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Federal Highway Administration; Hong Kong Polytechnic University; National Research Foundation of Korea; National Research Foundation; U.S. Department of Transportation","keywords":"Key (lock); Traffic flow (computer networking); Set (abstract data type); Process (computing); Computer science; Stage (stratigraphy); Operations research; Measure (data warehouse); TRIPS architecture; Transport engineering; Engineering; Data mining; Computer network; Computer security","score_opus":0.029532846597111265,"score_gpt":0.2890340038063142,"score_spread":0.25950115720920297,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W617279286","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.663964,0.00025362556,0.33114636,0.00041829576,0.0026805846,0.00025952514,0.00006528195,0.00010750452,0.0011048745],"genre_scores_gemma":[0.9967995,0.00005852932,0.0019293583,0.000007657075,0.00029662086,0.000006635456,0.000015243881,0.000016750259,0.000869667],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986226,0.000027576314,0.0005523577,0.00010034802,0.00051033247,0.0001868022],"domain_scores_gemma":[0.9988861,0.000035835314,0.0006116031,0.00012627109,0.0001872646,0.00015294718],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007659686,0.000105692736,0.00021546592,0.00012249571,0.00038704337,0.00018112508,0.00022364673,0.000072038194,0.00001615491],"category_scores_gemma":[0.00004454854,0.0001054218,0.000108264365,0.00006609264,0.000037020793,0.0005978689,6.228366e-7,0.00013787318,0.0000029631392],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014162667,0.000024575365,0.0026673537,0.000033930006,0.000031287014,0.000017941538,0.004210345,0.9900104,0.000121723024,0.0026038194,0.00015242308,0.00011205463],"study_design_scores_gemma":[0.0023010706,0.00009918957,0.035834942,0.00066220935,0.00018236191,0.000005341989,0.0036693693,0.9080285,0.00013378533,0.000013878026,0.04855169,0.0005177031],"about_ca_topic_score_codex":0.00011435711,"about_ca_topic_score_gemma":0.00015795795,"teacher_disagreement_score":0.33283558,"about_ca_system_score_codex":0.0000643165,"about_ca_system_score_gemma":0.00014107722,"threshold_uncertainty_score":0.4298976},"labels":[],"label_agreement":null},{"id":"W617601019","doi":"","title":"TORONTO'S NEW TRANSIT CONTROL CENTRE: A STUDY IN COOPERATIVE DESIGN, DEVELOPMENT AND PROJECT CONTROL","year":2001,"lang":"en","type":"article","venue":"Rail Transit ConferenceAmerican Public Transportation Association","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Control (management); Transit (satellite); Presentation (obstetrics); Process (computing); Reliability (semiconductor); Engineering; Service (business); Transport engineering; Engineering management; Process management; Public transport; Computer science; Business; Power (physics); Marketing","score_opus":0.03219725449200153,"score_gpt":0.2814923097552463,"score_spread":0.24929505526324477,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W617601019","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.35412303,0.00015309044,0.6312979,0.007503159,0.00022578162,0.0039440566,0.00009714365,0.0004225686,0.002233306],"genre_scores_gemma":[0.9967563,0.00015629201,0.001469291,0.00036721965,0.000066400724,0.00013218413,0.00021767509,0.000028553579,0.0008060534],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9960879,0.00088929065,0.0008728285,0.00059537543,0.00089922803,0.00065535045],"domain_scores_gemma":[0.9982369,0.0003888025,0.00041106102,0.00012453672,0.000546383,0.000292324],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0015081854,0.00033381005,0.0005653756,0.00022617877,0.00049062364,0.00026587164,0.00018944878,0.00022604468,0.00041322567],"category_scores_gemma":[0.00015549961,0.0003671586,0.0000751341,0.0008429771,0.00009490186,0.0011597503,6.846025e-7,0.0002470707,0.000007124131],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00051073177,0.00076424255,0.66867447,0.000013029927,0.000320527,0.000030471794,0.2992935,0.0037907723,0.00007054293,0.004087997,0.00016840741,0.022275344],"study_design_scores_gemma":[0.012737916,0.00027468853,0.8734779,0.00005013206,0.00021669362,6.495034e-7,0.09759055,0.0009612214,0.000021338261,0.000044351964,0.01387679,0.000747814],"about_ca_topic_score_codex":0.015236013,"about_ca_topic_score_gemma":0.23331441,"teacher_disagreement_score":0.6426333,"about_ca_system_score_codex":0.0007079788,"about_ca_system_score_gemma":0.0021624793,"threshold_uncertainty_score":0.99987805},"labels":[],"label_agreement":null},{"id":"W618725557","doi":"","title":"Bus-Train Systems","year":2013,"lang":"fr","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Train; Transport engineering; Public transport; Bus rapid transit; Fuel efficiency; Trailer; Consumption (sociology); Engineering; Automotive engineering; Geography","score_opus":0.019540344383002756,"score_gpt":0.2606420162975694,"score_spread":0.24110167191456663,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W618725557","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.049450956,0.006242934,0.04448311,0.031441104,0.008051409,0.0006621073,0.00002082352,0.00032945804,0.8593181],"genre_scores_gemma":[0.62498915,0.00019743097,0.0015111275,0.00022105633,0.00026406403,0.000015078411,0.000016737706,0.000006120662,0.37277922],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992257,0.00009546578,0.00016611088,0.00011952186,0.00018440885,0.00020883187],"domain_scores_gemma":[0.9996122,0.000060454488,0.00004876657,0.00006363988,0.00010549304,0.00010941944],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0001872672,0.00006716969,0.00008038624,0.000027658127,0.00018019165,0.00012743584,0.00007988237,0.00011297833,0.0065384503],"category_scores_gemma":[0.00001673944,0.00006590228,0.00003057379,0.0002522615,0.00013163815,0.00037706702,0.0000016429311,0.00006033162,0.002841684],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000013816724,0.000071853654,0.008976246,0.00006279739,0.000028389186,0.000003360061,0.017204817,0.03350049,0.000021640284,0.74112344,0.18290477,0.016100805],"study_design_scores_gemma":[0.00028828467,0.000042436408,0.02746522,0.00012428216,0.00003697571,0.0000018504091,0.022356994,0.03699805,0.000010177332,0.0012221202,0.91111445,0.00033918032],"about_ca_topic_score_codex":0.023959039,"about_ca_topic_score_gemma":0.00038998,"teacher_disagreement_score":0.7399013,"about_ca_system_score_codex":0.00002974693,"about_ca_system_score_gemma":0.000055821114,"threshold_uncertainty_score":0.9979347},"labels":[],"label_agreement":null},{"id":"W619062645","doi":"","title":"Tax-free employer-provided benefits for public transport: background, history and 2008 status","year":2008,"lang":"en","type":"article","venue":"EUROPEAN TRANSPORT CONFERENCE 2008; PROCEEDINGS","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Incentive; Legislation; Public transport; Transit (satellite); Business; Payroll; Tax incentive; Public economics; Cost–benefit analysis; Tax credit; Transport engineering; Economics; Political science; Engineering; Accounting","score_opus":0.0820022886667152,"score_gpt":0.251534898678235,"score_spread":0.16953261001151976,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W619062645","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.90185577,0.0017645909,0.0069262604,0.001788214,0.0005987263,0.0013400728,0.00043360336,0.0009506708,0.084342085],"genre_scores_gemma":[0.98393637,0.0031043245,0.0055585066,0.0003590966,0.00025622523,0.000058160105,0.0003491196,0.00009634055,0.006281845],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9968262,0.000047108708,0.0007012285,0.00080715807,0.00067361677,0.00094473304],"domain_scores_gemma":[0.9978463,0.000054124772,0.00031124405,0.00021199552,0.00096143817,0.00061491126],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00078041514,0.00040083088,0.00042230033,0.00026458089,0.0009943788,0.000078788114,0.0005737412,0.00017350433,0.00037949687],"category_scores_gemma":[0.00009150449,0.00044273492,0.00015105541,0.00036816165,0.00071872404,0.0014213762,0.000008858654,0.00030788514,0.000025043304],"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":[0.0005645089,0.00044551952,0.655544,0.00044847385,0.00018959516,0.00014403935,0.16096146,0.000113729126,0.0003605493,0.13473265,0.040739834,0.005755688],"study_design_scores_gemma":[0.0026777457,0.00024723873,0.4205857,0.00013495829,0.00014433362,0.000016480295,0.004868268,0.0001010488,0.00004187956,0.00017983401,0.5700497,0.0009528309],"about_ca_topic_score_codex":0.0008252898,"about_ca_topic_score_gemma":0.0015825353,"teacher_disagreement_score":0.52930987,"about_ca_system_score_codex":0.00016271774,"about_ca_system_score_gemma":0.000754431,"threshold_uncertainty_score":0.9998025},"labels":[],"label_agreement":null},{"id":"W619638767","doi":"","title":"Putting Transportation Emission Reduction Strategies in Perspective: Why Incremental Improvements Will Not Do","year":2008,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Greenhouse gas; Per capita; Metropolitan area; Natural resource economics; Investment (military); Sustainability; Business; Service (business); Population; Transport engineering; Agricultural economics; Economics; Geography; Engineering","score_opus":0.020399091245867078,"score_gpt":0.30188595825577347,"score_spread":0.2814868670099064,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W619638767","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9787621,0.00003062017,0.005623854,0.0009962638,0.00021074705,0.0002742851,0.0000069901557,0.00015380798,0.013941362],"genre_scores_gemma":[0.99527544,0.00010581039,0.003766573,0.000065652275,0.000078558136,0.0000118182425,0.00006972569,0.000008047097,0.0006183913],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.99892163,0.00006211816,0.00024505606,0.00023075921,0.00035282885,0.0001876217],"domain_scores_gemma":[0.9996655,0.000016426962,0.000088463836,0.000055172022,0.00011593882,0.00005847523],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027680772,0.000087861874,0.000088504385,0.00012465678,0.00044957426,0.00004921065,0.000069284695,0.00008204179,0.00019122066],"category_scores_gemma":[0.000021174974,0.00009118874,0.000032805787,0.00033667943,0.00008608103,0.0011437875,0.000001226013,0.00009679136,0.0000030934902],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034930866,0.0006174797,0.22066441,0.000051351108,0.000052330724,0.00005214389,0.54801315,0.02411148,0.068961814,0.12698142,0.0024356958,0.0077093993],"study_design_scores_gemma":[0.0031029508,0.00021337999,0.40960574,0.0001701061,0.000041319676,0.0000044474073,0.55985135,0.0011046572,0.014350668,0.006917352,0.0038722153,0.00076582644],"about_ca_topic_score_codex":0.010940143,"about_ca_topic_score_gemma":0.0020128593,"teacher_disagreement_score":0.18894133,"about_ca_system_score_codex":0.00017183754,"about_ca_system_score_gemma":0.00015468818,"threshold_uncertainty_score":0.9956461},"labels":[],"label_agreement":null},{"id":"W620014431","doi":"","title":"CONGESTION PRICING - ITS DECISION REPORT","year":2000,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Toll; Road pricing; Payment; Business; Congestion pricing; Dynamic pricing; Traffic congestion; Computer science; Telecommunications; Finance; Transport engineering; Marketing; Engineering","score_opus":0.020702921473884927,"score_gpt":0.31519537993140295,"score_spread":0.29449245845751804,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W620014431","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.547626,0.000032986372,0.014892232,0.00075084856,0.00018699002,0.000114033806,7.891682e-7,0.00025260609,0.4361435],"genre_scores_gemma":[0.95910186,0.000084271596,0.0047155595,0.00009609931,0.000059323927,0.0000021514104,0.000018946752,0.0000030730673,0.035918735],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99947983,0.00002209272,0.00012237279,0.00009722801,0.00019176687,0.000086704735],"domain_scores_gemma":[0.9997703,0.000052393654,0.000026553244,0.000048708873,0.00005816206,0.000043921405],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00030513704,0.000029933639,0.000038677113,0.000026091891,0.00023905412,0.000032110427,0.000038203067,0.000043939857,0.0035699704],"category_scores_gemma":[0.00008940341,0.000028100505,0.000014577728,0.00015652158,0.000018338085,0.00017910176,6.025355e-7,0.00002943369,0.00014529737],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010507521,0.0001223816,0.07724881,0.00000911244,0.000017971855,0.00021388852,0.02770438,0.14037663,0.00012089307,0.0587326,0.013646346,0.6817019],"study_design_scores_gemma":[0.00054751453,0.000039480008,0.2970497,0.00008413881,0.000027052698,0.000017559027,0.0017745559,0.0040305266,0.00024530716,0.0015291712,0.6943089,0.00034613695],"about_ca_topic_score_codex":0.00036728094,"about_ca_topic_score_gemma":0.00039883473,"teacher_disagreement_score":0.6813558,"about_ca_system_score_codex":0.0000170075,"about_ca_system_score_gemma":0.000042369506,"threshold_uncertainty_score":0.9973409},"labels":[],"label_agreement":null},{"id":"W620428028","doi":"","title":"Giving the Edge to Pedestrians: Food for Thought","year":2012,"lang":"en","type":"article","venue":"ITE journal","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Pedestrian; Legislation; Equity (law); Business; Transport engineering; Transportation planning; Order (exchange); Accommodation; Engineering; Finance; Political science; Psychology","score_opus":0.05111475857092955,"score_gpt":0.3366680904608617,"score_spread":0.28555333188993215,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W620428028","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4466103,0.0015843492,0.4794957,0.038753804,0.006432012,0.000852192,0.000047845162,0.00014741125,0.026076382],"genre_scores_gemma":[0.9890701,0.00003842094,0.0059895855,0.00044362276,0.0021219375,0.000006713421,0.0000017910457,0.0000060998987,0.002321738],"study_design_codex":"qualitative","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994273,0.00005905527,0.00010979255,0.000038143393,0.0001592832,0.00020641822],"domain_scores_gemma":[0.999564,0.00014533951,0.0000605503,0.000037893784,0.000071668444,0.000120584744],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009927014,0.000036124286,0.000043979075,0.000033537264,0.0008030573,0.00011252114,0.00010726634,0.00003068278,0.0000655185],"category_scores_gemma":[0.00020837889,0.000025123454,0.000043522567,0.00013388597,0.000018790826,0.00025390496,0.00000202637,0.00008119708,0.000011026058],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001434974,0.00020830763,0.21989633,0.000025934265,0.0001351724,0.0000027469896,0.42669564,0.011489607,0.00028656944,0.099745125,0.147515,0.09385606],"study_design_scores_gemma":[0.00019075922,0.000038700833,0.04270337,0.000022073391,0.000018500097,0.000004605933,0.0061524147,0.00003185607,0.000035868754,0.00040811618,0.95031804,0.000075711236],"about_ca_topic_score_codex":0.000013791759,"about_ca_topic_score_gemma":0.00013808183,"teacher_disagreement_score":0.80280304,"about_ca_system_score_codex":0.000025083591,"about_ca_system_score_gemma":0.000065744534,"threshold_uncertainty_score":0.61765486},"labels":[],"label_agreement":null},{"id":"W620915859","doi":"10.22215/etd/2009-06658","title":"Adaptive urban bimodal (car and transit) transportation system management in saturated and oversaturated conditions","year":2009,"lang":"en","type":"dissertation","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Heritage; University of Toronto; Library and Archives Canada","funders":"University of Ottawa","keywords":"Transit (satellite); Transport engineering; Bus rapid transit; Geography; Engineering; Public transport","score_opus":0.008976230638173405,"score_gpt":0.25889169504711224,"score_spread":0.24991546440893883,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W620915859","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9353045,0.0007089129,0.00068661256,0.00024403406,0.00047926747,0.00188295,0.0003096376,0.00049357116,0.059890486],"genre_scores_gemma":[0.9891782,0.0004980401,0.0006872022,0.0000372107,0.000031052015,0.00003490542,0.0067054117,0.000018412607,0.0028096086],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9985732,0.00010729194,0.00038084015,0.00040094476,0.00030757373,0.00023017266],"domain_scores_gemma":[0.9995003,0.00004207173,0.00015371344,0.00007035497,0.00012493056,0.00010862602],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00017756587,0.00023204194,0.0002766256,0.00035164613,0.0003265698,0.000094388626,0.00006419784,0.0003607831,0.000026379537],"category_scores_gemma":[0.000004270943,0.00024858819,0.00004036983,0.0005119126,0.000065620814,0.00030673805,4.188298e-7,0.00020805231,0.000002034769],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0023569558,0.0003725959,0.013194398,0.001601963,0.00076155027,0.00049540057,0.30363357,0.010166054,0.00035101245,0.64835685,0.0027067596,0.0160029],"study_design_scores_gemma":[0.0014819993,0.00008028753,0.8755962,0.0006266771,0.00033122726,8.4893196e-7,0.1189545,0.0015191585,0.00003953446,0.00026144434,0.0005495106,0.000558568],"about_ca_topic_score_codex":0.0032990237,"about_ca_topic_score_gemma":0.048895575,"teacher_disagreement_score":0.86240184,"about_ca_system_score_codex":0.0001017605,"about_ca_system_score_gemma":0.00009050627,"threshold_uncertainty_score":0.99999666},"labels":[],"label_agreement":null},{"id":"W621476703","doi":"","title":"ERP: DOES ONE SIZE FIT IT ALL?","year":2000,"lang":"en","type":"article","venue":"Traffic Technology International","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Electronic toll collection; Road pricing; Toll; Scheme (mathematics); Business; Norwegian; Emerging technologies; Transport engineering; Computer science; Marketing; Process management; Engineering; Traffic congestion","score_opus":0.01771630877742884,"score_gpt":0.2986634929135049,"score_spread":0.28094718413607606,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W621476703","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8574006,0.000025667998,0.00029849642,0.090248205,0.0005506081,0.00014216996,0.00003308273,0.0010870887,0.050214063],"genre_scores_gemma":[0.9753791,0.0001410603,0.0031076146,0.00058395194,0.00012539254,0.000019013796,0.00003282284,0.000007992388,0.020603048],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991175,0.000021059732,0.00018935825,0.00019834841,0.0002856241,0.00018813768],"domain_scores_gemma":[0.9996532,0.000072045346,0.00005183863,0.0000988858,0.00008312403,0.000040875195],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00015068067,0.00007614235,0.00008766931,0.00013517065,0.00016783985,0.00003590666,0.00035602908,0.00020887345,0.010911295],"category_scores_gemma":[0.000105134604,0.00007135318,0.000037993792,0.00023607521,0.00023966598,0.00013740065,0.000004725443,0.00015490018,0.0002834601],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030211595,0.0012296968,0.03066569,0.000016743757,0.00069280324,0.00010198699,0.040687386,0.07887882,0.00030799196,0.26107153,0.04536802,0.54067725],"study_design_scores_gemma":[0.00085511303,0.000048344908,0.010986129,0.000040069437,0.000035370555,0.000004821563,0.0029314733,0.001855953,0.00014852217,0.0024598283,0.98032695,0.00030741125],"about_ca_topic_score_codex":0.000060772945,"about_ca_topic_score_gemma":0.00058605004,"teacher_disagreement_score":0.93495893,"about_ca_system_score_codex":0.00005461215,"about_ca_system_score_gemma":0.000056481094,"threshold_uncertainty_score":0.98999286},"labels":[],"label_agreement":null},{"id":"W621949671","doi":"","title":"Transitways and the RouteAhead for Calgary Transit","year":2013,"lang":"en","type":"article","venue":"2013 CONFERENCE AND EXHIBITION OF THE TRANSPORTATION ASSOCIATION OF CANADA - TRANSPORTATION: BETTER - FASTER - SAFER","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Bus rapid transit; Public transport; Transport engineering; Context (archaeology); Service (business); Transit (satellite); Payment; Business; Engineering; Geography; Marketing; Finance","score_opus":0.009740797186438128,"score_gpt":0.20815555655833967,"score_spread":0.19841475937190153,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W621949671","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8824479,0.00016948246,0.03537524,0.07712234,0.00050834205,0.0024214403,0.00085169467,0.00005654366,0.0010470179],"genre_scores_gemma":[0.9960595,0.00016129958,0.00083996804,0.0011656877,0.00005404959,0.00015042772,0.00035405112,0.000019180477,0.0011958323],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9975846,0.00019850142,0.00086624175,0.000282808,0.0007620174,0.0003058609],"domain_scores_gemma":[0.9979381,0.00038301796,0.00060766045,0.00016234595,0.00079125154,0.000117638396],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00061649346,0.0002167217,0.0003977622,0.000076733806,0.00051840866,0.0000616945,0.00018408388,0.00018469631,0.0002842768],"category_scores_gemma":[0.000031309188,0.00016669268,0.00015370699,0.00024367093,0.00033596542,0.00056534994,9.706869e-7,0.00016764794,7.60421e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0019056536,0.0004220955,0.35124096,0.0015215407,0.0013117499,0.000004144959,0.2807504,0.00847877,0.0035254522,0.29289705,0.03690408,0.021038096],"study_design_scores_gemma":[0.0053078774,0.00006433685,0.9715845,0.00021827461,0.000474338,3.4295266e-7,0.00564149,0.0010800108,0.00093585317,0.0044180094,0.009845076,0.00042986328],"about_ca_topic_score_codex":0.13897245,"about_ca_topic_score_gemma":0.63003874,"teacher_disagreement_score":0.62034357,"about_ca_system_score_codex":0.00006599664,"about_ca_system_score_gemma":0.00046840424,"threshold_uncertainty_score":0.8667612},"labels":[],"label_agreement":null},{"id":"W622537145","doi":"","title":"Stop watch: ITS Spain focus: part 5","year":2005,"lang":"en","type":"article","venue":"Traffic Technology International","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Service (business); Focus (optics); Engineering; Global Positioning System; Intelligent transportation system; Transport engineering; The Internet; Telecommunications; Computer security; Automatic vehicle location; Interdependence; Computer science; World Wide Web","score_opus":0.017224403723376455,"score_gpt":0.2899693642490758,"score_spread":0.27274496052569935,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W622537145","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.70758617,0.00021274455,0.006812325,0.23368728,0.0014748775,0.00029189474,0.00004970214,0.0023853914,0.04749963],"genre_scores_gemma":[0.9900873,0.00007346327,0.002128479,0.00018279595,0.0003206261,0.000020870068,0.000036494,0.0000082102315,0.007141769],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99913764,0.000026811596,0.0001883624,0.00018614596,0.00026530403,0.00019575038],"domain_scores_gemma":[0.9996367,0.00003641298,0.00007145377,0.000085799846,0.00012701421,0.00004262168],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00025048832,0.000076405326,0.00007855768,0.0001788297,0.00018545591,0.000028520111,0.00032686602,0.00019480236,0.0010655456],"category_scores_gemma":[0.00012492067,0.000082326864,0.000035837766,0.00026628003,0.00015165767,0.0001807403,0.000008212805,0.00015580254,0.00028665728],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000046578843,0.00030104612,0.010280408,0.000004192577,0.000122738,0.000024391209,0.015139076,0.056546975,0.00008317534,0.47270805,0.026365636,0.41837773],"study_design_scores_gemma":[0.0004852932,0.000023961213,0.0011635597,0.000021095053,0.000012169253,0.0000041068124,0.0021788892,0.0031456596,0.00047376237,0.0003746007,0.9919362,0.00018068246],"about_ca_topic_score_codex":0.000014466781,"about_ca_topic_score_gemma":0.0004741709,"teacher_disagreement_score":0.96557057,"about_ca_system_score_codex":0.00008367302,"about_ca_system_score_gemma":0.00007019033,"threshold_uncertainty_score":0.9998476},"labels":[],"label_agreement":null},{"id":"W622905561","doi":"","title":"Issues and Strategies Involved in Developing Agent-Based Multimodal Network Simulation Model for Transportation Planning: Lessons from a Case Study on the Troronto and Hamilton Area","year":2013,"lang":"en","type":"article","venue":"2013 CONFERENCE AND EXHIBITION OF THE TRANSPORTATION ASSOCIATION OF CANADA - TRANSPORTATION: BETTER - FASTER - SAFER","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Network simulation; Transit (satellite); Computer science; Geocoding; Network planning and design; Transport engineering; Flow network; Multimodal transport; Transport network; Street network; Engineering; Public transport; Computer network; Geography","score_opus":0.051476490213024535,"score_gpt":0.29101556354133934,"score_spread":0.2395390733283148,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W622905561","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.961857,0.000045007353,0.027572775,0.008496006,0.00010166322,0.0013922716,0.0004910561,0.000025015905,0.00001918835],"genre_scores_gemma":[0.99718505,0.000036442314,0.0013378224,0.0005482836,0.00003256489,0.00019193067,0.0005610515,0.000020066253,0.00008681378],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99769,0.00019512966,0.0008542231,0.00036946943,0.00060665444,0.00028450598],"domain_scores_gemma":[0.99808544,0.0005453805,0.0006453133,0.00014365227,0.00049516675,0.00008501901],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046816934,0.00025605626,0.00036187758,0.00009967054,0.00046654313,0.00009899429,0.00011191982,0.00017437626,0.000041481333],"category_scores_gemma":[0.00002225544,0.0002169927,0.00006508466,0.00022257153,0.00012033272,0.00065382017,8.743062e-7,0.00016212158,1.3768694e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018627153,0.00011356501,0.4487806,0.000095683754,0.00010989166,0.0000072173693,0.10306457,0.4429385,0.00011926528,0.0038000236,0.00040805017,0.00037636817],"study_design_scores_gemma":[0.0017949187,0.00007079448,0.89046174,0.0002564466,0.00015505232,8.3278195e-8,0.033846207,0.07153809,0.0000931911,0.0013625673,0.00013955374,0.00028132185],"about_ca_topic_score_codex":0.17296144,"about_ca_topic_score_gemma":0.85713404,"teacher_disagreement_score":0.6841726,"about_ca_system_score_codex":0.00010600089,"about_ca_system_score_gemma":0.00045085218,"threshold_uncertainty_score":0.8848706},"labels":[],"label_agreement":null},{"id":"W623111192","doi":"","title":"DRIVERLESS RAPID TRANSIT SYSTEMS TAKE HOLD","year":2001,"lang":"en","type":"article","venue":"Rail Transit ConferenceAmerican Public Transportation Association","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Transit (satellite); Public transport; Procurement; Transport engineering; Transit system; Service (business); Information system; Rapid transit; Business; Geography; Telecommunications; Engineering; Marketing; Electrical engineering","score_opus":0.029408667240203218,"score_gpt":0.26034573032302283,"score_spread":0.2309370630828196,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W623111192","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.35442725,0.00044736674,0.54778004,0.028251708,0.0026308517,0.0022782509,0.00080241385,0.0025473156,0.060834832],"genre_scores_gemma":[0.99244964,0.0013520902,0.00053073175,0.00045987952,0.00025805653,0.00011199179,0.001641461,0.00004948681,0.0031466554],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9954824,0.000531335,0.0009317704,0.00064536807,0.0015204911,0.00088865915],"domain_scores_gemma":[0.9972015,0.00029665502,0.0007026766,0.00025820354,0.0010875425,0.0004534363],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0012792443,0.00037080306,0.0005579534,0.00041624386,0.0009498999,0.00048736567,0.00040102203,0.00042466723,0.001056574],"category_scores_gemma":[0.00013188072,0.00043467165,0.00024965336,0.0017706511,0.00022649909,0.0013535345,0.0000010057107,0.0003926585,0.00006891357],"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":[0.0002864705,0.00086466345,0.51811516,0.00012253376,0.00072174706,0.0000785083,0.11436083,0.024161374,0.00070389203,0.27382702,0.0035817863,0.06317603],"study_design_scores_gemma":[0.0030355218,0.00016394143,0.45968673,0.000101412414,0.00034462003,0.0000026276161,0.029695423,0.002623864,0.000032577613,0.00029876875,0.5027611,0.001253384],"about_ca_topic_score_codex":0.003457311,"about_ca_topic_score_gemma":0.00783322,"teacher_disagreement_score":0.6380224,"about_ca_system_score_codex":0.0004971409,"about_ca_system_score_gemma":0.00093269144,"threshold_uncertainty_score":0.9998566},"labels":[],"label_agreement":null},{"id":"W624253976","doi":"","title":"Critical Parameters Governing the Planning of Calgary's Airport Trail as an Effective East-West Corridor","year":2013,"lang":"en","type":"article","venue":"2013 CONFERENCE AND EXHIBITION OF THE TRANSPORTATION ASSOCIATION OF CANADA - TRANSPORTATION: BETTER - FASTER - SAFER","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Runway; Transport engineering; International airport; Redevelopment; Lease; Prioritization; SAFER; Engineering; Geometric design; Civil engineering; Computer science; Business; Geography; Computer security","score_opus":0.012430128369302029,"score_gpt":0.2383736294783908,"score_spread":0.22594350110908878,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W624253976","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9855705,0.000035742287,0.004569485,0.007383374,0.00037044415,0.00081192737,0.00034850783,0.00003131841,0.000878684],"genre_scores_gemma":[0.9976387,0.000022566339,0.00066432735,0.0006582584,0.00004234302,0.0000878117,0.00042995837,0.000020946247,0.0004350865],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99685466,0.00031221673,0.0009804725,0.00031733417,0.0012023076,0.00033300137],"domain_scores_gemma":[0.99714005,0.00044160444,0.00094678695,0.00021322673,0.0010997037,0.00015862941],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00058309507,0.00023953558,0.00039947726,0.00009052025,0.0004323945,0.00006060563,0.00025969144,0.00019947307,0.0006467432],"category_scores_gemma":[0.000106341104,0.00019675623,0.00014233652,0.00032621308,0.0003499593,0.0009418822,0.0000018669178,0.00025625728,0.000002746498],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035830302,0.00032139535,0.8170148,0.00042650467,0.0004028544,0.000008105608,0.13043006,0.013608204,0.0022046536,0.024323663,0.0042368504,0.0066645932],"study_design_scores_gemma":[0.00076319906,0.000107128115,0.9870995,0.00024404719,0.00021155842,3.9645744e-7,0.0080858255,0.00053783244,0.0010556963,0.0007897079,0.0008561636,0.0002489223],"about_ca_topic_score_codex":0.17555834,"about_ca_topic_score_gemma":0.37386698,"teacher_disagreement_score":0.19830863,"about_ca_system_score_codex":0.00010753656,"about_ca_system_score_gemma":0.00060405285,"threshold_uncertainty_score":0.8299317},"labels":[],"label_agreement":null},{"id":"W626090880","doi":"","title":"Urban transport patterns in a global sample of cities & their linkages to transport infrastructure, land use, economics & environment","year":2002,"lang":"en","type":"article","venue":"World Transport Policy and Practice","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Metropolitan area; Regional science; Context (archaeology); Sustainability; Public transport; Urban planning; Sustainable transport; Geography; China; Latin Americans; Land use; Transportation planning; Environmental planning; Sample (material); Sustainable development; Economic growth; Economic geography; Transport engineering; Political science; Economics; Engineering; Civil engineering","score_opus":0.027204114977124303,"score_gpt":0.2751254148252774,"score_spread":0.24792129984815311,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W626090880","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97664845,0.00019452252,0.0041348096,0.004983119,0.000119067925,0.00051604083,0.0020367792,0.000073902695,0.011293312],"genre_scores_gemma":[0.9921631,0.002840199,0.0030001756,0.0009407196,0.00014019996,0.000020214504,0.00018189034,0.000018905677,0.00069460075],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99835116,0.00011856869,0.0005628969,0.00035200786,0.00022918449,0.0003862092],"domain_scores_gemma":[0.99883306,0.0005379602,0.00018572542,0.00018235647,0.000038248363,0.00022262946],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047461764,0.00023047415,0.00034747078,0.0002030386,0.0001894219,0.000026185093,0.00016638605,0.00013092688,0.0002757773],"category_scores_gemma":[0.00006738889,0.00024165394,0.000086302,0.00048432068,0.00017183601,0.00077522616,0.0000025773325,0.00019232435,0.0000028192806],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013474835,0.00010983574,0.9375758,0.000037295224,0.000045003744,0.000014340298,0.038634855,0.012738011,0.000002084497,0.008479269,0.000053475997,0.0021752638],"study_design_scores_gemma":[0.00048617044,0.000044433575,0.770673,0.000047553054,0.00006538753,0.0000029421672,0.0015606433,0.000045856534,0.0000081841235,0.00023656109,0.2265947,0.00023458555],"about_ca_topic_score_codex":0.027449746,"about_ca_topic_score_gemma":0.04961361,"teacher_disagreement_score":0.22654122,"about_ca_system_score_codex":0.000093662304,"about_ca_system_score_gemma":0.000107134656,"threshold_uncertainty_score":0.98543614},"labels":[],"label_agreement":null},{"id":"W626437776","doi":"","title":"BRT Projects Grow, Systems Diversify","year":2006,"lang":"en","type":"article","venue":"Metrologia","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Bus rapid transit; Revenue; Business; Capital expenditure; Capital cost; Transport engineering; Order (exchange); Agency (philosophy); Plan (archaeology); Service (business); Computer science; Finance; Operations management; Marketing; Economics; Geography; Engineering; Public transport","score_opus":0.022232549672234637,"score_gpt":0.2612653837933143,"score_spread":0.23903283412107967,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W626437776","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.785223,0.0004072431,0.015039172,0.00083710573,0.0012400522,0.00039350876,0.000019445377,0.0006191752,0.1962213],"genre_scores_gemma":[0.9954154,0.000008405644,0.0006938957,0.000046302903,0.0001504496,0.000008024683,0.000031794272,0.0000033964934,0.0036423246],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9993436,0.00008778439,0.00009717699,0.000115318544,0.00019303452,0.00016312755],"domain_scores_gemma":[0.99976194,0.000050114835,0.000051835945,0.000055800887,0.000054154225,0.000026179086],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003376628,0.000046687044,0.00007251259,0.00006842701,0.00026979134,0.000049197428,0.0000919115,0.000080052356,0.000042893415],"category_scores_gemma":[0.000046759247,0.0000429456,0.000024398949,0.0002604462,0.000064710286,0.00010326866,0.0000031644065,0.000051622752,0.000046367284],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022692268,0.000058904658,0.85989225,0.000013608649,0.0000149606685,0.000017626702,0.002489545,0.0166214,0.000073453375,0.10149328,0.019017404,0.0002848717],"study_design_scores_gemma":[0.00086948264,0.00012798164,0.81987226,0.000020833075,0.00006657443,0.0000012311523,0.0062103844,0.0017444241,0.00008698801,0.00074300735,0.16986322,0.00039359147],"about_ca_topic_score_codex":0.007227524,"about_ca_topic_score_gemma":0.00056624966,"teacher_disagreement_score":0.21019241,"about_ca_system_score_codex":0.000035544017,"about_ca_system_score_gemma":0.000037485817,"threshold_uncertainty_score":0.99938345},"labels":[],"label_agreement":null},{"id":"W627937787","doi":"","title":"THE VALUE OF THE RARF FREEWAY SYSTEM: A CORPORATE-STYLE FINANCIAL ANALYSIS","year":2000,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Mile; Revenue; Transport engineering; Finance; Business; Highway system; Quarter (Canadian coin); Capital (architecture); Engineering; Geography","score_opus":0.012034800384302075,"score_gpt":0.2338615665284756,"score_spread":0.22182676614417352,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W627937787","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.79604995,0.00010270475,0.0052115396,0.0016547019,0.00031461287,0.00031488686,0.000030759722,0.00013080442,0.19619003],"genre_scores_gemma":[0.9859718,0.00002339069,0.00021497415,0.000047812293,0.000036378522,0.000004504151,0.0000057569855,0.0000021415372,0.013693246],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9992471,0.00014718351,0.00016842819,0.00008027639,0.00025334067,0.00010367923],"domain_scores_gemma":[0.9995675,0.00008791346,0.00011694215,0.0001352461,0.00006682735,0.000025568499],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044777576,0.000040640865,0.00008381862,0.000024029116,0.0006625803,0.000034844154,0.00019644345,0.000045446563,0.00026210136],"category_scores_gemma":[0.000042325086,0.000021983158,0.00009429328,0.0009564118,0.00013842135,0.00005388301,0.0000022121806,0.00004006335,0.000012086731],"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":[0.000054415814,0.000051792504,0.14906655,0.000011598729,0.00017502731,0.0000016249835,0.021766553,0.27167392,0.000015379806,0.547373,0.0050580655,0.0047520655],"study_design_scores_gemma":[0.0005201162,0.00003542107,0.891005,0.000045896693,0.000759697,3.0339865e-7,0.011465337,0.029622883,0.00024517748,0.000954532,0.06507084,0.00027484001],"about_ca_topic_score_codex":0.0042054583,"about_ca_topic_score_gemma":0.008892346,"teacher_disagreement_score":0.7419384,"about_ca_system_score_codex":0.000017271188,"about_ca_system_score_gemma":0.00011923223,"threshold_uncertainty_score":0.6357423},"labels":[],"label_agreement":null},{"id":"W628359784","doi":"10.22215/etd/2009-09012","title":"Estimation of direct and indirect energy requirements for bus rapid transit (BRT) and light rail transit (LRT)","year":2009,"lang":"en","type":"dissertation","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Government of Ontario","keywords":"Bus rapid transit; Transit (satellite); Light rail transit; Transport engineering; Computer science; Engineering; Public transport","score_opus":0.01608955260146262,"score_gpt":0.28676846625250557,"score_spread":0.27067891365104296,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W628359784","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.35853586,0.01812855,0.15561573,0.0033799612,0.0029050605,0.0060596154,0.0008521936,0.001399353,0.45312366],"genre_scores_gemma":[0.98057455,0.0024210706,0.004338631,0.0000794083,0.00007213793,0.000052360174,0.003331202,0.000031241994,0.009099406],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99857485,0.00008289458,0.00043566516,0.0003497915,0.00034512905,0.00021169164],"domain_scores_gemma":[0.9992896,0.00012023283,0.0002517705,0.00008484751,0.00015561041,0.00009796626],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037337435,0.00021581582,0.00039521145,0.00023939447,0.00033211734,0.00006484732,0.0000855513,0.00034250735,0.000047947306],"category_scores_gemma":[0.000052777308,0.00021911577,0.00007887357,0.00025709605,0.000052481755,0.000277987,7.2433016e-7,0.0000656413,3.0948974e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011293686,0.00027610175,0.00038557698,0.000973286,0.00036302,0.0000049663086,0.10199113,0.005175932,0.0015371034,0.015409869,0.0030361884,0.8697175],"study_design_scores_gemma":[0.032600522,0.0063671935,0.25277555,0.008282675,0.010596226,0.000012241087,0.07513365,0.051110923,0.21450387,0.030104121,0.30608815,0.012424853],"about_ca_topic_score_codex":0.0007739996,"about_ca_topic_score_gemma":0.0071258466,"teacher_disagreement_score":0.8572926,"about_ca_system_score_codex":0.000027606651,"about_ca_system_score_gemma":0.00014140886,"threshold_uncertainty_score":0.89352816},"labels":[],"label_agreement":null},{"id":"W629811779","doi":"","title":"Time-Based Model of Passenger Dispersion on Subway Platforms","year":2015,"lang":"en","type":"article","venue":"Transportation Research Board 94th Annual MeetingTransportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Train; Dwell time; Dispersion (optics); Computer science; Limiting; Transport engineering; Simulation; Engineering; Geography","score_opus":0.1161622513018408,"score_gpt":0.39752192984486373,"score_spread":0.2813596785430229,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W629811779","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97817844,0.00009751056,0.004417075,0.003139599,0.00022091279,0.0020115497,0.0008049935,0.00041324383,0.010716652],"genre_scores_gemma":[0.99037874,0.00015995136,0.0040619806,0.00012691486,0.00018016217,0.00023989985,0.0012881155,0.00010277577,0.0034614787],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.986678,0.00110525,0.0013680119,0.0010523883,0.008048123,0.0017481947],"domain_scores_gemma":[0.98895705,0.0016018633,0.00034872568,0.00061260915,0.007158969,0.0013207999],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.012013616,0.00042585522,0.0006165239,0.0019285755,0.0013848046,0.00014642673,0.00087442854,0.0005650622,0.00036633635],"category_scores_gemma":[0.0011023056,0.00042627333,0.00028833118,0.003174591,0.0019110888,0.0010671085,0.0000067862647,0.0015070465,0.00025560107],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00578185,0.0013940639,0.05856283,0.00041547292,0.00012478068,0.00009993138,0.10273432,0.75404376,0.0020200468,0.049593642,0.023246506,0.001982788],"study_design_scores_gemma":[0.028277002,0.009746889,0.31475213,0.0038008925,0.00045070634,5.697913e-7,0.27105907,0.20918941,0.023385512,0.023651935,0.109893814,0.005792076],"about_ca_topic_score_codex":0.008704568,"about_ca_topic_score_gemma":0.018127058,"teacher_disagreement_score":0.54485434,"about_ca_system_score_codex":0.00041503186,"about_ca_system_score_gemma":0.002734618,"threshold_uncertainty_score":0.99991524},"labels":[],"label_agreement":null},{"id":"W631880921","doi":"","title":"Active transit signal priority for streetcars: experience in Melbourne and Toronto","year":2008,"lang":"en","type":"article","venue":"Transportation Research Board 87th Annual MeetingTransportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Public transport; Transport engineering; Traffic congestion; Bus priority; Operations research; Transit (satellite); Computer science; Traffic signal; Futures contract; Engineering; Business; Real-time