{"id":"W3011408924","doi":"10.1371/journal.pcbi.1006869","title":"The use of mixture density networks in the emulation of complex epidemiological individual-based models","year":2020,"lang":"en","type":"article","venue":"PLoS Computational Biology","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Engineering and Physical Sciences Research Council; Medical Research Council; Children's Investment Fund Foundation","keywords":"Emulation; Computer science; Macro; Statistical model; Range (aeronautics); Data mining; Machine learning; Artificial intelligence; Engineering","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":[0.003911551,0.000754675,0.0008078981,0.001214389,0.0005864081,0.0009940676,0.001805453,0.001282683,0.002048573],"category_scores_gemma":[0.01974028,0.0008277309,0.0009058469,0.0008031207,0.00109943,0.001829414,0.001629151,0.001674801,0.0003578385],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001241131,"about_ca_system_score_gemma":0.001012086,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008643524,"about_ca_topic_score_gemma":0.006761804,"domain_scores_codex":[0.99905,0.0005956491,0.00004459028,0.0001197333,0.0001391458,0.00005100847],"domain_scores_gemma":[0.9930941,0.005632329,0.0003453222,0.000347073,0.000435276,0.0001459198],"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":[0.00002198589,0.00001050015,0.0008033357,0.000021389,0.00001941186,0.00002195414,0.00004325058,0.9780881,0.0001827549,0.01544937,0.000195843,0.005142109],"study_design_scores_gemma":[0.000002721585,0.000003193727,0.00005712614,0.00000482897,0.00000248165,0.000006513184,0.000004186201,0.9928274,0.000100746,0.006761041,0.0002259111,0.000003961594],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01724852,0.0001339697,0.9807246,0.0002410605,0.00002583247,0.00004479177,0.0001045841,0.0002424783,0.001234196],"genre_scores_gemma":[0.5020184,0.0004754689,0.4928387,0.0002345732,0.00006841012,0.0005414711,0.0005905946,0.0002507243,0.002981642],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008643524,"threshold_uncertainty_score":0.02068651,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6537787670534551,"score_gpt":0.4270716860789822,"score_spread":0.2267070809744729,"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."}}