{"id":"W2159553498","doi":"10.3390/jrfm8020266","title":"Network Analysis of the Shanghai Stock Exchange Based on Partial Mutual Information","year":2015,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Complex Systems and Time Series Analysis","field":"Economics, Econometrics and Finance","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Complex network; Stock exchange; Econophysics; Stock market; Stock (firearms); Network analysis; Econometrics; Financial market; Computer science; Dependency (UML); Financial economics; Complex system; Mutual information; Economics; Artificial intelligence; Finance; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009707173,0.00009056407,0.0004322868,0.0004450713,0.00008635312,0.00004371444,0.0001601493,0.00003964555,0.00006626348],"category_scores_gemma":[0.00007697262,0.00007021763,0.0002674078,0.0009094036,0.00002803293,0.0001712622,0.00006752495,0.00009541898,0.000008646114],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004911203,"about_ca_system_score_gemma":0.00001295613,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001981145,"about_ca_topic_score_gemma":0.00008756655,"domain_scores_codex":[0.9989104,0.000029659,0.0007293996,0.0000872387,0.0001109101,0.0001323776],"domain_scores_gemma":[0.9985435,0.00002613122,0.001101596,0.0001949725,0.00007317483,0.0000606445],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0007937258,0.0002797671,0.4260736,0.0001282215,0.001413295,0.00001058631,0.003581172,0.2385295,1.545812e-7,0.1721839,0.0228658,0.1341404],"study_design_scores_gemma":[0.001265809,0.0003341269,0.4786035,0.0000420284,0.0007554809,8.549145e-7,0.0002404625,0.07081508,0.000001013521,0.004677557,0.4430915,0.0001725516],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6056712,0.003962993,0.3664672,0.0009205228,0.002625862,0.000679003,0.0003939987,0.00001693225,0.0192622],"genre_scores_gemma":[0.9990689,0.0001374197,0.0003198194,0.0001756152,0.0001861812,0.000003244813,0.00000373744,0.000003781887,0.0001012645],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4202257,"threshold_uncertainty_score":0.2863392,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02120655942397769,"score_gpt":0.1985474132023836,"score_spread":0.1773408537784059,"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."}}