{"id":"W4391656599","doi":"10.1016/j.epidem.2024.100748","title":"Ensemble <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" altimg=\"si10.svg\" display=\"inline\" id=\"d1e331\"> <mml:msup> <mml:mrow/> <mml:mrow> <mml:mn>2</mml:mn> </mml:mrow> </mml:msup> </mml:math> : 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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow","sts","research_integrity","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.005380993,0.001045799,0.0009792774,0.0005027558,0.001889071,0.0008531781,0.001731905,0.001900703,0.003869034],"category_scores_gemma":[0.01190392,0.001402351,0.001837239,0.001454201,0.001193521,0.001090219,0.002467525,0.002027616,0.0008696103],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008499005,"about_ca_system_score_gemma":0.0007078478,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001201756,"about_ca_topic_score_gemma":0.001144961,"domain_scores_codex":[0.9912503,0.0006794892,0.002765181,0.001861018,0.001345293,0.002098769],"domain_scores_gemma":[0.9799556,0.01453782,0.001876773,0.002668237,0.0002689178,0.0006926709],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006141543,0.0003056504,0.0002225781,0.003803568,0.003319657,0.000197321,0.003805066,0.004711104,0.0005352958,0.9380377,0.03959257,0.004855316],"study_design_scores_gemma":[0.0009223799,0.0008638572,0.0002536391,0.001438477,0.004302032,0.0002819145,0.001668728,0.9561098,0.01501674,0.0008846416,0.01702795,0.001229834],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.952024,0.008072887,0.01307144,0.004505428,0.001260483,0.0002181349,0.0005961022,0.000479519,0.01977197],"genre_scores_gemma":[0.9639256,0.01126817,0.01800663,0.002370825,0.001268217,0.001052295,0.001143908,0.0004371135,0.0005272334],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9513987,"threshold_uncertainty_score":0.9999083,"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."}}