{"id":"W4255903987","doi":"10.34883/pi.2020.9.4.026","title":"Global Monitoring Information System for Epidemiologists (on the Material of Big Data on COVID-19)","year":2021,"lang":"ru","type":"article","venue":"Клиническая инфектология и паразитология","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Factorial; Factorial experiment; Factorial analysis; Statistics; Extrapolation; Fractional factorial design; Mathematics; Coronavirus disease 2019 (COVID-19); Matrix (chemical analysis); Main effect; Medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01193467,0.001153051,0.001229768,0.007361601,0.0008591204,0.003649285,0.001866832,0.001606834,0.01536018],"category_scores_gemma":[0.03035131,0.0008017191,0.001439706,0.008956783,0.0004076481,0.003753519,0.005314652,0.001830318,0.008951384],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001822217,"about_ca_system_score_gemma":0.008338794,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01667432,"about_ca_topic_score_gemma":0.01197281,"domain_scores_codex":[0.9928654,0.003062277,0.001381292,0.001098053,0.001169834,0.0004231721],"domain_scores_gemma":[0.9754671,0.006902599,0.004226546,0.004808788,0.006208972,0.002385788],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0007821798,0.0001383976,0.08026772,0.003563586,0.0008612089,0.0002107287,0.001020392,0.003025907,0.001752206,0.01929483,0.6613735,0.2277094],"study_design_scores_gemma":[0.0003840153,0.0002174865,0.1347662,0.002136695,0.0005545375,0.0002509615,0.0009944158,0.01305253,0.002936698,0.02082347,0.8236435,0.0002395482],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"methods","genre_scores_codex":[0.01806956,0.00468234,0.05497052,0.01663732,0.002486816,0.002394747,0.8285475,0.01830607,0.0539051],"genre_scores_gemma":[0.121849,0.005727063,0.1092729,0.005610015,0.001563033,0.004616655,0.7367131,0.001607202,0.01304113],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.01667432,"threshold_uncertainty_score":0.06311733,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2347261728101327,"score_gpt":0.4133354498857459,"score_spread":0.1786092770756132,"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."}}