computing; Finance","score_opus":0.09827760439205936,"score_gpt":0.4243807061306364,"score_spread":0.32610310173857704,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W631880921","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9881784,0.0005210416,0.0037558107,0.0015589299,0.00015110456,0.0032030323,0.00059744134,0.00022455341,0.0018097241],"genre_scores_gemma":[0.99008673,0.0026718092,0.0043990356,0.000065363936,0.00021960911,0.0010219153,0.0005029266,0.00007004925,0.0009625496],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.99067134,0.001343799,0.0011846218,0.001228695,0.003679307,0.0018922612],"domain_scores_gemma":[0.993296,0.0018746788,0.00019638901,0.00032922524,0.0034737461,0.00082990975],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.005787619,0.00038627695,0.00061287096,0.00082578085,0.0026569576,0.000150302,0.0005810076,0.00047660392,0.00038067077],"category_scores_gemma":[0.00047132338,0.0004368768,0.00018473122,0.0018097576,0.0019433673,0.0021444294,0.000005883423,0.0011247152,0.000010561484],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0036817156,0.0006914582,0.29435524,0.00039251804,0.00009067802,0.00021553946,0.666253,0.0034204032,0.0010263101,0.01697031,0.0015247638,0.011378085],"study_design_scores_gemma":[0.0032640216,0.00059989543,0.8049741,0.00025577983,0.000028709579,6.030472e-7,0.16469978,0.0004575558,0.00097318867,0.0010488594,0.02305638,0.00064117677],"about_ca_topic_score_codex":0.109428115,"about_ca_topic_score_gemma":0.42243513,"teacher_disagreement_score":0.5106188,"about_ca_system_score_codex":0.00047780454,"about_ca_system_score_gemma":0.0013152192,"threshold_uncertainty_score":0.9998083},"labels":[],"label_agreement":null},{"id":"W633061063","doi":"","title":"BRT - MODE DU JOUR OR LONG-TERM SOLUTION?","year":2002,"lang":"fr","type":"article","venue":"Metrologia","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Bus rapid transit; Miami; Transport engineering; Light rail transit; Public transport; Transit (satellite); Term (time); Business; Telecommunications; Computer science; Engineering; Environmental science","score_opus":0.05114738599113314,"score_gpt":0.3046912819960613,"score_spread":0.2535438960049282,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W633061063","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7780138,0.010029247,0.13398306,0.041416667,0.0069100843,0.00044875135,0.0001147123,0.0005212168,0.028562455],"genre_scores_gemma":[0.93572575,0.0016312215,0.0029657886,0.0005637037,0.0005670616,0.00000967251,0.000031651565,0.000013206897,0.05849193],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99848413,0.0002381324,0.00028143174,0.0002646122,0.00030683112,0.00042485265],"domain_scores_gemma":[0.99934375,0.00014583884,0.00014043531,0.00013768974,0.00010044945,0.00013185399],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003853894,0.00014847529,0.00019488203,0.00009880121,0.000527936,0.00007735584,0.00021491598,0.00026800614,0.008007351],"category_scores_gemma":[0.00017569847,0.00013447447,0.000090316855,0.00061507407,0.0003028992,0.00033305204,0.000009569048,0.00019762991,0.0007134901],"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":[0.000108816275,0.00063685176,0.845219,0.00006482363,0.00015106381,0.0001987201,0.013037078,0.031420853,0.000040058112,0.013711597,0.06387458,0.031536542],"study_design_scores_gemma":[0.002140888,0.00068399665,0.80286163,0.00012715039,0.00044211076,0.00003344892,0.0008701691,0.11151135,0.000054233908,0.00062321726,0.07968659,0.00096519693],"about_ca_topic_score_codex":0.00067257305,"about_ca_topic_score_gemma":0.001580747,"teacher_disagreement_score":0.15771195,"about_ca_system_score_codex":0.00011455557,"about_ca_system_score_gemma":0.00004552394,"threshold_uncertainty_score":0.9928995},"labels":[],"label_agreement":null},{"id":"W633210036","doi":"","title":"TORONTO INT'L AIRPORT INSTALLS POF PARKING REVENUE CONTROL SYSTEM","year":2003,"lang":"en","type":"article","venue":"Parking Today","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Ticket; Payment; Credit card; Revenue; Business; Debit card; Smart card; Transport engineering; Finance; Advertising; Computer security; Computer science; Engineering","score_opus":0.013115351449157412,"score_gpt":0.2655379637818338,"score_spread":0.2524226123326764,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W633210036","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13665313,0.0032901682,0.06443795,0.00083194475,0.005204204,0.0013209401,0.00009023395,0.0011787949,0.7869926],"genre_scores_gemma":[0.9969062,0.000089471636,0.0011534584,0.00014076797,0.00017416761,0.00002481771,0.000015395884,0.000016139704,0.0014795797],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99853855,0.00028924108,0.00028677747,0.00022932405,0.00034662074,0.0003094538],"domain_scores_gemma":[0.9993584,0.00011680045,0.0001720749,0.00015314727,0.00008858645,0.000110949666],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012544721,0.00011263247,0.00018032962,0.000031704163,0.00052836206,0.000083721716,0.00013627358,0.000113687856,0.0001123906],"category_scores_gemma":[0.00018294575,0.00011965921,0.00006157341,0.00014351959,0.000056869834,0.00025247576,0.0000030793158,0.000092953676,0.000026042062],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001221068,0.00020484766,0.4236384,0.00022732024,0.00022915065,0.00014696918,0.10273281,0.011891251,0.00040537564,0.43094856,0.01522294,0.014230271],"study_design_scores_gemma":[0.0011332091,0.00003485695,0.022946116,0.00041016255,0.00008276221,0.0000059481713,0.01249017,0.00049340754,0.00009517975,0.00035194526,0.96144754,0.0005087105],"about_ca_topic_score_codex":0.0028309822,"about_ca_topic_score_gemma":0.005383073,"teacher_disagreement_score":0.9462246,"about_ca_system_score_codex":0.00023720392,"about_ca_system_score_gemma":0.00015571015,"threshold_uncertainty_score":0.4879561},"labels":[],"label_agreement":null},{"id":"W634294833","doi":"","title":"Targeting Markets: Targeting Different Market Segments Makes This Agency's Numbers Grow","year":2007,"lang":"en","type":"article","venue":"Mass transit","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Transit (satellite); Schedule; Agency (philosophy); Business; Transport engineering; Transit system; Public transport; Transit-oriented development; Rail transit; Shore; Finance; Agricultural economics; Marketing; Engineering; Economics","score_opus":0.010934670793889082,"score_gpt":0.25705225474257415,"score_spread":0.24611758394868508,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W634294833","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3853596,0.00060569664,0.29053766,0.0020438053,0.0021074803,0.0008419351,0.0000690139,0.0008540365,0.3175808],"genre_scores_gemma":[0.9845004,0.00018230725,0.007842131,0.00020559004,0.00022478297,0.000007149966,0.00012512879,0.000029068435,0.0068834517],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99790347,0.00018954408,0.00041526277,0.0003242264,0.0005854384,0.00058207277],"domain_scores_gemma":[0.9993152,0.00018995718,0.00012130644,0.00010345415,0.00008491301,0.00018516152],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0015787897,0.00019064109,0.00019358342,0.000102896316,0.00064928737,0.000094320145,0.00020226695,0.00015929696,0.003286135],"category_scores_gemma":[0.00008362656,0.00019225974,0.00010430164,0.0003062924,0.00010087787,0.0002602605,0.0000038847056,0.00019199413,0.000032564734],"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":[0.0010267509,0.0005211855,0.68184495,0.00039809465,0.0003888894,0.0003414585,0.18847261,0.005907891,0.01191682,0.0034214682,0.07907833,0.026681561],"study_design_scores_gemma":[0.004278885,0.00014390225,0.6482423,0.00051957555,0.0004224018,0.0000031522445,0.041766573,0.006329976,0.0039469055,0.0017176225,0.28965053,0.002978195],"about_ca_topic_score_codex":0.0003961496,"about_ca_topic_score_gemma":0.00038051992,"teacher_disagreement_score":0.5991408,"about_ca_system_score_codex":0.00009900307,"about_ca_system_score_gemma":0.00004123333,"threshold_uncertainty_score":0.997625},"labels":[],"label_agreement":null},{"id":"W634929579","doi":"","title":"Bacs of university students returning home at night","year":2000,"lang":"en","type":"article","venue":"Proceedings International Council on Alcohol, Drugs and Traffic Safety Conference","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Quarter (Canadian coin); Psychology; Test (biology); Sample (material); Geography; Archaeology","score_opus":0.04171907640449652,"score_gpt":0.26745045885708535,"score_spread":0.22573138245258884,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W634929579","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.85045576,0.000024065293,0.000023291836,0.0008625914,0.00014572132,0.00010633825,0.00003373745,0.000069571455,0.1482789],"genre_scores_gemma":[0.98600125,0.0012664851,0.0001785172,0.00009017984,0.00005273468,0.0000013083161,0.000024632225,0.0000068107797,0.012378083],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99802893,0.000021542099,0.00021681299,0.00027599547,0.0012927883,0.00016389739],"domain_scores_gemma":[0.9987147,0.00009030959,0.00013987528,0.000042161242,0.0009159591,0.00009699727],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0008374085,0.00011615509,0.00014508932,0.00006826805,0.000370586,0.000076035845,0.00031983992,0.0000858027,0.000959827],"category_scores_gemma":[0.00008402036,0.00012213095,0.00004320865,0.00017718879,0.00018857593,0.0002955991,0.000016333743,0.00012307252,0.000020868807],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.008204965,0.0010476813,0.16946384,0.00016342872,0.0006683472,0.0000342365,0.34702837,0.01043031,0.0005229612,0.35796717,0.006188362,0.09828031],"study_design_scores_gemma":[0.0065486734,0.00030322815,0.20179665,0.00071728154,0.00014855678,0.000008612369,0.02310742,0.009342452,0.00019913618,0.00053453376,0.75623703,0.0010564427],"about_ca_topic_score_codex":0.000102594306,"about_ca_topic_score_gemma":0.00012894328,"teacher_disagreement_score":0.75004864,"about_ca_system_score_codex":0.0003806586,"about_ca_system_score_gemma":0.00024073964,"threshold_uncertainty_score":0.99995345},"labels":[],"label_agreement":null},{"id":"W635323861","doi":"","title":"2005 OUTLOOK : GOOD TIMES, BUT NO BOOM","year":2005,"lang":"en","type":"article","venue":"Better roads","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Boom; Government (linguistics); State (computer science); State government; Economics; Business; Public economics; Regional science; Local government; Engineering; Geography; Political science; Public administration; Computer science; Environmental engineering","score_opus":0.011219079839662997,"score_gpt":0.261188149060963,"score_spread":0.2499690692213,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W635323861","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.121893756,0.00040741696,0.00712153,0.04335457,0.0009842801,0.0004058713,0.000048295293,0.00075043493,0.82503384],"genre_scores_gemma":[0.7929652,0.00005367069,0.012978509,0.0053756614,0.0018450305,0.000016831425,0.000110121466,0.000021986805,0.186633],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99918497,0.000047013003,0.00014212696,0.00014667188,0.00023658854,0.00024260556],"domain_scores_gemma":[0.99969316,0.000023829221,0.000046360426,0.00010140974,0.000046550453,0.00008871387],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00021386893,0.00007203504,0.000077489036,0.00004247583,0.00022998886,0.000059106766,0.00012338159,0.000079693295,0.0019090817],"category_scores_gemma":[0.000018762597,0.000073495285,0.000039852388,0.00008243501,0.00004918157,0.00023480838,0.000004244869,0.00007853305,0.0014503504],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000040785402,0.00021765035,0.15101378,0.00001433526,0.000066293935,0.000018301393,0.022873603,0.006107266,0.0002538858,0.010247757,0.7478919,0.061254445],"study_design_scores_gemma":[0.0002094839,0.00000852009,0.016083889,0.000008427555,0.000012068203,2.661456e-7,0.00012946727,0.00034751775,0.000077713536,0.00005747144,0.98294187,0.00012330821],"about_ca_topic_score_codex":0.0003723566,"about_ca_topic_score_gemma":0.00067472976,"teacher_disagreement_score":0.6710714,"about_ca_system_score_codex":0.000037597874,"about_ca_system_score_gemma":0.00006212311,"threshold_uncertainty_score":0.9993271},"labels":[],"label_agreement":null},{"id":"W635869933","doi":"","title":"LET IT RIDE : TRANSIT AND TOURIST ROADS ARE PLACING THEIR PASSENGER- GROWTH CHIPS ON NEW MARKETING STRATEGIES, CAPACITY ADDITIONS AND CREATURE COMFORTS","year":2003,"lang":"en","type":"article","venue":"Progressive railroading","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Tourism; Revenue; Transport engineering; Business; Restructuring; Transit (satellite); Cruise; Ticket; Monorail; Destinations; NonStop; Advertising; Marketing; Public transport; Engineering; Geography; Finance; Computer science; Civil engineering","score_opus":0.02479905758654104,"score_gpt":0.2638666546703538,"score_spread":0.23906759708381276,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W635869933","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9568733,0.0014302866,0.0026169042,0.0034199893,0.00024068893,0.00067284395,0.000127249,0.00029103766,0.034327723],"genre_scores_gemma":[0.99645925,0.00020934644,0.0027076865,0.00013717565,0.00015777093,0.000021128757,0.000057932335,0.000018703944,0.0002309857],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9985283,0.00032406597,0.00021643225,0.00034595645,0.00024091265,0.00034431386],"domain_scores_gemma":[0.99897516,0.00041847964,0.00020230594,0.00008436008,0.00009449654,0.00022517964],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006681486,0.00019885245,0.00021499512,0.00010288384,0.001267008,0.00035818716,0.00007235037,0.00015194365,0.000102329774],"category_scores_gemma":[0.00039835606,0.00018729577,0.000043902466,0.0002495502,0.00018922484,0.0004559467,0.0000045558018,0.00030278935,8.275834e-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":[0.0007055814,0.0006020684,0.42103893,0.0007710602,0.00057977164,0.00041009806,0.3713579,0.006077146,0.0006289981,0.12725689,0.02941104,0.041160554],"study_design_scores_gemma":[0.007056576,0.0006038425,0.54194015,0.008308617,0.0006258575,0.00020148822,0.30598062,0.0025050312,0.0032211845,0.01696684,0.10841323,0.0041765976],"about_ca_topic_score_codex":0.0001845034,"about_ca_topic_score_gemma":0.0009352364,"teacher_disagreement_score":0.12090121,"about_ca_system_score_codex":0.00004664279,"about_ca_system_score_gemma":0.00012897534,"threshold_uncertainty_score":0.9744929},"labels":[],"label_agreement":null},{"id":"W636161244","doi":"","title":"Truck Parking In Urban Areas: Application of Choice Modeling Within Traffic Microsimulation","year":2014,"lang":"en","type":"article","venue":"Transportation Research Board 93rd Annual MeetingTransportation Research Board","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":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Truck; Transport engineering; Microsimulation; Parking guidance and information; Enforcement; Parking space; Computer science; Engineering; Automotive engineering","score_opus":0.052262594852185845,"score_gpt":0.3832513437804785,"score_spread":0.33098874892829266,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W636161244","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97217935,0.00013286823,0.022617785,0.0009663965,0.00019575357,0.002007196,0.00012449441,0.00028853244,0.0014875949],"genre_scores_gemma":[0.9940467,0.00017871421,0.00381577,0.00004508038,0.0002969236,0.00038625358,0.0008519533,0.00009599307,0.0002826472],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9880745,0.0025459235,0.00217352,0.0012222098,0.0044436557,0.001540226],"domain_scores_gemma":[0.992255,0.002173001,0.0004885995,0.00055866607,0.003996755,0.0005279905],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.014351702,0.00040061327,0.00065795955,0.0022132194,0.001340493,0.00018350214,0.000805113,0.00056219177,0.00008178102],"category_scores_gemma":[0.0012876253,0.00046560945,0.00021749247,0.0044385903,0.000932501,0.0012417003,0.000007447302,0.0015423443,0.00003247563],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00052035815,0.0003210863,0.2727379,0.00030440016,0.000032768978,0.000008054942,0.07345559,0.6303636,0.0010513808,0.017234156,0.00016540974,0.0038053286],"study_design_scores_gemma":[0.004489649,0.0005691247,0.5979421,0.001104405,0.00008063145,2.0202673e-7,0.050562445,0.32595578,0.0010423113,0.0030878976,0.013971971,0.0011934646],"about_ca_topic_score_codex":0.0382737,"about_ca_topic_score_gemma":0.14532967,"teacher_disagreement_score":0.32520425,"about_ca_system_score_codex":0.00033947418,"about_ca_system_score_gemma":0.00071955204,"threshold_uncertainty_score":0.99995965},"labels":[],"label_agreement":null},{"id":"W636869367","doi":"","title":"Adaptation and Implementation of a System for Collecting and Analyzing Cyclist Route Data Using Smartphones","year":2014,"lang":"en","type":"article","venue":"Transportation Research Board 93rd Annual MeetingTransportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Global Positioning System; Computer science; TRIPS architecture; Android (operating system); Adaptability; Real-time computing; Transport engineering; Data collection; Simulation; Engineering; Telecommunications; Statistics","score_opus":0.15566162998572722,"score_gpt":0.4476749960258979,"score_spread":0.2920133660401707,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W636869367","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94922376,0.00025648912,0.045901038,0.0006950655,0.00017205425,0.002160174,0.0011310772,0.00017356304,0.0002868041],"genre_scores_gemma":[0.9737674,0.00040095448,0.02369069,0.000014787253,0.00020542643,0.00016954979,0.0015717822,0.00006257231,0.00011685911],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9929681,0.0014925811,0.0012632773,0.0010438317,0.0022171456,0.0010150621],"domain_scores_gemma":[0.9928717,0.0024110363,0.00045268403,0.00042924334,0.003419859,0.00041547525],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0134894885,0.0002811018,0.0005163311,0.0011906822,0.0025118208,0.00027674757,0.0004729428,0.00026393431,0.000022905073],"category_scores_gemma":[0.0006306647,0.0003180306,0.00008545789,0.0019096403,0.00088798796,0.001379756,0.000016373757,0.0005160384,0.0000015944672],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016761654,0.00023586903,0.6444735,0.004329531,0.00034145103,0.000018924262,0.20670564,0.016068075,0.00532312,0.07979374,0.00062056136,0.04041343],"study_design_scores_gemma":[0.0050953296,0.000752653,0.514713,0.0012443612,0.00031968567,9.1204987e-7,0.41121113,0.050862055,0.0015670777,0.0012791484,0.01203907,0.00091560016],"about_ca_topic_score_codex":0.070979185,"about_ca_topic_score_gemma":0.12083787,"teacher_disagreement_score":0.20450549,"about_ca_system_score_codex":0.00018184439,"about_ca_system_score_gemma":0.00071462506,"threshold_uncertainty_score":0.99992716},"labels":[],"label_agreement":null},{"id":"W637017719","doi":"","title":"Transport revolution. International perspectives","year":2011,"lang":"fi","type":"article","venue":"Liikenneministeriön julkaisuja","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Ministry of Transport; Christian ministry; Civil servants; Business; Public administration; Engineering; Political science; Transport engineering","score_opus":0.03870515605269415,"score_gpt":0.28125296486371276,"score_spread":0.24254780881101862,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W637017719","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.059249513,0.0012718068,0.0063833697,0.0024412517,0.0061059194,0.0005596774,0.00038833107,0.00052622525,0.9230739],"genre_scores_gemma":[0.9502015,0.0017448394,0.0057558306,0.00023212144,0.0007940412,0.00003331165,0.00022250289,0.00005046582,0.0409654],"study_design_codex":"qualitative","study_design_gemma":"not_applicable","domain_scores_codex":[0.99710834,0.00015094575,0.00072712597,0.00074208976,0.0006723009,0.00059919927],"domain_scores_gemma":[0.99853396,0.000065459186,0.0003690071,0.00036397006,0.00035796477,0.00030964153],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005434906,0.00036739392,0.00035852627,0.00022108531,0.00063252583,0.000084790496,0.0007024883,0.00037977332,0.008982432],"category_scores_gemma":[0.000075449294,0.0004177932,0.0002715743,0.000547105,0.00067548745,0.0007042405,0.000024052855,0.00033593862,0.0005012488],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006788629,0.0012689638,0.07579806,0.00011344857,0.0003979128,0.00019144765,0.8214674,0.00009770834,0.00011723047,0.08660257,0.0070858765,0.0061805393],"study_design_scores_gemma":[0.0016930224,0.00041069815,0.28554267,0.00040737516,0.0003743163,0.000025933998,0.14795563,0.00040380095,0.00018381093,0.0011179736,0.5604288,0.0014559933],"about_ca_topic_score_codex":0.0011078274,"about_ca_topic_score_gemma":0.0004768923,"teacher_disagreement_score":0.890952,"about_ca_system_score_codex":0.0001753628,"about_ca_system_score_gemma":0.00026736318,"threshold_uncertainty_score":0.9998274},"labels":[],"label_agreement":null},{"id":"W637711567","doi":"","title":"Enhanced Customer Service Key to Improving Transit Systems","year":2008,"lang":"en","type":"article","venue":"Metrologia","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Workflow; Customer relationship management; Computer science; Service (business); Business; Telecommunications; Marketing; Database","score_opus":0.025885016264330166,"score_gpt":0.2687484322246362,"score_spread":0.24286341596030603,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W637711567","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8523296,0.0000800265,0.12239426,0.00092730287,0.00055953773,0.0002634155,0.0000086404125,0.00030918067,0.023128016],"genre_scores_gemma":[0.9957824,0.000013343274,0.002014201,0.00059503684,0.00009851951,0.000021936108,0.0000108653085,0.0000072256134,0.0014564719],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99913025,0.000089142064,0.0001498498,0.00017084894,0.00023311822,0.00022677735],"domain_scores_gemma":[0.99957824,0.000053621618,0.000047336358,0.000088986315,0.00013454778,0.00009726712],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003205067,0.00007041367,0.00011971621,0.00009542151,0.0003859218,0.000026814963,0.00013827573,0.00009202647,0.000065958484],"category_scores_gemma":[0.000066875764,0.00006761305,0.000027091575,0.00058580225,0.000033687058,0.00012247183,0.0000029437742,0.00007443459,0.00023157643],"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":[0.00036624822,0.0002384284,0.05641275,0.00014702277,0.00012576806,0.00008663332,0.27297148,0.60114443,0.033484895,0.020945974,0.0074650347,0.0066113356],"study_design_scores_gemma":[0.0049159336,0.0007412455,0.5626802,0.00016356859,0.0003341049,0.000019330988,0.04344533,0.01709398,0.0101497825,0.00010010847,0.35720423,0.0031522247],"about_ca_topic_score_codex":0.00346254,"about_ca_topic_score_gemma":0.00097046245,"teacher_disagreement_score":0.5840504,"about_ca_system_score_codex":0.000039487313,"about_ca_system_score_gemma":0.000071885144,"threshold_uncertainty_score":0.5234348},"labels":[],"label_agreement":null},{"id":"W637807408","doi":"","title":"Methods of Increasing Transportation Funding","year":2006,"lang":"en","type":"article","venue":"2006 ITE Annual Meeting and Exhibit Compendium of Technical PapersInstitute of Transportation Engineers (ITE)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Agency (philosophy); Public transport; State (computer science); Payment; Finance; Business; Transport engineering; Economics; Engineering; Computer science","score_opus":0.010657011787379976,"score_gpt":0.27953068059580083,"score_spread":0.26887366880842084,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W637807408","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.91781324,0.0008003287,0.069657765,0.00032063012,0.00032768404,0.0004653693,0.00066659483,0.0002663958,0.009681959],"genre_scores_gemma":[0.90727675,0.00011949157,0.091868214,0.000015251675,0.00005623698,0.000005366916,0.0005903299,0.000019877803,0.000048494006],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9976454,0.0001373049,0.0010793041,0.0003132139,0.0005143188,0.00031047725],"domain_scores_gemma":[0.9985038,0.0003883185,0.00051604374,0.00014413077,0.00032722554,0.000120505894],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0013586236,0.00022127217,0.0005046519,0.00034597574,0.00021051329,0.000014463712,0.00017390733,0.0002396003,0.000023809991],"category_scores_gemma":[0.00010682366,0.00024534136,0.00018317207,0.0006818422,0.00042061415,0.00043027996,0.000002025637,0.00018244401,4.3297285e-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":[0.0007654541,0.0007118353,0.14565797,0.001612027,0.00019299207,0.000023745752,0.025318878,0.5270965,0.18999101,0.101201005,0.0004836279,0.0069449446],"study_design_scores_gemma":[0.0035225195,0.00044051863,0.91673875,0.002370731,0.0009708246,0.000005800171,0.014504342,0.0015677065,0.03763947,0.0006782546,0.020145232,0.0014158351],"about_ca_topic_score_codex":0.0058614234,"about_ca_topic_score_gemma":0.0018311524,"teacher_disagreement_score":0.7710808,"about_ca_system_score_codex":0.00004026587,"about_ca_system_score_gemma":0.00011608825,"threshold_uncertainty_score":0.9999999},"labels":[],"label_agreement":null},{"id":"W638223336","doi":"","title":"A Sophisticated Transit Signal Priority System – How It Works","year":2006,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Transit (satellite); Bus priority; Transport engineering; Schedule; Computer science; SIGNAL (programming language); Operations research; Public transport; Engineering","score_opus":0.014928776347663911,"score_gpt":0.24743211974015053,"score_spread":0.23250334339248663,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W638223336","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023911968,0.000102810285,0.73478067,0.013527911,0.00033820144,0.00037819598,0.000016115418,0.0010581352,0.22588597],"genre_scores_gemma":[0.9925636,0.0000039971155,0.0024754417,0.000050243896,0.00014830002,0.0000049484293,0.000040724688,0.000004802744,0.004707953],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993171,0.000059246042,0.00011055186,0.00012653582,0.00022796486,0.00015861576],"domain_scores_gemma":[0.99972385,0.00005148688,0.000037656646,0.000050898885,0.00008351066,0.0000525873],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021886821,0.000057207657,0.000080441525,0.000036755522,0.00030675993,0.00010626247,0.0000736632,0.00008982183,0.00016087054],"category_scores_gemma":[0.000008570598,0.00005477375,0.000033109918,0.00023506927,0.00006392441,0.00014424183,8.0817364e-7,0.000059040733,0.000021350676],"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":[0.00010208644,0.00022960595,0.030798823,0.000112396,0.00004620542,0.000070684044,0.01525935,0.033376895,0.00043776748,0.8847842,0.029753689,0.0050282935],"study_design_scores_gemma":[0.007141974,0.0002840075,0.3104819,0.001230069,0.00073290523,0.000013491933,0.10700789,0.1294752,0.0012817417,0.0045915637,0.434021,0.0037382613],"about_ca_topic_score_codex":0.0013005198,"about_ca_topic_score_gemma":0.0023998232,"teacher_disagreement_score":0.9686516,"about_ca_system_score_codex":0.000050774706,"about_ca_system_score_gemma":0.00007556008,"threshold_uncertainty_score":0.23593804},"labels":[],"label_agreement":null},{"id":"W638451784","doi":"10.1016/b978-008043924-2/50034-1","title":"A Large-scale Model System for the Copenhagen-Ringsted Railway Project","year":2001,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":23,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Transport Canada","funders":"","keywords":"Scale (ratio); Environmental science; Computer science; Geography; Cartography","score_opus":0.03227846413242091,"score_gpt":0.28608227784107887,"score_spread":0.25380381370865795,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W638451784","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000008540793,0.0005891434,0.008099799,0.00011207451,0.00037181104,0.0021188399,0.00020158857,0.00024233123,0.98825586],"genre_scores_gemma":[0.0027533933,0.00010856995,0.0012433619,0.00015115767,0.00034416717,0.00024951872,0.00011673692,0.000068283574,0.99496484],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9984389,0.000039845618,0.0003839869,0.00036132985,0.00042125213,0.00035471944],"domain_scores_gemma":[0.9989729,0.00015480755,0.0002875386,0.0002822588,0.0002263554,0.000076128796],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00079967856,0.00025472062,0.0003256764,0.00009009334,0.0009827708,0.00012315174,0.0003422507,0.0003423894,0.00011605543],"category_scores_gemma":[0.000020519958,0.00019957738,0.00021978465,0.000031675263,0.00012090225,0.00006174758,0.000013984664,0.00022934025,0.00007592601],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024307128,0.00004245593,0.000018280152,0.0007290362,0.00048404504,0.000030299028,0.12043066,0.009531429,0.0000063568286,0.25899428,0.017552115,0.59193796],"study_design_scores_gemma":[0.00034579646,0.000016969,0.0000026698206,0.00030385915,0.00018571863,0.0000012828644,0.0013984403,0.005755237,0.0000022955808,0.00030428998,0.99142313,0.00026031723],"about_ca_topic_score_codex":0.000011640502,"about_ca_topic_score_gemma":0.0010754517,"teacher_disagreement_score":0.973871,"about_ca_system_score_codex":0.0001230771,"about_ca_system_score_gemma":0.00043440866,"threshold_uncertainty_score":0.8138529},"labels":[],"label_agreement":null},{"id":"W640143065","doi":"","title":"How Are We Doing? Opinion Mining Customer Sentiment in US Transit Agencies and Airlines via Twitter","year":2012,"lang":"en","type":"article","venue":"Transportation Research Board 91st Annual MeetingTransportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Sentiment analysis; Lexicon; Cluster analysis; Social media; Simple (philosophy); Advertising; Transit (satellite); Computer science; Public opinion; Business; Data science; Political science; Artificial intelligence; World Wide Web; Public transport; Law","score_opus":0.09195928560226266,"score_gpt":0.3939173482296893,"score_spread":0.30195806262742664,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W640143065","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97364956,0.0020586653,0.0054039136,0.013511041,0.0006421416,0.0021792646,0.00020993092,0.0004220525,0.001923426],"genre_scores_gemma":[0.9875962,0.003700873,0.005221669,0.00012682416,0.0005563304,0.00040502616,0.00044630986,0.00009857823,0.0018481901],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9891557,0.0018740814,0.0011313881,0.0010289593,0.0043707923,0.0024390637],"domain_scores_gemma":[0.9950639,0.0011054227,0.00030131402,0.00038271045,0.002184373,0.00096226996],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.008587228,0.0004873218,0.0006194276,0.002052565,0.0018402741,0.00046171076,0.00049897854,0.0005312286,0.0001940139],"category_scores_gemma":[0.0002360061,0.00052112987,0.00019257526,0.0031406851,0.0013689861,0.0025155223,0.000010304817,0.001415328,0.000046412602],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005525077,0.00038202794,0.7806096,0.0005422394,0.00008160657,0.000078045356,0.20255034,0.0037117498,0.0006851336,0.002055545,0.004821918,0.0039292886],"study_design_scores_gemma":[0.001703914,0.00016623427,0.7104014,0.00067931315,0.00004289825,6.582772e-7,0.15387654,0.000464829,0.0004991112,0.00014667187,0.13130479,0.00071364077],"about_ca_topic_score_codex":0.012920065,"about_ca_topic_score_gemma":0.045606695,"teacher_disagreement_score":0.12648286,"about_ca_system_score_codex":0.00035948466,"about_ca_system_score_gemma":0.0004722944,"threshold_uncertainty_score":0.99972403},"labels":[],"label_agreement":null},{"id":"W640379923","doi":"","title":"P-TRANE: Modeling Bus Transit Network Evolution in a GIS-Based Framework","year":2012,"lang":"en","type":"article","venue":"Transportation Research Board 91st Annual MeetingTransportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Transit (satellite); Transport engineering; Component (thermodynamics); Public transport; Service (business); Computer science; Process (computing); Geographic information system; Overcrowding; Engineering; Geography; Business","score_opus":0.07729415988429858,"score_gpt":0.4030854607749061,"score_spread":0.3257913008906075,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W640379923","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8731603,0.0014815689,0.11370531,0.003439814,0.0007873317,0.0030004794,0.00028545508,0.00067344843,0.0034662818],"genre_scores_gemma":[0.98317367,0.0005804168,0.013016896,0.00014141333,0.0010118737,0.00068505167,0.00082611584,0.00014811511,0.00041644156],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9829169,0.0033973786,0.0018758551,0.0012430812,0.006420207,0.0041465973],"domain_scores_gemma":[0.9919615,0.0023214666,0.00025806087,0.00063967524,0.003522907,0.0012963926],"candidate_categories":["metaepi_narrow","sts","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.019144278,0.0005740744,0.0007373097,0.0021929736,0.0028214604,0.00029994617,0.00092990417,0.0010161026,0.00067625125],"category_scores_gemma":[0.0009078176,0.00066842773,0.00034618977,0.0073377443,0.0012212027,0.0022189445,0.0000065106974,0.003235281,0.0001696121],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001292761,0.00068982405,0.35755387,0.0003060221,0.00006009234,0.00007215831,0.07542299,0.50104994,0.00016494401,0.06146839,0.000823906,0.0010950834],"study_design_scores_gemma":[0.0037769,0.00048398037,0.86053306,0.0019384663,0.00011979493,4.3243705e-7,0.086971335,0.018187685,0.00017954245,0.008851679,0.017234005,0.0017231113],"about_ca_topic_score_codex":0.05963474,"about_ca_topic_score_gemma":0.12746905,"teacher_disagreement_score":0.5029792,"about_ca_system_score_codex":0.0008514333,"about_ca_system_score_gemma":0.0017434026,"threshold_uncertainty_score":0.9995767},"labels":[],"label_agreement":null},{"id":"W641399911","doi":"","title":"Reliability Considerations in Bus Route Service Planning (Poster)","year":2014,"lang":"fr","type":"article","venue":"Transportation 2014: Past, Present, Future - 2014 Conference and Exhibition of the Transportation Association of Canada // Transport 2014 : Du passé vers l'avenir - 2014 Congrès et Exposition de 'Association des transports du Canada","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Schedule; Reliability (semiconductor); Scheduling (production processes); Transit (satellite); Computer science; Service (business); Transport engineering; Bus rapid transit; Reliability engineering; Travel time; Public transport; Engineering; Operations management; Business","score_opus":0.009104743226856882,"score_gpt":0.21959277129909807,"score_spread":0.21048802807224118,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W641399911","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.89787406,0.0010204007,0.010844955,0.07165169,0.0046606585,0.0024314641,0.009105725,0.00018671928,0.0022243203],"genre_scores_gemma":[0.98755795,0.0027052255,0.0006661157,0.0018628343,0.000653705,0.00015683104,0.00495342,0.000111791174,0.0013321124],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.98917115,0.0016258924,0.0034835772,0.0012353184,0.0030675232,0.0014165626],"domain_scores_gemma":[0.98960376,0.0013478621,0.004100251,0.0006416805,0.0035932711,0.00071318226],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0036189456,0.0010349165,0.0015585659,0.00036331377,0.0010725623,0.00014554412,0.0005707946,0.0011476191,0.001347715],"category_scores_gemma":[0.00016429786,0.0011652295,0.00051911065,0.0009791267,0.00042610988,0.0020883002,0.000006101244,0.0010885098,0.000006019164],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040629026,0.00040384638,0.78710353,0.0010798495,0.0004325242,0.00004579874,0.022194289,0.10935213,0.00032994535,0.012795699,0.06558986,0.0002662667],"study_design_scores_gemma":[0.004162052,0.00014388259,0.912299,0.0008064299,0.0010996182,0.000005824459,0.0031153725,0.0011897231,0.00074996316,0.0013156431,0.073965065,0.0011474635],"about_ca_topic_score_codex":0.8008841,"about_ca_topic_score_gemma":0.98990786,"teacher_disagreement_score":0.1890237,"about_ca_system_score_codex":0.0033859587,"about_ca_system_score_gemma":0.0075511034,"threshold_uncertainty_score":0.9995652},"labels":[],"label_agreement":null},{"id":"W641743180","doi":"","title":"Ontario Transportation Demand Management - Municipal Grant Program","year":2009,"lang":"en","type":"article","venue":"2009 ANNUAL CONFERENCE AND EXHIBITION OF THE TRANSPORTATION ASSOCIATION OF CANADA - TRANSPORTATION IN A CLIMATE OF CHANGE","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Outreach; Business; Ministry of Transport; Christian ministry; Incentive; Demand management; Transport engineering; Program evaluation; Transportation planning; Public administration; Economic growth; Engineering; Economics; Political science","score_opus":0.026001869648690414,"score_gpt":0.2640989797264885,"score_spread":0.23809711007779807,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W641743180","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9939977,0.00007071229,0.00019374106,0.0016774756,0.00025825432,0.0010677476,0.0012474749,0.000038185248,0.0014486919],"genre_scores_gemma":[0.99745494,0.0005867809,0.00065454456,0.00010957609,0.000020964724,0.000062146086,0.00086438796,0.000010860156,0.00023580165],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99699485,0.00013159233,0.0011781746,0.00027656887,0.0010584636,0.00036033368],"domain_scores_gemma":[0.9978432,0.00008761975,0.0011434439,0.00014967364,0.0006726496,0.00010340564],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006874717,0.00021166063,0.0004303148,0.00021655095,0.00019906985,0.000017393131,0.00018961266,0.00019456216,0.00012173571],"category_scores_gemma":[0.000020444268,0.00021476956,0.00012824219,0.00061806326,0.00011687161,0.00056300574,7.0384544e-7,0.00017512057,2.1528926e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016922674,0.0010162039,0.5555014,0.0009273728,0.00020334758,0.000015916581,0.33382773,0.008050753,0.00028900933,0.087907255,0.00060346304,0.00996527],"study_design_scores_gemma":[0.0018602123,0.00016140798,0.9850292,0.0003797007,0.00016212066,5.218198e-8,0.009332401,0.000080079975,0.0003966245,0.0006299797,0.0017535107,0.00021469622],"about_ca_topic_score_codex":0.32836,"about_ca_topic_score_gemma":0.98238075,"teacher_disagreement_score":0.6540207,"about_ca_system_score_codex":0.00021163496,"about_ca_system_score_gemma":0.00043371422,"threshold_uncertainty_score":0.87580484},"labels":[],"label_agreement":null},{"id":"W642104730","doi":"","title":"Data Requirements for a Province-wide Travel Demand Model","year":2014,"lang":"fr","type":"article","venue":"Transportation 2014: Past, Present, Future - 2014 Conference and Exhibition of the Transportation Association of Canada // Transport 2014 : Du passé vers l'avenir - 2014 Congrès et Exposition de 'Association des transports du Canada","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Representativeness heuristic; Truck; Transport engineering; Population; Boom; Geography; Christian ministry; Travel survey; Business; Operations research; Regional science; Environmental planning; Environmental resource management; Engineering; Travel behavior; Economics","score_opus":0.018735029192765124,"score_gpt":0.24002770727367945,"score_spread":0.22129267808091432,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W642104730","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.43663475,0.001955115,0.36421362,0.10716121,0.008328382,0.007189252,0.07124987,0.00033663417,0.0029311874],"genre_scores_gemma":[0.96970457,0.0053797304,0.0020930783,0.0014079544,0.00083637197,0.00020921604,0.016155198,0.00015000347,0.0040638577],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9893501,0.0010031089,0.0033009148,0.0014656878,0.0033674368,0.0015127783],"domain_scores_gemma":[0.98910445,0.0011210035,0.0047950386,0.00091051246,0.0032773344,0.0007916526],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.004183206,0.0010882594,0.0015863078,0.00028392504,0.0013023149,0.00014634553,0.0010845939,0.0010949362,0.0006292598],"category_scores_gemma":[0.0001675706,0.0011996152,0.00058682176,0.0005883603,0.0004934084,0.002463716,0.000008785775,0.00076904584,0.0000033470665],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010491137,0.0006457063,0.47415885,0.0022350075,0.0012774493,0.00002188049,0.01279787,0.10654268,0.0005614431,0.028132277,0.37172523,0.0008524987],"study_design_scores_gemma":[0.008850053,0.00035548265,0.69742894,0.0010726504,0.0040225517,0.0000043642917,0.0034271593,0.019383222,0.0015620291,0.002264816,0.25944108,0.0021876711],"about_ca_topic_score_codex":0.5091957,"about_ca_topic_score_gemma":0.98138213,"teacher_disagreement_score":0.53306985,"about_ca_system_score_codex":0.0025406387,"about_ca_system_score_gemma":0.009844611,"threshold_uncertainty_score":0.99999785},"labels":[],"label_agreement":null},{"id":"W643551565","doi":"","title":"Growth Plan to Half a Million - Growing Forward! Shaping Saskatoon (Poster)","year":2014,"lang":"fr","type":"article","venue":"Transportation 2014: Past, Present, Future - 2014 Conference and Exhibition of the Transportation Association of Canada // Transport 2014 : Du passé vers l'avenir - 2014 Congrès et Exposition de 'Association des transports du Canada","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Plan (archaeology); Variety (cybernetics); Population growth; Sustainable growth rate; Growth management; Business; Population; Strategic planning; Economic growth; Economics; Geography; Marketing; Finance; Engineering; Computer science; Civil engineering; Land use; Sociology","score_opus":0.009259837355223644,"score_gpt":0.21077477685908994,"score_spread":0.20151493950386629,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W643551565","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.82491297,0.001084695,0.038059454,0.111898296,0.0071138022,0.0034520528,0.010680397,0.000305789,0.0024925643],"genre_scores_gemma":[0.9847174,0.0033683132,0.00081225164,0.002157775,0.0009486408,0.00017343464,0.0055290326,0.00015310635,0.00214001],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9886868,0.0013093143,0.0032526867,0.001352608,0.0037591117,0.0016394642],"domain_scores_gemma":[0.99007314,0.0008858139,0.00392801,0.0006043098,0.0034301663,0.0010785753],"candidate_categories":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0033342827,0.0011800716,0.0016391304,0.00045448667,0.0013471483,0.00018324117,0.0007791677,0.0011341501,0.0010876356],"category_scores_gemma":[0.00012255282,0.0013199034,0.0007242195,0.001030426,0.00039978887,0.0025126901,0.000007595779,0.00094794156,0.000010408636],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010285054,0.0005424764,0.67072093,0.0022159403,0.0011867618,0.00005573586,0.032602172,0.04622435,0.0013088548,0.028344108,0.2147216,0.0010485436],"study_design_scores_gemma":[0.0048009586,0.0003876924,0.78956413,0.0013755481,0.0020631293,0.000007516445,0.0042800964,0.00084023847,0.0029841396,0.0008896244,0.19097531,0.0018316166],"about_ca_topic_score_codex":0.7353451,"about_ca_topic_score_gemma":0.97781724,"teacher_disagreement_score":0.24247211,"about_ca_system_score_codex":0.0031463567,"about_ca_system_score_gemma":0.005286419,"threshold_uncertainty_score":0.999953},"labels":[],"label_agreement":null},{"id":"W644831713","doi":"","title":"Guidelines for Planning and Implementation of Transit Priority Measures (TPM) in Urban Areas","year":2013,"lang":"en","type":"article","venue":"2013 CONFERENCE AND EXHIBITION OF THE TRANSPORTATION ASSOCIATION OF CANADA - TRANSPORTATION: BETTER - FASTER - SAFER","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Transport engineering; Transit (satellite); Reliability (semiconductor); Process (computing); Traffic congestion; Consistency (knowledge bases); Computer science; Transit system; Public transport; Business; Engineering","score_opus":0.029169738425052254,"score_gpt":0.28341347326654076,"score_spread":0.2542437348414885,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W644831713","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9785997,0.0001063185,0.0078756185,0.011582004,0.00017220578,0.0009900448,0.0005358123,0.000017085933,0.00012120964],"genre_scores_gemma":[0.99763817,0.00008780359,0.0012518289,0.0003337314,0.00003714686,0.00007551299,0.0003655331,0.000013656174,0.00019662565],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9975694,0.00011501355,0.0011199394,0.00024882192,0.00070353947,0.00024329606],"domain_scores_gemma":[0.9974287,0.00016306863,0.00081789156,0.00010958158,0.0013960979,0.00008466075],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056148856,0.00017819252,0.00036301962,0.00014154249,0.0001800496,0.000028434495,0.00011915766,0.00014978385,0.00015314345],"category_scores_gemma":[0.000041739226,0.00017056736,0.000085943895,0.00026188412,0.00011052243,0.0005648538,9.990948e-7,0.00010301258,1.5186689e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007927557,0.000048511683,0.95362484,0.0002488618,0.00008615458,4.589806e-7,0.032053117,0.0024394963,0.001486845,0.0041880873,0.0029446562,0.0027996644],"study_design_scores_gemma":[0.0013933753,0.000042529653,0.98616815,0.00021034777,0.00010720009,7.3082425e-8,0.0075230035,0.00016756305,0.0019939775,0.0007271152,0.0014816042,0.00018508518],"about_ca_topic_score_codex":0.14957426,"about_ca_topic_score_gemma":0.67309684,"teacher_disagreement_score":0.5235226,"about_ca_system_score_codex":0.00007611917,"about_ca_system_score_gemma":0.0005303475,"threshold_uncertainty_score":0.8560888},"labels":[],"label_agreement":null},{"id":"W644991872","doi":"","title":"Charging for Road Use When Road Systems Have Multiple Independent Road Owners","year":2013,"lang":"en","type":"article","venue":"Transportation Research Board 92nd Annual MeetingTransportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Payment; Transport engineering; Revenue; Incentive; Business; Finance; Engineering; Economics","score_opus":0.09268947270781983,"score_gpt":0.38210596361213195,"score_spread":0.2894164909043121,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W644991872","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97242486,0.00035751506,0.0075038304,0.005188161,0.0008075942,0.009366395,0.0014604504,0.00079950626,0.0020916671],"genre_scores_gemma":[0.9768342,0.0005460207,0.0049064592,0.000117181255,0.0005795206,0.0035600646,0.002521727,0.00018841539,0.010746418],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.98433244,0.0022238628,0.0020004367,0.0017602829,0.0066014105,0.0030815399],"domain_scores_gemma":[0.985392,0.0022275273,0.0004989423,0.00072776625,0.009785975,0.0013678236],"candidate_categories":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.010208635,0.0006505945,0.0008312368,0.0020124807,0.0038918022,0.0015886367,0.001160027,0.00078480935,0.00067128317],"category_scores_gemma":[0.0018503938,0.00067617116,0.00042942754,0.0019155464,0.00133423,0.003900864,0.000013665928,0.0018529352,0.00026008193],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0026025756,0.0014529872,0.5585392,0.0015704533,0.0006683349,0.00022459586,0.281715,0.050401848,0.0048428765,0.021516496,0.04968279,0.026782835],"study_design_scores_gemma":[0.0040983777,0.0005428189,0.77856386,0.0006637875,0.00009480022,5.2585597e-7,0.12629361,0.010242499,0.0004334481,0.00083279284,0.07696189,0.0012715787],"about_ca_topic_score_codex":0.2910041,"about_ca_topic_score_gemma":0.07551237,"teacher_disagreement_score":0.22002468,"about_ca_system_score_codex":0.000581957,"about_ca_system_score_gemma":0.0012393548,"threshold_uncertainty_score":0.99956894},"labels":[],"label_agreement":null},{"id":"W645282532","doi":"","title":"BUS TRAVEL TIME PREDICTION MODEL FOR DYNAMIC OPERATIONS CONTROL AND PASSENGER INFORMATION SYSTEMS","year":2003,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":93,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Microsimulation; Schedule; Kalman filter; Real-time computing; Real-time data; Computer science; Automatic vehicle location; VisSim; Train; Dwell time; Software; Control (management); Transit (satellite); Engineering; Public transport; Transport engineering; Global Positioning System","score_opus":0.008072739529346196,"score_gpt":0.23906588429265616,"score_spread":0.23099314476330995,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W645282532","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0070215575,0.000023604971,0.98405355,0.00025699404,0.00011710001,0.00054983026,0.000115730894,0.000086549146,0.0077750823],"genre_scores_gemma":[0.9920815,0.000024146924,0.0040233107,0.000079119585,0.0000132319865,0.000073180585,0.0001618369,0.000003606083,0.0035400558],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995104,0.000038367718,0.0001704892,0.000067251975,0.00011379544,0.000099708464],"domain_scores_gemma":[0.9996945,0.000043141456,0.000030782307,0.000037407248,0.00014673852,0.00004740778],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003160704,0.000048069352,0.000065722204,0.000051730854,0.0003745184,0.000111663474,0.000022673525,0.00006990977,0.000012128575],"category_scores_gemma":[0.00006636841,0.000045898265,0.000015509784,0.000070237424,0.000026536038,0.00066117896,3.4780348e-7,0.00002675796,0.00000696855],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009224482,0.000012454609,0.00034260945,0.000011071309,0.000010912206,2.9418008e-8,0.006825793,0.8839663,0.000095117604,0.10804842,0.0004734196,0.00020460063],"study_design_scores_gemma":[0.00049514923,0.000012871943,0.0012355353,0.0000050779377,0.000018727938,3.149188e-7,0.0014540167,0.99525434,0.000002110482,0.00008324292,0.0013832453,0.000055354736],"about_ca_topic_score_codex":0.00012705459,"about_ca_topic_score_gemma":0.00020404319,"teacher_disagreement_score":0.98506,"about_ca_system_score_codex":0.00002864321,"about_ca_system_score_gemma":0.000084794825,"threshold_uncertainty_score":0.28805307},"labels":[],"label_agreement":null},{"id":"W645555944","doi":"","title":"The Impact of a Non-Violation Business Model on a Greenfield All Electronic Toll (AET) Operations","year":2011,"lang":"en","type":"article","venue":"18th ITS World CongressTransCoreITS AmericaERTICO - ITS EuropeITS Asia-Pacific","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Toll; Demographics; Bridge (graph theory); Electronic toll collection; Business; Business model; Computer security; Advertising; Computer science; Marketing; Demography; Sociology","score_opus":0.039919854003425174,"score_gpt":0.3062808766550125,"score_spread":0.26636102265158734,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W645555944","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.55646163,0.0012670403,0.029405514,0.0036409504,0.0010276607,0.0040012384,0.00034288375,0.0007902621,0.40306282],"genre_scores_gemma":[0.9918389,0.00074020785,0.0004325079,0.00008436752,0.000096763564,0.000061080435,0.00008323352,0.00007118719,0.0065918076],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.996521,0.00034035076,0.00085729425,0.0006153891,0.00070885225,0.00095713016],"domain_scores_gemma":[0.9977076,0.00021363611,0.00037470087,0.00050349196,0.00091418385,0.00028639124],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00056791323,0.0004457185,0.00045632914,0.00034805294,0.000993732,0.00020982369,0.00071101,0.00013332827,0.00030199424],"category_scores_gemma":[0.00019589852,0.00033809425,0.00024373333,0.0018824787,0.00039996463,0.0006845484,0.000014210477,0.00047973337,0.00008164453],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0031688185,0.0020735972,0.014650423,0.0001439014,0.0013272676,0.00010774634,0.07998995,0.6987272,0.014450856,0.15170448,0.0059671076,0.027688667],"study_design_scores_gemma":[0.0052633714,0.0025830425,0.4318289,0.0010458794,0.0010151591,0.000022619512,0.0078940755,0.5191175,0.007382192,0.00062654546,0.019453233,0.0037675253],"about_ca_topic_score_codex":0.0010289953,"about_ca_topic_score_gemma":0.0015867652,"teacher_disagreement_score":0.4353772,"about_ca_system_score_codex":0.00010407462,"about_ca_system_score_gemma":0.00090359163,"threshold_uncertainty_score":0.99990714},"labels":[],"label_agreement":null},{"id":"W647223410","doi":"","title":"NO. AMERICAN TRAFFIC GROWTH STRONG","year":2000,"lang":"en","type":"article","venue":"RCC's public works financing","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Toll; Toll road; Annual growth %; Growth management; Transport engineering; Economics; Business; Agricultural economics; Engineering; Civil engineering","score_opus":0.011800388033554356,"score_gpt":0.2479581975006992,"score_spread":0.23615780946714485,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W647223410","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8381239,0.00017424353,0.0075590126,0.0034423626,0.00045042046,0.00022899531,0.000007133119,0.0006496058,0.14936431],"genre_scores_gemma":[0.99174535,0.00021479404,0.0024861756,0.00020510233,0.0002873575,0.0000127632875,0.000041884592,0.000015553214,0.0049909973],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9987191,0.000088302106,0.00021164205,0.00023960936,0.0002964158,0.00044494594],"domain_scores_gemma":[0.99944144,0.00008244431,0.000089935675,0.000118703465,0.00013346592,0.00013400994],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040749885,0.00010720814,0.00014794897,0.00009376297,0.0006157517,0.00023628768,0.00019794721,0.00007818089,0.0008009414],"category_scores_gemma":[0.00014081891,0.000116225594,0.00005870438,0.0008675477,0.00016649117,0.00059253175,0.000003363938,0.00017931485,0.0001748886],"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":[0.000038888393,0.00013293275,0.02637297,0.000014525325,0.0000277007,0.000016406624,0.017630832,0.058309063,0.0000039740466,0.017669503,0.008519445,0.87126374],"study_design_scores_gemma":[0.0013977061,0.00017976244,0.19842863,0.00034812107,0.000054492506,0.000002901114,0.010892509,0.016177515,0.000010013778,0.00021658963,0.7710116,0.0012801884],"about_ca_topic_score_codex":0.000994063,"about_ca_topic_score_gemma":0.0010752827,"teacher_disagreement_score":0.86998355,"about_ca_system_score_codex":0.00007744379,"about_ca_system_score_gemma":0.0002026297,"threshold_uncertainty_score":0.87697464},"labels":[],"label_agreement":null},{"id":"W648506033","doi":"","title":"OTTAWA IN TRANSIT: RAIL IS THE FUTURE","year":2003,"lang":"en","type":"article","venue":"Railway age","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Public transport; Light rail; Transit (satellite); Transport engineering; Light rail transit; Rail transit; Engineering; Service (business); Transit system; Rapid transit; Bus rapid transit; Business; Marketing","score_opus":0.012071675409444222,"score_gpt":0.2616918068428662,"score_spread":0.24962013143342196,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W648506033","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.30421844,0.0018787202,0.006466883,0.037526775,0.0017982519,0.0008083109,0.000034745266,0.00030088835,0.646967],"genre_scores_gemma":[0.99278706,0.0001209584,0.00053143624,0.0009159989,0.00010565257,0.0000068017494,0.00000848381,0.000005679392,0.0055179163],"study_design_codex":"qualitative","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993297,0.00013742334,0.00011007898,0.00010481174,0.0001676015,0.00015039332],"domain_scores_gemma":[0.99978596,0.0000527451,0.000026382404,0.00008072325,0.000019116635,0.000035085992],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047445996,0.00005149603,0.00006173182,0.000031960204,0.00024113858,0.000036096193,0.000096330885,0.000070874645,0.00055896386],"category_scores_gemma":[0.000027920105,0.000039289196,0.000034531997,0.0003174623,0.00006255869,0.00009148106,5.523216e-7,0.00010031607,0.000029741708],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025952486,0.0001409402,0.056538876,0.000023768069,0.000027429487,0.00009543288,0.52634543,0.0065414584,0.00012725485,0.35231048,0.045125995,0.012696959],"study_design_scores_gemma":[0.00026139815,0.000005928091,0.032530166,0.000008349908,0.0000056077106,3.019368e-7,0.007171888,0.0000221706,0.00005813625,0.0008166611,0.9590342,0.00008518692],"about_ca_topic_score_codex":0.00026291347,"about_ca_topic_score_gemma":0.008170772,"teacher_disagreement_score":0.9139082,"about_ca_system_score_codex":0.000016297256,"about_ca_system_score_gemma":0.000056391105,"threshold_uncertainty_score":0.6120262},"labels":[],"label_agreement":null},{"id":"W649405508","doi":"","title":"THE MARVEL THAT IS L.A.'S METROLINK","year":2001,"lang":"en","type":"article","venue":"Trains","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Mile; Transit (satellite); Train; Transport engineering; Geography; Engineering; Public transport; Archaeology","score_opus":0.05023043466099871,"score_gpt":0.3166474635613146,"score_spread":0.26641702890031593,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W649405508","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20927274,0.00055756536,0.022729455,0.063638404,0.00092399155,0.00036219982,0.000029242563,0.0004869066,0.7019995],"genre_scores_gemma":[0.98255956,0.00042145097,0.00035280915,0.0006794738,0.0000992095,0.000004332449,0.0000063861544,0.0000043377236,0.015872449],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995018,0.00003846985,0.000062222214,0.00006960284,0.0001803702,0.00014751841],"domain_scores_gemma":[0.9997532,0.0000820145,0.000026884425,0.00006726609,0.000026750868,0.000043928598],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000302144,0.000035307094,0.000036698388,0.000018069366,0.00061770726,0.000058775066,0.00010930176,0.00003616408,0.0002270751],"category_scores_gemma":[0.000029016417,0.000025655565,0.000030733878,0.00015660089,0.00008723219,0.00008047818,0.0000012451219,0.000047596874,0.000043982513],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000073824725,0.000113972805,0.22022898,0.000006646375,0.00010867154,0.00003053179,0.21244885,0.0013893009,0.000043570206,0.31766078,0.06171484,0.18618003],"study_design_scores_gemma":[0.00016434968,0.000008550363,0.10579478,0.0000035056967,0.000012076253,4.402827e-7,0.0051707565,0.00022344633,0.000015343636,0.0011737854,0.88736165,0.00007130085],"about_ca_topic_score_codex":0.00035499554,"about_ca_topic_score_gemma":0.002195301,"teacher_disagreement_score":0.8256468,"about_ca_system_score_codex":0.000014317182,"about_ca_system_score_gemma":0.00004023432,"threshold_uncertainty_score":0.47509673},"labels":[],"label_agreement":null},{"id":"W649628654","doi":"","title":"Economies of Scale in Operating Costs for LRT and Streetcars","year":2014,"lang":"en","type":"article","venue":"Transportation Research Board 93rd Annual MeetingTransportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Economies of scale; Transit (satellite); Mile; Transport engineering; Unit (ring theory); Service (business); Public transport; Scale (ratio); Light rail transit; Business; Modal; Modal shift; Light rail; Kilometer; Engineering; Geography","score_opus":0.050302111516304475,"score_gpt":0.393825506614341,"score_spread":0.34352339509803653,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W649628654","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9888731,0.00010992813,0.0028836133,0.0022327618,0.0001325026,0.0019283673,0.00035627524,0.00011286319,0.00337056],"genre_scores_gemma":[0.9861973,0.00045878027,0.011595184,0.000045955734,0.0001502528,0.0004412927,0.00040743547,0.000057424368,0.0006464058],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9932906,0.0014304584,0.0012494557,0.0008439685,0.0019660643,0.001219473],"domain_scores_gemma":[0.9935277,0.002636051,0.00022821137,0.00029262577,0.0028872872,0.0004281099],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.012545297,0.00026469852,0.00053696166,0.0013306276,0.0011508256,0.00016894063,0.00044804244,0.00033795153,0.00008807246],"category_scores_gemma":[0.0009830463,0.0002996701,0.00013205697,0.0015845001,0.001326963,0.0009731931,0.0000063172074,0.0007614651,0.000008089454],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00081093155,0.00032056888,0.7947532,0.00068346725,0.000056894114,0.000008813543,0.09182272,0.013652755,0.0014534892,0.079377845,0.0012775846,0.015781736],"study_design_scores_gemma":[0.002826313,0.0006417907,0.8959942,0.00058461184,0.000030774896,8.059012e-8,0.07436431,0.0022280728,0.0015876876,0.0028103902,0.018422442,0.000509322],"about_ca_topic_score_codex":0.031206604,"about_ca_topic_score_gemma":0.19348136,"teacher_disagreement_score":0.16227475,"about_ca_system_score_codex":0.00018799654,"about_ca_system_score_gemma":0.0005495381,"threshold_uncertainty_score":0.9999455},"labels":[],"label_agreement":null},{"id":"W650291818","doi":"","title":"A new look at projected gradient method for equilibrium assignment","year":2010,"lang":"en","type":"article","venue":"EUROPEAN TRANSPORT CONFERENCE 2008; PROCEEDINGS","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computation; Mathematical optimization; Flow network; Computer science; Assignment problem; Algorithm; Path (computing); Scale (ratio); Work (physics); Mathematics; Applied mathematics; Engineering","score_opus":0.030576622645711954,"score_gpt":0.2936049994057261,"score_spread":0.2630283767600141,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W650291818","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.30049917,0.00004667043,0.41470486,0.0033568197,0.0014433198,0.002877626,0.00012473551,0.0014110345,0.27553576],"genre_scores_gemma":[0.8574769,0.000027811428,0.11993607,0.00020722345,0.00045919,0.000049216284,0.0001860983,0.00006427225,0.021593243],"study_design_codex":"qualitative","study_design_gemma":"not_applicable","domain_scores_codex":[0.9979535,0.000036213438,0.0004347519,0.00055918895,0.00047208735,0.0005442636],"domain_scores_gemma":[0.9988055,0.00004111496,0.00021686581,0.00011779405,0.0004365224,0.0003821548],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010960641,0.0002430058,0.00023815037,0.0001259289,0.00045533667,0.00012018766,0.00039380285,0.000114714094,0.0006746235],"category_scores_gemma":[0.00008010729,0.00024362668,0.00012202908,0.00033131885,0.00013633598,0.00038172211,0.000009040147,0.00026503095,0.000051267813],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012957623,0.0006374824,0.15871418,0.00046912447,0.0002732436,0.00008163513,0.30923936,0.00045655514,0.13060442,0.28528157,0.06972426,0.043222424],"study_design_scores_gemma":[0.0034415517,0.00044045493,0.10864625,0.00021244095,0.00030999715,0.000014224661,0.005119104,0.0021943182,0.007803241,0.001009064,0.86927676,0.0015325821],"about_ca_topic_score_codex":0.00032605632,"about_ca_topic_score_gemma":0.0008049776,"teacher_disagreement_score":0.7995525,"about_ca_system_score_codex":0.000054782653,"about_ca_system_score_gemma":0.00038637628,"threshold_uncertainty_score":0.99348074},"labels":[],"label_agreement":null},{"id":"W650893311","doi":"","title":"Travel Time Estimation in Urban Networks Using Neighbor Links Travel Time Data","year":2010,"lang":"en","type":"article","venue":"Transportation Research Board 89th Annual MeetingTransportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Data mining; Outlier; Sample (material); Travel time; Estimation; Engineering; Transport engineering; Artificial intelligence","score_opus":0.0723375882140397,"score_gpt":0.399162211816715,"score_spread":0.32682462360267533,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W650893311","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9574007,0.00021345928,0.022859842,0.003522013,0.0007038564,0.0044286684,0.0018273018,0.00057723216,0.008466966],"genre_scores_gemma":[0.9740592,0.0002596297,0.015164969,0.00010158783,0.0006096701,0.00021193066,0.0065902025,0.0001753753,0.0028273985],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9856716,0.0021990524,0.002031671,0.0019340371,0.0054562003,0.0027074597],"domain_scores_gemma":[0.9915583,0.0022120413,0.00042168182,0.0012766431,0.0033933013,0.0011380203],"candidate_categories":["metaepi_narrow","sts","research_integrity","insufficient_payload"],"consensus_categories":["research_integrity"],"category_scores_codex":[0.017444612,0.0005988399,0.0008213323,0.002323043,0.0024076453,0.00054238975,0.0020366814,0.001448244,0.0014830411],"category_scores_gemma":[0.0014223274,0.0006866169,0.00020724104,0.0051781284,0.0017653955,0.0028200352,0.000024773102,0.005423083,0.0003073147],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0054685515,0.0035980474,0.33231363,0.0009792112,0.00051068974,0.0011145412,0.20161615,0.31549332,0.03477663,0.045237873,0.038638923,0.020252427],"study_design_scores_gemma":[0.0036613233,0.0003901947,0.66501534,0.00055480655,0.00012484644,0.000001179998,0.016967552,0.3025893,0.00047325745,0.0014410891,0.007304634,0.0014765017],"about_ca_topic_score_codex":0.043178104,"about_ca_topic_score_gemma":0.06905952,"teacher_disagreement_score":0.33270168,"about_ca_system_score_codex":0.00034792745,"about_ca_system_score_gemma":0.002102797,"threshold_uncertainty_score":0.99984807},"labels":[],"label_agreement":null},{"id":"W651240926","doi":"","title":"Edmonton’s LRT Setting the Stage for Heading Further South","year":2010,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Metropolitan area; TRIPS architecture; Public transport; Geography; Population; Capital city; Population growth; Transport engineering; Business; Economic growth; Engineering; Demography; Economics; Archaeology; Economic geography; Sociology","score_opus":0.01867426391352408,"score_gpt":0.3056501667610031,"score_spread":0.28697590284747904,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W651240926","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6016083,0.00002874344,0.15040441,0.027204053,0.0017939089,0.001148325,0.00005892738,0.00064932666,0.21710403],"genre_scores_gemma":[0.9749869,0.0000010488463,0.007638682,0.0003232813,0.00019854565,0.000014295014,0.0000101459,0.000006040394,0.016821062],"study_design_codex":"qualitative","study_design_gemma":"not_applicable","domain_scores_codex":[0.99956965,0.000020926536,0.00007924538,0.00008234472,0.00010994208,0.00013789187],"domain_scores_gemma":[0.99967396,0.00013051443,0.00003854071,0.00006280036,0.000060518425,0.00003366602],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005911855,0.000036632777,0.000036336805,0.000015365138,0.00052080606,0.00006926728,0.00008846328,0.000044000546,0.0002944348],"category_scores_gemma":[0.00011658358,0.000025173915,0.00002496923,0.00007643589,0.000047695317,0.00009212201,0.0000017173851,0.0000706624,0.000010032658],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030719188,0.000034224893,0.15719973,0.000017532851,0.000028867706,9.3051443e-7,0.52221966,0.00416382,0.0011988564,0.2953784,0.00778422,0.011943051],"study_design_scores_gemma":[0.0008294094,0.00003589153,0.043329142,0.000019636045,0.000041526982,2.7803017e-7,0.17279156,0.011645869,0.000672591,0.0016396404,0.76856184,0.00043261636],"about_ca_topic_score_codex":0.00042446962,"about_ca_topic_score_gemma":0.0068370574,"teacher_disagreement_score":0.76077765,"about_ca_system_score_codex":0.0000060376788,"about_ca_system_score_gemma":0.0000550329,"threshold_uncertainty_score":0.40056717},"labels":[],"label_agreement":null},{"id":"W652464253","doi":"","title":"Towards Systematic Design of Urban Bus Stations, Reinforcing a Weak Link in a Public Transport Chain","year":2009,"lang":"en","type":"article","venue":"European Transport Conference, 2009Association for European Transport (AET)","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":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Visibility; Pedestrian; Link (geometry); Quarter (Canadian coin); Public transport; Phenomenon; Transport engineering; Telecommunications; Computer science; Engineering; Geography; Meteorology; Computer network; Archaeology; Physics","score_opus":0.03869982094500814,"score_gpt":0.25856798223160476,"score_spread":0.21986816128659664,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W652464253","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05248654,0.0005121997,0.7763351,0.0027850696,0.00067410583,0.006517999,0.0008664011,0.0012898849,0.15853266],"genre_scores_gemma":[0.9875531,0.00042506747,0.005546854,0.00024554634,0.00023742909,0.00005674732,0.0022124862,0.00013342893,0.0035892967],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.9921522,0.0015052722,0.0029754639,0.0009215209,0.001317507,0.0011279989],"domain_scores_gemma":[0.9963689,0.00023102957,0.0014017187,0.00051734975,0.0010406753,0.00044034718],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.008086316,0.00065612746,0.0011687644,0.000925938,0.0006725148,0.000107030166,0.0009263051,0.00024141067,0.00013813848],"category_scores_gemma":[0.00025615635,0.00072352984,0.0004936626,0.0016614868,0.00024090633,0.0010523514,0.0000034157404,0.0004902429,0.000038491802],"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":[0.0019489733,0.002984638,0.26737162,0.0092037385,0.0011533337,0.00086743524,0.3096879,0.11492927,0.001333213,0.26735285,0.0017160896,0.021450896],"study_design_scores_gemma":[0.008030841,0.001086155,0.9534233,0.0048388015,0.00096035755,0.000005472284,0.0067054178,0.0028600853,0.00012467729,0.0008296739,0.018725364,0.0024098603],"about_ca_topic_score_codex":0.0003014697,"about_ca_topic_score_gemma":0.0008196981,"teacher_disagreement_score":0.9350666,"about_ca_system_score_codex":0.00027625175,"about_ca_system_score_gemma":0.0010619542,"threshold_uncertainty_score":0.99952155},"labels":[],"label_agreement":null},{"id":"W653259970","doi":"","title":"Evolution of Interchange Design in North America","year":2014,"lang":"fr","type":"article","venue":"Transportation 2014: Past, Present, Future - 2014 Conference and Exhibition of the Transportation Association of Canada // Transport 2014 : Du passé vers l'avenir - 2014 Congrès et Exposition de 'Association des transports du Canada","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Geometric design; Port (circuit theory); Transport engineering; Engineering; Crossover; Design elements and principles; Operations research; Computer science; Systems engineering; Mechanical engineering","score_opus":0.008836961352825915,"score_gpt":0.2075696025172982,"score_spread":0.19873264116447228,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W653259970","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8606316,0.0015441357,0.089698754,0.0294132,0.004506277,0.0028799402,0.0099950405,0.00014826102,0.0011828234],"genre_scores_gemma":[0.9871971,0.0057597877,0.0009750629,0.0005048206,0.0005120385,0.00013780876,0.0037511827,0.000098856144,0.0010633289],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.98980373,0.0017350628,0.0032564458,0.0010092494,0.0029572237,0.0012382966],"domain_scores_gemma":[0.99027085,0.0010115659,0.00478653,0.0005354153,0.002823593,0.0005720563],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0028456263,0.0009137295,0.0015594999,0.00045318156,0.0006072469,0.00006628337,0.00065564323,0.000904698,0.0009772304],"category_scores_gemma":[0.0001170301,0.0010224803,0.0005499082,0.0011979992,0.00055742584,0.0015868958,0.0000052933547,0.00084538775,0.000004143938],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007284921,0.00049242424,0.7993072,0.0011476037,0.0005105989,0.000023472458,0.02183345,0.11147625,0.00035917293,0.009421774,0.05377719,0.00092236913],"study_design_scores_gemma":[0.0038511436,0.0002768547,0.92265904,0.0006862416,0.0011489987,0.0000025745496,0.0040660687,0.0020952444,0.0008833136,0.00062011904,0.06267179,0.0010386427],"about_ca_topic_score_codex":0.7883222,"about_ca_topic_score_gemma":0.9903854,"teacher_disagreement_score":0.20206316,"about_ca_system_score_codex":0.0036595897,"about_ca_system_score_gemma":0.006317258,"threshold_uncertainty_score":0.999936},"labels":[],"label_agreement":null},{"id":"W653698129","doi":"","title":"Investigating Economic Viability of Personal Rapid Transit (PRT) System for a University Campus and Its Surroundings","year":2013,"lang":"en","type":"article","venue":"Transportation Research Board 92nd Annual MeetingTransportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Microsimulation; Investment (military); Internal rate of return; Abu dhabi; Transport engineering; Transit (satellite); Public transport; Cost–benefit analysis; Revenue; Business; Environmental economics; Environmental science; Engineering; Metropolitan area; Economics; Finance; Geography","score_opus":0.05152418957223117,"score_gpt":0.34149302877215365,"score_spread":0.2899688391999225,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W653698129","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98993737,0.00019431485,0.001398087,0.0025368156,0.00016890865,0.0032659716,0.0009697097,0.00022855905,0.0013002638],"genre_scores_gemma":[0.9951564,0.00030533082,0.0029065365,0.000019401,0.00014282198,0.00021215859,0.00042783257,0.00005517355,0.0007743476],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.99317265,0.001429871,0.0010407132,0.0009808138,0.0021062861,0.0012696772],"domain_scores_gemma":[0.9921738,0.0023071861,0.00033196912,0.00023364,0.0042137536,0.0007396347],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.008006249,0.00031575517,0.00056450977,0.0009858736,0.002140452,0.00019202859,0.00048819292,0.0003909631,0.0003032512],"category_scores_gemma":[0.00046036477,0.0003650449,0.00022395574,0.001302246,0.0014549098,0.001569929,0.0000062486815,0.0007928557,0.000031709977],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002203082,0.0007228221,0.34466463,0.008105625,0.0005675411,0.00006092647,0.40589285,0.009560794,0.015758747,0.19747168,0.0064691342,0.008522171],"study_design_scores_gemma":[0.0053809755,0.0010357574,0.635161,0.0009919693,0.00017284969,6.552943e-7,0.3229407,0.008929679,0.0030110474,0.0015864219,0.019630354,0.001158598],"about_ca_topic_score_codex":0.03792809,"about_ca_topic_score_gemma":0.017999817,"teacher_disagreement_score":0.29049635,"about_ca_system_score_codex":0.00046421873,"about_ca_system_score_gemma":0.0011904787,"threshold_uncertainty_score":0.9999191},"labels":[],"label_agreement":null},{"id":"W653999591","doi":"","title":"Optimizing Mass Transit Utilization in Emergency Evacuation of Congested Urban Areas","year":2010,"lang":"en","type":"article","venue":"Transportation Research Board 89th Annual MeetingTransportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Vehicle routing problem; Transit (satellite); Traffic congestion; Transport engineering; Computer science; Scheduling (production processes); Constraint (computer-aided design); Routing (electronic design automation); Emergency evacuation; Budget constraint; Operations research; Public transport; Engineering; Geography; Operations management; Computer network","score_opus":0.08692597758181969,"score_gpt":0.4108771684545918,"score_spread":0.32395119087277213,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W653999591","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9788713,0.00023572354,0.007445422,0.0022921397,0.0006817945,0.0027234822,0.00041779035,0.00032680988,0.0070055444],"genre_scores_gemma":[0.9897491,0.001180401,0.0057457713,0.000023864031,0.0002505887,0.00037912253,0.0013869009,0.00010038161,0.0011838974],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.98757637,0.0019962527,0.0022140092,0.0011714569,0.005252619,0.0017892906],"domain_scores_gemma":[0.99052656,0.0011272238,0.00047743908,0.00058400474,0.0065670996,0.0007176731],"candidate_categories":["metaepi_narrow","sts","research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.011987066,0.00043622064,0.0006526798,0.002775724,0.0013379746,0.00014975118,0.0008701413,0.000753082,0.0014379839],"category_scores_gemma":[0.0012366978,0.00050892716,0.00025350446,0.0059434483,0.0012612678,0.0016448214,0.0000055283836,0.0024168766,0.000049472073],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018206751,0.00093504845,0.6968447,0.0006990349,0.00011649883,0.00011227442,0.18419363,0.02529733,0.021682715,0.06087607,0.0021294896,0.005292529],"study_design_scores_gemma":[0.0030826193,0.00045092838,0.9181555,0.0004309902,0.00007488775,2.247626e-7,0.050052676,0.002042599,0.004576222,0.0032363266,0.01702608,0.00087095634],"about_ca_topic_score_codex":0.02140448,"about_ca_topic_score_gemma":0.17027108,"teacher_disagreement_score":0.22131078,"about_ca_system_score_codex":0.00020298749,"about_ca_system_score_gemma":0.0012970507,"threshold_uncertainty_score":0.99996215},"labels":[],"label_agreement":null},{"id":"W654385060","doi":"","title":"An Analytical Alternative to Transit Signal Priority Micro-simulation Modeling: Model and Application","year":2011,"lang":"en","type":"article","venue":"Transportation Research Board 90th Annual MeetingTransportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"VisSim; Intersection (aeronautics); Transit (satellite); Microsimulation; Traffic simulation; Software deployment; Bus priority; Computer science; SIGNAL (programming language); Simulation modeling; Signal timing; Transport engineering; Simulation; Operations research; Public transport; Engineering; Real-time computing; Traffic signal","score_opus":0.12904984897797453,"score_gpt":0.4335539687078983,"score_spread":0.30450411972992375,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W654385060","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7046349,0.000049244132,0.2896082,0.0008848251,0.00006995867,0.0022517254,0.00034006394,0.00034287258,0.0018182012],"genre_scores_gemma":[0.9772171,0.00019659563,0.020751504,0.00015287576,0.0002131464,0.0004311052,0.0006003906,0.00009676255,0.00034051147],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9898574,0.0015170445,0.0012792384,0.0015723341,0.004080761,0.0016932621],"domain_scores_gemma":[0.99131656,0.0005966213,0.00018442773,0.0005448831,0.0054083117,0.0019492065],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.007996884,0.0004461521,0.00052426656,0.001668769,0.0023436463,0.0002787722,0.00079759007,0.0005094162,0.00024700808],"category_scores_gemma":[0.00033380993,0.000508389,0.00016199017,0.0026445298,0.0009840919,0.0018743108,0.00000859196,0.0013777467,0.000059978604],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015884676,0.0004976567,0.026556069,0.00011043833,0.000058997473,0.000033648514,0.16924399,0.76831234,0.001017299,0.028307647,0.000127104,0.0041463217],"study_design_scores_gemma":[0.0015731606,0.0008054119,0.109629765,0.00016120367,0.00009894862,2.197388e-7,0.03265245,0.8443059,0.00054898864,0.007464663,0.0018627286,0.0008965951],"about_ca_topic_score_codex":0.03196766,"about_ca_topic_score_gemma":0.040105756,"teacher_disagreement_score":0.2725822,"about_ca_system_score_codex":0.0002782073,"about_ca_system_score_gemma":0.0010504138,"threshold_uncertainty_score":0.9997368},"labels":[],"label_agreement":null},{"id":"W655041936","doi":"","title":"DEVELOPMENT OF A PUBLIC TRANSIT INFORMATION SYSTEM: USE OF GIS AND ITS TECHNOLOGIES","year":2003,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Geographic information system; Transit (satellite); Transport engineering; Public transport; Computer science; Schedule; Information system; Operations research; Geography; Engineering; Cartography","score_opus":0.05063758783977261,"score_gpt":0.2507854433555461,"score_spread":0.20014785551577347,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W655041936","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.90291077,0.00005380364,0.08621356,0.00021153536,0.00004501447,0.00020474827,0.0000062240238,0.00021435219,0.010139975],"genre_scores_gemma":[0.9749272,0.00001811535,0.024981774,0.0000033195374,4.1523919e-7,0.0000031839772,0.000005196494,8.360932e-7,0.000059969425],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","domain_scores_codex":[0.9995899,0.000020128746,0.00018170598,0.000031630447,0.0001202538,0.000056351426],"domain_scores_gemma":[0.9997508,0.000019536312,0.000072472096,0.000026224807,0.00011740618,0.00001359482],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023889476,0.00002653112,0.000057187055,0.000088966124,0.00007688793,0.000017323033,0.000028544411,0.000048487047,0.000009077505],"category_scores_gemma":[0.00010404255,0.000023540502,0.000007208532,0.00019877865,0.000033889013,0.00051580736,9.775725e-7,0.00001530934,8.800187e-7],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007198215,0.000028680952,0.022691872,0.00028641292,0.00002964758,2.0073787e-7,0.09780415,0.0009051272,0.00027987864,0.859864,0.00006128548,0.018041518],"study_design_scores_gemma":[0.00198078,0.000104935934,0.10201614,0.0004551556,0.000082615334,0.0000029950431,0.6084958,0.0035375725,0.06991326,0.00021956675,0.21244715,0.0007440744],"about_ca_topic_score_codex":0.000042760286,"about_ca_topic_score_gemma":0.0001863848,"teacher_disagreement_score":0.8596445,"about_ca_system_score_codex":0.000011189777,"about_ca_system_score_gemma":0.00008861121,"threshold_uncertainty_score":0.09599538},"labels":[],"label_agreement":null},{"id":"W655343278","doi":"","title":"ACCESS ALL AREAS: CONTROLLING THE SQUARE","year":2001,"lang":"en","type":"article","venue":"PARKING NEWS","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Metropolitan area; Square (algebra); Transport engineering; Quarter (Canadian coin); Traffic congestion; Geography; Telecommunications; Engineering; Archaeology","score_opus":0.057214745596017594,"score_gpt":0.3454895021911466,"score_spread":0.288274756595129,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W655343278","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.51716876,0.001258691,0.08275867,0.0342719,0.0017468951,0.0010126492,0.000012235063,0.00086031837,0.36090985],"genre_scores_gemma":[0.9975078,0.00026151948,0.00015778677,0.00095832784,0.0002619313,0.000012282056,0.00001770331,0.0000059965905,0.00081663084],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.999309,0.000092254675,0.00011944631,0.00010919872,0.00019352898,0.00017656191],"domain_scores_gemma":[0.9996003,0.00015257095,0.000068928006,0.000086003194,0.000046556648,0.000045635155],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033309986,0.000052093073,0.000066017004,0.000026212514,0.00047240342,0.00019228576,0.0002079217,0.000040578427,0.00014031096],"category_scores_gemma":[0.000093235,0.00003903507,0.00003544003,0.00019047866,0.000046692956,0.00017622247,0.000004306754,0.00006946439,0.000021113496],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000057637415,0.000036742666,0.8828527,0.0000056617982,0.00003460773,0.000016576503,0.03710226,0.037839204,0.0000150738415,0.008680508,0.0059538186,0.027405228],"study_design_scores_gemma":[0.000304518,0.0000067162346,0.051378503,0.00002487236,0.000024685316,7.692618e-7,0.003861096,0.001991609,0.0000076103206,0.00078671635,0.94149524,0.00011766742],"about_ca_topic_score_codex":0.0025541882,"about_ca_topic_score_gemma":0.0053095007,"teacher_disagreement_score":0.9355414,"about_ca_system_score_codex":0.000018977818,"about_ca_system_score_gemma":0.000037937818,"threshold_uncertainty_score":0.3861186},"labels":[],"label_agreement":null},{"id":"W655571371","doi":"","title":"SETTING THE STAGE FOR SMART GROWTH","year":2004,"lang":"en","type":"article","venue":"Mass transit","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Transport engineering; TRIPS architecture; Traffic congestion; Transit (satellite); Service (business); Public transport; Smart growth; Population; Business; Geography; Engineering; Urban planning; Civil engineering","score_opus":0.016818473710773524,"score_gpt":0.2714590763973192,"score_spread":0.25464060268654565,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W655571371","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04336388,0.00005570009,0.9064339,0.030386776,0.00034453266,0.00054469955,0.0000583892,0.0002283189,0.018583803],"genre_scores_gemma":[0.99133193,0.000012116893,0.0069926553,0.00039076593,0.000080922124,0.000018996996,0.000026757563,0.0000069557695,0.001138898],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995204,0.000025901381,0.000085886764,0.00008824038,0.00013438181,0.00014515923],"domain_scores_gemma":[0.99977654,0.00007075352,0.000024242132,0.0000467752,0.000050285536,0.00003142408],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034002858,0.000042764856,0.00004527007,0.00001732271,0.00051524583,0.0000452134,0.00008803282,0.000038290884,0.000032178028],"category_scores_gemma":[0.00002865145,0.00003398312,0.000040391726,0.00011345525,0.000053918633,0.00009103755,4.23124e-7,0.00004605188,0.0000057322545],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009615299,0.00007630773,0.020175284,0.000094634706,0.00006112443,0.000009191906,0.19139424,0.14533933,0.00083903933,0.6370808,0.0011990962,0.0036348172],"study_design_scores_gemma":[0.008278737,0.00030300106,0.10698616,0.00022517268,0.00033414303,0.0000014524901,0.06364825,0.0025900858,0.007472704,0.08247223,0.7261848,0.0015032922],"about_ca_topic_score_codex":0.00053505274,"about_ca_topic_score_gemma":0.0012265763,"teacher_disagreement_score":0.94796807,"about_ca_system_score_codex":0.000020290236,"about_ca_system_score_gemma":0.00006540623,"threshold_uncertainty_score":0.39629063},"labels":[],"label_agreement":null},{"id":"W658215482","doi":"","title":"RESOLUTION OF LRT DELAYS IN DOWNTOWN CALGARY: A LOW COST TECH, HIGH BENEEFITS SOLUTION","year":2003,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Downtown; TRIPS architecture; Transit (satellite); Per capita; Transport engineering; Work (physics); Geography; Service (business); Business; Agricultural economics; Public transport; Engineering; Marketing; Economics; Archaeology; Demography","score_opus":0.015140000049612176,"score_gpt":0.26209669809866093,"score_spread":0.24695669804904874,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W658215482","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.521955,0.0002113068,0.26445323,0.0012344713,0.00064116553,0.0010903393,0.000030005871,0.00031103773,0.21007343],"genre_scores_gemma":[0.99241775,0.00007440563,0.006617476,0.000048691476,0.000014573988,0.00001419621,0.000048329286,0.0000049893056,0.00075961155],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9990709,0.00010993706,0.00023466456,0.00014302901,0.00024287614,0.00019860685],"domain_scores_gemma":[0.9996435,0.00004732169,0.000077662735,0.000078184734,0.00010122227,0.00005210004],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00053609064,0.00006334234,0.00010230339,0.00012787228,0.00015153903,0.000013922711,0.000066026725,0.00012237212,0.00027301282],"category_scores_gemma":[0.00013218154,0.00006486014,0.000026352072,0.0005208035,0.00008081385,0.00018491766,0.000002160386,0.000070936025,0.000009785158],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000101547244,0.00036834503,0.115403526,0.00003886521,0.000018411663,0.000007543971,0.013377159,0.08422306,0.0009074345,0.7713836,0.0019899635,0.012180532],"study_design_scores_gemma":[0.00653634,0.00033425892,0.8744654,0.0005906252,0.00011901075,0.0000047578374,0.01087379,0.036922734,0.011846732,0.011044324,0.04563781,0.0016242116],"about_ca_topic_score_codex":0.012303633,"about_ca_topic_score_gemma":0.01994223,"teacher_disagreement_score":0.76033926,"about_ca_system_score_codex":0.00008986107,"about_ca_system_score_gemma":0.00013852265,"threshold_uncertainty_score":0.99794126},"labels":[],"label_agreement":null},{"id":"W68121058","doi":"","title":"Route