{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":11,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":11,"direct_label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline (scores rank; they never assert a category)","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12","author_layer_release":"2026-06-26"},"query_hash":"89e01b623d8e","filters":{"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,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"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","authors":[{"name":"Anae Sobhani","is_ca":true},{"name":"Hamzeh Alizadeh Aliabadi","is_ca":true},{"name":"Bilal Farooq","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.09012465269356584,"gpt":0.4016408995392342,"spread":0.3115162468456684,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002912155,0.001008038,0.001153127,0.001445598,0.000622776,0.001523383,0.00266177,0.001205118,0.00785983],"category_scores_gemma":[0.006322286,0.0006273261,0.001697633,0.002042805,0.0009162661,0.00165536,0.001251826,0.002244841,0.0008003707],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001828148,"about_ca_system_score_gemma":0.001651476,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05151714,"about_ca_topic_score_gemma":0.04832722,"domain_scores_codex":[0.9979542,0.001271872,0.00006009414,0.0003520875,0.0001479939,0.000213661],"domain_scores_gemma":[0.9966074,0.002528098,0.0002617924,0.0001877071,0.0003136731,0.0001012566],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003852863,0.0002447231,0.04056457,0.0001762898,0.0002662551,0.0005166339,0.0004774418,0.869061,0.0004147524,0.06161815,0.002806791,0.0234681],"study_design_scores_gemma":[0.00002564582,0.00004778575,0.002455235,0.00001122167,0.0000299969,0.00003252607,0.00009582363,0.989203,0.00005193004,0.007263178,0.0007689478,0.00001470358],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5078002,0.0008205051,0.4778096,0.001185791,0.0001415689,0.0006790212,0.004362762,0.0006278097,0.006572626],"genre_scores_gemma":[0.9490429,0.0003772274,0.03845282,0.00008709569,0.00005084764,0.0005936749,0.00235263,0.00004302365,0.008999893],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05151714,"threshold_uncertainty_score":0.1024345,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"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","authors":[{"name":"Khaled Shaaban","is_ca":false},{"name":"Ali Hamdi","is_ca":false},{"name":"Mohammad Ghanim","is_ca":false},{"name":"Khaled Shaban","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.007783723158604833,"gpt":0.2555395266858949,"spread":0.2477558035272901,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008091461,0.0008230339,0.0005853698,0.001055145,0.0002287142,0.0004599732,0.0007448374,0.0004750096,0.0007107378],"category_scores_gemma":[0.002040684,0.0002884337,0.0006111061,0.001145285,0.0001743025,0.000739431,0.0003068264,0.0008929617,0.000303717],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005433153,"about_ca_system_score_gemma":0.0007589321,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02434315,"about_ca_topic_score_gemma":0.02584682,"domain_scores_codex":[0.9996492,0.00009382731,0.00001992358,0.00008959701,0.00009858539,0.00004898086],"domain_scores_gemma":[0.9992772,0.0003634282,0.0001142685,0.00004033506,0.0001862888,0.00001857113],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004079551,0.00008364654,0.00996238,0.00004386686,0.00005944509,0.00004760957,0.00003126305,0.9327249,0.001467807,0.0005771215,0.0004816892,0.05447944],"study_design_scores_gemma":[0.000001126816,0.00001643272,0.001472403,0.000003028475,0.000005199937,0.000006807619,0.000006513195,0.9979579,0.0002912967,0.000137821,0.00009812957,0.000003303611],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2689827,0.0005087082,0.7265329,0.0001622921,0.00006414075,0.00005279684,0.0002743663,0.001135248,0.002286753],"genre_scores_gemma":[0.9428192,0.0002223025,0.05503833,0.00002879524,0.00001827318,0.00005043159,0.0004010028,0.00004342577,0.001378144],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02434315,"threshold_uncertainty_score":0.04840291,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"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","authors":[{"name":"Brian