{"id":"W4392225730","doi":"","title":"Forecasting and visualization of passenger demand in public transport network with machine learning methods","year":2019,"lang":"fr","type":"preprint","venue":"theses.fr (ABES)","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Public transport; Visualization; Computer science; Passenger transport; Engineering; Artificial intelligence; Transport engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004700825,0.0007930211,0.0004421338,0.001527668,0.0002192404,0.0009686956,0.0004784722,0.0006700078,0.002822291],"category_scores_gemma":[0.001396648,0.0002490392,0.0007462341,0.00119962,0.0001626094,0.0007880714,0.0003767486,0.0006706218,0.0004534981],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000879421,"about_ca_system_score_gemma":0.000674796,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05031827,"about_ca_topic_score_gemma":0.02965115,"domain_scores_codex":[0.9998291,0.00003965671,0.00001113025,0.00004395558,0.00004525592,0.00003091674],"domain_scores_gemma":[0.9996938,0.000152456,0.00002963883,0.00001889083,0.00008263009,0.00002272393],"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":[0.0004772871,0.0002424477,0.02552049,0.0002847083,0.0001150378,0.0003270135,0.0003948533,0.7221996,0.00707262,0.002086349,0.009630216,0.2316494],"study_design_scores_gemma":[0.000002832633,0.00001159355,0.002436143,0.000008307678,0.000004099903,0.0000102486,0.00005793008,0.9958882,0.0005936638,0.0002930876,0.0006881041,0.00000585164],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6804346,0.002720285,0.2876461,0.002005908,0.0004910298,0.0001312068,0.005676406,0.008733423,0.01216097],"genre_scores_gemma":[0.9409167,0.0007208586,0.05164978,0.00006448411,0.00004723317,0.00006918128,0.002090381,0.0000953476,0.004346041],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05031827,"threshold_uncertainty_score":0.1000507,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0343666139798151,"score_gpt":0.2761845245897306,"score_spread":0.2418179106099155,"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."}}