{"id":"W3125004740","doi":"10.15353/jcvis.v6i1.3551","title":"Why Can’t Neural Networks Forecast Pandemics Better","year":2021,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Universities Space Research Association","keywords":"Pandemic; Computer science; Artificial neural network; Coronavirus disease 2019 (COVID-19); Artificial intelligence; Time series; Econometrics; Machine learning; Economics; Infectious disease (medical specialty)","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003181717,0.000111767,0.000318441,0.0001390256,0.00008262826,0.0001405718,0.00004737932,0.00003629427,0.00000934795],"category_scores_gemma":[0.000114685,0.00008893196,0.0001064201,0.0001499731,0.00004137164,0.000142419,0.00003487347,0.0002544226,6.235148e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000852833,"about_ca_system_score_gemma":0.0001494749,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002441402,"about_ca_topic_score_gemma":0.000001944146,"domain_scores_codex":[0.9987416,0.00009196987,0.0004923214,0.0001175944,0.000418347,0.0001381551],"domain_scores_gemma":[0.9984173,0.0003911736,0.0002794848,0.00007972673,0.0006766056,0.0001557392],"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":[0.000144058,0.0002294993,0.1084126,0.0002967675,0.0001878717,0.000973315,0.0004903532,0.4731547,0.0004714511,0.0001186062,0.3750407,0.04048008],"study_design_scores_gemma":[0.001562457,0.0001027588,0.02840361,0.0008060106,0.00008498481,0.005743964,0.0001743132,0.9108105,0.0000128868,0.0001588753,0.05203358,0.0001060703],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.497191,0.009127825,0.2358267,0.2546743,0.002830657,0.000232696,0.000009012239,0.00004276305,0.00006502384],"genre_scores_gemma":[0.9442983,0.0000451462,0.001210779,0.0537122,0.0006657151,8.505855e-7,0.00001348898,0.00001646964,0.00003707244],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4471073,"threshold_uncertainty_score":0.362654,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01897117722083076,"score_gpt":0.3094163156293788,"score_spread":0.290445138408548,"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."}}