{"id":"W2354743835","doi":"","title":"The Construction of China Monetary Condition Index and Empirical Analysis of Economic Growth","year":2012,"lang":"en","type":"article","venue":"Jingji yu guanli yanjiu","topic":"Evaluation Methods in Various Fields","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Economics; Index (typography); Econometrics; Exchange rate; Monetary policy; Variable (mathematics); Macro; Interest rate; China; Scale (ratio); Money supply; Macroeconomics; Monetary economics; Mathematics","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":[],"consensus_categories":[],"category_scores_codex":[0.00105531,0.00008528309,0.0002027735,0.00008402693,0.0001076035,0.000009620574,0.0001166398,0.00009001391,0.0008005139],"category_scores_gemma":[0.0001337324,0.00006729321,0.00008349001,0.0002606975,0.0004009723,0.0001964062,0.00009404319,0.0001045584,0.00001243418],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007392451,"about_ca_system_score_gemma":0.00001039004,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004291918,"about_ca_topic_score_gemma":0.0001207192,"domain_scores_codex":[0.9989603,0.0001948855,0.0003351464,0.0001506151,0.0002051493,0.0001538919],"domain_scores_gemma":[0.9991573,0.0003021301,0.0002561132,0.0002182608,0.000009412174,0.0000567995],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001450774,0.00002220193,0.9905433,0.000004720679,0.0001269377,6.169257e-8,0.000568633,0.001242312,0.0006029796,0.001546005,0.0002344204,0.00509392],"study_design_scores_gemma":[0.0001582428,0.00003464146,0.9712288,0.00000251164,0.000285011,0.000003432174,0.0001452636,0.02036373,0.001701582,0.005845825,0.0001546343,0.00007634894],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9896002,0.00008534214,0.005930657,0.0002535074,0.0001574901,0.0001065256,0.00001082447,0.00001029643,0.003845167],"genre_scores_gemma":[0.9906937,0.00005372975,0.009113435,0.00005106197,0.00003121249,0.00000503764,0.000009069165,0.000005641072,0.00003704649],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01931453,"threshold_uncertainty_score":0.8765066,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02357188651435198,"score_gpt":0.3356333153167556,"score_spread":0.3120614288024036,"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."}}