{"meta":{"query_hash":"321d12e706d4","filters":{"venue":"Multimodal Transportation"},"cohort_total":6,"direct_labels_cover":0,"predictions_cover":6,"exported":6,"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/321d12e706d4","api":"https://metacan.xera.ac/api/v1/cohort?venue=Multimodal+Transportation"},"results":[{"id":"W4309565825","doi":"10.1016/j.multra.2022.100062","title":"High-speed rail and carbon emissions","year":2022,"lang":"en","type":"article","venue":"Multimodal Transportation","topic":"Aviation Industry Analysis and Trends","field":"Economics, Econometrics and Finance","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 British Columbia","funders":"","keywords":"Environmental science; Greenhouse gas; Automotive engineering; Engineering; Geology; Oceanography","score_opus":0.026658444702782252,"score_gpt":0.21728891794896096,"score_spread":0.1906304732461787,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4309565825","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9958823,0.00018851215,0.00050969387,0.0007495476,0.0001617446,0.000069297814,0.00034600106,0.000033522036,0.0020594175],"genre_scores_gemma":[0.9979477,0.000015546584,0.0003101021,0.00006980305,0.00003715791,0.000023240194,0.00036462478,0.000010999703,0.0012208554],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99924004,0.000011500263,0.000344381,0.0002466341,0.000044877383,0.00011259463],"domain_scores_gemma":[0.9996413,0.000016581203,0.00016493372,0.00010952876,0.000012149826,0.000055504264],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001645376,0.000080917634,0.00017384507,0.00013333582,0.00022301653,0.000016023956,0.000061281055,0.000042593292,0.0014422067],"category_scores_gemma":[0.000007848919,0.00009994565,0.000055152013,0.00023376923,0.000017526369,0.000082577004,0.0000048894303,0.00014902306,0.000010087171],"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":[0.000046157584,0.00021967238,0.85569316,0.000016606218,0.000112304086,0.000013105882,0.0025665644,0.027147738,0.00030591516,0.11062799,0.00020999569,0.0030407996],"study_design_scores_gemma":[0.0011633402,0.000057353773,0.9556955,0.0000027615406,0.000026602005,0.0000010389209,0.00039812634,0.026795002,0.00011619816,0.00561893,0.009874307,0.00025081335],"about_ca_topic_score_codex":0.0011863534,"about_ca_topic_score_gemma":0.000046828387,"teacher_disagreement_score":0.10500906,"about_ca_system_score_codex":0.000037475464,"about_ca_system_score_gemma":0.0000075239404,"threshold_uncertainty_score":0.9994706},"labels":[],"label_agreement":null},{"id":"W4312199691","doi":"10.1016/j.multra.2022.100067","title":"Frequent public transit users views and attitudes toward cycling in Canada in the context of the COVID-19 pandemic","year":2022,"lang":"en","type":"article","venue":"Multimodal Transportation","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"The Scarborough Hospital; Institut National de la Recherche Scientifique; University of Toronto","funders":"Fonds de Recherche du Québec - Santé; Canadian Institutes of Health Research","keywords":"Public transport; Cycling; Context (archaeology); Pandemic; TRIPS architecture; Travel behavior; Transport engineering; Coronavirus disease 2019 (COVID-19); Business; Transit (satellite); Geography; Engineering; Medicine","score_opus":0.11409885085980939,"score_gpt":0.3351283546271296,"score_spread":0.2210295037673202,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312199691","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.994437,0.00023741936,0.00007058773,0.004372069,0.000116765455,0.0005865509,0.00010233005,0.00000988775,0.00006743386],"genre_scores_gemma":[0.9988136,0.00004403302,0.000022435454,0.0009877097,0.000010902201,0.00007281633,0.00003718735,0.0000050335548,0.0000062437534],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99831533,0.00036672084,0.00040886752,0.00021279237,0.00048504924,0.00021121195],"domain_scores_gemma":[0.9994919,0.00018824807,0.00011343344,0.00012072808,0.000021816972,0.00006388998],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012311626,0.00009062763,0.00017103374,0.000049568287,0.00032177425,0.000017593926,0.00034626428,0.000035479385,0.0001022804],"category_scores_gemma":[0.000040611914,0.00006606386,0.000057256148,0.00045130053,0.00015748417,0.000176033,0.0000027199796,0.00026007835,6.8328866e-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":[0.000023429257,0.000049724847,0.9313616,0.000036894013,0.00000451842,0.0000051599714,0.06615589,0.0009427119,0.00008287313,0.00025234112,0.000007279568,0.0010775356],"study_design_scores_gemma":[0.00057654246,0.000007892298,0.96704507,0.0000071779455,0.000010768428,1.4723874e-7,0.030490113,0.000089455905,0.000011996758,0.0001652007,0.0015159711,0.00007967775],"about_ca_topic_score_codex":0.96226114,"about_ca_topic_score_gemma":0.9991969,"teacher_disagreement_score":0.036935743,"about_ca_system_score_codex":0.0004893303,"about_ca_system_score_gemma":0.0012759088,"threshold_uncertainty_score":0.2694006},"labels":[],"label_agreement":null},{"id":"W4388553313","doi":"10.1016/j.multra.2023.100110","title":"A