Choice Modelling for Urban Commuters: Considering Bridge choice as a key determinant of selected routes","year":2013,"lang":"en","type":"article","venue":"Transportation Research Board 92nd Annual MeetingTransportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"TRIPS architecture; Bridge (graph theory); Transport engineering; Discrete choice; Travel behavior; Choice set; Mode choice; Population; Declaration; Value of time; Computer science; Set (abstract data type); Scale (ratio); Aggregate (composite); Operations research; Travel time; Geography; Econometrics; Public transport; Engineering; Economics; Demography","score_opus":0.09456174737871358,"score_gpt":0.39506147142628995,"score_spread":0.30049972404757636,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W68121058","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97453624,0.00025773369,0.01527689,0.0029121926,0.00021885397,0.004348996,0.0006913941,0.000398958,0.0013587135],"genre_scores_gemma":[0.9808715,0.00039387503,0.014221767,0.00008732917,0.0003214816,0.0012337886,0.0012396276,0.00012813974,0.0015025189],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.98964375,0.0013880127,0.0017858834,0.0011529436,0.0039053287,0.0021240993],"domain_scores_gemma":[0.98189825,0.0052499394,0.00048397464,0.0005526363,0.010999971,0.0008152183],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.00555031,0.00048214017,0.0007652061,0.0014092643,0.0023461906,0.0004423239,0.00095054484,0.00050374295,0.00034445318],"category_scores_gemma":[0.0017193884,0.00053343957,0.00031504102,0.0032008982,0.0013947746,0.002048044,0.000011061497,0.0013096341,0.000065085864],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013893385,0.0011136214,0.6357877,0.0019666348,0.00048021693,0.000068970316,0.23013566,0.07432237,0.0066634896,0.024922581,0.017459616,0.0056897555],"study_design_scores_gemma":[0.004793174,0.0012355258,0.86703974,0.0013195821,0.00017370832,6.26356e-7,0.03670062,0.014852487,0.0039017177,0.002861373,0.065691315,0.0014301476],"about_ca_topic_score_codex":0.17681573,"about_ca_topic_score_gemma":0.061491553,"teacher_disagreement_score":0.23125198,"about_ca_system_score_codex":0.00026849099,"about_ca_system_score_gemma":0.0014842117,"threshold_uncertainty_score":0.9997117},"labels":[],"label_agreement":null},{"id":"W6888741166","doi":"10.22004/ag.econ.319310","title":"Expansion of a GPS Truck Trip Sample to Remove Bias and Obtain Representative Flows for Ontario","year":2016,"lang":"en","type":"article","venue":"RePEc: Research Papers in Economics","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Global Positioning System; Sample (material); Truck; Data collection; Estimation","score_opus":0.06660280482210493,"score_gpt":0.3548119945395938,"score_spread":0.2882091897174889,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6888741166","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98929226,0.000008853122,0.00067742105,0.00096657063,0.00007820957,0.0006512199,0.00006433222,0.000015423164,0.008245687],"genre_scores_gemma":[0.9808293,0.0005325379,0.014324714,0.000042707095,0.000051215276,0.00008038247,0.000017027529,0.000013226393,0.004108865],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99882364,0.00016667438,0.0002777394,0.00029484418,0.00015215104,0.000284976],"domain_scores_gemma":[0.9976578,0.0018684765,0.00007147819,0.00015292472,0.00012384955,0.00012548578],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016416667,0.00006570435,0.0001624654,0.00018858277,0.00017521532,0.000027323817,0.00012252474,0.00008010511,0.00006662988],"category_scores_gemma":[0.0018406656,0.000056840134,0.000039107224,0.00013054899,0.00014569484,0.00015127963,0.000017504617,0.000077967765,9.592867e-7],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016286606,0.00012583836,0.32228118,0.000047805923,0.00006214337,0.0000061126552,0.13634671,0.0061000134,0.0019239604,0.014002274,0.0004891546,0.51698613],"study_design_scores_gemma":[0.010148238,0.001654945,0.50945646,0.00077250303,0.000036154648,0.0000027389638,0.08710177,0.004439048,0.0055624214,0.02021762,0.35935587,0.0012522516],"about_ca_topic_score_codex":0.012913896,"about_ca_topic_score_gemma":0.16683576,"teacher_disagreement_score":0.5157339,"about_ca_system_score_codex":0.0003072105,"about_ca_system_score_gemma":0.0004254514,"threshold_uncertainty_score":0.9936592},"labels":[],"label_agreement":null},{"id":"W6889142282","doi":"10.25384/sage.19641169","title":"sj-docx-1-trr-10.1177_03611981221085531 – Supplemental material for The Impact of a Single Bus Rapid Transit Corridor on Transit Ridership: The Winnipeg Example","year":2022,"lang":"en","type":"article","venue":"Sage Journals Data","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Transit (satellite); Rail transit; Public transport; Rapid transit; Transit system; Urban transit","score_opus":0.0990027772788817,"score_gpt":0.34307595531687507,"score_spread":0.2440731780379934,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6889142282","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.46120796,0.00988034,0.058977522,0.047928773,0.006062414,0.007988276,0.40412062,0.00039664534,0.0034374685],"genre_scores_gemma":[0.993455,0.0004331927,0.00025054574,0.00020426286,0.0004144599,0.000045654313,0.0046665994,0.000028196086,0.0005021006],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9977066,0.00044381717,0.00047386228,0.0002814295,0.00070033,0.0003939823],"domain_scores_gemma":[0.99837893,0.0006147126,0.00030872665,0.0005221287,0.000080602054,0.00009489224],"candidate_categories":["sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0025953401,0.00016481805,0.00022949302,0.00008565495,0.0018641158,0.00021256688,0.0011853941,0.000049586095,0.05834545],"category_scores_gemma":[0.00008124938,0.000107502616,0.00018635756,0.00037541724,0.00017285476,0.00040414988,0.00003357029,0.00023197787,0.0000041694457],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0060675014,0.0018794761,0.0029875136,0.00007561217,0.0013902285,0.00004823284,0.14773892,0.1599136,0.015859522,0.0002924665,0.5283495,0.13539739],"study_design_scores_gemma":[0.009937827,0.0042877793,0.03463254,0.00036047123,0.0012354814,0.00006281792,0.100025155,0.008478098,0.0031118402,0.0009556903,0.83560777,0.0013045608],"about_ca_topic_score_codex":0.004362393,"about_ca_topic_score_gemma":0.0013881644,"teacher_disagreement_score":0.53224707,"about_ca_system_score_codex":0.00010130951,"about_ca_system_score_gemma":0.00026250214,"threshold_uncertainty_score":0.9994353},"labels":[],"label_agreement":null},{"id":"W6892563265","doi":"10.5281/zenodo.10690697","title":"Market demand analysis and forecasting through applied econometrics: Empirical research in the public transit sector in the Toronto City Area (1969-2019)","year":2023,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Public transport; Equity (law); Empirical research; Demand forecasting; Public sector; Supply and demand; Transit (satellite); Time series","score_opus":0.21742802260266558,"score_gpt":0.34061073434594613,"score_spread":0.12318271174328055,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6892563265","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.52785325,0.0002353052,0.007084276,0.013287767,0.00007773022,0.0014872741,0.00018424925,0.00047507585,0.44931507],"genre_scores_gemma":[0.99856496,0.00031929897,0.000098747296,0.00013540829,0.00005989294,4.6157848e-7,0.00051890616,0.000103903316,0.00019841429],"study_design_codex":"qualitative","study_design_gemma":"not_applicable","domain_scores_codex":[0.9968155,0.0014902883,0.0002729839,0.0003413287,0.0006496694,0.00043025627],"domain_scores_gemma":[0.998885,0.0005370799,0.00006414305,0.00023095444,0.00021221268,0.00007060801],"candidate_categories":["sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.008789855,0.000086663385,0.00014125608,0.0005559987,0.0025409819,0.0010086086,0.0007662015,0.0000776534,0.0021419995],"category_scores_gemma":[0.000841852,0.00006802815,0.000040188843,0.0061738375,0.00023270253,0.00038540235,0.00008394234,0.0002985717,0.000095335694],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038693924,0.0006622554,0.03680892,0.00017241565,0.00033381802,0.00010776599,0.6035176,0.0054930486,0.00006953117,0.04200088,0.22049293,0.089953914],"study_design_scores_gemma":[0.0007047799,0.00008866339,0.3127662,0.000018466313,0.00003918438,0.0000059184094,0.056407075,0.00552536,0.0000029466141,0.0010287351,0.62318194,0.0002307439],"about_ca_topic_score_codex":0.00084500253,"about_ca_topic_score_gemma":0.0013929642,"teacher_disagreement_score":0.5471105,"about_ca_system_score_codex":0.0001533162,"about_ca_system_score_gemma":0.000012663876,"threshold_uncertainty_score":0.9987702},"labels":[],"label_agreement":null},{"id":"W6902048494","doi":"10.6084/m9.figshare.20317514","title":"Additional file 1 of Does group-based trajectory modeling estimate spurious trajectories?","year":2022,"lang":"en","type":"article","venue":"Open MIND","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique; Université de Montréal","funders":"","keywords":"Trajectory; Table (database); Spurious relationship; Bayesian probability; Code (set theory); Entropy (arrow of time)","score_opus":0.030125090035967063,"score_gpt":0.29994495963570206,"score_spread":0.269819869599735,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6902048494","genre_codex":"dataset","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.036974095,0.000017390166,0.0028744822,0.00022818013,0.00036489393,0.00059112615,0.9119789,0.000022883247,0.04694805],"genre_scores_gemma":[0.6910722,0.0000014510819,0.11536913,0.00007626915,0.00012086323,0.00052227697,0.18742013,0.000023778573,0.0053938976],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99911404,0.00009481835,0.0001733238,0.00016678427,0.000321402,0.00012964438],"domain_scores_gemma":[0.9993582,0.0003644698,0.00010336895,0.000074946336,0.000051418025,0.000047612488],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00021066162,0.000059581547,0.00010025122,0.000055363995,0.00057710585,0.000049291804,0.00024577972,0.000029549316,0.8817004],"category_scores_gemma":[0.00011762897,0.000059185288,0.00003975825,0.00021889023,0.00006024995,0.00016695436,0.000013503374,0.00008465042,0.000022689168],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009323328,0.00018856568,0.0001570914,0.000006778782,0.000019145973,0.000011256643,0.0097025065,0.74358815,0.000022960849,0.00014558193,0.23721224,0.008852479],"study_design_scores_gemma":[0.00044714106,0.000085653446,0.0006874966,0.00005444449,0.000022563096,5.9508164e-7,0.006799558,0.09277165,0.00003980637,0.00017596383,0.8986745,0.00024062577],"about_ca_topic_score_codex":0.0005530578,"about_ca_topic_score_gemma":0.0014356149,"teacher_disagreement_score":0.8816777,"about_ca_system_score_codex":0.000047090725,"about_ca_system_score_gemma":0.0003690688,"threshold_uncertainty_score":0.443869},"labels":[],"label_agreement":null},{"id":"W6907459497","doi":"10.22004/ag.econ.319252","title":"Planning For Real Or For Show? An Analysis of the Completion of Montreal's Transportation Plans","year":2016,"lang":"en","type":"article","venue":"RePEc: Research Papers in Economics","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Completion (oil and gas wells); Work (physics); Plan (archaeology); Production (economics); Data collection","score_opus":0.05907674873899932,"score_gpt":0.3688289072842517,"score_spread":0.3097521585452524,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6907459497","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9943439,0.000002947391,0.0020936423,0.00031762294,0.00005593692,0.0005552588,0.00085336325,0.000013067717,0.0017642945],"genre_scores_gemma":[0.99820095,0.00023283207,0.000919997,0.0000067807596,0.000027107837,0.00006059356,0.00018152874,0.0000075041426,0.00036270288],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9990729,0.00009628558,0.00032287245,0.00017238219,0.00014399832,0.00019158017],"domain_scores_gemma":[0.99865264,0.00086089986,0.00015134578,0.00013937785,0.00015245064,0.000043286782],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010875522,0.000051016872,0.00018412876,0.0002131877,0.00017441518,0.000010034533,0.00016699318,0.00007677504,0.000016712906],"category_scores_gemma":[0.00017055108,0.00003500893,0.00009649416,0.0002448054,0.00018438387,0.00012687761,0.000001772632,0.000038778206,3.682736e-8],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015535805,0.00016222033,0.67148143,0.00006841822,0.00025708045,5.54538e-7,0.028786471,0.22093613,0.0013831767,0.013266448,0.000028302993,0.062076204],"study_design_scores_gemma":[0.00094178,0.00016513554,0.968349,0.0000618371,0.00009701203,3.5771283e-8,0.008162519,0.019593345,0.00035348083,0.00033872735,0.001838398,0.00009874058],"about_ca_topic_score_codex":0.0011233197,"about_ca_topic_score_gemma":0.05188473,"teacher_disagreement_score":0.29686758,"about_ca_system_score_codex":0.000088215,"about_ca_system_score_gemma":0.00016315527,"threshold_uncertainty_score":0.9654159},"labels":[],"label_agreement":null},{"id":"W6911045320","doi":"10.5281/zenodo.10253557","title":"Should Translink Expand The Public Transit Network in Vancouver CMA: An analysis on possibility for public transit system restructure and expansion","year":2023,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Restructuring; Public transport; Transit (satellite); Rail transit; Transit system; Rapid transit","score_opus":0.06765466438612178,"score_gpt":0.292568803225526,"score_spread":0.2249141388394042,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6911045320","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9175404,0.00013148438,0.057385374,0.010454999,0.00048867567,0.0025259533,0.00060540886,0.002470284,0.008397456],"genre_scores_gemma":[0.99766743,0.000070390015,0.00015535975,0.000103374165,0.00013483335,5.2907427e-7,0.0014761094,0.0002448678,0.00014712379],"study_design_codex":"qualitative","study_design_gemma":"not_applicable","domain_scores_codex":[0.9973503,0.0008275239,0.00033234584,0.0005093899,0.0005014695,0.00047897923],"domain_scores_gemma":[0.9988983,0.00011325912,0.000087049244,0.00032919168,0.0003631332,0.00020901194],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0025350256,0.00014548356,0.00021359515,0.0003910392,0.003545713,0.00079033413,0.00052662607,0.00014934043,0.00043253222],"category_scores_gemma":[0.00018103134,0.00012538872,0.0000863835,0.0027295717,0.00020788054,0.0004907485,0.000025678792,0.00024999873,0.0000345424],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0020052672,0.00070587924,0.004845361,0.00073349616,0.0009523072,0.00007978345,0.27474472,0.26252452,0.0006135597,0.1184752,0.061201572,0.27311832],"study_design_scores_gemma":[0.0037121119,0.0008244244,0.10214268,0.00014856116,0.00047592414,0.0000089781215,0.0781812,0.07916462,0.000040707127,0.0012068725,0.7331141,0.0009798256],"about_ca_topic_score_codex":0.00016099057,"about_ca_topic_score_gemma":0.0014472986,"teacher_disagreement_score":0.67191255,"about_ca_system_score_codex":0.0001223775,"about_ca_system_score_gemma":0.000018573579,"threshold_uncertainty_score":0.99775153},"labels":[],"label_agreement":null},{"id":"W6939620894","doi":"10.6084/m9.figshare.19722683","title":"Choice set robustness and internal consistency in correlation-based logit stochastic user equilibrium models","year":2022,"lang":"en","type":"article","venue":"Figshare","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Consistency (knowledge bases); Logit; Robustness (evolution); Set (abstract data type); Mixed logit; Choice set; Key (lock)","score_opus":0.06061140742671158,"score_gpt":0.29238948002530823,"score_spread":0.23177807259859665,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6939620894","genre_codex":"dataset","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.36920053,0.002070154,0.0611598,0.0052825417,0.0021791847,0.0043551144,0.51547927,0.0015381703,0.038735252],"genre_scores_gemma":[0.9773601,2.2479031e-7,0.00011118559,0.00013207567,0.00002567493,0.00009529593,0.021710664,0.0000073440233,0.00055739516],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992738,0.00008874753,0.00013510842,0.00015215401,0.00021636575,0.00013383778],"domain_scores_gemma":[0.9995344,0.00022918169,0.000070217175,0.00005832677,0.00005870109,0.000049164253],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000095619755,0.00005947654,0.000074508025,0.00007895726,0.00022518254,0.00004653182,0.00010595311,0.000039583017,0.022729727],"category_scores_gemma":[0.00030651342,0.00007014989,0.000018830031,0.00022083354,0.000015052937,0.00020023345,0.000016952701,0.00013454426,0.000011083414],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007944136,0.000012488732,0.0020108537,0.000011206486,0.0000018149917,0.0000042240354,0.0017462493,0.99035585,3.7617502e-7,0.00015336461,0.0056371847,0.000058447888],"study_design_scores_gemma":[0.00053458824,0.000025909818,0.013499904,0.00021755637,0.000006934736,8.8214944e-7,0.0014201803,0.9804829,7.5354745e-7,0.00012882888,0.0035126659,0.00016891242],"about_ca_topic_score_codex":0.00035143257,"about_ca_topic_score_gemma":0.0007155056,"teacher_disagreement_score":0.6081596,"about_ca_system_score_codex":0.00005644175,"about_ca_system_score_gemma":0.0001755525,"threshold_uncertainty_score":0.97816366},"labels":[],"label_agreement":null},{"id":"W6948875792","doi":"10.5281/zenodo.10253556","title":"Should Translink Expand The Public Transit Network in Vancouver CMA: An analysis on possibility for public transit system restructure and expansion","year":2023,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Restructuring; Public transport; Transit (satellite); Rail transit; Transit system; Rapid transit","score_opus":0.06765466438612178,"score_gpt":0.292568803225526,"score_spread":0.2249141388394042,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6948875792","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9175404,0.00013148438,0.057385374,0.010454999,0.00048867567,0.0025259533,0.00060540886,0.002470284,0.008397456],"genre_scores_gemma":[0.99766743,0.000070390015,0.00015535975,0.000103374165,0.00013483335,5.2907427e-7,0.0014761094,0.0002448678,0.00014712379],"study_design_codex":"qualitative","study_design_gemma":"not_applicable","domain_scores_codex":[0.9973503,0.0008275239,0.00033234584,0.0005093899,0.0005014695,0.00047897923],"domain_scores_gemma":[0.9988983,0.00011325912,0.000087049244,0.00032919168,0.0003631332,0.00020901194],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0025350256,0.00014548356,0.00021359515,0.0003910392,0.003545713,0.00079033413,0.00052662607,0.00014934043,0.00043253222],"category_scores_gemma":[0.00018103134,0.00012538872,0.0000863835,0.0027295717,0.00020788054,0.0004907485,0.000025678792,0.00024999873,0.0000345424],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0020052672,0.00070587924,0.004845361,0.00073349616,0.0009523072,0.00007978345,0.27474472,0.26252452,0.0006135597,0.1184752,0.061201572,0.27311832],"study_design_scores_gemma":[0.0037121119,0.0008244244,0.10214268,0.00014856116,0.00047592414,0.0000089781215,0.0781812,0.07916462,0.000040707127,0.0012068725,0.7331141,0.0009798256],"about_ca_topic_score_codex":0.00016099057,"about_ca_topic_score_gemma":0.0014472986,"teacher_disagreement_score":0.67191255,"about_ca_system_score_codex":0.0001223775,"about_ca_system_score_gemma":0.000018573579,"threshold_uncertainty_score":0.99775153},"labels":[],"label_agreement":null},{"id":"W6949094329","doi":"10.5281/zenodo.10690698","title":"Market demand analysis and forecasting through applied econometrics: Empirical research in the public transit sector in the Toronto City Area (1969-2019)","year":2023,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Public transport; Equity (law); Empirical research; Demand forecasting; Public sector; Supply and demand; Transit (satellite); Time series","score_opus":0.21742802260266558,"score_gpt":0.34061073434594613,"score_spread":0.12318271174328055,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6949094329","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.52785325,0.0002353052,0.007084276,0.013287767,0.00007773022,0.0014872741,0.00018424925,0.00047507585,0.44931507],"genre_scores_gemma":[0.99856496,0.00031929897,0.000098747296,0.00013540829,0.00005989294,4.6157848e-7,0.00051890616,0.000103903316,0.00019841429],"study_design_codex":"qualitative","study_design_gemma":"not_applicable","domain_scores_codex":[0.9968155,0.0014902883,0.0002729839,0.0003413287,0.0006496694,0.00043025627],"domain_scores_gemma":[0.998885,0.0005370799,0.00006414305,0.00023095444,0.00021221268,0.00007060801],"candidate_categories":["sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.008789855,0.000086663385,0.00014125608,0.0005559987,0.0025409819,0.0010086086,0.0007662015,0.0000776534,0.0021419995],"category_scores_gemma":[0.000841852,0.00006802815,0.000040188843,0.0061738375,0.00023270253,0.00038540235,0.00008394234,0.0002985717,0.000095335694],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038693924,0.0006622554,0.03680892,0.00017241565,0.00033381802,0.00010776599,0.6035176,0.0054930486,0.00006953117,0.04200088,0.22049293,0.089953914],"study_design_scores_gemma":[0.0007047799,0.00008866339,0.3127662,0.000018466313,0.00003918438,0.0000059184094,0.056407075,0.00552536,0.0000029466141,0.0010287351,0.62318194,0.0002307439],"about_ca_topic_score_codex":0.00084500253,"about_ca_topic_score_gemma":0.0013929642,"teacher_disagreement_score":0.5471105,"about_ca_system_score_codex":0.0001533162,"about_ca_system_score_gemma":0.000012663876,"threshold_uncertainty_score":0.9987702},"labels":[],"label_agreement":null},{"id":"W6963671859","doi":"10.22004/ag.econ.305969","title":"Structure of Road-User Taxes for Large Commercial Trucks in Canada","year":2020,"lang":"en","type":"article","venue":"AgEcon Search (University of Minnesota, USA)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Truck; Government (linguistics); Work (physics); Payment","score_opus":0.024668058938733684,"score_gpt":0.24861049827683726,"score_spread":0.22394243933810357,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6963671859","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99462545,0.0000140820885,0.0012873937,0.002586751,0.000047853802,0.00017971385,0.0006804611,0.0000057682673,0.00057251944],"genre_scores_gemma":[0.9985452,0.000023555947,0.0009008359,0.00010011399,0.00002152469,6.177552e-8,0.000068261805,0.0000035876994,0.00033685105],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99928844,0.00006853636,0.000099739555,0.0001253109,0.0002367718,0.00018119864],"domain_scores_gemma":[0.99956757,0.00009110662,0.00007399583,0.00005504734,0.000117711694,0.00009457567],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014134691,0.000051263574,0.00015653594,0.00005836399,0.00014224212,0.0000047353346,0.00020359085,0.00005826606,0.0005848651],"category_scores_gemma":[0.000042665804,0.000066809334,0.000037099304,0.00026453397,0.000069898364,0.00016834313,0.0000121862395,0.00008553811,0.0000013827678],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00052068,0.00007983006,0.77943355,0.00024698093,0.000078097575,0.000044598888,0.16880089,0.005795749,0.0012246136,0.013723376,0.020520972,0.009530671],"study_design_scores_gemma":[0.002497743,0.00011461876,0.8439773,0.00003904826,0.000033115408,1.6547088e-7,0.10655535,0.002558774,0.00059006386,0.000085680775,0.043309268,0.00023890687],"about_ca_topic_score_codex":0.80246496,"about_ca_topic_score_gemma":0.98978007,"teacher_disagreement_score":0.18731508,"about_ca_system_score_codex":0.0000660111,"about_ca_system_score_gemma":0.0006927255,"threshold_uncertainty_score":0.6403863},"labels":[],"label_agreement":null},{"id":"W6968888198","doi":"10.5281/zenodo.5635137","title":"Polypedilum gomphus Townes 1945","year":2016,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Natural (archaeology); Type (biology); Biodiversity; Taxonomy (biology)","score_opus":0.0350423036391441,"score_gpt":0.26779455749975023,"score_spread":0.23275225386060613,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6968888198","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.045934267,0.0000994019,0.043941993,0.013098073,0.00055236585,0.0005814645,0.00027098818,0.0032306388,0.89229083],"genre_scores_gemma":[0.98640645,0.00018726013,0.0005281507,0.00015578863,0.0003092792,3.1022353e-8,0.0003629514,0.00047501008,0.011575103],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9987127,0.00024415142,0.00015972568,0.0002391038,0.00035109822,0.00029321504],"domain_scores_gemma":[0.9991866,0.000037732858,0.00006910615,0.0001781691,0.00036811762,0.00016032654],"candidate_categories":["sts","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0005861541,0.000076597105,0.00007765404,0.00010921206,0.0045428923,0.000643944,0.00048547215,0.000060853574,0.011355932],"category_scores_gemma":[0.00054964167,0.00006480066,0.000034249824,0.00037523208,0.00022033417,0.00033086358,0.000075445445,0.000072833274,0.0045578433],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010368421,0.00016329727,0.00023542995,0.000026034417,0.000045488312,0.00001783435,0.01562594,0.000094344585,0.0035415855,0.07056603,0.39854428,0.51103604],"study_design_scores_gemma":[0.00029034528,0.000047903126,0.0016384966,0.0000226152,0.0000065078884,0.0000043352825,0.0007321447,0.000019872261,0.000114173585,0.00026775987,0.99674594,0.00010987948],"about_ca_topic_score_codex":0.00012032903,"about_ca_topic_score_gemma":0.000007648844,"teacher_disagreement_score":0.9404722,"about_ca_system_score_codex":0.00009182307,"about_ca_system_score_gemma":0.000007228973,"threshold_uncertainty_score":0.99675304},"labels":[],"label_agreement":null},{"id":"W6969635401","doi":"10.5683/sp3/uqpmuk","title":"Labour Force Survey, October 2020 [Canada] [Rebased 2025]","year":2023,"lang":"en","type":"dataset","venue":"Borealis","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Statistics Canada","funders":"","keywords":"Unemployment; Current Population Survey; Government (linguistics); Population; Wage; Descriptive statistics; Discouraged worker; Minimum wage","score_opus":0.02467548478279203,"score_gpt":0.29539188054481696,"score_spread":0.27071639576202494,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6969635401","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00001617459,0.0000455887,0.000026155314,0.00076652155,0.0006344661,0.00018731225,0.9973568,0.000108963315,0.00085807295],"genre_scores_gemma":[0.000019208179,0.00052827806,0.000039653718,0.00056677207,0.00034572868,0.000020169202,0.9881115,0.00002247757,0.010346222],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.998003,0.00029648835,0.00029205877,0.00032166095,0.0007343983,0.00035237553],"domain_scores_gemma":[0.9987271,0.0003753819,0.00019107244,0.00030969517,0.00021714372,0.00017962596],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007856924,0.0001870833,0.00024991148,0.00007793986,0.00038673816,0.00010122163,0.0004038154,0.00029180074,0.00026680127],"category_scores_gemma":[0.0006073029,0.00020048619,0.000056970894,0.00055801513,0.00007253239,0.0000810452,0.000012955176,0.00022529742,0.00007768185],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011044737,0.000010063627,0.0005323598,0.000025651923,0.000027019005,0.00004299814,0.00011834191,0.0003772993,3.5392816e-8,0.00009944165,0.9987213,0.00003445062],"study_design_scores_gemma":[0.00012242595,0.000007072722,0.049825933,0.000032252392,0.000032611515,1.14976785e-7,0.00015677647,0.0000074566706,3.2362803e-7,0.000024897436,0.9495663,0.00022380617],"about_ca_topic_score_codex":0.9988088,"about_ca_topic_score_gemma":0.9998172,"teacher_disagreement_score":0.049293574,"about_ca_system_score_codex":0.0001359782,"about_ca_system_score_gemma":0.001694463,"threshold_uncertainty_score":0.81755894},"labels":[],"label_agreement":null},{"id":"W6976822380","doi":"10.6084/m9.figshare.19722683.v1","title":"Choice set robustness and internal consistency in correlation-based logit stochastic user equilibrium models","year":2022,"lang":"en","type":"article","venue":"Figshare","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Consistency (knowledge bases); Logit; Robustness (evolution); Set (abstract data type); Mixed logit; Choice set; Key (lock)","score_opus":0.06061140742671158,"score_gpt":0.29238948002530823,"score_spread":0.23177807259859665,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6976822380","genre_codex":"dataset","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.36920053,0.002070154,0.0611598,0.0052825417,0.0021791847,0.0043551144,0.51547927,0.0015381703,0.038735252],"genre_scores_gemma":[0.9773601,2.2479031e-7,0.00011118559,0.00013207567,0.00002567493,0.00009529593,0.021710664,0.0000073440233,0.00055739516],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992738,0.00008874753,0.00013510842,0.00015215401,0.00021636575,0.00013383778],"domain_scores_gemma":[0.9995344,0.00022918169,0.000070217175,0.00005832677,0.00005870109,0.000049164253],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000095619755,0.00005947654,0.000074508025,0.00007895726,0.00022518254,0.00004653182,0.00010595311,0.000039583017,0.022729727],"category_scores_gemma":[0.00030651342,0.00007014989,0.000018830031,0.00022083354,0.000015052937,0.00020023345,0.000016952701,0.00013454426,0.000011083414],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007944136,0.000012488732,0.0020108537,0.000011206486,0.0000018149917,0.0000042240354,0.0017462493,0.99035585,3.7617502e-7,0.00015336461,0.0056371847,0.000058447888],"study_design_scores_gemma":[0.00053458824,0.000025909818,0.013499904,0.00021755637,0.000006934736,8.8214944e-7,0.0014201803,0.9804829,7.5354745e-7,0.00012882888,0.0035126659,0.00016891242],"about_ca_topic_score_codex":0.00035143257,"about_ca_topic_score_gemma":0.0007155056,"teacher_disagreement_score":0.6081596,"about_ca_system_score_codex":0.00005644175,"about_ca_system_score_gemma":0.0001755525,"threshold_uncertainty_score":0.97816366},"labels":[],"label_agreement":null},{"id":"W6977081180","doi":"10.6084/m9.figshare.20317514.v1","title":"Additional file 1 of Does group-based trajectory modeling estimate spurious trajectories?","year":2022,"lang":"en","type":"article","venue":"Figshare","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique; Université de Montréal","funders":"","keywords":"Trajectory; Table (database); Spurious relationship; Bayesian probability; Code (set theory); Entropy (arrow of time)","score_opus":0.02693521946820873,"score_gpt":0.2654624848574811,"score_spread":0.23852726538927235,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6977081180","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00010471078,0.000012338077,0.000039883398,0.000027525148,0.000050378694,0.00009873839,0.9966777,0.00011388725,0.0028748342],"genre_scores_gemma":[0.05972096,1.549036e-7,0.0019106226,0.000056241362,0.00006595192,0.0006724263,0.93717283,0.000010313184,0.0003905241],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991295,0.00006857226,0.00014561544,0.00014018397,0.00037550004,0.00014063795],"domain_scores_gemma":[0.99913865,0.0005612121,0.00010000791,0.00006820966,0.000083138664,0.000048799127],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000044900076,0.00006443193,0.0000838075,0.00006511113,0.00051759864,0.000018833298,0.00013665292,0.00003641988,0.99429846],"category_scores_gemma":[0.00065200333,0.00006588811,0.000059656893,0.00023828453,0.00001217567,0.00010185777,0.000007173926,0.00010264047,0.00006278824],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007468597,0.000025313384,0.000005838298,0.000016720098,0.0000033853607,0.000002992079,0.0008021381,0.19895406,7.7391945e-7,0.000020711816,0.8000052,0.00015541604],"study_design_scores_gemma":[0.00016921922,0.000038607075,0.00068752014,0.00028887158,0.0000062234894,3.5487056e-7,0.0011614244,0.08246896,0.0000066680104,0.00007572097,0.9149264,0.00017001676],"about_ca_topic_score_codex":0.00009504248,"about_ca_topic_score_gemma":0.00030426885,"teacher_disagreement_score":0.99423563,"about_ca_system_score_codex":0.000050795763,"about_ca_system_score_gemma":0.00028413723,"threshold_uncertainty_score":0.39810026},"labels":[],"label_agreement":null},{"id":"W6980022833","doi":"","title":"Applying Agreed-Upon Procedures: Highway Trust Fund Excise Taxes","year":2001,"lang":"en","type":"article","venue":"University of North Texas Digital Library (University of North Texas)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"U.S. Department of Transportation","keywords":"Excise; Treasury; Revenue; Debt; Service (business); Quarter (Canadian coin); Tax revenue; Fund accounting","score_opus":0.01648531427173738,"score_gpt":0.19683968411295757,"score_spread":0.1803543698412202,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6980022833","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.965989,0.00005261809,0.0019660445,0.00037390704,0.00008092269,0.00048358267,0.00085602736,0.00030472202,0.029893156],"genre_scores_gemma":[0.9733728,0.00048809306,0.0012877077,0.00003709571,0.000045329234,6.1009686e-8,0.0007401943,0.000018299115,0.02401038],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9983774,0.00006842452,0.0002031458,0.0004281681,0.0005332365,0.00038962535],"domain_scores_gemma":[0.99879086,0.000117847936,0.00039014113,0.00025203387,0.00015707123,0.00029205246],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00007532722,0.00022370333,0.00035559296,0.0003934925,0.0007323139,0.000072329916,0.0008388346,0.00013943875,0.00064974604],"category_scores_gemma":[0.000025661322,0.00030976406,0.00021706782,0.0011068676,0.00060353486,0.0041540004,0.000118087424,0.00018450407,0.000094595795],"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":[0.00029404112,0.0001905726,0.98545027,0.00007233348,0.00006753573,0.00012873088,0.0012146748,0.0025819875,8.0643964e-8,0.0005644099,0.0032996067,0.006135772],"study_design_scores_gemma":[0.0006806596,0.00008419146,0.82350326,0.00006846667,0.000060361617,0.0000020161096,0.0013088102,0.00020971961,0.0000012666496,0.000049143182,0.1737437,0.00028842318],"about_ca_topic_score_codex":0.0015337563,"about_ca_topic_score_gemma":0.014408954,"teacher_disagreement_score":0.17044409,"about_ca_system_score_codex":0.000053808548,"about_ca_system_score_gemma":0.00040062697,"threshold_uncertainty_score":0.99993545},"labels":[],"label_agreement":null},{"id":"W6980127327","doi":"","title":"Availability Payments And Key Performance Indicators: Challenges For Effective Implementation Of Performance Management Systems In Transportation Public-Private Partnerships","year":2014,"lang":"en","type":"article","venue":"Journal of International Crisis and Risk Communication Research","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Performance indicator; Performance measurement; Payment; Performance management; Key (lock); Agency (philosophy)","score_opus":0.07807103482059011,"score_gpt":0.3985074380227803,"score_spread":0.32043640320219025,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6980127327","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99557674,0.00095255487,0.00091424613,0.001650049,0.00006682723,0.0004230932,0.000014441638,0.0000047625304,0.0003972646],"genre_scores_gemma":[0.9364033,0.062243484,0.0012186948,0.000006105784,0.000028768387,0.000054415203,0.000027601383,0.0000049297755,0.000012698063],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99797994,0.0006731648,0.00052220485,0.00011793128,0.0005714758,0.00013528074],"domain_scores_gemma":[0.9982273,0.0004662089,0.00046237113,0.00011714592,0.0006636806,0.00006330579],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0076048127,0.00006246723,0.00014362391,0.000508354,0.0002855266,0.00006672138,0.0002563491,0.000056264165,0.000008431352],"category_scores_gemma":[0.00012403248,0.000055936835,0.000028434835,0.00021924888,0.00013891267,0.0005031121,0.00001415515,0.00019994067,2.8740575e-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":[0.00015511927,0.00009923117,0.88247263,0.00018545533,0.000081731094,1.3029731e-7,0.013997457,0.0005098416,0.000006714909,0.032841682,0.000056284378,0.06959374],"study_design_scores_gemma":[0.0009934191,0.00019415337,0.9612809,0.00014310532,0.000019504958,3.71129e-7,0.019381417,0.001742233,0.00007938323,0.00038314125,0.0157209,0.00006148146],"about_ca_topic_score_codex":0.00026388746,"about_ca_topic_score_gemma":0.0006285015,"teacher_disagreement_score":0.07880828,"about_ca_system_score_codex":0.00008847448,"about_ca_system_score_gemma":0.000038874587,"threshold_uncertainty_score":0.26356915},"labels":[],"label_agreement":null},{"id":"W6980290295","doi":"","title":"Brain lesion detection and tumor segmentation in MRI using 3D fully convolutional networks","year":2019,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Strong","keywords":"Segmentation; Convolutional neural network; Context (archaeology); Pattern recognition (psychology); Image segmentation; Magnetic resonance imaging; Deep learning; Focus (optics)","score_opus":0.01966966273593595,"score_gpt":0.27588454823069314,"score_spread":0.2562148854947572,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6980290295","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9908389,0.00024326851,0.00011547749,0.000025768235,0.0016268966,0.0009305551,0.00015747432,0.00017173869,0.0058899256],"genre_scores_gemma":[0.99400294,0.00022437965,0.0017788674,0.00017219815,0.00008062103,0.00004954547,0.0015416329,0.000077415185,0.0020724111],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99668866,0.00057781744,0.00071044656,0.0007653827,0.00074427016,0.0005133941],"domain_scores_gemma":[0.998459,0.0002943381,0.0005925239,0.00018229375,0.00029081418,0.0001810562],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0013349551,0.000396853,0.00041000897,0.0005119037,0.0016319234,0.00013421767,0.00019470903,0.00068143563,0.00010678562],"category_scores_gemma":[0.00030532255,0.000489603,0.00010925924,0.0007195899,0.0000893403,0.00092928857,0.00001561647,0.00082228286,0.000024477482],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0029706638,0.00074635015,0.018487038,0.0011859208,0.00040156924,0.00017675437,0.0027483283,0.4556832,0.081810616,0.09814842,0.000021665872,0.33761948],"study_design_scores_gemma":[0.024606526,0.001705994,0.45267525,0.010470749,0.0021923508,0.00013337728,0.07405001,0.29963505,0.026643783,0.022326764,0.07029177,0.015268377],"about_ca_topic_score_codex":0.00320341,"about_ca_topic_score_gemma":0.024338853,"teacher_disagreement_score":0.43418822,"about_ca_system_score_codex":0.00085002853,"about_ca_system_score_gemma":0.00015136565,"threshold_uncertainty_score":0.99975556},"labels":[],"label_agreement":null},{"id":"W6980379858","doi":"","title":"BusNet : a prototype implementation of an Intelligent Transportation System for Winnipeg urban transit","year":2003,"lang":"en","type":"dissertation","venue":"Mspace (University of Manitoba)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Transit (satellite); Global Positioning System; Bluetooth; Intelligent transportation system; Public transport; Information system; Wireless; Fuzzy logic; Management information systems; Management system","score_opus":0.0179890864380709,"score_gpt":0.2621897715349307,"score_spread":0.24420068509685983,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6980379858","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97318786,0.00010476274,0.019856626,0.00011857011,0.0005775244,0.0032594153,0.0005610978,0.00013405009,0.0022000978],"genre_scores_gemma":[0.9867249,0.00008503423,0.0051840534,0.00000533414,0.000050968563,0.000012344503,0.0066578076,0.000033187545,0.00124635],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.9986872,0.00012628676,0.00027005348,0.00029434782,0.00042053685,0.00020157255],"domain_scores_gemma":[0.99863964,0.00003735856,0.00057128654,0.0001405261,0.00052253745,0.000088623914],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036209606,0.0001704098,0.00032935574,0.0002502001,0.00035354696,0.000017187267,0.0002182883,0.00027911493,0.000028216216],"category_scores_gemma":[0.000006692737,0.00023992523,0.0001587698,0.00032203848,0.00006966893,0.0003282848,6.573436e-7,0.00009974913,0.0000021276494],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.009215063,0.0015797716,0.1408736,0.01806287,0.0015693848,0.00006142293,0.6399505,0.020160692,0.003803085,0.12240095,0.0045619304,0.03776074],"study_design_scores_gemma":[0.0015128581,0.0005728347,0.13826594,0.0004038759,0.0006891744,2.4329742e-7,0.8437648,0.00024711326,0.002686192,0.00007449592,0.011335529,0.00044698454],"about_ca_topic_score_codex":0.012676068,"about_ca_topic_score_gemma":0.749963,"teacher_disagreement_score":0.7372869,"about_ca_system_score_codex":0.00013444138,"about_ca_system_score_gemma":0.00035817735,"threshold_uncertainty_score":0.99389863},"labels":[],"label_agreement":null},{"id":"W6986143402","doi":"","title":"Oil Palm Plantations, Migration and Indian Youth Culture in Contemporary Malaysia","year":2016,"lang":"en","type":"other","venue":"York University Digital Library (York University)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Universiti Malaya","keywords":"Palm oil; Palm; Youth culture; Livelihood","score_opus":0.01377953630408063,"score_gpt":0.18833344173840488,"score_spread":0.17455390543432425,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6986143402","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0034645384,0.00023186093,0.000068395595,0.00034303355,0.00011903832,0.0001312981,0.0033314687,0.00047166008,0.9918387],"genre_scores_gemma":[0.075607695,0.0015491077,0.00022611681,0.00004498841,0.00009447029,8.941505e-8,0.0032350305,0.00006412928,0.91917837],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99892896,0.0001242929,0.00011767159,0.00038461777,0.00021006436,0.00023436375],"domain_scores_gemma":[0.99938834,0.000052926353,0.00021077701,0.000131174,0.00001689403,0.00019991261],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000030002477,0.00022453381,0.0002310375,0.0009129721,0.00026459893,0.00017991329,0.00033048997,0.00044150732,0.00034682738],"category_scores_gemma":[0.000007283522,0.00025506466,0.00007358399,0.0007526854,0.00027843245,0.0016801183,0.000039414525,0.0001831498,0.000064209155],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00049206143,0.00019703974,0.40291566,0.00016341833,0.00019876585,0.0011160823,0.0421165,0.00006517299,0.0000011446596,0.045009512,0.50369036,0.004034285],"study_design_scores_gemma":[0.0007386056,0.000021063384,0.0010314687,0.00035798518,0.00002939615,7.370978e-7,0.040056653,0.0000027085891,4.9648156e-7,0.00002455884,0.9574036,0.00033274738],"about_ca_topic_score_codex":0.0010333458,"about_ca_topic_score_gemma":0.0018355817,"teacher_disagreement_score":0.4537132,"about_ca_system_score_codex":0.00007899641,"about_ca_system_score_gemma":0.00037869893,"threshold_uncertainty_score":0.99999017},"labels":[],"label_agreement":null},{"id":"W6986165489","doi":"","title":"Out