{"meta":{"query_hash":"89e01b623d8e","filters":{"venue":"International Journal of Transportation Science and Technology"},"cohort_total":11,"direct_labels_cover":0,"predictions_cover":11,"exported":11,"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/89e01b623d8e","api":"https://metacan.xera.ac/api/v1/cohort?venue=International+Journal+of+Transportation+Science+and+Technology"},"results":[{"id":"W2902665519","doi":"10.1016/j.ijtst.2018.11.002","title":"Metropolis-Hasting based Expanded Path Size Logit model for cyclists’ route choice using GPS data","year":2018,"lang":"en","type":"article","venue":"International Journal of Transportation Science and Technology","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Polytechnique Montréal; Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Logit; Global Positioning System; Logistic regression; Transport engineering; Mixed logit; Computer science; Geography; Statistics; Econometrics; Mathematics; Engineering; Telecommunications","score_opus":0.09012465269356584,"score_gpt":0.4016408995392342,"score_spread":0.31151624684566837,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2902665519","genre_codex":"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":[0.5078002,0.0008205051,0.47780964,0.0011857909,0.00014156892,0.00067902124,0.0043627615,0.00062780967,0.0065726256],"genre_scores_gemma":[0.94904286,0.0003772274,0.03845282,0.00008709569,0.000050847644,0.0005936749,0.0023526305,0.000043023654,0.0089998925],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99795425,0.0012718722,0.00006009414,0.00035208746,0.00014799387,0.00021366101],"domain_scores_gemma":[0.9966074,0.002528098,0.00026179242,0.00018770708,0.00031367308,0.0001012566],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002912155,0.0010080381,0.0011531272,0.001445598,0.000622776,0.001523383,0.0026617695,0.0012051184,0.00785983],"category_scores_gemma":[0.006322286,0.00062732614,0.0016976326,0.0020428046,0.0009162661,0.00165536,0.0012518258,0.0022448408,0.0008003707],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003852863,0.00024472308,0.04056457,0.0001762898,0.0002662551,0.0005166339,0.00047744176,0.869061,0.00041475243,0.061618146,0.0028067906,0.023468098],"study_design_scores_gemma":[0.000025645822,0.000047785754,0.0024552348,0.000011221667,0.000029996903,0.000032526073,0.00009582363,0.98920304,0.00005193004,0.007263178,0.00076894776,0.00001470358],"about_ca_topic_score_codex":0.051517144,"about_ca_topic_score_gemma":0.04832722,"teacher_disagreement_score":0.051517144,"about_ca_system_score_codex":0.0018281483,"about_ca_system_score_gemma":0.0016514764,"threshold_uncertainty_score":0.102434516},"labels":[],"label_agreement":null},{"id":"W2908505535","doi":"10.1016/j.ijtst.2018.12.002","title":"Vehicle stacking estimation at signalized intersections with unmanned aerial systems","year":2018,"lang":"en","type":"article","venue":"International Journal of Transportation Science and Technology","topic":"Vehicle emissions and performance","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakes Environmental (Canada); University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Monte Carlo method; Headway; Vehicle type; Stacking; Computer science; Intersection (aeronautics); Simulation; Engineering; Transport engineering; Statistics; Mathematics","score_opus":0.008430704441143367,"score_gpt":0.25234114676738056,"score_spread":0.2439104423262372,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2908505535","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":[0.8508318,0.00014824524,0.14724742,0.000024341818,0.0000159239,0.000019933885,0.00017484439,0.00046098503,0.0010764165],"genre_scores_gemma":[0.976396,0.000041333933,0.023140203,0.0000040624263,0.0000051567663,0.000008868367,0.0001586934,0.000010628227,0.00023504057],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99979764,0.000036502268,0.000008734167,0.000050969975,0.000064984946,0.000041267765],"domain_scores_gemma":[0.9997844,0.00005770337,0.00005845922,0.00003035669,0.000049011134,0.000020189176],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017413938,0.00048533783,0.0003459131,0.0010097937,0.00020063481,0.00035268714,0.0003953356,0.00028022646,0.0005028505],"category_scores_gemma":[0.0006886684,0