{"id":"W4413228950","doi":"10.59075/c7sdz289","title":"Association of Covid-19 and Macroeconomic Growth: Lessons Learned in Managing with Future Pandemics and Preventing Economic System Degeneration","year":2025,"lang":"en","type":"article","venue":"˜The œcritical review of social sciences studies","topic":"COVID-19 Pandemic Impacts","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Pandemic; Coronavirus disease 2019 (COVID-19); Quarter (Canadian coin); Demographic economics; Index (typography); Demography; Population; Economics; Development economics; Medicine; Geography; Disease; Internal medicine","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":[0.005844278,0.0008642211,0.001081145,0.00120904,0.0007402813,0.003992898,0.001030573,0.001677136,0.004616053],"category_scores_gemma":[0.02171692,0.000339792,0.001074916,0.001370418,0.001078808,0.00443574,0.001791841,0.00432743,0.0005087245],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001872425,"about_ca_system_score_gemma":0.004991542,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02034333,"about_ca_topic_score_gemma":0.02319668,"domain_scores_codex":[0.9978231,0.001179223,0.0001481535,0.0002782913,0.0002426636,0.0003285019],"domain_scores_gemma":[0.9880676,0.005573782,0.001645148,0.0005351777,0.002371814,0.001806526],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005814984,0.0005549602,0.5735612,0.001636046,0.001030443,0.0008287071,0.002034216,0.005221454,0.0003766759,0.01850277,0.04935235,0.3463197],"study_design_scores_gemma":[0.0001193768,0.0008511393,0.7444131,0.006988939,0.001085656,0.001009412,0.01810355,0.03031508,0.0009806433,0.1030957,0.09264686,0.0003905116],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"commentary","genre_gemma":"review","genre_scores_codex":[0.3036812,0.2195399,0.0163924,0.4273239,0.004272231,0.0001802892,0.002815461,0.0003180383,0.02547668],"genre_scores_gemma":[0.8930153,0.07948461,0.01318137,0.007726899,0.002834027,0.0001293173,0.001147711,0.00006227512,0.002418514],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.02034333,"threshold_uncertainty_score":0.04044986,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08662640535481357,"score_gpt":0.3746139402704241,"score_spread":0.2879875349156105,"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."}}