{"id":"W2520024035","doi":"10.1109/vhcie.2016.7563566","title":"Using synthetic crowds to inform building pillar placements","year":2016,"lang":"en","type":"article","venue":"","topic":"Evacuation and Crowd Dynamics","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Crowds; Computer science; Crowd simulation; Level design; Range (aeronautics); Human–computer interaction; Simulation; Distributed computing; Engineering; Computer security; Game design","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007359031,0.00008498568,0.00006950342,0.00008065582,0.00004389925,0.0000267214,0.00007972105,0.00003603238,0.0004181594],"category_scores_gemma":[0.00002798233,0.00006190928,0.00002228896,0.00012801,0.000007066558,0.0001283144,0.00002865137,0.0000248015,0.0002327532],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001176165,"about_ca_system_score_gemma":0.000007724193,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002228202,"about_ca_topic_score_gemma":0.000006212195,"domain_scores_codex":[0.9994882,0.000002994872,0.0001373153,0.00007595887,0.0001174025,0.0001780807],"domain_scores_gemma":[0.9997252,0.00002275909,0.000008966148,0.0001390656,0.00002315473,0.0000808824],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002102197,0.00002812208,0.003431805,0.0000897244,0.0001144604,0.000005855257,0.0003995032,0.1721361,0.6369239,0.03426999,0.002245197,0.1503343],"study_design_scores_gemma":[0.001360612,0.00007886363,0.001427976,0.0003110402,0.00003874542,0.000024,0.0001931026,0.7699942,0.1156742,0.0008716801,0.1089474,0.001078126],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4579974,0.000009436861,0.506941,0.0001154881,0.000209554,0.00008899973,0.000003096405,0.0002713667,0.03436365],"genre_scores_gemma":[0.9846425,0.000006985903,0.01322153,0.000146141,0.00002593761,0.000004264011,4.527899e-7,0.0000217649,0.001930412],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5978581,"threshold_uncertainty_score":0.4578552,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02373416700813972,"score_gpt":0.2763755878723489,"score_spread":0.2526414208642092,"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."}}