{"id":"W3016924233","doi":"10.1016/j.heliyon.2020.e03729","title":"Visualizing public transit system operation with GTFS data: A case study of Calgary, Canada","year":2020,"lang":"en","type":"article","venue":"Heliyon","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":59,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates","keywords":"Headway; Visualization; Computer science; Public transport; Cluster analysis; Transit (satellite); Data science; Data mining; Facilitator; Representation (politics); Data visualization; Transport engineering; Engineering; Simulation; Machine learning","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":[],"consensus_categories":[],"category_scores_codex":[0.0004745625,0.00007105181,0.0001701601,0.00003286171,0.0004659624,0.00007666631,0.0002171855,0.00003014224,0.00006753656],"category_scores_gemma":[0.0000648889,0.00006285498,0.000015175,0.0004245955,0.00004618564,0.0002292022,0.0000206065,0.00007269801,0.000002537041],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000139204,"about_ca_system_score_gemma":0.001123805,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9451548,"about_ca_topic_score_gemma":0.998046,"domain_scores_codex":[0.9985799,0.0003520593,0.0002288905,0.0002658266,0.000432816,0.0001404918],"domain_scores_gemma":[0.9993299,0.0000588024,0.00006697113,0.0002602407,0.0001407038,0.0001433862],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.0003002509,0.002349542,0.1044707,0.004797046,0.001227008,0.005163722,0.828116,0.007653141,0.0006660317,0.0136098,0.001256498,0.0303903],"study_design_scores_gemma":[0.001176893,0.0005554036,0.0007452688,0.0001481675,0.0003240579,0.00002279611,0.9539366,0.0337752,0.00008884761,6.487363e-7,0.008874127,0.0003519529],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9930937,0.0001612531,0.00430989,0.001261309,0.000035681,0.0004196652,0.00001638388,0.00004336614,0.0006587962],"genre_scores_gemma":[0.9996459,0.000007921537,0.00002686525,0.0001558442,0.00008701633,0.00001341069,0.00003255745,0.0000059712,0.00002444302],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1258207,"threshold_uncertainty_score":0.3583854,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08259737704302134,"score_gpt":0.3167087434008218,"score_spread":0.2341113663578004,"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."}}