{"id":"W4386279429","doi":"10.1155/2023/1987210","title":"Inferring Travel Modes from Cellular Signaling Data Based on the Gated Recurrent Unit Neural Network","year":2023,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Computer science; Recurrent neural network; Artificial neural network; Identification (biology); Artificial intelligence; Deep learning; Cellular network; Global Positioning System; Machine learning; Ground truth; Data mining; Computer network; Telecommunications","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.000291224,0.0007636943,0.0004019889,0.0009950631,0.0001749451,0.0003840222,0.0006961381,0.0004559763,0.0006581794],"category_scores_gemma":[0.001206546,0.0002353992,0.0004977816,0.0007553768,0.0001780552,0.0007186757,0.0004388673,0.0006640757,0.000359996],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003968498,"about_ca_system_score_gemma":0.0005014572,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01308854,"about_ca_topic_score_gemma":0.01563901,"domain_scores_codex":[0.9997824,0.00004429151,0.00001548164,0.00007019175,0.00004599561,0.00004167422],"domain_scores_gemma":[0.9997515,0.00007079603,0.00004270225,0.00003277694,0.0000861837,0.00001605823],"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":[0.0004008602,0.0002688699,0.03972055,0.0002104005,0.0002557927,0.0006564598,0.0002715509,0.5115319,0.01947752,0.003302666,0.006982755,0.4169207],"study_design_scores_gemma":[0.000002995917,0.0000173451,0.002463392,0.00000635966,0.0000149994,0.00003415021,0.00002685012,0.9949769,0.001515204,0.0006755416,0.000258589,0.000007565093],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4150135,0.0008499924,0.5767956,0.0003250234,0.0001237499,0.00008213522,0.001569313,0.002185712,0.003054969],"genre_scores_gemma":[0.9544173,0.0002765469,0.04211386,0.00005853239,0.00002995192,0.00004757651,0.001326823,0.00003263848,0.001696773],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01308854,"threshold_uncertainty_score":0.0260247,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07045943971410289,"score_gpt":0.3214302862904481,"score_spread":0.2509708465763452,"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."}}