{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008353064,0.0004776749,0.0003046598,0.001938701,0.002986489,0.002425144,0.001179095,0.0006846224,0.00215038],"category_scores_gemma":[0.002687562,0.0002675902,0.000465952,0.005428975,0.00127115,0.0008610541,0.0009904183,0.000651601,0.0002815177],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01520976,"about_ca_system_score_gemma":0.01149345,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9751894,"about_ca_topic_score_gemma":0.9887924,"domain_scores_codex":[0.9992571,0.0001525603,0.00003282434,0.0001066719,0.0002935568,0.0001572406],"domain_scores_gemma":[0.9981506,0.0005526607,0.00007348138,0.0001186704,0.0008998505,0.0002046533],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0009581572,0.001268943,0.3046355,0.001732261,0.0003525339,0.02742629,0.2365984,0.04809215,0.02393211,0.0106163,0.05263167,0.2917558],"study_design_scores_gemma":[0.0001148084,0.0003326372,0.4502988,0.0005576398,0.0001964874,0.001594141,0.3173722,0.0917808,0.009546961,0.001390712,0.126534,0.0002807729],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9834107,0.0002440223,0.002370652,0.0007602741,0.00001750629,0.0002064091,0.002882894,0.0003099375,0.009797765],"genre_scores_gemma":[0.9764528,0.0005449457,0.01289947,0.0001111256,0.000007251976,0.00006278686,0.002867511,0.0001440716,0.006910075],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02481055,"threshold_uncertainty_score":0.1103551,"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."}}