{"id":"W2376711778","doi":"","title":"Systemic Risk Measurement of Commercial Banks in China Based on Principal Components Analysis","year":2013,"lang":"en","type":"article","venue":"Journal of Changsha University","topic":"Evaluation and Optimization Models","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"China; Systemic risk; Listing (finance); Business; Quarter (Canadian coin); Principal component analysis; Principal (computer security); Government (linguistics); Financial crisis; Finance; Economics; Statistics; Computer science; Geography; Mathematics; Computer security","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":[0.0005659385,0.00006824369,0.00022655,0.0006170241,0.00002624549,0.000006598367,0.0001260559,0.00004515766,0.0002612953],"category_scores_gemma":[0.00002316476,0.00006801295,0.0001181637,0.0003896434,0.000009786397,0.0001276789,0.0000110407,0.0001434786,0.000003675252],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003590299,"about_ca_system_score_gemma":0.00003226806,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007168089,"about_ca_topic_score_gemma":0.00005797236,"domain_scores_codex":[0.9990305,0.0001102525,0.0002322556,0.0000488493,0.0004985748,0.00007950899],"domain_scores_gemma":[0.9992903,0.00002137873,0.0002094918,0.0000876262,0.00033111,0.00006007932],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003172416,0.00005356191,0.0187442,0.00002261008,0.0001220123,0.000002940167,0.0003404863,0.9798641,0.0002450775,0.00001435438,0.0001692338,0.0003897244],"study_design_scores_gemma":[0.0008560746,0.00003546833,0.3076489,0.00007200184,0.0001309562,6.096601e-7,0.00005937748,0.6909556,0.0001176996,0.000002952288,0.00006718937,0.00005310417],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9285123,0.00002405793,0.07029012,0.00006564113,0.00007726702,0.000103583,0.0000185058,0.000009797521,0.0008986976],"genre_scores_gemma":[0.9993884,0.00002915009,0.0005345822,0.0000127171,0.0000155112,1.341008e-7,0.000003516999,0.000004412624,0.00001155154],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2889084,"threshold_uncertainty_score":0.2861,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.026829420261432,"score_gpt":0.203753978529383,"score_spread":0.1769245582679509,"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."}}