{"id":"W2035971144","doi":"10.1080/15472450601122256","title":"Individual Trip Destination Estimation in a Transit Smart Card Automated Fare Collection System","year":2007,"lang":"en","type":"article","venue":"Journal of Intelligent Transportation Systems","topic":"Transportation and Mobility Innovations","field":"Engineering","cited_by":368,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Smart card; Transit (satellite); Computer science; Data collection; Estimation; Process (computing); Transport engineering; Real-time computing; Public transport; Engineering; Computer security; Systems engineering; Operating system","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":[0.0003967268,0.0003096082,0.0005322793,0.000438422,0.0003082208,0.0005622523,0.0004523622,0.0005201039,0.0008714051],"category_scores_gemma":[0.001310816,0.0001829769,0.0001855422,0.0004814668,0.0001167479,0.0004399796,0.0002637559,0.0002442803,0.0003499557],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006101022,"about_ca_system_score_gemma":0.0006515721,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02480709,"about_ca_topic_score_gemma":0.01392855,"domain_scores_codex":[0.9997998,0.00004515854,0.00001429785,0.00006211711,0.00005523693,0.00002347581],"domain_scores_gemma":[0.9994591,0.0001710098,0.00005272769,0.00009105976,0.0001961359,0.00003000656],"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":[0.001247711,0.0004987195,0.06764477,0.0001327307,0.0001742066,0.0004934645,0.0004111036,0.7020362,0.01836967,0.001067463,0.002806167,0.2051178],"study_design_scores_gemma":[0.00001623746,0.0001071888,0.00631805,0.00000278787,0.00001859714,0.00006238872,0.00003684727,0.9894521,0.00347173,0.0001104936,0.0003910403,0.00001269395],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8835611,0.00006276664,0.1121781,0.0000978414,0.00002167734,0.00007177376,0.0004944692,0.002088247,0.001423925],"genre_scores_gemma":[0.9815041,0.00001949696,0.01735142,0.00001403348,0.000003192547,0.00001579274,0.0002606421,0.00001194799,0.0008193635],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02480709,"threshold_uncertainty_score":0.04932541,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01960926572771334,"score_gpt":0.2575466345719113,"score_spread":0.237937368844198,"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."}}