.00023940709,0.00035098742,0.000556962,0.00016394032,0.00060297793,0.0004646367,0.00031481503,0.00018041102],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025121015,0.00010327504,0.072040476,0.00007176449,0.00014551509,0.0003358372,0.00025382274,0.7539584,0.01752257,0.0012591997,0.0005508228,0.15350705],"study_design_scores_gemma":[0.000003693552,0.000061898696,0.021013163,0.000006163089,0.000017507014,0.000054929496,0.00010392133,0.9738666,0.0039608893,0.00055360503,0.00034585714,0.00001187364],"about_ca_topic_score_codex":0.007102284,"about_ca_topic_score_gemma":0.006546431,"teacher_disagreement_score":0.007102284,"about_ca_system_score_codex":0.00026272758,"about_ca_system_score_gemma":0.00035777042,"threshold_uncertainty_score":0.01412189},"labels":[],"label_agreement":null},{"id":"W4220793985","doi":"10.1016/j.ijtst.2022.02.003","title":"Machine learning-based multi-target regression to effectively predict turning movements at signalized intersections","year":2022,"lang":"en","type":"article","venue":"International Journal of Transportation Science and Technology","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Artificial neural network; Machine learning; Calibration; Random forest; Regression analysis; Computer science; Artificial intelligence; Regression; Predictive modelling; Engineering; Statistics; Mathematics","score_opus":0.0077837231586048335,"score_gpt":0.2555395266858949,"score_spread":0.24775580352729007,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220793985","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":[0.2689827,0.0005087082,0.72653294,0.00016229213,0.00006414075,0.000052796837,0.00027436626,0.0011352481,0.002286753],"genre_scores_gemma":[0.94281924,0.00022230254,0.05503833,0.000028795243,0.00001827318,0.000050431594,0.00040100276,0.000043425774,0.0013781441],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99964917,0.00009382731,0.000019923582,0.00008959701,0.00009858539,0.000048980863],"domain_scores_gemma":[0.9992772,0.00036342817,0.00011426847,0.000040335064,0.00018628882,0.000018571134],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008091461,0.0008230339,0.00058536977,0.0010551453,0.0002287142,0.00045997318,0.0007448374,0.00047500958,0.0007107378],"category_scores_gemma":[0.0020406835,0.00028843372,0.0006111061,0.0011452853,0.00017430252,0.00073943095,0.0003068264,0.00089296175,0.00030371695],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000040795512,0.00008364654,0.00996238,0.000043866858,0.000059445094,0.000047609574,0.000031263047,0.9327249,0.0014678068,0.0005771215,0.00048168923,0.054479443],"study_design_scores_gemma":[0.0000011268155,0.000016432723,0.0014724034,0.0000030284748,0.0000051999373,0.0000068076192,0.000006513195,0.99795794,0.0002912967,0.00013782104,0.00009812957,0.000003303611],"about_ca_topic_score_codex":0.024343148,"about_ca_topic_score_gemma":0.025846822,"teacher_disagreement_score":0.024343148,"about_ca_system_score_codex":0.0005433153,"about_ca_system_score_gemma":0.00075893215,"threshold_uncertainty_score":0.048402905},"labels":[],"label_agreement":null},{"id":"W4284669228","doi":"10.1016/j.ijtst.2022.06.006","title":"CGAN-EB: A non-parametric empirical Bayes method for crash frequency modeling using conditional generative adversarial networks as safety performance functions","year":2022,"lang":"en","type":"article","venue":"International Journal of Transportation Science and Technology","topic":"Traffic and Road Safety","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Crash; Bayes' theorem; Parametric statistics; Artificial neural network; Computer science; Machine learning; Nonparametric statistics; Artificial intelligence; Algorithm; Statistics; Mathematics; Bayesian