{"id":"W3149566804","doi":"10.1155/2021/6675605","title":"A Data-Driven Urban Metro Management Approach for Crowd Density Control","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"National Natural Science Foundation of China","keywords":"Crowds; Pedestrian; Crowding; Transport engineering; Constraint (computer-aided design); Computer science; Range (aeronautics); Public transport; Computer security; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.0007934259,0.001041626,0.001149472,0.0006441569,0.0006883325,0.001143574,0.002163304,0.0009411662,0.002148893],"category_scores_gemma":[0.001255501,0.0005827008,0.0007768631,0.0006971795,0.000555625,0.001033559,0.001301868,0.0009346081,0.0002514178],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001185739,"about_ca_system_score_gemma":0.00133165,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01675941,"about_ca_topic_score_gemma":0.01515739,"domain_scores_codex":[0.9996164,0.00007684494,0.00001758152,0.0001250086,0.00008328586,0.00008079466],"domain_scores_gemma":[0.9993923,0.0002288724,0.00009261209,0.00003456667,0.0001792316,0.00007254684],"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":[0.0000162838,0.00002255085,0.0003405698,0.00002295504,0.00001179105,0.00003181845,0.000022257,0.9911489,0.0004137431,0.00223373,0.0003871784,0.005348198],"study_design_scores_gemma":[0.000001428653,0.000005633767,0.00003018167,0.00000103032,0.000001924154,0.000001872478,0.000006982151,0.9993458,0.0000511805,0.0004150379,0.0001373279,0.000001584915],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02734387,0.0003041618,0.9665889,0.0002922834,0.0001280027,0.00009170261,0.0001838718,0.0003131347,0.004754006],"genre_scores_gemma":[0.9352013,0.0002051268,0.06044699,0.0001036195,0.00006580036,0.00018293,0.0002396762,0.00006015524,0.00349438],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01675941,"threshold_uncertainty_score":0.03332376,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02511287941769374,"score_gpt":0.300200873280179,"score_spread":0.2750879938624853,"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."}}