{"meta":{"query_hash":"04caa2223bcc","filters":{"venue":"Transportation Planning and Technology"},"cohort_total":42,"direct_labels_cover":0,"predictions_cover":42,"exported":42,"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/04caa2223bcc","api":"https://metacan.xera.ac/api/v1/cohort?venue=Transportation+Planning+and+Technology"},"results":[{"id":"W1965106804","doi":"10.1080/03081060500322599","title":"A Trip Reconstruction Tool for GPS-based Personal Travel Surveys","year":2005,"lang":"en","type":"article","venue":"Transportation Planning and Technology","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":160,"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":"Global Positioning System; Transport engineering; TRIPS architecture; Map matching; Modal; Matching (statistics); Travel survey; Respondent; Computer science; Downtown; Travel behavior; Software; Geography; Engineering; Telecommunications; Statistics","score_opus":0.02263479197427972,"score_gpt":0.2973810975346593,"score_spread":0.2747463055603796,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1965106804","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0340148,0.00008443916,0.84013623,0.00009867816,0.00005908976,0.0006998741,0.0042604497,0.11857079,0.002075726],"genre_scores_gemma":[0.2185793,0.00014034015,0.76371956,0.00008774794,0.000036905774,0.0014916388,0.008310994,0.0038310154,0.003802444],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99852604,0.0005238272,0.00018902017,0.00021369477,0.00049190334,0.00005557034],"domain_scores_gemma":[0.9863988,0.008622542,0.0008102933,0.0017158162,0.0021709723,0.00028149475],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003032181,0.00083752914,0.00087378843,0.00355284,0.00033780967,0.0008650528,0.001207892,0.00046398345,0.011889417],"category_scores_gemma":[0.016220005,0.00061716215,0.00055606,0.0020717077,0.00023683523,0.001218477,0.0010808546,0.0006796861,0.003590208],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00092129136,0.00038860476,0.017770367,0.00056836894,0.0001826851,0.00053679745,0.0011847217,0.019552093,0.013554523,0.0023007488,0.037695713,0.9053442],"study_design_scores_gemma":[0.0005703871,0.00081699895,0.04570672,0.00042672115,0.00024769717,0.0016716778,0.0011350017,0.7819721,0.053093374,0.0061031035,0.10791249,0.00034382576],"about_ca_topic_score_codex":0.0028991974,"about_ca_topic_score_gemma":0.002078716,"teacher_disagreement_score":0.011889417,"about_ca_system_score_codex":0.00036844471,"about_ca_system_score_gemma":0.0006832539,"threshold_uncertainty_score":0.03977412},"labels":[],"label_agreement":null},{"id":"W1973615193","doi":"10.1080/0308106042000263078","title":"The origin-destination matrix as an indicator of intrahousehold travel allocation","year":2004,"lang":"en","type":"article","venue":"Transportation Planning and Technology","topic":"Urban Transport and Accessibility","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 Saskatchewan","funders":"","keywords":"Daytime; Destinations; Context (archaeology); Metropolitan area; Residence; Matrix (chemical analysis); Geography; Economics; Demographic economics; Tourism","score_opus":0.019465764698977598,"score_gpt":0.3208854141429901,"score_spread":0.3014196494440125,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1973615193","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8852194,0.00008651751,0.08167865,0.00019829303,0.000040438135,0.00016297674,0.006022097,0.00041656662,0.026175048],"genre_scores_gemma":[0.96312535,0.00007148647,0.0315778,0.000008849177,0.0000065796066,0.00006358084,0.0019880624,0.00002712234,0.0031311694],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99946076,0.00016446055,0.000057513855,0.00007296884,0.00018751221,0.000056763845],"domain_scores_gemma":[0.9984958,0.0006979706,0.00024461545,0.00013421602,0.0003406627,0.00008674121],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00057556684,0.00029660907,0.00027198135,0.002739425,0.0003985032,0.0012658108,0.00027360112,0.00018260168,0.009895194],"category_scores_gemma":[0.0040669227,0.000108021435,0.00024562835,0.0036522627,0.00040509022,0.00084115344,0.0008487073,0.00031615316,0.00073100894],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006712726,0.00022349987,0.62716633,0.00032582396,0.0002534326,0.0005245077,0.002545017,0.026191164,0.0104884235,0.072751015,0.006266264,0.2525932],"study_design_scores_gemma":[0.000043207452,0.00038490855,0.7558843,0.00006589449,0.00012567882,0.0013284645,0.007500293,0.14202204,0.01326763,0.04177866,0.03747161,0.00012734304],"about_ca_topic_score_codex":0.0074787866,"about_ca_topic_score_gemma":0.009873742,"teacher_disagreement_score":0.009895194,"about_ca_system_score_codex":0.0005890676,"about_ca_system_score_gemma":0.0005245391,"threshold_uncertainty_score":0.03310275},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10478585,0.0011197034,0.82645893,0.002937828,0.00018120978,0.00061878096,0.005656413,0.0008669159,0.05737438],"genre_scores_gemma":[0.88284355,0.0012776322,0.04403101,0.00035016774,0.00012053857,0.0010437799,0.0021228795,0.00011963533,0.068090886],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982672,0.00068909494,0.000058136397,0.0003612292,0.00028301755,0.00034134428],"domain_scores_gemma":[0.99769336,0.0012878109,0.0002998489,0.00007517665,0.00039886267,0.00024493408],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018826365,0.0017927644,0.0018242204,0.0016801842,0.0010172215,0.0036816716,0.0035707585,0.0033315148,0.021627348],"category_scores_gemma":[0.0034458716,0.001047185,0.0012228033,0.002344563,0.0013425414,0.002556237,0.001210811,0.002338698,0.0033410755],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009450144,0.000068517315,0.00046971644,0.00006886313,0.00004824599,0.00025796075,0.00006510256,0.9476019,0.00039496043,0.04418388,0.0016095102,0.0051368563],"study_design_scores_gemma":[0.000043874534,0.000048340713,0.00013146384,0.000007914248,0.00001862693,0.000032942527,0.000026944406,0.9871114,0.000064581,0.011090106,0.0014099434,0.000013808118],"about_ca_topic_score_codex":0.01681471,"about_ca_topic_score_gemma":0.007282435,"teacher_disagreement_score":0.021627348,"about_ca_system_score_codex":0.0032987848,"about_ca_system_score_gemma":0.0017483226,"threshold_uncertainty_score":0.07235062},"labels":[],"label_agreement":null},{"id":"W1989630095","doi":"10.1080/03081060.2012.671028","title":"A framework for neighbour links travel time estimation in an urban network","year":2012,"lang":"en","type":"article","venue":"Transportation Planning and Technology","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of British Columbia","funders":"University of British Columbia","keywords":"Computer science; Travel time; Estimation; Sample (material); Sensor fusion; Data mining; Real-time data; Scheme (mathematics); Transport engineering; Operations research; Engineering; Artificial intelligence; Mathematics; World Wide Web","score_opus":0.011613982847270004,"score_gpt":0.25193588991494137,"score_spread":0.24032190706767137,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1989630095","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0025130026,0.00010614912,0.9966493,0.00003349393,0.000013679411,0.000013678471,0.000074607065,0.00011391472,0.00048221814],"genre_scores_gemma":[0.3781714,0.0009205799,0.61661786,0.000052747742,0.0001485783,0.00029804578,0.00090252375,0.00013768938,0.0027505753],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991277,0.0002700388,0.000048656355,0.00023038751,0.00024346724,0.00007972366],"domain_scores_gemma":[0.9989819,0.0004790699,0.00014712858,0.00010211271,0.00023449723,0.00005531146],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013893718,0.0007727326,0.0007783701,0.0023074425,0.00064342434,0.0013733591,0.0024764827,0.00087253295,0.0013162418],"category_scores_gemma":[0.0040911343,0.00053371035,0.0009480651,0.0026053651,0.0006326724,0.0020116877,0.0014214978,0.0010602827,0.00047449503],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018165549,0.000018294633,0.0009401631,0.000041518386,0.00003184315,0.00006740693,0.00007422418,0.9305273,0.00056915084,0.03371551,0.00077560276,0.033220842],"study_design_scores_gemma":[0.0000018052618,0.000008774184,0.00018815468,0.000005890888,0.0000062894196,0.000021647043,0.000015469352,0.99092907,0.00015853946,0.00740433,0.0012500588,0.000009907072],"about_ca_topic_score_codex":0.02357455,"about_ca_topic_score_gemma":0.014405885,"teacher_disagreement_score":0.02357455,"about_ca_system_score_codex":0.0011348877,"about_ca_system_score_gemma":0.0012346355,"threshold_uncertainty_score":0.046874702},"labels":[],"label_agreement":null},{"id":"W2016903344","doi":"10.1080/03081060802364505","title":"Imputation of Missing Traffic Data during Holiday Periods","year":2008,"lang":"en","type":"article","venue":"Transportation Planning and Technology","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":50,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"Natural Sciences and Engineering Research Council of Canada; University of Regina","keywords":"Adaptability; Imputation (statistics); Transport engineering; Computer science; Parametric statistics; Data collection; Regression; Missing data; Data mining; Engineering; Statistics; Mathematics; Machine learning","score_opus":0.017934968787953726,"score_gpt":0.23591085325623293,"score_spread":0.2179758844682792,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2016903344","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7090336,0.000458707,0.2782861,0.0005244302,0.0004530063,0.00013676926,0.008261543,0.0006942961,0.002151502],"genre_scores_gemma":[0.9467694,0.00012976008,0.041591436,0.00007935048,0.00009155388,0.00012578027,0.0095299445,0.00007821383,0.0016046171],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99360645,0.0033985835,0.0005338935,0.0011443697,0.00067209447,0.0006446946],"domain_scores_gemma":[0.9573077,0.019019596,0.005540976,0.013225858,0.0043055965,0.00060019805],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012757825,0.00049281353,0.0014936499,0.0016906471,0.0010667187,0.0013255426,0.002791492,0.0011754351,0.0022282337],"category_scores_gemma":[0.03698939,0.00056545297,0.0012318376,0.0032030663,0.0005678058,0.0009827277,0.0012123343,0.002089843,0.0007410594],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0024108419,0.0006703403,0.63975555,0.00054347795,0.0012320176,0.001455497,0.0011936695,0.1487068,0.0020570722,0.006647499,0.011887855,0.18343937],"study_design_scores_gemma":[0.00008976687,0.0005781186,0.3689644,0.00021204549,0.00036941926,0.00072491827,0.0016976999,0.58900946,0.008110279,0.01974106,0.0103262095,0.0001766205],"about_ca_topic_score_codex":0.0056110336,"about_ca_topic_score_gemma":0.006250579,"teacher_disagreement_score":0.012757825,"about_ca_system_score_codex":0.000750516,"about_ca_system_score_gemma":0.0012014363,"threshold_uncertainty_score":0.06747061},"labels":[],"label_agreement":null},{"id":"W2018845946","doi":"10.1080/03081060.2010.512225","title":"Bus running time prediction using a statistical pattern recognition technique","year":2010,"lang":"en","type":"article","venue":"Transportation Planning and Technology","topic":"Traffic Prediction and Management Techniques","field":"Engineering","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":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Basis (linear algebra); Smoothing; Predictive modelling; Public transport; Data mining; Intelligent transportation system; Arrival time; Real-time data; Machine learning; Automatic vehicle location; Travel time; Artificial intelligence; Real-time computing; Pattern recognition (psychology); Simulation; Engineering; Transport engineering; Computer vision; Global Positioning System","score_opus":0.00839327067652494,"score_gpt":0.21649587233755177,"score_spread":0.20810260166102684,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2018845946","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.33029652,0.00013660236,0.6657926,0.00011717048,0.00003126762,0.00003582663,0.00023692517,0.0020584571,0.0012946345],"genre_scores_gemma":[0.95064765,0.000044323446,0.048333813,0.000011661641,0.000012010211,0.000027246793,0.00018970395,0.00002283953,0.0007107619],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99984133,0.000028846765,0.000011283205,0.000051551015,0.000048819766,0.000018067296],"domain_scores_gemma":[0.9994054,0.00024845544,0.00009481491,0.00006552603,0.00016264453,0.00002323517],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029523228,0.0003151324,0.00035477662,0.0009100434,0.00014216252,0.0003848416,0.00036150182,0.00025395633,0.00061999465],"category_scores_gemma":[0.0011995839,0.00014507512,0.00037341705,0.00082295475,0.00019897868,0.00032960926,0.00014779392,0.000319248,0.00027406323],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027510672,0.00018727791,0.029189913,0.000055624754,0.000099546254,0.00013410534,0.00010308556,0.62398934,0.021767074,0.001571568,0.0011937573,0.32143354],"study_design_scores_gemma":[0.0000024417034,0.000023489212,0.002399413,0.0000013888783,0.000006125137,0.000015442829,0.000005607662,0.99581236,0.0013880766,0.00022745357,0.00011509627,0.0000031139568],"about_ca_topic_score_codex":0.012975284,"about_ca_topic_score_gemma":0.010397828,"teacher_disagreement_score":0.012975284,"about_ca_system_score_codex":0.0002842208,"about_ca_system_score_gemma":0.0005750237,"threshold_uncertainty_score":0.025799513},"labels":[],"label_agreement":null},{"id":"W2035670852","doi":"10.1080/03081060008717659","title":"Estimation of time‐dependent, stochastic route travel times using artificial neural networks","year":2000,"lang":"en","type":"article","venue":"Transportation Planning and Technology","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Artificial neural network; Travel time; Estimation; Computer science; Transport engineering; Engineering; Artificial intelligence","score_opus":0.008304728214769465,"score_gpt":0.2190699148662046,"score_spread":0.21076518665143515,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2035670852","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21469958,0.00013588576,0.78304666,0.00011081274,0.000033866923,0.000023032242,0.00019089146,0.0005033463,0.0012559304],"genre_scores_gemma":[0.92157054,0.00012461914,0.0769157,0.000018977693,0.000023463035,0.000045702036,0.0003263618,0.000024748582,0.0009498748],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997216,0.00007399586,0.000020650692,0.00005642057,0.00010463719,0.000022675848],"domain_scores_gemma":[0.99895644,0.0005531791,0.000207107,0.000060269624,0.00020169158,0.000021296204],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004711876,0.00045623,0.0003014492,0.00067752734,0.00020131316,0.0003989791,0.00049371936,0.0004913941,0.00042350852],"category_scores_gemma":[0.0038151066,0.00030148256,0.00032775206,0.0006365682,0.00017916987,0.0006452242,0.00021318819,0.00045426763,0.00011249178],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026299771,0.00002162799,0.002275128,0.000013842454,0.000022975148,0.000018640421,0.000014475742,0.97270536,0.0006374513,0.00057570584,0.00013553664,0.023552902],"study_design_scores_gemma":[0.0000012788162,0.000005598988,0.0006098785,0.0000013301362,0.0000021746384,0.000004855257,0.0000027121016,0.9987863,0.00023595935,0.00028214656,0.0000648441,0.0000030608674],"about_ca_topic_score_codex":0.015872413,"about_ca_topic_score_gemma":0.016307456,"teacher_disagreement_score":0.015872413,"about_ca_system_score_codex":0.0006198106,"about_ca_system_score_gemma":0.00055424136,"threshold_uncertainty_score":0.031560063},"labels":[],"label_agreement":null},{"id":"W2039479607","doi":"10.1080/03081060.2011.651878","title":"A GPS-aided survey for assessing trip reporting accuracy and travel of students without telephone land lines","year":2012,"lang":"en","type":"article","venue":"Transportation Planning and Technology","topic":"Urban and Freight Transport Logistics","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto","funders":"","keywords":"TRIPS architecture; Global Positioning System; Travel behavior; Transport engineering; Sample (material); Geography; Travel survey; Computer science; Engineering; Telecommunications","score_opus":0.0704727557759861,"score_gpt":0.3207575826559606,"score_spread":0.2502848268799745,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2039479607","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99635786,0.00002311059,0.00069819693,0.0000157587,0.0000024801668,0.00026405722,0.002065718,0.000023005412,0.0005498197],"genre_scores_gemma":[0.9936668,0.000068630085,0.0022602917,0.000013692167,0.000007715231,0.00036708612,0.0026756024,0.000005866361,0.00093442044],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9983388,0.00053891906,0.00030535966,0.00017142802,0.0005057744,0.00013966471],"domain_scores_gemma":[0.9948421,0.0008887608,0.0017345479,0.0005301438,0.0016184904,0.0003859417],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016912217,0.00025651,0.0003195315,0.0018348004,0.00042538793,0.000398312,0.00056131044,0.00032713395,0.0021580893],"category_scores_gemma":[0.0066119465,0.00030202285,0.0003431935,0.002642667,0.0002261765,0.00042789424,0.0005555072,0.00031112946,0.0006619883],"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":[0.00012622839,0.00012161235,0.99028134,0.000044138007,0.000024709121,0.000049892857,0.00085603714,0.00013536416,0.0007469476,0.000019782592,0.0003656464,0.0072283573],"study_design_scores_gemma":[0.000006993636,0.00027089939,0.9984774,0.0000036817548,0.000009785987,0.000055914923,0.00040206965,0.00034480757,0.00013295526,0.0000033393883,0.00028796002,0.000004274048],"about_ca_topic_score_codex":0.06126534,"about_ca_topic_score_gemma":0.10376842,"teacher_disagreement_score":0.06126534,"about_ca_system_score_codex":0.001124405,"about_ca_system_score_gemma":0.0011874276,"threshold_uncertainty_score":0.12181747},"labels":[],"label_agreement":null},{"id":"W2062433653","doi":"10.1080/715020598","title":"Impact of telecommuting and intelligent transportation systems on residential location choice","year":2003,"lang":"en","type":"article","venue":"Transportation Planning and Technology","topic":"Urban Transport and Accessibility","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":true,"ca_institutions":"Carleton University","funders":"Science and Engineering Research Board; Natural Sciences and Engineering Research Council of Canada","keywords":"Telecommuting; Mixed logit; Discrete choice; Logit; Preference; Transport engineering; Transportation planning; Estimation; Choice modelling; Urban planning; Public transport; Computer science; Business; Logistic regression; Econometrics; Economics; Engineering; Marketing; Microeconomics; Civil engineering","score_opus":0.023721029024787753,"score_gpt":0.3317951124801324,"score_spread":0.30807408345534465,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2062433653","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9963319,0.000054536184,0.000173058,0.0001040972,0.0000024300182,0.000005546698,0.000023641174,0.0000018953086,0.0033030682],"genre_scores_gemma":[0.99947613,0.00004449085,0.000068076224,0.000011055032,0.0000028490424,0.0000027701383,0.000012746647,7.706781e-7,0.00038115864],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9970126,0.0018988063,0.0000868413,0.000099809506,0.00034131572,0.0005605533],"domain_scores_gemma":[0.9832776,0.012978787,0.0016751103,0.0003303268,0.00086119137,0.0008769324],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002054113,0.00019269252,0.00021255108,0.000549606,0.0006890091,0.001486817,0.00029092815,0.0005335843,0.0060254433],"category_scores_gemma":[0.009586914,0.00012285545,0.0005751347,0.0012429488,0.0014093816,0.00081350765,0.0010215709,0.00047608477,0.00028133098],"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":[0.0020721788,0.0013130127,0.897127,0.00016450282,0.00020583671,0.0011398044,0.002639235,0.026098594,0.0026141887,0.006139566,0.00061643834,0.059869703],"study_design_scores_gemma":[0.000048267357,0.0019869492,0.9525946,0.00004006556,0.00023448453,0.0006018274,0.0104723545,0.024442978,0.0030375083,0.003781403,0.002699683,0.000059950373],"about_ca_topic_score_codex":0.010204314,"about_ca_topic_score_gemma":0.019897984,"teacher_disagreement_score":0.010204314,"about_ca_system_score_codex":0.001521202,"about_ca_system_score_gemma":0.00094310875,"threshold_uncertainty_score":0.020289838},"labels":[],"label_agreement":null},{"id":"W2063184644","doi":"10.1080/03081060802334995","title":"Analysis of Characteristics of the Dynamic Flow-Density Relation and its Application to Traffic Flow Models","year":2008,"lang":"en","type":"article","venue":"Transportation Planning and Technology","topic":"Traffic control and management","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Relation (database); Traffic flow (computer networking); Flow (mathematics); Microscopic traffic flow model; Computer science; Three-phase traffic theory; Traffic generation model; Simulation; Mechanics; Data mining; Engineering; Transport engineering; Traffic congestion reconstruction with Kerner's three-phase theory; Traffic congestion; Real-time computing; Physics","score_opus":0.00573569939270616,"score_gpt":0.1869191892492623,"score_spread":0.18118348985655613,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2063184644","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.469588,0.00019385458,0.5246091,0.00015560321,0.000014036084,0.000052104944,0.00010611379,0.00030188472,0.0049792975],"genre_scores_gemma":[0.992167,0.00003387216,0.0074934834,0.000004212683,0.0000038013045,0.000015910075,0.000048377642,0.000008286144,0.00022513371],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995264,0.0001460505,0.000019624596,0.0000972497,0.00015853783,0.00005206583],"domain_scores_gemma":[0.99777883,0.0014793187,0.00024109255,0.00018855098,0.00026094588,0.000051284886],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011233146,0.00032526493,0.0003715733,0.001280215,0.00031387998,0.00088683056,0.00048145742,0.00051757257,0.00077730673],"category_scores_gemma":[0.0057616993,0.00020563121,0.00035760368,0.0008699695,0.00061939337,0.0010324365,0.0005226142,0.0005810745,0.00008313177],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000054899963,0.00007354804,0.0077141486,0.00003537142,0.00002193243,0.00011368412,0.00015698257,0.9159945,0.005075113,0.04401755,0.0002959388,0.026446251],"study_design_scores_gemma":[5.4480427e-7,0.000005945103,0.00052232057,0.0000015466175,0.0000013506663,0.00001168907,0.000006233641,0.99707186,0.0003164384,0.0019844705,0.00007476259,0.0000028169986],"about_ca_topic_score_codex":0.0069741732,"about_ca_topic_score_gemma":0.0022231943,"teacher_disagreement_score":0.0069741732,"about_ca_system_score_codex":0.00085599156,"about_ca_system_score_gemma":0.00043843724,"threshold_uncertainty_score":0.01386714},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07568926,0.0014323364,0.90536267,0.0036181135,0.00015741869,0.00014567014,0.00022776726,0.000064990156,0.013301814],"genre_scores_gemma":[0.9325589,0.001140118,0.064139105,0.00022717404,0.00016206792,0.00016389781,0.00008849688,0.0000271491,0.0014929808],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9948488,0.0033121668,0.00017465132,0.00043348875,0.0007984138,0.0004325041],"domain_scores_gemma":[0.98732215,0.010501562,0.000986505,0.00024444962,0.0006165551,0.00032876225],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007184109,0.001373044,0.0017599392,0.0013566989,0.001299693,0.004091041,0.001613788,0.002750689,0.0015979124],"category_scores_gemma":[0.015673121,0.001158813,0.0015016806,0.0015126092,0.0024504028,0.0044349423,0.0025400526,0.0030722837,0.00012448042],"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":[0.00001894221,0.000020237374,0.00079987454,0.000035698762,0.00005344473,0.00009853568,0.00007206226,0.94211555,0.000077081095,0.051847935,0.00020665166,0.0046539633],"study_design_scores_gemma":[0.000011237333,0.00004342891,0.0003732918,0.000049003793,0.000031609343,0.000037577887,0.00011981494,0.88881457,0.00011100839,0.1094147,0.0009581471,0.000035585465],"about_ca_topic_score_codex":0.013056732,"about_ca_topic_score_gemma":0.013648906,"teacher_disagreement_score":0.013056732,"about_ca_system_score_codex":0.004089573,"about_ca_system_score_gemma":0.003340058,"threshold_uncertainty_score":0.03799361},"labels":[],"label_agreement":null},{"id":"W2892388188","doi":"10.1080/03081060.2018.1526879","title":"Strategies