S. Freeman","is_ca":true},{"name":"Jamal Ahmad Al Matawah","is_ca":false},{"name":"Musaed Al Najjar","is_ca":false},{"name":"Jesse Van Griensven Thé","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.008430704441143367,"gpt":0.2523411467673806,"spread":0.2439104423262372,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001741394,0.0004853378,0.0003459131,0.001009794,0.0002006348,0.0003526871,0.0003953356,0.0002802265,0.0005028505],"category_scores_gemma":[0.0006886684,0.0002394071,0.0003509874,0.000556962,0.0001639403,0.0006029779,0.0004646367,0.000314815,0.000180411],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002627276,"about_ca_system_score_gemma":0.0003577704,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007102284,"about_ca_topic_score_gemma":0.006546431,"domain_scores_codex":[0.9997976,0.00003650227,0.000008734167,0.00005096997,0.00006498495,0.00004126777],"domain_scores_gemma":[0.9997844,0.00005770337,0.00005845922,0.00003035669,0.00004901113,0.00002018918],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0002512101,0.000103275,0.07204048,0.00007176449,0.0001455151,0.0003358372,0.0002538227,0.7539584,0.01752257,0.0012592,0.0005508228,0.1535071],"study_design_scores_gemma":[0.000003693552,0.0000618987,0.02101316,0.000006163089,0.00001750701,0.0000549295,0.0001039213,0.9738666,0.003960889,0.000553605,0.0003458571,0.00001187364],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8508318,0.0001482452,0.1472474,0.00002434182,0.0000159239,0.00001993388,0.0001748444,0.000460985,0.001076416],"genre_scores_gemma":[0.976396,0.00004133393,0.0231402,0.000004062426,0.000005156766,0.000008868367,0.0001586934,0.00001062823,0.0002350406],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007102284,"threshold_uncertainty_score":0.01412189,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"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","authors":[{"name":"Mohammad Zarei","is_ca":true},{"name":"Bruce Hellinga","is_ca":true},{"name":"Pedram Izadpanah","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.009879900757652745,"gpt":0.2903560433886933,"spread":0.2804761426310405,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002943024,0.0009010423,0.0008328005,0.001019283,0.0003312275,0.0005950828,0.001438885,0.001009495,0.00122615],"category_scores_gemma":[0.007748597,0.0005752073,0.0006799761,0.000516853,0.001019547,0.0009224069,0.001474069,0.001779774,0.0001795126],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001067863,"about_ca_system_score_gemma":0.0009917491,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01385978,"about_ca_topic_score_gemma":0.01184241,"domain_scores_codex":[0.9991763,0.0004195331,0.0000338491,0.0001759125,0.0001232156,0.00007124947],"domain_scores_gemma":[0.9957488,0.00338318,0.000256608,0.0001896376,0.0003199718,0.000101768],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005528135,0.00002212795,0.002062265,0.00002396845,0.00004593671,0.00004800276,0.00003166347,0.9722058,0.0003095013,0.004954439,0.00051861,0.01972236],"study_design_scores_gemma":[0.000001699144,0.000005441085,0.0001775977,0.000004491334,0.000003623511,0.00000982982,0.000002920591,0.9965931,0.0001152077,0.00300482,0.00007805349,0.000003257512],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06959598,0.0009223146,0.9256698,0.00073374,0.00006907313,0.00006236215,0.0001891435,0.0006607414,0.002096866],"genre_scores_gemma":[0.9140829,0.0004654742,0.0824321,0.0003550058,0.00006642886,0.00009042918,0.0004448379,0.00007543394,0.001987298],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01385978,"threshold_uncertainty_score":0.02755821,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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","authors":[{"name":"Mohammad Zarei","is_ca":true},{"name":"Bruce Hellinga","is_ca":true},{"name":"Pedram