{"id":"W4318474716","doi":"10.36948/ijfmr.2023.v05i01.1495","title":"Macro-economic Analysis Between the Indian Economy and Indonesian Economy","year":2023,"lang":"en","type":"article","venue":"International Journal For Multidisciplinary Research","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Gross domestic product; Indonesian; Economy; Economics; Recession; Inflation (cosmology); Overtaking; World economy; Descriptive statistics; Quarter (Canadian coin); Economic statistics; Economic recovery; Geography; Economic growth; Political science; Macroeconomics","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":[0.004695932,0.0002044395,0.0004792062,0.002238302,0.000956911,0.000776489,0.00108941,0.0001350823,0.0004219222],"category_scores_gemma":[0.0001157917,0.0001889823,0.0003869401,0.0003872591,0.0002577512,0.0007537152,0.0004444759,0.0005876663,0.001226859],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004320549,"about_ca_system_score_gemma":0.00007257956,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005199179,"about_ca_topic_score_gemma":0.0001122494,"domain_scores_codex":[0.9976394,0.00007804592,0.0009570481,0.0005247118,0.00007135277,0.000729475],"domain_scores_gemma":[0.9980565,0.0008430956,0.0003759693,0.0003511538,0.00007722391,0.0002960137],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001766809,0.0000526252,0.9332063,0.00002817047,0.004032786,0.00004881392,0.0032635,0.008482033,0.000003776186,0.03435143,0.01230915,0.004044732],"study_design_scores_gemma":[0.001821255,0.0001998722,0.472995,0.00001493168,0.00005949209,0.00009942879,0.001411611,0.09467991,0.00002957908,0.3189501,0.1092574,0.0004814125],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9672874,0.0002639921,0.0005311977,0.02409927,0.0007238233,0.000460739,0.0007383249,0.00003332453,0.005861904],"genre_scores_gemma":[0.9954265,0.0002952838,0.0001231699,0.0001167666,0.001634312,0.00009298179,0.0001729515,0.00003613733,0.002101863],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4602113,"threshold_uncertainty_score":0.9995508,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2098648818531679,"score_gpt":0.410494773214014,"score_spread":0.2006298913608461,"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."}}