to achieve deep reductions in metropolitan transportation GHG emissions: the case of Philadelphia","year":2018,"lang":"en","type":"article","venue":"Transportation Planning and Technology","topic":"Urban Transport and Accessibility","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; McGill University","funders":"Drexel University","keywords":"Greenhouse gas; Metropolitan area; Market penetration; Electricity; Public transport; Baseline (sea); Transport engineering; Battery electric vehicle; Electric vehicle; Population; Engineering; Environmental science; Environmental engineering; Environmental economics; Agricultural economics; Business; Natural resource economics; Economics; Geography","score_opus":0.024030226483588005,"score_gpt":0.3428979237618753,"score_spread":0.3188676972782873,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2892388188","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8191513,0.001991068,0.027553635,0.013004169,0.00012492052,0.00035093146,0.00038945983,0.00021113588,0.1372235],"genre_scores_gemma":[0.98676294,0.0005856267,0.00846664,0.0003281918,0.000018755534,0.0000597953,0.00006819532,0.0000137977295,0.003696144],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9995221,0.00020107462,0.00001039778,0.000039542392,0.000062504594,0.00016437672],"domain_scores_gemma":[0.9997031,0.00008165728,0.000046084828,0.000023001126,0.00007864423,0.00006738329],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00065723696,0.0003628361,0.00011225883,0.00032944026,0.0010028004,0.0013913567,0.00043617905,0.0009573072,0.0023471776],"category_scores_gemma":[0.0010113998,0.00017515737,0.0003950105,0.0004024676,0.0005394999,0.001553156,0.001483442,0.00057471823,0.00025397667],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006583262,0.0011797044,0.06250235,0.0010842625,0.0004605855,0.010741923,0.006219797,0.2138326,0.03108997,0.24894218,0.041375145,0.38191316],"study_design_scores_gemma":[0.00041905636,0.002813457,0.079055205,0.000560368,0.0005247782,0.0033053644,0.030271132,0.18785103,0.039903972,0.09625172,0.55872244,0.00032157893],"about_ca_topic_score_codex":0.021441758,"about_ca_topic_score_gemma":0.042071573,"teacher_disagreement_score":0.021441758,"about_ca_system_score_codex":0.0021252504,"about_ca_system_score_gemma":0.0015942907,"threshold_uncertainty_score":0.04263389},"labels":[],"label_agreement":null},{"id":"W2902266504","doi":"10.1080/03081060.2018.1541279","title":"Validation of an agent-based microscopic pedestrian simulation model in a crowded pedestrian walking environment","year":2018,"lang":"en","type":"article","venue":"Transportation Planning and Technology","topic":"Evacuation and Crowd Dynamics","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of British Columbia; McMaster University","funders":"","keywords":"Pedestrian; Computer science; Simulation; Downtown; Calibration; Artificial intelligence; Transport engineering; Engineering; Statistics; Mathematics; Geography","score_opus":0.01541875205711673,"score_gpt":0.2596092468974919,"score_spread":0.24419049484037514,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2902266504","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.86703795,0.000078485944,0.12655552,0.00015278191,0.000047542566,0.00009893339,0.00021588628,0.00044922228,0.005363633],"genre_scores_gemma":[0.9879048,0.00004184376,0.011219794,0.000012145264,0.000002641,0.000041761552,0.00009196646,0.0000117370455,0.00067329296],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99981445,0.000059955648,0.000010010559,0.000028933631,0.000057183846,0.000029453553],"domain_scores_gemma":[0.9995316,0.00019440483,0.000052448897,0.0000585338,0.000115180825,0.0000478641],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040358986,0.00045163985,0.0005612088,0.0003832708,0.00042263718,0.0005834859,0.0007401066,0.0007016406,0.00076946564],"category_scores_gemma":[0.0011096008,0.0002508656,0.00037591512,0.0002927065,0.00038232616,0.00034863417,0.00053252385,0.00038548585,0.0001456973],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018123083,0.000025274752,0.0010112786,0.000008097564,0.0000072140356,0.00004379694,0.000021061793,0.99651504,0.00081726874,0.0003284971,0.00004212545,0.0011621753],"study_design_scores_gemma":[0.000004013407,0.000017135819,0.0002187982,0.00000123885,0.0000022941492,0.000005313033,0.000008733076,0.9992859,0.0003102057,0.000060388244,0.00008378315,0.0000023472458],"about_ca_topic_score_codex":0.03241079,"about_ca_topic_score_gemma":0.0133712115,"teacher_disagreement_score":0.03241079,"about_ca_system_score_codex":0.0006273384,"about_ca_system_score_gemma":0.0012201001,"threshold_uncertainty_score":0.0644443},"labels":[],"label_agreement":null},{"id":"W2979733112","doi":"10.1080/03081060.2019.1675321","title":"GIS-based transit trip allocation methods converting stop-level boarding and alighting trips into TAZ trips","year":2019,"lang":"en","type":"article","venue":"Transportation Planning and Technology","topic":"Urban Transport and Accessibility","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":"TRIPS architecture; Transport engineering; Transit (satellite); Computer science; Public transport; Engineering","score_opus":0.03381816516095198,"score_gpt":0.352525553319223,"score_spread":0.318707388158271,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2979733112","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03884904,0.000096836586,0.95681775,0.00007464035,0.00006645377,0.00013096904,0.0006040253,0.0014211778,0.001939029],"genre_scores_gemma":[0.399947,0.00017341811,0.5951468,0.000039701306,0.00006448757,0.00034644696,0.0015801551,0.0001351231,0.0025669166],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99965036,0.00009770177,0.000029312992,0.00009677407,0.000097595796,0.000028274138],"domain_scores_gemma":[0.99925584,0.0002257493,0.00009776283,0.00009711867,0.00030014987,0.000023312525],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00052475464,0.0007400305,0.0005380951,0.0020659706,0.00025768092,0.00067788287,0.00072849006,0.00033414605,0.0029483445],"category_scores_gemma":[0.0026205434,0.00029208764,0.00044153276,0.0020527549,0.00020345375,0.00084787916,0.0005326487,0.00049183035,0.0010105483],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015965535,0.00015889709,0.0055748825,0.00015385142,0.00008914254,0.000039657236,0.00017610576,0.36862662,0.0044815266,0.003784277,0.0030244219,0.61373097],"study_design_scores_gemma":[0.000010873855,0.00002966195,0.00290066,0.00000878384,0.000011103845,0.00003405053,0.00008645783,0.99098235,0.0018987776,0.0019109242,0.002110738,0.00001569992],"about_ca_topic_score_codex":0.009901588,"about_ca_topic_score_gemma":0.009092037,"teacher_disagreement_score":0.009901588,"about_ca_system_score_codex":0.00046885852,"about_ca_system_score_gemma":0.0008796955,"threshold_uncertainty_score":0.019687891},"labels":[],"label_agreement":null},{"id":"W3159799002","doi":"10.1080/03081060.2021.1919350","title":"Does the use of smartphones affect discretionary trips? An analysis of smartphone use data from Halifax, Nova Scotia","year":2021,"lang":"en","type":"article","venue":"Transportation Planning and Technology","topic":"Urban Transport and Accessibility","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":"Dalhousie University; McMaster University","funders":"","keywords":"TRIPS architecture; Nova scotia; Advertising; Perception; Smartphone application; Affect (linguistics); Travel behavior; Transport engineering; Business; Geography; Psychology; Engineering; Computer science; Multimedia","score_opus":0.09042516334321465,"score_gpt":0.3379171986758825,"score_spread":0.24749203533266784,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3159799002","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99524635,0.00013965857,0.00005106284,0.000076921475,0.000002979624,0.000026329908,0.0028990328,0.0000033721085,0.0015543664],"genre_scores_gemma":[0.9969112,0.00020086771,0.000102381775,0.000042085812,0.0000019135805,0.00002777016,0.001586919,0.0000029951298,0.0011239138],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9994467,0.000082451385,0.000047765963,0.00006822162,0.00018106235,0.00017377477],"domain_scores_gemma":[0.99681574,0.00050938653,0.00077206723,0.00013342938,0.0013153757,0.00045413274],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043561394,0.00026961975,0.0003371771,0.0015432318,0.0011087629,0.0010285671,0.0005745397,0.0002525202,0.0013132166],"category_scores_gemma":[0.0020863574,0.00018248038,0.00033408488,0.003733866,0.000595983,0.00021484695,0.0007735603,0.00030027033,0.00030507802],"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":[0.000055962806,0.000016000587,0.9933482,0.00005128613,0.000029838886,0.00021875936,0.002027581,0.00015559448,0.00039594338,0.00004559365,0.00047254006,0.0031828587],"study_design_scores_gemma":[8.69105e-7,0.0000074484997,0.99724686,0.000009333036,0.000003694568,0.000025666255,0.002249296,0.00007840733,0.00003561039,0.0000021134313,0.000337068,0.0000036525907],"about_ca_topic_score_codex":0.981868,"about_ca_topic_score_gemma":0.9908092,"teacher_disagreement_score":0.018131971,"about_ca_system_score_codex":0.007861034,"about_ca_system_score_gemma":0.00778812,"threshold_uncertainty_score":0.057036042},"labels":[],"label_agreement":null},{"id":"W3162299972","doi":"10.1080/03081060.2021.1927303","title":"An inductive experimental approach to developing a web-based travel survey builder: developing guidelines to design an efficient web-survey platform","year":2021,"lang":"en","type":"article","venue":"Transportation Planning and Technology","topic":"Urban Transport and Accessibility","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":"Respondent; Usability; Web application; Survey data collection; Web testing; Web survey; Computer science; Survey methodology; World Wide Web; Engineering; Web application security; The Internet; Web development; Human–computer