of Service: Identifying Route-level Determinants of Bus 2 Ridership over Time in Montreal, Quebec, Canada","year":2019,"lang":"en","type":"article","venue":"eScholarship@McGill (McGill)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Data collection; Identification (biology); Public transport; Government (linguistics); Work (physics)","score_opus":0.028375346049028107,"score_gpt":0.26271393009509936,"score_spread":0.23433858404607125,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6986165489","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98707557,0.000040146944,4.8500067e-7,0.00004823442,0.00040672318,0.00031228378,0.0005249194,0.000037469992,0.0115541555],"genre_scores_gemma":[0.99367386,0.000025922216,0.00018290328,0.00014357037,0.000011438628,0.000007847683,0.000056692603,0.000028116532,0.005869654],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9974677,0.0002623535,0.0007045131,0.00037409863,0.0007664941,0.0004248705],"domain_scores_gemma":[0.9985541,0.00025465098,0.0004557617,0.00028923526,0.00029315805,0.00015306147],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008122415,0.00020518953,0.00042751682,0.00018826731,0.0002555368,0.000027711418,0.000395872,0.00021088161,0.000435227],"category_scores_gemma":[0.00025509557,0.00023195149,0.00008297134,0.0005893217,0.000055337616,0.0007626021,0.00003040754,0.000255155,0.000048283433],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006300639,0.0006768546,0.8724237,0.0014102774,0.00027393774,0.00015080106,0.0036189503,0.021480616,0.02432223,0.02122466,0.00012671275,0.053661197],"study_design_scores_gemma":[0.0021729104,0.00005295537,0.9734017,0.00077870535,0.000088632216,9.727393e-7,0.0026783287,0.0007188017,0.012634661,0.0018101236,0.004879702,0.0007825054],"about_ca_topic_score_codex":0.93775403,"about_ca_topic_score_gemma":0.9978354,"teacher_disagreement_score":0.10097801,"about_ca_system_score_codex":0.00045301375,"about_ca_system_score_gemma":0.00030830418,"threshold_uncertainty_score":0.94587076},"labels":[],"label_agreement":null},{"id":"W6986560097","doi":"","title":"Pricing toll roads under uncertainty","year":2016,"lang":"en","type":"article","venue":"Research Portal (Queen's University Belfast)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Nucleofection; Gestational period; TSG101; Dysgeusia; Liquation; Diafiltration; Emperipolesis; Triacetin; Durvalumab","score_opus":0.030760570488395072,"score_gpt":0.31181867434108995,"score_spread":0.28105810385269486,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6986560097","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.647884,0.00001790849,0.01981199,0.030325837,0.00037564881,0.0008434476,0.00008986882,0.00063443533,0.30001688],"genre_scores_gemma":[0.9485343,0.00031351787,0.00029626305,0.00003650876,0.000096833064,8.359712e-7,0.000024686355,0.00001079954,0.05068626],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99753433,0.00042061473,0.00013764131,0.00032856598,0.0009683123,0.00061055785],"domain_scores_gemma":[0.9986711,0.0004265876,0.00006517266,0.00019097276,0.0003389796,0.00030717396],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0011536259,0.00010219187,0.00013058029,0.00033021596,0.0011947148,0.00005370821,0.00034271835,0.0001466958,0.0011601368],"category_scores_gemma":[0.00021383188,0.00008987908,0.00007323708,0.0007805303,0.00047592627,0.0005457104,0.000041900224,0.00022243537,0.00011651893],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004606394,0.00041192022,0.10296859,0.00005323279,0.00020639812,0.0008995526,0.018079614,0.01120605,0.00042938584,0.8069,0.040344533,0.01804004],"study_design_scores_gemma":[0.0025393895,0.00026780614,0.104721464,0.00028898055,0.00005068495,0.0000020553014,0.06154456,0.000204982,0.00024849616,0.0028340754,0.82649714,0.0008003946],"about_ca_topic_score_codex":0.008865975,"about_ca_topic_score_gemma":0.0013245036,"teacher_disagreement_score":0.80406594,"about_ca_system_score_codex":0.00029231855,"about_ca_system_score_gemma":0.00060654426,"threshold_uncertainty_score":0.99975294},"labels":[],"label_agreement":null},{"id":"W6989910647","doi":"","title":"On choice models in the context of MDPs","year":2023,"lang":"fr","type":"dissertation","venue":"Papyrus : Institutional Repository (Université de Montréal)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Alliance de recherche numérique du Canada","keywords":"Context (archaeology); Limiting; Markov chain; Determinism","score_opus":0.013332617668892023,"score_gpt":0.21195597611093933,"score_spread":0.19862335844204732,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6989910647","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9430511,0.0030509229,0.0001955287,0.0008154185,0.0013440826,0.00045517142,0.000096298034,0.00007183189,0.050919637],"genre_scores_gemma":[0.96640486,0.0006309287,0.000096811695,0.00014911378,0.00008271858,0.000021732123,0.00055868004,0.000018089531,0.03203709],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.9977555,0.00030258528,0.00041757978,0.00036866296,0.00085759745,0.00029805102],"domain_scores_gemma":[0.9984112,0.00064413744,0.0004037851,0.00021301932,0.00022415302,0.00010372446],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0004025272,0.00024868335,0.00029231815,0.0002929785,0.0038438991,0.000030619998,0.00045499683,0.00038024285,0.000023240691],"category_scores_gemma":[0.00009755173,0.0002483183,0.00020176618,0.0008655324,0.0003345579,0.0003143885,0.000013718749,0.00037049505,0.0000431239],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033940797,0.00020398383,0.00894816,0.00009021313,0.00008960152,0.0002846323,0.443975,0.20190749,0.00014052562,0.34131986,0.00052453356,0.0021765882],"study_design_scores_gemma":[0.0017606346,0.00021358914,0.21396963,0.0011048765,0.0003488519,0.00002293201,0.7404564,0.024523394,0.00052285154,0.0036674116,0.0127183,0.0006911201],"about_ca_topic_score_codex":0.2177851,"about_ca_topic_score_gemma":0.1521571,"teacher_disagreement_score":0.33765244,"about_ca_system_score_codex":0.0015821496,"about_ca_system_score_gemma":0.001068784,"threshold_uncertainty_score":0.9999969},"labels":[],"label_agreement":null},{"id":"W6991022716","doi":"","title":"Equitable Congestion Pricing","year":2020,"lang":"en","type":"article","venue":"RePEc: Research Papers in Economics","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Congestion pricing; Equity (law); Downtown; Revenue; Singapore Area Licensing Scheme; Road pricing; Balance (ability)","score_opus":0.05364915558326517,"score_gpt":0.3433227058002813,"score_spread":0.28967355021701613,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6991022716","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.42774817,0.000054701417,0.00015437783,0.008493347,0.00018328929,0.00040270426,0.000009100972,0.00013602433,0.5628183],"genre_scores_gemma":[0.99701935,0.0008977646,0.0006593808,0.0001818627,0.00011749576,0.000012336523,0.000016051834,0.000008408687,0.0010873694],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99902946,0.00012404482,0.00016064559,0.00019420967,0.00014982425,0.00034181573],"domain_scores_gemma":[0.99950874,0.00019768057,0.00003488627,0.00007140699,0.00005645257,0.00013084916],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000987598,0.00004546298,0.000085970234,0.00007441945,0.00029853405,0.00006484289,0.00013281322,0.000070625036,0.00011565089],"category_scores_gemma":[0.00048106507,0.000053502426,0.000020692818,0.0002023013,0.00011456871,0.00017419131,0.000014703634,0.00019139347,0.000016413422],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019067769,0.00011454576,0.15957434,0.00007033654,0.00003472062,0.000036725367,0.065433644,0.33414018,0.0006358817,0.05106251,0.0006220895,0.38808435],"study_design_scores_gemma":[0.0023238831,0.00034598206,0.090301916,0.0001766935,0.000015137961,0.0000014667189,0.058073755,0.08015815,0.00048468227,0.0013813912,0.76588345,0.00085348205],"about_ca_topic_score_codex":0.00020901104,"about_ca_topic_score_gemma":0.00058171665,"teacher_disagreement_score":0.76526135,"about_ca_system_score_codex":0.00014426399,"about_ca_system_score_gemma":0.0002066165,"threshold_uncertainty_score":0.22961128},"labels":[],"label_agreement":null},{"id":"W6991970107","doi":"","title":"“Is There a Bus?”: Ridership Changes from All-day, Every-day Transit Service in Toronto","year":2024,"lang":"en","type":"dissertation","venue":"QSpace (Queen's University Library)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Public transport; Metropolitan area; Context (archaeology); Transit (satellite); Service (business); Work (physics); Level of service; Private transport","score_opus":0.0143713278963007,"score_gpt":0.23485507104858327,"score_spread":0.22048374315228259,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6991970107","genre_codex":"empirical","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.37020007,0.002571019,0.0002450796,0.33181435,0.00319244,0.0019215523,0.0018482489,0.002275164,0.28593206],"genre_scores_gemma":[0.44635132,0.007127519,0.0007475411,0.0010950988,0.00040256325,0.0000069285766,0.009103723,0.00013693562,0.53502834],"study_design_codex":"qualitative","study_design_gemma":"not_applicable","domain_scores_codex":[0.9978802,0.00036605017,0.00018294447,0.00065822795,0.00049951096,0.00041306418],"domain_scores_gemma":[0.9990996,0.00020571514,0.00016642764,0.0002410448,0.000082365164,0.00020482164],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00013920634,0.00035269491,0.0003675795,0.00025901932,0.00028122202,0.00020145156,0.0005472567,0.00071292743,0.0031068001],"category_scores_gemma":[0.000015629064,0.00042645156,0.00014503383,0.0007533224,0.00004863171,0.0016093774,0.000016214506,0.0004371123,0.000097449316],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00054430973,0.00017688559,0.013695038,0.00057783176,0.00049595995,0.00040916997,0.7463028,0.00069082674,0.000009694807,0.006334558,0.22956398,0.0011989486],"study_design_scores_gemma":[0.0006935842,0.00006240705,0.07325267,0.0011787842,0.0004304948,2.4266415e-8,0.29867905,0.000059446225,0.00025499458,0.00059063884,0.6237441,0.0010538463],"about_ca_topic_score_codex":0.42198178,"about_ca_topic_score_gemma":0.6269802,"teacher_disagreement_score":0.44762376,"about_ca_system_score_codex":0.00033769646,"about_ca_system_score_gemma":0.00051767036,"threshold_uncertainty_score":0.99981874},"labels":[],"label_agreement":null},{"id":"W6996982938","doi":"","title":"Texas Transit Statistics","year":2011,"lang":"en","type":"report","venue":"The Portal to Texas History (University of North Texas)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Texas Department of Transportation; Federal Transit Administration; Petroleum Technology Alliance Canada; U.S. Department of Transportation","keywords":"Transit (satellite); Public transport; Transit system; State (computer science); Information system; Statistical analysis; State government","score_opus":0.04277790767018284,"score_gpt":0.2431763370438487,"score_spread":0.20039842937366587,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6996982938","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01668229,0.0012090737,0.025072562,0.00059228094,0.0037415642,0.0019457092,0.0025292947,0.00045273517,0.94777447],"genre_scores_gemma":[0.23774852,0.003289938,0.007838635,0.00030962162,0.00055704033,0.0000014176363,0.0021269703,0.0001380374,0.74798983],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99718463,0.00019445912,0.00039546372,0.00048234622,0.0013153211,0.0004277949],"domain_scores_gemma":[0.9975633,0.00011618944,0.0006964486,0.00050522777,0.0007981486,0.0003206966],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00086713344,0.00031678803,0.000554381,0.00033828075,0.00048823757,0.000013185015,0.0009823688,0.0003177352,0.005604703],"category_scores_gemma":[0.000097970646,0.000351415,0.00024052142,0.0003798394,0.0008191968,0.0002057682,0.00003711194,0.00047759336,0.00045836822],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018506049,0.0002432689,0.15764818,0.00024580772,0.00035661578,0.00038322087,0.025851479,0.0011920255,2.6945054e-7,0.0042005973,0.801298,0.00839545],"study_design_scores_gemma":[0.00013856738,0.00005695601,0.322039,0.00005349882,0.00030484874,0.0000026357395,0.00025533728,0.000006538628,2.0610521e-7,0.000030607072,0.6768138,0.00029803626],"about_ca_topic_score_codex":0.03310582,"about_ca_topic_score_gemma":0.15153797,"teacher_disagreement_score":0.22106624,"about_ca_system_score_codex":0.0007203506,"about_ca_system_score_gemma":0.003169602,"threshold_uncertainty_score":0.9998938},"labels":[],"label_agreement":null},{"id":"W6997060079","doi":"","title":"Traffic Mitigation And Congestion In Ibadan, Oyo State Nigeria: Causes And Solutions","year":2021,"lang":"en","type":"article","venue":"UND Scholarly Commons (University of North Dakota)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Traffic congestion; Intersection (aeronautics); State (computer science); Congestion management; Public transport; Politics; Road traffic; Government (linguistics)","score_opus":0.0216450510399417,"score_gpt":0.23582245337191265,"score_spread":0.21417740233197097,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6997060079","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99678695,0.00033891443,0.0008887091,0.0012921223,0.00004459014,0.00008619901,0.000055903052,0.00003533185,0.00047130318],"genre_scores_gemma":[0.99789774,0.0005454557,0.001019251,0.000019175384,0.000006077538,2.0091403e-7,0.00010619098,0.0000033227122,0.0004025942],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99927735,0.0001811104,0.00009576373,0.00015844048,0.00015615711,0.00013119244],"domain_scores_gemma":[0.999486,0.0000991503,0.00007052444,0.000076061464,0.00018094778,0.00008729772],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023489229,0.00005837186,0.00011115379,0.00013425789,0.00064469106,0.00009404495,0.00006369822,0.00006037687,0.000020610325],"category_scores_gemma":[0.0000770287,0.000083729305,0.000018910838,0.0004096647,0.00023461321,0.0011397165,0.000017149892,0.00014702963,0.0000016184462],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020104886,0.000057087716,0.97492576,0.000014508517,0.000023175704,0.000032337826,0.014947037,0.0043319515,0.000035442496,0.001338708,0.000090417205,0.0041834973],"study_design_scores_gemma":[0.00040895253,0.00001830827,0.9924482,0.00003140894,0.000024320609,0.0000011295937,0.004473468,0.0002779344,0.00000281771,0.00012131133,0.002104741,0.00008737541],"about_ca_topic_score_codex":0.0013315865,"about_ca_topic_score_gemma":0.36842972,"teacher_disagreement_score":0.36709812,"about_ca_system_score_codex":0.00003777535,"about_ca_system_score_gemma":0.00015106544,"threshold_uncertainty_score":0.6430948},"labels":[],"label_agreement":null},{"id":"W6998923911","doi":"","title":"BHLS, Bus, tram: tesi, antitesi, sintesi.","year":2013,"lang":"it","type":"article","venue":"UPM Digital Archive (Technical University of Madrid)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Fish <Actinopterygii>; Air transport; Quality standard","score_opus":0.010039406856074325,"score_gpt":0.20325663383559223,"score_spread":0.1932172269795179,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6998923911","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25370163,0.00020376401,0.14323162,0.008324773,0.00055766443,0.0021735502,0.0022411824,0.0011523578,0.5884135],"genre_scores_gemma":[0.9866037,0.00020928687,0.00531663,0.00007659246,0.000057544483,5.3547717e-7,0.00028352634,0.000020741098,0.0074314745],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9979813,0.000105967796,0.00036587266,0.0005012616,0.0005539834,0.00049161277],"domain_scores_gemma":[0.998383,0.00047455347,0.00028796462,0.0002858167,0.00022162979,0.00034703818],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00016570422,0.00026162626,0.00041036733,0.00020997811,0.0004466915,0.00016892389,0.00071943324,0.00023129016,0.0012208434],"category_scores_gemma":[0.00017808953,0.00030510852,0.00030217372,0.0005501756,0.0013492005,0.0011467871,0.00009971661,0.00036284878,0.00058502937],"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":[0.001516324,0.008152245,0.26140806,0.0008652021,0.001120353,0.0006541319,0.044094384,0.012780652,0.0070978682,0.24140804,0.30857083,0.11233191],"study_design_scores_gemma":[0.0037403933,0.0014319321,0.6856949,0.0012393626,0.00050072157,0.000021783904,0.023045698,0.0038940576,0.00039823365,0.023137972,0.25437513,0.0025198422],"about_ca_topic_score_codex":0.0014134109,"about_ca_topic_score_gemma":0.0002881206,"teacher_disagreement_score":0.73290205,"about_ca_system_score_codex":0.00007285182,"about_ca_system_score_gemma":0.00020194566,"threshold_uncertainty_score":0.9999401},"labels":[],"label_agreement":null},{"id":"W6998924311","doi":"","title":"Beyond Generating Transit Performance Measures: Visualizations and Statistical Analysis Using Historical Data","year":2009,"lang":"en","type":"article","venue":"eScholarship@McGill (McGill)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Oregon Department of Transportation; Portland State University; U.S. Department of Transportation; Federal Highway Administration; National Science Foundation","keywords":"Transit (satellite); Public transport; Futures studies; Automatic vehicle location; Visualization; Performance indicator; Intelligent transportation system; Enhanced Data Rates for GSM Evolution","score_opus":0.05810340502215107,"score_gpt":0.309765399997046,"score_spread":0.25166199497489494,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6998924311","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9719417,0.00024967943,0.018047847,0.00014806443,0.00027013797,0.00030414303,0.0010002704,0.0003027894,0.0077353544],"genre_scores_gemma":[0.9706775,0.00017369339,0.028182229,0.00023187113,0.000045802215,0.0000030845626,0.00046537447,0.00001773675,0.00020268362],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976052,0.0003337624,0.00046847019,0.00056594633,0.00065299525,0.0003736136],"domain_scores_gemma":[0.99888533,0.00015351712,0.0001423143,0.0003304906,0.00021474162,0.0002736116],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0011919925,0.00019051557,0.00029937434,0.0002617224,0.0026239867,0.000121786536,0.00030676764,0.00017066453,0.00007471873],"category_scores_gemma":[0.00044578945,0.00021156517,0.00005578154,0.0012221065,0.00008668836,0.0011066493,0.000019102712,0.0002650917,0.000004627781],"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":[0.00013654432,0.000556545,0.01523222,0.00007270237,0.0006698235,0.000060965936,0.0011466874,0.18026339,0.008322999,0.43506888,0.000038309583,0.35843092],"study_design_scores_gemma":[0.004166854,0.0006850323,0.082012534,0.00028704657,0.00845145,0.000035214107,0.0031502293,0.7048608,0.0017954631,0.014186995,0.17562005,0.0047483374],"about_ca_topic_score_codex":0.0007243347,"about_ca_topic_score_gemma":0.0018354462,"teacher_disagreement_score":0.5245974,"about_ca_system_score_codex":0.00039104887,"about_ca_system_score_gemma":0.00006532525,"threshold_uncertainty_score":0.99867445},"labels":[],"label_agreement":null},{"id":"W7000031104","doi":"","title":"Effect of Transit Systems’ Long-Term Disruptions on Travel Behaviour","year":2023,"lang":"en","type":"dissertation","venue":"University Library (University of Saskatchewan)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"McGill University","keywords":"Transit (satellite); TRIPS architecture; Public transport; Service (business); Travel behavior; Sustainable transport; Transit system; Service provider; Mode of transport","score_opus":0.009216632611084115,"score_gpt":0.22409656543873466,"score_spread":0.21487993282765055,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7000031104","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9886994,0.000039698105,0.00037418265,0.00011279192,0.00055357517,0.0005207394,0.00074310857,0.0002523969,0.008704103],"genre_scores_gemma":[0.8793554,0.0002601871,0.00008414607,0.0000026112602,0.00003452889,1.1158195e-7,0.0036427996,0.000027777045,0.1165925],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.9984214,0.00033266415,0.00017501337,0.00035498047,0.00047695125,0.00023904824],"domain_scores_gemma":[0.9989127,0.00024288424,0.0003718441,0.00021457765,0.000095129864,0.00016290108],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00018916986,0.00022849654,0.0004918264,0.00062051165,0.00057015073,0.000022922828,0.00052927405,0.00049661094,0.0003134353],"category_scores_gemma":[0.0000076244896,0.0003140635,0.0003131126,0.0007694288,0.00021984369,0.00072959845,0.000013300197,0.00028423616,0.00003407141],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0049827397,0.0005881015,0.18912902,0.003369666,0.0007787017,0.00085659605,0.78327,0.006630074,0.00018927491,0.0033854032,0.0022658173,0.004554601],"study_design_scores_gemma":[0.001661768,0.0005422299,0.33912992,0.0011998552,0.0009868877,5.2482426e-7,0.6552067,0.000049792638,0.00030767362,0.000011798159,0.00035848268,0.00054435094],"about_ca_topic_score_codex":0.0069184448,"about_ca_topic_score_gemma":0.011425193,"teacher_disagreement_score":0.15000091,"about_ca_system_score_codex":0.00007209216,"about_ca_system_score_gemma":0.00048687065,"threshold_uncertainty_score":0.99993116},"labels":[],"label_agreement":null},{"id":"W7000929161","doi":"","title":"Implementing Connectivity Conservation in Canada","year":2021,"lang":"en","type":"article","venue":"Scholars Commons (Wilfrid Laurier University)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Government (linguistics); Work (physics); Agency (philosophy); Variety (cybernetics)","score_opus":0.017058198150328722,"score_gpt":0.23510145814291464,"score_spread":0.21804325999258592,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7000929161","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.952578,0.000033838456,0.00059939554,0.002940981,0.00017128146,0.000089842004,0.000060257826,0.000044505177,0.043481868],"genre_scores_gemma":[0.99738365,0.000025848367,0.0005449449,0.00023008494,0.000014527534,4.135059e-7,0.00010169917,0.000005078801,0.0016937207],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9988453,0.00033725466,0.00012160993,0.0001822877,0.00022493378,0.0002886166],"domain_scores_gemma":[0.99938965,0.00015972642,0.00006742757,0.00012101788,0.00017105287,0.000091099246],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041177226,0.000067465124,0.00010483395,0.00012507036,0.0006747632,0.000054910084,0.00013621153,0.000046695302,0.00021508161],"category_scores_gemma":[0.00015361095,0.000096057534,0.00002721405,0.0010048486,0.000044955326,0.0005214866,0.000026017387,0.00019552503,0.0000025339666],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010298988,0.000020373669,0.9629859,0.0000036444578,0.000011204833,0.00016865863,0.0012043351,0.0006197109,0.000031848802,0.03367052,0.00070878584,0.00056475156],"study_design_scores_gemma":[0.00065009244,0.0000045669444,0.4427983,0.000029783556,0.00001938533,8.54543e-7,0.020309854,0.0001880349,0.00019816702,0.00017726695,0.53543705,0.00018662386],"about_ca_topic_score_codex":0.8300003,"about_ca_topic_score_gemma":0.99881965,"teacher_disagreement_score":0.5347283,"about_ca_system_score_codex":0.00056030805,"about_ca_system_score_gemma":0.0030758393,"threshold_uncertainty_score":0.54564065},"labels":[],"label_agreement":null},{"id":"W7000968147","doi":"","title":"The Impact of Utilizing Point-of-Choice Prompts to Promote Active Transportation on a Canadian University Campus","year":2018,"lang":"en","type":"article","venue":"Scholarship@Western (Western University)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"University campus; Higher education; Work (physics); Government (linguistics)","score_opus":0.06546315397678946,"score_gpt":0.34164157911238324,"score_spread":0.27617842513559376,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7000968147","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9973911,0.0000039561564,0.00023318018,0.0002462896,0.00011684209,0.00043396238,0.00012468331,0.000047589376,0.0014023716],"genre_scores_gemma":[0.9985194,0.000012267571,0.00005275505,0.000025201705,0.000038005757,2.577175e-7,0.000029241499,0.000012380297,0.0013104687],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9986577,0.00022965367,0.00016919039,0.00027489735,0.00031478467,0.00035377094],"domain_scores_gemma":[0.998617,0.00015278187,0.00022179016,0.00021473467,0.00046673534,0.00032699356],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036644068,0.0001458454,0.00017262732,0.00042021376,0.00078853406,0.000051774405,0.000459317,0.0001382638,0.00002275675],"category_scores_gemma":[0.00009963747,0.00014316793,0.000112311776,0.000908663,0.0002598986,0.00077335816,0.000008761815,0.00019360994,0.000018000643],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036342687,0.000046425077,0.97462267,0.000010014388,0.000070319205,0.000024161254,0.022199608,0.00052665186,0.0002153612,0.0006006617,0.0000020064417,0.001318693],"study_design_scores_gemma":[0.0004037192,0.00031053257,0.9916321,0.0001313533,0.000044723292,3.0905775e-7,0.004468983,0.0000011128394,0.0007943904,0.000030991912,0.0020283142,0.00015348355],"about_ca_topic_score_codex":0.01953484,"about_ca_topic_score_gemma":0.7096269,"teacher_disagreement_score":0.690092,"about_ca_system_score_codex":0.00044364852,"about_ca_system_score_gemma":0.00077444507,"threshold_uncertainty_score":0.98699415},"labels":[],"label_agreement":null},{"id":"W7001363979","doi":"","title":"Investigation of the Relationship between Transit Network Structure and the Network Effect - The Toronto &amp; Melbourne Experience","year":2009,"lang":"en","type":"dissertation","venue":"TSpace (University of Toronto)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Transit (satellite); Mode (computer interface); Network structure; Urban transit; Public transport; Network planning and design; Mode choice","score_opus":0.018984597308968572,"score_gpt":0.26635762912688316,"score_spread":0.2473730318179146,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7001363979","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.986425,0.0028249354,0.00048013724,0.0013997569,0.00038413703,0.00076862925,0.000019246287,0.000037489717,0.007660682],"genre_scores_gemma":[0.99597734,0.00029717086,0.00047785498,0.000026176509,0.00018764757,8.8025473e-7,0.00017913239,0.000008787437,0.0028450284],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.9982518,0.0007355835,0.00017192608,0.00019948412,0.00044525246,0.0001959868],"domain_scores_gemma":[0.9982242,0.00081134,0.0005256136,0.00026014855,0.00011890866,0.000059789258],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.00074594223,0.00016032266,0.0002980873,0.000007554681,0.0015344582,0.000030602256,0.00046218044,0.0002921366,0.00038507557],"category_scores_gemma":[0.0001037079,0.000111334724,0.00013354898,0.00017079909,0.0005462297,0.00037299722,0.000009824406,0.00022296325,3.7743018e-7],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00042711364,0.000004380837,0.2284893,0.00007699444,0.00010477219,3.1882294e-7,0.74284315,0.0068741343,0.000010455833,0.01449077,0.0010561709,0.0056224535],"study_design_scores_gemma":[0.00043365048,0.000024087265,0.96187973,0.00016382133,0.0002764052,1.2713362e-7,0.03583362,0.00003735656,0.0000043249515,0.00068107387,0.0005489314,0.00011689363],"about_ca_topic_score_codex":0.18244886,"about_ca_topic_score_gemma":0.7918962,"teacher_disagreement_score":0.7333904,"about_ca_system_score_codex":0.00012441847,"about_ca_system_score_gemma":0.00014337752,"threshold_uncertainty_score":0.9997654},"labels":[],"label_agreement":null},{"id":"W7001845645","doi":"","title":"Market segmentation study of potential light rail users in Montreal QC, Canada.","year":2021,"lang":"en","type":"article","venue":"eScholarship@McGill (McGill)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Market segmentation; Segmentation; Light rail","score_opus":0.010658884047944454,"score_gpt":0.2352882886649825,"score_spread":0.22462940461703804,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7001845645","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9736794,0.000030387431,0.0000021046646,0.00012074564,0.00041210846,0.00033041587,0.0001886876,0.00004676028,0.025189362],"genre_scores_gemma":[0.9977417,0.00003828485,0.0003315792,0.000092548726,0.000018613082,0.000022572098,0.00008104122,0.000019200661,0.0016544786],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99748164,0.0005357815,0.00049935846,0.00038293653,0.0007762562,0.0003240377],"domain_scores_gemma":[0.9990672,0.000107903936,0.00020130069,0.00019054025,0.00027984325,0.00015324088],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000674317,0.0001602686,0.0002427961,0.00013179968,0.00060467457,0.00003884137,0.00018825252,0.00012165193,0.00038380793],"category_scores_gemma":[0.00024413326,0.00018815308,0.00005341042,0.0007447218,0.00003300652,0.00045945065,0.000020934935,0.00023617673,0.000004549685],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0020454766,0.008398132,0.4993848,0.00041722794,0.0012343894,0.004205062,0.007918097,0.14590691,0.038356595,0.038581204,0.0010635518,0.25248855],"study_design_scores_gemma":[0.0069897296,0.00024832177,0.907559,0.00018502102,0.0002721084,0.000007687383,0.050585028,0.0005459013,0.011583339,0.0019617192,0.01893867,0.0011234938],"about_ca_topic_score_codex":0.5694986,"about_ca_topic_score_gemma":0.9865891,"teacher_disagreement_score":0.41709048,"about_ca_system_score_codex":0.00041405356,"about_ca_system_score_gemma":0.00023738726,"threshold_uncertainty_score":0.767266},"labels":[],"label_agreement":null},{"id":"W7006233622","doi":"","title":"Texas Department of Transportation Requests for Legislative Appropriations: Fiscal Years 2008 and 2009","year":2006,"lang":"en","type":"book","venue":"The Portal to Texas History (University of North Texas)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Federal Highway Administration; Texas Department of Transportation; Federal Transit Administration; Petroleum Technology Alliance Canada; U.S. Department of Transportation","keywords":"Legislature; Fiscal year; Government (linguistics); Legislation; Work (physics)","score_opus":0.018061991888536362,"score_gpt":0.22142684127537682,"score_spread":0.20336484938684046,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7006233622","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.120940045,0.0036575322,0.030063476,0.0016416202,0.0016289423,0.00939139,0.01357149,0.00046416555,0.81864136],"genre_scores_gemma":[0.076266654,0.0002659853,0.004972264,0.00008168004,0.0001687715,0.000002909217,0.0050912616,0.00005000241,0.9131005],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9985873,0.000074817275,0.00033025836,0.0003299592,0.00046767658,0.0002099702],"domain_scores_gemma":[0.99865633,0.00013042912,0.000640647,0.00020349644,0.00025585984,0.000113226895],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032165233,0.00019572239,0.00037582617,0.00023103897,0.00022196738,0.0000067346955,0.00029421155,0.00021083224,0.00015037826],"category_scores_gemma":[0.000021060481,0.00022730914,0.00016400623,0.00015349661,0.0005441004,0.00021056308,0.000008050764,0.00016292473,0.000013183543],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005691742,0.00032901997,0.097314104,0.00033983865,0.0004141664,0.000059024453,0.034928113,0.007947676,0.0000010676602,0.027650021,0.82566434,0.0047834646],"study_design_scores_gemma":[0.00034044456,0.00010191798,0.40114,0.00006718705,0.00024595254,3.0815806e-7,0.00010804651,0.000022652735,3.7355449e-7,0.00022493444,0.59754294,0.00020526863],"about_ca_topic_score_codex":0.002545304,"about_ca_topic_score_gemma":0.05950663,"teacher_disagreement_score":0.30382589,"about_ca_system_score_codex":0.00030464565,"about_ca_system_score_gemma":0.000663787,"threshold_uncertainty_score":0.95765495},"labels":[],"label_agreement":null},{"id":"W7019563944","doi":"","title":"2001-02 House Bill 5089. An Act Providing For The Transfer Of A Portion Of The Turnpike And The Financing Of The Metropolitan Highway System From Non-Toll Revenues.","year":2002,"lang":"en","type":"other","venue":"State Library's electronic repository (State Library of Massachusetts)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Legislature; Metropolitan area; Legislation; Quarter (Canadian coin); Lump sum; Legislative history","score_opus":0.008266548190911558,"score_gpt":0.21021944323039277,"score_spread":0.20195289503948122,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7019563944","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6654397,0.05697699,0.010239129,0.023748366,0.0071698213,0.033818908,0.015982913,0.0026273686,0.1839968],"genre_scores_gemma":[0.92237794,0.0026261767,0.00053194904,0.00007458443,0.00022538286,0.000080117185,0.00010100451,0.00035165562,0.073631205],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99619234,0.001039148,0.0011021186,0.00046305434,0.0006683756,0.00053498865],"domain_scores_gemma":[0.9968585,0.0006024004,0.0015811606,0.0008278372,0.000052407235,0.00007769904],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00058558845,0.0003766668,0.00076315535,0.00017039452,0.0004357994,0.000092468654,0.0011447727,0.00025310792,0.000050318034],"category_scores_gemma":[0.000031176856,0.00018989717,0.00039962554,0.0005861118,0.00093487004,0.00093953003,0.000048990598,0.0004333067,2.7245756e-7],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.008710529,0.0014952252,0.093514934,0.010816762,0.007085514,0.00003942476,0.18717973,0.02100652,0.009047724,0.061324947,0.59261197,0.0071667437],"study_design_scores_gemma":[0.018187411,0.0024186275,0.04751297,0.014110476,0.0058436315,0.00003261322,0.02353979,0.01627751,0.180145,0.014624162,0.6734469,0.0038609467],"about_ca_topic_score_codex":0.0035279742,"about_ca_topic_score_gemma":0.00024943464,"teacher_disagreement_score":0.25693822,"about_ca_system_score_codex":0.00006596526,"about_ca_system_score_gemma":0.000762346,"threshold_uncertainty_score":0.7743782},"labels":[],"label_agreement":null},{"id":"W7024572656","doi":"","title":"Squaring the circle: The BHLS concept","year":2010,"lang":"en","type":"article","venue":"UPM Digital Archive (Technical University of Madrid)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Flexibility (engineering); Public transport; Quality (philosophy); Service (business); Point (geometry); Service quality; Bus rapid transit; Perspective (graphical)","score_opus":0.007558281203982942,"score_gpt":0.2151057426453132,"score_spread":0.20754746144133027,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7024572656","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4214952,0.000024697343,0.085269585,0.012073814,0.00044715442,0.0007082285,0.00025535517,0.00046760382,0.47925836],"genre_scores_gemma":[0.99850893,0.0000089987925,0.000580328,0.00006053988,0.000042330565,1.9966234e-7,0.000022593063,0.000003797084,0.0007722663],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9993602,0.00004549809,0.00008740316,0.00012689453,0.00023268252,0.00014734035],"domain_scores_gemma":[0.9992267,0.00042252045,0.000074677926,0.00016574924,0.000048604918,0.00006175047],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001910079,0.000054481603,0.00007671771,0.000027765434,0.0006027296,0.000052173702,0.0005054623,0.000052738164,0.000088059605],"category_scores_gemma":[0.00012708732,0.000042677573,0.0000820055,0.00015839991,0.0012340414,0.00021942353,0.00004472085,0.00027059333,0.000017819184],"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":[0.00022800823,0.00037719987,0.043700844,0.000021331187,0.00010446593,0.000044502016,0.06616903,0.004934611,0.0017396354,0.76213306,0.016432136,0.10411518],"study_design_scores_gemma":[0.0008462257,0.000112799076,0.5029069,0.000050589584,0.000076110555,0.0000058896326,0.020527015,0.0004461256,0.00018459129,0.021496475,0.45294115,0.00040614593],"about_ca_topic_score_codex":0.00037921322,"about_ca_topic_score_gemma":0.0019861865,"teacher_disagreement_score":0.7406366,"about_ca_system_score_codex":0.000009557459,"about_ca_system_score_gemma":0.00007159065,"threshold_uncertainty_score":0.46357697},"labels":[],"label_agreement":null},{"id":"W7026611212","doi":"","title":"Alleviating traffic congestion in Montreal's Champlain Bridge corridor : commuter rail or express bus?","year":2005,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Bridge (graph theory); Public transport; Rail transit; Traffic congestion; Mode (computer interface); Light rail transit; Work (physics); Urban rail transit","score_opus":0.02794243288446455,"score_gpt":0.2814632586073371,"score_spread":0.25352082572287254,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7026611212","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9445818,0.00022935994,0.0000018349721,0.00009084347,0.0013228646,0.0011231386,0.0008656612,0.00047781598,0.05130669],"genre_scores_gemma":[0.9755769,0.000812851,0.00096103986,0.00024212942,0.00017458813,0.00022884719,0.0046450696,0.00013981751,0.0172188],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99491334,0.0009138516,0.0012173943,0.0009811645,0.0010755755,0.0008986516],"domain_scores_gemma":[0.9972622,0.0006601183,0.0008445692,0.00044574557,0.0004342397,0.0003531736],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0017678431,0.0006674352,0.000786938,0.0005674935,0.0018728877,0.00021805009,0.00067534443,0.0011267754,0.00070208486],"category_scores_gemma":[0.0010185717,0.00072722184,0.00023194906,0.00084107573,0.00010546395,0.001182927,0.000021979407,0.0013854981,0.00014768665],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0025132385,0.0012533132,0.0015005277,0.00073475053,0.0003138746,0.0004321026,0.00825925,0.08087395,0.0010077379,0.021889545,0.00025458192,0.88096714],"study_design_scores_gemma":[0.015851168,0.00081616495,0.4519247,0.009143398,0.0010485823,0.000048034402,0.042309966,0.0038795397,0.0019042274,0.0027220396,0.4606615,0.009690677],"about_ca_topic_score_codex":0.006908753,"about_ca_topic_score_gemma":0.34838027,"teacher_disagreement_score":0.87127644,"about_ca_system_score_codex":0.00074131484,"about_ca_system_score_gemma":0.00017349744,"threshold_uncertainty_score":0.9997043},"labels":[],"label_agreement":null},{"id":"W7027514299","doi":"","title":"Comparison between MATSim &amp; EMME: Developing a Dynamic, Activity-based Microsimulation Transit Assignment Model for Toronto","year":2012,"lang":"en","type":"dissertation","venue":"TSpace (University of Toronto)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Public transport; Transit (satellite); Microsimulation; Schedule; Traffic congestion; Urban transit; Data collection; Rail transit","score_opus":0.05291134564165917,"score_gpt":0.35136479538581483,"score_spread":0.29845344974415566,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7027514299","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14062764,0.00038196103,0.8492572,0.00033371945,0.00029607263,0.00091253157,0.0002086666,0.00014029403,0.007841947],"genre_scores_gemma":[0.94800323,0.000076880686,0.040413965,0.000008553082,0.000043362856,0.0000028936045,0.003296686,0.000028151968,0.008126257],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.9986209,0.0001001428,0.00019307114,0.0003195199,0.00042924107,0.00033707087],"domain_scores_gemma":[0.9988111,0.00013126899,0.00050131074,0.00016543285,0.00025720763,0.00013362677],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003436809,0.0002323943,0.00046154833,0.00004800337,0.0007093924,0.00003142636,0.00025376672,0.00047118886,0.0008128763],"category_scores_gemma":[0.000013024842,0.00033211088,0.0001883781,0.00005666709,0.00005969166,0.00075580453,0.0000048596166,0.000108290864,0.0000038505877],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018898558,0.00045653552,0.01201819,0.0013153685,0.00047868557,9.170261e-7,0.72293824,0.18052512,0.0028133178,0.002139893,0.0007983339,0.07462551],"study_design_scores_gemma":[0.0027221604,0.0001156504,0.7407662,0.00057796045,0.0013028963,4.7824393e-8,0.07490225,0.17101648,0.00021201206,0.000053290547,0.0068886676,0.0014423286],"about_ca_topic_score_codex":0.1109395,"about_ca_topic_score_gemma":0.7950138,"teacher_disagreement_score":0.8088432,"about_ca_system_score_codex":0.0014777586,"about_ca_system_score_gemma":0.00048746273,"threshold_uncertainty_score":0.9999131},"labels":[],"label_agreement":null},{"id":"W7030101480","doi":"","title":"Lubbock urban transportation study 1975 annual report","year":2013,"lang":"en","type":"report","venue":"ThinkTech (Texas Tech University)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Annual report; Technical report; Work (physics); Quarter (Canadian coin); Annual cycle","score_opus":0.02431605262070235,"score_gpt":0.27709892417026055,"score_spread":0.2527828715495582,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7030101480","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13426414,0.00034399738,0.0055595743,0.0006912675,0.004086697,0.0060429987,0.0009914767,0.004521023,0.8434988],"genre_scores_gemma":[0.40811634,0.0010474125,0.0018909742,0.00004240071,0.00076318515,0.000012325869,0.0026908529,0.000120636876,0.5853159],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9954571,0.00026035414,0.00066858076,0.0009174182,0.0021255892,0.00057095813],"domain_scores_gemma":[0.99637556,0.00010569052,0.00087542844,0.0006400528,0.0017081369,0.0002951457],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0014712077,0.00046134228,0.00063106575,0.0010403729,0.0009638053,0.00012193163,0.0008061469,0.0009537939,0.0006700803],"category_scores_gemma":[0.00019137004,0.00054421084,0.00025506897,0.0016379943,0.00025657052,0.0008335636,0.000020950138,0.0008599057,0.0001281587],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002358716,0.0023693144,0.45332244,0.00038637163,0.0013280328,0.012259331,0.08692608,0.0015981145,0.000011476015,0.009081076,0.42595902,0.006522883],"study_design_scores_gemma":[0.0005731236,0.00017042166,0.056001414,0.00012773302,0.00051526574,0.000009720845,0.014291412,0.0000072285643,0.000007097998,0.000075738324,0.9275004,0.0007204161],"about_ca_topic_score_codex":0.025216972,"about_ca_topic_score_gemma":0.011426775,"teacher_disagreement_score":0.50154144,"about_ca_system_score_codex":0.0007086353,"about_ca_system_score_gemma":0.002655671,"threshold_uncertainty_score":0.99970096},"labels":[],"label_agreement":null},{"id":"W7030713658","doi":"","title":"NTA