{"id":"W3024259511","doi":"","title":"Joint Modelling of Propensity and Distance for Walking Trip Generation","year":2012,"lang":"en","type":"article","venue":"Transportation Research Board 91st Annual MeetingTransportation Research Board","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"TRIPS architecture; Econometric model; Baseline (sea); Econometrics; Work (physics); Trip generation; Travel behavior; Preferred walking speed; Distance decay; Economics; Transport engineering; Computer science; Engineering; Physical medicine and rehabilitation","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":[0.001710426,0.0003979313,0.0005636812,0.0007561254,0.0003109557,0.001504634,0.0009699312,0.0007586385,0.004656507],"category_scores_gemma":[0.006502874,0.0004244969,0.001181569,0.001440912,0.0007836889,0.001094454,0.001095903,0.0009755861,0.0005870126],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001130493,"about_ca_system_score_gemma":0.001003303,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02245392,"about_ca_topic_score_gemma":0.02225221,"domain_scores_codex":[0.9989705,0.0004371851,0.00005826131,0.0002401021,0.000109271,0.0001846786],"domain_scores_gemma":[0.9955036,0.002683291,0.0009388084,0.000356564,0.0002670108,0.0002507727],"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":[0.0001931352,0.0001629487,0.1850494,0.0001000806,0.0002685852,0.0005612475,0.000556789,0.7394694,0.001026851,0.04297547,0.001097131,0.02853901],"study_design_scores_gemma":[0.00001785963,0.0001331268,0.05137781,0.0000225423,0.0001220376,0.0001549753,0.0004004434,0.9298398,0.0003441052,0.01537819,0.002158977,0.00005020588],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7546645,0.000339628,0.2385739,0.0004786499,0.0000436379,0.0001815259,0.001189535,0.0001544551,0.004374074],"genre_scores_gemma":[0.9862447,0.0001633,0.007076913,0.00001943424,0.00001316118,0.00005074825,0.0006120902,0.00001896606,0.005800645],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02245392,"threshold_uncertainty_score":0.0446465,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2905167700668444,"score_gpt":0.4129203287131816,"score_spread":0.1224035586463372,"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."}}