{"id":"W4234051823","doi":"10.1109/dasc-picom-cbdcom-cyberscitech52372.2021.00164","title":"Analyzing COVID-19 Epidemiological Data","year":2021,"lang":"en","type":"article","venue":"2021 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech)","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; University of Manitoba","keywords":"Data science; Big data; Computer science; Coronavirus disease 2019 (COVID-19); Epidemiology; Disease; Data mining; Infectious disease (medical specialty); Medicine","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow","sts","scholarly_communication","open_science","research_integrity"],"consensus_categories":["metaepi_narrow","sts","open_science","research_integrity"],"category_scores_codex":[0.008169481,0.002908428,0.004434807,0.003035192,0.00370444,0.00218348,0.005857695,0.002049863,0.0004364775],"category_scores_gemma":[0.01153184,0.00267255,0.0004091193,0.003380942,0.009213245,0.0007437545,0.009346434,0.005843276,0.000163599],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009203031,"about_ca_system_score_gemma":0.003960306,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001295545,"about_ca_topic_score_gemma":0.000469328,"domain_scores_codex":[0.9805478,0.001196305,0.003825929,0.009152227,0.001944673,0.003333029],"domain_scores_gemma":[0.9782383,0.007847383,0.002689712,0.006508092,0.002400206,0.002316324],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001798369,0.002929288,0.03077313,0.001266778,0.00237186,0.00451288,0.003861839,0.002239704,0.004160261,0.1387213,0.04767628,0.7596883],"study_design_scores_gemma":[0.008821715,0.007770485,0.004947332,0.008478061,0.001397335,0.004466285,0.006660134,0.5181811,0.0201401,0.004962528,0.4072028,0.006972162],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9125629,0.004919786,0.02746682,0.04051772,0.007479345,0.003085344,0.001034687,0.001388412,0.001545002],"genre_scores_gemma":[0.9526934,0.003110919,0.002273814,0.03920912,0.001334142,0.00002821836,0.0007371426,0.0001986325,0.000414582],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7527161,"threshold_uncertainty_score":0.9995211,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1216574048700541,"score_gpt":0.3644049178686058,"score_spread":0.2427475129985517,"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."}}