{"id":"W4310784875","doi":"10.1016/j.procs.2022.11.088","title":"On the accuracy of Covid-19 forecasting methods in Russia for two years","year":2022,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Research Council Canada; Russian Foundation for Basic Research","keywords":"Mean absolute percentage error; Statistic; Computer science; Coronavirus disease 2019 (COVID-19); Statistics; Artificial neural network; Population; Mean absolute error; Set (abstract data type); Simple (philosophy); Artificial intelligence; Econometrics; Mean squared error; Demography; Mathematics; 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":[0.008812526,0.0001066759,0.0002609378,0.0001240576,0.0004201787,0.00002556331,0.0009682775,0.00001464512,0.00002426997],"category_scores_gemma":[0.04384126,0.00007092558,0.00006927815,0.0009827442,0.0003048338,0.00007796968,0.001100631,0.0001874406,8.017901e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002258441,"about_ca_system_score_gemma":0.0003075381,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003618758,"about_ca_topic_score_gemma":0.000009182349,"domain_scores_codex":[0.9983137,0.0002309944,0.0003628368,0.0004081992,0.0003461775,0.0003380715],"domain_scores_gemma":[0.9690075,0.03033012,0.0002618661,0.0002719843,0.00005594299,0.00007262012],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001147089,0.000363241,0.006743017,0.0004091316,0.00002569527,0.00001119304,0.01316226,0.0227262,0.0009355125,0.8181591,0.008215039,0.1291349],"study_design_scores_gemma":[0.0003726458,0.000220375,0.001513487,0.00002093365,0.000006212575,0.000005993018,0.000121507,0.3489181,0.0002587976,0.6460322,0.002391701,0.0001381376],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1351262,0.00005267824,0.8581721,0.005262733,0.0002814518,0.0009030297,0.000008320791,0.0000617287,0.0001317831],"genre_scores_gemma":[0.5434969,0.000001801991,0.4523808,0.003764572,0.00005009977,0.0002908723,2.920427e-7,0.000006992888,0.000007683353],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.4083707,"threshold_uncertainty_score":0.9642129,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4613923431627227,"score_gpt":0.5232448043944713,"score_spread":0.06185246123174859,"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."}}