{"id":"W4399024944","doi":"10.1515/snde-2023-0106","title":"Divisia Monetary Aggregates for India","year":2024,"lang":"en","type":"article","venue":"Studies in Nonlinear Dynamics and Econometrics","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University; University of Calgary","funders":"","keywords":"Divisia index; Divisia monetary aggregates index; Economics; Econometrics; Keynesian economics; Monetary economics; Monetary policy; Mathematics; Statistics; Central bank; Quantitative easing","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009234403,0.0002794702,0.0007083784,0.001449898,0.0001301528,0.0001600304,0.0001950847,0.0001429711,0.00006077038],"category_scores_gemma":[0.0003275617,0.0003111388,0.000162248,0.0006912545,0.0001698683,0.0003835656,0.0001474946,0.0002213045,0.0001547197],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003049739,"about_ca_system_score_gemma":0.00001451491,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001401187,"about_ca_topic_score_gemma":0.0001181504,"domain_scores_codex":[0.997897,0.000007856323,0.0008614199,0.0006816942,0.00002114878,0.0005308834],"domain_scores_gemma":[0.9989798,0.0004820965,0.0001580234,0.0002590092,0.00001282644,0.0001082083],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000911968,0.0002857,0.4070056,0.002389952,0.001852016,0.00006744816,0.004728735,0.00702733,5.371435e-7,0.492643,0.004828787,0.07907973],"study_design_scores_gemma":[0.0006000585,0.0001566784,0.01140592,0.00007057249,0.00001816528,0.0000133345,0.0004468837,0.8896326,0.000001713959,0.06936679,0.02778653,0.0005007204],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7793685,0.2043824,0.002492902,0.001514732,0.002770237,0.0006748018,0.002543433,0.0001065028,0.006146535],"genre_scores_gemma":[0.9655592,0.02797329,0.004197806,0.0002851917,0.0003821436,0.00007263038,0.0001596989,0.00005825796,0.001311715],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8826053,"threshold_uncertainty_score":0.9999341,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1424694205419128,"score_gpt":0.2883487150579674,"score_spread":0.1458792945160546,"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."}}