{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00624116,0.0004449208,0.0005638783,0.001599741,0.003390124,0.001242182,0.0009395776,0.0006248172,0.0006354441],"category_scores_gemma":[0.001537775,0.0004775978,0.000227196,0.002598964,0.001243846,0.00350764,0.00001487542,0.001545171,0.00005285085],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002537631,"about_ca_system_score_gemma":0.0006089765,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.05396292,"about_ca_topic_score_gemma":0.06382069,"domain_scores_codex":[0.9906034,0.001324257,0.001377242,0.001259243,0.003566396,0.001869502],"domain_scores_gemma":[0.9861785,0.006074771,0.0003103875,0.0005052785,0.006010488,0.0009206305],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001191698,0.0008873141,0.7123863,0.0006864359,0.0002052824,0.00004678112,0.1034408,0.01004133,0.00126724,0.1053153,0.01921305,0.04531853],"study_design_scores_gemma":[0.001980499,0.0003728672,0.9200342,0.0005006914,0.00009853633,1.88715e-7,0.02603529,0.0013023,0.0002979765,0.01872989,0.02989573,0.0007518323],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8840522,0.0001980349,0.1041023,0.004723189,0.0002686334,0.004899388,0.0008603903,0.0003605921,0.000535321],"genre_scores_gemma":[0.9411598,0.0008422043,0.05243547,0.0001440664,0.0003432176,0.002631563,0.001459657,0.0001227738,0.0008612081],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2076479,"threshold_uncertainty_score":0.9997946,"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."}}