{"id":"W3094731120","doi":"10.21272/fmir.4(3).80-94.2020","title":"Trends, Cycles and Seasonal Variations of Ukrainian Gross Domestic Product","year":2020,"lang":"en","type":"article","venue":"Financial Markets Institutions and Risks","topic":"Economic Issues in Ukraine","field":"Economics, Econometrics and Finance","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Hodrick–Prescott filter; Econometrics; Quarter (Canadian coin); Seasonal adjustment; Economics; Autoregressive integrated moving average; Filter (signal processing); Volatility (finance); Lag; Context (archaeology); Mathematics; Statistics; Business cycle; Time series; Macroeconomics; Geography; Computer science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003280278,0.0001180574,0.0001301034,0.001526954,0.0001450365,0.0006832324,0.0001296971,0.0001035885,0.0004034156],"category_scores_gemma":[0.000993547,0.00007546199,0.0001667598,0.00178234,0.0001715413,0.0005745622,0.0002581381,0.0001425128,0.00009489191],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006458844,"about_ca_system_score_gemma":0.0004707008,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01171819,"about_ca_topic_score_gemma":0.008244535,"domain_scores_codex":[0.9998425,0.00002716502,0.00001935652,0.00004591668,0.00004354059,0.00002151013],"domain_scores_gemma":[0.9997043,0.00007424467,0.0001066701,0.00002411105,0.00007947838,0.00001117895],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001028769,0.00004468858,0.7985193,0.0002868308,0.0002332964,0.0006595154,0.002434846,0.02457219,0.003177159,0.02008028,0.002876295,0.1470127],"study_design_scores_gemma":[0.000002146173,0.00006785603,0.9518603,0.00006608545,0.00006568113,0.0002325405,0.001266912,0.02481803,0.001449574,0.003399939,0.01675232,0.00001851353],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9865373,0.001437509,0.003113897,0.0002571805,0.00003008298,0.000008848437,0.00128291,0.00003734473,0.007294913],"genre_scores_gemma":[0.9967335,0.0007752391,0.0007339449,0.00001296433,0.00001496033,0.000006262291,0.0006940585,0.000008867324,0.001020233],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01171819,"threshold_uncertainty_score":0.02329993,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07128045158486633,"score_gpt":0.2825501915586174,"score_spread":0.211269739973751,"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."}}