{"id":"W4388405488","doi":"10.1109/iv60283.2023.00061","title":"Visual Knowledge Discovery from Public Transit Performance Data","year":2023,"lang":"en","type":"article","venue":"","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; University of Manitoba","keywords":"Public transport; Computer science; Process (computing); Transit (satellite); Service (business); Component (thermodynamics); Destinations; Service provider; Work (physics); Mode (computer interface); Knowledge extraction; Transport engineering; Data science; Business; Data mining; Engineering; Human–computer interaction; Marketing; Geography","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.001710355,0.0009962295,0.0005417272,0.007803803,0.0006333636,0.003169778,0.001316874,0.0009385992,0.002536059],"category_scores_gemma":[0.009716438,0.0003825169,0.001037328,0.004357017,0.0005484262,0.002309222,0.002478676,0.0009481916,0.001026113],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001267166,"about_ca_system_score_gemma":0.001663547,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01381942,"about_ca_topic_score_gemma":0.0258692,"domain_scores_codex":[0.998531,0.0002327036,0.0001242259,0.0003411471,0.0006426622,0.000128219],"domain_scores_gemma":[0.9941696,0.0029413,0.0005941455,0.0007916605,0.001292135,0.0002112306],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009960663,0.0006918338,0.03269456,0.002670343,0.0002995616,0.003028535,0.009142624,0.06401015,0.0250011,0.0210343,0.07352935,0.7669016],"study_design_scores_gemma":[0.0001031,0.0002636695,0.0241535,0.00103086,0.0002789652,0.001467433,0.009546926,0.6408318,0.04571008,0.0646522,0.2117686,0.0001927523],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.238463,0.002624904,0.6161919,0.006117451,0.0002797823,0.001331644,0.07909948,0.02574342,0.03014842],"genre_scores_gemma":[0.5027403,0.001421822,0.4400169,0.0003160712,0.00008952933,0.0004262534,0.04985026,0.0005344911,0.004604341],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01381942,"threshold_uncertainty_score":0.02747798,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1022240187151564,"score_gpt":0.3461272566686147,"score_spread":0.2439032379534583,"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."}}