{"id":"W2293442411","doi":"","title":"Investigating the Performance of Large-Scale Intermodal Facilities Using Agent-Based Crowd Simulation: Case Study of Union Station in Toronto","year":2016,"lang":"en","type":"article","venue":"Transportation Research Board 95th Annual Meeting","topic":"Urban and Freight Transport Logistics","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Scale (ratio); Computer science; Operations research; Transport engineering; Engineering; Geography; Cartography","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001274174,0.0001440047,0.0002170943,0.0001519556,0.0001351775,0.00001234575,0.0001389001,0.00006841264,0.00003883657],"category_scores_gemma":[0.00006231406,0.0001073116,0.00004192233,0.0003195214,0.0001671465,0.0003712952,0.000004974368,0.0001856112,5.898159e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001709123,"about_ca_system_score_gemma":0.00006061723,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.01054304,"about_ca_topic_score_gemma":0.05424151,"domain_scores_codex":[0.9979802,0.0002108724,0.0007181905,0.00018865,0.0005593227,0.0003427395],"domain_scores_gemma":[0.998807,0.0004341461,0.00009910703,0.0001992017,0.0003905256,0.00006998787],"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":[0.00005909901,0.000128957,0.2570776,0.0005157575,0.00002477266,0.00004059927,0.04027221,0.6933092,0.007289585,0.00005495644,0.000004845317,0.001222405],"study_design_scores_gemma":[0.002061054,0.0004952427,0.1380567,0.0005359114,0.00002999728,0.000001137682,0.1291622,0.7232843,0.006075984,0.00001778125,0.00004244961,0.000237213],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9908846,0.00006702912,0.008126278,0.00001097653,0.00006549836,0.0004762521,0.0002368496,0.00004710151,0.00008541771],"genre_scores_gemma":[0.9989541,0.00001034717,0.0009237654,0.000002938105,0.00002614582,0.00002274259,0.00002115305,0.00002315663,0.00001566803],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1190208,"threshold_uncertainty_score":0.9960458,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0663009893147325,"score_gpt":0.329257724944832,"score_spread":0.2629567356300995,"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."}}