{"id":"W4226097264","doi":"10.1111/poms.13726","title":"Pandemic lockdown, isolation, and exit policies based on machine learning predictions","year":2022,"lang":"en","type":"article","venue":"Production and Operations Management","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Agence Nationale de la Recherche","keywords":"Isolation (microbiology); Pandemic; Coronavirus disease 2019 (COVID-19); Computer science; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); 2019-20 coronavirus outbreak; Business; Operations research; Operations management; Economics; Virology; Medicine; 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":[0.002786869,0.000741265,0.0007069127,0.0006624461,0.0004784238,0.001204253,0.0009452295,0.001003065,0.002823561],"category_scores_gemma":[0.01058062,0.0003310684,0.0006083101,0.0003499633,0.0009560342,0.00152831,0.0008788518,0.001562236,0.0001494731],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002003261,"about_ca_system_score_gemma":0.001346665,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02120765,"about_ca_topic_score_gemma":0.01153154,"domain_scores_codex":[0.999297,0.0003690677,0.00001934388,0.0000998969,0.0000525602,0.0001621238],"domain_scores_gemma":[0.9931934,0.00514398,0.0008601878,0.0001847867,0.0003239809,0.0002937653],"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":[0.00004845815,0.00002718733,0.00318582,0.000008845243,0.00000949452,0.00002726507,0.00001502384,0.9910801,0.00006341206,0.0038052,0.0003117318,0.00141747],"study_design_scores_gemma":[0.000009111238,0.00002097661,0.000481248,0.000005805196,0.000004262072,0.000004417481,0.00001820781,0.9959798,0.0000589736,0.003317035,0.00009620775,0.000003895756],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8612742,0.000423902,0.1185349,0.003844307,0.0001248711,0.0001409829,0.001007607,0.0002981049,0.01435104],"genre_scores_gemma":[0.9943876,0.00008680938,0.00415348,0.0001053186,0.00001639226,0.00003533614,0.0001776837,0.00001119838,0.001026318],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02120765,"threshold_uncertainty_score":0.04216844,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1276165337843003,"score_gpt":0.3657592505872012,"score_spread":0.2381427168029009,"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."}}