{"id":"W2971854718","doi":"10.3390/app9173624","title":"Congestion Evaluation of Pedestrians in Metro Stations Based on Normal-Cloud Theory","year":2019,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Evacuation and Crowd Dynamics","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Science Foundation of Zhejiang Province; Fundamental Research Funds for the Central Universities; Natural Science Foundation of Ningbo Municipality","keywords":"Metro station; Transport engineering; Pedestrian; Computer science; Traffic congestion; Channel (broadcasting); Cloud computing; Engineering; Telecommunications","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008781311,0.0008894276,0.0004984442,0.001665155,0.0006509639,0.0009967041,0.001007108,0.0004594384,0.000818146],"category_scores_gemma":[0.002320216,0.0003059413,0.0006166424,0.0009512003,0.0007491462,0.001402653,0.001014561,0.0003784026,0.0001147363],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001866114,"about_ca_system_score_gemma":0.000943996,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03655762,"about_ca_topic_score_gemma":0.02396255,"domain_scores_codex":[0.9991467,0.0001897425,0.00004885247,0.0001689697,0.0002600889,0.0001855666],"domain_scores_gemma":[0.9988932,0.0002111968,0.0001943952,0.00006535474,0.0004848256,0.0001509947],"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":[0.0009569464,0.0004006085,0.3057105,0.0002738838,0.0001994732,0.0006039902,0.000709759,0.6340525,0.009340685,0.005465758,0.001617493,0.04066836],"study_design_scores_gemma":[0.00001615377,0.0001718107,0.04179817,0.00001747513,0.00004688945,0.00008492851,0.0005940807,0.9544456,0.001282588,0.001221773,0.0002848274,0.00003564034],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9136083,0.0002208958,0.08176518,0.0001245035,0.00005607252,0.0001169643,0.0003379208,0.0001881114,0.00358197],"genre_scores_gemma":[0.9978315,0.00005129733,0.001803964,0.000006512152,0.000007618947,0.00001425735,0.0001000754,0.000003210376,0.0001814862],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03655762,"threshold_uncertainty_score":0.07268965,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02285828087162318,"score_gpt":0.2722478159948725,"score_spread":0.2493895351232493,"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."}}