{"id":"W2377650939","doi":"","title":"Monetary Policy,Financing Constraints and Corporate Investment","year":2012,"lang":"en","type":"article","venue":"Jingji yu guanli yanjiu","topic":"Environmental and Agricultural Sciences","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Monetary policy; Constraint (computer-aided design); Investment (military); Economics; Monetary economics; Credit channel; Incentive; Finance; China; Investment policy; Quarter (Canadian coin); Inflation targeting; Market economy","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":[0.0002401032,0.0001805056,0.0001411922,0.00002364189,0.0002384805,0.00003613322,0.0001590777,0.00005575027,0.0008084947],"category_scores_gemma":[0.0000201221,0.0001217891,0.00004035547,0.0002256892,0.0006770315,0.0005650469,0.0002961475,0.0001000249,0.0005350811],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001436341,"about_ca_system_score_gemma":0.000005775637,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006129228,"about_ca_topic_score_gemma":0.00004917928,"domain_scores_codex":[0.9986784,0.00003714701,0.000184809,0.0002861791,0.0002995932,0.0005138768],"domain_scores_gemma":[0.9994002,0.00002752185,0.0001298789,0.0001261258,0.000001569829,0.0003146991],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000005396199,0.0001341404,0.8921617,0.000008144396,0.00001086992,0.000008956986,0.001088143,0.0001289743,0.06800042,0.001644621,0.00286193,0.0339467],"study_design_scores_gemma":[0.0001883121,0.00006004305,0.9850302,0.00001393039,0.00001132078,0.0000531741,0.0003360316,0.0001038357,0.003787763,0.001431128,0.00871324,0.0002709667],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.977695,0.0002275867,0.00002106792,0.0007649065,0.0001246491,0.000165863,0.000007162039,0.00003909644,0.02095464],"genre_scores_gemma":[0.9955041,0.00006861722,0.001602358,0.001628917,0.0001401364,0.00001030704,0.000007176079,0.000006799345,0.001031543],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09286856,"threshold_uncertainty_score":0.885245,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01650497256015153,"score_gpt":0.2004835770433822,"score_spread":0.1839786044832306,"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."}}