interaction","score_opus":0.15709498269337754,"score_gpt":0.38625884518181197,"score_spread":0.22916386248843443,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3162299972","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018760312,0.00007958202,0.892922,0.0006205185,0.00011825186,0.07898083,0.00046240224,0.0008994425,0.0071567674],"genre_scores_gemma":[0.015610747,0.00006418988,0.9104146,0.0002478086,0.000016297585,0.07247961,0.00010794995,0.000109655564,0.00094916],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.85247344,0.12736717,0.0053512324,0.00484969,0.00798379,0.0019746742],"domain_scores_gemma":[0.72952926,0.21924344,0.0065457467,0.020150714,0.022407902,0.002123],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.13926883,0.0022248619,0.0013678754,0.0044384487,0.003818961,0.0050277435,0.0052114516,0.0026158653,0.011816689],"category_scores_gemma":[0.21829207,0.0025279257,0.0017188502,0.0024899347,0.005474137,0.004499375,0.007042546,0.004238084,0.0030478453],"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":[0.0016495956,0.01307695,0.009484058,0.0102644665,0.00027948333,0.0010496238,0.11484235,0.022678558,0.032461725,0.16906124,0.014346993,0.61080503],"study_design_scores_gemma":[0.0063609807,0.021508599,0.011185756,0.008025437,0.0006970607,0.00088252465,0.079291716,0.1267128,0.0978924,0.2270043,0.4194124,0.0010260376],"about_ca_topic_score_codex":0.0022145105,"about_ca_topic_score_gemma":0.0041949353,"teacher_disagreement_score":0.13926883,"about_ca_system_score_codex":0.0058168154,"about_ca_system_score_gemma":0.012860455,"threshold_uncertainty_score":0.7365328},"labels":[],"label_agreement":null},{"id":"W3192144090","doi":"10.1080/03081060.2021.1956806","title":"Modeling the impacts of electric bicycle purchase incentive program designs","year":2021,"lang":"en","type":"article","venue":"Transportation Planning and Technology","topic":"Urban Transport and Accessibility","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 British Columbia","funders":"","keywords":"Incentive; Revenue; Incentive program; Yield (engineering); Business; Key (lock); Marketing; Environmental economics; Transport engineering; Public economics; Economics; Microeconomics; Finance; Computer science; Computer security; Engineering","score_opus":0.032345701021934035,"score_gpt":0.33140914282955136,"score_spread":0.29906344180761735,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3192144090","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92793393,0.0003681786,0.04714457,0.00081654394,0.00005967344,0.0004063296,0.0012311116,0.00013392404,0.02190578],"genre_scores_gemma":[0.98612344,0.00018884472,0.0052026534,0.000072803625,0.0000095495025,0.00018594797,0.00022715805,0.000018798462,0.007970846],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986797,0.0006753354,0.000032695247,0.00015385257,0.000103215716,0.00035513888],"domain_scores_gemma":[0.9941719,0.0045893467,0.0005048503,0.00009965486,0.00041722186,0.00021700504],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024379615,0.0009302221,0.00073520915,0.0006720335,0.00033407533,0.0014634585,0.001187686,0.0014641058,0.0075597875],"category_scores_gemma":[0.0073461183,0.0006771781,0.00086539,0.0007267649,0.0005752814,0.001506622,0.0009264086,0.0013083454,0.00031864597],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010117091,0.00017808008,0.0026469808,0.00003324324,0.000021065718,0.000035077715,0.00001932452,0.9912322,0.0002526962,0.0024212515,0.00023863428,0.0028203407],"study_design_scores_gemma":[0.00003747186,0.00026306542,0.0019161026,0.000010552905,0.000038187172,0.000009305688,0.00008052983,0.9949137,0.00025227387,0.0020501763,0.00041861247,0.000009959383],"about_ca_topic_score_codex":0.026301656,"about_ca_topic_score_gemma":0.024410503,"teacher_disagreement_score":0.026301656,"about_ca_system_score_codex":0.0034252815,"about_ca_system_score_gemma":0.0022430206,"threshold_uncertainty_score":0.052297115},"labels":[],"label_agreement":null},{"id":"W4315486068","doi":"10.1080/03081060.2022.2162518","title":"Who will adopt private automated vehicles and automated shuttle buses? Testing the roles of past experience and performance expectancy","year":2023,"lang":"en","type":"article","venue":"Transportation Planning and Technology","topic":"Transportation and Mobility Innovations","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"McMaster University; Toronto Metropolitan University","funders":"Federation for the Humanities and Social Sciences","keywords":"Expectancy theory; Public transport; Transport engineering; Unified theory of acceptance and use of technology; Engineering; Plan (archaeology); Master plan; Operations management; Business; Psychology; Engineering management; Geography; Social psychology","score_opus":0.01567148060598169,"score_gpt":0.24061779469822003,"score_spread":0.22494631409223834,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315486068","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9988771,0.00005431167,0.00005651421,0.00009717189,0.000002153707,0.0000049451737,0.00003136506,7.8784024e-7,0.00087572],"genre_scores_gemma":[0.999746,0.000024972702,0.000017848324,0.000011644227,0.0000013574981,0.0000023746213,0.000029428009,4.76245e-7,0.00016588965],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99813557,0.0007449932,0.000089510344,0.00021501367,0.00033714678,0.00047778495],"domain_scores_gemma":[0.97461104,0.013982282,0.0055184644,0.00087301544,0.001795702,0.0032195242],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036638076,0.0001489319,0.00025920916,0.0005248056,0.0005628097,0.0016047407,0.0005905877,0.0005161778,0.002879918],"category_scores_gemma":[0.017456751,0.00021515672,0.0004888002,0.00057188096,0.0013796681,0.0011644119,0.0006701998,0.0008391,0.00024528924],"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":[0.00010347623,0.00011793602,0.9934772,0.000008148899,0.000041452928,0.00003723777,0.002929021,0.00007997046,0.0000578703,0.0001517988,0.00005186026,0.0029439963],"study_design_scores_gemma":[0.000005360835,0.0001337562,0.9923712,0.000014056409,0.000019332836,0.000033185057,0.006482057,0.00050767546,0.000053641004,0.00008096832,0.0002917923,0.00000698078],"about_ca_topic_score_codex":0.16389082,"about_ca_topic_score_gemma":0.18429124,"teacher_disagreement_score":0.16389082,"about_ca_system_score_codex":0.0015574583,"about_ca_system_score_gemma":0.0017433795,"threshold_uncertainty_score":0.3258738},"labels":[],"label_agreement":null},{"id":"W4381571936","doi":"10.1080/03081060.2023.2214144","title":"Lane-based analysis of the saturation flow rate considering traffic composition","year":2023,"lang":"en","type":"article","venue":"Transportation Planning and Technology","topic":"Traffic control and management","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Qatar National Library","keywords":"Intersection (aeronautics); Transport engineering; Outcome (game theory); Saturation (graph theory); Metric (unit); Traffic flow (computer networking); Computer science; Econometrics; Simulation; Engineering; Mathematics; Economics; Computer security; Operations management; Microeconomics","score_opus":0.00752674181485991,"score_gpt":0.200282180319996,"score_spread":0.1927554385051361,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4381571936","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.53375137,0.00031373897,0.4520901,0.00012058937,0.00008513198,0.00019149034,0.0018347001,0.0012033749,0.010409479],"genre_scores_gemma":[0.9834773,0.00008629996,0.014016741,0.000010026443,0.000016380784,0.00006234321,0.0006922236,0.000050633367,0.0015879897],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9991825,0.00018363689,0.000047630532,0.00022544597,0.00025504967,0.00010574907],"domain_scores_gemma":[0.9987632,0.0004668293,0.00017287667,0.00010234226,0.000441916,0.0000527655],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011263769,0.00071739574,0.00049980415,0.0036657492,0.00034762276,0.000923086,0.00083364005,0.00042625173,0.0029270854],"category_scores_gemma":[0.003221308,0.0002649328,0.0010729398,0.0020187055,0.00032450978,0.0010905146,0.0006767097,0.0004258107,0.0006766327],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002766135,0.0002946548,0.23289604,0.00023359693,0.00033751683,0.00039592892,0.00076778076,0.5975069,0.011693275,0.010839889,0.0022613145,0.14249662],"study_design_scores_gemma":[0.0000053089293,0.000090779875,0.028650874,0.000014433218,0.00006190437,0.00008210348,0.00018946007,0.965076,0.0022876342,0.0021409206,0.0013541464,0.000046442783],"about_ca_topic_score_codex":0.014418266,"about_ca_topic_score_gemma":0.0078468,"teacher_disagreement_score":0.014418266,"about_ca_system_score_codex":0.0007038269,"about_ca_system_score_gemma":0.0007384159,"threshold_uncertainty_score":0.028668702},"labels":[],"label_agreement":null},{"id":"W4381851880","doi":"10.1080/03081060.2023.2226636","title":"A fuzzy rule-based system for terrain classification in highway design","year":2023,"lang":"en","type":"article","venue":"Transportation Planning and Technology","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Terrain; Classifier (UML); Fuzzy logic; Data mining; Computer science; Artificial intelligence; Fuzzy rule; Machine learning; Fuzzy set; Geography; Cartography","score_opus":0.019606081448231196,"score_gpt":0.2400947796796594,"score_spread":0.2204886982314282,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4381851880","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.051695284,0.00050742726,0.92638665,0.00032228438,0.00016630013,0.00093046867,0.0013650636,0.009662711,0.008963856],"genre_scores_gemma":[0.34539622,0.00029985496,0.6480508,0.00018865024,0.00005428771,0.00063684885,0.0015339424,0.00007469524,0.0037647062],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987348,0.00018323594,0.0002166656,0.00036297814,0.00042118813,0.00008112967],"domain_scores_gemma":[0.9982425,0.00056779577,0.00013210117,0.00016534046,0.000831478,0.000060834693],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001908692,0.00073025975,0.0009922567,0.0021699984,0.0010603666,0.0019018875,0.0017073983,0.0012697115,0.0045658494],"category_scores_gemma":[0.005784849,0.0003343229,0.00086311094,0.0012845583,0.0003995493,0.0012208109,0.00052821497,0.0007336606,0.0023645589],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00059998425,0.00064885704,0.008727739,0.0006563172,0.00018024314,0.0011070733,0.00095179,0.07375317,0.0278277,0.006020985,0.014520148,0.86500597],"study_design_scores_gemma":[0.00009174558,0.00023904898,0.004363646,0.00023776034,0.00020647758,0.00038324352,0.00021963687,0.96312654,0.013478348,0.005010159,0.01254352,0.000099934055],"about_ca_topic_score_codex":0.013403143,"about_ca_topic_score_gemma":0.012374194,"teacher_disagreement_score":0.013403143,"about_ca_system_score_codex":0.0010990169,"about_ca_system_score_gemma":0.001695206,"threshold_uncertainty_score":0.02665025},"labels":[],"label_agreement":null},{"id":"W4384408061","doi":"10.1080/03081060.2023.2230969","title":"Communication and mobility issues of visually impaired pedestrians with connected autonomous vehicles","year":2023,"lang":"en","type":"article","venue":"Transportation Planning and Technology","topic":"Transportation and Mobility Innovations","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"CNIB Foundation; University of Toronto","funders":"","keywords":"Pedestrian; Structural equation modeling; Context (archaeology); Computer science; Visually impaired; Confirmatory factor analysis; Latent variable; Econometrics; Transport engineering; Artificial intelligence; Human–computer interaction; Engineering; Machine