{"id":"W4415523121","doi":"10.1073/pnas.2508575122","title":"Improving outbreak forecasts through model augmentation","year":2025,"lang":"en","type":"article","venue":"Proceedings of the National Academy of Sciences","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Centers for Disease Control and Prevention; Council of State and Territorial Epidemiologists","keywords":"Reliability (semiconductor); Outbreak; Consensus forecast; Ensemble forecasting; Public health; Probabilistic forecasting; Healthcare system; Predictive modelling","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.002943626,0.001198919,0.00093789,0.0007807933,0.0003275583,0.0009738847,0.001050798,0.0007109302,0.001445513],"category_scores_gemma":[0.01159136,0.0005957383,0.0008481967,0.0004530067,0.0003734387,0.001921864,0.001270337,0.001988003,0.0004304875],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004786409,"about_ca_system_score_gemma":0.001228037,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0069232,"about_ca_topic_score_gemma":0.00736127,"domain_scores_codex":[0.9993893,0.0002845512,0.00004254648,0.0001324755,0.00009490366,0.00005619773],"domain_scores_gemma":[0.9956164,0.002972768,0.0003277489,0.0005097835,0.0004892301,0.00008412041],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009751249,0.00004958177,0.002529137,0.00003844287,0.00005369213,0.00003153461,0.00003352686,0.9514905,0.0008701747,0.001368685,0.001153903,0.04228336],"study_design_scores_gemma":[0.000003211468,0.00001022171,0.0001016355,0.000003229866,0.000005656899,0.000003377547,0.000001890939,0.9985042,0.0001941911,0.0009669709,0.000202925,0.000002632411],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1475962,0.001255356,0.8391468,0.001690247,0.0003720072,0.0001379415,0.0009519252,0.003576085,0.005273458],"genre_scores_gemma":[0.8863007,0.0004830915,0.1100867,0.0003111633,0.0002051538,0.0001423637,0.001011733,0.000114906,0.00134414],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9970564,"threshold_uncertainty_score":0.01556754,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.289506109772484,"score_gpt":0.4567257920849241,"score_spread":0.1672196823124401,"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."}}