{"id":"W4251433094","doi":"10.1109/wsc.2014.7019944","title":"Towards closed loop modeling: Evaluating the prospects for creating recurrently regrounded aggregate simulation models using particle filtering","year":2014,"lang":"en","type":"article","venue":"Proceedings of the Winter Simulation Conference 2014","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Particle filter; Computer science; Aggregate (composite); Context (archaeology); Risk analysis (engineering); Data science; Data mining; Artificial intelligence; Kalman filter","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.002817977,0.0002900612,0.0004857374,0.00004403403,0.0005271222,0.0001573995,0.0005146348,0.0001103089,0.00001403867],"category_scores_gemma":[0.01388203,0.0001775996,0.0002220091,0.0001872342,0.0001137258,0.0004644647,0.0003515839,0.0002089449,0.000001330699],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001421605,"about_ca_system_score_gemma":0.00004423209,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003639364,"about_ca_topic_score_gemma":0.000005612819,"domain_scores_codex":[0.9976687,0.00009499508,0.0009032538,0.0004712067,0.0004628843,0.0003989637],"domain_scores_gemma":[0.9947022,0.002586196,0.0009783057,0.0002875441,0.001387021,0.00005879662],"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":[0.0001781373,0.00004393514,0.001209542,0.0002790588,0.00005069338,1.17513e-8,0.002144901,0.9592742,0.006152487,0.02623261,0.00001533201,0.0044191],"study_design_scores_gemma":[0.0004875885,0.0001190656,0.000108677,0.0003961793,0.00009346313,3.1137e-7,0.0001693656,0.7289944,0.002163243,0.2673003,0.000007877433,0.000159516],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5510408,0.0000176335,0.447197,0.0004736969,0.00009635626,0.0008565083,0.000003062594,0.00007028651,0.0002447075],"genre_scores_gemma":[0.9849887,0.000002669359,0.01450365,0.0001341795,0.0001848611,0.00007710109,0.000001354389,0.00003414399,0.0000733191],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4339479,"threshold_uncertainty_score":0.9944245,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4873570493848329,"score_gpt":0.4694200751424156,"score_spread":0.01793697424241736,"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."}}