{"id":"W3177544553","doi":"10.3386/w27224","title":"Global Supply Chains in the Pandemic","year":2020,"lang":"en","type":"article","venue":"National Bureau of Economic Research","topic":"COVID-19 Pandemic Impacts","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Supply chain; Shock (circulatory); Economics; Supply shock; Coronavirus disease 2019 (COVID-19); Quarter (Canadian coin); Pandemic; Recession; International economics; International trade; Monetary economics; Business; Macroeconomics; Monetary policy; Geography","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":[0.001261313,0.0004191303,0.0003257782,0.0008678578,0.0004750619,0.001716459,0.0003576778,0.0009652376,0.007291721],"category_scores_gemma":[0.003950655,0.0002402256,0.0005514071,0.001347141,0.0009627733,0.002827774,0.001748123,0.001049261,0.000230334],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001910176,"about_ca_system_score_gemma":0.001036111,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0190266,"about_ca_topic_score_gemma":0.01062707,"domain_scores_codex":[0.9995496,0.0002081435,0.00001222071,0.00005677885,0.0000465341,0.00012657],"domain_scores_gemma":[0.998642,0.0005700852,0.0004280033,0.00007711432,0.0001400275,0.0001427812],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.000167031,0.0001369274,0.07237128,0.0001241364,0.0001392037,0.0004805385,0.000677932,0.6550744,0.0007576023,0.2486668,0.004725857,0.01667834],"study_design_scores_gemma":[0.0001951096,0.0003246416,0.05116535,0.0001886772,0.0001143387,0.0001894486,0.003843849,0.5274236,0.0008596382,0.3895395,0.02606767,0.00008824565],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9333354,0.001475281,0.01829313,0.008827606,0.0001031761,0.00006202683,0.001094611,0.00009303555,0.03671571],"genre_scores_gemma":[0.9967408,0.0005151518,0.0005835451,0.0001852102,0.00002634433,0.00001783174,0.0001493734,0.00001016136,0.001771566],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0190266,"threshold_uncertainty_score":0.03783172,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4401466174968498,"score_gpt":0.4812172178329736,"score_spread":0.04107060033612375,"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."}}