{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002108067,0.000475024,0.0007706539,0.0003572139,0.00007213042,0.00005991074,0.0002412451,0.0003808246,0.00007466698],"category_scores_gemma":[0.00007727586,0.0004631602,0.00009715854,0.0004863262,0.00008563285,0.0002737024,0.0001756796,0.0008268761,0.000002709409],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001136764,"about_ca_system_score_gemma":0.00003762445,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007620016,"about_ca_topic_score_gemma":0.0008308578,"domain_scores_codex":[0.9975767,0.0004004736,0.000717868,0.0005169674,0.0002765686,0.0005114714],"domain_scores_gemma":[0.998864,0.0002900926,0.0003017885,0.000355367,0.00008102038,0.000107766],"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":[0.00006148607,0.0001043271,0.1291165,0.002427623,0.0003702992,0.00001813592,0.003461039,0.7317381,0.0002160553,0.07040188,0.00005157637,0.06203297],"study_design_scores_gemma":[0.0007749951,0.0001562238,0.02118676,0.0015736,0.0002426088,0.00001531257,0.0004702364,0.9458389,0.0003710425,0.0005077386,0.0282875,0.0005751385],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04241244,0.001916551,0.9283375,0.000122515,0.0004630089,0.0009520858,0.00001143164,0.001213222,0.02457123],"genre_scores_gemma":[0.9413453,0.002135654,0.05596171,0.00002048835,0.0001293085,0.00007505965,0.0001141273,0.0001186618,0.0000996755],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8989329,"threshold_uncertainty_score":0.999782,"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."}}