{"id":"W7149034321","doi":"10.70675/e2936539z2511z4449z8c58z5896073e0069","title":"Prévision et visualisation de l'affluence dans les transports en commun à l'aide de méthodes d'apprentissage automatique","year":2019,"lang":"","type":"dissertation","venue":"","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Transportation infrastructure; Rail transportation; High speed train; Georeference","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":[0.002827087,0.001129307,0.0007690943,0.002047058,0.001044053,0.00591398,0.001007041,0.001168107,0.007930697],"category_scores_gemma":[0.007720966,0.000562338,0.001267773,0.002217892,0.001097435,0.002691407,0.002354427,0.001517087,0.001279972],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001347306,"about_ca_system_score_gemma":0.002217725,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02562715,"about_ca_topic_score_gemma":0.02465718,"domain_scores_codex":[0.9979018,0.0006327941,0.00008999904,0.0003175253,0.0009078665,0.0001499706],"domain_scores_gemma":[0.9956316,0.00247289,0.0002859719,0.000377131,0.001113825,0.0001187036],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008258677,0.0002584812,0.04280427,0.003334997,0.0003959153,0.0007251255,0.02204028,0.1162399,0.04507509,0.04739365,0.01335495,0.7075514],"study_design_scores_gemma":[0.0001622223,0.0006146803,0.08506422,0.002189953,0.0004206996,0.0009898058,0.02472795,0.5037102,0.06565749,0.04271862,0.2731316,0.0006126393],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1895589,0.002720702,0.7557788,0.002088521,0.0003785079,0.0003406989,0.001808097,0.006727654,0.04059796],"genre_scores_gemma":[0.5902262,0.002525994,0.3862833,0.0002233947,0.00006348316,0.0004187464,0.001266193,0.001374184,0.0176184],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02562715,"threshold_uncertainty_score":0.05095595,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.029336866956793,"score_gpt":0.3752107669018332,"score_spread":0.3458738999450403,"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."}}