Mystery Passenger Survey : Direct Award : 2024 : Quarter 1","year":2024,"lang":"en","type":"report","venue":"Trinity's Access to Research Output (TARA) (Trinity College Dublin)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Quarter (Canadian coin); Service (business); Measure (data warehouse); Data collection; Quality (philosophy); Public transport","score_opus":0.2987401709933374,"score_gpt":0.4838454134965535,"score_spread":0.18510524250321614,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7030713658","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07389015,0.0023433543,0.00046368447,0.025178432,0.020455383,0.010308329,0.022094188,0.0020604888,0.843206],"genre_scores_gemma":[0.72937506,0.0029771025,0.00017884564,0.00025638708,0.0029586593,0.000874914,0.0033880626,0.00027157515,0.25971937],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.97661346,0.00595703,0.0022294496,0.0026405072,0.009755333,0.0028042446],"domain_scores_gemma":[0.98154414,0.0064358944,0.0005668428,0.0017224356,0.007928709,0.001801955],"candidate_categories":["metaresearch","metaepi_narrow","sts","scholarly_communication","research_integrity","insufficient_payload"],"consensus_categories":["metaresearch","research_integrity","insufficient_payload"],"category_scores_codex":[0.034346126,0.001120112,0.0019998844,0.004978354,0.0022155838,0.004554322,0.0036809242,0.0018296154,0.0027406942],"category_scores_gemma":[0.01113111,0.0011249306,0.0007281671,0.010258944,0.00074034603,0.0017493588,0.00077638024,0.004918821,0.002352244],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006389889,0.00051924377,0.015288744,0.0009559102,0.0005298443,0.0009451567,0.0034202891,0.00041721386,0.0000019455922,0.0011370084,0.9729507,0.0031949629],"study_design_scores_gemma":[0.0008050228,0.00030169432,0.06331355,0.0008276452,0.00018127335,0.000007743197,0.0017797468,0.00013133732,0.000025693047,0.00023229558,0.9310421,0.0013519481],"about_ca_topic_score_codex":0.07417829,"about_ca_topic_score_gemma":0.15022531,"teacher_disagreement_score":0.6554849,"about_ca_system_score_codex":0.0025123253,"about_ca_system_score_gemma":0.016795479,"threshold_uncertainty_score":0.99946624},"labels":[],"label_agreement":null},{"id":"W7036169819","doi":"","title":"Assessing MoDOT's Efforts to Provide the Right Transportation Solution [2007]","year":2024,"lang":"en","type":"other","venue":"Rosa P: A digital library for transportation research (United States Department of Transportation)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Missouri Department of Transportation; University of Missouri; U.S. Department of Transportation","keywords":"Margin (machine learning); Right of way; Set (abstract data type); Public transport; Customer satisfaction; Measure (data warehouse); Public opinion; Feature (linguistics); Market research; Quarter (Canadian coin)","score_opus":0.03643334169877026,"score_gpt":0.3355894919495722,"score_spread":0.29915615025080194,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7036169819","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017290752,0.0020141508,0.07433899,0.011560154,0.0019666522,0.0148768285,0.85767317,0.003280897,0.016998427],"genre_scores_gemma":[0.089402385,0.0007936577,0.0059937243,0.00032783565,0.00046174022,0.0017011177,0.8044628,0.0009907798,0.09586594],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9940213,0.0002175125,0.0014313813,0.0010789997,0.002171106,0.0010797107],"domain_scores_gemma":[0.9972703,0.0006036219,0.00059071934,0.00046637518,0.00055679807,0.00051220733],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.000878717,0.0006512336,0.0006752488,0.001760016,0.000737848,0.0011097648,0.0006958538,0.0005537412,0.00089506095],"category_scores_gemma":[0.000051414947,0.00059172144,0.0005084974,0.0032487514,0.00058393547,0.0026847827,0.0000032474345,0.0006102697,0.00012251499],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043464405,0.00042522076,0.0007293165,0.0016206541,0.00045795357,0.00007297632,0.011603685,0.004243914,0.0000033907302,0.036529176,0.94188726,0.0019918203],"study_design_scores_gemma":[0.00071961625,0.00025463852,0.0012723212,0.0010131124,0.00031736976,2.3679618e-7,0.0031798556,0.0003299843,0.00010128286,0.003122718,0.98905987,0.00062900624],"about_ca_topic_score_codex":0.00036392375,"about_ca_topic_score_gemma":0.0053666816,"teacher_disagreement_score":0.07886752,"about_ca_system_score_codex":0.00011941043,"about_ca_system_score_gemma":0.0006989169,"threshold_uncertainty_score":0.99992716},"labels":[],"label_agreement":null},{"id":"W7036549220","doi":"","title":"Business Ideation for Creative Entrepreneurs","year":2023,"lang":"en","type":"other","venue":"OCAD University Open Research Repository (OCAD University)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Ideation; Conversation; Leverage (statistics); Business plan; Creativity; Revenue; Entrepreneurship; Business model","score_opus":0.07845399420367452,"score_gpt":0.34831554538040954,"score_spread":0.26986155117673505,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7036549220","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00024988092,0.00002849204,0.0022242537,0.000481887,0.00044699147,0.0015547074,0.0001931238,0.0005661221,0.9942545],"genre_scores_gemma":[0.0017505558,0.0008137471,0.0006019671,0.000007864011,0.00021407111,0.0000019306458,0.0002286041,0.00018896876,0.9961923],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9970471,0.00082221464,0.00013926334,0.00071067555,0.0007391717,0.0005415511],"domain_scores_gemma":[0.9978787,0.00034228,0.00024186731,0.00035854548,0.00090590393,0.00027271183],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0007834189,0.00021896133,0.0003231338,0.0011644315,0.0022015413,0.00025540325,0.0012869935,0.0005286393,0.000020664256],"category_scores_gemma":[0.0001855114,0.00029024025,0.0001199361,0.0021633268,0.0006352044,0.0006706898,0.000103623235,0.0003528094,0.000065831315],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026024962,0.000067121335,0.001842581,0.000046704805,0.00013466747,0.00020543179,0.0020805616,0.0002782903,0.000009845932,0.9201546,0.07441976,0.00050021854],"study_design_scores_gemma":[0.0009499242,0.000057634414,0.0027400411,0.00028586114,0.000099696204,5.0508976e-7,0.008905799,0.000049032235,0.000014328547,0.000051722032,0.9865306,0.00031486523],"about_ca_topic_score_codex":0.05329598,"about_ca_topic_score_gemma":0.027268965,"teacher_disagreement_score":0.92010283,"about_ca_system_score_codex":0.0008260045,"about_ca_system_score_gemma":0.0013151397,"threshold_uncertainty_score":0.999955},"labels":[],"label_agreement":null},{"id":"W7043766995","doi":"","title":"Temporal Variability of Public Transit Supply-Demand Gaps in London, ON, Canada","year":2022,"lang":"en","type":"article","venue":"Scholarship@Western (Western University)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Public transport; Transit (satellite); Supply and demand; Mains electricity; Transit system","score_opus":0.05729698325864178,"score_gpt":0.28993645020897446,"score_spread":0.2326394669503327,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7043766995","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9971884,0.000023643437,0.00038387338,0.0010345759,0.0002238264,0.00025266828,0.00014388832,0.00005358158,0.00069556024],"genre_scores_gemma":[0.99796164,0.00001660924,0.000023391181,0.00017665503,0.000020686964,0.0000020073808,0.0001094549,0.000012830374,0.0016767248],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9971458,0.0011511508,0.00028457108,0.00036173564,0.0007004016,0.0003563708],"domain_scores_gemma":[0.99909383,0.00018841692,0.0001693483,0.00023675969,0.00012728147,0.00018434811],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014582771,0.00014331045,0.00023476942,0.00035407627,0.00051750254,0.000059117396,0.0005329119,0.00008651322,0.00020244958],"category_scores_gemma":[0.00007318653,0.00018527165,0.00006244969,0.0010125004,0.00012250358,0.000662613,0.000038830156,0.00043032193,0.000002502386],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007874394,0.00011894706,0.991864,0.0000187526,0.00001437942,0.00009561946,0.00268285,0.0042229793,0.000014178552,0.0008006573,0.0000033873014,0.00008546736],"study_design_scores_gemma":[0.0008052712,0.00006592058,0.98698074,0.00002657054,0.000018314187,0.0000012369575,0.002626454,0.000001165443,0.000053788295,0.00009238078,0.0091315545,0.00019660058],"about_ca_topic_score_codex":0.030030988,"about_ca_topic_score_gemma":0.88802695,"teacher_disagreement_score":0.8579959,"about_ca_system_score_codex":0.00060752,"about_ca_system_score_gemma":0.0012404289,"threshold_uncertainty_score":0.9764281},"labels":[],"label_agreement":null},{"id":"W7070529851","doi":"","title":"Perspectives on the externalities of road usage in South Africa","year":2011,"lang":"en","type":"other","venue":"SUNScholar (Stellenbosch University)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Externality; Traffic congestion; Consumption (sociology); Variety (cybernetics); Value of time; Vehicle miles of travel; Road traffic; Value (mathematics)","score_opus":0.03180612841908892,"score_gpt":0.2291105240993849,"score_spread":0.19730439568029598,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7070529851","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0034096106,0.00061418005,0.00013412787,0.00006556775,0.00012495487,0.00022015828,0.000060241604,0.00008088003,0.9952903],"genre_scores_gemma":[0.17130916,0.00086136657,0.00021695414,0.000011316594,0.000064819644,5.7871665e-7,0.000008124259,0.000056640056,0.827471],"study_design_codex":"qualitative","study_design_gemma":"not_applicable","domain_scores_codex":[0.99877876,0.00037121624,0.00011396531,0.0002459368,0.00029610243,0.00019402207],"domain_scores_gemma":[0.9993972,0.00008231753,0.00019724555,0.00021137338,0.00005825026,0.000053602776],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00034730995,0.00014180946,0.00018838068,0.0005920497,0.00017899499,0.000026302463,0.00041961897,0.00022695912,0.0033649239],"category_scores_gemma":[0.00007586729,0.00012877895,0.00008991839,0.0004982188,0.00028572843,0.00012420383,0.000014673064,0.0003068277,0.000053667743],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012680123,0.0001853174,0.007854759,0.000048380363,0.000121033954,0.000061174236,0.68685335,0.00009318712,0.0000038594835,0.29241937,0.011893388,0.0003393922],"study_design_scores_gemma":[0.00026600476,0.000034451114,0.0014011814,0.00024479776,0.00003375332,5.1497945e-8,0.18777151,0.0000020343577,0.0000049869495,0.000079307094,0.80997306,0.0001888631],"about_ca_topic_score_codex":0.0028337743,"about_ca_topic_score_gemma":0.0016311574,"teacher_disagreement_score":0.79807967,"about_ca_system_score_codex":0.000067117944,"about_ca_system_score_gemma":0.00013917826,"threshold_uncertainty_score":0.99754614},"labels":[],"label_agreement":null},{"id":"W7083309867","doi":"10.22329/uwdj.v2i1.9003","title":"Amplifying the Voices of Canadian Muslim Excellence","year":2024,"lang":"en","type":"article","venue":"UWill Discover Journal","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Islamophobia; Excellence; Work (physics); Islam; Racism","score_opus":0.026572240949902747,"score_gpt":0.2946252296790047,"score_spread":0.26805298872910194,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7083309867","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.80039114,0.004720408,0.016197521,0.02622109,0.0048163882,0.00027690164,0.00009834727,0.00010146646,0.14717674],"genre_scores_gemma":[0.9971624,0.0007992437,0.00017396444,0.00011930467,0.0002421185,8.810202e-7,0.0000029666642,0.000004821028,0.0014942728],"study_design_codex":"qualitative","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993382,0.00006148385,0.00013800344,0.00006666368,0.00023629557,0.00015932707],"domain_scores_gemma":[0.99966145,0.00009684279,0.000044984517,0.0000469781,0.000048767764,0.000100954574],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005277945,0.000042470554,0.00004863189,0.00018758529,0.0005479624,0.00024607335,0.00015373522,0.000031093943,0.00029875743],"category_scores_gemma":[0.000050938077,0.000028433782,0.00004814028,0.00042823274,0.0000979007,0.0003348299,0.0000022871063,0.00015038547,0.000012108825],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017904096,0.00003826455,0.077385925,0.000093879295,0.0001662403,0.00017017502,0.6822949,0.047497693,0.00021146849,0.11730826,0.043259323,0.031555973],"study_design_scores_gemma":[0.00018857095,0.000036200727,0.06066705,0.00054185995,0.000086824955,0.000025997133,0.11557925,0.0014325677,0.000070913375,0.0041230298,0.8169801,0.00026762503],"about_ca_topic_score_codex":0.0989727,"about_ca_topic_score_gemma":0.2658167,"teacher_disagreement_score":0.7737208,"about_ca_system_score_codex":0.00004290728,"about_ca_system_score_gemma":0.0005618739,"threshold_uncertainty_score":0.9070273},"labels":[],"label_agreement":null},{"id":"W7095723577","doi":"","title":"Word Count: 6,374","year":2004,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Preference; Public transport; Affect (linguistics); Travel behavior; Mode choice; Work (physics); Travel time","score_opus":0.016700292189348067,"score_gpt":0.2858817570050858,"score_spread":0.26918146481573774,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7095723577","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13360608,0.00004601978,0.05553097,0.0063311625,0.00036742046,0.00011617387,0.0000029481128,0.00042520653,0.803574],"genre_scores_gemma":[0.9902048,0.000024621913,0.0039369464,0.0002985668,0.00004885202,0.000001510055,0.000007651733,0.0000019340387,0.005475091],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99971235,0.0000072795165,0.000045302488,0.00004885094,0.00011293797,0.00007330608],"domain_scores_gemma":[0.99989116,0.00001002767,0.000011527369,0.000027516138,0.00002722812,0.000032556614],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009294768,0.00001984346,0.00002312118,0.00001629882,0.00016340922,0.000022759055,0.00003805072,0.00002522426,0.00035052985],"category_scores_gemma":[0.000013741828,0.000018710422,0.000010499405,0.000110351386,0.000032198368,0.00008937858,5.6256846e-7,0.000020201183,0.000095576295],"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":[0.000006919545,0.000041772102,0.012645002,0.0000024017813,0.0000057791526,0.0000047973376,0.032119256,0.02909957,0.00001330808,0.91953313,0.0022739551,0.0042541344],"study_design_scores_gemma":[0.0013147014,0.000034141318,0.11425425,0.00003941097,0.000020865198,7.0154886e-7,0.015683759,0.000092689115,0.00027617055,0.02610977,0.84177405,0.0003994776],"about_ca_topic_score_codex":0.0014892886,"about_ca_topic_score_gemma":0.002848157,"teacher_disagreement_score":0.8934233,"about_ca_system_score_codex":0.000020461423,"about_ca_system_score_gemma":0.000065738655,"threshold_uncertainty_score":0.3838056},"labels":[],"label_agreement":null},{"id":"W7096619559","doi":"","title":"Transportation Planning in the Portland/Vancouver Metropolitan Area Metro","year":2008,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Metropolitan area; Transportation planning; Table (database); Public transport; Regional planning","score_opus":0.03910454294356939,"score_gpt":0.2965911803797768,"score_spread":0.25748663743620737,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7096619559","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.80160546,0.00008799095,0.013708677,0.00067431654,0.00026055216,0.00022767902,0.000009846888,0.0001371385,0.18328835],"genre_scores_gemma":[0.997047,0.00003258395,0.0013225705,0.00034336254,0.000052147036,0.000011446455,0.000062537525,0.0000053904537,0.0011229784],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9990214,0.00008579258,0.00020058041,0.00012946807,0.00036707727,0.0001956555],"domain_scores_gemma":[0.99965996,0.00011679045,0.000055656627,0.00007947969,0.00004543725,0.000042676525],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041627148,0.000069767855,0.00009071935,0.0001147217,0.0003844167,0.000018144476,0.00013064618,0.000058804435,0.00024436685],"category_scores_gemma":[0.00003746605,0.00005118993,0.000040327817,0.0005079903,0.00008939357,0.00022773827,4.3773352e-7,0.00009651469,0.000005293113],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023468294,0.000059137015,0.88368565,0.0000034158718,0.000009291054,0.0000800823,0.07903189,0.006734702,0.000004534924,0.017323121,0.012869091,0.00017561461],"study_design_scores_gemma":[0.00047600808,0.000031296833,0.87163883,0.00001446934,0.000017907985,7.9930254e-7,0.10689833,0.00026984423,0.000029439107,0.0005533496,0.019887455,0.0001822383],"about_ca_topic_score_codex":0.00490637,"about_ca_topic_score_gemma":0.037485916,"teacher_disagreement_score":0.19544153,"about_ca_system_score_codex":0.00006266824,"about_ca_system_score_gemma":0.00007682443,"threshold_uncertainty_score":0.98007745},"labels":[],"label_agreement":null},{"id":"W7096810196","doi":"","title":"University of British Columbia’s A Cappella Club Sing TransLink Praise","year":2015,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Praise; Club; Public transport; Ticket; Cost–benefit analysis","score_opus":0.02698959194573364,"score_gpt":0.24546421138378377,"score_spread":0.21847461943805013,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7096810196","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7257072,0.00008245279,0.048234448,0.00074385427,0.00019058982,0.00018611553,0.000042812135,0.00018432898,0.22462824],"genre_scores_gemma":[0.9759011,0.00002723602,0.0049248063,0.000025505122,0.000016635477,6.218602e-8,0.000013234192,0.0000023978062,0.019088972],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.99958885,0.000045532273,0.00006256074,0.000072946386,0.0001499054,0.00008022938],"domain_scores_gemma":[0.99972165,0.00001892728,0.00003212076,0.00003352266,0.000113332215,0.000080427],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023387487,0.000017205706,0.000055068143,0.0000105596955,0.000097036995,0.00003178929,0.0000596483,0.000046396246,0.00019471817],"category_scores_gemma":[0.000019809304,0.000033456407,0.000021150327,0.00013465398,0.00009052365,0.0001124759,0.0000012628881,0.000028023544,0.000005999293],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000093685434,0.00040104444,0.43891093,0.000061426676,0.00006765908,0.00011917206,0.24622086,0.027734386,0.000081685415,0.0030437568,0.22763571,0.055629686],"study_design_scores_gemma":[0.004583213,0.0001982461,0.13406499,0.00017191122,0.00014437812,0.000005512336,0.20209616,0.0049919826,0.000023505487,0.001867453,0.65109754,0.0007551293],"about_ca_topic_score_codex":0.27871558,"about_ca_topic_score_gemma":0.6680401,"teacher_disagreement_score":0.4234618,"about_ca_system_score_codex":0.000016270022,"about_ca_system_score_gemma":0.0001846756,"threshold_uncertainty_score":0.7260875},"labels":[],"label_agreement":null},{"id":"W7096862888","doi":"","title":"and","year":2004,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Sound (geography); Traffic congestion; Trip generation; Population; Transit (satellite); Sound analysis; Congestion pricing","score_opus":0.01370181941499608,"score_gpt":0.2827135259815686,"score_spread":0.26901170656657253,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7096862888","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4209179,0.000040451345,0.03939905,0.006387832,0.000077749704,0.000046575118,4.6440286e-7,0.00015445742,0.53297555],"genre_scores_gemma":[0.99445605,0.000018727118,0.004177485,0.0001056315,0.000010120452,2.629241e-7,7.541639e-7,4.2259566e-7,0.0012305597],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9999162,0.000002271406,0.000013097061,0.000018629125,0.000027227605,0.00002255572],"domain_scores_gemma":[0.9999668,0.0000034255145,0.0000026175676,0.000006945071,0.0000058671844,0.000014369732],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000032985205,0.000006111563,0.000007502813,0.0000056015033,0.000071659524,0.000008048167,0.0000067853293,0.000007817243,0.00004304416],"category_scores_gemma":[0.000005496659,0.000005570814,0.0000017469309,0.000025788415,0.000018505238,0.000035791756,2.1845773e-7,0.000005518422,0.0000056325266],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[7.031144e-7,0.000004110779,0.01159386,4.87672e-7,6.8520484e-7,5.3425845e-7,0.013204676,0.0013359351,0.0000065569157,0.97190434,0.00010547389,0.001842613],"study_design_scores_gemma":[0.0015580924,0.000049739006,0.55125445,0.000024247343,0.000014642063,0.0000010011955,0.026469156,0.00008014861,0.00039952795,0.09034701,0.3294793,0.00032267452],"about_ca_topic_score_codex":0.0006166309,"about_ca_topic_score_gemma":0.00073266524,"teacher_disagreement_score":0.88155735,"about_ca_system_score_codex":0.0000025666543,"about_ca_system_score_gemma":0.000012743172,"threshold_uncertainty_score":0.09321655},"labels":[],"label_agreement":null},{"id":"W7096873411","doi":"","title":"© Association for European Transport and contributors 2009 1 SIMULATING CAR-PEDESTRIAN INTERACTIONS DURING MASS EVENTS WITH DTA MODELS: THE CASE OF VANCOUVER WINTER OLYMPIC","year":2015,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Association (psychology); Event (particle physics); European region; Field (mathematics)","score_opus":0.027987181182447328,"score_gpt":0.2805798939575968,"score_spread":0.2525927127751495,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7096873411","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92648935,0.000018465204,0.06447066,0.00035279867,0.00028521862,0.00042045466,0.00007263884,0.00006733058,0.007823094],"genre_scores_gemma":[0.995608,0.0000042474217,0.00084030576,0.000022920358,0.00006902589,0.000007058863,0.000021008844,0.0000101101805,0.0034173266],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992618,0.00010893808,0.00021362408,0.00012207174,0.00015086737,0.00014271277],"domain_scores_gemma":[0.99926805,0.0001657035,0.00017862502,0.000061315004,0.00025656534,0.00006972073],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006443561,0.00007040534,0.00010007999,0.000039426894,0.00029862465,0.000022965753,0.000051479386,0.000032646287,0.000009248421],"category_scores_gemma":[0.000088869245,0.0000516305,0.000031855594,0.000118841854,0.00002828227,0.00035117575,0.0000019875993,0.000058781592,3.822853e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030371564,0.00007750738,0.34875557,0.000039740593,0.00020652263,0.000043751425,0.06316895,0.5849152,0.000036748854,0.0009555679,0.000923418,0.0005732902],"study_design_scores_gemma":[0.029444726,0.0008693925,0.16779028,0.00084926945,0.002143346,0.000056047244,0.36978528,0.4041896,0.00069047965,0.0053011375,0.016685829,0.0021946463],"about_ca_topic_score_codex":0.0018180382,"about_ca_topic_score_gemma":0.031243907,"teacher_disagreement_score":0.30661634,"about_ca_system_score_codex":0.00008279163,"about_ca_system_score_gemma":0.000060795624,"threshold_uncertainty_score":0.9864334},"labels":[],"label_agreement":null},{"id":"W7097266169","doi":"","title":"Calgary Transit","year":2006,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Transit (satellite); Light rail transit; Public transport; Transit-oriented development; Market segmentation; Mode (computer interface)","score_opus":0.009892729588477946,"score_gpt":0.2538918461230812,"score_spread":0.24399911653460324,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7097266169","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025749892,0.000023399783,0.08952102,0.0011414255,0.00008083922,0.000042352684,8.284919e-7,0.00017837426,0.88326186],"genre_scores_gemma":[0.9751161,0.0000045185907,0.0034759745,0.0001086232,0.000059579586,0.000001179789,0.000017393519,0.000001754761,0.021214891],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9997556,0.0000130067065,0.00004521787,0.00004131797,0.00008243946,0.00006241875],"domain_scores_gemma":[0.99992895,0.000011544506,0.000006942351,0.000019356597,0.000016720614,0.000016506407],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006671005,0.000016841614,0.0000203073,0.000014453569,0.00012833605,0.000013593516,0.00002472641,0.000022882883,0.00045437366],"category_scores_gemma":[0.000002389265,0.000016074906,0.000011888667,0.00007738786,0.000025326755,0.0000580698,1.8678612e-7,0.000014832685,0.000028331291],"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":[0.0000060612824,0.000041907468,0.046259254,0.000002489076,0.0000027993128,0.0000053953586,0.0050617885,0.007997754,0.00008245192,0.92021865,0.01587964,0.004441827],"study_design_scores_gemma":[0.0004335617,0.000014695237,0.31746033,0.0000074056948,0.000015368865,3.2893178e-7,0.0018794552,0.001171523,0.0002919868,0.0066303657,0.6718598,0.00023517922],"about_ca_topic_score_codex":0.0061628525,"about_ca_topic_score_gemma":0.0057881824,"teacher_disagreement_score":0.9493662,"about_ca_system_score_codex":0.000005611062,"about_ca_system_score_gemma":0.000021735268,"threshold_uncertainty_score":0.93164307},"labels":[],"label_agreement":null},{"id":"W7097281562","doi":"","title":"Past Presidents’Award for Merit in Transportation Engineering: Assessing Transportation Policy Using an Activity-Based Microsimulation","year":2014,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Microsimulation; Transportation planning; Scheduling (production processes); Policy analysis; Demand management; Transport policy; Transit (satellite); Economic model; Decision support system","score_opus":0.033245387711083055,"score_gpt":0.34138600192408775,"score_spread":0.3081406142130047,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7097281562","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.51234514,0.0000018140156,0.48692614,0.0002367524,0.00007750842,0.00025463683,0.000015818177,0.000100900295,0.00004131611],"genre_scores_gemma":[0.96200657,0.0000017882354,0.037121158,0.00007445491,0.00018738923,0.000027670896,0.0005248084,0.00002578184,0.00003037748],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9987726,0.00009318901,0.00031744153,0.00027294815,0.00026796007,0.00027581988],"domain_scores_gemma":[0.9993833,0.00015474658,0.00014056831,0.000093629686,0.00013046336,0.000097334225],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00052589906,0.00013747683,0.00016451621,0.000318553,0.0002653516,0.00013193747,0.00008849927,0.00014849355,0.000012638513],"category_scores_gemma":[0.00005902495,0.00016431778,0.000062704894,0.00047411572,0.000034284665,0.0011405768,2.330103e-7,0.000077564735,5.809576e-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":[0.000045552417,0.00008770087,0.04966526,0.000046731788,0.000004368112,3.7948686e-7,0.004630715,0.9330893,0.00737972,0.003278215,0.0000036431945,0.0017683728],"study_design_scores_gemma":[0.0011394281,0.00004873579,0.5468422,0.00006913655,0.000037385245,3.947899e-8,0.00048055817,0.44715485,0.0026095207,0.0002106337,0.0011245498,0.0002829709],"about_ca_topic_score_codex":0.0050417474,"about_ca_topic_score_gemma":0.007911735,"teacher_disagreement_score":0.49717695,"about_ca_system_score_codex":0.000120757446,"about_ca_system_score_gemma":0.00019812248,"threshold_uncertainty_score":0.7621648},"labels":[],"label_agreement":null},{"id":"W7097354987","doi":"","title":"Design and Assessment of the Toronto Area Computerized Household Activity Scheduling Survey","year":2003,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Data quality; Scheduling (production processes); Survey data collection; World class; Quality (philosophy); Travel survey; Process (computing)","score_opus":0.09332423214135677,"score_gpt":0.3279780422386605,"score_spread":0.23465381009730374,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7097354987","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.41019714,0.000014566384,0.58487463,0.00004932536,0.000090631176,0.00011738816,0.0000022724753,0.000024954514,0.004629112],"genre_scores_gemma":[0.9349024,0.000032274016,0.064905606,0.000023665962,0.000003500478,0.0000016148737,0.0000011831377,0.000002320041,0.00012740112],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9992214,0.00043167343,0.00007033213,0.00007435611,0.0001336845,0.000068575886],"domain_scores_gemma":[0.9996351,0.00019092776,0.000052316816,0.000057188205,0.000038365008,0.000026121146],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000934983,0.000035322184,0.00006439123,0.000005805224,0.00017793375,0.000025300395,0.00004869556,0.000031148506,0.000033828554],"category_scores_gemma":[0.000042571304,0.000025763346,0.000012501852,0.00006983225,0.000048188384,0.00011542806,0.0000025270585,0.000030267536,4.9402e-8],"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":[0.000033564276,0.00017171998,0.7664815,0.000014060644,0.0000419656,4.771891e-7,0.00768983,0.18993011,0.0013025163,0.03192853,0.00019331239,0.0022123724],"study_design_scores_gemma":[0.0002705129,0.000013518035,0.9852993,0.000010125971,0.0000071698632,8.683606e-8,0.00043565847,0.013181109,0.0004087262,0.0000553515,0.00025212218,0.000066352906],"about_ca_topic_score_codex":0.004859279,"about_ca_topic_score_gemma":0.00541363,"teacher_disagreement_score":0.5247053,"about_ca_system_score_codex":0.000025234845,"about_ca_system_score_gemma":0.00011485317,"threshold_uncertainty_score":0.7345809},"labels":[],"label_agreement":null},{"id":"W7097795280","doi":"","title":"2003) A Prototype Model of Household Activity/Travel Scheduling, forthcoming in","year":2014,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Operationalization; Scheduling (production processes); Microsimulation; Conceptual model; Time budget","score_opus":0.045394862970009595,"score_gpt":0.2908968985478439,"score_spread":0.2455020355778343,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7097795280","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6051071,0.000004602388,0.35660473,0.0001694608,0.00004334289,0.00032301736,0.000002994783,0.00005474389,0.037690002],"genre_scores_gemma":[0.9730506,0.000008032568,0.026095606,0.000034571232,0.00001467959,0.000015368087,0.0000024147905,0.000005278751,0.0007734701],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994766,0.000037148187,0.00011964501,0.00009609486,0.00014887961,0.00012163127],"domain_scores_gemma":[0.9997623,0.000024411092,0.00005662268,0.00006041466,0.00006214364,0.00003411048],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039598416,0.00004307379,0.00008686218,0.00007001876,0.00006894899,0.000011242765,0.00006654369,0.00006073708,0.000015882948],"category_scores_gemma":[0.000108078515,0.000042939017,0.000015355454,0.00028036293,0.000040187533,0.00014664132,0.0000022260024,0.000058917263,0.0000012002006],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006416879,0.00012271361,0.09610498,0.000030607593,0.0000058520914,3.396813e-7,0.0134279,0.7653159,0.00093523366,0.11965935,0.00006205812,0.004270897],"study_design_scores_gemma":[0.0005992307,0.000048040853,0.026361236,0.00006460694,0.000008128157,5.5764335e-8,0.001112047,0.96823066,0.0017211206,0.001231833,0.0004491644,0.00017389609],"about_ca_topic_score_codex":0.0015102836,"about_ca_topic_score_gemma":0.0030363973,"teacher_disagreement_score":0.36794347,"about_ca_system_score_codex":0.000013418803,"about_ca_system_score_gemma":0.00011238887,"threshold_uncertainty_score":0.2283107},"labels":[],"label_agreement":null},{"id":"W7097945644","doi":"","title":"SEE PROFILE","year":2004,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Calibration; Matrix (chemical analysis); Traffic flow (computer networking); Flow (mathematics); Flow network; Traffic network","score_opus":0.01647261713516857,"score_gpt":0.28794442076277327,"score_spread":0.2714718036276047,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7097945644","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04803734,0.0000097142365,0.031455908,0.0022924694,0.00012793319,0.00007757368,0.0000015020311,0.00023487334,0.9177627],"genre_scores_gemma":[0.9711533,0.000005767063,0.010881763,0.0001308375,0.000037183858,0.0000023772768,0.000008525851,0.0000015078562,0.017778706],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9997927,0.0000061393885,0.000032081887,0.000037724785,0.00007573216,0.000055635293],"domain_scores_gemma":[0.9999219,0.000005301558,0.000007863025,0.00002051513,0.000020104033,0.0000243218],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00006417385,0.000013875819,0.000015880692,0.000011567471,0.00012582193,0.0000132171,0.000026335154,0.00001815906,0.0011956812],"category_scores_gemma":[0.000011949832,0.000012743113,0.0000076279716,0.00007960118,0.00002166556,0.00006919755,4.4821485e-7,0.000014188443,0.00029796115],"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":[0.0000045238307,0.000041522933,0.009560356,0.0000028064308,0.0000041726257,0.000003275938,0.03201657,0.016433029,0.000046105815,0.93328536,0.0058440045,0.0027583037],"study_design_scores_gemma":[0.0022023893,0.000093912626,0.1602028,0.000050056984,0.000025916903,9.84646e-7,0.030890223,0.00024245343,0.0028606574,0.035969123,0.76682,0.0006414534],"about_ca_topic_score_codex":0.0008607593,"about_ca_topic_score_gemma":0.0008818062,"teacher_disagreement_score":0.92311597,"about_ca_system_score_codex":0.000011765789,"about_ca_system_score_gemma":0.000057638434,"threshold_uncertainty_score":0.99971735},"labels":[],"label_agreement":null},{"id":"W7100264979","doi":"","title":"CRT-971 Introducing Spatial Competition through an Autoregressive Contiguous Distributed (AR-C-D) Process in Intercity Generation-Distribution Models","year":2002,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Process (computing); Christian ministry; Work (physics); Competition (biology); Ministry of Transport; German; Software","score_opus":0.04741027453722211,"score_gpt":0.30237441239209556,"score_spread":0.25496413785487343,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7100264979","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.36938792,0.00003190462,0.6265913,0.0010276245,0.00028283385,0.0002941588,0.0001739679,0.00020774704,0.0020025205],"genre_scores_gemma":[0.9943432,0.00005875041,0.0012712673,0.00008748895,0.00031564885,0.000033176268,0.0037795617,0.000009338483,0.00010154152],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984586,0.00019947234,0.0003599383,0.0003612888,0.00034666416,0.00027403454],"domain_scores_gemma":[0.999281,0.000034352255,0.00014902928,0.00013123476,0.00031982595,0.00008456603],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028914533,0.0001361945,0.00017561398,0.00005291787,0.00045962463,0.00014611584,0.0001391864,0.0001492062,0.00048205798],"category_scores_gemma":[0.00011815309,0.00014043,0.00003547942,0.00031210337,0.0001288258,0.001619765,0.000005190403,0.00015401398,0.000011103827],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006294935,0.0005737757,0.010752515,0.00002810914,0.000020326595,0.000015248197,0.081809215,0.8050191,0.00019597833,0.096945725,0.0017581899,0.0028188974],"study_design_scores_gemma":[0.0009348885,0.00007849504,0.0076714978,0.00007087438,0.00001786576,0.0000013275616,0.0044472315,0.98253894,0.0007450581,0.0024105795,0.0007271107,0.0003561135],"about_ca_topic_score_codex":0.0057178414,"about_ca_topic_score_gemma":0.015218372,"teacher_disagreement_score":0.62532,"about_ca_system_score_codex":0.00019147141,"about_ca_system_score_gemma":0.000064531145,"threshold_uncertainty_score":0.8643704},"labels":[],"label_agreement":null},{"id":"W7100353213","doi":"","title":"CONCEPTION TO RESULTS.","year":2013,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Cape; Toll; Payment; Population; Table (database); Quarter (Canadian coin); Socioeconomic status; Bridge (graph theory)","score_opus":0.02251911907855196,"score_gpt":0.3024768271454955,"score_spread":0.27995770806694353,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7100353213","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12083743,0.0000039887855,0.029698428,0.015603152,0.00023412466,0.0002929033,0.0000037415055,0.00027889898,0.83304733],"genre_scores_gemma":[0.9699081,0.0000033626002,0.006174449,0.00061617035,0.00006256148,0.000008790646,0.00001317342,0.0000014072458,0.023211945],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9997104,0.00001709811,0.00006172093,0.00005627463,0.00008723815,0.00006730942],"domain_scores_gemma":[0.9998209,0.000019134126,0.000011304953,0.00003112266,0.00006164213,0.00005590488],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.000099162564,0.000017055092,0.000020090945,0.000019131974,0.00010816354,0.000034208155,0.000033452794,0.000020588022,0.00095469854],"category_scores_gemma":[0.00005300853,0.000015603147,0.0000070800074,0.000108498505,0.000017147722,0.00012262372,8.264483e-7,0.000013313251,0.00095600507],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026676315,0.000044308086,0.016362656,0.0000026146868,0.000007195735,7.7973664e-7,0.11630961,0.012718475,0.00048515698,0.19714846,0.5995103,0.057383727],"study_design_scores_gemma":[0.00045243884,0.00004956525,0.20842022,0.000011785454,0.000004398388,5.6713297e-8,0.01810538,0.0005532795,0.0001924059,0.0013116385,0.7706947,0.00020416593],"about_ca_topic_score_codex":0.0035076542,"about_ca_topic_score_gemma":0.0007575569,"teacher_disagreement_score":0.8490707,"about_ca_system_score_codex":0.000009224295,"about_ca_system_score_gemma":0.000015672169,"threshold_uncertainty_score":0.9999586},"labels":[],"label_agreement":null},{"id":"W7100506556","doi":"","title":"Transportation Association of Canada","year":2006,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Traffic congestion; Greenhouse gas; Air pollutants; Congestion pricing; Service (business); Traffic volume; Alternative fuels; Air pollution","score_opus":0.004280282694260168,"score_gpt":0.2124575191862834,"score_spread":0.20817723649202322,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7100506556","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6163103,0.00002252653,0.009381213,0.003269988,0.00034953808,0.00013902453,0.000045802382,0.00009907008,0.37038252],"genre_scores_gemma":[0.9841137,0.000002920757,0.0006702654,0.00003297027,0.000031669908,0.000001170241,0.00006654869,0.0000016939416,0.015079061],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9995,0.00001930237,0.000116033145,0.00004069598,0.0002478067,0.0000761904],"domain_scores_gemma":[0.9997493,0.00004144035,0.00007606989,0.000019569696,0.00009819223,0.000015433092],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001543061,0.000020955164,0.000038509315,0.000015914837,0.000088029396,0.0000048664583,0.000025388441,0.0000296869,0.00016186766],"category_scores_gemma":[0.000017959639,0.000022409924,0.000011618771,0.000121107485,0.000008069932,0.00006277509,8.393987e-8,0.000016032118,5.630518e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000064046226,0.00002730035,0.748448,0.0000070524034,0.000007506398,9.446247e-7,0.0023935356,0.026117537,0.00010181116,0.17392212,0.048687518,0.00028023912],"study_design_scores_gemma":[0.00017882977,0.0000044007165,0.93162775,0.000004582326,0.00001116742,7.810736e-9,0.0011682368,0.000109722125,0.00062582945,0.0007583004,0.065440685,0.00007050381],"about_ca_topic_score_codex":0.90849537,"about_ca_topic_score_gemma":0.9940156,"teacher_disagreement_score":0.36780336,"about_ca_system_score_codex":0.00008748363,"about_ca_system_score_gemma":0.0003222345,"threshold_uncertainty_score":0.17723374},"labels":[],"label_agreement":null},{"id":"W7100601420","doi":"","title":"Transportation Planning in the Portland/Vancouver Metropolitan Area Metro","year":2009,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Metropolitan area; Transportation planning; Public transport; Urban planning; Regional planning","score_opus":0.02495151338129306,"score_gpt":0.30549233022090533,"score_spread":0.2805408168396123,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7100601420","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.54517925,0.00014564506,0.028022727,0.0037482195,0.00034583223,0.000404644,0.000012553854,0.00024682804,0.42189428],"genre_scores_gemma":[0.9970514,0.0000126289315,0.0013434687,0.0008605579,0.00005187915,0.0000046589753,0.00005881118,0.000003216554,0.0006133685],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99907464,0.00007649106,0.0001958458,0.00012695814,0.00032885856,0.00019723372],"domain_scores_gemma":[0.99970514,0.000082302606,0.00005444018,0.000077800345,0.00003920962,0.00004110575],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005296352,0.000069459296,0.000086908345,0.00011331552,0.0002196584,0.00004181797,0.00013290653,0.000058646525,0.00017573537],"category_scores_gemma":[0.000033759366,0.000050599258,0.000038100483,0.0004998683,0.00003594369,0.0002323344,2.1730523e-7,0.0000936184,0.00000331996],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009105189,0.00024069309,0.5896702,0.0000071118784,0.000019551342,0.000094085975,0.13958658,0.023269655,0.000032312884,0.21285352,0.028875493,0.00525978],"study_design_scores_gemma":[0.00046091864,0.00006532801,0.87637275,0.000020783165,0.000024170708,1.7307981e-7,0.10414418,0.00032928272,0.000031821608,0.0036836658,0.0146784745,0.00018847907],"about_ca_topic_score_codex":0.0017394457,"about_ca_topic_score_gemma":0.022937488,"teacher_disagreement_score":0.45187214,"about_ca_system_score_codex":0.00006140185,"about_ca_system_score_gemma":0.00004755226,"threshold_uncertainty_score":0.99489135},"labels":[],"label_agreement":null},{"id":"W7100730217","doi":"","title":"Assistant Professor","year":2008,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Service (business); Transit (satellite); Travel time; Public transport; Yield (engineering); Range (aeronautics)","score_opus":0.04174472320649011,"score_gpt":0.31287186726347543,"score_spread":0.27112714405698535,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7100730217","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23438106,0.00002199811,0.012109636,0.0033920233,0.00024035446,0.00010427586,0.000001766136,0.000295705,0.7494532],"genre_scores_gemma":[0.9449869,0.000024096975,0.0028895978,0.00012517438,0.000032971955,0.0000025438205,0.0000048656484,0.0000015780174,0.051932294],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9997182,0.000020061136,0.00004554812,0.000042568663,0.00010822535,0.0000654338],"domain_scores_gemma":[0.9998862,0.000015447813,0.000012627087,0.000025698173,0.00003126308,0.000028734632],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008064172,0.000016563537,0.000023102077,0.000013916454,0.00032584494,0.0000054086786,0.00003070979,0.000021315027,0.0003852811],"category_scores_gemma":[0.00001939488,0.000014594997,0.0000099434465,0.000088422064,0.000047255282,0.000066781766,5.6391303e-7,0.000019147468,0.00004452182],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022740329,0.00011204929,0.3128774,0.0000056540853,0.000013047997,0.000034987595,0.11565059,0.0005875739,0.00006298655,0.5031726,0.06504251,0.0024179304],"study_design_scores_gemma":[0.00031433508,0.000018435909,0.34454858,0.000008264777,0.0000060682933,0.0000011209692,0.006882694,0.00014599715,0.00014836439,0.00059800193,0.6471551,0.00017301382],"about_ca_topic_score_codex":0.00025496603,"about_ca_topic_score_gemma":0.0004937676,"teacher_disagreement_score":0.7106058,"about_ca_system_score_codex":0.000008165192,"about_ca_system_score_gemma":0.00006413795,"threshold_uncertainty_score":0.42185578},"labels":[],"label_agreement":null},{"id":"W7100846248","doi":"","title":"Computers, Environment