{"id":"W3087247166","doi":"10.1016/j.trpro.2020.08.192","title":"Predicting Carsharing Station-Based Trip Generation Using a Growth Model","year":2020,"lang":"en","type":"article","venue":"Transportation research procedia","topic":"Transportation and Mobility Innovations","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Transport engineering; Service (business); Level of service; Computer science; Operations research; Engineering; Business","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006849823,0.0007526414,0.0004980422,0.0006230216,0.000295624,0.0009405579,0.001074131,0.0009869965,0.001787177],"category_scores_gemma":[0.001704632,0.0003504633,0.0008334419,0.0008470329,0.0004111763,0.0006311325,0.0004873976,0.001021257,0.0003169729],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001861316,"about_ca_system_score_gemma":0.001111637,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1119817,"about_ca_topic_score_gemma":0.06073308,"domain_scores_codex":[0.9998034,0.00005151535,0.000009114773,0.00006039054,0.00002482731,0.00005087693],"domain_scores_gemma":[0.9990534,0.0006268803,0.00008551526,0.00003651032,0.0001534477,0.00004426402],"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":[0.00001679036,0.00001905537,0.004111602,0.00001027417,0.000008804231,0.00003024644,0.00001678112,0.9931133,0.0001722323,0.0004853463,0.0001563204,0.001859151],"study_design_scores_gemma":[0.000001194787,0.00000634549,0.0007302228,0.000001154457,0.000002511649,0.000002483887,0.00001077572,0.9990397,0.00004606545,0.0001042884,0.00005291962,0.000002241049],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9037528,0.0002047583,0.08835663,0.0003769822,0.0000437871,0.0001005068,0.002252709,0.0003118599,0.004600053],"genre_scores_gemma":[0.9923542,0.00008370559,0.005037101,0.00001144152,0.000006274999,0.00005595748,0.0008516217,0.00001358446,0.001586189],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1119817,"threshold_uncertainty_score":0.2226599,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1991005540041871,"score_gpt":0.3505172083669904,"score_spread":0.1514166543628032,"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."}}