{"id":"W2918569430","doi":"10.1155/2019/4319254","title":"Exploring Boarding Strategies for High-Speed Railway","year":2019,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Evacuation and Crowd Dynamics","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Cellular automaton; Transport engineering; Process (computing); Pedestrian; Computer science; Business; Simulation; Engineering","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":[0.0002030222,0.0006958731,0.0004067859,0.0004045771,0.0004788832,0.0008049185,0.0005432729,0.0007637376,0.001882036],"category_scores_gemma":[0.00099308,0.0002516564,0.0005837443,0.0002561607,0.0006221086,0.0007094311,0.0006295968,0.0003517445,0.0001283302],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008790692,"about_ca_system_score_gemma":0.0008017588,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02652173,"about_ca_topic_score_gemma":0.01536311,"domain_scores_codex":[0.9998317,0.00005190209,0.000004935683,0.00003996224,0.0000175098,0.00005406819],"domain_scores_gemma":[0.9996403,0.0001384038,0.00008838096,0.00001865252,0.00005282,0.00006138351],"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":[0.00004968508,0.00005515616,0.002865611,0.00003723293,0.00002787421,0.0001345363,0.0001229435,0.9791589,0.004282499,0.007137866,0.0002224604,0.005905187],"study_design_scores_gemma":[0.000006368673,0.00005341082,0.0006047256,0.000003148383,0.00001512901,0.00001224776,0.00009390999,0.9973081,0.000265055,0.001396929,0.0002355106,0.000005400555],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7985561,0.0004479252,0.1894651,0.0002346657,0.00004810646,0.0000612409,0.00006452229,0.0001325357,0.01098978],"genre_scores_gemma":[0.9949453,0.0001228101,0.003952466,0.00001081289,0.000003344848,0.00001222199,0.00002108613,0.000005549829,0.0009264339],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02652173,"threshold_uncertainty_score":0.05273467,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02521277931236732,"score_gpt":0.244750063547797,"score_spread":0.2195372842354297,"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."}}