{"id":"W3034450623","doi":"10.2139/ssrn.3684795","title":"The Fiscal Cost of Covid-19: Evidence from the States","year":2020,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"COVID-19 Pandemic Impacts","field":"Economics, Econometrics and Finance","cited_by":13,"is_retracted":false,"has_abstract":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Revenue; Counterfactual thinking; Economics; Coronavirus disease 2019 (COVID-19); Tax revenue; Panel data; State (computer science); Demographic economics; Monetary economics; Public economics; Econometrics; Finance","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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":[0.003107679,0.0001405539,0.0002735449,0.00003604811,0.0003967451,0.0001206274,0.0008823666,0.00006326682,0.0001409243],"category_scores_gemma":[0.005491241,0.0000938398,0.0001565616,0.0002655688,0.0001479946,0.0002179005,0.00009982273,0.00136797,0.0001382712],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009679184,"about_ca_system_score_gemma":0.001746828,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001419098,"about_ca_topic_score_gemma":0.00122244,"domain_scores_codex":[0.9976783,0.00008669993,0.0006293176,0.0002393332,0.00009866797,0.001267615],"domain_scores_gemma":[0.9970286,0.001847031,0.0005762814,0.000288734,0.00003160677,0.0002277629],"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":[0.0009372988,0.0000954438,0.4417078,0.00004808725,0.001136957,0.00001316351,0.0173996,0.006508053,0.0002444141,0.4748104,0.03737598,0.0197228],"study_design_scores_gemma":[0.001045132,0.0003560828,0.0088381,0.00002912965,0.0000301924,0.00004271718,0.003326948,0.005567755,0.00006932328,0.7504094,0.2300176,0.0002676176],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.3045678,0.1085773,0.1319258,0.4530011,0.0006035423,0.0006366816,0.0002404448,0.00005620405,0.0003910649],"genre_scores_gemma":[0.9646776,0.02820688,0.0000204151,0.006531451,0.0003503734,0.000005979261,0.000003501407,0.00001886241,0.0001849762],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6601097,"threshold_uncertainty_score":0.6573925,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05945577044614858,"score_gpt":0.2847008095350283,"score_spread":0.2252450390888797,"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."}}