probability","score_opus":0.018191098001040025,"score_gpt":0.3053526273259077,"score_spread":0.28716152932486766,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4284669228","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":[0.0050060716,0.00021052224,0.99350125,0.0000982125,0.000023268565,0.00004140861,0.00006326802,0.000309104,0.00074692053],"genre_scores_gemma":[0.5846289,0.0007532013,0.40566513,0.00042066685,0.00015941919,0.00048491263,0.00093981164,0.00032342802,0.0066245478],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99877363,0.000657405,0.00004120074,0.00018568974,0.0002660474,0.00007596348],"domain_scores_gemma":[0.9957129,0.0032048537,0.0002960396,0.00025355854,0.00043289215,0.00009983846],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003375586,0.00110202,0.0010338584,0.0014175653,0.0003833051,0.000727929,0.0023128046,0.0011841011,0.0029278286],"category_scores_gemma":[0.008405737,0.00053046556,0.00091582054,0.0006086389,0.0009780374,0.0012906705,0.0014336987,0.0019248556,0.0006491164],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000057280544,0.000043086722,0.0015339475,0.000054148717,0.00007346807,0.00006740935,0.000049508024,0.9227592,0.0007299064,0.013835856,0.00142515,0.059371043],"study_design_scores_gemma":[0.0000022918962,0.000009118241,0.0001210041,0.0000070545766,0.000004980376,0.000018628214,0.0000037160899,0.9946525,0.00022013579,0.004655573,0.000300101,0.0000047956964],"about_ca_topic_score_codex":0.0055474597,"about_ca_topic_score_gemma":0.005024741,"teacher_disagreement_score":0.0055474597,"about_ca_system_score_codex":0.00075268367,"about_ca_system_score_gemma":0.0010335471,"threshold_uncertainty_score":0.017852068},"labels":[],"label_agreement":null},{"id":"W4321502816","doi":"10.1016/j.ijtst.2023.02.005","title":"Application of Conditional Deep Generative Networks (CGAN) in empirical bayes estimation of road crash risk and identifying crash hotspots","year":2023,"lang":"en","type":"article","venue":"International Journal of Transportation Science and Technology","topic":"Traffic and Road Safety","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Transport Canada","keywords":"Crash; Transferability; Bayes' theorem; Computer science; Range (aeronautics); Statistics; Artificial neural network; Machine learning; Algorithm; Econometrics; Artificial intelligence; Mathematics; Engineering; Bayesian probability","score_opus":0.009879900757652745,"score_gpt":0.29035604338869325,"score_spread":0.2804761426310405,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321502816","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":[0.06959598,0.0009223146,0.92566985,0.00073374,0.00006907313,0.00006236215,0.00018914354,0.0006607414,0.0020968663],"genre_scores_gemma":[0.9140829,0.00046547424,0.0824321,0.0003550058,0.00006642886,0.00009042918,0.00044483793,0.00007543394,0.0019872983],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991763,0.00041953305,0.000033849097,0.00017591246,0.00012321562,0.00007124947],"domain_scores_gemma":[0.9957488,0.0033831797,0.000256608,0.00018963759,0.00031997176,0.000101767975],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002943024,0.0009010423,0.0008328005,0.0010192828,0.00033122746,0.0005950828,0.0014388846,0.0010094945,0.0012261501],"category_scores_gemma":[0.0077485973,0.00057520735,0.0006799761,0.000516853,0.001019547,0.0009224069,0.0014740693,0.0017797736,0.00017951257],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005528135,0.00002212795,0.0020622648,0.000023968452,0.000045936707,0.00004800276,0.00003166347,0.9722058,0.00030950128,0.0049544387,0.00051861,0.01972236],"study_design_scores_gemma":[0.0000016991444,0.000005441085,0.00017759767,0.0000044913336,0.0000036235106,0.00000982982,0.0000029205912,0.99659306,0.00011520771,0.0030048199,0.00007805349,0.000003257512],"about_ca_topic_score_codex":0.013859778,"about_ca_topic_score_gemma":0.011842414,"teacher_disagreement_score":0.013859778,"about_ca_system_score_codex":0.0010678632,"about_ca_system_score_gemma":0.0009917491,"threshold_uncertainty_score":0.027558208},"labels":[],"label_agreement":null},{"id":"W4383957239","doi":"10.1016/j.ijtst.2023.06.004","title":"Connected vehicle enabled hierarchical anomaly behavior management system for city-level networks","year":2023,"lang":"en","type":"article","venue":"International Journal of Transportation Science and Technology","topic":"Traffic control and management","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Reliability (semiconductor); Anomaly detection; Transport