{"id":"W2581699074","doi":"","title":"Analyzing Commuter Train User Behavior: Decision Framework for Access Mode and Station Choice","year":2013,"lang":"en","type":"article","venue":"Transportation Research Board 92nd Annual MeetingTransportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Mode (computer interface); Computer science; Hierarchy; Service (business); Function (biology); Mode choice; Transit (satellite); Transport engineering; Segmentation; Operations research; Public transport; Engineering; Artificial intelligence; 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":[0.006502304,0.0008337648,0.001276407,0.002906094,0.0008963592,0.00220216,0.001773462,0.001333939,0.006506833],"category_scores_gemma":[0.01188537,0.0005275815,0.001806506,0.002450768,0.001701259,0.002462311,0.00162636,0.001838693,0.0003932918],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002352578,"about_ca_system_score_gemma":0.002171272,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02885967,"about_ca_topic_score_gemma":0.02079132,"domain_scores_codex":[0.994372,0.003714288,0.0001438793,0.0008322748,0.0004497676,0.000487762],"domain_scores_gemma":[0.9904314,0.007817983,0.0007957534,0.0002923341,0.0003135582,0.0003489426],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.000430536,0.001619439,0.183655,0.0002815052,0.0006560666,0.0004972003,0.004309672,0.3382731,0.002166514,0.4068236,0.001996991,0.05929037],"study_design_scores_gemma":[0.00002228417,0.0001823939,0.01948182,0.00002544139,0.00004988723,0.00006858022,0.001099902,0.9300557,0.0001499869,0.04781037,0.001015183,0.00003847567],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4757269,0.000400814,0.5153841,0.001178224,0.00002927431,0.0005168116,0.0008002085,0.00009558994,0.005868081],"genre_scores_gemma":[0.9531552,0.0002232104,0.04323158,0.00006448852,0.00003356413,0.0003512538,0.0004422603,0.00001308723,0.002485391],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02885967,"threshold_uncertainty_score":0.05738336,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09709440915048527,"score_gpt":0.46752351638946,"score_spread":0.3704291072389748,"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."}}