{"id":"W1922496799","doi":"10.1017/s1365100518000731","title":"PRECAUTIONARY LEARNING AND INFLATIONARY BIASES","year":2018,"lang":"en","type":"preprint","venue":"Macroeconomic Dynamics","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Estimator; Inflation (cosmology); Context (archaeology); Econometrics; Mistake; Rational expectations; Adaptive learning; Function (biology); Economics; Ordinary least squares; Computer science; Mathematics; Statistics; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003349779,0.00049443,0.0007536519,0.0004897166,0.0004678727,0.001838607,0.0007369512,0.001292751,0.004052846],"category_scores_gemma":[0.03456599,0.0004556834,0.0004572046,0.000527619,0.002501506,0.003280561,0.001921177,0.002377202,0.0003565309],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009276063,"about_ca_system_score_gemma":0.001000599,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001880374,"about_ca_topic_score_gemma":0.001586481,"domain_scores_codex":[0.9989569,0.0003779662,0.0000638198,0.0002499137,0.0002076023,0.0001437677],"domain_scores_gemma":[0.9872528,0.007919827,0.002505577,0.001424474,0.0006130698,0.0002842183],"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":[0.0002582825,0.0001028779,0.01075683,0.000167509,0.0001272804,0.0003618709,0.0006043817,0.1009674,0.002525316,0.8363947,0.001506425,0.04622712],"study_design_scores_gemma":[0.00003332191,0.00003755903,0.003128207,0.00005113281,0.00002730638,0.0001227552,0.00004932476,0.1304232,0.001172163,0.8637808,0.001148177,0.00002600219],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5653247,0.002992594,0.3755492,0.01025304,0.000239179,0.00004777948,0.0001808721,0.0003105256,0.04510199],"genre_scores_gemma":[0.9860749,0.0007309023,0.008556897,0.0002972629,0.0001273074,0.00001576743,0.00003253863,0.00002989087,0.004134609],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004052846,"threshold_uncertainty_score":0.01771557,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05502451828117069,"score_gpt":0.2507562553199685,"score_spread":0.1957317370387978,"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."}}