Izadpanah","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01819109800104003,"gpt":0.3053526273259077,"spread":0.2871615293248677,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003375586,0.00110202,0.001033858,0.001417565,0.0003833051,0.000727929,0.002312805,0.001184101,0.002927829],"category_scores_gemma":[0.008405737,0.0005304656,0.0009158205,0.0006086389,0.0009780374,0.00129067,0.001433699,0.001924856,0.0006491164],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007526837,"about_ca_system_score_gemma":0.001033547,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00554746,"about_ca_topic_score_gemma":0.005024741,"domain_scores_codex":[0.9987736,0.000657405,0.00004120074,0.0001856897,0.0002660474,0.00007596348],"domain_scores_gemma":[0.9957129,0.003204854,0.0002960396,0.0002535585,0.0004328921,0.00009983846],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005728054,0.00004308672,0.001533948,0.00005414872,0.00007346807,0.00006740935,0.00004950802,0.9227592,0.0007299064,0.01383586,0.00142515,0.05937104],"study_design_scores_gemma":[0.000002291896,0.000009118241,0.0001210041,0.000007054577,0.000004980376,0.00001862821,0.00000371609,0.9946525,0.0002201358,0.004655573,0.000300101,0.000004795696],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005006072,0.0002105222,0.9935012,0.0000982125,0.00002326857,0.00004140861,0.00006326802,0.000309104,0.0007469205],"genre_scores_gemma":[0.5846289,0.0007532013,0.4056651,0.0004206669,0.0001594192,0.0004849126,0.0009398116,0.000323428,0.006624548],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00554746,"threshold_uncertainty_score":0.01785207,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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","authors":[{"name":"Sonia Mrad","is_ca":false},{"name":"Rafaa Mraïhi","is_ca":false},{"name":"Aparna Murthy","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01091777463262308,"gpt":0.2823597068118022,"spread":0.2714419321791792,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009049722,0.0006592391,0.0006175505,0.000395219,0.0003047277,0.0006576978,0.0007025111,0.0009563664,0.001734852],"category_scores_gemma":[0.001697474,0.0003839149,0.0006749549,0.0003497352,0.0003094513,0.0008240835,0.0005889918,0.001213561,0.0002531122],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000578442,"about_ca_system_score_gemma":0.0008066249,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01128307,"about_ca_topic_score_gemma":0.005116653,"domain_scores_codex":[0.9997904,0.00006808666,0.00001297824,0.00003967893,0.00005515755,0.00003373952],"domain_scores_gemma":[0.9995326,0.0002897082,0.00003106143,0.00002153369,0.0001124783,0.0000126285],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003322669,0.0000235968,0.0004569771,0.00002007591,0.00001633214,0.00003186658,0.0000120683,0.9866636,0.001313253,0.0007210217,0.0001007368,0.01060715],"study_design_scores_gemma":[5.289781e-7,0.000003464817,0.0000343981,7.214516e-7,0.000001055935,0.000001053059,0.000001128804,0.9997041,0.000156426,0.00007813617,0.00001815182,8.099215e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1413524,0.0002128066,0.8538619,0.0002274078,0.00004653811,0.00007049954,0.0001094884,0.0004812218,0.003637758],"genre_scores_gemma":[0.9549079,0.0001516905,0.04291405,0.00003594618,0.00001426343,0.0001214768,0.0001522309,0.00003616631,0.001666328],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01128307,"threshold_uncertainty_score":0.02243477,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"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","authors":[{"name":"Ehsan Nateghinia","is_ca":true},{"name":"Luis Miranda-Moreno","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03082856382537944,"gpt":0.3315516952212768,"spread":0.3007231313958973,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005436285,0.0005355227,0.0004612889,0.001433644,0.000306834,0.0007399353,0.001421418,0.0007626466,0.000785212],"category_scores_gemma":[0.001204774,0.0003912963,0.0008089041,0.00079404,0.0002909348,0.0007110921,0.0008531099,0.0006037802,0.0008118052],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004437776,"about_ca_system_score_gemma":0.001093858,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003479498,"about_ca_topic_score_gemma":0.007385675,"domain_scores_codex":[0.9992854,0.0000775244,0.00004596192,0.0002175756,0.0003148899,0.00005873676],"domain_scores_gemma":[0.9991733,0.0001215863,0.0001183471,0.0001033925,0.0004552494,0.0000280671],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006565927,0.0002988163,0.01469916,0.0002622853,0.00009954454,0.0001619501,0.000211638,0.1233674,0.1247787,0.002484601,0.002660886,0.7309094],"study_design_scores_gemma":[0.000008155439,0.00008804061,0.006535991,0.00002398368,0.00001639419,0.0001881739,0.00007612518,0.9538919,0.03518394,0.001162546,0.002796923,0.00002786423],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01924171,0.00004150752,0.9782904,0.00004089547,0.00001305279,0.0001425999,0.000187622,0.001395696,0.000646504],"genre_scores_gemma":[0.1733894,0.0000823004,0.8245031,0.00006445162,0.00001464157,0.0002299191,0.0007313516,0.00009778971,0.0008871675],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003479498,"threshold_uncertainty_score":0.00691849,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"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","authors":[{"name":"Md. Shahadat Hossain","is_ca":true},{"name":"Mahmudur Rahman Fatmi","is_ca":true},{"name":"Mostaq Ahmed","is_ca":true},{"name":"Bijoy Saha","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.008238396777302223,"gpt":0.2843324515480565,"spread":0.2760940547707543,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005901536,0.000149809,0.0001771217,0.0004880713,0.0004777771,0.001111423,0.0003134885,0.0003230051,0.002928888],"category_scores_gemma":[0.003194901,0.000114733,0.0002367993,0.0006042324,0.0002586922,0.0004864719,0.0003306873,0.0002906429,0.0006817286],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005822673,"about_ca_system_score_gemma":0.0004977868,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1033658,"about_ca_topic_score_gemma":0.2261719,"domain_scores_codex":[0.9996008,0.0001089149,0.00003838713,0.00003924236,0.000120117,0.00009249635],"domain_scores_gemma":[0.9983711,0.0004022033,0.000356287,0.00007301897,0.0005225376,0.0002748296],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002001588,0.0001130579,0.9600153,0.00007583155,0.000040949,0.0002006385,0.003062487,0.0002132761,0.001028427,0.0001103223,0.001143382,0.03379615],"study_design_scores_gemma":[0.000005846953,0.0001141507,0.9732441,0.00004740792,0.00003569005,0.0002833986,0.02023897,0.001388432,0.0003476918,0.0001135369,0.004150647,0.00003020823],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9963459,0.00008843851,0.0002560308,0.00008436335,0.000004488525,0.00001529033,0.0002036874,0.000006766858,0.002994912],"genre_scores_gemma":[0.9979972,0.000131485,0.0004440894,0.0000392747,0.00000250945,0.00001076039,0.0001973339,0.000002677879,0.001174766],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1033658,"threshold_uncertainty_score":0.2055284,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Reliability (semiconductor); Anomaly detection; Transport engineering; Engineering","authors":[{"name":"Hao Yang","is_ca":true},{"name":"Seyhan Uçar","is_ca":false},{"name":"Kentaro Oguchi","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01357677617210997,"gpt":0.2456839180325431,"spread":0.2321071418604332,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002579093,0.0004401141,0.000347221,0.0006945907,0.0004267404,0.0004472677,0.001184915,0.0003469684,0.00153865],"category_scores_gemma":[0.000586812,0.0001646503,0.0001972379,0.000332416,0.0002396544,0.0006996265,0.0008762782,0.0003575556,0.0003705337],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006667236,"about_ca_system_score_gemma":0.0007077155,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006126009,"about_ca_topic_score_gemma":0.007384114,"domain_scores_codex":[0.999781,0.00002477864,0.00001497528,0.00006732071,0.0000698842,0.00004210219],"domain_scores_gemma":[0.9995926,0.00005493888,0.00005953472,0.00007564585,0.0001564415,0.00006081214],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0012471,0.001131604,0.04509559,0.0002692771,0.0002757824,0.00121358,0.00077193,0.3791443,0.08938988,0.0105145,0.01424835,0.4566981],"study_design_scores_gemma":[0.00002427004,0.0001338294,0.003188056,0.000007686783,0.00004420735,0.0001100127,0.00005577003,0.9818865,0.009607362,0.002029478,0.002888113,0.00002460801],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3866466,0.0002909854,0.5785714,0.0003059361,0.0001477705,0.0003348669,0.0005365763,0.02566503,0.007500856],"genre_scores_gemma":[0.9792138,0.00004110624,0.01896168,0.00004364251,0.00001005431,0.00006722557,0.0002548561,0.00004221437,0.00136538],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006126009,"threshold_uncertainty_score":0.01218075,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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","authors":[{"name":"Jianhong