{"id":"W2756896347","doi":"10.1093/ofid/ofx166","title":"Seasonal Influenza Forecasting in Real Time Using the Incidence Decay With Exponential Adjustment Model","year":2017,"lang":"en","type":"article","venue":"Open Forum Infectious Diseases","topic":"Influenza Virus Research Studies","field":"Medicine","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Sunnybrook Hospital; Public Health Ontario; Provincial Laboratory of Public Health; University of Alberta; Alberta Health Services; Capital District Health Authority; Nova Scotia Health Authority; University of Toronto; Dalhousie University; Ottawa Public Health; St. Michael's Hospital","funders":"Pfizer","keywords":"Medicine; Incidence (geometry); Seasonal influenza; Exponential growth; Exponential decay; Exponential function; Coronavirus disease 2019 (COVID-19); Internal medicine; Mathematics; Nuclear physics; Infectious disease (medical specialty)","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":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0003455862,0.0002805735,0.0004407497,0.0001131067,0.001540575,0.0004573084,0.0006144579,0.00006382618,0.00009408],"category_scores_gemma":[0.0005160786,0.00018286,0.0001014355,0.0001400159,0.0004441462,0.0009235145,0.001394596,0.0002691964,0.00003405143],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004044621,"about_ca_system_score_gemma":0.0007386664,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003424883,"about_ca_topic_score_gemma":0.001224022,"domain_scores_codex":[0.9978353,0.00008425865,0.0003096538,0.00043351,0.0006372903,0.0006999301],"domain_scores_gemma":[0.9983141,0.0001319917,0.0002534168,0.0008138123,0.0002480854,0.0002385436],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001833212,0.0004168576,0.9780666,0.00007369645,0.0002501113,0.0002276804,0.0003469045,0.01125521,0.0008210127,0.0005571809,0.0007254137,0.005426104],"study_design_scores_gemma":[0.009350145,0.0008822703,0.6623022,0.001234611,0.0004834586,0.0001997132,0.0004615501,0.3221693,0.0006176893,0.001221377,0.0003918801,0.0006858022],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9797612,0.0003126209,0.0001372338,0.0002781203,0.00005873169,0.001654718,0.0000546073,0.00005567459,0.01768703],"genre_scores_gemma":[0.9981959,0.00006492026,0.0003815971,0.000643111,0.0001125484,0.0002724058,0.000009435201,0.00004472102,0.0002752954],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3157644,"threshold_uncertainty_score":0.9997593,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1100347976966551,"score_gpt":0.3948217333873985,"score_spread":0.2847869356907433,"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."}}