{"id":"W3109937920","doi":"10.18757/ejtir.2020.20.4.5429","title":"Modelling cellphone trace travel mode with neural networks using transit smartcard and home interview survey data","year":2020,"lang":"en","type":"article","venue":"European journal of transport and infrastructure research","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Universidad de la República Uruguay","keywords":"TRIPS architecture; Computer science; Smart card; Public transport; Artificial neural network; Travel survey; Mode choice; Transit (satellite); Mode (computer interface); Travel behavior; Survey data collection; TRACE (psycholinguistics); Data mining; Transport engineering; Computer security; Artificial intelligence; Engineering; Statistics; Human–computer interaction","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007740632,0.0007235624,0.0003626895,0.0008301593,0.0002196807,0.0006902767,0.001074667,0.0007673849,0.001225788],"category_scores_gemma":[0.003015841,0.0003505594,0.0005927591,0.001059641,0.000358955,0.000979795,0.0006467002,0.001134609,0.0003491927],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001250567,"about_ca_system_score_gemma":0.0006729939,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05996288,"about_ca_topic_score_gemma":0.06294683,"domain_scores_codex":[0.999755,0.00007585379,0.00001326467,0.00008592803,0.00002471477,0.00004533758],"domain_scores_gemma":[0.9993215,0.0004053497,0.00007662252,0.00004454629,0.0001175211,0.00003446974],"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":[0.00009256879,0.0001147112,0.03002164,0.0000389807,0.00007626012,0.00009746315,0.0001471261,0.9293873,0.0005113229,0.001989938,0.001135488,0.03638719],"study_design_scores_gemma":[0.000001692571,0.000006985229,0.001573935,0.000004663811,0.000003689962,0.000005452916,0.00001921784,0.9973647,0.00008537503,0.0007932486,0.0001379919,0.000003113768],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7170061,0.0005820798,0.27384,0.0009710758,0.000112467,0.000112936,0.003600141,0.0006870747,0.003088043],"genre_scores_gemma":[0.9746807,0.0001592819,0.02074846,0.00005904152,0.00002277921,0.00008014324,0.001924217,0.00001838949,0.002306993],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05996288,"threshold_uncertainty_score":0.1192277,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1927143739527558,"score_gpt":0.3554851757517922,"score_spread":0.1627708017990365,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}