{"id":"W3014724356","doi":"10.2196/12842","title":"An Automated Approach for Finding Spatio-Temporal Patterns of Seasonal Influenza in the United States: Algorithm Validation Study","year":2020,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Science Foundation","keywords":"Seasonal influenza; Computer science; Algorithm; Data mining; Coronavirus disease 2019 (COVID-19); Medicine; Pathology; Disease; Infectious disease (medical specialty)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00208933,0.0001930435,0.0005077742,0.000177422,0.000111001,0.00007331833,0.0002073055,0.00006054201,0.000010696],"category_scores_gemma":[0.0003517716,0.000150717,0.00004405345,0.0008480772,0.00004457947,0.0002050174,0.00003669798,0.0001880759,8.897152e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006386601,"about_ca_system_score_gemma":0.0004990404,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004359774,"about_ca_topic_score_gemma":0.00009816924,"domain_scores_codex":[0.9972044,0.0008217445,0.0006382246,0.0004412886,0.0004470272,0.000447344],"domain_scores_gemma":[0.9983681,0.0002130153,0.0002953836,0.0003509615,0.0002337687,0.0005387712],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002218522,0.0006819288,0.9841394,0.0006154776,0.00003071969,0.000005173523,0.005608827,0.0001007071,0.000001431401,0.00001583828,0.000969574,0.007609002],"study_design_scores_gemma":[0.002859205,0.001079659,0.6929451,0.00001472887,0.000001668789,0.000003882667,0.004658016,0.2920569,5.124908e-7,0.000002773155,0.006250259,0.0001273329],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9852295,0.00008017879,0.00740668,0.002935226,0.00002819405,0.002895102,0.001194959,0.0002159943,0.00001414909],"genre_scores_gemma":[0.9809685,0.0000222203,0.001308211,0.004893472,0.00008582554,0.0003040723,0.01239064,0.00002463387,0.000002409527],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2919562,"threshold_uncertainty_score":0.614606,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07068713116738694,"score_gpt":0.373530575953328,"score_spread":0.3028434447859411,"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."}}