{"id":"W3034578641","doi":"10.1109/tetci.2020.3046012","title":"Optimisation of Non-Pharmaceutical Measures in COVID-19 Growth via Neural Networks","year":2021,"lang":"en","type":"preprint","venue":"IEEE Transactions on Emerging Topics in Computational Intelligence","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; Perimeter Institute","funders":"Ministry of Colleges and Universities; Centers for Disease Control and Prevention; Scottish Funding Council; Government of Canada; Natural Sciences and Engineering Research Council of Canada; Johns Hopkins University; Dipartimento della Protezione Civile, Presidenza del Consiglio dei Ministri; Institut Périmètre de physique théorique; Innovation, Science and Economic Development Canada","keywords":"Timeline; Government (linguistics); Coronavirus disease 2019 (COVID-19); Lift (data mining); Pandemic; Enforcement; Set (abstract data type); Globe; Computer science; Operations research; Econometrics; Business; Political science; Economics; Psychology; Engineering; Statistics; Mathematics; Medicine; Machine learning","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001229822,0.0003898785,0.0007628269,0.0004137779,0.0001229778,0.00004010134,0.0004481724,0.0003739587,0.0001220001],"category_scores_gemma":[0.001061174,0.0004110865,0.0002801235,0.0005991748,0.0002187802,0.0000943031,0.00004074005,0.001601231,0.000001574067],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006814613,"about_ca_system_score_gemma":0.0002434227,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007104616,"about_ca_topic_score_gemma":0.0005479214,"domain_scores_codex":[0.9964272,0.0004987436,0.001404081,0.0007447618,0.0005616797,0.0003635307],"domain_scores_gemma":[0.9934444,0.005468249,0.0003293415,0.0003135042,0.0003052438,0.0001392107],"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":[0.00006759573,0.0003923006,0.0005568202,0.0004495456,0.00007508952,0.00002702597,0.000960912,0.9748446,0.0000076067,0.001336017,0.00002296384,0.02125957],"study_design_scores_gemma":[0.0001819177,0.00004068052,0.0005992358,0.0003104999,0.00004222041,0.000004268215,0.0001679309,0.882817,0.0003745995,0.1151449,0.00001228098,0.0003044907],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02586137,0.0002580875,0.9687672,0.003448813,0.001003853,0.0005374523,0.0000151911,0.00006954882,0.00003848662],"genre_scores_gemma":[0.954924,0.0005944204,0.04332541,0.0008747052,0.00008945957,0.0001375639,0.00001664928,0.00002517292,0.00001256282],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9290627,"threshold_uncertainty_score":0.9998341,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2956783636261572,"score_gpt":0.4714120132974769,"score_spread":0.1757336496713197,"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."}}