engineering; Engineering","score_opus":0.013576776172109973,"score_gpt":0.24568391803254314,"score_spread":0.23210714186043316,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383957239","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":[0.3866466,0.0002909854,0.57857144,0.00030593612,0.00014777054,0.00033486687,0.00053657626,0.025665034,0.0075008557],"genre_scores_gemma":[0.9792138,0.00004110624,0.018961685,0.000043642507,0.000010054312,0.00006722557,0.0002548561,0.00004221437,0.0013653802],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99978095,0.000024778637,0.00001497528,0.00006732071,0.0000698842,0.000042102194],"domain_scores_gemma":[0.9995926,0.000054938875,0.00005953472,0.000075645854,0.00015644151,0.00006081214],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025790927,0.0004401141,0.00034722098,0.0006945907,0.00042674044,0.00044726767,0.0011849147,0.00034696836,0.0015386502],"category_scores_gemma":[0.00058681204,0.0001646503,0.00019723787,0.00033241598,0.0002396544,0.0006996265,0.00087627815,0.0003575556,0.00037053367],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012471002,0.0011316042,0.045095585,0.00026927708,0.00027578243,0.0012135804,0.00077193,0.3791443,0.08938988,0.0105145,0.01424835,0.45669815],"study_design_scores_gemma":[0.000024270039,0.00013382937,0.0031880557,0.000007686783,0.000044207347,0.00011001273,0.00005577003,0.9818865,0.009607362,0.002029478,0.0028881133,0.000024608005],"about_ca_topic_score_codex":0.006126009,"about_ca_topic_score_gemma":0.0073841135,"teacher_disagreement_score":0.006126009,"about_ca_system_score_codex":0.0006667236,"about_ca_system_score_gemma":0.0007077155,"threshold_uncertainty_score":0.012180746},"labels":[],"label_agreement":null},{"id":"W4391693745","doi":"10.1016/j.ijtst.2024.02.004","title":"Efficient implementation of a wavelet neural network model for short-term traffic flow prediction: Sensitivity analysis","year":2024,"lang":"en","type":"article","venue":"International Journal of Transportation Science and Technology","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Professional Engineers Ontario","funders":"","keywords":"Sensitivity (control systems); Term (time); Artificial neural network; Wavelet; Computer science; Traffic flow (computer networking); Real-time computing; Engineering; Artificial intelligence; Computer network","score_opus":0.01091777463262308,"score_gpt":0.2823597068118022,"score_spread":0.27144193217917917,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391693745","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":[0.14135236,0.00021280657,0.85386187,0.00022740781,0.000046538105,0.00007049954,0.00010948839,0.00048122177,0.0036377583],"genre_scores_gemma":[0.9549079,0.00015169046,0.04291405,0.000035946177,0.000014263426,0.00012147684,0.00015223087,0.00003616631,0.0016663284],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997904,0.00006808666,0.000012978242,0.00003967893,0.000055157554,0.00003373952],"domain_scores_gemma":[0.9995326,0.0002897082,0.00003106143,0.000021533688,0.000112478345,0.000012628497],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009049722,0.00065923913,0.00061755045,0.00039521902,0.00030472773,0.00065769785,0.0007025111,0.00095636636,0.0017348521],"category_scores_gemma":[0.0016974743,0.0003839149,0.0006749549,0.00034973523,0.00030945125,0.0008240835,0.0005889918,0.0012135608,0.0002531122],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003322669,0.000023596804,0.00045697705,0.00002007591,0.000016332142,0.00003186658,0.000012068296,0.9866636,0.0013132528,0.00072102173,0.00010073678,0.010607146],"study_design_scores_gemma":[5.2897815e-7,0.0000034648174,0.0000343981,7.2145156e-7,0.0000010559348,0.0000010530588,0.0000011288043,0.99970406,0.00015642602,0.000078136174,0.000018151817,8.099215e-7],"about_ca_topic_score_codex":0.011283073,"about_ca_topic_score_gemma":0.005116653,"teacher_disagreement_score":0.011283073,"about_ca_system_score_codex":0.000578442,"about_ca_system_score_gemma":0.00080662494,"threshold_uncertainty_score":0.022434771},"labels":[],"label_agreement":null},{"id":"W4403280016","doi":"10.1016/j.ijtst.2024.09.004","title":"Identifying the key factors of intermodal travel using interpretative ensemble learning","year":2024,"lang":"en","type":"article","venue":"International Journal of Transportation Science and Technology","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Key (lock); Transport engineering; Travel behavior; Computer science; Business; Engineering; Computer