andUrban Systems","year":2008,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Microsimulation; Aggregate (composite); Work (physics); Real estate; Aggregate data; Land use; Urban planning; Subdivision","score_opus":0.02405251746481647,"score_gpt":0.23128053616263494,"score_spread":0.20722801869781848,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7100846248","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22101432,0.00042900612,0.47005937,0.0017362604,0.0013547215,0.00045942634,0.000009447994,0.0006360666,0.30430138],"genre_scores_gemma":[0.98783857,0.00012113981,0.0025961364,0.0000539346,0.00006411006,0.0000022758136,0.000013099544,0.000002105676,0.0093086595],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996463,0.000026850861,0.000061075734,0.00006119517,0.00013241603,0.00007214537],"domain_scores_gemma":[0.9998803,0.000017507553,0.000017103026,0.00003596305,0.0000075474672,0.00004157672],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000070599956,0.00002475112,0.00003493028,0.00001570924,0.00026344688,0.00001192844,0.000038082937,0.000024160048,0.00011890186],"category_scores_gemma":[0.0000023279147,0.000023155684,0.000010762135,0.000037702743,0.000051080464,0.00006136044,0.0000011116782,0.000017736971,0.00005793143],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001557554,0.00016718896,0.3809531,0.000014799551,0.000036940488,0.000059651626,0.09154574,0.22137514,0.00003577534,0.1852092,0.11799075,0.0025961334],"study_design_scores_gemma":[0.0004180592,0.000033628203,0.12137392,0.000014181467,0.0000082595225,0.0000037385003,0.0041731303,0.008706259,0.000018068878,0.000040112445,0.86498255,0.00022810318],"about_ca_topic_score_codex":0.00081688137,"about_ca_topic_score_gemma":0.00002603781,"teacher_disagreement_score":0.7668242,"about_ca_system_score_codex":0.000016425925,"about_ca_system_score_gemma":0.000018324125,"threshold_uncertainty_score":0.20262471},"labels":[],"label_agreement":null},{"id":"W7112645522","doi":"","title":"Post-secondary Students' Travel Behavior through the Lens of Urban and Rural Contexts","year":2025,"lang":"en","type":"dissertation","venue":"VTechWorks (Virginia Tech)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Multinomial logistic regression; Mixed logit; Discrete choice; Travel behavior; Mode choice; Travel survey; Preference; Multinomial probit; Revealed preference","score_opus":0.01006208428194184,"score_gpt":0.3010805376902207,"score_spread":0.29101845340827887,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7112645522","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9282641,0.006335883,0.0007698022,0.0012943035,0.002487643,0.002690154,0.0010370031,0.00043971505,0.056681395],"genre_scores_gemma":[0.9713262,0.00094190164,0.00033686377,0.00019782712,0.00009968463,0.00011814801,0.001634455,0.00003490969,0.025310013],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.99813676,0.00012752817,0.00051836675,0.00032032956,0.00060602603,0.00029096394],"domain_scores_gemma":[0.99878174,0.00020980394,0.00038961653,0.00028194007,0.00028672238,0.000050198134],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039446767,0.000275948,0.0003982427,0.00016984278,0.0006235363,0.00012816158,0.00056800304,0.00063263177,0.00018247523],"category_scores_gemma":[0.000097075084,0.00023889956,0.00013857424,0.00043518,0.00023521131,0.00025693738,0.000016770508,0.00064112694,0.0000047387744],"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":[0.0010217264,0.0014136259,0.07731905,0.001132544,0.00097303744,0.00006322027,0.5489692,0.00027059464,0.0018410722,0.19816099,0.017882839,0.15095209],"study_design_scores_gemma":[0.0018421048,0.00024204305,0.7554265,0.0015410377,0.0011675215,0.0000018030378,0.17289679,0.00001492219,0.00061870826,0.0009837593,0.064175665,0.0010890865],"about_ca_topic_score_codex":0.0030794227,"about_ca_topic_score_gemma":0.0057029077,"teacher_disagreement_score":0.6781075,"about_ca_system_score_codex":0.00004430392,"about_ca_system_score_gemma":0.00037938196,"threshold_uncertainty_score":0.9742042},"labels":[],"label_agreement":null},{"id":"W7114895526","doi":"10.3390/su172411144","title":"A Comparative Analysis of the Effect of Route Set Size in Logit and Weibit-Based Stochastic Traffic Assignment","year":2025,"lang":"en","type":"article","venue":"Sustainability","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Inje University","keywords":"Logit; Set (abstract data type); Mixed logit; Path (computing); Flow network; Traffic flow (computer networking); Flow (mathematics); Logistic regression","score_opus":0.008573308997430433,"score_gpt":0.3221585085307379,"score_spread":0.3135851995333075,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7114895526","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99538016,0.000024852992,0.0034269851,0.0005464195,0.000020605376,0.00044013592,0.000019367442,0.000009137981,0.00013234484],"genre_scores_gemma":[0.99987835,3.3496687e-7,0.000029084289,0.000009497777,0.0000011651639,0.000015702613,0.0000059895997,7.2510477e-7,0.00005913732],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9989233,0.000509651,0.00019520088,0.00012770758,0.0001491823,0.00009495913],"domain_scores_gemma":[0.9985626,0.0010837957,0.00008953559,0.000097983415,0.00014845107,0.000017673592],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009096359,0.00005340467,0.00024193048,0.000095668955,0.00008255028,0.0000075039525,0.000070136615,0.00004217379,0.0000096330605],"category_scores_gemma":[0.0008239119,0.00003908259,0.000066700544,0.001234803,0.00030239136,0.000031455194,0.0000058129826,0.00005473499,2.0486892e-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":[0.00008037096,0.000029635341,0.39560232,0.000060614388,0.000043949396,1.4752895e-7,0.0072219865,0.59630394,0.000002228883,0.00045296416,0.0000045784723,0.00019728653],"study_design_scores_gemma":[0.00043377042,0.000060624858,0.9644258,0.000020813646,0.00026804404,2.5220515e-9,0.0058877054,0.02852639,0.000088371635,0.0002358334,0.000011556767,0.000041098316],"about_ca_topic_score_codex":0.0011639784,"about_ca_topic_score_gemma":0.0035315843,"teacher_disagreement_score":0.56882346,"about_ca_system_score_codex":0.00021393468,"about_ca_system_score_gemma":0.00034028167,"threshold_uncertainty_score":0.19707069},"labels":[],"label_agreement":null},{"id":"W7114930163","doi":"10.1061/jtepbs.teeng-8821","title":"Mitigating Grade Crossing Blockage Queues by Modifying Signal Timing Plans: A Network-Level Microsimulation Approach Using Train Detection and Probe-Based Traffic Data","year":2025,"lang":"en","type":"article","venue":"Journal of Transportation Engineering Part A Systems","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Microsimulation; Queue; Signal timing; Intersection (aeronautics); SIGNAL (programming language); Level crossing; Component (thermodynamics); Work (physics)","score_opus":0.06308874644854126,"score_gpt":0.29305367001109917,"score_spread":0.2299649235625579,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7114930163","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.31696215,0.0006541511,0.681615,0.00003181796,0.00034662514,0.00023395354,0.00008482774,0.000065328866,0.000006144969],"genre_scores_gemma":[0.97849816,0.000015753516,0.02096446,0.000013096034,0.0002274498,0.0000072671,0.00023138439,0.000022673583,0.000019773068],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980816,0.00013910483,0.0008705406,0.0002461414,0.00038491178,0.00027764912],"domain_scores_gemma":[0.99891514,0.00020866528,0.00050419633,0.00010696888,0.00016603284,0.0000989762],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001649817,0.00017983318,0.00030671182,0.00023749516,0.0006309799,0.00034850964,0.00016560797,0.00016085178,0.000001237107],"category_scores_gemma":[0.000052844254,0.00019722145,0.000064257176,0.00047877766,0.00006483929,0.0006588223,0.0000017538587,0.00024842762,6.183346e-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":[0.000029552006,0.00003133941,0.0019302326,0.00027923897,0.00005137062,0.0000035253352,0.0046471255,0.9850693,0.007303782,0.00006212592,0.000027667746,0.00056475913],"study_design_scores_gemma":[0.00073867064,0.000019641984,0.0018056757,0.0010899606,0.00014750956,0.000004271612,0.0012364022,0.9936968,0.0003645109,0.000004311448,0.00070024835,0.00019201651],"about_ca_topic_score_codex":0.000207962,"about_ca_topic_score_gemma":0.0001602226,"teacher_disagreement_score":0.661536,"about_ca_system_score_codex":0.00010067667,"about_ca_system_score_gemma":0.00023513631,"threshold_uncertainty_score":0.8042457},"labels":[],"label_agreement":null},{"id":"W7118158721","doi":"10.18280/ijsdp.201111","title":"Evaluation of the Palopo City Activity Service Center System in Terms of Settlement Patterns and Transportation Systems","year":2025,"lang":"","type":"article","venue":"International Journal of Sustainable Development and Planning","topic":"Transportation Planning and Optimization","field":"Social Sciences","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":"Settlement (finance); Center (category theory); Service (business); Human settlement; Service system","score_opus":0.022931630908320042,"score_gpt":0.2968824391979561,"score_spread":0.273950808289636,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7118158721","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9946561,0.00033808404,0.0025878367,0.00045022077,0.0008949397,0.00039172056,0.000049056434,0.0000032128203,0.00062882714],"genre_scores_gemma":[0.99962455,0.000081088976,0.00011642587,0.000024767229,0.000039080016,0.000007627344,0.00002550684,0.000004570374,0.00007636751],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99713606,0.00030767743,0.0009571958,0.00015156156,0.0012739664,0.00017354857],"domain_scores_gemma":[0.99648094,0.00012996771,0.0011139456,0.000055465058,0.0021680119,0.00005166123],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003560116,0.00013663873,0.0002815419,0.00043805398,0.00015851986,0.00007793147,0.00022782295,0.000104232146,0.000009262669],"category_scores_gemma":[0.00012184222,0.000116157804,0.00004370292,0.00039532367,0.00005480565,0.00043299972,0.00002040321,0.0001718653,3.6906833e-8],"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":[0.0002214636,0.00010500599,0.88746935,0.00085154217,0.00030714425,0.000030506193,0.06614563,0.039021786,0.000029327242,0.0021403944,0.00002478324,0.003653059],"study_design_scores_gemma":[0.0017154928,0.000021002645,0.8811957,0.0038927228,0.0001570031,0.0000035257322,0.107144155,0.0049562375,0.00033559153,0.00012813306,0.00034526427,0.00010512673],"about_ca_topic_score_codex":0.00070988893,"about_ca_topic_score_gemma":0.000353026,"teacher_disagreement_score":0.040998526,"about_ca_system_score_codex":0.00043018736,"about_ca_system_score_gemma":0.0008484383,"threshold_uncertainty_score":0.47367778},"labels":[],"label_agreement":null},{"id":"W7119497377","doi":"10.1007/s10958-025-08162-9","title":"IDENTIFICATION OF THE BRAESS PARADOX IN A STABLE DYNAMIC MODEL IN NETWORK WITH ONE SOURCE AND MULTIPLE SINKS","year":2025,"lang":"en","type":"article","venue":"Journal of Mathematical Sciences","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Flow network; Toll; Residual; Graph; Sensitivity (control systems); Maximum flow problem; Path (computing); Identification (biology)","score_opus":0.01634526198011459,"score_gpt":0.2883397583623684,"score_spread":0.2719944963822538,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7119497377","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9027711,0.00008019244,0.09507984,0.001357251,0.000026018859,0.000095505624,5.2095845e-7,0.0000026746764,0.0005868975],"genre_scores_gemma":[0.99460226,0.000027223892,0.005184939,0.00002320517,0.000004431692,9.0580585e-7,7.2113416e-8,0.0000011209986,0.00015584697],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99908054,0.000078360776,0.00034918066,0.00006540373,0.0003197626,0.000106735504],"domain_scores_gemma":[0.99938834,0.00026855333,0.00022619756,0.00004005735,0.00005458342,0.000022245302],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018912327,0.000034528137,0.00012202709,0.00007882965,0.00013433292,0.000044145116,0.00016831179,0.000032579697,0.00000333369],"category_scores_gemma":[0.00024521392,0.000021372576,0.000016367892,0.00069641403,0.00031905534,0.00019763358,0.0000068418403,0.000087281405,1.1530789e-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":[0.00002380555,0.00010241187,0.074913934,0.000033691267,0.0000039703505,3.999985e-7,0.007207248,0.8954614,0.00014493708,0.021567216,0.000010535692,0.0005304937],"study_design_scores_gemma":[0.0003956264,0.00002909759,0.08186212,0.0006861722,0.000017220957,0.000001021088,0.004381981,0.8413429,0.00007385879,0.07114154,0.000011661539,0.000056765788],"about_ca_topic_score_codex":0.000057208603,"about_ca_topic_score_gemma":0.0011107974,"teacher_disagreement_score":0.091831155,"about_ca_system_score_codex":0.000025663088,"about_ca_system_score_gemma":0.00016594531,"threshold_uncertainty_score":0.117557265},"labels":[],"label_agreement":null},{"id":"W7128545614","doi":"10.64903/1480-6800.22.1.78","title":"Bus Public Transport Improvements and Service Integration in Penang, Malaysia","year":2019,"lang":"","type":"article","venue":"Arab world geographer","topic":"Transportation Planning and Optimization","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":"Public transport; Ticket; Service (business); Reliability (semiconductor); Smart card; Bus network; Passenger information; Data collection","score_opus":0.011245299537096567,"score_gpt":0.2438792384291744,"score_spread":0.2326339388920778,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7128545614","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97180307,0.0005476909,0.00022084049,0.0039090184,0.00071015535,0.0008866251,0.000024891506,0.000077011384,0.021820683],"genre_scores_gemma":[0.99224746,0.00048308593,0.0003873154,0.001163544,0.000058249025,0.000038897615,0.00018998669,0.000026570802,0.0054049073],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9975649,0.00014587655,0.0005970969,0.00062807056,0.0004874817,0.0005765586],"domain_scores_gemma":[0.9990558,0.00006228798,0.00023741976,0.00024631206,0.00020817482,0.00019001341],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00070963305,0.00029108248,0.00031338123,0.0005934765,0.00023865902,0.00017305867,0.00024307915,0.00017843704,0.00225347],"category_scores_gemma":[0.000009878611,0.00030335688,0.00009946053,0.0033561732,0.00012317502,0.0010273034,0.000011357449,0.00032399522,0.000109421955],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000054024986,0.00015246111,0.9731635,0.000059464193,0.00003700829,0.0000048915954,0.010976428,0.0010204875,0.00008028218,0.004398934,0.000074825126,0.009977729],"study_design_scores_gemma":[0.0015025383,0.00006961177,0.98034096,0.00024885032,0.000063603555,4.3463896e-7,0.004970167,0.0036328721,0.000022176895,0.0006391354,0.008057742,0.00045193764],"about_ca_topic_score_codex":0.0055764443,"about_ca_topic_score_gemma":0.06214273,"teacher_disagreement_score":0.056566287,"about_ca_system_score_codex":0.000039753628,"about_ca_system_score_gemma":0.00010750003,"threshold_uncertainty_score":0.9999418},"labels":[],"label_agreement":null},{"id":"W7132870278","doi":"","title":"Optimal bus service design in competitive contracting","year":2005,"lang":"","type":"dissertation","venue":"TSpace","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Transit (satellite); Procurement; Public transport; Service (business); Point (geometry); Perfect competition; Call for bids; Operator (biology)","score_opus":0.029880840931286443,"score_gpt":0.3557076460127605,"score_spread":0.32582680508147405,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7132870278","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7657544,0.0014571775,0.024165332,0.00449084,0.0026431696,0.002893602,0.00003659212,0.00039793932,0.19816093],"genre_scores_gemma":[0.96287894,0.00073541055,0.020120174,0.00035467267,0.00045717676,0.00006680778,0.0010129891,0.000069514506,0.014304311],"study_design_codex":"simulation_or_modeling","study_design_gemma":"qualitative","domain_scores_codex":[0.9967617,0.0005686764,0.00068801554,0.00064834586,0.0006748852,0.00065837067],"domain_scores_gemma":[0.997507,0.00083757023,0.0007213821,0.00016875933,0.0005761106,0.00018922277],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0011030829,0.0004354796,0.00054511364,0.0002925352,0.0006272125,0.00019888132,0.0003355083,0.0006563954,0.0011494295],"category_scores_gemma":[0.0002260013,0.0005443259,0.00010048119,0.0012481933,0.000080549435,0.0004434275,0.000006175092,0.0007487458,0.00020917327],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039127414,0.00015477072,0.0009964907,0.000075795986,0.000042165128,0.000030639905,0.44078514,0.5542389,0.0001374496,0.0019421629,0.0000644122,0.0011407916],"study_design_scores_gemma":[0.0019805462,0.00012048926,0.03252457,0.0013826306,0.00021248969,0.0000024473354,0.9081472,0.045475513,0.0006393828,0.000021186415,0.008273462,0.001220089],"about_ca_topic_score_codex":0.00690623,"about_ca_topic_score_gemma":0.026217673,"teacher_disagreement_score":0.50876343,"about_ca_system_score_codex":0.00022770373,"about_ca_system_score_gemma":0.0008103684,"threshold_uncertainty_score":0.99976367},"labels":[],"label_agreement":null},{"id":"W7132985749","doi":"","title":"Express bus modelling for the GTA","year":2003,"lang":"","type":"dissertation","venue":"TSpace","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Local bus; Bus network; Representation (politics); Mode (computer interface); Transit (satellite); Public transport; Software; System bus","score_opus":0.048658629130698156,"score_gpt":0.3685867121353562,"score_spread":0.319928083004658,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7132985749","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020343963,0.0037087433,0.94087636,0.0011494758,0.004241681,0.0019903474,0.000038794784,0.00012458947,0.027526064],"genre_scores_gemma":[0.39030352,0.0065982593,0.023144506,0.00037348384,0.000902858,0.00063224364,0.0020269305,0.00016747581,0.5758507],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980599,0.00012438983,0.00038766966,0.0004604286,0.0005187019,0.00044889262],"domain_scores_gemma":[0.9981216,0.0006460077,0.00042675066,0.00026679286,0.00043051387,0.00010831935],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.00067344966,0.00029857684,0.00027840585,0.000077469755,0.0019619784,0.00022701616,0.00038487586,0.0004256209,0.00033214167],"category_scores_gemma":[0.00009952209,0.00025694113,0.00020945504,0.00040684384,0.00010924276,0.0001538158,0.0000025032837,0.00027788244,0.000025781694],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009842179,0.000034366356,0.000039571707,0.0000848031,0.00005892841,5.838243e-7,0.18873225,0.80180913,0.000026484138,0.006544036,0.0018371264,0.0007342874],"study_design_scores_gemma":[0.00084791885,0.000076844175,0.0001037307,0.00033365318,0.0006707614,4.1569422e-7,0.34426218,0.3563099,0.00042229658,0.0007178911,0.29538703,0.00086737436],"about_ca_topic_score_codex":0.0017334181,"about_ca_topic_score_gemma":0.00053339184,"teacher_disagreement_score":0.9177318,"about_ca_system_score_codex":0.00004486763,"about_ca_system_score_gemma":0.00033335754,"threshold_uncertainty_score":0.99998826},"labels":[],"label_agreement":null},{"id":"W7144605759","doi":"","title":"Fare System of Edmonton Transit Service","year":2019,"lang":"ja","type":"article","venue":"Institutional Repositories DataBase (IRDB)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Service (business); Transit (satellite); Public transport; Rapid transit; Transit system","score_opus":0.014486369913270198,"score_gpt":0.2541106052231572,"score_spread":0.239624235309887,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7144605759","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8907208,0.0019301849,0.006110885,0.0012962036,0.015153089,0.0013819828,0.0060441964,0.0004305428,0.076932155],"genre_scores_gemma":[0.9941583,0.000099862424,0.0015464169,0.000087658125,0.0006916099,0.000016651333,0.0022595916,0.000019804818,0.0011200996],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99684703,0.00015867042,0.0008088728,0.0005560633,0.0012624807,0.0003668533],"domain_scores_gemma":[0.99781615,0.00014751936,0.00042770628,0.00049708155,0.0009114158,0.00020010368],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00048069595,0.0002872813,0.0004232677,0.00013584236,0.00076757086,0.00013305632,0.00042640383,0.0002652228,0.0001714232],"category_scores_gemma":[0.00007532987,0.00030395517,0.00014200369,0.0009526768,0.00028421325,0.001287965,0.000031613465,0.00025085747,0.0001793535],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016716744,0.00090433354,0.05470282,0.008508533,0.00076746946,0.00037390727,0.071127184,0.15117955,0.017041076,0.690172,0.0029786217,0.0005728356],"study_design_scores_gemma":[0.009193379,0.0007272062,0.10038612,0.018257761,0.001818429,0.00024370359,0.116526954,0.022510849,0.01924141,0.00008859308,0.70699376,0.0040118597],"about_ca_topic_score_codex":0.0094913505,"about_ca_topic_score_gemma":0.0015365696,"teacher_disagreement_score":0.70401514,"about_ca_system_score_codex":0.0003049521,"about_ca_system_score_gemma":0.00105735,"threshold_uncertainty_score":0.99994123},"labels":[],"label_agreement":null},{"id":"W7161955158","doi":"10.82308/15082","title":"Alleviating traffic congestion in Montreal's Champlain Bridge corridor : commuter rail or express bus?","year":2005,"lang":"en","type":"dissertation","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Bridge (graph theory); Public transport; Rail transit; Traffic congestion; Mode (computer interface); Light rail transit; Work (physics); Urban rail transit","score_opus":0.02927786506877865,"score_gpt":0.31231433583342927,"score_spread":0.2830364707646506,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7161955158","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8980073,0.0003491118,0.0006287958,0.00080485217,0.0014612956,0.0013062935,0.00009454323,0.00054682104,0.096801005],"genre_scores_gemma":[0.9208444,0.00059347256,0.001370428,0.0001766403,0.000351478,0.00010889735,0.0052777757,0.000043602664,0.07123334],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.997909,0.00024583537,0.0005818315,0.00039380434,0.0004826323,0.00038687073],"domain_scores_gemma":[0.99889874,0.00030940055,0.00033180794,0.00016733211,0.00017442407,0.00011831554],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00060600665,0.00026977947,0.00036702026,0.00026229685,0.00039614068,0.0001526587,0.00025547773,0.0004980884,0.0012448417],"category_scores_gemma":[0.0001894262,0.0002614631,0.00008233578,0.00035389466,0.000049928152,0.00034492376,0.0000035022947,0.00035567838,0.00005054678],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001511387,0.00068233267,0.010069637,0.00038169933,0.000117813164,0.000115094495,0.46612996,0.23323156,0.000040422805,0.0023048338,0.026816316,0.25859892],"study_design_scores_gemma":[0.0037102934,0.00014850896,0.8419081,0.0016719855,0.00015273948,0.000003276267,0.08775156,0.011654998,0.00003665891,0.000061920065,0.051043227,0.0018567577],"about_ca_topic_score_codex":0.010184943,"about_ca_topic_score_gemma":0.59835565,"teacher_disagreement_score":0.8318384,"about_ca_system_score_codex":0.00013737347,"about_ca_system_score_gemma":0.00025944505,"threshold_uncertainty_score":0.9999838},"labels":[],"label_agreement":null},{"id":"W7162014878","doi":"10.82308/2059","title":"A comprehensive study of household transportation expenditure, vehicle ownership, and usage patterns: tale of Canadian cities","year":2015,"lang":"en","type":"dissertation","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Pooling; Context (archaeology); Destinations; Population; Travel behavior; Econometric model; Hierarchy; Car ownership; Discrete choice; Empirical research","score_opus":0.05150811726398704,"score_gpt":0.2929354360981129,"score_spread":0.24142731883412585,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7162014878","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9936719,0.00018544146,0.0000078172425,0.000019737654,0.00022350122,0.0004931578,0.00037203907,0.000034551107,0.0049918504],"genre_scores_gemma":[0.99565077,0.00015958777,0.00007506142,0.000017362438,0.000030348547,0.000020260008,0.002065266,0.00001917357,0.0019621614],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.998666,0.00009680249,0.00039238043,0.00021248472,0.00046525063,0.00016710788],"domain_scores_gemma":[0.9990604,0.00005918393,0.00030102668,0.000104885235,0.00031970127,0.00015483936],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014129037,0.00014958644,0.0003060234,0.0005500227,0.00014626261,0.000026248055,0.00011830394,0.00021183265,0.00008581714],"category_scores_gemma":[0.000014618048,0.000150086,0.000039427374,0.00031748132,0.000064438056,0.0001780671,6.8706737e-7,0.00011434024,4.69975e-7],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015471733,0.0002406477,0.21904431,0.00045098292,0.00014584373,0.000018599854,0.77390647,0.0024404582,0.00011805098,0.0012174757,0.0012299218,0.001032506],"study_design_scores_gemma":[0.00057144614,0.00013648359,0.4765766,0.00010563187,0.000095828225,5.9838044e-8,0.5212459,0.000012184685,0.00010411234,0.000073813826,0.0009009565,0.00017698987],"about_ca_topic_score_codex":0.6718809,"about_ca_topic_score_gemma":0.95933527,"teacher_disagreement_score":0.28745437,"about_ca_system_score_codex":0.000034545734,"about_ca_system_score_gemma":0.00037214233,"threshold_uncertainty_score":0.61203295},"labels":[],"label_agreement":null},{"id":"W7162072385","doi":"10.82308/37799","title":"Fostering happiness among public transit users: Analyzing customer satisfaction surveys through non-traditional approaches","year":2019,"lang":"en","type":"dissertation","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Customer satisfaction; Happiness; Public transport; Service quality; Quality (philosophy); Market segmentation; Service (business)","score_opus":0.13301354521898576,"score_gpt":0.3056036176153476,"score_spread":0.17259007239636184,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7162072385","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4488999,0.00013224164,0.14385079,0.00040996983,0.0038139627,0.0013957953,0.00015703344,0.00063571316,0.4007046],"genre_scores_gemma":[0.9798096,0.00012144568,0.0012208332,0.000026760586,0.00026102609,0.000065130545,0.009035142,0.000055803383,0.009404297],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9973902,0.00034293914,0.00051088794,0.00057328166,0.00075599167,0.0004266914],"domain_scores_gemma":[0.9989249,0.00015755296,0.0003541051,0.00018801393,0.00025418538,0.00012120947],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001039651,0.0003312598,0.00039898997,0.000324918,0.00072318554,0.00042724342,0.00022723747,0.0005918626,0.00075128424],"category_scores_gemma":[0.00005009844,0.00035883224,0.00019521697,0.0007327395,0.00010062381,0.001863186,0.0000059094673,0.00038083756,0.00007916497],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001479794,0.0003525854,0.6300213,0.0013238483,0.0010450605,0.000017353337,0.17394795,0.059326474,0.00018183872,0.04158463,0.002415244,0.08963575],"study_design_scores_gemma":[0.00052682904,0.000023417351,0.96670187,0.00024997382,0.00016015093,5.964249e-7,0.028351031,0.001807521,0.00007255507,0.00036592904,0.0009110346,0.00082909095],"about_ca_topic_score_codex":0.0076893284,"about_ca_topic_score_gemma":0.047345817,"teacher_disagreement_score":0.53090966,"about_ca_system_score_codex":0.00016727298,"about_ca_system_score_gemma":0.00039628817,"threshold_uncertainty_score":0.9998864},"labels":[],"label_agreement":null},{"id":"W7162716836","doi":"","title":"Fare System of Edmonton Transit Service","year":2019,"lang":"ja","type":"article","venue":"Institutional Repositories DataBase (IRDB)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Service (business); Transit (satellite); Public transport; Rapid transit; Transit system","score_opus":0.014486369913270198,"score_gpt":0.2541106052231572,"score_spread":0.239624235309887,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7162716836","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8907208,0.0019301849,0.006110885,0.0012962036,0.015153089,0.0013819828,0.0060441964,0.0004305428,0.076932155],"genre_scores_gemma":[0.9941583,0.000099862424,0.0015464169,0.000087658125,0.0006916099,0.000016651333,0.0022595916,0.000019804818,0.0011200996],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99684703,0.00015867042,0.0008088728,0.0005560633,0.0012624807,0.0003668533],"domain_scores_gemma":[0.99781615,0.00014751936,0.00042770628,0.00049708155,0.0009114158,0.00020010368],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00048069595,0.0002872813,0.0004232677,0.00013584236,0.00076757086,0.00013305632,0.00042640383,0.0002652228,0.0001714232],"category_scores_gemma":[0.00007532987,0.00030395517,0.00014200369,0.0009526768,0.00028421325,0.001287965,0.000031613465,0.00025085747,0.0001793535],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016716744,0.00090433354,0.05470282,0.008508533,0.00076746946,0.00037390727,0.071127184,0.15117955,0.017041076,0.690172,0.0029786217,0.0005728356],"study_design_scores_gemma":[0.009193379,0.0007272062,0.10038612,0.018257761,0.001818429,0.00024370359,0.116526954,0.022510849,0.01924141,0.00008859308,0.70699376,0.0040118597],"about_ca_topic_score_codex":0.0094913505,"about_ca_topic_score_gemma":0.0015365696,"teacher_disagreement_score":0.70401514,"about_ca_system_score_codex":0.0003049521,"about_ca_system_score_gemma":0.00105735,"threshold_uncertainty_score":0.99994123},"labels":[],"label_agreement":null},{"id":"W7163411105","doi":"10.18192/politika.8172","title":"Free Transportation in Canadian Municipalities","year":2021,"lang":"","type":"article","venue":"Politika – Undergraduate Journal of International Affairs Politics and Policy","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"","score_opus":0.016348693429308482,"score_gpt":0.29603689731369337,"score_spread":0.27968820388438487,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7163411105","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.389536,0.004227998,0.0002781899,0.36345175,0.0050349273,0.000335652,0.0064329626,0.000037827227,0.23066469],"genre_scores_gemma":[0.981331,0.011345995,0.0005851378,0.0010652083,0.0012837618,0.0000024991382,0.00018767786,0.000033318207,0.0041654087],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9961535,0.0003115779,0.0014169827,0.0002792882,0.0008577333,0.000980922],"domain_scores_gemma":[0.99647826,0.0003042757,0.00064275664,0.00020465994,0.0012870787,0.0010829935],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00073566486,0.0003112918,0.00048376402,0.0014690408,0.0003502749,0.00033924018,0.0005239905,0.00027088067,0.00019669921],"category_scores_gemma":[0.00082755036,0.00035584386,0.00023941367,0.00071121426,0.00039616623,0.00071438454,0.000018550647,0.00051383075,0.000010086619],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002584465,0.00011920088,0.029830333,0.000055814828,0.0001785617,0.000271389,0.023431981,0.0018317773,0.00000817745,0.9427548,0.0012398899,0.00025224214],"study_design_scores_gemma":[0.0047309506,0.00026853743,0.15278922,0.0012951479,0.00029944608,0.000293991,0.10736132,0.0016906192,0.0002576605,0.5086925,0.22125663,0.0010639655],"about_ca_topic_score_codex":0.62819934,"about_ca_topic_score_gemma":0.7585335,"teacher_disagreement_score":0.591795,"about_ca_system_score_codex":0.0010870174,"about_ca_system_score_gemma":0.007157458,"threshold_uncertainty_score":0.9998894},"labels":[],"label_agreement":null},{"id":"W72014014","doi":"","title":"Driving mad: public perceptions of road pricing and the costs of congestion in Metro Vancouver","year":2010,"lang":"en","type":"dissertation","venue":"Summit (Simon Fraser University)","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":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Singapore Area Licensing Scheme; Road pricing; Transport engineering; Traffic congestion; Public transport; Perception; Congestion pricing; Business; Advertising; Engineering; Psychology","score_opus":0.008814301372163968,"score_gpt":0.23565546532025541,"score_spread":0.22684116394809145,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W72014014","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98902184,0.000047567748,0.00027776454,0.00009155794,0.00035876414,0.00026786444,0.00001822345,0.000025573514,0.009890844],"genre_scores_gemma":[0.9970135,0.00029302321,0.0002115905,0.000006542826,0.000020942478,0.0000010528693,0.00020546945,0.000009153267,0.0022387193],"study_design_codex":"observational","study_design_gemma":"qualitative","domain_scores_codex":[0.99897784,0.0002015226,0.00021935451,0.00018688136,0.00027080002,0.00014361032],"domain_scores_gemma":[0.99902517,0.00020101079,0.0003269463,0.00011708908,0.00027419842,0.00005557385],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043053238,0.000111070745,0.0002418228,0.0005760977,0.00024235457,0.000023211429,0.00019751815,0.0002876863,0.00008644775],"category_scores_gemma":[0.00018621238,0.00010970058,0.0000677264,0.00079770223,0.00024393288,0.0003155923,0.000007872122,0.00030068474,8.196676e-7],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009890563,0.000041675732,0.98466265,0.000040140123,0.000028913728,0.000002956836,0.0027853681,0.00046057723,0.0000075294433,0.008664153,0.00024009615,0.0029670205],"study_design_scores_gemma":[0.0017894926,0.000028990444,0.32634327,0.00031284394,0.00020393332,4.2423914e-10,0.66599005,0.00038157075,0.00006339411,0.00014725271,0.004479903,0.0002592925],"about_ca_topic_score_codex":0.006312109,"about_ca_topic_score_gemma":0.94957143,"teacher_disagreement_score":0.9432593,"about_ca_system_score_codex":0.00009412255,"about_ca_system_score_gemma":0.0001989983,"threshold_uncertainty_score":0.9542063},"labels":[],"label_agreement":null},{"id":"W7378902","doi":"10.1139/y80-018","title":"Audit of Car Ownership Models","year":2002,"lang":"en","type":"article","venue":"Canadian Journal of Physiology and Pharmacology","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Car ownership; Audit; Christian ministry; Business; Operations research; Accounting; Public transport; Transport engineering; Engineering","score_opus":0.043033861581918055,"score_gpt":0.2880805624820693,"score_spread":0.24504670090015127,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7378902","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9911126,0.0010927933,0.00016013019,0.0023746737,0.00084972667,0.000052877444,0.000015625035,0.000004128767,0.0043374393],"genre_scores_gemma":[0.9988083,0.0002971008,0.00011752554,0.00035795147,0.00017203491,5.4684136e-7,0.000001686383,0.0000025434897,0.00024229329],"study_design_codex":"qualitative","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994704,0.00013992847,0.00015379876,0.00005159977,0.000039003593,0.0001452652],"domain_scores_gemma":[0.9994312,0.000077757206,0.00013873764,0.000020628277,0.000115577655,0.00021613385],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000177069,0.0000397529,0.0001246589,0.00011388306,0.00015002493,0.0000027631513,0.00008562152,0.00008276452,0.001976092],"category_scores_gemma":[0.000015367383,0.000037146907,0.00002982377,0.000085281834,0.00030874906,0.00010240067,9.1071473e-7,0.000114750634,0.0000020271505],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030678522,0.00020577895,0.032855514,0.00026398635,0.00084253104,0.00034206468,0.23926882,0.20174848,0.23452319,0.10499145,0.17570354,0.008947833],"study_design_scores_gemma":[0.015516135,0.003477381,0.13799989,0.00030938708,0.001989643,0.00024713663,0.02298057,0.03276353,0.045468744,0.14346507,0.5933428,0.002439732],"about_ca_topic_score_codex":0.0016946793,"about_ca_topic_score_gemma":0.0037461894,"teacher_disagreement_score":0.41763923,"about_ca_system_score_codex":0.000018511671,"about_ca_system_score_gemma":0.00023316257,"threshold_uncertainty_score":0.99893624},"labels":[],"label_agreement":null},{"id":"W754805827","doi":"","title":"Toronto thinks big on rapid transit","year":2013,"lang":"en","type":"article","venue":"International railway journal","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Public transport; Transit (satellite); Investment (military); Population; Transport engineering; Business; Order (exchange); Finance; Rail transit; Engineering; Political science; Demography; Sociology","score_opus":0.019589220527201016,"score_gpt":0.2866081008162374,"score_spread":0.26701888028903636,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W754805827","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12177328,0.00034130705,0.025983904,0.032684002,0.008906961,0.00031840312,0.000022587265,0.00021144414,0.8097581],"genre_scores_gemma":[0.9887096,0.00036875054,0.0011653632,0.0010247759,0.0012936996,0.000007221192,0.000017068007,0.00000990976,0.0074035726],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9987798,0.00007553514,0.00022953708,0.000111245085,0.0006322688,0.00017163868],"domain_scores_gemma":[0.9992952,0.000085022744,0.00010759877,0.00005366523,0.00031367806,0.00014488278],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00037277714,0.00007835342,0.000074252006,0.000068446825,0.00038049376,0.00028100732,0.00027161694,0.00007608911,0.010001307],"category_scores_gemma":[0.00009365336,0.000070147435,0.0000813063,0.000056548786,0.000052031155,0.00056152383,0.0000026205798,0.00018614519,0.00024189598],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024568563,0.00053203385,0.014901446,0.000007093464,0.0004246736,0.00007848853,0.091686204,0.020413872,0.00084148877,0.1377977,0.14089216,0.5921792],"study_design_scores_gemma":[0.0017484354,0.00019633108,0.08922215,0.00012738646,0.000028277153,0.0000392147,0.009199184,0.0014721092,0.00021236861,0.007941204,0.8893544,0.00045893833],"about_ca_topic_score_codex":0.0011820681,"about_ca_topic_score_gemma":0.0009944726,"teacher_disagreement_score":0.8669364,"about_ca_system_score_codex":0.00017478556,"about_ca_system_score_gemma":0.000109986584,"threshold_uncertainty_score":0.9909037},"labels":[],"label_agreement":null},{"id":"W780091081","doi":"","title":"Analysis of Routine Weekly Activity/Travel Patterns","year":2007,"lang":"en","type":"article","venue":"11th World Conference on Transport ResearchWorld Conference on Transport Research Society","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Schedule; Flexibility (engineering); Time-use survey; Work schedule; Transport engineering; Computer science; Work (physics); Operations management; Operations research; Statistics; Engineering; Mathematics; Scheduling (production processes)","score_opus":0.16099998960773923,"score_gpt":0.41133449577017156,"score_spread":0.25033450616243236,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W780091081","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7344,0.00006670593,0.037757177,0.014890161,0.0003379674,0.0026814332,0.0009234009,0.00051974086,0.20842342],"genre_scores_gemma":[0.977102,0.0017344664,0.0005184636,0.000174041,0.00017841908,0.00011626593,0.00084964617,0.00007776437,0.01924897],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9863499,0.000819393,0.0014863387,0.0017490671,0.006657999,0.002937312],"domain_scores_gemma":[0.9927164,0.0013207493,0.0004237753,0.0012825824,0.0029069642,0.0013495722],"candidate_categories":["metaepi_narrow","sts","research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.012815294,0.0007308019,0.0014231479,0.0030032087,0.0016658133,0.0001815978,0.0019079518,0.00053035625,0.0049094367],"category_scores_gemma":[0.00011650192,0.00073326146,0.0012268907,0.010126951,0.0022844188,0.0006252869,0.000025713483,0.0031055356,0.00005595345],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0028520115,0.0025559282,0.47215882,0.00038905532,0.0023292918,0.0002957347,0.038107987,0.0016935925,0.00259238,0.45970565,0.0004976051,0.016821938],"study_design_scores_gemma":[0.0015282931,0.0006040094,0.97538304,0.00064699975,0.00044810364,4.0688852e-7,0.011560048,0.00218328,0.0032023045,0.0010195874,0.0024505837,0.00097331946],"about_ca_topic_score_codex":0.013912055,"about_ca_topic_score_gemma":0.11966986,"teacher_disagreement_score":0.50322425,"about_ca_system_score_codex":0.00049583294,"about_ca_system_score_gemma":0.0025712617,"threshold_uncertainty_score":0.9996339},"labels":[],"label_agreement":null},{"id":"W789085925","doi":"","title":"SMART COMMUTE INITIATIVE - GREATER TORONTO'S SHOWCASE FOR TRANSPORTATION DEMAND MANAGEMENT","year":2004,"lang":"en","type":"article","venue":"ITE 2004 Annual Meeting and ExhibitInstitute of Transportation Engineers (ITE)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Urban sprawl; TRIPS architecture; Geography; Population; Kilometer; Population density; Agricultural economics; Demography; Urban planning; Transport engineering; Economics; Engineering; Sociology","score_opus":0.013322615064206492,"score_gpt":0.2522361690539527,"score_spread":0.2389135539897462,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W789085925","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.79924744,0.0006716442,0.1879714,0.0008616191,0.00071452494,0.0015011729,0.0027356343,0.0004178906,0.0058786753],"genre_scores_gemma":[0.9779731,0.0003531012,0.019388918,0.00013254936,0.000091270056,0.00006993387,0.0018131913,0.000029807103,0.00014814525],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9982417,0.000039611023,0.0006136184,0.0003557305,0.0003815004,0.00036782082],"domain_scores_gemma":[0.9990885,0.000082451836,0.00022121353,0.000118860844,0.00030983813,0.000179105],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00053852325,0.000252803,0.00030186962,0.0001467072,0.00044151917,0.000042963144,0.00011357313,0.0001642588,0.000019674437],"category_scores_gemma":[0.000025970785,0.00028025906,0.00012825459,0.0002093277,0.00016046895,0.0008169551,0.0000010217288,0.00009933613,0.0000014450254],"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":[0.00051411695,0.00026606637,0.013392389,0.0009247536,0.00033191757,0.00008810853,0.27308258,0.64089495,0.00018038857,0.06643571,0.00039532364,0.0034937211],"study_design_scores_gemma":[0.04434603,0.0020498673,0.4843583,0.008279291,0.004372499,0.000016286578,0.30379143,0.002317476,0.012419147,0.005373796,0.12600982,0.0066660275],"about_ca_topic_score_codex":0.0024771094,"about_ca_topic_score_gemma":0.01271166,"teacher_disagreement_score":0.63857746,"about_ca_system_score_codex":0.00009339265,"about_ca_system_score_gemma":0.00010123871,"threshold_uncertainty_score":0.99996495},"labels":[],"label_agreement":null},{"id":"W791557178","doi":"","title":"BRT