Ye","is_ca":false},{"name":"Lei Gao","is_ca":false},{"name":"Jihao Deng","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03225883535504963,"gpt":0.3640420255464345,"spread":0.3317831901913849,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001773855,0.0009409336,0.000777186,0.001123985,0.0003677979,0.001087378,0.001020434,0.0007730508,0.001267688],"category_scores_gemma":[0.005084399,0.0002488201,0.001131685,0.0009723693,0.0003221194,0.001666022,0.0008479087,0.001345789,0.0002756875],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006038927,"about_ca_system_score_gemma":0.0006572003,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008180676,"about_ca_topic_score_gemma":0.009747793,"domain_scores_codex":[0.9993083,0.0002441176,0.00003573965,0.0002246487,0.000101275,0.00008589587],"domain_scores_gemma":[0.9983003,0.0008897887,0.0002022774,0.0001928415,0.0003531567,0.00006160204],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0001283961,0.0002028755,0.05426403,0.00007484437,0.0003439894,0.0001824471,0.0002611152,0.8145966,0.002272752,0.004832394,0.002110764,0.1207299],"study_design_scores_gemma":[0.000002235549,0.00001606951,0.002993748,0.000007654109,0.00002339813,0.00001474719,0.00003335289,0.993804,0.0002209904,0.002609842,0.0002665839,0.000007372568],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2192876,0.0003906692,0.7763695,0.0006617488,0.00007313053,0.00007470166,0.000570987,0.0004456654,0.00212608],"genre_scores_gemma":[0.9597824,0.0001724277,0.03774771,0.00007883968,0.00004358864,0.00007229411,0.0008366824,0.00003141653,0.001234497],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008180676,"threshold_uncertainty_score":0.01626611,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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","authors":[{"name":"Mahdi Mohammadizadeh","is_ca":false},{"name":"Arash Mozhdehi","is_ca":false},{"name":"Xin Wang","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01782883525290078,"gpt":0.3585817779203076,"spread":0.3407529426674068,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001392118,0.000966233,0.001151227,0.0006146005,0.0004845668,0.001049642,0.002963708,0.001736469,0.007668498],"category_scores_gemma":[0.004206614,0.0005048949,0.0006138029,0.0004626018,0.0005136585,0.001209235,0.001565088,0.002179489,0.001091916],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001022866,"about_ca_system_score_gemma":0.00224623,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0239842,"about_ca_topic_score_gemma":0.03062008,"domain_scores_codex":[0.9993905,0.0001994446,0.00002593362,0.0001399268,0.0001607263,0.00008345211],"domain_scores_gemma":[0.9989018,0.0005309884,0.00006480944,0.00008091249,0.0003121644,0.0001091889],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001734214,0.0002082925,0.0007897071,0.00007127925,0.00004895536,0.00006270827,0.00005120819,0.8768886,0.0007858159,0.005965964,0.005817099,0.1091369],"study_design_scores_gemma":[0.00001206053,0.00001244926,0.00002609011,0.000002986131,0.000003058158,0.000003977202,0.000003032916,0.998004,0.0001493665,0.001341272,0.0004388069,0.000002882374],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007690085,0.0001584065,0.9861298,0.0001969005,0.00008152987,0.0001046175,0.000265429,0.003339408,0.002033826],"genre_scores_gemma":[0.4266102,0.0001893319,0.5642269,0.0003646566,0.00008390767,0.0003917265,0.0006969846,0.0004774948,0.006958921],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0239842,"threshold_uncertainty_score":0.0476892,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}