{"id":"W7149009131","doi":"10.71465/ajbd863","title":"Exploring the Use of Big Data in Improving Public Transportation Systems","year":2025,"lang":"","type":"article","venue":"American Journal Of Big Data","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Big data; Public transport; Sustainability; Analytics; Intelligent transportation system; Predictive analytics; Resource (disambiguation); Data collection","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":[0.001592117,0.0002232407,0.0005417948,0.0007098698,0.00006569055,0.0002225098,0.002680119,0.0000364382,0.000001604311],"category_scores_gemma":[0.0003216213,0.0001893759,0.0000635878,0.001483784,0.0001947628,0.002761983,0.0003692408,0.0005509825,6.892782e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001032169,"about_ca_system_score_gemma":0.0002256161,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001606019,"about_ca_topic_score_gemma":0.0005597472,"domain_scores_codex":[0.9973008,0.0001769938,0.001449331,0.0003335364,0.0004286187,0.0003107295],"domain_scores_gemma":[0.9964668,0.0002538957,0.000810485,0.002252856,0.0001252281,0.00009076],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004763961,0.00008899431,0.001580377,0.0002981077,0.0003392857,0.00002338697,0.000368354,0.00353668,0.0003213712,0.0003231518,0.006717665,0.986355],"study_design_scores_gemma":[0.002000258,0.0006249209,0.06171215,0.004585622,0.001203158,0.00004488889,0.01481128,0.4180227,0.0002538298,0.00001299014,0.4959439,0.0007842541],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1285497,0.001915006,0.8589091,0.001444338,0.006918592,0.0005307802,0.001341791,0.0002541948,0.0001365664],"genre_scores_gemma":[0.9908912,0.007091909,0.001406909,0.00005946335,0.0003097583,0.000007554118,0.0001942637,0.00002708681,0.00001185307],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9855707,"threshold_uncertainty_score":0.7722526,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2950769392846395,"score_gpt":0.2828777519297793,"score_spread":0.0121991873548602,"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."}}