{"id":"W2952699036","doi":"10.48550/arxiv.1412.4933","title":"GPU accelerated Nature Inspired Methods for Modelling Large Scale Bi-Directional Pedestrian Movement","year":2014,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Evacuation and Crowd Dynamics","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Computer science; Pedestrian; CUDA; Speedup; Path (computing); Movement (music); Grid; Scale (ratio); Visualization; Parallel computing; Graphics; Distributed computing; Artificial intelligence; Computer graphics (images)","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":[0.0001702941,0.00058077,0.0004913597,0.0004750886,0.0003622295,0.0007594299,0.001027583,0.001175204,0.002434103],"category_scores_gemma":[0.0007663658,0.0004144759,0.0007329077,0.0005767189,0.0004364247,0.0003914864,0.0006146221,0.0006711317,0.0004582716],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005332449,"about_ca_system_score_gemma":0.0007605119,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01262683,"about_ca_topic_score_gemma":0.009312775,"domain_scores_codex":[0.9999038,0.0000271405,0.00000463263,0.00001375807,0.00003500778,0.00001556856],"domain_scores_gemma":[0.9997851,0.00009607899,0.00003346178,0.00001605992,0.00004727516,0.00002192536],"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":[0.00001770534,0.00001211852,0.0005883818,0.00003049275,0.00001433885,0.00008379148,0.00003114302,0.989746,0.001048361,0.003167973,0.000540134,0.004719533],"study_design_scores_gemma":[0.000001902143,0.000002514859,0.00006617743,0.000002355862,0.000001215246,0.000009353786,0.000003991356,0.9988733,0.00006284854,0.0004308267,0.0005436541,0.000001816702],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06758194,0.0008576898,0.9134591,0.0003324574,0.0002312267,0.0001012644,0.0003591352,0.0007439802,0.01633323],"genre_scores_gemma":[0.7344294,0.001072233,0.2465288,0.000194059,0.00009428873,0.0003924013,0.000547121,0.0002783285,0.0164634],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01262683,"threshold_uncertainty_score":0.02510667,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07236634342631638,"score_gpt":0.2446114420185477,"score_spread":0.1722450985922313,"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."}}