Projects Roll Along Despite Economic Slump","year":2009,"lang":"en","type":"article","venue":"Metrologia","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Transit (satellite); Range (aeronautics); Business; Agency (philosophy); Peninsula; Bus rapid transit; Agricultural economics; Transport engineering; Public transport; Geography; Engineering; Economics","score_opus":0.023268671419015168,"score_gpt":0.2936652268351949,"score_spread":0.27039655541617974,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W791557178","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9505604,0.00009250009,0.002755454,0.001706914,0.00029969192,0.00018104973,0.0000052825194,0.00026529265,0.044133414],"genre_scores_gemma":[0.99569577,0.00002694244,0.0028292288,0.0005176868,0.000112775706,0.00000472285,0.00001752993,0.00000364948,0.00079169037],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99932736,0.000074437965,0.00013354588,0.00015683519,0.000101783706,0.00020603581],"domain_scores_gemma":[0.9997309,0.0000473947,0.00006247317,0.00008089883,0.000025130208,0.00005324416],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000451435,0.00006276557,0.00009682594,0.00008563746,0.00022193445,0.000049917573,0.00012087482,0.0000882129,0.00011528022],"category_scores_gemma":[0.00007098538,0.00006114012,0.000037479786,0.00014376792,0.000047414414,0.0001605956,0.0000019459385,0.000069364636,0.000099017256],"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":[0.00012962804,0.00012475075,0.8574295,0.000006435971,0.000044485863,0.00003074831,0.01594801,0.035111062,0.00026546244,0.056723703,0.007770071,0.026416134],"study_design_scores_gemma":[0.00057162484,0.00021512882,0.9669622,0.00000722723,0.000030441275,7.846538e-7,0.00089833525,0.0013171186,0.00019684099,0.0018350851,0.027714115,0.00025108465],"about_ca_topic_score_codex":0.00059822004,"about_ca_topic_score_gemma":0.00090593746,"teacher_disagreement_score":0.10953271,"about_ca_system_score_codex":0.00006038404,"about_ca_system_score_gemma":0.000089080975,"threshold_uncertainty_score":0.24932218},"labels":[],"label_agreement":null},{"id":"W791580567","doi":"","title":"OVERCOMING TRAFFIC CONGESTION. A DISCUSSION OF REDUCTION STRATEGIES AND BEHAVIORAL RESPONSES FROM A NORTH-AMERICAN PERSPECTIVE","year":2002,"lang":"en","type":"article","venue":"European journal of transport and infrastructure research","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"McMaster University","funders":"","keywords":"Perspective (graphical); Traffic congestion; Balance (ability); Travel behavior; Transport engineering; Congestion pricing; Demand management; Public economics; Economics; Computer science; Business; Operations research; Engineering; Psychology","score_opus":0.049693962839663736,"score_gpt":0.34799340810129,"score_spread":0.2982994452616262,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W791580567","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9978522,0.00044207284,0.00024944663,0.000721277,0.00006983104,0.00007974438,0.000023485441,0.000010925583,0.00055098184],"genre_scores_gemma":[0.9968077,0.0011823337,0.0017737311,0.0000031876948,0.00014099716,3.5117353e-7,0.000006648144,0.000010583355,0.00007445582],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.9982622,0.0005971516,0.00035072345,0.00014321951,0.00048352548,0.00016314586],"domain_scores_gemma":[0.9991019,0.00007500971,0.00021901079,0.00006031539,0.00040321314,0.00014054615],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009359387,0.00008516964,0.00018261587,0.00024907436,0.0003147985,0.000072025454,0.0001066544,0.00002646525,0.00007039295],"category_scores_gemma":[0.000054343098,0.00006168676,0.00004373107,0.0003501491,0.00079717906,0.00050966424,0.0000041527433,0.00038101006,3.2181973e-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":[0.0018783091,0.00023450932,0.34196997,0.00004932779,0.00013903127,0.00030389664,0.514884,0.011656616,0.0029179407,0.001432569,0.0004959643,0.12403788],"study_design_scores_gemma":[0.00043779777,0.00041475787,0.9170999,0.00008095881,0.000038574206,0.000014060539,0.080669865,0.0000467606,0.00001800865,0.00008825981,0.0010079629,0.000083123494],"about_ca_topic_score_codex":0.0003420415,"about_ca_topic_score_gemma":0.00030487246,"teacher_disagreement_score":0.5751299,"about_ca_system_score_codex":0.00003565848,"about_ca_system_score_gemma":0.000097101845,"threshold_uncertainty_score":0.29372394},"labels":[],"label_agreement":null},{"id":"W810211516","doi":"","title":"Estimation of a Weekend Mode Choice Model for Calgary","year":2007,"lang":"en","type":"article","venue":"Transportation Research Board 86th Annual MeetingTransportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Mode (computer interface); Mode choice; Estimation; Transport engineering; Logit; Transit (satellite); Nested logit; Computer science; Mixed logit; Logistic regression; Operations research; Public transport; Econometrics; Engineering; Economics","score_opus":0.08488505425403316,"score_gpt":0.4431881186400647,"score_spread":0.35830306438603154,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W810211516","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6158311,0.00016234246,0.373755,0.0014077078,0.0002494259,0.00342239,0.00088027085,0.00035303258,0.003938729],"genre_scores_gemma":[0.95716685,0.00024454386,0.037587903,0.00006745504,0.00023425807,0.00042861953,0.0015167773,0.00010252938,0.00265106],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9890853,0.0007715074,0.0018356567,0.0010462401,0.005140038,0.002121215],"domain_scores_gemma":[0.9879866,0.0038258545,0.00042535053,0.0005001169,0.0063997954,0.00086225214],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.01577508,0.0003830354,0.0005897269,0.0019747894,0.0020752824,0.00013907603,0.0007873739,0.00060477725,0.00012658117],"category_scores_gemma":[0.0017111195,0.00043671252,0.00031949073,0.0030688278,0.0014491151,0.0012705405,0.000006527323,0.0012020719,0.00002293857],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.004244545,0.0010779862,0.06084385,0.0013970508,0.00021016729,0.000051319,0.17373632,0.56396776,0.0026447328,0.15665673,0.007370202,0.027799316],"study_design_scores_gemma":[0.008928001,0.0017008176,0.45243132,0.0012188306,0.00029539425,4.3341979e-7,0.07101481,0.39272586,0.009079216,0.021512868,0.038883287,0.0022091826],"about_ca_topic_score_codex":0.030727923,"about_ca_topic_score_gemma":0.12261607,"teacher_disagreement_score":0.39158747,"about_ca_system_score_codex":0.00031851372,"about_ca_system_score_gemma":0.0015421996,"threshold_uncertainty_score":0.9998085},"labels":[],"label_agreement":null},{"id":"W816010327","doi":"10.1002/atr.217","title":"Trip generation modeling using data collected in single and repeated cross‐sectional surveys","year":2012,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","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":true,"ca_institutions":"","funders":"","keywords":"Cross-sectional data; Metropolitan area; Trip generation; Econometrics; Variance (accounting); Data collection; Travel survey; Linear model; Estimation; Statistics; Computer science; Demand forecasting; Travel behavior; Geography; Operations research; Transport engineering; Mathematics; Engineering; Economics","score_opus":0.1243911921483098,"score_gpt":0.36190303461412127,"score_spread":0.23751184246581147,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W816010327","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.89028883,0.00027017642,0.108804785,0.0000281406,0.00044083403,0.00009668612,0.000023536546,0.000014814405,0.000032221687],"genre_scores_gemma":[0.9808554,0.00013559028,0.01837812,0.000011625733,0.00023537035,8.961455e-7,0.0003505555,0.000009687841,0.00002272915],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9985612,0.0002046561,0.000604369,0.0001283066,0.00033035077,0.00017110599],"domain_scores_gemma":[0.9991075,0.000059980684,0.00033647838,0.00007357161,0.00033382166,0.000088682296],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001907443,0.00007689662,0.00013716203,0.00019976488,0.00022257687,0.00005126414,0.000084523446,0.000089338966,0.000015318352],"category_scores_gemma":[0.00014566683,0.00008258578,0.000025656896,0.00041699098,0.000039052007,0.0021176222,0.0000010302824,0.00012780634,1.8991055e-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":[0.000060018894,0.000059377515,0.3286777,0.000005486847,0.000008587608,0.0000023450866,0.005464485,0.6616299,0.0035057357,0.00005992791,0.000002564818,0.00052386837],"study_design_scores_gemma":[0.001129662,0.000035569406,0.9507276,0.000038893275,0.000028980561,0.0000040011278,0.0010507491,0.04656532,0.00016125644,0.000052327006,0.0000859529,0.00011971865],"about_ca_topic_score_codex":0.00028734683,"about_ca_topic_score_gemma":0.0023228878,"teacher_disagreement_score":0.62204987,"about_ca_system_score_codex":0.000097158554,"about_ca_system_score_gemma":0.00013669807,"threshold_uncertainty_score":0.33677503},"labels":[],"label_agreement":null},{"id":"W818516275","doi":"","title":"Transit Projects Seek Voter Approval for Financial Support","year":2008,"lang":"en","type":"article","venue":"Metrologia","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Finance; Transit (satellite); Mile; Dozen; Investment (military); Ballot; Business; Government (linguistics); Light rail transit; Transport engineering; Bond; Rail transit; Public transport; Transit system; Political science; Engineering; Geography; Politics; Voting","score_opus":0.051233812449404406,"score_gpt":0.2902542388831354,"score_spread":0.239020426433731,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W818516275","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.72680265,0.00003597337,0.25211328,0.0022951982,0.00076347176,0.0009025069,0.000062306186,0.0003966218,0.016628008],"genre_scores_gemma":[0.9840678,0.000011453484,0.012671794,0.0003885491,0.00017589093,0.00005114119,0.000067409695,0.000006912305,0.0025590814],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9992403,0.000046590307,0.00013495085,0.00016854203,0.00017125605,0.0002383878],"domain_scores_gemma":[0.9997053,0.00006144902,0.000045477485,0.000060581653,0.000079080106,0.000048070768],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036264927,0.000068047855,0.00011321244,0.000067555906,0.00041255524,0.0000133893955,0.00009733294,0.00011868585,0.00008347438],"category_scores_gemma":[0.00015981852,0.00006365633,0.00006509056,0.00020583582,0.000118180425,0.0001108204,0.0000015233967,0.000067523295,0.000014890396],"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":[0.001691795,0.0008266188,0.5472235,0.0001485286,0.00013854292,0.00020843686,0.19707143,0.012204624,0.0009378568,0.061806716,0.15651268,0.021229263],"study_design_scores_gemma":[0.0045493315,0.0014094595,0.539421,0.000015080155,0.00015466654,0.000014412999,0.0025060484,0.001929319,0.0012266164,0.0025686258,0.4452093,0.0009961192],"about_ca_topic_score_codex":0.00013134978,"about_ca_topic_score_gemma":0.00026426298,"teacher_disagreement_score":0.28869662,"about_ca_system_score_codex":0.000020500214,"about_ca_system_score_gemma":0.00022171118,"threshold_uncertainty_score":0.3173083},"labels":[],"label_agreement":null},{"id":"W820091312","doi":"","title":"2008 Passenger Rail At-A-Glance","year":2008,"lang":"en","type":"article","venue":"Progressive railroading","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Transport engineering; TRIPS architecture; Transit (satellite); Stock (firearms); Rail transit; Engineering; Business; Public transport","score_opus":0.033093222809859456,"score_gpt":0.3032562222626954,"score_spread":0.27016299945283595,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W820091312","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9081721,0.0028763092,0.0022145251,0.0025456864,0.0007557277,0.0005118161,0.000014891197,0.0006992101,0.08220979],"genre_scores_gemma":[0.9882591,0.00027320324,0.0035821507,0.00014164197,0.00027748465,0.00003745511,0.0000328565,0.000015160198,0.0073809554],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9988135,0.00009430232,0.0001746332,0.00022928689,0.0003501046,0.00033814693],"domain_scores_gemma":[0.99938387,0.000098238736,0.00013559905,0.00010062015,0.00015221453,0.00012948066],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024311768,0.00010151794,0.00012725702,0.00007035583,0.0012988433,0.000032852407,0.00013741449,0.00008863658,0.0004933364],"category_scores_gemma":[0.00017754715,0.000102753664,0.00005299084,0.00030457415,0.0002534825,0.00026534277,0.000010677019,0.00010273616,0.00013651447],"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":[0.00010109764,0.00014368829,0.8266829,0.00003354371,0.000077957244,0.0006377525,0.110487804,0.0016542462,0.0005377087,0.0061748126,0.040477432,0.01299101],"study_design_scores_gemma":[0.0018440456,0.000084651336,0.19609591,0.00023183216,0.000053649015,0.00008442256,0.0035417352,0.001233431,0.0021583948,0.0003998973,0.79319483,0.0010772281],"about_ca_topic_score_codex":0.000054687847,"about_ca_topic_score_gemma":0.00008733805,"teacher_disagreement_score":0.7527174,"about_ca_system_score_codex":0.00008591079,"about_ca_system_score_gemma":0.00010074084,"threshold_uncertainty_score":0.99897844},"labels":[],"label_agreement":null},{"id":"W820582257","doi":"","title":"Can Road User Charging Be Made Publicly Acceptable? Lessons Learnt – Why Did the Manchester Congestion Charging TIF Submission Fail to Win Public Support","year":2009,"lang":"en","type":"article","venue":"16th ITS World Congress and Exhibition on Intelligent Transport Systems and ServicesITS AmericaERTICOITS Japan","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Emotive; Road pricing; Point (geometry); Government (linguistics); Business; Champion; Politics; State (computer science); Finance; Ballot; Quarter (Canadian coin); Public transport; Voting; Economics; Traffic congestion; Marketing; Public economics; Computer science; Transport engineering; Political science; Law; Engineering","score_opus":0.04204812618853012,"score_gpt":0.28742374662981535,"score_spread":0.24537562044128522,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W820582257","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8849817,0.00048099176,0.0014471207,0.10774059,0.0007789934,0.0014484507,0.00010034288,0.00033840854,0.0026833983],"genre_scores_gemma":[0.98894703,0.00061808265,0.00003650249,0.0056088744,0.00021704854,0.00009424947,0.00028153206,0.000038097256,0.0041585574],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9966279,0.00025011133,0.00077731523,0.00078700855,0.0007978059,0.0007598611],"domain_scores_gemma":[0.99821836,0.00009638641,0.00034954725,0.00030263263,0.00038118585,0.00065189233],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.00085501693,0.00041879242,0.00051064166,0.00042686734,0.0013978102,0.00078957994,0.00029803178,0.00015971546,0.0003247973],"category_scores_gemma":[0.000028849701,0.00033863625,0.00009702499,0.00095513754,0.00013592296,0.000832704,0.000014844896,0.0003542854,0.000018400693],"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":[0.0011979773,0.0012691587,0.33018807,0.0012979697,0.00056191796,0.00015698347,0.24836889,0.026585761,0.0042481734,0.24931245,0.0057419753,0.13107069],"study_design_scores_gemma":[0.0014238381,0.00058387086,0.61940384,0.0017854433,0.00029296684,0.000027892565,0.04920166,0.005262536,0.0012107575,0.00008839749,0.31888297,0.0018358356],"about_ca_topic_score_codex":0.0028109136,"about_ca_topic_score_gemma":0.0058539486,"teacher_disagreement_score":0.313141,"about_ca_system_score_codex":0.00009260491,"about_ca_system_score_gemma":0.00010551059,"threshold_uncertainty_score":0.99990654},"labels":[],"label_agreement":null},{"id":"W824671066","doi":"","title":"Transportation Modeling for the 2010 Winter Olympic Games","year":2009,"lang":"en","type":"article","venue":"2009 ANNUAL CONFERENCE AND EXHIBITION OF THE TRANSPORTATION ASSOCIATION OF CANADA - TRANSPORTATION IN A CLIMATE OF CHANGE","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Christian ministry; Plan (archaeology); Agency (philosophy); Government (linguistics); Ministry of Transport; Transportation planning; Business; Transport engineering; Geography; Operations research; Political science; Engineering; Sociology","score_opus":0.03291740932557699,"score_gpt":0.2680372400584598,"score_spread":0.2351198307328828,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W824671066","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97771776,0.00026775157,0.0057372297,0.010235319,0.000630568,0.0013663833,0.0037640193,0.00003769022,0.00024328717],"genre_scores_gemma":[0.99787706,0.0007674185,0.00030122613,0.00021332334,0.00004731944,0.00006278451,0.0005902852,0.000012442006,0.00012812536],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.9975227,0.00009581252,0.0010561412,0.00023591025,0.0007805926,0.0003088099],"domain_scores_gemma":[0.99750906,0.00027935285,0.0009842918,0.00013897006,0.0010200273,0.000068279034],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00079007994,0.00018278527,0.00036247584,0.00014692501,0.00025530855,0.000018111748,0.00019858776,0.00018271094,0.000055903816],"category_scores_gemma":[0.000073836534,0.00015766866,0.00014860563,0.00046074498,0.00010319116,0.000534566,4.7512168e-7,0.00013995278,1.113515e-7],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.003770575,0.00074243854,0.30958885,0.0013917359,0.0003488704,0.0000041463713,0.39045006,0.1468958,0.0018241076,0.1300949,0.0028802955,0.012008207],"study_design_scores_gemma":[0.002356174,0.00014952378,0.97241515,0.0004029851,0.00023670662,5.293004e-8,0.017863158,0.0033145752,0.0008969636,0.0012736252,0.0008224198,0.00026868915],"about_ca_topic_score_codex":0.05246419,"about_ca_topic_score_gemma":0.7368015,"teacher_disagreement_score":0.6843373,"about_ca_system_score_codex":0.00009628446,"about_ca_system_score_gemma":0.0004345745,"threshold_uncertainty_score":0.95384556},"labels":[],"label_agreement":null},{"id":"W832955445","doi":"","title":"Advanced traveller information system for developing countries","year":2008,"lang":"en","type":"article","venue":"ARRB Conference, 23rd, 2008, Adelaide, South Australia, Australia","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Developing country; Developed country; Business; Economic growth; Development economics; Economics; Population; Environmental health; Medicine","score_opus":0.0842320128454675,"score_gpt":0.3114813253012132,"score_spread":0.2272493124557457,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W832955445","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8904754,0.000031707386,0.09801818,0.0013898757,0.0024530946,0.0023098555,0.0009650097,0.00089937204,0.003457515],"genre_scores_gemma":[0.9727326,0.00008500905,0.012754043,0.00014557045,0.00023635881,0.00017715315,0.0006448181,0.000028565462,0.013195896],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99648523,0.0001315727,0.0010789332,0.00049277715,0.00080713915,0.0010043689],"domain_scores_gemma":[0.9977242,0.000121412915,0.00056992116,0.00030434408,0.0009427398,0.0003373874],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0005543884,0.00044862885,0.00051086495,0.00033245626,0.0015183142,0.00025368104,0.00045768078,0.0004912207,0.00043277658],"category_scores_gemma":[0.00013876421,0.00048016573,0.00018687475,0.00061660184,0.00050421286,0.0017963529,0.000012917184,0.0003003344,0.0005632957],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011338972,0.00013382224,0.15884283,0.0013207188,0.00045952608,0.00009253082,0.35968503,0.02865579,0.00009006465,0.401589,0.046369366,0.0016274063],"study_design_scores_gemma":[0.010211842,0.0005413333,0.18086158,0.0014790033,0.0005100361,0.00007438914,0.10868418,0.0014993581,0.0040113265,0.0010546165,0.68633276,0.004739585],"about_ca_topic_score_codex":0.0015305016,"about_ca_topic_score_gemma":0.00059081975,"teacher_disagreement_score":0.6399634,"about_ca_system_score_codex":0.0002481827,"about_ca_system_score_gemma":0.00075380405,"threshold_uncertainty_score":0.99978155},"labels":[],"label_agreement":null},{"id":"W836154542","doi":"","title":"Temporal transferability of mode-destination models: summary of literature, initial findings","year":2011,"lang":"en","type":"article","venue":"European Transport Conference 2011Association for European Transport (AET)Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Transferability; Context (archaeology); Empirical evidence; Set (abstract data type); Data science; Computer science; Empirical research; Strengths and weaknesses; Mode (computer interface); Econometrics; Operations research; Benchmarking; Geography; Marketing; Psychology; Economics; Statistics; Business; Engineering; Machine learning; Mathematics; Epistemology; Social psychology","score_opus":0.1330719304546206,"score_gpt":0.35263740123699144,"score_spread":0.21956547078237085,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W836154542","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.733,0.00018215593,0.16096246,0.0003119006,0.00053163717,0.0030380425,0.0046036798,0.0005730416,0.096797064],"genre_scores_gemma":[0.9857164,0.0006397948,0.0070528276,0.000042067975,0.00015037964,0.00006232844,0.0051897266,0.00016790282,0.0009785871],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99118423,0.001769289,0.0025914027,0.0011141442,0.0022656145,0.0010753261],"domain_scores_gemma":[0.9933468,0.00036454274,0.0008154339,0.00058297283,0.0044075516,0.0004826586],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.007993553,0.00057567353,0.0009012641,0.00095111947,0.00081845967,0.00006384924,0.001068612,0.00033024498,0.00035877348],"category_scores_gemma":[0.00016171233,0.0006491074,0.00065265613,0.0016130586,0.0011089285,0.0018390277,0.0000053986555,0.0008242771,0.000018540792],"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":[0.0035767616,0.00235208,0.49765003,0.0017712709,0.0005285562,0.00018532752,0.32479244,0.004851158,0.0021303047,0.15588666,0.00075672194,0.0055186795],"study_design_scores_gemma":[0.0036329469,0.0007580456,0.97691905,0.0009968068,0.0003805648,5.4397356e-7,0.0044326656,0.00058598304,0.002238285,0.0049695834,0.0040296977,0.001055797],"about_ca_topic_score_codex":0.0039138445,"about_ca_topic_score_gemma":0.0068406276,"teacher_disagreement_score":0.47926906,"about_ca_system_score_codex":0.0001524252,"about_ca_system_score_gemma":0.00075984484,"threshold_uncertainty_score":0.999596},"labels":[],"label_agreement":null},{"id":"W84208449","doi":"10.22260/isarc2013/0146","title":"Criticality-Based Model for Rehabilitating Subway Stations","year":2013,"lang":"en","type":"article","venue":"Proceedings of the ... ISARC","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Criticality; Subway station; Cluster analysis; Computer science; Process (computing); Transit (satellite); Interdependence; Transport engineering; Operations research; Set (abstract data type); Fuzzy set; Fuzzy logic; Engineering; Artificial intelligence; Public transport","score_opus":0.03014654048263516,"score_gpt":0.30768968849654177,"score_spread":0.2775431480139066,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W84208449","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.82956606,0.000026451005,0.0862283,0.046125352,0.00022938052,0.0021493637,0.000100482976,0.0002522882,0.035322297],"genre_scores_gemma":[0.9363547,7.619055e-7,0.062568046,0.00021665003,0.00002684458,0.000099795994,0.0000039126276,0.0000075742996,0.00072172785],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99929464,0.000008238378,0.00019402345,0.00011572626,0.00022280765,0.00016455587],"domain_scores_gemma":[0.9988254,0.00026516733,0.00009096123,0.000041036463,0.0007292624,0.00004818839],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045507422,0.00005253008,0.00007825299,0.000035258,0.00040153056,0.00005515072,0.00017626719,0.000044515597,0.000029435603],"category_scores_gemma":[0.001116093,0.000042879856,0.00006527065,0.00018581857,0.00019066973,0.0002625808,0.0000039175993,0.000052785937,0.0000024481599],"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":[0.000034070275,0.00019189458,0.058913384,0.00044018528,0.000019944422,1.30378215e-8,0.08571865,0.040546972,0.0048069647,0.79490614,0.013171022,0.0012507848],"study_design_scores_gemma":[0.00085054256,0.00008189609,0.022114778,0.00020668878,0.00008180691,8.207503e-8,0.03450337,0.75445867,0.0021108596,0.18321905,0.0020279072,0.00034439308],"about_ca_topic_score_codex":0.00025535145,"about_ca_topic_score_gemma":0.00005942785,"teacher_disagreement_score":0.71391165,"about_ca_system_score_codex":0.000026612624,"about_ca_system_score_gemma":0.000088820554,"threshold_uncertainty_score":0.3088289},"labels":[],"label_agreement":null},{"id":"W843001687","doi":"","title":"Descriptive Analysis of Mode Choice Patterns Before and After the Introduction of a Bus Rapid Transit System: A Case Study of VIVA in York Region, Canada","year":2015,"lang":"en","type":"article","venue":"Transportation Research Board 94th Annual MeetingTransportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"TRIPS architecture; Transit (satellite); Transport engineering; Public transport; Service (business); Work (physics); Mode choice; Descriptive statistics; Transit system; Payment; Business; Geography; Engineering; Marketing; Finance; Statistics","score_opus":0.06149441853824621,"score_gpt":0.35878173110773853,"score_spread":0.2972873125694923,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W843001687","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9954958,0.00014662111,0.0006910556,0.00078514253,0.0001055808,0.0019913975,0.00066228386,0.000038246915,0.000083860104],"genre_scores_gemma":[0.9989295,0.00008220724,0.00022124701,0.000008083086,0.00008680143,0.00029177195,0.00027427415,0.000033246175,0.000072888055],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99045557,0.002540959,0.0016700169,0.0007224713,0.0038363796,0.0007745786],"domain_scores_gemma":[0.99254966,0.00089749746,0.00048477965,0.0004707165,0.005184731,0.00041262512],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006458945,0.0002680015,0.0007712895,0.0019064717,0.0004622461,0.00004875595,0.00041965675,0.00021328943,0.000032055934],"category_scores_gemma":[0.0004145162,0.0002434472,0.00014162668,0.0061589563,0.00086963974,0.0004938634,0.00000598278,0.00076617155,3.1503254e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014729417,0.0003449677,0.62712455,0.00035750275,0.00033355984,0.00033420234,0.33298427,0.035744477,0.00002745125,0.00066025235,0.00017077003,0.0004450392],"study_design_scores_gemma":[0.0011640317,0.00054821034,0.5149153,0.00012603594,0.0002927436,0.0000012137771,0.48164594,0.00087210233,0.00006200043,0.000022770013,0.00020435448,0.00014527979],"about_ca_topic_score_codex":0.95594203,"about_ca_topic_score_gemma":0.9950024,"teacher_disagreement_score":0.14866166,"about_ca_system_score_codex":0.00033599098,"about_ca_system_score_gemma":0.0016109177,"threshold_uncertainty_score":0.99274886},"labels":[],"label_agreement":null},{"id":"W845890631","doi":"","title":"An Evaluation of the Impacts of Introducing a New Transit System on Commuting Mode Choice and Transit Ridership: A Case Study of the VIVA BRT-Lite System in Toronto","year":2012,"lang":"en","type":"article","venue":"Transportation Research Board 91st Annual MeetingTransportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"TRIPS architecture; Transit (satellite); Public transport; Transport engineering; Ticket; Service (business); Work (physics); Mode choice; Mode (computer interface); Business; Transit system; Geography; Computer science; Marketing; Engineering; Computer security","score_opus":0.09567294675971046,"score_gpt":0.4336774062137098,"score_spread":0.3380044594539994,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W845890631","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9939483,0.00036867824,0.00025234776,0.00032845882,0.00022348877,0.0039817817,0.00019677012,0.00006965735,0.0006305535],"genre_scores_gemma":[0.99923337,0.000051597966,0.00026772975,0.000007904131,0.00015082452,0.00015159138,0.000048174003,0.00005313414,0.000035688157],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.98360306,0.00678543,0.0018075949,0.00066502346,0.0060617374,0.00107716],"domain_scores_gemma":[0.99310267,0.0015569667,0.0006337546,0.0007959481,0.0034445117,0.0004661453],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.020561663,0.00032687205,0.0006463056,0.0006252714,0.0012066402,0.0000773832,0.00074329285,0.00030184045,0.00004112514],"category_scores_gemma":[0.0005862363,0.000265112,0.00018876321,0.0025731314,0.00072866894,0.0012453387,0.000007875038,0.0009904319,7.9451235e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00064948335,0.00077602273,0.48366198,0.0010336977,0.00009393488,0.000011243072,0.44354495,0.06299143,0.0015952715,0.0042054257,0.00003279161,0.0014037659],"study_design_scores_gemma":[0.002211913,0.0004593854,0.51679736,0.0011192695,0.00015509759,0.0000011772812,0.4755251,0.0025085437,0.0009880203,0.000017342412,0.00003323813,0.00018356447],"about_ca_topic_score_codex":0.5312735,"about_ca_topic_score_gemma":0.6781485,"teacher_disagreement_score":0.14687502,"about_ca_system_score_codex":0.0007619611,"about_ca_system_score_gemma":0.0011536053,"threshold_uncertainty_score":0.9999801},"labels":[],"label_agreement":null},{"id":"W846674078","doi":"","title":"Transit Oriented Development From Both Sides of the Tracks: How the City is Promoting it and How Developers are Building it","year":2009,"lang":"en","type":"article","venue":"2009 ANNUAL CONFERENCE AND EXHIBITION OF THE TRANSPORTATION ASSOCIATION OF CANADA - TRANSPORTATION IN A CLIMATE OF CHANGE","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Transit-oriented development; Bus rapid transit; Transport engineering; Public transport; Transit (satellite); Plan (archaeology); Population; Business; Urban planning; Process (computing); Land-use planning; Land use; Dependency (UML); Engineering; Geography; Computer science; Civil engineering","score_opus":0.0329875572517,"score_gpt":0.25186165546654976,"score_spread":0.21887409821484977,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W846674078","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9657591,0.00007691345,0.00042856095,0.03169227,0.00012745603,0.0005383469,0.0012870213,0.000011950341,0.00007836645],"genre_scores_gemma":[0.9985937,0.00032364833,0.00046179118,0.00035612,0.000017591337,0.000018521343,0.00015228799,0.000008384049,0.00006798894],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9977748,0.00016555148,0.0006800885,0.00023078888,0.0009080018,0.00024080995],"domain_scores_gemma":[0.99749094,0.00019391505,0.0014035392,0.00011550846,0.0007358338,0.000060276467],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00075040763,0.00017545067,0.00035170146,0.00009237044,0.00032001108,0.000025987274,0.00018423534,0.00015168801,0.00003230524],"category_scores_gemma":[0.00010354027,0.00013483225,0.000082063,0.0005424368,0.00015743864,0.00046503107,0.0000014485541,0.000155128,2.2634008e-8],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031224164,0.00015619639,0.53709245,0.00037448524,0.00012256518,0.0000016460839,0.45194337,0.00078990665,0.0016110453,0.0044735246,0.0005172348,0.0026053507],"study_design_scores_gemma":[0.0008997219,0.000035662237,0.9537091,0.00063624734,0.00009446321,5.6258802e-8,0.03758064,0.000099500794,0.0059092757,0.0001563106,0.0007282749,0.00015071272],"about_ca_topic_score_codex":0.020926528,"about_ca_topic_score_gemma":0.62062514,"teacher_disagreement_score":0.5996986,"about_ca_system_score_codex":0.00009346609,"about_ca_system_score_gemma":0.0005060846,"threshold_uncertainty_score":0.9855932},"labels":[],"label_agreement":null},{"id":"W86391898","doi":"","title":"News and Social Media as Performance Indicators for Public Involvement in Transportation Planning: Eglinton Crosstown Project in Toronto, Canada","year":2015,"lang":"en","type":"article","venue":"Transportation Research Board 94th Annual MeetingTransportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Outreach; Newspaper; Social media; Variety (cybernetics); Computer science; Process (computing); Meaning (existential); Content analysis; World Wide Web; Public relations; Knowledge management; Psychology; Sociology; Political science; Advertising; Business; Artificial intelligence; Social science","score_opus":0.11715916786021573,"score_gpt":0.4105824186330385,"score_spread":0.2934232507728228,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W86391898","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9898824,0.00038918713,0.00018356454,0.0036744007,0.0003032388,0.0030920983,0.000578406,0.00014286685,0.0017538382],"genre_scores_gemma":[0.993517,0.00063361356,0.0011079338,0.00013655597,0.00022952571,0.001259403,0.0027574066,0.00008186338,0.00027671116],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.98918074,0.001200445,0.0016507977,0.0011987883,0.004675588,0.0020936523],"domain_scores_gemma":[0.994633,0.0013717528,0.00032468396,0.0002675734,0.002632435,0.0007705611],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.009502256,0.00043985466,0.0006331673,0.0018074602,0.001302006,0.00029061147,0.0006247656,0.000519263,0.000099780445],"category_scores_gemma":[0.0008058308,0.0005021765,0.00010777624,0.0031609843,0.0008934731,0.0018933846,0.000005656343,0.0012294503,0.0000045855477],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0022532542,0.00033551012,0.63743687,0.0004414129,0.00004200961,0.00012399182,0.3355832,0.002229421,0.000034683406,0.012744909,0.004372966,0.0044017467],"study_design_scores_gemma":[0.0036770217,0.00039484235,0.6702096,0.00021545311,0.000019995552,1.3548369e-7,0.2754257,0.00023907938,0.00008364935,0.00047245377,0.04872221,0.00053987786],"about_ca_topic_score_codex":0.8621348,"about_ca_topic_score_gemma":0.99492294,"teacher_disagreement_score":0.13278812,"about_ca_system_score_codex":0.001995503,"about_ca_system_score_gemma":0.00786092,"threshold_uncertainty_score":0.99999815},"labels":[],"label_agreement":null},{"id":"W89704614","doi":"","title":"Evaluation of Proof-of-Payment and Multiple-Unit Operations in Different Streetcar Route Configurations","year":2007,"lang":"en","type":"article","venue":"Transportation Research Board 86th Annual MeetingTransportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Headway; Payment; Computer science; Unit (ring theory); Replicate; Reliability (semiconductor); Service (business); Transport engineering; Operations research; Reliability engineering; Simulation; Statistics; Mathematics; Engineering; Business; Physics","score_opus":0.12225047519899336,"score_gpt":0.43983635419224865,"score_spread":0.3175858789932553,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W89704614","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9880128,0.0002874515,0.0045827185,0.0009614385,0.00013697152,0.003174042,0.00048170745,0.00007078894,0.0022921097],"genre_scores_gemma":[0.9961997,0.00027210725,0.0021189165,0.000014035559,0.00008934601,0.00022569014,0.00081914203,0.000043961903,0.00021708809],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.98613113,0.0025168257,0.0019595872,0.0007958864,0.0073857307,0.0012108466],"domain_scores_gemma":[0.9872415,0.0020300578,0.00029203136,0.00037797834,0.009560989,0.0004974567],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.025909813,0.0003085815,0.0005411356,0.0021589072,0.0009464861,0.000091425354,0.00042794223,0.00037079782,0.0002453912],"category_scores_gemma":[0.0017518498,0.00031858814,0.00013628,0.0028272353,0.0013715439,0.00085848704,0.000007334189,0.0009644841,0.000003960595],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009828215,0.0011705732,0.74401766,0.00041533244,0.00015311064,0.000028428703,0.12943877,0.07470375,0.0051217345,0.029565865,0.00015738905,0.014244575],"study_design_scores_gemma":[0.0026829822,0.0003670431,0.9195622,0.0003847906,0.00010295651,9.87158e-8,0.06250213,0.0037722918,0.008211987,0.0010389831,0.0010550581,0.0003194527],"about_ca_topic_score_codex":0.04351271,"about_ca_topic_score_gemma":0.44954994,"teacher_disagreement_score":0.40603724,"about_ca_system_score_codex":0.00029874683,"about_ca_system_score_gemma":0.0014794843,"threshold_uncertainty_score":0.9999266},"labels":[],"label_agreement":null},{"id":"W90980035","doi":"10.1007/0-306-47155-8_6","title":"Life-Cycle and Across-the-Week Allocation of Time to Daily Activities","year":2002,"lang":"en","type":"book-chapter","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":25,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Time allocation; Sociology; Social science","score_opus":0.020500227129812122,"score_gpt":0.2629210159674523,"score_spread":0.2424207888376402,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W90980035","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014583297,0.00010139902,0.0009228764,0.0019204036,0.00006578416,0.00023143938,0.000046697594,0.00006995828,0.9951831],"genre_scores_gemma":[0.05244468,0.00016201961,0.00032752345,0.00027478582,0.00008851286,0.000004892902,0.000039301638,0.000015034649,0.94664323],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993259,0.000015890544,0.00016566332,0.00014286146,0.00024169532,0.000108006316],"domain_scores_gemma":[0.9995248,0.00010736445,0.00010973539,0.00009461885,0.00007214983,0.000091368114],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00019721297,0.000092858005,0.00013414549,0.000041339907,0.00024542093,0.000050409457,0.00009345745,0.00013858853,0.0018517076],"category_scores_gemma":[0.000046153575,0.000080367296,0.000032570544,0.00003411036,0.00017572328,0.00011436847,0.0000072741705,0.00007139,0.00010665017],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014978391,0.0001383709,0.0006667208,0.00016100696,0.00031821275,0.00000511129,0.29560384,0.011399465,0.00012243455,0.51730466,0.1176809,0.05644948],"study_design_scores_gemma":[0.00040501248,0.00017828196,0.0026457247,0.00021372407,0.00009081848,7.40097e-7,0.0050825113,0.00076492084,0.000059439924,0.005002383,0.9848833,0.000673157],"about_ca_topic_score_codex":0.00026949434,"about_ca_topic_score_gemma":0.00041305757,"teacher_disagreement_score":0.8672024,"about_ca_system_score_codex":0.000015994103,"about_ca_system_score_gemma":0.00006347171,"threshold_uncertainty_score":0.99906075},"labels":[],"label_agreement":null},{"id":"W9106118","doi":"10.1007/0-306-48220-7_7","title":"A New General Equilibrium Model","year":2006,"lang":"en","type":"book-chapter","venue":"Kluwer Academic Publishers eBooks","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Convergence (economics); Mathematical optimization; Computer science; General equilibrium theory; Flow network; Inverse; Applied mathematics; Mathematical economics; Mathematics; Economics","score_opus":0.030594556128230727,"score_gpt":0.2762199355730211,"score_spread":0.24562537944479038,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W9106118","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000047490455,0.0002958151,0.010019542,0.0011534214,0.00078200625,0.00037323768,0.000054569617,0.0005261412,0.9867478],"genre_scores_gemma":[0.00091783167,0.000019303494,0.003919491,0.00082701334,0.0017418269,0.000012182718,0.00042344996,0.000105645384,0.99203324],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99719405,0.000036002664,0.0006074889,0.0006066608,0.0009646411,0.00059113343],"domain_scores_gemma":[0.9986894,0.00005353597,0.00037643826,0.00028432108,0.00018009919,0.00041618262],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.000497463,0.0004012643,0.00039193273,0.0003253805,0.0002579876,0.00042487375,0.00072027853,0.0017759255,0.0004860975],"category_scores_gemma":[0.000042546246,0.00045142396,0.00022273093,0.00004726577,0.00024226769,0.000860865,0.000037164078,0.0013738642,0.00006230485],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013596349,0.0000019230813,0.000035310877,0.000009650442,0.00003317132,0.000005878942,0.0021904905,0.010420481,0.00001047523,0.17781655,0.80620664,0.003255806],"study_design_scores_gemma":[0.00033366162,0.000009930434,0.000010951714,0.00008339124,0.0000972921,0.0000010998962,0.00004313013,0.002566047,0.0000049851383,0.059946,0.9363375,0.0005660185],"about_ca_topic_score_codex":0.0010826967,"about_ca_topic_score_gemma":0.0001921606,"teacher_disagreement_score":0.13013083,"about_ca_system_score_codex":0.00023454815,"about_ca_system_score_gemma":0.0016758492,"threshold_uncertainty_score":0.99979377},"labels":[],"label_agreement":null},{"id":"W93412482","doi":"10.1007/978-3-642-22194-1_78","title":"Predictive Data Mining Driven Architecture to Guide Car Seat Model Parameter Initialization","year":2011,"lang":"en","type":"book-chapter","venue":"Smart innovation, systems and technologies","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Replicate; Initialization; Architecture; Computer science; Constraint (computer-aided design); Data mining; Quality (philosophy); Work (physics); Government (linguistics); Machine learning; Data science; Artificial intelligence; Engineering; Geography; Statistics","score_opus":0.08036314571998736,"score_gpt":0.29855421766696016,"score_spread":0.2181910719469728,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W93412482","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0021259494,0.0008008452,0.66806465,0.0017447751,0.0011910324,0.002391839,0.0016889736,0.0028981187,0.31909382],"genre_scores_gemma":[0.65472627,0.0017572972,0.067092925,0.00046977538,0.00071086746,0.00037929267,0.010171242,0.00026259752,0.26442972],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.998202,0.000028678593,0.00060258695,0.0005700537,0.00036688952,0.00022975638],"domain_scores_gemma":[0.9982628,0.000080458114,0.00039495452,0.00055993855,0.00066687417,0.000034926117],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00048047438,0.0002649891,0.00033431753,0.0007173321,0.00043862703,0.00013862441,0.0004649746,0.00069949694,0.0000093698945],"category_scores_gemma":[0.0004441416,0.00025761552,0.000022614178,0.0003807447,0.00027052202,0.00028778744,0.00011175953,0.00024063134,0.0000053400963],"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":[0.000030255473,0.000013025833,0.0032843398,0.00014092831,0.00016855191,0.000007524074,0.013081493,0.013980327,0.000011751531,0.9396178,0.018222556,0.011441426],"study_design_scores_gemma":[0.0005780841,0.0002634106,0.00047122384,0.0021113108,0.00030757324,0.000011141901,0.013225156,0.021066671,0.000034985223,0.06651401,0.8937154,0.0017010316],"about_ca_topic_score_codex":0.0004167996,"about_ca_topic_score_gemma":0.0005454792,"teacher_disagreement_score":0.8754929,"about_ca_system_score_codex":0.00007440087,"about_ca_system_score_gemma":0.00023556977,"threshold_uncertainty_score":0.9999876},"labels":[],"label_agreement":null}]}