{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002296775,0.00008906565,0.00009903681,0.0001225981,0.0001023575,0.0006368731,0.001925888,0.00003042491,0.00008884141],"category_scores_gemma":[0.00003186449,0.00007478485,0.00002184319,0.001087061,0.00002668069,0.00370797,0.0009501568,0.00005753249,0.001654539],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001000283,"about_ca_system_score_gemma":0.00009756961,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001753077,"about_ca_topic_score_gemma":0.00005543381,"domain_scores_codex":[0.9989872,0.00002985875,0.0001630747,0.0004056224,0.0001962057,0.0002180377],"domain_scores_gemma":[0.9988202,0.00005925145,0.00002628608,0.0009753514,0.00003623515,0.00008264369],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000005652303,0.0005345633,0.01076774,0.00006722544,0.000123552,0.00002508789,0.001448696,0.0001095871,0.0007346665,0.266745,0.4162733,0.303165],"study_design_scores_gemma":[0.0001410025,0.00001445169,0.00388228,0.000007065294,0.000003407713,5.638953e-7,0.00003858289,0.8992905,0.0002195828,0.0001428696,0.0961394,0.0001202858],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02412235,0.00003544849,0.9667353,0.001439824,0.0004003803,0.00005222306,0.0001086702,0.0006248014,0.006480979],"genre_scores_gemma":[0.9861465,0.0001535615,0.002165328,0.000542438,0.0001363251,0.000002183377,0.001775412,0.00001060144,0.00906762],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.96457,"threshold_uncertainty_score":0.9991228,"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."}}