{"id":"W3025300857","doi":"10.1049/ccs.2020.0017","title":"Predicting COVID‐19 trends in Canada: a tale of four models","year":2020,"lang":"en","type":"article","venue":"Cognitive Computation and Systems","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada Research Chairs; University of Toronto; Lakehead University; University of Windsor","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); Logistic regression; Pandemic; Econometrics; 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Turning point; Regression; Regression analysis; Demography; Statistics; Geography; Computer science; Medicine; Economics; Mathematics; Virology; Period (music); Sociology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001379942,0.00009027262,0.0003004447,0.0001270372,0.00003312205,0.00001250789,0.00002214438,0.00003256182,0.000008784464],"category_scores_gemma":[0.0004150414,0.00009081957,0.00002557267,0.0003211913,0.00002936764,0.00005173012,0.00002330938,0.0000890586,7.982338e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002215324,"about_ca_system_score_gemma":0.0008355736,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.2788266,"about_ca_topic_score_gemma":0.07385375,"domain_scores_codex":[0.9990436,0.00009399426,0.0003144811,0.0002157754,0.000219841,0.0001123227],"domain_scores_gemma":[0.9990104,0.0004362478,0.0001257204,0.0000314156,0.0001704204,0.0002257981],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007694945,0.0002342101,0.6710582,0.004587725,0.0003019718,0.0007489587,0.0216108,0.2124484,0.0005400899,0.0002665306,0.01259064,0.07484304],"study_design_scores_gemma":[0.002293912,0.0001237568,0.02670624,0.0004397341,0.00004665018,0.00002840927,0.003364834,0.9666373,0.00002859004,0.00001769444,0.0002254371,0.00008745625],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9236216,0.0006541891,0.06500888,0.009210627,0.0001517974,0.0004973864,0.00009649259,0.00005167861,0.0007073725],"genre_scores_gemma":[0.9934093,0.000008224356,0.0000329015,0.006392577,0.00005647959,0.00001743595,0.00004965925,0.00001064804,0.00002271872],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.754189,"threshold_uncertainty_score":0.943046,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1163314383346385,"score_gpt":0.3323212106462635,"score_spread":0.215989772311625,"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."}}