{"id":"W4320804545","doi":"10.58902/tcnckhpt.v1i1.6","title":"CURRENT SITUATION AND SOLUTIONS TO VIETNAM'S ECONOMIC RECOVERY AFTER THE COVID-19 PANDEMIC","year":2022,"lang":"en","type":"article","venue":"Tạp chí Nghiên cứu Khoa học và Phát triển","topic":"COVID-19 Pandemic Impacts","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Quarter (Canadian coin); Coronavirus disease 2019 (COVID-19); Economic recovery; Pandemic; Business; Goods and services; 2019-20 coronavirus outbreak; Recovery rate; Economy; Economics; Development economics; Geography; Macroeconomics","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":[0.001175108,0.0002036346,0.0002180405,0.0005958678,0.0008052461,0.002639946,0.000369512,0.001074596,0.01001428],"category_scores_gemma":[0.002440075,0.0001141308,0.0002645847,0.0009617748,0.0007928802,0.001869112,0.0008594209,0.001570954,0.0004345643],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004290083,"about_ca_system_score_gemma":0.004838464,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06489071,"about_ca_topic_score_gemma":0.04576603,"domain_scores_codex":[0.9994479,0.0002017119,0.00001727832,0.00003121815,0.00007091858,0.0002309672],"domain_scores_gemma":[0.9989899,0.0002579394,0.0001880449,0.00001674797,0.0002565095,0.0002908142],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004320804,0.0004097449,0.12645,0.002111729,0.0001536852,0.006295444,0.004699183,0.02164672,0.000898011,0.4277513,0.2609426,0.1482095],"study_design_scores_gemma":[0.00006973477,0.0002795215,0.227264,0.003348998,0.00008950374,0.001350059,0.0798134,0.02036526,0.0006043439,0.146153,0.5205142,0.0001479167],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.2864395,0.05726986,0.002289447,0.5296849,0.002087036,0.0001010688,0.003727871,0.0000667229,0.1183336],"genre_scores_gemma":[0.9376455,0.03755067,0.0008169468,0.007377389,0.0008252896,0.00004161387,0.001180482,0.0000226868,0.01453945],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.06489071,"threshold_uncertainty_score":0.129026,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09715223648589205,"score_gpt":0.2991185141836821,"score_spread":0.2019662776977901,"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."}}