{"id":"W4388450990","doi":"10.25071/2817-5344/54","title":"Drawing Insights from the COVID-19 Pandemic","year":2023,"lang":"en","type":"article","venue":"Canadian Journal for the Academic Mind","topic":"Disaster Response and Management","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Pandemic; Preparedness; Misinformation; Coronavirus disease 2019 (COVID-19); Equity (law); Health care; Business; Public relations; Vulnerability (computing); Political science; Economic growth; Economics; Computer security; Computer science; Medicine; Infectious disease (medical specialty); Disease","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00717143,0.001002492,0.0005832437,0.002921909,0.02135052,0.01698909,0.002596185,0.0067058,0.00363488],"category_scores_gemma":[0.01106158,0.00044527,0.0004918285,0.002513295,0.02765022,0.01020777,0.008414358,0.01157331,0.0005657805],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04940504,"about_ca_system_score_gemma":0.05939284,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.5371972,"about_ca_topic_score_gemma":0.7061827,"domain_scores_codex":[0.9946091,0.002942979,0.0001048419,0.0002488025,0.0008109738,0.001283253],"domain_scores_gemma":[0.9913612,0.004901003,0.0003786268,0.0002421894,0.001570219,0.001546671],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00005540817,0.00005675352,0.004639353,0.0004328862,0.0000175342,0.002391945,0.5242645,0.001285707,0.0005002969,0.3292981,0.1088006,0.02825689],"study_design_scores_gemma":[0.000008820156,0.00001892265,0.002307374,0.0009139812,0.00001356936,0.0003361616,0.588729,0.0005993266,0.0002140142,0.07532667,0.331479,0.00005313809],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.06332853,0.02539326,0.005599752,0.7337999,0.003749533,0.0001229462,0.0004610979,0.00008082551,0.1674641],"genre_scores_gemma":[0.8536952,0.03852449,0.005615157,0.08086829,0.001624395,0.00007384269,0.0002262195,0.0001432333,0.0192291],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5371972,"threshold_uncertainty_score":0.9310566,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2676223057231304,"score_gpt":0.4624914319257664,"score_spread":0.1948691262026359,"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."}}