mixed integer programming approach to improve oil spill response resource allocation in the Canadian arctic","year":2023,"lang":"en","type":"article","venue":"Multimodal Transportation","topic":"Oil Spill Detection and Mitigation","field":"Environmental Science","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":"Dalhousie University","funders":"Canada First Research Excellence Fund; Ocean Frontier Institute; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Geospatial analysis; Arctic; Integer programming; Operations research; Computer science; Sensitivity (control systems); Oil spill; Resource allocation; Environmental science; Decision support system; Resource (disambiguation); Engineering; Geography; Environmental engineering; Remote sensing","score_opus":0.018894294416821233,"score_gpt":0.23201228086300837,"score_spread":0.21311798644618715,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388553313","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9950422,0.0000019334375,0.000872949,0.0019775776,0.000095318625,0.0004392897,0.00001091362,0.00008420819,0.0014755977],"genre_scores_gemma":[0.99705595,0.0000012317354,0.0015715767,0.00042786705,0.000021353371,0.00035632902,0.00018310764,0.000016164962,0.0003664376],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9986783,0.00014672929,0.00023272312,0.00032240033,0.00031711016,0.00030270385],"domain_scores_gemma":[0.999569,0.000055570992,0.00004534798,0.00018332679,0.000017218897,0.00012953863],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010565239,0.00011782499,0.00007963348,0.00018046344,0.00019964657,0.000052754152,0.00015673492,0.000084530155,0.000036263657],"category_scores_gemma":[0.0001106034,0.00010091944,0.00004375414,0.0010847297,0.000052951335,0.00013300474,0.000003904413,0.00016374422,0.00028902854],"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":[0.0009885136,0.00032831356,0.05742365,0.0001251195,0.000022063485,0.000045109326,0.07961117,0.14377797,0.05156286,0.0007246208,0.0006765077,0.6647141],"study_design_scores_gemma":[0.00043755214,0.00007541965,0.9665616,0.000018080384,0.000009690168,0.0000014207056,0.0024200731,0.016232362,0.0008403513,0.00005576792,0.013173003,0.00017469005],"about_ca_topic_score_codex":0.14678359,"about_ca_topic_score_gemma":0.55350393,"teacher_disagreement_score":0.90913796,"about_ca_system_score_codex":0.0004134948,"about_ca_system_score_gemma":0.00003502605,"threshold_uncertainty_score":0.85889804},"labels":[],"label_agreement":null},{"id":"W4394993096","doi":"10.1016/j.multra.2024.100135","title":"Understanding the predictability of path flow distribution in urban road networks using an information entropy approach","year":2024,"lang":"en","type":"article","venue":"Multimodal Transportation","topic":"Human Mobility and Location-Based Analysis","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":"Natural Science Foundation for Distinguished Young Scholars of Hunan Province; National Natural Science Foundation of China; Department of Transportation of Hunan Province","keywords":"Predictability; Entropy (arrow of time); Computer science; Path (computing); Flow (mathematics); Data mining; Mathematics; Statistics; Physics; Computer network","score_opus":0.052181683423009544,"score_gpt":0.28882120238510783,"score_spread":0.23663951896209828,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394993096","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.38530824,0.000030419997,0.6141179,0.000050388393,0.00006779694,0.0002595633,0.000071479764,0.000037811184,0.00005638354],"genre_scores_gemma":[0.9979611,0.000015193038,0.00032544794,0.000008467011,0.00007660767,0.000019059007,0.0015882603,0.000003956929,0.0000019023902],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99872875,0.00023751508,0.00039517105,0.00015619514,0.00031901133,0.00016335568],"domain_scores_gemma":[0.9995951,0.00008320217,0.00007807728,0.00012412752,0.000073884235,0.000045596164],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013280466,0.0000813757,0.000114532784,0.00006892027,0.000284981,0.0000840745,0.000103437655,0.0001030583,0.000035504876],"category_scores_gemma":[0.00004498786,0.000068711655,0.00007889302,0.00061785267,0.0001823207,0.00090278353,0.0000013642955,0.00015104534,6.8340995e-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":[0.000040973988,0.00014971699,0.0614724,0.000106664884,0.00002349455,4.3136194e-7,0.083775,0.8336541,0.000030326526,0.013728305,0.0000065795543,0.007012008],"study_design_scores_gemma":[0.00012875786,0.000014104469,0.10756694,0.000032732343,0.000043084656,2.7871643e-8,0.010546821,0.88101697,0.000006707836,0.0005394148,0.000040009905,0.00006440706],"about_ca_topic_score_codex":0.013022382,"about_ca_topic_score_gemma":0.008956735,"teacher_disagreement_score":0.6137925,"about_ca_system_score_codex":0.00041726933,"about_ca_system_score_gemma":0.00014734111,"threshold_uncertainty_score":0.99355},"labels":[],"label_agreement":null},{"id":"W4406229046","doi":"10.1016/j.multra.2025.100190","title":"Multimodal