{"id":"W4281788403","doi":"10.1155/2022/7096153","title":"A Hierarchical Passenger Mobility Prediction Model Applicable to Large Crowding Events","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Science Foundation for Distinguished Young Scholars of Hunan Province; National Natural Science Foundation of China; Department of Transportation of Hunan Province; U.S. Department of Transportation","keywords":"Crowding; Computer science; Transport engineering; Engineering; Psychology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001639819,0.00007501923,0.0001772379,0.0001605945,0.000784561,0.00001502785,0.0001732091,0.00003736473,0.0003105722],"category_scores_gemma":[0.00007347148,0.00008099597,0.0001565778,0.0005070751,0.00002935542,0.0003478567,0.000004257863,0.0003031263,0.0000024238],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003066904,"about_ca_system_score_gemma":0.0002567366,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001057332,"about_ca_topic_score_gemma":0.001389217,"domain_scores_codex":[0.9982163,0.0001648117,0.0005041947,0.000171765,0.0007368014,0.0002061793],"domain_scores_gemma":[0.9991964,0.00007968568,0.0002303216,0.0001100929,0.0002230602,0.0001604123],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0001866744,0.0003816068,0.007154136,0.00001332189,0.00002734129,0.000002845564,0.01831349,0.9665928,0.00148681,0.002197947,0.00008487968,0.003558112],"study_design_scores_gemma":[0.005942548,0.001493547,0.6408309,0.0001482037,0.0007128677,0.000004883289,0.08969126,0.07018328,0.0007809892,0.08381437,0.1053457,0.00105145],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8539345,0.00002972506,0.1445862,0.0008483373,0.0001433071,0.0002446192,0.00006480561,0.00002260752,0.000125884],"genre_scores_gemma":[0.9974382,0.00002032015,0.002004084,0.0001629087,0.0001166429,0.00005612021,0.00003347436,0.000007936592,0.0001602984],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8964096,"threshold_uncertainty_score":0.6034288,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01339063462436305,"score_gpt":0.3003199694344114,"score_spread":0.2869293348100483,"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."}}