learning; Mathematics; Geography","score_opus":0.01362594107256283,"score_gpt":0.2575904927464965,"score_spread":0.24396455167393366,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4384408061","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99660325,0.00013846745,0.0010073814,0.0005556586,0.0000068530576,0.000010091159,0.00010208295,0.0000039180263,0.001572081],"genre_scores_gemma":[0.9993844,0.0000826258,0.00023154753,0.000023084449,0.0000034731397,0.0000049272403,0.00003732005,6.139161e-7,0.00023199733],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9988193,0.0005576183,0.0000846023,0.00012419827,0.00017412471,0.00024018442],"domain_scores_gemma":[0.9922404,0.003665877,0.0027637046,0.0002904133,0.0005774793,0.00046208035],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014473881,0.00031177202,0.00031881733,0.0012264409,0.00098824,0.0023036222,0.0004765595,0.0009112426,0.0028650968],"category_scores_gemma":[0.0118917925,0.00023841257,0.00043617468,0.0009960942,0.0012326897,0.0015179471,0.0020111327,0.0010115134,0.00031236437],"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":[0.0001594357,0.0003235024,0.9431115,0.00014850333,0.00009531706,0.0012763422,0.018512398,0.004885698,0.0005058133,0.0054305964,0.0008739206,0.024676954],"study_design_scores_gemma":[0.000023634339,0.0006540002,0.79143816,0.0003011314,0.0002131471,0.001584202,0.1611817,0.026331292,0.00071315886,0.012274251,0.005193224,0.00009211036],"about_ca_topic_score_codex":0.033730514,"about_ca_topic_score_gemma":0.023569465,"teacher_disagreement_score":0.033730514,"about_ca_system_score_codex":0.0015516616,"about_ca_system_score_gemma":0.0012777615,"threshold_uncertainty_score":0.06706834},"labels":[],"label_agreement":null},{"id":"W4387558843","doi":"10.1080/03081060.2023.2268601","title":"Freight last mile delivery: a literature review","year":2023,"lang":"en","type":"review","venue":"Transportation Planning and Technology","topic":"Urban and Freight Transport Logistics","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Mile; Last mile (transportation); Transport engineering; Engineering; Forensic engineering; Geography","score_opus":0.03948716499677697,"score_gpt":0.2661767225157942,"score_spread":0.22668955751901726,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387558843","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00033400988,0.9978796,0.00011528649,0.00048214506,0.00017157925,0.000014173051,0.00007919385,0.0000068167838,0.0009172671],"genre_scores_gemma":[0.0017709876,0.99742365,0.00018883093,0.0002213518,0.00009165057,0.000014375826,0.0000762428,0.0000017724919,0.00021109515],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9988734,0.00021934455,0.0002965149,0.00014567295,0.0003815574,0.000083506275],"domain_scores_gemma":[0.99443465,0.0031665526,0.0008330392,0.000080805476,0.0013147991,0.00017016995],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017696265,0.0011335732,0.0016227645,0.020602705,0.0009349394,0.0025661683,0.0012569759,0.0015328608,0.006306199],"category_scores_gemma":[0.0059838463,0.00060680485,0.0014235359,0.024086291,0.00061507634,0.0030970338,0.0010483839,0.0010941488,0.0014294289],"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":[0.000070552494,0.00009403549,0.0010522938,0.25277492,0.00039171675,0.00064211484,0.0006679116,0.0005018918,0.0006538528,0.0048221513,0.04249248,0.695836],"study_design_scores_gemma":[0.000017679238,0.00013127667,0.006357916,0.27858993,0.00174992,0.0022077751,0.001607283,0.00019668473,0.00044059908,0.0023223755,0.70631903,0.000059465423],"about_ca_topic_score_codex":0.0057002017,"about_ca_topic_score_gemma":0.012097769,"teacher_disagreement_score":0.020602705,"about_ca_system_score_codex":0.0021380372,"about_ca_system_score_gemma":0.0077434336,"threshold_uncertainty_score":0.021096349},"labels":[],"label_agreement":null},{"id":"W4393990604","doi":"10.1080/03081060.2024.2335514","title":"Temporal analysis of factors affecting injury severities of expressway rear-end crashes during weekdays and weekends","year":2024,"lang":"en","type":"article","venue":"Transportation Planning and Technology","topic":"Traffic and Road Safety","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Science Foundation of Tibet Autonomous Region","keywords":"Transport engineering; Psychology; Engineering","score_opus":0.005737905651148587,"score_gpt":0.21556195131523118,"score_spread":0.20982404566408258,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393990604","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9987525,0.00012027931,0.00037790995,0.000022804841,0.00000343065,0.0000068048184,0.00036302023,0.0000046092077,0.0003486497],"genre_scores_gemma":[0.99917513,0.000057664303,0.000073732874,0.0000041352487,0.0000039116594,0.000008425881,0.00043365854,0.00000162592,0.00024170641],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9995671,0.00008612188,0.000052478128,0.00011161763,0.00008085002,0.00010179982],"domain_scores_gemma":[0.9984762,0.00023661947,0.00071961543,0.00012010976,0.0002980009,0.000149369],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00073745253,0.00021205998,0.00017648225,0.0010678555,0.00027654864,0.0004530125,0.0003021202,0.0001970358,0.0014562898],"category_scores_gemma":[0.0019512771,0.00012979719,0.0005524802,0.0008501379,0.0001910726,0.0005079703,0.0005940447,0.00029936136,0.00017571222],"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":[0.00005934526,0.000017151375,0.9955472,0.000015056995,0.00005317524,0.00009445761,0.00024097029,0.00031496925,0.00035440474,0.000081698476,0.00010085577,0.003120754],"study_design_scores_gemma":[4.5835452e-7,0.000018655068,0.998705,0.0000040469695,0.000012678364,0.00004225416,0.0003315152,0.00060923485,0.00007489719,0.00003208665,0.00016572038,0.0000033986526],"about_ca_topic_score_codex":0.014268045,"about_ca_topic_score_gemma":0.0255407,"teacher_disagreement_score":0.014268045,"about_ca_system_score_codex":0.000374602,"about_ca_system_score_gemma":0.00040300257,"threshold_uncertainty_score":0.028369963},"labels":[],"label_agreement":null},{"id":"W4394608731","doi":"10.1080/03081060.2024.2338873","title":"Optimization of E-bike networks","year":2024,"lang":"en","type":"article","venue":"Transportation Planning and Technology","topic":"Transportation and Mobility Innovations","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Group for Research in Decision Analysis","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Transport engineering; Poison control; Engineering; Computer science; Medical emergency; Medicine","score_opus":0.006393701261182617,"score_gpt":0.2181879870261782,"score_spread":0.2117942857649956,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394608731","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28262058,0.003592701,0.6343818,0.0020444908,0.00028023566,0.0005259382,0.002262363,0.00076030206,0.07353169],"genre_scores_gemma":[0.8945035,0.0011661412,0.08638863,0.00024678296,0.00004218928,0.0004740669,0.0010579628,0.00019147705,0.01592914],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993561,0.00029607705,0.000019041454,0.00011020554,0.00007377957,0.000144854],"domain_scores_gemma":[0.9977927,0.0017236447,0.0001321607,0.00004998261,0.00018194257,0.00011950319],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010112455,0.0019770374,0.0016508948,0.00087295164,0.0005919588,0.0018349086,0.0013115016,0.0022079884,0.0101355575],"category_scores_gemma":[0.0040533734,0.00073775015,0.0008419099,0.0011546413,0.00083031086,0.0013189254,0.0014084599,0.0014744723,0.0007849774],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000047328427,0.000032865424,0.00027892168,0.000046990575,0.000017012611,0.00004119572,0.000009479786,0.99220884,0.00012598906,0.0024997038,0.000545035,0.0041466192],"study_design_scores_gemma":[0.000013613909,0.000030778643,0.00016642793,0.000011248342,0.000007704735,0.000011194245,0.00003183532,0.99559104,0.00008803755,0.0033839357,0.00066054065,0.0000037613179],"about_ca_topic_score_codex":0.009156751,"about_ca_topic_score_gemma":0.006805897,"teacher_disagreement_score":0.0101355575,"about_ca_system_score_codex":0.001668269,"about_ca_system_score_gemma":0.0014259884,"threshold_uncertainty_score":0.033906817},"labels":[],"label_agreement":null},{"id":"W4396617936","doi":"10.1080/03081060.2024.2348713","title":"Systematic review and research gaps on wildfire evacuations: infrastructure, transportation modes, networks, and planning","year":2024,"lang":"en","type":"article","venue":"Transportation Planning and Technology","topic":"Evacuation and Crowd Dynamics","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Transport engineering; Environmental planning; Geography; Business; Engineering","score_opus":0.014071275503244912,"score_gpt":0.2958397439549198,"score_spread":0.2817684684516749,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396617936","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012737024,0.9940194,0.0007896378,0.0019669451,0.00034802113,0.00030520456,0.0005627991,0.0000172466,0.0007170349],"genre_scores_gemma":[0.021807864,0.97218794,0.0023403708,0.0019713405,0.00016004074,0.00087264314,0.0004191874,0.000014202309,0.00022639465],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.9839708,0.0068621803,0.0053179483,0.0010552624,0.00233797,0.000455819],"domain_scores_gemma":[0.8846135,0.098213054,0.008760667,0.0020557924,0.005743282,0.00061363674],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.019317636,0.0012792764,0.004460386,0.010331379,0.00093751197,0.004085143,0.0019548861,0.0022618242,0.0050961226],"category_scores_gemma":[0.118853934,0.0010314048,0.0059761507,0.010322305,0.0015638398,0.004089398,0.0023621258,0.0018491951,0.0004624693],"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":[0.00011159019,0.000021591586,0.0009408228,0.90786076,0.0037699067,0.00013098924,0.00078039925,0.00027401268,0.00013193229,0.001924573,0.0051903315,0.078862965],"study_design_scores_gemma":[0.0000612883,0.00005905342,0.0020080218,0.94649756,0.013562411,0.00015595359,0.00076064334,0.00010857122,0.000109853005,0.0015732859,0.03507816,0.00002529674],"about_ca_topic_score_codex":0.0117367115,"about_ca_topic_score_gemma":0.03949018,"teacher_disagreement_score":0.019317636,"about_ca_system_score_codex":0.003973234,"about_ca_system_score_gemma":0.026879787,"threshold_uncertainty_score":0.10216266},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10198309,0.000056215555,0.8844389,0.00009716624,0.00005533494,0.0002116424,0.00039192982,0.0040690484,0.008696696],"genre_scores_gemma":[0.73014045,0.0001301246,0.2641942,0.00003416653,0.00001365691,0.0004787893,0.00063370174,0.0003241506,0.0040506893],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99986255,0.000032927685,0.000008781172,0.000029223185,0.000046183966,0.000020338246],"domain_scores_gemma":[0.9998049,0.000059559803,0.000024043668,0.00003357067,0.000055650165,0.000022228533],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035524517,0.0006060237,0.00046415307,0.0003145291,0.00038842938,0.000595497,0.0010334512,0.00049607427,0.0028134664],"category_scores_gemma":[0.00056302105,0.0003429706,0.0006608734,0.0002102785,0.00021828827,0.00058185676,0.0006018792,0.00065236463,0.0005801243],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004237705,0.00006344718,0.0010068923,0.000035258214,0.00002933137,0.000037629583,0.00004168812,0.9759611,0.006618785,0.002977268,0.00033000685,0.012856218],"study_design_scores_gemma":[0.000006722082,0.000024452724,0.00011563848,0.0000023991786,0.000006350123,0.000006467887,0.000006688618,0.9963321,0.0020418055,0.00021068421,0.0012423443,0.000004341183],"about_ca_topic_score_codex":0.009234827,"about_ca_topic_score_gemma":0.006764972,"teacher_disagreement_score":0.009234827,"about_ca_system_score_codex":0.00061373535,"about_ca_system_score_gemma":0.0010803707,"threshold_uncertainty_score":0.018362105},"labels":[],"label_agreement":null},{"id":"W4399787718","doi":"10.1080/03081060.2024.2366241","title":"International travel patterns: exploring destination preferences and airfare trends to and from the USA","year":2024,"lang":"en","type":"article","venue":"Transportation Planning and Technology","topic":"Diverse Aspects of Tourism Research","field":"Social Sciences","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Texas Department of Transportation","keywords":"Air travel; Economic geography; Geography; Business; Aviation; Engineering","score_opus":0.08389209719644905,"score_gpt":0.3497368021068684,"score_spread":0.26584470491041934,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399787718","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.990573,0.0001969822,0.00057064526,0.00016011178,0.000008716345,0.0000108792665,0.0055590826,0.000018850826,0.0029017702],"genre_scores_gemma":[0.9895873,0.00031036,0.0007380084,0.000047572055,0.000009005598,0.000023947827,0.008399959,0.000014340351,0.00086955115],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99984384,0.000035031368,0.000011613879,0.000049067876,0.000028021857,0.00003247782],"domain_scores_gemma":[0.9995289,0.00008597466,0.00013917305,0.000041279975,0.00013867728,0.000065960434],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031939292,0.0002892025,0.0001857914,0.0014010652,0.00027243057,0.00091773306,0.00021843462,0.00021793162,0.0021277133],"category_scores_gemma":[0.0013380846,0.00011294764,0.0006249469,0.003389885,0.00013673275,0.0006563284,0.0005348081,0.00041163494,0.00042516578],"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":[0.000049998398,0.000048555732,0.98310983,0.000034475688,0.0001462897,0.000049684255,0.0003797105,0.002618611,0.00012188809,0.00039751365,0.0025937194,0.010449571],"study_design_scores_gemma":[0.0000047655944,0.00006334849,0.9801021,0.00003384071,0.00007317712,0.00009966467,0.0027116819,0.011177809,0.00010086768,0.00027644556,0.0053417906,0.000014498808],"about_ca_topic_score_codex":0.11896884,"about_ca_topic_score_gemma":0.15222742,"teacher_disagreement_score":0.11896884,"about_ca_system_score_codex":0.0005079691,"about_ca_system_score_gemma":0.000526389,"threshold_uncertainty_score":0.23655272},"labels":[],"label_agreement":null},{"id":"W4402378021","doi":"10.1080/03081060.2024.2399635","title":"Access to green and gray urban nature amenities: exploring equity in Montreal's built environment","year":2024,"lang":"en","type":"article","venue":"Transportation Planning and Technology","topic":"Urban Green Space and Health","field":"Environmental Science","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":"Polytechnique Montréal; McGill University","funders":"Mitacs","keywords":"Transport engineering; Equity (law); Built environment; Regional science; Geography; Engineering; Business; Environmental planning; Political science; Civil engineering","score_opus":0.04094525229263528,"score_gpt":0.3085293609977384,"score_spread":0.26758410870510313,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402378021","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9891761,0.00015022523,0.00026572106,0.00032909983,0.0000029176626,0.000028663877,0.00026527068,0.0000037565958,0.009778162],"genre_scores_gemma":[0.9993711,0.000024779303,0.00008824549,0.000016921414,0.0000011726873,0.000008885584,0.0000455038,9.382877e-7,0.00044249647],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99948764,0.00013057822,0.000010331848,0.00005086532,0.00010056103,0.00022001845],"domain_scores_gemma":[0.998998,0.0002861935,0.00020267835,0.000044151406,0.00018728744,0.00028165386],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008921722,0.00018959807,0.00016067247,0.0013735285,0.0015206974,0.0016031448,0.0006380043,0.00027101068,0.0045622205],"category_scores_gemma":[0.0027667456,0.00007299537,0.0003383826,0.001769122,0.0015332928,0.0012377688,0.0016386237,0.00045582332,0.00007247904],"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":[0.00012234124,0.00018181639,0.93828076,0.000047644935,0.00008002397,0.00014074169,0.015465723,0.0009197523,0.00032111065,0.014209873,0.001334028,0.028896177],"study_design_scores_gemma":[0.0000060762377,0.000061039915,0.9727649,0.00003771,0.000028252805,0.000024550349,0.02101731,0.0012004661,0.00011233438,0.001489497,0.0032452731,0.000012485657],"about_ca_topic_score_codex":0.80264276,"about_ca_topic_score_gemma":0.89627683,"teacher_disagreement_score":0.19735724,"about_ca_system_score_codex":0.010799569,"about_ca_system_score_gemma":0.003985644,"threshold_uncertainty_score":0.397039},"labels":[],"label_agreement":null},{"id":"W4402567823","doi":"10.1080/03081060.2024.2401507","title":"What influences intention to use a first-mile/last-mile automated shuttle service in a suburban area? A case study in Toronto, Canada","year":2024,"lang":"en","type":"article","venue":"Transportation Planning and Technology","topic":"Transportation and Mobility Innovations","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Mile; Last mile (transportation); Transport engineering; Vehicle miles of travel; Service (business); Engineering; Business; Geography; Marketing","score_opus":0.01602389775136318,"score_gpt":0.26258400199623533,"score_spread":0.24656010424487215,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402567823","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9985605,0.000073886105,0.000040582963,0.00018264589,0.0000030870046,0.00002358067,0.000088998204,0.0000015604378,0.0010251023],"genre_scores_gemma":[0.99893445,0.00014096328,0.00009678226,0.000051307914,0.0000020186396,0.000011581647,0.00008375439,0.0000026900734,0.0006765225],"study_design_codex":"observational","study_design_gemma":"qualitative","domain_scores_codex":[0.9988034,0.00024081157,0.00004841384,0.00010086222,0.00024399965,0.00056250684],"domain_scores_gemma":[0.996305,0.00084901083,0.00042042776,0.000101403755,0.0012830714,0.001041065],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009670252,0.0003500244,0.00047029645,0.0009259078,0.005116889,0.002799658,0.0010285734,0.0007160587,0.0019082024],"category_scores_gemma":[0.0033846484,0.00033195043,0.00065610925,0.0020845155,0.0016927884,0.0006904132,0.0009264166,0.0012763087,0.00016627327],"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":[0.00009736712,0.00054536515,0.9324123,0.000060699887,0.000046908954,0.0015789599,0.055282783,0.00034006883,0.00045412118,0.0005761012,0.0010428552,0.007562388],"study_design_scores_gemma":[0.000016142354,0.0001581012,0.80861026,0.0001115099,0.0000654323,0.00028420083,0.18572998,0.0019883073,0.00026616134,0.00009003706,0.0026319022,0.00004798625],"about_ca_topic_score_codex":0.9923929,"about_ca_topic_score_gemma":0.9965815,"teacher_disagreement_score":0.041327316,"about_ca_system_score_codex":0.041327316,"about_ca_system_score_gemma":0.031896263,"threshold_uncertainty_score":0.299852},"labels":[],"label_agreement":null},{"id":"W4403197003","doi":"10.1080/03081060.2024.2411611","title":"Travel mode choice prediction: developing new techniques to prioritize variables and interpret black-box machine learning techniques","year":2024,"lang":"en","type":"article","venue":"Transportation Planning and Technology","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; Concordia University; Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Black box; Mode choice; Mode (computer interface); Machine learning; Computer science; Engineering; Transport engineering; Artificial intelligence; Public transport; Human–computer interaction","score_opus":0.007807652114184692,"score_gpt":0.2425755632876937,"score_spread":0.234767911173509,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403197003","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02118061,0.00025994677,0.97663194,0.00013927543,0.0000433474,0.000061002036,0.00016071898,0.00071031146,0.0008128968],"genre_scores_gemma":[0.28683153,0.0004583653,0.7098473,0.00010678296,0.00008402442,0.00020295428,0.00038936833,0.00010116324,0.0019785033],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99939156,0.0001479572,0.000046941233,0.00016753504,0.00018818138,0.00005778564],"domain_scores_gemma":[0.99830186,0.0008747388,0.00015780589,0.00014323731,0.00047877367,0.000043532782],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014938761,0.0011582,0.0008771791,0.002162891,0.00035772592,0.0010527861,0.0009160689,0.00064554863,0.0017854805],"category_scores_gemma":[0.004056526,0.00032774487,0.0008026845,0.0019092088,0.00032435977,0.0020250606,0.0008004877,0.0010512738,0.000518799],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016088509,0.00031010553,0.020409813,0.0002644285,0.00020893294,0.00014968976,0.00036369634,0.15660441,0.016593184,0.009951311,0.0029792667,0.79200435],"study_design_scores_gemma":[0.000007556541,0.000045634926,0.0036281503,0.000030999465,0.000031024065,0.000044472526,0.000059290513,0.9854035,0.0038295493,0.0053432835,0.0015519115,0.000024530029],"about_ca_topic_score_codex":0.0068821055,"about_ca_topic_score_gemma":0.0067678727,"teacher_disagreement_score":0.0068821055,"about_ca_system_score_codex":0.00045437785,"about_ca_system_score_gemma":0.00082776084,"threshold_uncertainty_score":0.013684094},"labels":[],"label_agreement":null},{"id":"W4403616101","doi":"10.1080/03081060.2024.2416248","title":"An Impact Assessment of Cordon Pricing Relaxation on Modal Shift During the COVID-19 Pandemic","year":2024,"lang":"en","type":"article","venue":"Transportation Planning and Technology","topic":"Energy, Environment, and Transportation Policies","field":"Energy","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); 2019-20 coronavirus outbreak; Pandemic; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Modal; Virology; Medicine; Chemistry","score_opus":0.023064355709192758,"score_gpt":0.3287401140258828,"score_spread":0.3056757583166901,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403616101","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9974492,0.00014234347,0.00020852321,0.00023150614,0.000018357297,0.000056288944,0.00015473652,0.0000065534905,0.0017324514],"genre_scores_gemma":[0.9992867,0.00015115736,0.000168653,0.00004269111,0.000007932726,0.000023478442,0.00008717636,0.0000014912018,0.0002306392],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9984635,0.0007573857,0.000050941395,0.00009980168,0.00026713434,0.00036132592],"domain_scores_gemma":[0.99644,0.0014802036,0.00086749176,0.00012674142,0.00065820874,0.00042741463],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001975227,0.0003845864,0.00028859737,0.00054946763,0.0005367568,0.00095307536,0.000596671,0.0006666489,0.0024580294],"category_scores_gemma":[0.0064076358,0.0001476884,0.00086791615,0.0006352506,0.0004121509,0.0008881273,0.00097457157,0.0008214563,0.00019105093],"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":[0.002458541,0.003541891,0.8707566,0.0006266063,0.00047104328,0.0011665213,0.0026426513,0.019552339,0.0023659335,0.0023864303,0.0021832366,0.09184812],"study_design_scores_gemma":[0.000058785463,0.004256397,0.95325565,0.00019422224,0.00036204295,0.00010873269,0.0142457625,0.022111285,0.0015128611,0.0009545878,0.0028709923,0.000068637506],"about_ca_topic_score_codex":0.021988917,"about_ca_topic_score_gemma":0.024826935,"teacher_disagreement_score":0.021988917,"about_ca_system_score_codex":0.00197347,"about_ca_system_score_gemma":0.0023496062,"threshold_uncertainty_score":0.043721855},"labels":[],"label_agreement":null},{"id":"W4403800979","doi":"10.1080/03081060.2024.2420384","title":"Editorial","year":2024,"lang":"en","type":"editorial","venue":"Transportation Planning and Technology","topic":"Urban Transport and Accessibility","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":"Engineering; Transport