{"id":"W4289543625","doi":"10.48550/arxiv.1809.05781","title":"Modelling Latent Travel Behaviour Characteristics with Generative\\n Machine Learning","year":2018,"lang":"","type":"preprint","venue":"arXiv (Cornell University)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Latent variable; Latent variable model; Bayesian network; Generative grammar; Computer science; Machine learning; Restricted Boltzmann machine; Artificial intelligence; Generative model; Mode (computer interface); Preference; Graphical model; Latent class model; Dynamic Bayesian network; Bayesian probability; Artificial neural network; Statistics; Mathematics; Human–computer interaction","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002347228,0.0006564733,0.001008547,0.001361982,0.0003936277,0.001243862,0.002329032,0.001398197,0.002272085],"category_scores_gemma":[0.009901167,0.0008124661,0.001508481,0.001283696,0.001434388,0.001951769,0.001293342,0.002013886,0.0004620095],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001323871,"about_ca_system_score_gemma":0.0009095701,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008578783,"about_ca_topic_score_gemma":0.01126459,"domain_scores_codex":[0.9988542,0.0006219365,0.00004093061,0.0002596464,0.0001281076,0.00009521301],"domain_scores_gemma":[0.9936977,0.005170634,0.0004310978,0.0003948619,0.0002039736,0.0001017783],"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":[0.00003465398,0.00004542976,0.002708239,0.00004288838,0.00005861878,0.00003782576,0.0001153852,0.9475613,0.0002689519,0.03401709,0.0003439398,0.01476557],"study_design_scores_gemma":[0.000002324855,0.00000512966,0.0002076414,0.000003872794,0.000003440611,0.000005505421,0.000006401401,0.9795537,0.00005903664,0.02002389,0.000124723,0.000004248575],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04066628,0.0001476931,0.9575521,0.0003216598,0.00002211683,0.00003873304,0.0002450629,0.0002554354,0.0007509133],"genre_scores_gemma":[0.8617374,0.0002228021,0.1342268,0.0001874978,0.00006047821,0.0002277831,0.0008516543,0.00008773016,0.00239782],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008578783,"threshold_uncertainty_score":0.01705766,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08243694847824926,"score_gpt":0.2007683677259841,"score_spread":0.1183314192477349,"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."}}