{"id":"W4391656599","doi":"10.1016/j.epidem.2024.100748","title":"Ensemble 2 : Scenarios ensembling for communication and performance analysis","year":2024,"lang":"lv","type":"article","venue":"Epidemics","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fonds de recherche du Québec – Nature et technologies; Centers for Disease Control and Prevention; Pennsylvania State University; National Sleep Foundation; Council of State and Territorial Epidemiologists; National Institutes of Health; U.S. Department of Health and Human Services; National Science Foundation","keywords":"Weighting; Computer science; Ensemble forecasting; Process (computing); Machine learning; Artificial intelligence; Data mining","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.002402794,0.001012356,0.0006213547,0.001189561,0.0005766568,0.001795727,0.001258672,0.0008452523,0.01319758],"category_scores_gemma":[0.009657047,0.0004362005,0.001025117,0.00120909,0.0001894448,0.002483512,0.001325166,0.001220989,0.003143756],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007186568,"about_ca_system_score_gemma":0.001383167,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01100732,"about_ca_topic_score_gemma":0.01592899,"domain_scores_codex":[0.9993443,0.0002515837,0.00003801977,0.0001181124,0.0001910745,0.00005689103],"domain_scores_gemma":[0.9971366,0.001225508,0.0001301085,0.0008432348,0.0005745944,0.00008993063],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001915693,0.0001516116,0.01052641,0.0001711927,0.0002843416,0.0001245744,0.0001939812,0.7492391,0.003547579,0.03939274,0.04697242,0.1492045],"study_design_scores_gemma":[0.00001481407,0.00004166497,0.002045644,0.00004102218,0.00002920912,0.00004382789,0.00005669716,0.9578253,0.003318109,0.01940651,0.01713859,0.0000386789],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07099328,0.000317705,0.8517558,0.0008419653,0.0002479063,0.0003191739,0.02703628,0.0116319,0.03685588],"genre_scores_gemma":[0.5276065,0.0006662573,0.396216,0.000298782,0.0001396146,0.0006131751,0.0647086,0.002531713,0.007219376],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01319758,"threshold_uncertainty_score":0.04415029,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06325202780964104,"score_gpt":0.3189309795940396,"score_spread":0.2556789517843986,"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."}}