adaptive traffic signal control: A decentralized multiagent reinforcement learning approach","year":2025,"lang":"en","type":"article","venue":"Multimodal Transportation","topic":"Traffic control and management","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Reinforcement learning; Traffic signal; Computer science; Reinforcement; SIGNAL (programming language); Multi-agent system; Control (management); Artificial intelligence; Psychology; Real-time computing; Social psychology","score_opus":0.008751713872585103,"score_gpt":0.20967656596800865,"score_spread":0.20092485209542355,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406229046","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19321309,0.0004102145,0.8010715,0.000067872854,0.00038807237,0.001552197,0.000025257279,0.0011870803,0.0020846857],"genre_scores_gemma":[0.99361587,0.000082986706,0.005233189,0.00007477094,0.00004207883,0.00033868142,0.00028693562,0.00004227313,0.00028320315],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980914,0.000055503708,0.00059685146,0.00040921223,0.00032033928,0.00052666885],"domain_scores_gemma":[0.9994798,0.0000698756,0.00006916839,0.00018027789,0.00007690439,0.00012398463],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00019425502,0.00037629216,0.00040049388,0.00022642552,0.00015382277,0.000047947695,0.0001808737,0.00014186665,0.00009463574],"category_scores_gemma":[0.0000080610225,0.00038727256,0.00022197828,0.0002840858,0.000045266155,0.0001931125,0.0000043786335,0.00032614774,0.000020708256],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033051983,0.00012856629,0.0002466579,0.00013177979,0.00033289846,0.00001005049,0.0011261371,0.94602454,0.0011788481,0.0006700033,0.00009564583,0.049724355],"study_design_scores_gemma":[0.0076061627,0.00007194767,0.035360575,0.00005364425,0.00020817714,3.1315432e-7,0.0003919114,0.9530368,0.00019687873,0.000009424643,0.0027105587,0.00035363538],"about_ca_topic_score_codex":0.0001124349,"about_ca_topic_score_gemma":0.00013501146,"teacher_disagreement_score":0.80040276,"about_ca_system_score_codex":0.00016514408,"about_ca_system_score_gemma":0.000033010296,"threshold_uncertainty_score":0.9998579},"labels":[],"label_agreement":null},{"id":"W4408605475","doi":"10.1016/j.multra.2025.100223","title":"Evaluating the usefulness of VGI for citizen co-producing city services from citizen perspective: A case study of crowdsourcing pedestrian navigation","year":2025,"lang":"en","type":"article","venue":"Multimodal Transportation","topic":"Mobile Crowdsensing and Crowdsourcing","field":"Computer Science","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":"National Natural Science Foundation of China","keywords":"Crowdsourcing; Volunteered geographic information; Perspective (graphical); Pedestrian; Citizen science; Computer science; Data science; Transport engineering; Engineering; World Wide Web; Artificial intelligence","score_opus":0.06637986463921254,"score_gpt":0.36916059473677737,"score_spread":0.30278073009756484,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408605475","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.84584296,0.00005318972,0.15254915,0.00009982977,0.00019079277,0.0011039632,0.00005284799,0.000078490564,0.000028772463],"genre_scores_gemma":[0.9892214,8.0412906e-7,0.010482428,0.00002289836,0.000061038,0.00013614404,0.000046355664,0.000015542888,0.000013413926],"study_design_codex":"qualitative","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977926,0.00021298927,0.00072525756,0.00063389144,0.00040430782,0.00023094968],"domain_scores_gemma":[0.99752635,0.00064826774,0.00048060104,0.0006374291,0.0006664327,0.000040944535],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001172979,0.00021361883,0.00037749857,0.00015612524,0.00048777048,0.00011490102,0.000430157,0.000086166016,0.000004119865],"category_scores_gemma":[0.00009542801,0.00018859914,0.00013199676,0.0005790134,0.00006587436,0.00034404514,0.000018506109,0.0001732505,2.513958e-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":[0.000521695,0.001043233,0.1357346,0.0012392408,0.00054764823,0.00012363256,0.59588194,0.13749631,0.07166526,0.0016581144,0.00000871232,0.0540796],"study_design_scores_gemma":[0.0055380175,0.00066212675,0.20285767,0.0008538651,0.00044115158,0.00002008126,0.15699652,0.5659616,0.06311673,0.003106349,0.0000038979656,0.00044201064],"about_ca_topic_score_codex":0.026764747,"about_ca_topic_score_gemma":0.0022217957,"teacher_disagreement_score":0.43888545,"about_ca_system_score_codex":0.00007583384,"about_ca_system_score_gemma":0.00011962097,"threshold_uncertainty_score":0.9797161},"labels":[],"label_agreement":null}]}