{"id":"W4404258538","doi":"10.1016/j.epidem.2024.100801","title":"Forecasting SARS-CoV-2 epidemic dynamic in Poland with the pDyn agent-based model","year":2024,"lang":"en","type":"article","venue":"Epidemics","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Interdyscyplinarne Centrum Modelowania Matematycznego i Komputerowego UW; Institut de Cardiologie de Montréal; Ministerstwo Edukacji i Nauki; Nuclear Decommissioning Authority","keywords":"Pandemic; Coronavirus disease 2019 (COVID-19); Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Econometrics; Disease; Geography; Medicine; Infectious disease (medical specialty); Mathematics; Internal medicine","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"],"consensus_categories":[],"category_scores_codex":[0.004418714,0.0004182077,0.0007827574,0.0001501499,0.0001941862,0.00005024269,0.0004555354,0.0002046425,0.000008317838],"category_scores_gemma":[0.01154665,0.0002380364,0.0002138744,0.0006061891,0.0002329881,0.0001222708,0.0001980347,0.0008925501,0.00003061367],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004769927,"about_ca_system_score_gemma":0.0001516269,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002513022,"about_ca_topic_score_gemma":0.00315019,"domain_scores_codex":[0.9969624,0.0004224883,0.0008591298,0.0006758577,0.0002958347,0.0007842379],"domain_scores_gemma":[0.9788502,0.02025853,0.0002174694,0.0005552839,0.00005430865,0.00006419295],"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":[0.0007970515,0.0006266134,0.1480016,0.005079315,0.001115544,0.0009690803,0.006474278,0.4038673,0.005603806,0.142274,0.2508268,0.03436464],"study_design_scores_gemma":[0.00027101,0.00004936629,0.0003857978,0.0003584985,0.00006803017,0.00001187652,0.00006363677,0.8623198,0.00004717994,0.1337627,0.00237945,0.0002827294],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5947685,0.002255382,0.3712471,0.02899269,0.0002001469,0.0008784103,0.00005371467,0.0004949188,0.00110914],"genre_scores_gemma":[0.9679692,0.00009594169,0.02469974,0.006709572,0.00009636167,0.0001352809,0.00001006225,0.00008231829,0.0002015662],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4584525,"threshold_uncertainty_score":0.9967795,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3295447613569682,"score_gpt":0.4320936688497189,"score_spread":0.1025489074927507,"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."}}