engineering","score_opus":0.009646726440714076,"score_gpt":0.3077637884339975,"score_spread":0.2981170619932834,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403800979","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000020894415,0.001264284,0.000059778184,0.016197326,0.9788578,0.000023925153,0.0000714986,0.00007543365,0.0034290361],"genre_scores_gemma":[0.00039070318,0.0017409753,0.00008508322,0.016557068,0.9477918,0.000031897263,0.00009291255,0.0000781084,0.033231445],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9956572,0.00059272326,0.000386338,0.00068160245,0.0023384804,0.00034367893],"domain_scores_gemma":[0.9794094,0.004080006,0.0010688271,0.00067710195,0.010633992,0.0041307304],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.004256751,0.003526467,0.002950104,0.0032339473,0.00338156,0.007594228,0.0031757953,0.009031104,0.07329408],"category_scores_gemma":[0.024825145,0.0010034546,0.0020563253,0.001185088,0.0014886666,0.0044704997,0.0015151558,0.01170525,0.06795209],"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":[0.000015938,0.000005139001,0.0000076291067,0.00005672551,0.0000032727344,0.000040366787,0.000003577191,0.0000071907,0.000020854184,0.00008182953,0.9977767,0.0019807336],"study_design_scores_gemma":[0.00004717121,0.000021025147,0.00010230547,0.00017860574,0.000012219404,0.0001310329,0.000025996238,0.00006025205,0.00006452842,0.00049047096,0.99885666,0.000009744983],"about_ca_topic_score_codex":0.0014615587,"about_ca_topic_score_gemma":0.003902954,"teacher_disagreement_score":0.9267059,"about_ca_system_score_codex":0.0023502659,"about_ca_system_score_gemma":0.0029062706,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W4404050854","doi":"10.1080/03081060.2024.2423013","title":"Robust evaluation of big data-driven winter weather traffic models using six weigh-in-motion sites as testbeds in Alberta's highway network","year":2024,"lang":"en","type":"article","venue":"Transportation Planning and Technology","topic":"Transport Systems and Technology","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Transport engineering; Meteorology; Environmental science; Big data; Computer science; Geography; Engineering; Data mining","score_opus":0.06445988452619943,"score_gpt":0.26332977455000056,"score_spread":0.19886989002380112,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404050854","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98451287,0.00014237587,0.012486112,0.00029739263,0.000058034366,0.000041792493,0.0006109621,0.00076089764,0.0010896415],"genre_scores_gemma":[0.99502075,0.00003860154,0.0031376372,0.000034305922,0.000008226011,0.0000129947475,0.0014299765,0.000033021428,0.00028441084],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9990864,0.00036854236,0.00005232144,0.00020394305,0.00017282313,0.000116029616],"domain_scores_gemma":[0.9961384,0.0020814822,0.00026690628,0.00046608804,0.0008072672,0.00023979049],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0040480276,0.0010998469,0.00045167687,0.0005106053,0.0005431605,0.00095687585,0.0017533203,0.00077457976,0.0005241508],"category_scores_gemma":[0.008533144,0.00048752356,0.00068177446,0.0004179972,0.0008058889,0.001024141,0.00075319095,0.0012220878,0.00014898062],"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":[0.00017658046,0.0001532647,0.020545995,0.000031947875,0.000088678025,0.000054819477,0.000063691,0.9704258,0.00067974103,0.00046623292,0.00059388485,0.006719346],"study_design_scores_gemma":[0.000011776215,0.0000613997,0.00519271,0.0000043283203,0.000011135299,0.000006087366,0.00005738254,0.993816,0.000522537,0.00016858189,0.00014037937,0.000007694036],"about_ca_topic_score_codex":0.31286505,"about_ca_topic_score_gemma":0.24441211,"teacher_disagreement_score":0.687135,"about_ca_system_score_codex":0.0033638363,"about_ca_system_score_gemma":0.002319964,"threshold_uncertainty_score":0.62208796},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99952435,0.000033029028,0.0002219854,0.000017113709,0.0000022577076,0.0000053752874,0.00012242071,0.0000019054751,0.00007163989],"genre_scores_gemma":[0.99927384,0.000035389894,0.00012047151,0.0000052472865,0.0000032479138,0.0000088456,0.0002969434,0.0000015147289,0.0002545811],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99863416,0.000668749,0.000082333936,0.00027122835,0.00012310708,0.000220361],"domain_scores_gemma":[0.9922328,0.004140993,0.0015545023,0.0009794744,0.00038589118,0.00070639624],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002460196,0.00045760328,0.0004398863,0.00079414557,0.0005101349,0.00094305794,0.0006652336,0.0005238376,0.002630395],"category_scores_gemma":[0.005398373,0.0003606674,0.0014787152,0.0011433228,0.00072449463,0.00066363695,0.0006769007,0.0007453795,0.00035602463],"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":[0.00012830317,0.00010437353,0.99591184,0.000008952816,0.00027699588,0.00007359419,0.00031408406,0.0013180864,0.000060430906,0.00012256863,0.00008087266,0.0015998936],"study_design_scores_gemma":[0.0000111581185,0.00025428503,0.98204994,0.000011384791,0.0002362337,0.00009133497,0.0015705051,0.01505641,0.00013084977,0.00023492704,0.00033416526,0.000018801324],"about_ca_topic_score_codex":0.029034305,"about_ca_topic_score_gemma":0.02515213,"teacher_disagreement_score":0.029034305,"about_ca_system_score_codex":0.0006840152,"about_ca_system_score_gemma":0.0006136324,"threshold_uncertainty_score":0.057730615},"labels":[],"label_agreement":null},{"id":"W4406000960","doi":"10.1080/03081060.2024.2447571","title":"RecoMap – a semi-automated tool for analysing railway accident recommendations across jurisdictions and over time","year":2025,"lang":"en","type":"article","venue":"Transportation Planning and Technology","topic":"Occupational Health and Safety Research","field":"Health Professions","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; Accident (philosophy); Human factors and ergonomics; Business; Occupational safety and health; Poison control; Engineering; Medical emergency; Political science; Medicine","score_opus":0.03203620102346983,"score_gpt":0.47012925984086135,"score_spread":0.4380930588173915,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406000960","genre_codex":"methods","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.092132956,0.0011031632,0.6125802,0.003501976,0.0004951742,0.011012244,0.11775193,0.13153723,0.029885137],"genre_scores_gemma":[0.080615364,0.00046757306,0.8578414,0.00027333465,0.00009382278,0.0060651405,0.0460486,0.0021902912,0.0064045135],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9802679,0.008818018,0.0045182174,0.0024527924,0.0036431975,0.00029990755],"domain_scores_gemma":[0.75899935,0.18087038,0.017473686,0.014680883,0.026215848,0.0017597842],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02607043,0.0020289537,0.0012275951,0.01692078,0.0013644944,0.004025922,0.0030159384,0.0015614593,0.014954747],"category_scores_gemma":[0.11646864,0.0011258879,0.0013021054,0.008027946,0.00070133584,0.005843148,0.0039504864,0.001580055,0.009048442],"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":[0.00085617445,0.000620043,0.022084553,0.008724056,0.000385987,0.0017643257,0.03620169,0.0076363366,0.013032139,0.0052575846,0.15171622,0.75172085],"study_design_scores_gemma":[0.0005488282,0.0009571706,0.060315736,0.006245663,0.00045968094,0.0018807884,0.053042363,0.116032936,0.0328953,0.027882347,0.69852126,0.0012179893],"about_ca_topic_score_codex":0.008898274,"about_ca_topic_score_gemma":0.01911785,"teacher_disagreement_score":0.02607043,"about_ca_system_score_codex":0.0015852298,"about_ca_system_score_gemma":0.0065117367,"threshold_uncertainty_score":0.13787526},"labels":[],"label_agreement":null},{"id":"W4407260520","doi":"10.1080/03081060.2025.2462970","title":"Are online shoppers ready to use smart mobile city bus lockers?","year":2025,"lang":"en","type":"article","venue":"Transportation Planning and Technology","topic":"Consumer Retail Behavior Studies","field":"Business, Management and Accounting","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":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Transport engineering; Advertising; Engineering; Business; Computer science","score_opus":0.03731538928802592,"score_gpt":0.2868559746397734,"score_spread":0.2495405853517475,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407260520","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9975458,0.00010567608,0.000038986513,0.00053125975,0.000007333086,0.0000060897632,0.000060305683,0.0000028282002,0.0017017457],"genre_scores_gemma":[0.99823195,0.00026423694,0.000067749665,0.00022478322,0.00001320745,0.0000042800484,0.000058598234,0.0000018323539,0.0011333432],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9997054,0.000042952597,0.000013901886,0.00003164874,0.000082367245,0.00012369199],"domain_scores_gemma":[0.9979189,0.00038270027,0.0009500677,0.00007288171,0.00027097308,0.00040455852],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042180988,0.00010287781,0.00013968268,0.0005816319,0.0006422171,0.0013332474,0.00025439932,0.00048299562,0.006900449],"category_scores_gemma":[0.0027116667,0.00018342149,0.00014778659,0.0005798858,0.00054623926,0.0010760941,0.0002605315,0.00047506753,0.0007160512],"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":[0.00008615264,0.00020749198,0.9572038,0.000053123385,0.000016936527,0.00027323014,0.010125693,0.000021768717,0.0006617163,0.00027124712,0.0012037494,0.029875163],"study_design_scores_gemma":[0.000004947446,0.00011038855,0.9367994,0.000043936845,0.000017509736,0.00040780523,0.05826282,0.00012071547,0.0001796562,0.00009961976,0.003939486,0.000013717372],"about_ca_topic_score_codex":0.048569005,"about_ca_topic_score_gemma":0.07638221,"teacher_disagreement_score":0.048569005,"about_ca_system_score_codex":0.00043710682,"about_ca_system_score_gemma":0.00069620414,"threshold_uncertainty_score":0.09657258},"labels":[],"label_agreement":null},{"id":"W4407666926","doi":"10.1080/03081060.2025.2465557","title":"Assessing traffic vulnerability to climate hazards in cold regions: the impact of harsh winters conditions on highway traffic volumes","year":2025,"lang":"en","type":"article","venue":"Transportation Planning and Technology","topic":"Traffic and Road Safety","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Vulnerability (computing); Transport engineering; Environmental science; Traffic volume; Poison control; Road traffic; Engineering; Forensic engineering; Computer science; Computer security; Environmental health","score_opus":0.011588006661605086,"score_gpt":0.2943748748688494,"score_spread":0.2827868682072443,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407666926","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99758506,0.00001781836,0.0016662907,0.0000178627,0.000003406205,0.000012256614,0.00019318926,0.000018902716,0.0004852571],"genre_scores_gemma":[0.9991725,0.000018582015,0.00050128833,0.0000032080595,0.0000027858525,0.0000072160765,0.00018737983,0.0000035378457,0.00010356637],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9996724,0.000119554315,0.000019770465,0.000056196503,0.000054051015,0.000077991055],"domain_scores_gemma":[0.9990144,0.00045679984,0.00018719837,0.00008640101,0.00012456598,0.00013060217],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009877731,0.00069034635,0.00033037187,0.0008427997,0.00032341646,0.0008519414,0.0005106698,0.00052580854,0.0006649902],"category_scores_gemma":[0.0019033643,0.00027088614,0.0010714353,0.0005674736,0.00034551977,0.00077650015,0.000735173,0.00036208058,0.0001217854],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031409616,0.00018940607,0.3933282,0.00002996482,0.00023053981,0.00015376476,0.00014742043,0.5955394,0.0022804819,0.0004000472,0.00019496056,0.007191739],"study_design_scores_gemma":[0.000021170976,0.00073435955,0.27278385,0.0000119664965,0.00014242007,0.000096385724,0.00057461264,0.7222616,0.0024168263,0.000687614,0.00023670754,0.000032496122],"about_ca_topic_score_codex":0.030477049,"about_ca_topic_score_gemma":0.027772846,"teacher_disagreement_score":0.030477049,"about_ca_system_score_codex":0.0008441009,"about_ca_system_score_gemma":0.0006884811,"threshold_uncertainty_score":0.060599327},"labels":[],"label_agreement":null},{"id":"W4407892403","doi":"10.1080/03081060.2025.2467453","title":"Development