security","score_opus":0.03225883535504963,"score_gpt":0.3640420255464345,"score_spread":0.33178319019138486,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403280016","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":[0.21928765,0.00039066919,0.77636945,0.00066174875,0.00007313053,0.00007470166,0.000570987,0.00044566538,0.0021260795],"genre_scores_gemma":[0.9597824,0.00017242767,0.03774771,0.00007883968,0.000043588636,0.00007229411,0.00083668245,0.000031416534,0.0012344968],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99930835,0.00024411765,0.00003573965,0.00022464865,0.00010127504,0.00008589587],"domain_scores_gemma":[0.99830025,0.00088978873,0.00020227736,0.00019284146,0.00035315668,0.00006160204],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017738551,0.0009409336,0.000777186,0.0011239852,0.00036779794,0.0010873781,0.0010204342,0.0007730508,0.0012676875],"category_scores_gemma":[0.0050843987,0.0002488201,0.0011316851,0.0009723693,0.00032211942,0.0016660223,0.00084790867,0.0013457892,0.00027568755],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001283961,0.00020287554,0.05426403,0.00007484437,0.0003439894,0.00018244711,0.0002611152,0.8145966,0.002272752,0.0048323944,0.0021107637,0.120729886],"study_design_scores_gemma":[0.000002235549,0.000016069509,0.0029937483,0.000007654109,0.000023398128,0.000014747195,0.00003335289,0.993804,0.00022099043,0.002609842,0.0002665839,0.000007372568],"about_ca_topic_score_codex":0.008180676,"about_ca_topic_score_gemma":0.009747793,"teacher_disagreement_score":0.008180676,"about_ca_system_score_codex":0.00060389267,"about_ca_system_score_gemma":0.00065720035,"threshold_uncertainty_score":0.016266108},"labels":[],"label_agreement":null},{"id":"W4406714013","doi":"10.1016/j.ijtst.2025.01.011","title":"Development of an unsupervised 3D LiDAR-based methodology for automated safety monitoring of railway facilities","year":2025,"lang":"en","type":"article","venue":"International Journal of Transportation Science and Technology","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Transport Canada","keywords":"Lidar; Safety monitoring; Transport engineering; Computer science; Engineering; Remote sensing; Geography","score_opus":0.030828563825379438,"score_gpt":0.3315516952212768,"score_spread":0.30072313139589735,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406714013","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":[0.01924171,0.000041507523,0.9782904,0.00004089547,0.000013052791,0.00014259989,0.00018762198,0.0013956957,0.000646504],"genre_scores_gemma":[0.17338938,0.0000823004,0.82450306,0.00006445162,0.000014641574,0.00022991907,0.00073135155,0.000097789714,0.00088716747],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992854,0.000077524404,0.000045961922,0.00021757564,0.00031488994,0.000058736765],"domain_scores_gemma":[0.99917334,0.000121586316,0.000118347125,0.00010339247,0.0004552494,0.000028067101],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054362847,0.0005355227,0.0004612889,0.0014336441,0.000306834,0.0007399353,0.0014214178,0.0007626466,0.00078521203],"category_scores_gemma":[0.001204774,0.00039129634,0.0008089041,0.00079404004,0.00029093478,0.0007110921,0.00085310993,0.00060378015,0.0008118052],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006565927,0.00029881633,0.014699155,0.0002622853,0.00009954454,0.00016195005,0.00021163805,0.12336736,0.12477867,0.002484601,0.0026608864,0.7309094],"study_design_scores_gemma":[0.000008155439,0.00008804061,0.0065359906,0.00002398368,0.000016394193,0.00018817387,0.00007612518,0.9538919,0.035183936,0.0011625458,0.002796923,0.000027864226],"about_ca_topic_score_codex":0.0034794984,"about_ca_topic_score_gemma":0.0073856753,"teacher_disagreement_score":0.0034794984,"about_ca_system_score_codex":0.00044377756,"about_ca_system_score_gemma":0.0010938576,"threshold_uncertainty_score":0.00691849},"labels":[],"label_agreement":null},{"id":"W4411024422","doi":"10.1016/j.ijtst.2025.05.008","title":"Where to plug in? Assessing the users’ preferences for EV charging location","year":2025,"lang":"en","type":"article","venue":"International Journal of Transportation Science and Technology","topic":"Electric Vehicles and Infrastructure","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia, Okanagan