{"id":"W4253371817","doi":"10.32920/ryerson.14664273.v1","title":"Generative Modelling and Machine Learning Methods for Travel Behaviour Analysis","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Generative grammar; Generative model; Artificial intelligence; Machine learning; Computer science; Inference; Bayesian inference; Representation (politics); Artificial neural network; Process (computing); Bayesian probability","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":[0.01089089,0.001426073,0.001588696,0.002429435,0.0006713338,0.003123068,0.002598591,0.002240401,0.007366683],"category_scores_gemma":[0.03723996,0.0009458709,0.003860658,0.002803931,0.003424064,0.003521014,0.002431094,0.004914025,0.001172904],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003071822,"about_ca_system_score_gemma":0.001874891,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005533816,"about_ca_topic_score_gemma":0.003893544,"domain_scores_codex":[0.9917648,0.005618992,0.0003593841,0.0009836977,0.001061715,0.000211348],"domain_scores_gemma":[0.9652712,0.03053991,0.001159595,0.002074987,0.0008072293,0.0001471392],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002589294,0.00006370177,0.00126695,0.0002967321,0.0002071575,0.00004603835,0.0003852316,0.1437995,0.0003534968,0.8157486,0.001753007,0.03605378],"study_design_scores_gemma":[0.00001996039,0.00003080524,0.0005991395,0.00009689966,0.0000274331,0.00003635807,0.0000773884,0.3687007,0.0002109943,0.6249439,0.005218949,0.00003765069],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002222532,0.0006571424,0.9933501,0.0006061462,0.00008051569,0.00008104154,0.0001462246,0.0001181004,0.002738229],"genre_scores_gemma":[0.2142209,0.003592859,0.7701234,0.0008171774,0.0004869227,0.001645924,0.0008059754,0.0003135682,0.007993381],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01089089,"threshold_uncertainty_score":0.05759722,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09234453186759631,"score_gpt":0.3644365852346217,"score_spread":0.2720920533670255,"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."}}