{"id":"W2918458045","doi":"","title":"iCity: big data and visualization urban transportation strategies","year":2018,"lang":"en","type":"article","venue":"Computer Science and Software Engineering","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Esri (Canada); University of Saskatchewan; University of Toronto","funders":"","keywords":"Transportation planning; Computer science; Big data; Sustainable transport; Visualization; Urban planning; Transport engineering; Intelligent transportation system; Data science; Engineering; Sustainability; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000199555,0.00007856976,0.00006123639,0.0001449361,0.00008282519,0.0001640936,0.0001701442,0.00002371013,5.288553e-7],"category_scores_gemma":[0.00001322951,0.00008149181,0.000004182915,0.0002685857,0.00009336511,0.000678948,0.00004595597,0.0000387589,7.056503e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001198024,"about_ca_system_score_gemma":0.00001287155,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002763385,"about_ca_topic_score_gemma":0.000005436005,"domain_scores_codex":[0.9994639,0.000002141913,0.00008860537,0.0001915604,0.0001279272,0.0001258894],"domain_scores_gemma":[0.9997191,0.0000123571,0.000008373397,0.0001663169,0.00004106647,0.00005274531],"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":[0.000002859742,0.00001930848,0.003479563,0.0003442194,0.00003489338,0.000007038284,0.003538089,0.01415221,0.002830569,0.009633816,0.01210889,0.9538485],"study_design_scores_gemma":[0.00006368957,0.00002751241,0.01596439,0.0000301672,0.000005520824,0.000002025597,0.00002074758,0.9757895,0.000379318,0.00001626391,0.007591415,0.0001094263],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0726696,0.00009613399,0.9243726,0.000006669304,0.000474123,0.00005999173,0.00000468129,0.002295882,0.00002036991],"genre_scores_gemma":[0.9765881,0.00007816287,0.02307369,0.000029138,0.0002060351,0.000002646953,0.00001218373,0.000008683616,0.000001331209],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9616373,"threshold_uncertainty_score":0.332314,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01794465574664724,"score_gpt":0.2278201609880907,"score_spread":0.2098755052414435,"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."}}