{"id":"W2938241264","doi":"10.3934/mbe.2019152","title":"Using cultural, historical, and epidemiological data to inform, calibrate, and verify model structures in agent-based simulations","year":2019,"lang":"en","type":"article","venue":"Mathematical Biosciences & Engineering","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Calibration; Replication (statistics); Process (computing); Variety (cybernetics); Agent-based model; Econometrics; Data mining; Data science; Machine learning; Artificial intelligence; Statistics; Mathematics","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0008680546,0.0002498711,0.0006065153,0.0001231389,0.0001170797,0.00006628278,0.0003852173,0.0001306893,0.00003130277],"category_scores_gemma":[0.009333077,0.0001621346,0.00003470884,0.0004482712,0.0001102893,0.0002950821,0.0005957466,0.0002000778,0.000004143475],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001715894,"about_ca_system_score_gemma":0.00002554172,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003095247,"about_ca_topic_score_gemma":0.000007007,"domain_scores_codex":[0.998189,0.00004716598,0.000570884,0.0005318473,0.0002376495,0.0004235051],"domain_scores_gemma":[0.9962969,0.003006946,0.00008237349,0.0003967214,0.00002600858,0.0001910624],"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":[0.00002408766,0.0001366762,0.01602994,0.0008677226,0.00002783354,0.000007492597,0.0007444391,0.7690911,0.02136382,0.1907975,0.0005551743,0.0003541949],"study_design_scores_gemma":[0.0001432216,0.00004359204,0.001602878,0.00007398835,0.00001325914,0.000002530184,0.00004304105,0.9679363,0.0000469267,0.02949481,0.0003685125,0.0002309741],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8722186,0.0001159564,0.1260823,0.0009649136,0.00005912861,0.0003976309,0.00002309811,0.00009476217,0.00004360885],"genre_scores_gemma":[0.7797104,0.000008044787,0.2199224,0.0003099036,0.00001444391,0.000007280567,0.000002568425,0.000008827546,0.00001609512],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1988452,"threshold_uncertainty_score":0.9990118,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4859336613600561,"score_gpt":0.4478138763806587,"score_spread":0.03811978497939739,"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."}}