{"id":"W4299356032","doi":"10.17016/ifdp.2013.1093","title":"Surprise and Uncertainty Indexes: Real-Time Aggregation of Real-Activity Macro Surprises","year":2013,"lang":"en","type":"article","venue":"International Finance Discussion Paper","topic":"Market Dynamics and Volatility","field":"Economics, Econometrics and Finance","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Surprise; Index (typography); Economics; Econometrics; Pessimism; Construct (python library); Optimism; Measure (data warehouse); Macro; Computer science","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003985802,0.0001683851,0.0003362363,0.0001344407,0.00007527461,0.00006389502,0.0002186695,0.0001178197,0.002157944],"category_scores_gemma":[0.0001747485,0.0001311775,0.0001019816,0.0001343866,0.0001067489,0.0006164882,0.00009095163,0.0001162092,0.00008514328],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009157386,"about_ca_system_score_gemma":0.00002376186,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003014758,"about_ca_topic_score_gemma":0.00009205689,"domain_scores_codex":[0.9987015,0.0000295421,0.000558893,0.000423356,0.00009890759,0.0001878149],"domain_scores_gemma":[0.998949,0.00009437746,0.0004763454,0.0002990509,0.0001236299,0.00005760454],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001571243,0.0003008667,0.919744,0.00006532639,0.00007625091,0.000002668053,0.0005004347,0.000105004,0.003910023,0.03607519,0.001920496,0.03714263],"study_design_scores_gemma":[0.0004584709,0.00003639433,0.9191172,0.0000692316,0.000003345952,0.00000169832,0.00002677849,0.03923009,0.0001508031,0.03481097,0.005857708,0.0002373263],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9659358,0.00007741015,0.0002952202,0.001329995,0.0003032026,0.0002412256,0.0002671262,0.00002481899,0.03152515],"genre_scores_gemma":[0.991242,0.001856107,0.0003406847,0.00004659042,0.00004775472,0.00004467525,0.00005226451,0.00001707233,0.006352851],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03912508,"threshold_uncertainty_score":0.9987542,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01126981594867509,"score_gpt":0.2295828325069715,"score_spread":0.2183130165582964,"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."}}