{"id":"W3172177573","doi":"10.3389/frai.2021.684609","title":"Insights Into Co-Morbidity and Other Risk Factors Related to COVID-19 Within Ontario, Canada","year":2021,"lang":"en","type":"article","venue":"Frontiers in Artificial Intelligence","topic":"COVID-19 Clinical Research Studies","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Pandemic; Coronavirus disease 2019 (COVID-19); Identification (biology); Test (biology); Demography; Comorbidity; Medicine; Actuarial science; Geography; Business; Disease; Sociology; Psychiatry","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0007202431,0.0002558376,0.0003656592,0.001225659,0.002127085,0.001413708,0.0008677007,0.0003157564,0.003305921],"category_scores_gemma":[0.005317257,0.0001948037,0.0005677877,0.003594817,0.0006394144,0.0004106543,0.001062108,0.0006481159,0.0002758008],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.03059201,"about_ca_system_score_gemma":0.04598097,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9965964,"about_ca_topic_score_gemma":0.9980634,"domain_scores_codex":[0.999315,0.00008873776,0.00004747499,0.0001136498,0.0002097757,0.0002253374],"domain_scores_gemma":[0.9973505,0.0004205339,0.0004201825,0.0001249651,0.001174799,0.0005090303],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00008577714,0.00002527605,0.9689006,0.00008605728,0.00008500724,0.0002989482,0.001916559,0.001828238,0.0001457007,0.001400373,0.00918881,0.01603868],"study_design_scores_gemma":[0.00001023493,0.00001638727,0.9776459,0.0001859708,0.00005689617,0.0001497273,0.005530013,0.005377724,0.000108877,0.000679515,0.01020539,0.00003323513],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9476892,0.002682227,0.001718692,0.006419688,0.0000600668,0.00010773,0.02533648,0.00004466293,0.01594119],"genre_scores_gemma":[0.9875644,0.001423592,0.001019391,0.0002770067,0.00001512477,0.00002757183,0.006666157,0.00001102446,0.002995757],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03059201,"threshold_uncertainty_score":0.2219616,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07727848306740467,"score_gpt":0.3984726454392954,"score_spread":0.3211941623718908,"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."}}