{"id":"W4310517465","doi":"10.1101/2020.04.29.20084707","title":"Pandemic Lock-down, Isolation, and Exit Policies Based on Machine Learning Predictions","year":2020,"lang":"en","type":"preprint","venue":"medRxiv","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Isolation (microbiology); Pandemic; Operationalization; Population; Risk analysis (engineering); Population health; Work (physics); Computer science; Operations research; Business; Coronavirus disease 2019 (COVID-19); Engineering; Medicine; Environmental health; Disease","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":[0.002885753,0.0006926971,0.0007264093,0.0006013537,0.0004352576,0.001218395,0.001006869,0.001019617,0.003470798],"category_scores_gemma":[0.01138782,0.0003305713,0.0006218932,0.0003244003,0.0009577089,0.001451524,0.0009300542,0.001701804,0.0002101963],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001962638,"about_ca_system_score_gemma":0.001468531,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0175287,"about_ca_topic_score_gemma":0.01050166,"domain_scores_codex":[0.9992571,0.0003923413,0.00001932199,0.0001072295,0.00005551846,0.0001684722],"domain_scores_gemma":[0.9938181,0.004642981,0.0007296674,0.0001944777,0.0002913218,0.0003234904],"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":[0.00007298489,0.00003462498,0.003813633,0.00001493496,0.00001307615,0.00003514641,0.0000237584,0.9860723,0.00008842997,0.006775384,0.0006767467,0.002378893],"study_design_scores_gemma":[0.00001662915,0.00002946414,0.0005580425,0.0000099954,0.000005966102,0.000007005157,0.00002205007,0.9932823,0.00007655699,0.005789985,0.0001969001,0.000005150866],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8031266,0.0006842818,0.1666283,0.007012366,0.0001966899,0.0002087805,0.001712358,0.000463488,0.01996709],"genre_scores_gemma":[0.9912338,0.0001316803,0.006551454,0.0001905201,0.0000242963,0.00005390868,0.0002781649,0.00001945577,0.001516648],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0175287,"threshold_uncertainty_score":0.03485334,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.235260584145284,"score_gpt":0.4004960436187338,"score_spread":0.1652354594734498,"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."}}