{"id":"W2889724758","doi":"10.1109/itsc.2018.8569581","title":"Modelling Latent Travel Behaviour Characteristics with Generative Machine Learning","year":2018,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Latent variable; Computer science; Bayesian network; Latent variable model; Machine learning; Generative grammar; Artificial intelligence; Generative model; Restricted Boltzmann machine; Graphical model; Mode (computer interface); Dynamic Bayesian network; Preference; Latent class model; Artificial neural network; Statistics; Mathematics; Human–computer interaction","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.002892421,0.0008383955,0.00116048,0.001572074,0.0004469295,0.001261628,0.002362887,0.001478893,0.00263388],"category_scores_gemma":[0.01291152,0.0009593423,0.001857625,0.001506966,0.001474785,0.001996395,0.001382724,0.002179243,0.0005177315],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001599392,"about_ca_system_score_gemma":0.001039505,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01079893,"about_ca_topic_score_gemma":0.01371039,"domain_scores_codex":[0.9984933,0.0008892447,0.00004899105,0.000291251,0.0001665684,0.0001106349],"domain_scores_gemma":[0.990469,0.008031693,0.0005999152,0.0004914053,0.000287758,0.0001202055],"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":[0.00003189625,0.00004393287,0.002640369,0.00004400448,0.00007069632,0.0000459084,0.0001334047,0.9467598,0.0002234191,0.03878669,0.0002858432,0.010934],"study_design_scores_gemma":[0.000003475642,0.000006379248,0.0002486188,0.000005814568,0.000005634361,0.000009545384,0.000009637623,0.9731541,0.00005664054,0.0263574,0.0001364339,0.000006298149],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03405416,0.0001171161,0.9643775,0.0002552434,0.00001760039,0.0000460146,0.0002384113,0.000212948,0.0006808653],"genre_scores_gemma":[0.8267895,0.0002417882,0.1687261,0.0001843389,0.00005399893,0.0003469282,0.0009622583,0.0001089286,0.002586129],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01079893,"threshold_uncertainty_score":0.02147216,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03299488692954841,"score_gpt":0.2703924480165857,"score_spread":0.2373975610870373,"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."}}