{"id":"W2299212302","doi":"10.18488/journal.aefr/2016.6.1/102.1.54.65","title":"Corporate Failure Prediction Models for Advanced Research in China: Identifying the Optimal Cut Off Point","year":2016,"lang":"en","type":"article","venue":"Asian Economic and Financial Review","topic":"Financial Distress and Bankruptcy Prediction","field":"Business, Management and Accounting","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"China; Creditor; Quarter (Canadian coin); Point (geometry); Value (mathematics); Business; Financial distress; Logistic regression; Debt; Economics; Finance; Financial system; Statistics; 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":[0.001115061,0.0001486211,0.0002879171,0.0001178021,0.0002835139,0.0001061771,0.0001810606,0.00007498761,0.00003045146],"category_scores_gemma":[0.0001189006,0.00009725423,0.00008906036,0.0001733829,0.0001012892,0.001454549,0.0001084832,0.0001351868,0.00005677058],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008089384,"about_ca_system_score_gemma":0.00005627377,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008667419,"about_ca_topic_score_gemma":0.0003654725,"domain_scores_codex":[0.9987844,0.00002094767,0.0004263099,0.0003561932,0.00008992798,0.0003222354],"domain_scores_gemma":[0.9994339,0.00003448279,0.0002533474,0.0002011413,0.00006311326,0.00001406633],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001062602,0.00003330943,0.001441317,0.001318564,0.000007987638,0.000003573272,0.00004639797,0.00006891893,0.00003341982,0.1674482,0.01344742,0.8160446],"study_design_scores_gemma":[0.003520058,0.0001395305,0.1167583,0.01355549,0.0001415674,0.00001339945,0.0002446667,0.01392867,0.00002654629,0.1967525,0.654142,0.000777245],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7001606,0.1070075,0.06546906,0.08071234,0.004515894,0.01328325,0.0006139257,0.0004174111,0.02781999],"genre_scores_gemma":[0.9738059,0.02421662,0.0001004478,0.0004115661,0.00088024,0.0003608205,0.00003199838,0.00002434224,0.0001680441],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8152674,"threshold_uncertainty_score":0.3965912,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0510074925544799,"score_gpt":0.2756746264039843,"score_spread":0.2246671338495044,"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."}}