of a microsimulation-based mass evacuation model for persons needing mobility assistance","year":2025,"lang":"en","type":"article","venue":"Transportation Planning and Technology","topic":"Evacuation and Crowd Dynamics","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Microsimulation; Transport engineering; Computer science; Operations research; Engineering","score_opus":0.015384301813261024,"score_gpt":0.2667969423084246,"score_spread":0.2514126404951636,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407892403","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.050564285,0.00016919614,0.9380615,0.00026182327,0.00006107272,0.00010525102,0.00023667327,0.0002674251,0.010272905],"genre_scores_gemma":[0.89312,0.0003787932,0.09834886,0.00007770697,0.00003881088,0.0005376574,0.0003621961,0.00010400385,0.0070319967],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99986446,0.000042161646,0.0000072803887,0.000026298621,0.000031639287,0.000028256794],"domain_scores_gemma":[0.99974555,0.000117063675,0.00003875315,0.000013497734,0.0000633894,0.000021711987],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035846347,0.0005813398,0.0005556785,0.00038583585,0.00039482658,0.00057571003,0.00093023403,0.0008296114,0.0017620886],"category_scores_gemma":[0.0009119718,0.00036329194,0.0009746022,0.00023542476,0.00042547393,0.0005385992,0.00081858854,0.00073297153,0.0002563039],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000048461293,0.0000055222536,0.00013587398,0.000008316601,0.0000053092203,0.000013473016,0.000011568314,0.9964636,0.00032241506,0.0022764693,0.000055122535,0.00069747074],"study_design_scores_gemma":[0.000001664726,0.000005968303,0.000034245983,0.000002058923,0.0000020972022,0.0000024461651,0.0000058608975,0.9991622,0.000094556905,0.0004518194,0.00023516131,0.0000018662637],"about_ca_topic_score_codex":0.019719828,"about_ca_topic_score_gemma":0.009206095,"teacher_disagreement_score":0.019719828,"about_ca_system_score_codex":0.00083072355,"about_ca_system_score_gemma":0.0014369512,"threshold_uncertainty_score":0.03921014},"labels":[],"label_agreement":null},{"id":"W4408274031","doi":"10.1080/03081060.2025.2476083","title":"Battery electric bus transit system optimization with battery degradation and energy consumption uncertainty","year":2025,"lang":"en","type":"article","venue":"Transportation Planning and Technology","topic":"Advanced Battery Technologies Research","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Automotive engineering; Battery (electricity); Energy consumption; Public transport; Transit (satellite); Battery electric vehicle; Engineering; Automotive battery; Computer science; Transport engineering; Electrical engineering; Power (physics)","score_opus":0.007037931855640836,"score_gpt":0.21912907556707983,"score_spread":0.212091143711439,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408274031","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.66685176,0.0012174223,0.302426,0.0016556595,0.00008376511,0.00016847606,0.0012131601,0.00031658463,0.026067091],"genre_scores_gemma":[0.9917536,0.00017412221,0.004715683,0.000035875753,0.000007825019,0.000044246983,0.00014856253,0.000022458164,0.003097607],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996834,0.00012985984,0.000010648612,0.000045512807,0.00005314576,0.000077545956],"domain_scores_gemma":[0.9994692,0.0003248022,0.000070146474,0.00002003675,0.000086565495,0.000029101411],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006652815,0.0007754641,0.0007571147,0.00038360906,0.0003410794,0.0011175206,0.00055033405,0.0008773257,0.0015976849],"category_scores_gemma":[0.0016683287,0.00050245237,0.00067612017,0.00057566556,0.00047278928,0.00083848136,0.0006802593,0.00081059587,0.000121817226],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015641734,0.0000058769383,0.00021791445,0.000010571258,0.0000063690095,0.000024198218,0.0000054611646,0.9979736,0.00012587602,0.00075133215,0.00010707212,0.000756012],"study_design_scores_gemma":[0.0000048580278,0.000017121896,0.00020069508,0.0000022137497,0.0000056436397,0.000006958014,0.000013733907,0.9988655,0.000094723386,0.0006434136,0.00014267275,0.000002470723],"about_ca_topic_score_codex":0.020162802,"about_ca_topic_score_gemma":0.01276936,"teacher_disagreement_score":0.020162802,"about_ca_system_score_codex":0.0016413991,"about_ca_system_score_gemma":0.001463135,"threshold_uncertainty_score":0.04009086},"labels":[],"label_agreement":null},{"id":"W4408904924","doi":"10.1080/03081060.2025.2480692","title":"Quantifying emergency response system risk caused by grade crossing blockages","year":2025,"lang":"en","type":"article","venue":"Transportation Planning and Technology","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Manitoba","funders":"Mitacs; University of Manitoba","keywords":"Emergency response; Level crossing; Transport engineering; Poison control; Engineering; Environmental science; Computer science; Risk analysis (engineering); Medical emergency; Business; Medicine","score_opus":0.008397392064182068,"score_gpt":0.2539094855262507,"score_spread":0.24551209346206862,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408904924","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95809454,0.00010957548,0.037226275,0.00011095534,0.000010347774,0.00013432578,0.00087542634,0.000122057165,0.0033163885],"genre_scores_gemma":[0.9937338,0.000048320217,0.0055613685,0.000005835736,0.0000024381197,0.00002688961,0.00033780833,0.0000070928336,0.00027631762],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975878,0.00073533686,0.00016876738,0.00034137332,0.00092820195,0.00023862217],"domain_scores_gemma":[0.9923074,0.0045816265,0.0015823541,0.0004178893,0.0009972656,0.00011325795],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027506824,0.0006124597,0.0003425724,0.0019468182,0.0003731076,0.0010979614,0.00060479325,0.00053518644,0.0011121321],"category_scores_gemma":[0.013365531,0.00025903326,0.00055867585,0.0015403521,0.00037593246,0.0011275993,0.00089144346,0.00041657293,0.00010039269],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021063798,0.00010639002,0.15969485,0.00012847572,0.0002788571,0.0004075137,0.00057278416,0.8045283,0.0042758086,0.0037329602,0.00061646645,0.025447039],"study_design_scores_gemma":[0.000019702575,0.00055878685,0.19583197,0.000051457588,0.00016288033,0.00042758588,0.0018306231,0.79150087,0.0040280856,0.0035438451,0.0019654343,0.000078770696],"about_ca_topic_score_codex":0.041385863,"about_ca_topic_score_gemma":0.04234497,"teacher_disagreement_score":0.041385863,"about_ca_system_score_codex":0.0019014584,"about_ca_system_score_gemma":0.0013041763,"threshold_uncertainty_score":0.082289934},"labels":[],"label_agreement":null},{"id":"W4412137856","doi":"10.1080/03081060.2025.2527323","title":"Modernizing Montreal’s household travel survey: adapting to evolving travel trends and technological shifts","year":2025,"lang":"en","type":"article","venue":"Transportation Planning and Technology","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Travel behavior; Transport engineering; Vehicle miles of travel; Engineering; Regional science; Geography; Business","score_opus":0.04116264578458948,"score_gpt":0.30460089781142585,"score_spread":0.26343825202683635,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412137856","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.42614064,0.015712244,0.2570148,0.13280588,0.0056700334,0.0094572315,0.063896164,0.0047811354,0.08452195],"genre_scores_gemma":[0.62093675,0.01351555,0.29675013,0.020430949,0.0033893054,0.0068322485,0.022636382,0.00055497675,0.014953845],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.98711026,0.006904605,0.00063995086,0.0012492397,0.0035232124,0.00057273917],"domain_scores_gemma":[0.9819472,0.004086585,0.0014143616,0.0019531075,0.00919658,0.0014021284],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.021179345,0.00073467544,0.00044681857,0.0043280423,0.0013882582,0.0023599986,0.0022974133,0.0006165771,0.0038734404],"category_scores_gemma":[0.029521545,0.00041421226,0.0005297675,0.0076024192,0.0009442519,0.0027416225,0.0021729607,0.0015009693,0.00065699365],"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":[0.00021774533,0.000155921,0.19176215,0.0014312244,0.00027031117,0.00020543391,0.006289792,0.00576279,0.0046172873,0.007100324,0.11231917,0.6698678],"study_design_scores_gemma":[0.000072356,0.00035811868,0.6836746,0.0008744421,0.00010915755,0.00013803427,0.00537596,0.011192341,0.0013505453,0.00423093,0.29241455,0.00020900693],"about_ca_topic_score_codex":0.70455617,"about_ca_topic_score_gemma":0.81758845,"teacher_disagreement_score":0.29544383,"about_ca_system_score_codex":0.010197742,"about_ca_system_score_gemma":0.014853612,"threshold_uncertainty_score":0.5943675},"labels":[],"label_agreement":null},{"id":"W561637617","doi":"10.1080/03081060903257053","title":"GIS-based travel demand modeling for estimating traffic on low-class roads","year":2009,"lang":"en","type":"article","venue":"Transportation Planning and Technology","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Cochrane; University of New Brunswick","funders":"","keywords":"Transport engineering; Computer science; Traffic count; Class (philosophy); Traffic volume; Floating car data; Traffic congestion; Engineering","score_opus":0.010855305532257706,"score_gpt":0.23669380502310894,"score_spread":0.22583849949085122,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W561637617","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92461413,0.00014752198,0.063781135,0.00022992678,0.000023139322,0.00009817808,0.0051750434,0.00082511036,0.0051058596],"genre_scores_gemma":[0.9850257,0.00006786393,0.011539686,0.000010338461,0.0000042836755,0.00005015745,0.001986762,0.000021869539,0.0012933866],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998062,0.00005600125,0.000010885136,0.000044413537,0.000048488295,0.00003399742],"domain_scores_gemma":[0.9996044,0.0001610805,0.000033171767,0.000024057492,0.0001554388,0.000021964841],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031361077,0.00061929686,0.0003132414,0.0012361599,0.00026951128,0.00067661173,0.00074591744,0.00042478237,0.0020284941],"category_scores_gemma":[0.0011378592,0.0003052424,0.0006581007,0.0012048779,0.00019786794,0.00043933716,0.0003280853,0.00036657226,0.00042021976],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005493629,0.000098637334,0.0401171,0.000048469505,0.000044731518,0.000077806224,0.00005784776,0.94258934,0.00071278925,0.0009298852,0.00091270177,0.014355666],"study_design_scores_gemma":[0.0000017814592,0.0000058232927,0.003925072,0.0000025606255,0.0000037844457,0.000005429588,0.00006006909,0.9954656,0.00015213309,0.00014861232,0.00022572197,0.000003542946],"about_ca_topic_score_codex":0.38589966,"about_ca_topic_score_gemma":0.26790416,"teacher_disagreement_score":0.38589966,"about_ca_system_score_codex":0.0024148605,"about_ca_system_score_gemma":0.0019755128,"threshold_uncertainty_score":0.7673069},"labels":[],"label_agreement":null}]}