{"id":"W4247085377","doi":"10.32920/ryerson.14647785","title":"Smart Transit Dynamic Optimization and Informatics","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Transportation and Mobility Innovations","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Dynamic pricing; Queue; Component (thermodynamics); Queueing theory; Operations research; Process (computing); Scheduling (production processes); Real-time computing; Mathematical optimization; Economics; Computer network; Engineering","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":[0.00004077532,0.0001113682,0.0001220303,0.00007827162,0.00002092321,0.00006698377,0.00004180048,0.0001418012,0.0001786283],"category_scores_gemma":[0.000002704001,0.0001248742,0.00002969749,0.00009803049,0.00001414208,0.00009203033,0.00001042885,0.0002015714,0.000001861396],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002340355,"about_ca_system_score_gemma":0.00002795342,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008195272,"about_ca_topic_score_gemma":0.0001835616,"domain_scores_codex":[0.9994915,0.000003153086,0.0002940143,0.00006854765,0.00006932712,0.00007351281],"domain_scores_gemma":[0.9997241,0.000007918037,0.0000188673,0.000154113,0.00006491722,0.0000300531],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[2.861554e-7,0.00000568518,0.00007540207,0.0003081021,0.00003220835,3.875579e-7,0.001076001,0.9970214,0.00002892632,0.0002271927,0.00003889488,0.001185487],"study_design_scores_gemma":[0.0001124241,0.000002273385,0.003174891,0.00003888263,0.00002979193,0.000001633536,0.0004685543,0.9955155,0.00007904905,0.00003307953,0.0003885863,0.000155295],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09665882,0.00007677714,0.898277,0.00008507746,0.0002584499,0.0001604722,0.00003021401,0.0003551521,0.004098065],"genre_scores_gemma":[0.91104,0.0004164395,0.08693021,0.00009496631,0.000006523709,0.00002782882,0.001341418,0.00001848042,0.0001241169],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8143812,"threshold_uncertainty_score":0.509222,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006371842414821381,"score_gpt":0.203016224653246,"score_spread":0.1966443822384246,"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."}}