{"id":"W2069814636","doi":"10.5038/2375-0901.17.4.4","title":"A Transit Technology Selection Model","year":2014,"lang":"en","type":"article","venue":"Journal of Public Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Transit (satellite); Selection (genetic algorithm); Computer science; Range (aeronautics); Operations research; Sensitivity (control systems); Emerging technologies; Model selection; Transport engineering; Engineering; Public transport","routes":{"ca_aff":true,"ca_fund":false,"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":[0.001059614,0.00007218176,0.0001445873,0.0004488229,0.0002233911,0.00004543277,0.0001325149,0.0001481947,0.00004454765],"category_scores_gemma":[0.00009953405,0.00007204701,0.00007979925,0.0006790537,0.00007534391,0.0006801004,2.006867e-7,0.0001750167,0.000002427641],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004824405,"about_ca_system_score_gemma":0.0002747687,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003773761,"about_ca_topic_score_gemma":0.001339614,"domain_scores_codex":[0.9988347,0.00008331694,0.000422558,0.00009254771,0.0003893437,0.0001775442],"domain_scores_gemma":[0.9988927,0.00003662742,0.0003224459,0.00004506611,0.000595557,0.0001075554],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.00009185934,0.0002413701,0.08191834,0.00003489212,0.00008368897,0.00000539525,0.02590549,0.3804541,0.002205743,0.4534054,0.0008107836,0.05484292],"study_design_scores_gemma":[0.01057349,0.001619007,0.373753,0.0003687585,0.0009059263,0.00004591858,0.02122612,0.2031833,0.002790291,0.1076207,0.2760999,0.001813621],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2347929,0.00003045437,0.759015,0.004717777,0.0001603944,0.00006184991,0.000003193422,0.00007492514,0.001143517],"genre_scores_gemma":[0.9842272,0.00005586291,0.01531074,0.00007834779,0.0001236128,0.000002530707,0.00001725531,0.000009366542,0.000175051],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7494343,"threshold_uncertainty_score":0.2937992,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02126889635970298,"score_gpt":0.277362448382717,"score_spread":0.256093552023014,"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."}}