Campus","funders":"Natural Sciences and Engineering Research Council of Canada; Environment and Climate Change Canada; Canada Foundation for Innovation","keywords":"Plug-in; Business; Transport engineering; Computer science; Engineering; Operating system","score_opus":0.008238396777302223,"score_gpt":0.2843324515480565,"score_spread":0.27609405477075427,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411024422","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":[0.99634594,0.00008843851,0.00025603082,0.00008436335,0.000004488525,0.000015290329,0.00020368742,0.0000067668584,0.0029949122],"genre_scores_gemma":[0.9979972,0.00013148495,0.00044408944,0.000039274695,0.00000250945,0.000010760394,0.00019733392,0.0000026778787,0.0011747661],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9996008,0.00010891492,0.00003838713,0.000039242364,0.00012011703,0.00009249635],"domain_scores_gemma":[0.99837106,0.00040220327,0.00035628697,0.00007301897,0.00052253756,0.00027482957],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005901536,0.00014980897,0.00017712169,0.00048807132,0.00047777712,0.001111423,0.0003134885,0.00032300508,0.0029288882],"category_scores_gemma":[0.0031949012,0.000114733,0.00023679933,0.00060423237,0.00025869216,0.0004864719,0.0003306873,0.00029064287,0.0006817286],"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":[0.00020015876,0.00011305788,0.9600153,0.00007583155,0.000040949002,0.00020063846,0.0030624873,0.00021327606,0.001028427,0.000110322275,0.0011433818,0.03379615],"study_design_scores_gemma":[0.0000058469527,0.00011415067,0.97324413,0.000047407924,0.000035690053,0.00028339858,0.020238966,0.0013884321,0.00034769182,0.000113536924,0.0041506467,0.000030208234],"about_ca_topic_score_codex":0.10336583,"about_ca_topic_score_gemma":0.22617193,"teacher_disagreement_score":0.10336583,"about_ca_system_score_codex":0.0005822673,"about_ca_system_score_gemma":0.0004977868,"threshold_uncertainty_score":0.20552838},"labels":[],"label_agreement":null},{"id":"W4411334025","doi":"10.1016/j.ijtst.2025.06.001","title":"EcoRouteQ: A Reinforcement Learning Framework for Green Route Recommendations","year":2025,"lang":"en","type":"article","venue":"International Journal of Transportation Science and Technology","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Research Council Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Reinforcement; Reinforcement learning; Business; Engineering; Transport engineering; Computer science; Operations management; Artificial intelligence; Structural engineering","score_opus":0.017828835252900778,"score_gpt":0.3585817779203076,"score_spread":0.3407529426674068,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411334025","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007690085,0.0001584065,0.9861298,0.00019690051,0.00008152987,0.00010461746,0.00026542903,0.0033394077,0.0020338257],"genre_scores_gemma":[0.42661017,0.00018933193,0.5642269,0.00036465662,0.000083907675,0.0003917265,0.00069698464,0.00047749482,0.006958921],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993905,0.00019944458,0.000025933621,0.00013992681,0.00016072627,0.00008345211],"domain_scores_gemma":[0.99890184,0.00053098844,0.000064809436,0.00008091249,0.00031216437,0.00010918891],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013921176,0.000966233,0.0011512268,0.0006146005,0.0004845668,0.0010496421,0.002963708,0.001736469,0.0076684975],"category_scores_gemma":[0.004206614,0.0005048949,0.00061380287,0.0004626018,0.00051365845,0.0012092354,0.0015650876,0.002179489,0.0010919159],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017342136,0.00020829248,0.00078970706,0.000071279246,0.000048955364,0.00006270827,0.00005120819,0.87688863,0.0007858159,0.005965964,0.005817099,0.10913689],"study_design_scores_gemma":[0.000012060534,0.000012449263,0.00002609011,0.000002986131,0.0000030581582,0.000003977202,0.0000030329159,0.998004,0.00014936646,0.0013412719,0.0004388069,0.0000028823745],"about_ca_topic_score_codex":0.023984198,"about_ca_topic_score_gemma":0.030620078,"teacher_disagreement_score":0.023984198,"about_ca_system_score_codex":0.0010228655,"about_ca_system_score_gemma":0.0022462297,"threshold_uncertainty_score":0.0476892},"labels":[],"label_agreement":null}]}