{"id":"W3197601510","doi":"10.3386/w29209","title":"What Can Stockouts Tell Us About Inflation? Evidence from Online Micro Data","year":2021,"lang":"en","type":"report","venue":"National Bureau of Economic Research","topic":"COVID-19 Pandemic Impacts","field":"Economics, Econometrics and Finance","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"Bank of Canada","funders":"","keywords":"Stockout; Inflation (cosmology); Keynesian economics; Economics; Computer science; Operations management","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":["metaresearch","metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.009054858,0.0004542268,0.001405791,0.001644323,0.0001923656,0.000616634,0.002661045,0.0008415077,0.003370269],"category_scores_gemma":[0.01551017,0.0005842497,0.0002654221,0.0005130066,0.0003305373,0.001752204,0.001682283,0.001559985,0.0007118536],"about_ca_system_candidate":true,"about_ca_system_consensus":true,"about_ca_system_score_codex":0.006053422,"about_ca_system_score_gemma":0.01144296,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03741475,"about_ca_topic_score_gemma":0.008864462,"domain_scores_codex":[0.9937052,0.0001731354,0.002480859,0.002014288,0.0008474957,0.0007790108],"domain_scores_gemma":[0.9896359,0.004291364,0.00162428,0.002372221,0.001779678,0.0002965359],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000291779,0.0008663203,0.4246349,0.001587563,0.002988477,0.00009247728,0.001544898,0.006234515,0.0005256485,0.02707727,0.5275455,0.006610672],"study_design_scores_gemma":[0.00203412,0.000188566,0.2088961,0.003833201,0.00008266292,0.00004837016,0.0002710086,0.01023265,0.0003610612,0.165145,0.6069261,0.001981094],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4443601,0.2631565,0.0001948384,0.01813234,0.01606632,0.005006495,0.08260897,0.0001711542,0.1703033],"genre_scores_gemma":[0.7466992,0.1527662,0.002165385,0.0008281246,0.007669032,0.00014808,0.05323824,0.0003293563,0.03615639],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3023392,"threshold_uncertainty_score":0.9996609,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6238900308056372,"score_gpt":0.5357653700431524,"score_spread":0.08812466076248482,"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."}}